Healthy People in a Healthy Environment

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Healthy People in a Healthy Environment C O M M E N TA R Y Comprehensive Environmental Public Health . . . . 3 CJ Portier

PRACTICE Teaching Home Environmental Health to Resident Physicians . . . . . . . . . . . . . . . . . . . . . . 7 JS Zickafoose, S Greenberg, DG Dearborn Healthy Homes University . . . . . . . . . . . . . . . . . . 14 TW Largo, M Borgialli, CL Wisinski, RL Wahl, WF Priem Oklahoma Healthy Homes Initiative . . . . . . . . . . 27 F Khan Making Child Care Centers SAFER . . . . . . . . . . . 34 TS Somers, ML Harvey, SM Rusnak Promoting Active Transportation: Columbus . . . . 41 CG Green, EG Klein Animal Sentinels for Environmental Health . . . . . 50 JS Reif The 2009 National Environmental Public Health Conference . . . . . . . . . . . . . . . . . . 58 PZ Ruckart, C Moore, D Burgin, MK Byrne

RESEARCH Health Outcomes and Green Renovation of Affordable Housing . . . . . . . . . . . . . . . . . . . . . 64 J Breysse, DE Jacobs, W Weber, S Dixon, C Kawecki, S Aceti, J Lopez The Philadelphia Lead Safe Homes Study . . . . . . 76 C Campbell, M Tran, E Gracely, N Starkey, H Kersten, P Palermo, N Rothman, L Line, T Hansen-Turton Nurse Case Management and Housing Interventions Reduce Allergen Exposures: Milwaukee . . . . . . . . 89 J Breysse, J Wendt, S Dixon, A Murphy, J Wilson, J Meurer, J Cohn, DE Jacobs Attitudes about Carbon Monoxide Safety: Results from the HealthStyles Survey . . . . . . . . . 100 ME King, SA Damon Carbon Monoxide Poisoning After an Ice Storm in Kentucky, 2009 . . . . . . . . . . . . . 108 EC Lutterloh, S Iqbal, JH Clower, HA Spiller, MA Riggs, TJ Sugg, KE Humbaugh, BL Cadwell, DA Thoroughman Hazards of Illicit Methamphetamine Production and Efforts at Reduction . . . . . . . . . 116 N Melnikova, WL Welles, RE Wilburn, N Rice, J Wu, M Stanbury Healthy Workplaces: The Effects of Nature Contact at Work on Employee Stress and Health . . . . . . . 124 E Largo-Wight, WW Chen, V Dodd, R Weiler

Photos: Earl Dotter, Hugh Mainzer, and Janice Huy

Residential Light and Risk for Depression and Falls: The LARES Study of Eight European Cities . . . . 131 MJ Brown, DE Jacobs Poverty, Sprawl, and Restaurant Types Influence BMI in California . . . . . . . . . . . . . . . . 141 J Gregson Effects of a TV Drama about Exposure to Toxic Substances . . . . . . . . . . . . . . . . . . . . . . 150 MG Kennedy, EE Turf, M Wilson-Genderson, K Wells, GC Huang, V Beck

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MAY/JUNE 2011

PUBLIC HEALTH REPORTS

V o l u m e 1 2 6 / S UPPLEMENT 1 PA G E S 1 – 1 6 0

ASPH

Photo of children in leaves courtesy of Hugh Mainzer. Photo of man in garden courtesy of Janice Huy. Photos of water testing and child with duckling by Earl Dotter.

Healthy People in a Healthy Environment Hugh M. Mainzer, MS, DVM, CAPT, USPHS, and Daphne B. Moffett, PhD, CAPT, USPHS, Guest Editors This supplement was funded by the U.S. Centers for Disease Control and Prevention, National Center for Environmental Health and the Agency for Toxic Substances and Disease Registry.

GUEST EDITORIAL Introduction to Healthy People in a Healthy Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Hugh M. Mainzer, Daphne B. Moffett

The 2009 National Environmental Public Health Conference: One Model for Planning Green and Healthy Conferences. . . . . . . . . . . . . . . . . . . . . . . . . 58 Perri Zeitz Ruckart, Cory Moore, Deborah Burgin, Maggie Kelly Byrne

C O M M E N TA R Y

RESEARCH ARTICLES

Comprehensive Environmental Public Health. . . . . . . . . . 3 Christopher J. Portier

Health Outcomes and Green Renovation of Affordable Housing. . . . . . . . . . . . . . . . . . . . . . . . . . . 64 Jill Breysse, David E. Jacobs, William Weber, Sherry Dixon, Carol Kawecki, Susan Aceti, Jorge Lopez

PRACTICE ARTICLES Teaching Home Environmental Health to Resident Physicians. . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Joseph S. Zickafoose, Stuart Greenberg, Dorr G. Dearborn Healthy Homes University: A Home-Based Environmental Intervention and Education Program for Families with Pediatric Asthma in Michigan. . . . . . . . 14 Thomas W. Largo, Michele Borgialli, Courtney L. Wisinski, Robert L. Wahl, Wesley F. Priem Oklahoma Healthy Homes Initiative . . . . . . . . . . . . . . . . 27 Fahad Khan Making Child Care Centers SAFER: A Non-Regulatory Approach to Improving Child Care Center Siting . . . . . . 34 Tarah S. Somers, Margaret L. Harvey, Sharee Major Rusnak Promoting Active Transportation as a Partnership Between Urban Planning and Public Health: The Columbus Healthy Places Program. . . . . . . . . . . . . . 41 Christine Godward Green, Elizabeth G. Klein Animal Sentinels for Environmental and Public Health. . 50 John S. Reif

Primary Prevention of Lead Exposure: The Philadelphia Lead Safe Homes Study. . . . . . . . . . . . 76 Carla Campbell, Mary Tran, Edward Gracely, Naomi Starkey, Hans Kersten, Peter Palermo, Nancy Rothman, Laura Line, Tine Hansen-Turton Nurse Case Management and Housing Interventions Reduce Allergen Exposures: The Milwaukee Randomized Controlled Trial . . . . . . . . . . . . . . . . . . . . . 89 Jill Breysse, Jean Wendt, Sherry Dixon, Amy Murphy, Jonathan Wilson, John Meurer, Jennifer Cohn, David E. Jacobs Attitudes about Carbon Monoxide Safety in the United States: Results from the 2005 and 2006 HealthStyles Survey. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 Michael E. King, Scott A. Damon Carbon Monoxide Poisoning After an Ice Storm in Kentucky, 2009 . . . . . . . . . . . . . . . . . . . . . 108 Emily C. Lutterloh, Shahed Iqbal, Jacquelyn H. Clower, Henry A. Spiller, Margaret A. Riggs, Tennis J. Sugg, Kraig E. Humbaugh, Betsy L. Cadwell, Douglas A. Thoroughman

Hazards of Illicit Methamphetamine Production and Efforts at Reduction: Data from the Hazardous Substances Emergency Events Surveillance System. . . . 116 Natalia Melnikova, Wanda Lizak Welles, Rebecca E. Wilburn, Nancy Rice, Jennifer Wu, Martha Stanbury Healthy Workplaces: The Effects of Nature Contact at Work on Employee Stress and Health. . . . . . . . . . . . . 124 Erin Largo-Wight, W. William Chen, Virginia Dodd, Robert Weiler

Residential Light and Risk for Depression and Falls: Results from the LARES Study of Eight European Cities . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Mary Jean Brown, David E. Jacobs Poverty, Sprawl, and Restaurant Types Influence Body Mass Index of Residents in California Counties. . 141 Jennifer Gregson Effects of a Television Drama about Environmental Exposure to Toxic Substances. . . . . . . . . . . . . . . . . . . . 150 May G. Kennedy, Elizabeth Eustis Turf, Maureen Wilson-Genderson, Kristen Wells, Grace C. Huang, Vicki Beck

The contents, findings, and views contained in this supplement are those of the authors and do not necessarily represent the official programs and policies of the Centers for Disease Control and Prevention, the Agency for Toxic Substances and Disease Registry or the U.S. Department of Health and Human Services.

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Guest Editorial Introduction to healthy people in a healthy environment Hugh M. Mainzer, MS, DVM Daphne B. Moffett, PhD

This special supplement of Public Health Reports, “Healthy People in a Healthy Environment,” represents an addendum to the Centers for Disease Control and Prevention’s 2009 National Environmental Public Health Conference of the same name, which drew a national audience of 1,300 to Atlanta, Georgia, from October 26–28, 2009. The meeting promoted the nation’s environmental health scientific and practice capacity by enhancing the expertise of environmental health professionals, including public health and health-care professionals, academic researchers, representatives from communities and organizations, and advocacy and business groups. The conference aimed to develop and encourage innovative strategies for addressing existing and emerging issues impacting environmental public health, the discipline that focuses on the interrelationships between people and their environment, promotes human health and well-being, and fosters a safe and healthful environment. The conference comprised workshops, presentations, posters, roundtables, and plenary sessions that accomplished the following: 1. Reviewed issues and policies related to allhazards preparedness, including anticipating, responding to, mitigating, and recovering from chemical, radiologic, and biological emergencies, natural disasters, and outbreaks. 2. Emphasized biomonitoring, exposure pathways, health effects, and interventions, and provided an opportunity to discuss specific hazardous substances, as well as emerging toxic exposures. 3. Highlighted research and programs related to healthy communities, schools, and housing; indoor and outdoor air quality; land reuse and revitalization; and the built environment. 4. Provided learning opportunities about new and emerging tools for use in science, research, and programs. Sessions focused on informatics, tracking, surveillance, geospatial research, laboratory science, modeling, workforce devel-

opment, capacity building, and program implementation and evaluation. 5. Provided a forum for discussing public health’s engagement with a variety of sustainability initiatives. These initiatives included efforts to mitigate or adapt to climate change, green health-care and energy initiatives, and other sustainability programs. 6. Promoted discussion about how changes in the earth’s ecosystems affect population health and included sessions on the interrelationships between human and animal health and the environmental public health systems that monitor, control, or prevent adverse health outcomes. Those readers who would like to explore the conference proceedings or view plenary presentations may access them at http://www.cdc.gov/nceh/conference/ index.htm. Indexed abstracts from presentations are available at http://www.publichealthreports.org/EOHabstracts. We encourage readers with an interest in healthy homes and environments, health promotion, and animals as sentinels to delve into the pages of this special supplement, which includes both research articles and practice notes. The healthy housing articles address the continued dangers of lead exposure among children, as well as a continued lack of public awareness of the dangers of carbon monoxide exposures and how to prevent them, particularly following a disaster. It has long been recognized among researchers in the injury field that while homes are generally considered safe havens, they can also be dangerous places. We now see the intersection of injury prevention and environmental health in the Healthy Homes movement; Healthy Homes programs in Oklahoma and Michigan are highlighted in this issue. Another article investigates the linkage of low lighting in homes to falls and depression and suggests that improved lighting may decrease risks for both. Findings of positive associations between “green” housing renovations and health outcomes are also featured. Improving ventilation and reducing moisture, mold, pests, and radon led to significant health improvements and decreased energy demands among residents of a low-income housing development. Interventions to reduce allergens in homes of asthmatic children utilizing home visits both in research and

Public Health Reports / 2011 Supplement 1 / Volume 126

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practice yielded positive outcomes. Providing health education materials and instructions to the homeowners was key to the success of the interventions. The use of television programming as a successful vehicle for disseminating important messages on environmental exposure to toxic substances is also explored. As discussed in the commentary by Dr. Portier, environmental health is a complex and broad field that has expanded beyond the once-accepted discreet limits of traditional exposure pathways—air, water, food, and soil. Environments are now recognized as much more holistic places: They are the spaces we occupy, the interrelationships of animals and plants—even the positive or negative behaviors we are exposed to are now included in the more comprehensive approach to environmental health. Environmental health research and practice will always include the foundations of the field—clean water, sanitation, and hygiene—but it has also grown to include new and equally important areas of interest, such as biomonitoring and climate change. As we recognize the broader implications our actions and choices have on the environment, we also

recognize the effects levied on the health of us all and the opportunities we have to be healthy people living in healthy environments. In closing, we hope that your interest in the contents of this supplement and review of the 2009 conference proceedings will serve to inspire your own efforts to understand, protect, and improve the well-being of our environment and our populations. Hugh M. Mainzer is a Captain in the U.S. Public Health Service (PHS) and Supervisory Medical Epidemiologist in the Division of Emergency and Environmental Health Services at the National Center for Environmental Health, Centers for Disease Control and Prevention (CDC). Additionally, he has collateral duties as the Ninth Chief Veterinary Officer of the PHS. Daphne B. Moffett is a Captain in the PHS and formerly the Associate Director of Science for the Agency for Toxic Substances and Disease Registry, Division of Health Assessment and Consultation. She currently is Deputy Director of the Health System’s Reconstruction Office in CDC’s Center for Global Health. The contents, findings, and views contained in this article are those of the authors and do not necessarily represent the official programs and policies of CDC, ATSDR, or the U.S. Department of Health and Human Services.


Commentary

Comprehensive Environmental Public Health

Christopher J. Portier, PhDa

Typically, people thinking about environmental health focus on how the environment can affect the four key physiological factors: air, water, food, and shelter. However, the environment can have a much broader impact on human health through changes to security, and personal and endogenous factors, such as genes, age, and past medical history. Every change in an external environmental factor can affect a broad array of diseases and alter morbidity and mortality in a population, sometimes in unpredictable ways. Our nation’s disease burden is due to numerous causes, and we must address the complexity of the environment in which we live in a comprehensive way if we are to make significant strides in reducing morbidity and mortality. Addressing single issues undoubtedly will help to reduce health risks, but not nearly as well as addressing a much broader range of exposures that can harm an individual. The human body consists of a series of interconnected systems. At the highest level is the entire human, where our major concerns are overall morbidity and mortality and general health. As defined by the World Health Organization, health is not merely the absence of disease or infirmity; rather, a healthy human being is one in a state of complete physical, mental, and social well-being.1 To achieve this state, our organ systems must function properly, doing their jobs to provide oxygen and nutrients to the body and to mount a comprehensive defense against environmental agents and pathogens that would otherwise overwhelm us. Paracrine, autocrine, and other signaling processes must function according to plan. Each cell contributes to this interplay, and for each cell to function properly, the intricate intercellular biochemistry that drives that function must be maintained and balanced. This happens through a complex array of organelles and intracellular components that form their own system, with each cell type in each different organ of the body maintaining its own special biochemistry. This cellular machinery comes about as a function of genetic and epigenetic controls during development and then functions throughout the life of that cell. Molecular control mechanisms under genetic control are subject to changes in nutrition and other environmental factors. Hence, from the molecular level to the functioning of the whole, humans are very complex biochemical reactors that have to be maintained throughout a lifetime.

Centers for Disease Control and Prevention, National Center for Environmental Health, and Agency for Toxic Substances and Disease Registry, Atlanta, GA a

Address correspondence to: Christopher J. Portier, PhD, Centers for Disease Control and Prevention, Agency for Toxic Substances and Disease Registry, 4770 Buford Hwy., MS F61, Atlanta, GA 30341; tel. 770-488-0604; fax 770-488-3385; e-mail .


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4  Commentary

However, because humans do not fully control the environments in which they live, they cannot perfectly maintain the physical systems that support them. Our environment has four basic components that can interfere with and alter human systems and thus increase morbidity and mortality. Our basic physiological needs—air, water, food, and shelter—are key to maintaining health. Two other aspects of the physical environment also can play a key role in sustaining human life—global and local ecosystems and spacerelated ecosystems. Global and local ecosystems allow for nutritious food, clean air, clean water, and the ability to shelter when necessary, thus supporting our physiological environment. Space-related ecosystems refer to the sun, which provides energy as well as harmful radiation, and gravity, which we use to move about and function. A final component of the environment we live in that can play a crucial role in maintaining health in a modern society is the social environment. The social

environment constitutes those elements of our built environment that allow us to work and play, maintain relationships, govern our behavior, and develop as the social animals that humans really are. Let’s consider an example of how complex the interplay between environmental components and disease can be. Asthma is a chronic inflammatory disease affecting the respiratory system. It is a recurring condition characterized by episodes of airflow obstruction and bronchospasms that lead to wheezing, coughing, chest tightness, and shortness of breath. The disease affects millions of children and adults in the U.S. and worldwide. In children, we now know that the frequency of asthmatic episodes is not simply the result of genetic predisposition to asthma and pollen and dust in the air, but is linked to the interplay of environmental factors affecting the child (Figure). For example, the quality and the type of shelter in which the child lives can be

Figure. Childhood asthma provides an example of the interplay of environmental factors and their impact on human health. External forces in the physical environment can effect changes in physiological, security, and personal factors, which can also have a tremendous impact on a child with asthma.


Comprehensive Environmental Public Health  5

associated with other exposures that have an impact on the quality of a child’s life, affecting the immune system and the child’s ability to fight exposures and avoid a prolonged attack. Access to health care has been shown to have a significant effect on the length and severity of asthma attacks. A child’s ability to get sufficient sleep, support and care from family members, and sufficient financial resources are security factors that also can alter asthma prognosis. In addition, the asthma itself can have an effect on these security factors and leave the child vulnerable to asthma attacks. Finally, prolonged illness can significantly reduce personal factors such as self-esteem, leaving a child depressed, greatly stressed, and once again vulnerable to increased frequency and severity of illness. All of these factors interact either to improve or reduce a child’s ability to fight the illness and live a healthy life. External forces in the physical environment can effect changes in physiological, security, and personal factors, which can also have a tremendous impact on a child with asthma (Figure). Clearly, air quality is an important factor. Numerous studies have demonstrated the impact of air quality on the frequency and severity of asthma episodes. But water quality and availability, natural disasters, degradation of ecosystems, soil quality, and chemicals in the environment can also play a major role. For example, reduced availability of potable water can reduce the quality of the water we drink, food we eat, and even the air we breathe, thereby increasing a child’s vulnerability to asthma. Soil quality can affect all of the physiological and security factors, and natural disasters can change everything for a child with asthma. Hence, asthma is a disease susceptible to environmental change in numerous interconnected ways that need to be understood and managed appropriately if we are to provide a child with asthma the ability to grow, develop, and overcome this disease. Some, if not all, of these same factors are involved in other chronic diseases such as cancer, cardiovascular disease, diabetes, chronic obstructive pulmonary disease, and birth defects. Perhaps with the exception of cigarette smoking, no single environmental issue in the U.S. drives any specific disease risk. Instead, our rates of disease are due to numerous causes; thus, the effective practice of environmental public health calls upon us to address the environment in which a person lives in a comprehensive way if we are to make significant strides in reducing morbidity and mortality. This type of response requires three areas of emphasis: 1. Surveillance and tracking: The nation needs to do a better job of tracking disease incidence and outcomes, and link this to exposures at

the local level. To truly understand the interplay between changes to our environment and human health, we need data collected continuously that monitors environmental factors (not just food, air, water, and housing, but the other environmental factors described previously), environmental exposures, and human disease rates. Such a system should be aimed at the local level, if not the individual home, and should include biomonitoring data to better characterize individual exposures. The Environmental Public Health Tracking Network at the Centers for Disease Control and Prevention’s National Center for Environmental Health2 is a good start, but it will need expansion if we are to really be able to use these data to understand complex interacting exposures. 2. Research: Having data is not enough. We also must extract knowledge from these data to take appropriate actions. This will require the analysis and interpretation of the surveillance and tracking data in ways that allow us to build models that can estimate disease risks in real time; predict trends in the data that can be used to optimize personal, local, state, and national strategies; and suggest hypotheses that can be followed up through additional scientific studies. 3. Implementation: Translating knowledge about environmental health risks into active strategies to reduce health risks is always a challenge. Regulations are useful and should be continued where appropriate. However, much of what protects and enhances our health happens at our kitchen tables. The everyday, personal choices we make about such factors as the quality of our indoor air, the types of food we eat, whether we smoke or drink alcohol, and whether we filter our water can have a profound effect on our health. Devising ways that empower people to create a healthy environment in which to live is a challenge that will need to be addressed. In summary, comprehensive environmental public health uses comprehensive information on human environments linked to surveillance data on human diseases to better understand and control hazards, personal choices, and other factors and their interactions in ways that will improve public health. Creating such a comprehensive practice will require discontinuing activities that look at the environment through “stove pipes” and embarking on activities that build upon a


6  Commentary

complex, interactive discipline that relies on science, economics, and common sense to improve public health. Only through approaching environmental public health in a comprehensive manner will we ever create sustainable healthy human environments.

REFERENCES 1. World Health Organization. WHO definitions of health [cited 2010 Dec 20]. Available from: URL: http://www.who.int/about/ definition/en/print.html 2. Centers for Disease Control and Prevention (US). National Environmental Public Health Tracking Network [cited 2010 Dec 20]. Available from: URL: http://ephtracking.cdc.gov/showHome .action


Practice Articles

Teaching Home Environmental Health to Resident Physicians

Joseph S. Zickafoose, MDa Stuart Greenberg, MSb Dorr G. Dearborn, PhD, MDc

ABSTRACT Healthy Homes programs seek to integrate the evaluation and management of a multitude of health and safety risks in households. The education of physicians in the identification, evaluation, and management of these home health and safety issues continues to be deficient. Healthy Homes programs represent a unique opportunity to educate physicians in the home environment and stimulate ongoing, specific patient-physician discussions and more general learning about home environmental health. The Case Healthy Homes and Patients Program addresses these deficiencies in physician training while providing direct services to high-risk households. Pediatric and family practice resident physicians participate in healthy home inspections and interventions for their primary care patients and follow up on identified risks during health maintenance and acute illness visits.

Rainbow Babies and Children’s Hospital, Division of General Academic Pediatrics, Cleveland, OH

a

Environmental Health Watch, Cleveland, OH

b

Case Western Reserve University, School of Medicine, Department of Environmental Health Sciences, Cleveland, OH

c

Address correspondence to: Dorr G. Dearborn, PhD, MD, Case Western Reserve University, School of Medicine, Department of Environmental Health Sciences, 10900 Euclid Ave., Cleveland, OH 44106; tel. 216-368-5961; fax 216-368-3194; e-mail . ©2011 Association of Schools of Public Health


 7

8  Practice Articles

Environmental exposures in the home are key determinants of health, particularly in children and the elderly. The normal physiology and developmental behavior of children put them at increased risk for harm from environmental hazards. Compared with adults, children consume more food and water, breathe more air per body size, spend more time on the ground, place their hands in their mouth more often, and spend more time indoors, increasing exposure to household risks.1,2 The chance of harm is amplified due to ongoing growth and development of essentially all body structures and systems during childhood, as well as the prenatal period.2 Millions of children in the U.S. live in substandard housing conditions that contribute to many of the most common causes of morbidity and mortality in childhood, including lead poisoning, asthma and other respiratory conditions, accidental trauma, burns, drowning, and sudden infant death syndrome.3–6 Many elderly people have an equally dangerous confluence of housing hazards, with increased risk due to medical conditions and loss of function. People older than 65 years of age are more likely to live in older homes that have deferred maintenance and lack safety modifications.7–9 Environmental risks in the home are major contributors to health problems, loss of independence, and death in the elderly, particularly from falls, respiratory illnesses including chronic obstructive pulmonary disease, and heat and cold stress.3,10–15 The expected rapid rise in the number of elderly people in the U.S. in the coming decades is likely to magnify these problems.16 Children and the elderly are likely to spend significant amounts of time in the same home environments due to provision of childcare, co-residence, and foster, adoptive, and kinship care.17 Thus, interventions targeting each group are likely to benefit the other in turn. Physicians who care for children generally have a high level of interest in environmental health but report low self-efficacy in dealing with common environmental exposures.18–21 The National Environmental Education and Training Foundation has promoted further integration of environmental health into medical education, and this position has been endorsed by multiple professional societies, including the American Academy of Pediatrics and the American Public Health Association.22 The accreditation requirements for residency training programs in pediatrics and family medicine include expectations for learning about environmental illness and injury.23,24 Despite these requirements, education of physicians in identifying and managing environmental health risks in the home has lagged behind in medical school and residency

training.25,26 Interventions to promote environmental health in medical education reported in the literature include didactics and practical evaluations for medical and nursing students,25,27–31 training of faculty to incorporate environmental health into curricula,27,32,33 training of public health nurses,34 and a few residencybased programs primarily focused on occupational health.35–38 Education of physicians in the home setting has not been reported. The Case Healthy Homes and Patients Program (CHHAP) seeks to address these deficiencies in training while providing meaningful services to high-risk households. The Mary Ann Swetland Center for Environmental Health at Case Western Reserve University in Cleveland, Ohio, has partnered with community environmental health and housing agencies, local health departments, and the family medicine and pediatrics residency programs to educate physicians-in-training (resident physicians) in the context of a healthy home inspection for their primary care patients while providing interventions for identified risks. PROGRAM After a successful pilot with medical and public health students funded by private foundations in 2005, the CHHAP received a three-year Healthy Homes Demonstration grant from the U.S. Department of Housing and Urban Development (HUD). Additional leveraged and matching funds provided the remainder of the financing. Initial goals of the program were (1) to provide home health and injury hazard assessments and interventions in 150 homes of pregnant, infant, and geriatric patients living in high-risk housing in lowincome neighborhoods of Cleveland and its inner-ring suburbs; and (2) to provide resident physicians at an academic family medicine clinic the opportunity to learn about housing-related health hazards through participation in assessments at their primary care patients’ homes. Home assessments are performed by a certified Healthy Homes practitioner employed by Environmental Health Watch (EHW), a local community-based environmental health organization. The assessment focuses on child and/or elderly health and injury hazards, including lead risk, respiratory illness triggers, pesticide exposure, carbon monoxide (CO) risk, accidental injury risk, safe sleep environment for infants, and potential for infant exposure to toxigenic mold. Specific elements of the inspection include an occupant interview; visual paint condition assessment; collection of dust and soil samples for lead analysis; visual evidence of smoking, mold, roaches, rodents, dust


Teaching Home Environmental Health to Resident Physicians  9

mites, pets, pesticides, space heaters, faulty combustion appliances, and smoke and CO detectors; tap water and refrigerator temperature measurements; observations of child and elderly fall and injury risks; and infant sleep environment. Inspection items are tracked using a personal data assistant-based tool. Items in the tool incorporate elements developed and refined in prior home environmental health projects performed by the Swetland Center and partner organizations.39 The leadership teams of the family medicine and pediatrics residency programs expressed strong interest prior to the start of the program, and faculty coordinators for the program were identified in each department. During their first year of training, residents in family medicine and pediatrics are provided a formal orientation to the program and a basic introduction to home environmental health hazards. They also receive written materials on general home environmental health principles and aspects specific to the Cleveland area. In their continuity clinics, they identify an infant patient (,6 months of age) to which they will provide ongoing primary care. Geriatric patients are identified through a program providing primary care in the homes of house-bound elderly run by the Department of Family Medicine. Nearly all families attending these clinics and involved in the house-call program are low income. After enrollment of the family, the residents accompany the home environmental health specialist on an inspection of the patient’s home. After completion of the inspection, the inspector reviews identified hazards with the resident physician and the home occupants and discusses corrective actions. Based on the identified hazards, the resident physicians provide ongoing assessment, family education, and behavior intervention during subsequent routine primary care visits. In-home interventions provided directly to the family by the program include education and advice on behavior change, a set of health and safety items tailored for the identified needs, low-level building interventions, and referral to appropriate agencies for more extensive interventions. EHW staff and resident physicians provide education and advice on behavior change throughout the inspection as hazards are identified. They also identify actions that family members are capable of taking and provide printed educational materials on specific items. The set of health and safety items made available to the family includes an allergen vacuum, fire extinguisher, smoke and CO detectors, digital thermometer (mercury thermometers are taken for disposal if identified), door mats, cleaning supplies, child gates, window guards, cabinet locks, allergen-barrier pillow and mattress covers, electric

heaters, and roach baits. Based on the inspection, the home environmental specialists also perform low-level building repairs and modifications and hazard remediation. Examples include installation of safety items, environmental cleaning to reduce lead dust and other contaminants, moisture-reduction measures, cockroach and rodent integrated pest management, and repair of simple fall hazards. RESULTS During the initial three years of the program, 150 homes with a total of 570 family members were inspected (Table). For each of the 150 patients in the program, there were generally two to five home visits by the EHW staff (mean of 2.8 visits, range of 1–5 visits). In addition to the initial hazards assessment with the physician, there were follow-up visits for further environmental sampling, to deliver additional health and safety items, to conduct low-level remediation, to explain sampling results, and to assist the family in completing referral forms. About half of the occupants were ,18 years of age, and about one-third of family Table. Households inspected, number and age of occupants, and home interventions provided, by patient type: Case Healthy Homes and Patients Program, Cleveland, 2006–2008 Patient type Pediatric N

Geriatric N

Total N

Total families

121

29

150

Age of occupants (in years) ,6 6–17 $18 Total

181 110 235 526

0 1 43 44

181 111 278 570

Family members educated by CHHAP staff

411

74

485

Home interventions provided by CHHAP staff Carbon monoxide detector Mold and moisture Other allergens Pesticides Lead Other toxic substances Home safety Other: plumbing leak Other: electrical Other: boiler repair

117 46 66 55 87 13 118 2 0 0

27 9 19 17 2 2 28 0 1 1

144 55 85 72 89 15 146 2 1 1

Demographics/interventions

CHHAP 5 Case Healthy Homes and Patients Program



members were ,6 years of age. Nearly 500 occupant and non-occupant family members were educated on environmental risks in the home. Program staff implemented low-level interventions in most homes, with home safety improvement; reduction of mold, moisture, and allergens; installation of CO detectors; and reduction of lead risk being the most common (Table). The mean direct cost per household for building interventions carried out by the program was $860. This included $477 for health and safety items (provided free of charge) and $383 for low-level interventions performed by program staff. Mean costs were $927 (range: $509–$4,197) for pediatric patient home inspections, health and safety items, and interventions, and were $577 (range: $247–$936) for geriatric patient home inspections and interventions. Visits for pediatric patients were more expensive due to the larger number of health and safety items provided. Program staff made referrals for more extensive repairs (i.e., lead abatement, weatherization, moisture control, and electrical/plumbing/carpentry) for 95 of the 150 families. Referrals were made directly to local health departments and affordable-housing organizations for their home repair and remediation programs. Staff assisted families in completing required forms and obtaining necessary documentation. Despite this assistance, for many families, completing the referral process was daunting and not always successful. Barriers included multiple eligibility requirements; the need to verify income, home ownership, and occupancy; difficulty in securing cooperation from the landlord; and long waiting periods. We were able to complete the referral process on far fewer units than anticipated primarily because of refusal by the landlord, failure by the landlord to provide information and documentation, or disqualification of the landlord because of referral program criteria. Overall, 38% (n=36/95) of referrals resulted in repairs or improvements to the homes, ranging from a few hundred to several thousand dollars in value, including repairs to roofs, railings, and stairs; lead hazard abatement; weatherization and furnace repair/replacement; and electrical and plumbing repairs. Home inspections were conducted by 143 health professionals, including 95 resident physicians, 38 medical students, and 10 social workers (Figure). A small number of resident physicians participated in visits for two different households. While resident physician education is a primary goal of the program, relationships with other training programs allowed for inclusion of other health professionals. Social workers in the geriatrics clinical program were already involved in home visits as part of the care and were included

to provide specific environmental health education. In a brief survey of participating physicians, 100% rated the program as a “useful experience” or better, with 61% rating it as a “very useful experience.” A majority (79%) indicated that the experience had changed their clinical practice: “I found the Healthy Homes visit very helpful . . . [to] get a better sense of how my patients live and what kind of things I could ask about in order to improve their child’s home safety.” Challenges for the program arose from the busy schedules of resident physicians, the often chaotic lives of low-income families, uncooperative landlords, years of deferred maintenance, and the coordination of referred work that was beyond the direct scope of the program. The program relied on residents to recruit their own primary care patients, which required monthly reminders to maintain priority among the myriad demands of their 60- to 80-hour work week. Scheduling with families was occasionally complicated by last-minute cancellations or no one present in the home at the scheduled time of inspection, prompting additional reminder calls prior to visits from the program staff and sometimes the physicians. Under Ohio landlord-tenant law, landlord approval is not required for inspection for environmental home health hazards, but property owners needed to be notified prior to any significant repair efforts and were often uncooperative. Lastly, the geriatric patients included later in the program often lived in homes requiring more extensive interventions due to many years of neglected maintenance. These interventions were provided through referral to an affordable-housing agency with funding for repairs to homes owned by the elderly. DISCUSSION The formalization of a federal commitment to integrated management of environmental health and safety risks in the home began in 1999 with the funding of the Healthy Homes Initiative within HUD’s Office of Healthy Homes and Lead Hazard Control.40 In a draft strategic plan from 2008, the program’s stated mission was “to reduce health and safety hazards in housing by supporting and promoting applied research, assessment and intervention protocols, policy guidelines, outreach, and capacity building for partners, practitioners, and the public” (emphasis added).40 At the same time, multiple professional organizations have called for improved education of physicians in the identification and management of health risks within the home.22 CHHAP seeks to integrate these two sets of goals through the education of resident physicians in the context of inspections, family education, and direct



Number (by patient type)

Figure. Number and type of health professionals, by patient type, educated by the Case Healthy Homes and Patients Program, Cleveland, 2006–2008

Health professional

interventions in the homes of primary care patients of physicians-in-training. Robust partnerships with a wide variety of stakeholders have been essential to the development and maintenance of CHHAP. The family medicine and pediatrics residency programs invest in educating their trainees in environmental health, provide an established recruitment population from their primary care clinics, and integrate follow-up into routine care. A community-based environmental health organization, EHW, provides home health inspectors who inspect homes, educate families, educate physicians, perform low-level interventions, and make referrals to other agencies. Local departments of health provide intervention and follow-up for identified hazards beyond the scope of CHHAP’s low-level interventions through their lead hazard control programs, and, similarly, affordable housing agencies provide services for weatherization, water conservation, and other home repairs. The initial phase of the program was funded as a demonstration to show the feasibility of integrating these often-separated goals and stakeholders. Future challenges will include expansion to more trainees, measuring discrete outcomes, and sustainability. The program began with a family medicine residency program that has approximately eight resident physicians per year. With renewal of funding, the program has been able to include an additional 30 pediatrics and combined internal medicine-pediatrics resident physicians. The second round of funding is allowing for the measurement of outcomes in both the pediatric and geriatric arms of the program. Children in participating

households are being compared with matched controls from the same primary care clinics for serum lead levels, acute respiratory illnesses in general, asthma exacerbations specifically, and injury-related primary care and emergency department visits. Elderly patients participating in the family medicine home visitation program are being enrolled in a randomized controlled trial with the environmental health home visit as an intervention in addition to typical physician home visit care. Outcomes for this trial include maintenance of interventions, falls, chronic obstructive pulmonary disease exacerbations, admission rates to long-term nursing facilities, quality-of-life surveys, and length of time of independent living. Both arms will measure the environmental home health knowledge of participating residents in a pre-post design. Sustainability of the programs beyond the current grant support and planned private foundation applications will depend on demonstrating the value of both the infant and elderly programs as effective patient service activities. If effectiveness can be demonstrated, local government and community agencies could incorporate home environmental screening by health personnel already going out to the homes, such as those involved in newborn and infant home visitation programs and home health-care agencies. Provision of home health and safety items will continue to depend on grant funding and/or donations until cost-effectiveness might justify payment by Medicaid or another third-party payer. Opportunities for replication of the physician education portion of the program are likely to exist anywhere there is an existing relationship or the prospect for a



relationship between a residency training program and a department of health and/or a community-based organization that performs home environmental health evaluations and interventions. Potential partnerships include programs that perform newborn home visits for at-risk families, lead abatement, weatherization and energy efficiency improvements, home-based asthma interventions, and broader Healthy Homes interventions, as well as environmental justice advocacy groups. Alternatively, residency and other health professional training programs that are implementing their own home visits with the goal of education in a community setting could use a Healthy Homes inspection as a framework for the visit that balances direct benefit to the families with the educational goals. In addition to meeting training requirements for environmental health, the program meets more general expectations for “community and child advocacy experiences”23 in pediatric residency programs and “community medicine”24 in family medicine residency programs. The pediatrics residency at Rainbow Babies and Children’s Hospital/University Hospitals of Cleveland applies CHHAP toward the community experience requirement, and the family medicine residency at University Hospitals of Cleveland incorporates CHHAP in community medicine and home visitation requirements. While this program was targeted to pediatric and family practice residents, the structure can be applied for other residencies (e.g., internal medicine and preventive medicine), as well as other professional trainees (e.g., public health, nursing, and social work). This program was housed in a department of environmental health science of a medical school, which provided the opportunity for leveraging multiple existing relationships with a department of health, a community organization with extensive experience, and targeted physician training programs. Investments from the training programs for physician education involved time commitments from faculty to provide an orientation to the program and generate recruitment reminders and to make adjustments to resident physician schedules that allowed for the home visits. The Swetland Center had a 50% full-time equivalent (FTE) coordinator and scheduler who also maintained the database and Institutional Review Board protocols. In addition to the healthy house specialist (70% FTE) who conducted the inspections and low-level interventions (whose time is included in the intervention costs cited previously), EHW staffing included a program manager (25% FTE), health educator (15% FTE), and data manager (15% FTE).

CONCLUSION The recognition of the home environment as a key determinant of health-care outcomes calls for innovative approaches that produce meaningful benefits for families and individuals in settings with the highest burden of risk. Integration of education for physicians and other health professionals in Healthy Homes projects will help spread awareness of the importance of the home environment to health care and, ideally, help generate significant improvements in health and decreases in health-care costs. This project was supported by U.S. Department of Housing and Urban Development (HUD) Healthy Homes Demonstration grants OHLHH 0141-05 and OHLHH0164-08. The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of HUD. The authors thank Margaret Pizzi, RN, for her assistance as project coordinator and housing inspector Akbar Tyler for his expert evaluation and education efforts.

REFERENCES 1. Woodruff TJ, Axelrad DA, Kyle AD, Nweke O, Miller GG. America’s children and the environment: measures of contaminants, body burdens, and illnesses. 2nd ed. Washington: Environmental Protection Agency (US); 2003. Report No.: EPA 240-R-03-001. 2. Environmental Protection Agency (US), National Center for Environmental Assessment. Child-specific exposure factors handbook. Final report. Washington: EPA; 2008. Report No.: EPA 600-R-06096F. 3. Department of Health and Human Services (US), Office of the Surgeon General. The Surgeon General’s call to action to promote healthy homes. Rockville (MD): Office of the Surgeon General; 2009. 4. Breysse P, Farr N, Galke W, Lanphear B, Morley R, Bergofsky L. The relationship between housing and health: children at risk. Environ Health Perspect 2004;112:1583-8. 5. Jacobs DE, Baeder A. Housing interventions and health: a review of the evidence. Columbia (MD): National Center for Healthy Housing; 2009. 6. Sandel M, Sharfstein J, Shaw R. There’s no place like home: how America’s housing crisis threatens our children. San Francisco: Housing America; 1999. 7. Golant SM. Low-income elderly homeowners in very old dwellings: the need for public policy debate. J Aging Soc Policy 2008;20:1-28. 8. Newman S. The living conditions of elderly Americans. Gerontologist 2003;43:99-109. 9. Joint Center for Housing Studies of Harvard University. Measuring the benefits of home remodeling. Cambridge (MA): Harvard University, Joint Center for Housing Studies; 2003. 10. Hypothermia prevention. MMWR Morb Mortal Wkly Rep 1988;37(50):780-2. 11. Self-reported falls and fall-related injuries among persons aged $65 years—United States, 2006. MMWR Morb Mortal Wkly Rep 2008;57(9):225-9. 12. Klinenberg E. Heat wave: a social autopsy of disaster in Chicago. Chicago: University of Chicago Press; 2002. 13. Heat-related deaths—United States, 1999–2003. MMWR Morb Mortal Wkly Rep 2006;55(29):796-8. 14. Smolander J. Effect of cold exposure on older humans. Int J Sports Med 2002;23:86-92. 15. Viegi G, Simoni M, Scognamiglio A, Baldacci S, Pistelli F, Carrozzi L, et al. Indoor air pollution and airway disease. Int J Tuberc Lung Dis 2004;8:1401-15.



16. Federal Interagency Forum on Aging-Related Statistics. Older Americans update 2006: key indicators of well-being. Washington: Government Printing Office (US); 2006. 17. Hughes ME, Waite LJ, LaPierre TA, Luo Y. All in the family: the impact of caring for grandchildren on grandparents’ health. J Gerontol B Psychol Sci Soc Sci 2007;62:S108-19. 18. Kilpatrick N, Frumkin H, Trowbridge J, Escoffery C, Geller R, Rubin L, et al. The environmental history in pediatric practice: a study of pediatricians’ attitudes, beliefs, and practices. Environ Health Perspect 2002;110:823-7. 19. Trasande L, Boscarino J, Graber N, Falk L, Schechter C, Galvez M, et al. The environment in pediatric practice: a study of New York pediatricians’ attitudes, beliefs, and practices towards children’s environmental health. J Urban Health 2006;83:760-72. 20. Trasande L, Schapiro ML, Falk R, Haynes KA, Behrmann A, Vohmann M, et al. Pediatrician attitudes, clinical activities, and knowledge of environmental health in Wisconsin. WMJ 2006;105:45-9. 21. Trasande L, Ziebold C, Schiff JS, Wallinga D, McGovern P, Oberg CN. The role of the environment in pediatric practice in Minnesota: attitudes, beliefs, and practices. Minn Med 2008;91:36-9. 22. National Environmental Education Foundation. Position statement: health professionals and environmental health education. Washington: National Environmental Education Foundation; 2004. Also available from: URL: http://www.neefusa.org/pdf/PositionStatement.pdf [cited 2010 Mar 5]. 23. Accreditation Council for Graduate Medical Education. ACGME program requirements for graduate medical education in pediatrics. Chicago: ACGME; 2007. 24. Accreditation Council for Graduate Medical Education. ACGME program requirements for graduate medical education in family medicine. Chicago: ACGME; 2007. 25. McCurdy LE, Roberts J, Rogers B, Love R, Etzel R, Paulson J, et al. Incorporating environmental health into pediatric medical and nursing education. Environ Health Perspect 2004;112:1755-60. 26. Merritt EF. Human health and the environment: are physician educators lagging behind? JAMA 1999;281:1661. 27. Goldman RH, Rosenwasser S, Armstrong E. Incorporating an environmental/occupational medicine theme into the medical school curriculum. J Occup Environ Med 1999;41:47-52. 28. Hewitt JB, Candek PR, Engel JM. Challenges and successes of infusing environmental health content in a nursing program. Public Health Nurs 2006;23:453-64.

29. Rapport DJ, Howard J, Lannigan R, McCauley W. Linking health and ecology in the medical curriculum. Environ Int 2003;29:353-8. 30. Backus AS, Hewitt JB, Chalupka SM. Using a site visit to a contaminated location as a focus for environmental health education for academic and public health nurses. Public Health Nursing 2006;23:410-32. 31. Mujuru P, Niezen C. Evaluation of an environmental health education program: assessing changes in knowledge of health professionals. AAOHN J 2004;52:436-41. 32. Frazier LM, Berberich NJ, Moser R Jr, Cromer JW Jr, Hitchcock MA, Monteiro FM, et al. Developing occupational and environmental medicine curricula for primary care residents: project EPOCH-Envi. Educating Physicians in Occupational Health and the Environment. J Occup Environ Med 1999;41:706-11. 33. Rogers B, McCurdy LE, Slavin K, Grubb K, Roberts JR. Children’s Environmental Health Faculty Champions Initiative: a successful model for integrating environmental health into pediatric health care. Environ Health Perspect 2009;117:850-5. 34. Edmondson ME, Williamson GC. Environmental health education for health professionals and communities. Using a train the trainer approach. AAOHN J 1998;46:14-9. 35. Eckstein TE, Teitelbaum HS. Occupational and environmental medicine in a family medicine residency. J Am Osteopath Assoc 2001;101:288-98. 36. Frazier LM, Cromer JW, Andolsek KM, Greenberg GN, Thomann WR, Stopford W. Teaching occupational and environmental medicine in primary care residency training programs: experience using three approaches during 1984–1991. Am J Med Sci 1991;302:42-5. 37. Bernard ME, Rohrer JE, Swenson-Dravis DM, Justice MW. Impact of an occupational and environmental medicine curriculum on lost workdays. J Occup Environ Med 2007;49:771-5. 38. Michas MG, Iacono CU. Overview of occupational medicine training among US family medicine residency programs. Fam Med 2008;40:102-6. 39. Environmental Health Watch. Healthy house: moisture control projects [cited 2010 Mar 5]. Available from: URL: http://www.ehw .org/Healthy_House/HH_Moist_Proj.htm 40. Department of Housing and Urban Development (US), Office of Healthy Homes and Lead Hazard Control. Leading our nation to healthier homes: the healthy homes strategic plan. Washington: HUD; 2009.


Practice Articles

Healthy Homes University: A Home-Based Environmental Intervention and Education Program for Families with Pediatric Asthma in Michigan Thomas W. Largo, MPHa Michele Borgialli, MPH, MSWa Courtney L. Wisinski, BSa Robert L. Wahl, DVM, MSa Wesley F. Priem, BSa

ABSTRACT Environmental conditions within the home can exacerbate asthmatic children’s symptoms. To improve health outcomes among this group, we implemented an in-home environmental public health program—Healthy Homes University— for low-income families in Lansing, Michigan, from 2005 to 2008. Families received four visits during a six-month intervention. Program staff assessed homes for asthma triggers and subsequently provided products and services to reduce exposures to cockroaches, dust mites, mold, tobacco smoke, and other triggers. We also provided asthma education that included identification of asthma triggers and instructions on specific behaviors to reduce exposures. Based on self-reported data collected from 243 caregivers at baseline and six months, the impact of asthma on these children was substantially reduced, and the proportion who sought acute unscheduled health care for their asthma decreased by more than 47%.

Michigan Department of Community Health, Division of Environmental Health, Lansing, MI

a

Address correspondence to: Michele Borgialli, MPH, MSW, Michigan Department of Community Health, Division of Environmental Health, Healthy Homes Section, P.O. Box 30195, Lansing, MI 48909; tel. 517-335-8948; fax 517-335-8800; e-mail . ©2011 Association of Schools of Public Health

14 


Home-Based Pediatric Asthma Intervention  15

Asthma prevalence, hospitalizations, and deaths have increased steadily among children over the past three decades, bringing this issue to the forefront of public health.1–3 This article describes and evaluates an environmental public health program intended to decrease asthma symptoms in children through environmental trigger identification and reduction in the home, coupled with multiple, face-to-face education sessions with caregivers. The program was designed in response to a growing body of literature suggesting that the home environment is associated with asthma symptom exacerbation in children.4–6 Asthma is a chronic inflammatory respiratory disease that ranges in severity. Episodic acute symptoms can be induced by upper respiratory infections, exposure to environmental pollutants and allergens, exercise, emotional distress, and excitement. Environmental risk factors in the home that are known to affect childhood asthma symptoms include cockroach, dust mite, and animal-derived allergens; second-hand tobacco smoke; mold; chemicals (e.g., household cleaning products and pesticides); and combustion byproducts from wood or natural gas stoves.7–12 Some research studies have attempted to control for a single asthma trigger in the home environment with varying success on respiratory health outcomes.10,13–16 Current trends in program practice design that address multiple environmental triggers in the intervention strategies reveal promising and consistent findings. The most successful programs are those that have combined environmental interventions with face-to-face education over multiple home visits.7,17–21 The Healthy Homes University Program Healthy Homes University (HHU) was a home-based environmental intervention and health promotion program whose target population was low-income families with asthmatic children residing in Ingham County, Michigan—home to Michigan State University. Household participation spanned six months from initial home assessment to completion, with four home education visits conducted within that time frame. HHU program objectives were to increase primary caregiver knowledge about asthma and its triggers, improve environmental conditions within the home, and reduce child asthma severity. The program was also designed to reduce unintentional injuries; however, this article focuses on the interventions and outcomes pertaining to asthma. In 2005, the asthma hospitalization rate for children 18 years of age in Ingham County was significantly higher than the corresponding statewide rate (41.2

vs. 23.4 per 10,000). Among the Medicaid population in 2005, 7.2% of children living in Ingham County showed health-care usage consistent with persistent asthma, compared with the 5.3% estimate for the state (Personal communication, Elizabeth A. Wasilevich, Division of Genomics, Perinatal Health, and Chronic Disease Epidemiology, Michigan Department of Community Health, May 2010). Demographic and housing data from the U.S. Census Bureau’s Census 2000 showed that the at-risk population in Ingham County was concentrated in the city of Lansing. In 2000, the city population was 119,128 (22% black, 10% Hispanic). Twenty-four percent of Lansing’s occupied housing stock was built before 1940, with renters in about one-third of these units. The city’s median family income was $28,550; less than one-third of these families made $14,275. According to the 2000 Comprehensive Housing Assessment Strategy Databook, 40% of renting households in Lansing had housing problems, defined as housing cost burden (affordability), overcrowding, an incomplete kitchen, or unfinished plumbing. Methods Selection of program participants From November 2005 to March 2006, HHU staff visited neighborhood coalitions, schools, health-care providers, community organizations, and governmental agencies to market the program. We recruited households through interest fliers distributed through these venues and subsequently sent applications to interested households. A household was eligible if there was at least one resident child 18 years of age with caregiverreported asthma and the household income was 80% of the area’s median income. Selection priority was based on a weighted and scored matrix of factors listed on the application, including age of housing, income status, single head of household, number of asthmatic children, asthma symptom severity, and the presence of environmental asthma triggers. The flow diagram in Figure 1 illustrates the number of participants and withdrawals at key stages throughout the program. Interventions We enrolled all participating households in a six-month basic intervention program, with a subset receiving custom interventions. Criteria for determining which households received custom interventions included condition of home, severity and number of residents in the home with asthma, household compliance with participation agreement, and availability of products. Households received an introductory pre-intervention



Figure 1. Recruitment and participant flow diagram illustrating number of participants and withdrawals at key stages throughout the program: HHU pediatric asthma intervention program, Lansing, Michigan, 2005–2008

21,119 fliers distributed announcing the program

926 fliers returned by households interested in the program

926 program applications sent to interested households

588 households provided no further response

338 program applications received

12 households found to be ineligible

326 households accepted into program and received pre-intervention home visit

25 households opted out, could not be reached, or did not sign consent form

301 households received baseline intervention home visit

28 households discontinued program

247 households received three-month home visit

30 households discontinued program

243 households received six-month home visit; 217 households received baseline, three-month, and six-month home visits; and 26 households received baseline and six-month home visits

HHU = Healthy Homes University



home visit, a baseline intervention home visit for health education and product installation, and three- and sixmonth post-intervention home visits. Figure 2 illustrates specific activities that occurred at various stages of the program. Before enrollment began, we acquired Michigan Department of Community Health Institutional Review Board clearance for human subject participation. Educational backgrounds of program field staff included degrees in biology, medical technology, and environmental science, with prior experience in clinical research, low-income housing, and environmental contaminant investigation. Additional program training entailed in-home assessment and asthma-trigger remediation, asthma management, survey techniques, and motivational speaking. Pre-intervention home visit. Each qualified household received a one-hour introductory visit. Program staff targeted interventions and health outcomes for one subject child in each household. The purpose of the first visit was to (1) introduce HHU staff to household members, (2) discuss program expectations and timelines, (3) obtain informed consent and participation agreement, and (4) perform a visual assessment to Figure 2. Program participation phases and activities: HHU pediatric asthma intervention program, Lansing, Michigan, 2005–2008 Phase Pre-intervention home visit

Activities • Complete informed consent and participation agreement. • Identify asthma triggers and safety hazards per visual assessment. • Determine basic and custom products and services for intervention.

Baseline intervention home visit

• Administer baseline household questionnaire. • Install basic products. • Educate on asthma-trigger reduction and injury prevention.

Three-month post-intervention home visit

• Assess intervention effectiveness (household questionnaire and visual assessment). • Install custom products and initiate custom services. • Repeat education on asthma-trigger reduction and injury prevention.

Six-month post-intervention home visit

• Assess intervention effectiveness (household questionnaire and visual assessment). • Repeat education on asthma-trigger reduction and injury prevention. • Provide program completion gift certificate and diploma.

HHU  Healthy Homes University

identify environmental asthma triggers and evaluate overall housing condition. Findings of the visual assessment determined which basic and custom intervention products we would provide to the household. Baseline intervention home visit. Program staff conducted a three-hour baseline intervention home visit within two weeks of the introductory visit. We administered a survey, installed products, and provided asthma education to the subject child’s primary caregiver. The survey captured demographic information; family history of asthma; knowledge and presence of asthma triggers; home cleaning frequency; and the subject child’s asthma symptoms, frequency of medical visits for asthma, and asthma medication usage. Staff designed the baseline questionnaire using the following nationally recognized assessment tools: the Centers for Disease Control and Prevention’s Behavioral Risk Factor Surveillance System Child Asthma Call-Back Survey Questionnaire,22 the Seattle-King County Healthy Homes Project Bimonthly Interim Questionnaire,12 and the ZAP Asthma Project Caregiver Asthma Knowledge Survey Instrument.23 While the survey was being conducted, the basic intervention products (Figure 3) were installed. After these tasks were completed, staff took the caregiver on a walk-through of the home and provided tailored, one-on-one education based on caregiver responses to the survey. HHU staff demonstrated techniques (e.g., furnace filter replacement, cleaning, and vacuuming) to reduce asthma triggers. In addition, we gave caregivers a HHU Course Manual, which included asthma information and local resources. Post-intervention follow-up home visits. Post-intervention follow-up visits were scheduled for three and six months after the baseline intervention home visit. Two HHU staffers were present at each two-hour home visit; one administered a survey similar to the baseline questionnaire. The staff reassessed the home for asthma triggers and determined if the intervention products provided at baseline were in use. Program staff also reinforced caregiver education based on their survey responses. When custom intervention products (Figure 3) were allocated to a household, the staff provided them at three- or six-month follow-up visits to encourage continued program participation. Households in which all four home visits were completed received gift certificates and a program diploma. Data analysis We evaluated the program using survey responses provided by caregivers at the baseline and six-month visits to measure changes in each of the following



areas: (a) caregiver knowledge about asthma triggers, (b) frequency of various actions to reduce in-home asthma triggers, (c) environmental conditions within the home, (d ) subject child’s asthma severity, and (e) acute, unscheduled medical care sought for treatment of the child’s asthma. We designated medical care utilization as “acute, unscheduled” to differentiate it from preventive, well-asthma medical care. In addition, we used visual assessment data collected by staff during the pre-intervention and six-month post-intervention visits to characterize key baseline home conditions and measure environmental changes. For the initial

95 home visits, these home conditions (e.g., presence of a bathroom fan) were ascertained via caregiver self-reporting. However, field staff noted discrepancies between what was observed and what was reported. Thus, for the remaining 148 participants, these environmental factors were based on staff visual assessment only. Our analyses of changes in home conditions were limited to these 148 households. We limited our analyses to households who completed the six-month program. To maximize study group size, we did not exclude households who did not receive a three-month visit. While there is a seasonality to asthma incidence,

Figure 3. Intervention products and services provided to participating households: HHU pediatric asthma intervention program, Lansing, Michigan, 2005–2008 Asthma trigger-related Basic intervention products

Custom intervention products/services

Caulk Carbon monoxide detector Trash can with lid Door mat Fan Foam crack sealant Food containers with securing lids Furnace filters HEPA vacuum and replacement bags Pest eradication gels and baits Mildew-proof shower curtain Nontoxic cleaning supplies Pillow and mattress covers Smoking cessation kit

Bathroom vent installation Beds and pillows Clothes dryer vent repair Carpet removal Dehumidifier Furniture slipcovers Garbage removal Gutter replacement/repair HEPA air filter unit House cleaning Landscaping for water drainage Minor roof repair Plumbing repair Pest extermination Stove vent installation Window air-conditioning unit

Injury hazard-related Basic intervention products Carbon monoxide and smoke detectors Cabinet safety locks Child safety gate Electrical outlet safety plugs Fire extinguisher First aid kit Flashlights Gun trigger locks Mercury-free thermometer Mini-blind cord wind-ups Night-lights Poison control sticker Skid-proof rug pads/rug gripper tape Skid-proof bathroom mat Step stool

Custom intervention products/services Outdoor child play area improvement Dead bolt for entry door Electrical repair Window repair Stairwell repair HVAC maintenance Household hazardous-waste removal

HHU  Healthy Homes University HEPA  high-efficiency particulate air HVAC  heating, ventilating, and air conditioning



we did not control for this potentially confounding factor because families were enrolled continuously during a 2½-year period. We analyzed responses to survey questions pertaining to asthma knowledge, cleaning behavior, and asthma severity as continuous data. For these topics, we compared baseline and six-month means and tested for two-tailed statistical significance using the paired t-test. Data on whether subject children sought care at an emergency department, were hospitalized overnight, or had any other acute, unscheduled visit to a health-care provider for treatment of asthma were binary—either a child sought this care in the previous six months or did not. Similarly, environmental conditions either were present or not. We used McNemar’s test to examine changes in the proportion of children requiring health-care visits for asthma and for the proportion of homes with environmental conditions relevant to asthma. Because our analyses involved paired data (e.g., caregiver responses at baseline and six months), a missing value at either baseline or six months necessitated excluding that data pair from analysis. We used SAS version 9.1.324 for statistical testing. Test results for which p-values were 0.05 were considered statistically significant. RESULTS We accepted 326 households for the intervention (Figure 1). Of the 301 households in which the baseline intervention home visit was completed, 243 (81%) completed the six-month program and comprised our study group. Table 1 characterizes the demographics of the 243 subject children and their households at baseline. Their median age was 7 years, and there were slightly more males than females. About 25% of the children were reported by their caregivers as multiracial, and 10% were reported as Hispanic. For one-quarter of the households, no other children lived in the home. Slightly more than half (56%) of the households rented their property. Median income was $16,640, and 81% of the households were enrolled in Medicaid. The biological father did not reside within 87% of the households. Fifty-eight households failed to complete the program because of relocation, eviction, foreclosure, or loss of contact with project staff. These 58 subject children had characteristics very similar to those seen in Table 1. The exception was that households of Hispanic children were much less likely to withdraw from the program. Table 2 illustrates baseline intervention home conditions relevant to asthma exacerbation. Asthma triggers

associated with these conditions include mold, dust, dust mites, cockroaches, aerosol pesticides, rodent urine, and animal dander. High relative humidity provides the necessary moisture for many of these triggers. More than half of the households had experienced water damage in the previous year. In addition, many Table 1. Characteristics of subject children, as reported by caregivers at baseline (n=243): HHU pediatric asthma intervention program, Lansing, Michigan, 2005–2008 Characteristic

N

Percent

Age (in years) 0–4 5–11 12–17

85 109 49

35.0 44.9 20.2

Gender Male Female

134 109

55.1 44.9

Race One race White African American Other Multiracial Not reported

166 67 94 5 63 14

68.3 27.6 38.7 2.1 25.9 5.8

Hispanic

25

10.3

Health insurance Medicaid Alone In combination with other type Parent’s employer Other None

197 123 74 31 14 1

81.1 50.6 30.5 12.8 5.8 0.4

Number of other children living in home 0 1–2 3–6

59 138 46

24.3 56.8 18.9

Room where subject child usually sleeps Own room Parent’s room Other

199 30 14

81.9 12.3 5.8

Biological father does not live in home

212

87.2

Household occupancy status Homeowner Renter

106 137

43.6 56.4

Annual household income $20,000 $20,000–$39,999 $40,000

142 78 23

58.4 32.1 9.5

Caregiver education Did not graduate from high school High school graduate; no college At least some college

34 72 137

14.0 29.6 56.4




Table 2. Characteristics commonly associated with the presence of asthma triggers in participating homes at baseline: HHU pediatric asthma intervention program, Lansing, Michigan, 2005–2008 Characteristic

N

Percent

Leak, flooding, or other water damage in the past yeara

133

54.7

Aerosol pesticides (spray or bug bomb) useda

35

14.4

No working heating systema

10

4.1

110

74.3

122

82.4

8 23

5.4 15.5

12

8.1

8

5.4

87 14

58.8 9.5

Carpeting and rugs Family room has carpeting or rugb Subject child’s bedroom has wall-to-wall carpeting or area rugsb Windows Family room has no windows that can openb Kitchen has no windows that can openb Primary bathroomc has neither a vent nor a window that can openb Subject child’s bedroom has no windows that can openb Air-quality control Subject child’s bedroom has neither central nor room air conditioningb Subject child’s bedroom has humidifierb Reported by all 243 caregivers

a

Data for these characteristics were collected by HHU staff on the visual assessment form for 148 of the 243 homes. The original visual assessment form used for the first 95 households did not include these environmental characteristics but was revised for use on the remaining 148 homes.

b

c

Bathroom in which the family normally showers or bathes


rooms lacked the ability to ventilate humidity though a window or vent, and nearly one in 10 subject children had humidifiers in their bedrooms. Most homes had carpeting and/or rugs in the family room and the subject child’s bedroom. Floor coverings are prime locations where children can be exposed to asthma triggers. The few homes without a working heating system presumably used an alternative heating source; many of these sources generate combustion by-products, which are also asthma triggers. Finally, air conditioning allows windows to remain closed during high-allergen seasons and filters the air. However, air conditioning was lacking in more than half of the subject children’s bedrooms. Caregiver knowledge of asthma triggers Program staff asked caregivers 37 mostly true-false/ agree-disagree questions that included identification of specific asthma triggers, appropriate ways to respond to asthma attacks, and effects of asthma on daily living

(Table 3). Overall, respondents answered an average of three more questions correctly at six months than at baseline, thereby improving their overall score from 82.5% to 90.5% (p0.0001). Scores improved for 83% of caregivers, while 10% showed no change, and 7% scored worse. Caregivers’ scores improved substantially at six months for many important topics, such as cockroaches (96.3% answered correctly) and birds (93.8%) as asthma triggers, inhaled steroids not having the same side effects as oral steroids (93.8%), and people with asthma knowing how well their lungs are working (88.6%). For several questions, however, the percent of caregivers responding correctly was low at six months. Less than half correctly indicated that asthma symptoms cannot be worsened by mosquitoes (49.0%), eggs (36.6%), and chocolate (46.1%), and that asthma episodes usually do not occur without warning (45.9%). Home environmental conditions During introductory and baseline assessments, staff ascertained, through caregiver reporting and staff visual observation, both the presence of particular risk factors for asthma exacerbation and the absence of products that could be used to reduce the subject child’s exposure to triggers. Table 4 illustrates the percentage of households with each risk factor at baseline and six months. Households demonstrated improvement for most of the risk factor measures. While there was no statistically significant change in the percentage of caregivers reporting the presence of household indoor pets, fewer reported allowing pets in the child’s bedroom: 59.6% at baseline and 50.5% at six months (p0.05). Also, substantially fewer caregivers reported evidence of mold in the home: 58.2% vs. 38.9% (p0.0001). There was no measurable change in the reported evidence of cockroaches, but there was a decrease in the reported evidence of mice or rats: 19.8% vs. 12.8% (p0.01). Fewer households reported allowing stuffed toys in the child’s bedroom: 68.3% vs. 48.3% (p0.0001). There was some reduction in reported exposure to tobacco smoke, either within the home (21.8% vs. 14.4%) or by anyone caring for the child (51.3% vs. 43.8%) (p0.005). HHU staff visually observed that high-efficiency particulate air filters and pillow/mattress covers designed to control dust mites were generally absent in the subject children’s bedrooms at baseline (absent for 98.6%, 97.9%, and 96.5%, respectively). These items were among the basic and custom products supplied or installed by HHU staff. Among the listed environmental changes, the greatest change from baseline



to six months occurred in the prevalence of pillow (absent for 9.9%) and mattress (absent for 15.6%) covers (p0.0001).

Home cleaning frequency HHU staff encouraged caregivers to frequently perform a number of actions to improve and maintain the

Table 3. Questions used to measure asthma knowledge of the subject child’s caregiver and percent of caregivers who answered correctly for each question at baseline and six months: HHU pediatric asthma intervention program, Lansing, Michigan, 2005–2008 Percent of caregivers who answered correctly Questiona

Baseline

Six months

Change

Smoking around a child with asthma may make them cough but it is not harmful.

93.0

97.5

4.5

Asthma symptoms can be made worse by: Dust Cockroaches Mosquitoes Mold, mildew, or fungi Tobacco smoke Hard, crisp, or crunchy foods Infections Eggs Exercise Pet fish Chocolate Birds Cats Pollen Air pollution Emotional stress or excitement Dogs Watching television Dust mites

98.4 62.8 25.5 97.1 99.2 65.0 93.0 29.2 97.1 66.1 45.3 74.5 95.5 98.4 98.8 92.6 94.6 87.7 95.9

100.0 96.3 49.0 100.0 99.6 86.8 99.2 36.6 98.8 84.7 46.1 93.8 100.0 100.0 99.6 97.9 100.0 91.8 98.4

1.6 33.5 23.5 2.9 0.4 21.8 6.2 7.4 1.7 18.6 0.8 19.3 4.5 1.6 0.8 5.3 5.4 4.1 2.5 2.6

Is asthma an acute or a chronic disease?

96.5

99.1

Asthma can make you feel bad even if not wheezing.

95.6

98.7

3.1

Asthma episodes usually occur without warning.

22.7

45.9

23.1

Not all asthma episodes need to be taken seriously.

96.9

99.6

2.6

Asthmatics only need to see doctor about asthma when having an attack.

97.4

98.7

1.3

People can die from having an asthma attack.

97.4

99.6

2.2

If someone takes asthma medication everyday, they do not have to stay away from things to which they are allergic.

98.3

100.0

1.7

It is best to wait and see if asthma symptoms go away on their own before taking “as needed” medications.

95.6

99.6

3.9

An inhaler will deliver a useful dose of medicine no matter how it is used.

85.6

95.6

10.0

A person with asthma can become addicted to their asthma medications.

52.0

71.6

19.6

People with asthma have no way to know how well their lungs are working.

68.1

88.6

20.5

During an asthma attack, it is hard to blow out air from the lungs.

93.0

97.8

4.8

Asthma cannot be cured, but it can be controlled.

96.1

96.9

0.9

People with asthma should not exercise.

99.1

99.6

0.4

There is nothing you can do to keep from getting an asthma attack.

79.5

90.0

10.5

Asthma is all psychological, that is, in people’s heads.

99.6

99.6

0.0

Inhaled steroids have the same side effects as oral steroids.

73.1

93.8

20.7

Total

82.5

90.5

8.0

Questions were true/false or agree/disagree except whether asthma was an acute or chronic disease.

a




Table 4. Percent of participating households with environmental risk factors associated with asthma exacerbation at baseline and six months: HHU pediatric asthma intervention program, Lansing, Michigan, 2005–2008 Percent of homes with factor Risk factor Per caregiver self-report Stuffed toys in child’s bedroom Home has indoor feathered or furry pets Pets allowed in subject child’s bedroom Mold has been seen or a musty odor has been smelled in the subject child’s bedroom (past 30 days) Mold has been seen or a musty odor has been smelled in the rest of the home (past 30 days) Evidence of cockroaches inside the home (past 30 days) Evidence of mice or rats inside the home (past 30 days) Someone has smoked inside the home (past week) Smoker among those who take care of the subject child Per HHU staff visual assessment b No HEPA air filterc in subject child’s bedroom Mattress cover for controlling dust mitesc not used/available Pillow cover for controlling dust mitesc not used/available

Na

Baseline

Six months

Change

P-value

240 242 99

68.3 43.8 59.6

48.3 44.6 50.5

20.0 0.8 9.1

0.0001 NS 0.05

241

8.7

5.4

3.3

NS

239 243 242 243 240

58.2 7.0 19.8 21.8 51.3

38.9 5.8 12.8 14.4 43.8

19.3 1.2 7.0 7.4 7.5

0.0001 NS 0.01 0.005 0.005

145 141 141

98.6 96.5 97.9

77.9 15.6 9.9

20.7 80.9 88.0

0.0001 0.0001 0.0001

a Number of valid baseline/six-month caregiver response or visual assessment pairs. If data for a caregiver response (or visual assessment) were missing, not applicable, or otherwise invalid for either baseline or six months, that pair was excluded from analysis.

Visual assessment data were collected for only 148 of the 243 homes. The original form used for the first 95 households did not capture environmental characteristics.

b

c

Items provided by HHU staff during the program

HHU  Healthy Homes University NS  not statistically significant HEPA  high-efficiency particulate air

hygiene of their homes. Table 5 lists the most relevant of these for minimizing asthma triggers. At six months, caregivers reported that they had increased the frequency with which they performed each action. The increases were all statistically significant except for washing sheets and pillowcases. However, the degree to which they increased varied by the type of activity. They increased their dusting and washing of blankets and covers only slightly, but increased vacuuming by nearly once per month. Most notably, they nearly doubled the rate of vacuuming upholstered furniture. In addition, at six months, they reported washing their child’s stuffed toys nearly once a month. The increase in the reported rate of changing their furnace filter was affected by HHU staff performing the task during the three- and six-month visits. Subject child’s asthma severity Caregivers reported monthly frequencies for subject children experiencing negative health effects due to asthma (Table 6). For each of the listed indicators of asthma impact, the number of monthly occurrences

reported at six months was less than reported at baseline, and all improvements were statistically significant. The reductions ranged from 51% (wheezing first thing in the morning) to 71% (missed school due to asthma). Unscheduled medical care for subject child’s asthma Caregivers were asked in baseline and six-month surveys if the subject child had visited an emergency department or been hospitalized overnight for asthma in the previous six months. They were subsequently asked if, besides these events, the child had seen a health-care provider for asthma in the past six months, in which the visit was unscheduled (i.e., not scheduled more than 24 hours in advance). Figure 4 illustrates caregiver responses for the three types of medical care queried. For each measure, the proportion of households who sought medical care for the child’s asthma decreased substantially—48% for unscheduled visits to a healthcare provider, 53% for emergency department visits, and 68% for hospitalizations. All three reductions were statistically significant (p0.0001).



Table 5. Changes in caregiver-reported frequency of actions to reduce in-home environmental asthma triggers from baseline to six months: HHU pediatric asthma intervention program, Lansing, Michigan, 2005–2008 Mean frequencyb Action Dusting the child’s bedroom Dusting the other rooms in the home Vacuuming the floor of the child’s bedroom Vacuuming the floors in the other rooms of the home Vacuuming the upholstered furniture in the home Washing the child’s sheets and pillowcases Washing the blankets or covers on the child’s bed Washing the stuffed toys in the child’s bedroom Changing the heating system filter

Na

Baseline

Six months

Change

241 241 234 234 233 242 238 109 146

2.7 3.3 3.1 3.8 1.2 3.4 2.8 0.5 3.3

3.1 3.5 4.0 4.6 2.2 3.5 3.2 0.8 5.1

0.4 0.2 0.9 0.8 1.0 0.1 0.4 0.3 1.8

P-value 0.0001 0.05 0.0001 0.0001 0.0001 NS 0.0001 0.005 0.0001

a Number of valid baseline/six-month caregiver response pairs. If data for a caregiver response were missing, not applicable, or otherwise invalid for either baseline or six months, that pair was excluded from analysis. b

Times per month, except for changing the heating system filter, for which frequency is times per year

HHU  Healthy Homes University NS  not statistically significant

DISCUSSION We found that families completing the HHU program had modest, yet statistically significant, improvements in asthma knowledge, self-reported cleaning habits, and in-home environmental conditions. Among asthmaknowledge gains, most noteworthy was that one-third of caregivers became aware that cockroaches are asthma triggers. The most notable gain in self-reported cleaning habits pertained to the frequency of vacuuming, especially upholstered furniture. The most impressive environmental improvement was the increase in the percentage of households in which the subject child was using pillow and mattress covers designed to control

dust mites. These items were provided by the program and required minimal behavior change by families. Consistent with the changes described above, there were statistically significant caregiver-reported reductions in pediatric asthma severity. The number of days that subject children were negatively impacted by their asthma decreased at least 50% by all of our measures. Thus, not only were children experiencing symptoms less frequently, but also their asthma was impacting them less, specifically with missed school days and reduced physical activity. In addition, the percentage of households seeking medical care for their child’s asthma substantially decreased for each of our three measures: emergency department visits,

Table 6. Changes in caregiver reports of subject child’s asthma severity from baseline to six months: HHU pediatric asthma intervention program, Lansing, Michigan, 2005–2008 Mean frequencyb Impact of subject child’s asthma Had wheezing first thing in the morning Woke up because of wheezing, tightness in chest, or a cough Had shortness of breath because of asthma Had wheezing, tightness in the chest, or cough Had to slow down or stop play or activities because of asthma, wheezing, tightness in chest, or cough Missed preschool or school because of asthma

Na

Baseline

Six months

Change

P-value

227 231 230 236

6.2 8.7 9.4 12.0

3.1 3.3 3.4 4.9

3.1 5.4 6.0 7.1

0.0001 0.0001 0.0001 0.0001

236 141

9.1 1.9

3.3 0.5

5.8 1.4

0.0001 0.0001

a Number of valid baseline/six-month caregiver response pairs. If data for a caregiver response were missing, not applicable, or otherwise invalid for either baseline or six months, that pair was excluded from analysis.

Within the past 30 days

b




ospitalizations, and all other acute, unscheduled h medical visits. When viewed in conjunction with the fairly modest improvements in knowledge, cleaning behavior, and home environments, these reductions were striking. Previous studies have demonstrated that effective healthy homes intervention programs require multiple home visits.7,17–21 We designed our program on this premise, and staff made four in-home visits with most of the participating households. One key to program success that studies have cited is the effectiveness of outreach workers. This is important because they are the connection between the program design and human subjects. Specific characteristics that are vital to outreach worker effectiveness include empathy, subject matter expertise, and persistence. While we did not gather quantitative data evaluating our staff, one indication of their effectiveness in gaining participant trust and buy-in is that 81% of families who received the baseline visit remained in the program for the

full six months. The provision of valuable products also may have contributed to the high participant retention rate. Healthy People 2010 is a national health-promotion and disease-prevention initiative25 that includes environmental health objectives pertaining to healthy homes and healthy community issues. HHU addressed three of these national objectives: • To reduce indoor allergen levels—HHU home visits provided asthma-trigger reduction products to households and educated caregivers on ways they could reduce indoor allergens. • To reduce the proportion of housing units that are substandard—HHU staff corrected physical housing problems including water leaks, electrical deficiencies, pest infestations, inoperable heating equipment, cracks and holes, hand rails, and peeling lead-based paint. • To reduce the population’s exposure to pesticides—

Figure 4. Percentage of households that sought unscheduled health care for the child’s asthma within the past six months, as reported by caregivers at baseline and six months, by type of medical care received (n=243 for each type): HHU pediatric asthma intervention program, Lansing, Michigan, 2005–2008a

b

Type of medical care Reductions were statistically significant (p0.0001) for each medical care type.

a

Excluded from this category were emergency department visits and hospitalizations. These were considered “unscheduled” visits because they were not scheduled more than 24 hours in advance.

b




HHU staff educated households about integrated pest-management techniques and provided them with traps, baits, food containers, and trash cans. Program costs The following costs pertain to the products and services provided for asthma and injury prevention efforts implemented within the comprehensive program. The mean cost for the basic products provided for all households at the baseline visit was $387. Twelve percent of the households received a custom service, with a mean cost of $2,647 per household. Staffing and travel costs associated with a home visit were $230, and administrative office function costs were $1,055 per household. Limitations Our program had several limitations. Some of these could have affected our findings, while others inhibit our ability to attribute the apparent health improvement to our intervention. The reduction in asthma severity may have been artificially inflated due to reporting bias. Caregivers could have overstated asthma severity at baseline to justify program inclusion and understated it at completion to provide “desirable” results. Improvements in cleaning habits, likewise, may have been the result of reporting bias. Because HHU staff had stressed good home hygiene, caregivers may have embellished their cleaning habits at program completion to avoid the embarrassment of not meeting perceived expectations. Our program did not utilize a control group. In this case, an appropriate control group would have been a set of households similar to our intervention group at baseline in terms of housing conditions, child asthma severity, and availability of a local asthma coalition. Use of a control group against which to compare intervention group results is crucial because factors other than our intervention could have influenced outcomes. Without a control group, we cannot estimate the effect our program alone had on reducing asthma severity. We did not design the program for the purpose of generalizing results to a larger population. Such a design would have required recruiting households using probability sampling methods. The 243 families evaluated here were motivated to alleviate childhood asthma, as evidenced by their self-selection into the program and their diligence to participate through the entire six months. However, our findings may be indicative of results that other similarly designed programs could have when working with motivated families. We did not collect data on all of the factors that could have contributed to the observed reduction in

asthma severity. The National Heart, Lung, and Blood Institute—National Asthma Education and Prevention Program’s “Expert Panel Report 3: Guidelines for the Diagnosis and Management of Asthma”26 cites that, in addition to reducing exposure to environmental asthma triggers, the following are key to the long-term control of asthma: providing optimal pharmacotherapy, ensuring proper use of asthma medications, having children maintain normal activity levels, and maintaining effective communications between patients and their health-care providers. Finally, we have no information on the impact of our program beyond six months, either in terms of pediatric asthma severity or improvements to the home environment. Changes in caregiver behavior may have been temporary and due to the Hawthorne effect. That is, they may have modified their behavior simply in response to the fact that they were being studied. Current status—Healthy Homes University II In 2008, the Michigan Department of Community Health received grant funding to continue HHU through 2011. For this second version (HHU II), several changes were made to improve the program, including redesigning the questionnaire; modifying the basic products provided; offering environmental sampling and additional products as incentives for program compliance; performing environmental sampling for dust mite, cockroach, and mouse urine allergens; requiring an asthma action plan and scheduled well-asthma doctor visits to promote proper medication usage; utilizing Medicaid claims data, rather than self-reporting, to identify health outcomes; and comparing outcomes to a control group to evaluate effectiveness. CONCLUSIONS Improving the health of a child with asthma requires a multifaceted strategy that addresses the physical home environment, health-care utilization, medication adherence, and other extrinsic factors (e.g., health behaviors and caregiver involvement). Through education with multiple in-home visits by trained staff, families can gain knowledge about asthma triggers, effective methods for improving their home environment to minimize these triggers, how to most effectively utilize the health-care system, and the importance of appropriate use of effective medication. In the HHU program, we conducted multiple home visits and had very good participant retention rates, thanks to dedicated, persistent, and empathetic staff. We found that caregivers increased their awareness of important asthma topics and reported greater



frequency of trigger-reducing behaviors. The program assisted families in improving home environments by providing and directly installing certain products. Program staff did not measure changes in the use of appropriate asthma medications or regular well-asthma doctor visits, but are doing so for HHU II. While we found statistically significant reductions in asthma severity, we cannot attribute these outcomes solely to our intervention because of the reliance on self-reported data and the lack of a control group to which we could compare outcomes. Overall, the HHU program is a promising model for reducing pediatric asthma severity among motivated families. In 2005, primary funding was provided by U.S. Department of Housing and Urban Development (HUD), Office of Healthy Homes and Lead Hazard Control grant #MILHH0140-05, with additional support from the Michigan Department of Community Health (MDCH). The findings and conclusions in this article are those of the authors and do not necessarily reflect the conclusions or opinions of HUD or MDCH. The authors thank project field specialists and housing technical assistants Linda Stewart, Lana Ashley, Margaret Demps, and Lina Goodwin for working enthusiastically and devotedly with their clients. The authors also thank asthma epidemiologist Elizabeth A. Wasilevich, PhD, MPH, and environmental epidemiologist Lorraine L. Cameron, PhD, MPH, for their support and expertise in the program and survey design.

References

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1. Akinbami LJ, Moorman JE, Garbe PL, Sondik EJ. Status of childhood asthma in the United States, 1980–2007. Pediatrics 2009;123 Suppl 3:S131-45. Also available from: URL: http://pediatrics .aappublications.org/cgi/content/full/123/Supplement_3/S131 [cited 2010 Mar 18]. 2. National Center for Health Statistics (US). Asthma prevalence, health care use and mortality, 2002. NCHS health e-stat [cited 2010 Mar 18]. Available from: URL: http://www.cdc.gov/nchs/ data/hestat/asthma/asthma.htm 3. Akinbami LJ, Schoendorf KC. Trends in childhood asthma: prevalence, health care utilization, and mortality. Pediatrics 2002;110: 315-22. Also available from: URL: http://pediatrics.aappublications .org/cgi/content/full/110/2/315 [cited 2010 Mar 18]. 4. Centers for Disease Control and Prevention (US). Important asthma triggers [cited 2010 Mar 18]. Available from: URL: http://www.cdc .gov/asthma/triggers.html 5. Lanphear BP, Aligne CA, Auinger P, Weitzman M, Byrd RS. Residential exposures associated with asthma in US children. Pediatrics 2001;107:505-11. Also available from: URL: http://pediatrics .aappublications.org/cgi/content/abstract/107/3/505 [cited 2010 Mar 18]. 6. Committee on the Assessment of Asthma and Indoor Air, Division of Health Promotion and Disease Prevention, Institute of Medicine. Clearing the air: asthma and indoor air exposures. Washington: National Academies Press; 2000. 7. Morgan WJ, Crain EF, Gruchalla RS, O’Connor GT, Kattan M, Evans R 3rd, et al. Results of a home-based environmental intervention among urban children with asthma. N Engl J Med 2004;351:1068-80. Also available from: URL: http://content.nejm .org/cgi/content/full/351/11/1068 [cited 2010 Mar 18]. 8. Chilmonczyk BA, Salmun LM, Megathlin KN, Neveux LM, Palomaki GE, Knight GJ, et al. Association between exposure to environmental tobacco smoke and exacerbations of asthma in children. N Engl J Med 1993;328:1665-9. Also available from: URL: http://

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content.nejm.org/cgi/content/full/328/23/1665 [cited 2010 Mar 18]. Martinez FD, Cline M, Burrows B. Increased incidence of asthma in children of smoking mothers. Pediatrics 1992;89:21-6. Wilson SR, Yamada EG, Sudhakar R, Roberto L, Mannino D, Mejia C, et al. A controlled trial of an environmental tobacco smoke reduction intervention in low-income children with asthma. Chest 2001;120:1709-22. Also available from: URL: http://chestjournal .chestpubs.org/content/120/5/1709.long [cited 2010 Mar 18]. Shapiro GG, Wighton TG, Chinn T, Zuckrman J, Eliassen AH, Picciano JF, et al. House dust mite avoidance for children with asthma in homes of low-income families. J Allergy Clin Immunol 1999;103:1069-74. Krieger JK, Takaro TK, Allen C, Song L, Weaver M, Chai S, et al. The Seattle-King County healthy homes project: implementation of a comprehensive approach to improving indoor environmental quality for low-income children with asthma. Environ Health Perspect 2002;110 Suppl 2:311-22. Greenberg RA, Strecher VJ, Bauman KE, Boat BW, Fowler MG, Keyes LL, et al. Evaluation of a home-based intervention program to reduce infant passive smoking and lower respiratory illness. J Behav Med 1994;17:273-90. Irvine L, Crombie IK, Clark RA, Slane PW, Feyerabend C, Goodman KE, et al. Advising parents of asthmatic children on passive smoking: randomised controlled trial. BMJ 1999;318:1456-9. Also available from: URL: http://www.bmj.com/cgi/content/full/ 318/7196/1456 [cited 2010 Mar 18]. Phipatanakul W, Cronin B, Wood RA, Eggleston PA, Shih MC, Song L, et al. Effect of environmental intervention on mouse allergen levels in homes of inner-city Boston children with asthma. Ann Allergy Asthma Immunol 2004;92:420-5. Recer GM. A review of the effects of impermeable bedding encasements on dust-mite allergen exposure and bronchial hyperresponsiveness in dust-mite-sensitized patients. Clin Exp Allergy 2004;34:268-75. Dixon SL, Fowler C, Harris J, Moffat S, Martinez Y, Walton H, et al. An examination of interventions to reduce respiratory health and injury hazards in homes of low-income families. Environ Res 2009; 109:123-30. Saegert SC, Klitzman S, Freudenberg N, Cooperman-Mroczek J, Nassar S. Healthy housing: a structured review of published evaluations of US interventions to improve health by modifying housing in the United States, 1990–2001. Am J Public Health 2003;93:1471-7. Chan-Yeung M, Manfreda J, Dimich-Ward H, Ferguson A, Watson W, Becker A. A randomized controlled study on the effectiveness of a multifaceted intervention program in the primary prevention of asthma in high-risk infants. Arch Pediatr Adolesc Med 2000;154: 657-63. Lin S, Gomez MI, Hwang SA, Franko EM, Bobier JK. An evaluation of the asthma intervention of the New York State Healthy Neighborhoods Program. J Asthma 2004;41:583-95. Takaro TK, Krieger JW, Song L. Effect of environmental interventions to reduce exposure to asthma triggers in homes of low-income children in Seattle. J Expo Anal Environ Epidemiol 2004;14 Suppl 1: S133-43. Centers for Disease Control and Prevention (US). Behavioral Risk Factor Surveillance System child asthma call-back survey questionnaire. Atlanta: CDC; 2005. Also available from: URL: http://www .cdc.gov/asthma/survey/brfss.html [cited 2011 Jan 5]. Williams S, Sehgal M, Falter K, Dennis R, Jones D, Boudreaux J, et al. Effect of asthma on the quality of life among children and their caregivers in the Atlanta Empowerment Zone. J Urban Health 2000;77:268-79. SAS Institute, Inc. SAS: Version 9.1.3. Cary (NC): SAS Institute, Inc.; 2004. Department of Health and Human Services (US). Healthy People: environmental health [cited 2010 Mar 18]. Available from: URL: http://healthypeople.gov/2020/topicsobjectives2020/overview .aspx?topicid=12 Department of Health and Human Services (US). National Institutes of Health, National Heart, Lung and Blood Institute, Asthma Education and Prevention Program. Expert panel report 3: guidelines for the diagnosis and management of asthma. Bethesda (MD): National Institutes of Health; 2007.


Practice Articles

Oklahoma Healthy Homes Initiative

Fahad Khan, MPHa

ABSTRACT Compelling scientific evidence suggests that a strong association exists between housing-related hazards and the health and safety of their residents. Health, safety, and environmental hazards (such as asthma and allergy triggers), unintentional injury hazards, lead-based paint hazards, and poor indoor air quality are interrelated with substandard housing conditions. This article describes a Healthy Homes initiative to address these hazards in a coordinated fashion in the home, rather than taking a categorical approach, even in the presence of multiple hazards. It also provides an overview of Oklahoma’s Healthy Homes initiative and its pilot project, the Tulsa Safe and Healthy Housing Project, which is currently administered in Tulsa in collaboration with Children First, Oklahoma’s Nurse-Family Partnership program. This pilot project seeks to open new areas of research that can lead to a greater understanding of environmental health issues related to substandard housing in the United States, which will eventually make homes safer and healthier.

Oklahoma State Department of Health, Oklahoma Childhood Lead Poisoning Prevention Program, Oklahoma City, OK

a

Address correspondence to: Fahad Khan, MPH, Oklahoma State Department of Health, Oklahoma Childhood Lead Poisoning Prevention Program, 1000 N.E. 10th St., Oklahoma City, OK 73117; tel. 405-271-9444 ext. 56754; fax 405-271-4971; e-mail . ©2011 Association of Schools of Public Health


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In 1999, the U.S. Department of Housing and Urban Development (HUD) launched its Healthy Homes Initiative (HHI) to address diseases, injuries, and unhealthy conditions related to substandard housing.1,2 The HHI is a comprehensive approach to address a broad range of housing deficiencies and hazards in a coordinated fashion rather than taking a categorical approach to health and safety hazards in the home, even in the presence of multiple issues.2,3 Several federal agencies, including the Centers for Disease Control and Prevention (CDC), have turned their attention to the HHI and have begun collaborating with HUD to investigate the causal relationships between housing and health.2 On June 9, 2009, then Acting Surgeon General Steven K. Galson issued The Surgeon General’s Call to Action to Promote Healthy Homes.3,4 OKLAHOMA HEALTHY HOMES INITIATIVE In response to the shift in focus of federal agencies to the HHI, the Oklahoma Childhood Lead Poisoning Prevention Program (OCLPPP) also began developing the capacity to meet this challenge. In partnership with the National Center for Healthy Housing (NCHH) and East Central University in Ada, Oklahoma, OCLPPP has offered the course Essentials for Healthy Homes Practitioners five times since May 2008. This two-day course is designed for professionals who routinely visit clients in their homes. The training is focused on reviewing Healthy Homes issues and challenges, including what conditions must be present to make a home healthy and how to recognize visible environmental health and safety hazards. In November 2008, OCLPPP offered another Healthy Homes course, Launching a Healthy Homes Initiative, targeted toward health and housing leaders and decision makers seeking to establish Healthy Homes programs and activities in their communities.5 In partnership with NCHH, the National Environmental Health Association (NEHA) offers the Healthy Homes specialist certification to recognize health and housing professionals with expertise in the area of Healthy Homes.6 As of June 18, 2010, 531 individuals— including 18 from Oklahoma—have been certified since 2007, when the credential was first offered. These courses have put OCLPPP on the path to solidifying Healthy Homes alliances and providing the basis for skills development in specialized areas of environmental hazards identification and control to approximately 300 health and housing professionals in Oklahoma.

TULSA SAFE AND HEALTHY HOUSING PROJECT In 2009, CDC selected OCLPPP as one of six sites in the nation for its two-year Healthy Homes pilot project, Building Strategic Alliances for Healthy Housing Pilot. Oklahoma’s project, the Tulsa Safe and Healthy Housing Project, is currently administered in collaboration with Children First (C1), Oklahoma’s Nurse-Family Partnership (NFP) program and a longtime partner of OCLPPP. Both C1 and OCLPPP are housed within the Oklahoma State Department of Health. OCLPPP has been providing training on childhood lead poisoning and its prevention to C1 nurses for the past five years, as part of their required continuing education. Several C1 nurses have attended the Essentials of Healthy Homes for Practitioners course. The C1 program is based on the NFP initiative, a highly acclaimed, evidence-based community health and nurse home-visitation program with partnering sites in 32 states.7 The NFP initiative meets the congressionally established Top Tier evidence standard.8 C1 registered nurses provide services to first-time mothers and their families during pregnancy and up to two years after the child is born.9 To enroll in the program, clients must be #29 weeks pregnant and meet the same income eligibility criteria as required for the Special Supplemental Nutrition Program for Women, Infants, and Children and Medicaid. These young, low-income C1 clients are likely to have poor birth outcomes due to a combination of multiple factors including age, race/ethnicity, high-risk behaviors, socioeconomic status, lack of health insurance, and environmental exposures. The impact of substandard and inadequate housing and living conditions on this population, who are already at high risk for adverse health outcomes, can be significant. OPERATIONAL APPROACH Training of C1 nurses During the first year, 25 C1 nurses in Tulsa will attend the National Healthy Homes Training Center and Network’s (NHHTC) one-day course, Healthy Homes for Community Health Workers.5 The nurses will be trained on Healthy Homes principles and how to educate clients about the connection between health and housing (Figure). NHHTC’s one-page assessment form, Visual Survey Report (VSR), has been adapted for the C1 nurses to collect information on visually evident structural problems and hazards during client visits.


Public Health Reports / 2011 Supplement 1 / Volume 126 continued on p. 30

Figure. Housing and health hazards matrix used in training Children First nurses for their role in the Tulsa Safe and Healthy Housing Project

Oklahoma Healthy Homes Initiative  29


ETS 5 environmental tobacco smoke

pCi/L 5 picocuries per liter

ER 5 emergency room

CO 5 carbon monoxide

EPA 5 Environmental Protection Agency

µg/dL 5 microgram per deciliter

CDC 5 Centers for Disease Control and Prevention

IQ 5 intelligence quotient

IPMC 5 International Property Maintenance Code

HP 5 Healthy People

Figure (continued). Housing and health hazards matrix used in training Children First nurses for their role in the Tulsa Safe and Healthy Housing Project



Client recruitment and retention OCLPPP will evaluate the completed VSR forms and select 25 eligible clients for an in-depth home assessment during the first year, based on factors identified through the VSR form, including housing age and hazards. Some of them will likely be Spanish-speaking, as a significant portion of Tulsa C1 clients are Hispanic (27% in 2009) and receive services from bilingual C1 nurses. These clients will receive Healthy Homes assessments from OCLPPP’s bilingual case manager, who also holds NEHA’s Healthy Homes specialist certification. Community outreach programs, including homevisitation programs, can struggle with client retention, and C1 is no exception.10 However, the intensive level of support provided by highly trained and experienced registered nurses in guiding many low-income, firsttime mothers during a challenging phase of their lives develops a trusted partnership between them, which can help in maximizing client retention. Home assessments The Pediatric Environmental Home Assessment form developed by NHHTC has been adapted to collect baseline data on general housing characteristics, home environment, sleep environment, home safety, and indoor pollutants during the home assessments. Additionally, tools such as carbon monoxide meters, carbon dioxide meters, moisture meters, lead-dust test kits, and borescopes will be used during home assessments to determine the presence of certain hazards. Following assessment and identification of health and safety hazards, C1 nurses will provide families appropriate low-cost health and safety items, including smoke alarms, carbon monoxide alarms, safety latches, nonslip area-rug pads, safety gates, night-lights, bathroom spout covers, infant bath pads, bathroom safety kits, anti-scald devices, oven locks, sticky mats for doorways, table edge guards, window guards, furnace filters, crawling-insect traps, humane mouse traps, crib mattress encasements, and steam vacuums. These items were chosen specifically because they can easily be transported to another residence if the family moves. C1 nurses will provide installation assistance and will educate families on how to reduce exposure to housing and safety hazards by following the seven Healthy Homes principles (i.e., keep it dry, clean, ventilated, pest-free, safe, contaminant-free, and maintained).5 Action plan OCLPPP will develop a client-specific action plan for the client and the C1 nurse assigned to the client based on the home-assessment findings. The action plan will identify areas of concern inside the home and goals

the family can set to improve the environmental safety of their homes. Achievement of these goals will serve as a source of positive reinforcement for the family as they begin developing a feeling of security about their success in improving their health, as well as their housing-safety profile. Referral partnerships Assistance will be provided in connecting families with community and government programs for services beyond the project resources. OCLPPP has been allied with partners such as the city of Tulsa’s Working in Neighborhoods department, the Tulsa Health Department’s Environmental Health Services division, the American Lung Association of the Central States, and the Community Action Project of Tulsa County. The Working in Neighborhoods department includes a Neighborhood Inspections section, which enforces nuisance and zoning ordinances to help maintain the highest level of safety and health standards in neighborhoods, and a Housing section, which oversees the city’s housing assistance programs and property maintenance enforcement. The Working in Neighborhoods department offers grant and loan programs to owner-occupants of low to moderate income who reside in the city of Tulsa. Areas of service include lead-based paint, electrical, plumbing, security (doors and windows), roofs, heating, interior issues, and weatherization. The Tulsa Health Department’s Environmental Health Services division inspects existing structures to ensure and enforce certain minimum building standards that must be in place if the structure is occupied or used. The standards include, but are not limited to, requirements pertaining to sanitation, maintenance, and electrical, mechanical, and plumbing systems. The American Lung Association of the Central States offers a variety of programs in Tulsa and throughout the central U.S. states to prevent, protect against, and control lung diseases in adults and children. Some of the programs currently offered in Tulsa include Breathe Smart from the Start, Freedom from Smoking, and Not on Tobacco. The Community Action Project of Tulsa County is a comprehensive anti-poverty agency with a history of providing a variety of services to low-income individuals and families, including access to safe and affordable housing in Tulsa County, including the city of Tulsa. The agency is a charter member of NeighborWorks, a national network of more than 230 communitybased organizations in 50 states dedicated to creating healthy communities. As the primary designated Head Start agency for Tulsa County, the Community Action



roject has 14 Early Head Start and Head Start centers P in Tulsa. Follow-up visit Three months after the initial home assessment, C1 nurses will conduct a follow-up home visit with the client to administer a three-month follow-up survey individually designed for that client, based on the specific hazard reductions completed. Additional follow-up visits can be provided as needed. Year two After putting the infrastructure in place for the Tulsa Safe and Healthy Housing Project in year one, OCLPPP will expand the Healthy Homes assessment and hazardreduction activities from 25 to 50 at-risk homes in year two. A refresher course in healthy housing principles will be offered to the C1 nurses, and partnerships and collaborations will be enhanced for the continuation of the project. Referral networks and services provided through community and government programs will be expanded where appropriate. Data entry Data collected during initial and follow-up visits will be entered into a Microsoft® Access database and regularly monitored to guarantee that comparable data are collected. Potential challenges Anticipated challenges include landlord-tenant issues and the possibility of rental clients suffering retaliatory eviction after reporting a problem needing remediation to the landlords. OCLPPP will refer these clients to the Tulsa Health Department’s Environmental Health Services for environmental inspections. Clients can potentially be protected from retaliatory eviction if health and safety hazards are identified and documented in environmental inspection reports. Other concerns could be maintaining contact with highly mobile clients and loss to follow-up, although the close association of C1 nurses with the clients can keep the retention rates higher. Finally, the three-month follow-up visits could prove insufficient, as research has shown that greater beneficial effects are found in home-visitation programs of longer duration.11 EVALUATION Evaluation measures have been developed to demonstrate the project’s progress and intended health and housing improvement. Data collected from initial and

follow-up visits will be compared and analyzed to determine improvements in the health and housing-safety profile of the family. Improvements may include behavior changes, such as reducing or eliminating sources of pest infestation and proper storage of hazardous household products, or reduction in the severity of asthma symptoms. CONCLUSION The Tulsa Safe and Healthy Housing Project has the potential to open up new areas of research that can lead to greater understanding of best practices to make homes healthier and safer in the U.S. On March 23, 2010, the Patient Protection and Affordable Care Act was signed into law. The Maternal, Infant, and Early Childhood Home Visiting Program, which was created as part of the Act to support evidence-based programs focused on improving the well-being of families with young children, was also modeled on the NFP initiative. Both C1 and OCLPPP’s Tulsa Safe and Healthy Housing Project are multidimensional, holistic programs focusing on various aspects of the family’s health and safety. It seems reasonable that such a partnership, characterized by evidence-based preventive interventions, with the potential to yield quantifiable health, social, and economic benefits to families and communities, should be expanded not only in Oklahoma but also across the country. The project was supported by Cooperative Agreement #1U88EH000570-01 from the Centers for Disease Control and Prevention (CDC). Its contents are solely the responsibility of the author and do not necessarily represent the official views of CDC.

REFERENCES 1. Department of Housing and Urban Development (US), Office of Healthy Homes and Lead Hazard Control. Leading our nation to healthier homes: the healthy homes strategic plan. 2009 [cited 2010 May 15]. Available from: URL: http://www.hud.gov/offices/lead/ library/hhi/hh_strategic_plan.pdf 2. Smith KD. From healthy homes to health equity. J Public Health Manag Pract 2010;16(5 Suppl):S3-4. 3. Meyer PA. Healthier homes for a healthier nation. J Public Health Manag Pract 2010;16(5 Suppl):S1-2. 4. Public Health Service (US). The Surgeon General’s call to action to promote healthy homes. 2009 [cited 2010 Oct 14]. Available from: URL: http://www.surgeongeneral.gov/topics/healthyhomes/ calltoactiontopromotehealthyhomes.pdf 5. Neltner T. National Healthy Homes Training Center and Network: building capacity for healthy homes. J Public Health Manag Pract 2010;16(5 Suppl):S75-8. 6. National Center for Healthy Housing. Healthy homes specialist credential. 2008 [cited 2010 Oct 14]. Available from: URL: http://www.nchh.org/LinkClick.aspx?fileticket=JoqoXgWsaLg% 3d&tabid=348 7. Nurse-Family Partnership. Overview [cited 2010 Oct 14]. Available from: URL: http://www.nursefamilypartnership.org/assets/PDF/ Fact-sheets/NFP_Overview



8. Coalition for Evidence-Based Policy. Top Tier evidence initiative: evidence summary for the Nurse-Family Partnership. 2010 [cited 2010 Oct 14]. Available from: URL: http://evidencebasedprograms .org/wordpress/wp-content/uploads//NFP-updated-summary-forrelease-Jan2010.pdf 9. Oklahoma State Department of Health, Children First program. Children First brochure [cited 2010 Oct 14]. Available from: URL: http://www.ok.gov/health/documents/Children%20First%20 Brochure2rev.pdf

10. Senturia YD, McNiff Mortimer K, Baker D, Gergen P, Mitchell H, Joseph C, et al. Successful techniques for retention of study participants in an inner-city population. Control Clin Trials 1998;19: 544-54. 11. Council on Community Pediatrics. The role of preschool homevisiting programs in improving children’s developmental and health outcomes. Pediatrics 2009;123:598-603.


Practice Articles

Making Child Care Centers SAFER: A Non-Regulatory Approach to Improving Child Care Center Siting

Tarah S. Somers, RN, MSN/ MPHa Margaret L. Harvey, MPHb Sharee Major Rusnak, MSPH, ScDb

ABSTRACT Licensed child care centers are generally considered to be safe because they are required to meet state licensing regulations. As part of their licensing requirements, many states inspect child care centers and include an assessment of the health and safety of the facility to look for hazardous conditions or practices that may harm children. However, most states do not require an environmental assessment of the child care center building or land to prevent a center from being placed on, next to, or inside contaminated buildings. Having worked on several sites where child care centers were affected by environmental contaminants, the Centers for Disease Control and Prevention and the Agency for Toxic Substances and Disease Registry (ATSDR) endeavor to raise awareness of this issue. One of ATSDR’s partner states, Connecticut, took a proactive, non-regulatory approach to the issue with the development its Child Day Care Screening Assessment for Environmental Risk Program.

Centers for Disease Control and Prevention, Agency for Toxic Substances and Disease Registry—Region 1, Boston, MA

a

Connecticut Department of Public Health, Environmental Health Section, Environmental and Occupational Health Assessment Program, Hartford, CT

b

Address correspondence to: Tarah S. Somers, RN, MSN/MPH, Centers for Disease Control and Prevention, Agency for Toxic Substances and Disease Registry—Region 1, 5 Post Office Sq., Ste. 1010, Mail Code ATSDR10-1, Boston, MA 02109; tel. 617-918-1493; fax 617-918-1494; e-mail .

34 


Improving Child Care Center Siting  35

As used in this article, the term child care center encompasses organized facilities that provide day care, preschool, or nursery school. It does not include “family day cares” or child care provided in an individual’s home to a small number of children. Child care centers include for-profit (such as large chain day cares), nonprofit (such as church-based preschools), and publicly funded (such as Head Start) centers. Child care centers are different from schools because most are privately owned and operated, while many schools receive some public funding, are publicly administered, or go through a public process when the location of the school is being determined. Unlike school, attendance at child care centers is not mandatory for children. Children who attend child care centers are generally infants to 5 years of age. The number of children in the United States who attend child care centers is difficult to quantify because of the many different types of child care arrangements in this country. A 2005 report based on a U.S. Census Bureau survey found 23.8% of the 11.2 million children younger than aged 5 years in some type of child care arrangement were in organized child care facilities.1 A separate report found there were more than six million licensed child care center spaces.2 In 2002, 42% of 3-year-olds and 67% of 4-year-olds attended preschool.3 Young children are at greater risk than adults from exposure to environmental contaminants because of children’s physiology (such as higher respiration rates than adults) and their rapidly developing bodies and behaviors (such as mouthing objects). Child care center workers also tend to be women of childbearing age who are particularly vulnerable to health risks from exposure to environmental hazards. Data from the 2000 Census showed that 95.5% of day care workers and 97.5% of preschool and kindergarten teachers were women.4 Despite the vulnerability of these children and their caregivers, most child care centers in the U.S. are not required to conduct a site history, environmental site assessment, or environmental audit before obtaining a license. Such an investigation could help prevent a center from being located on land or in a building that is contaminated from past industrial use or that is at risk of contamination from neighboring facilities. Without an environmental site history or site assessment, there is a risk that child care centers are operating on sites that could expose children to harmful contamination. In addition, most child care operators may expect that if their prospective child care center site is contaminated, someone would notify them. This is usually not the case. Each state regulates child care centers differently.

The “2005 Child Care Licensing Study: Final Report” found that only 12 states required some type of environmental testing in child care centers for lead, radon, or asbestos, while 39 states required an environmental inspection.2 In this report, the term “environmental inspection” included fire, health, or building code inspections. These inspections look at the overall physical environment of the center and document safety and health practices such as the presence of smoke detectors, cleanliness of food service areas and food handling procedures, toileting and diapering of children, playground safety, and sanitation of the facility. An environmental inspection may also include checking for cracking, peeling, or chipping paint (which could contain lead) or looking for the proper handling and storage of chemicals, such as cleaning products and pesticides. While critical for keeping children safe from many physical and chemical hazards, these limited environmental inspections do not address environmental contamination that may be present from past use of the property. Additionally, these inspections do not address environmental contamination from nearby facilities that may be affecting the child care center, such as a dry cleaner or nail salon. Caring for Our Children: National Health and Safety Performance Standards: Guidelines for Out-of-Home Child Care is a collaborative publication from the American Academy of Pediatrics, American Public Health Association, and National Resource Center for Health and Safety in Child Care and Early Education, and it provides voluntary guidance for child care center safety, including a section on conducting an environmental audit: An environmental audit should be conducted before construction of a new building or renovation of an older building. The environmental audit should include assessments of the following: (a) historical land use, seeking possibility of toxic contamination of soil; (b) the possibility of lead and asbestos in older buildings; (c) potential sources of infestation, noise, air pollution, and toxic exposures; (d) the location of the playground in relation to infested stagnant water, roadways, industrial emissions, building exhaust outlets, and any other hazards to children.5

Although this guidance was available, no data were readily available that show which states had this type of language in their child care center licensing regulations. In July 2009, a review of the state licensing regulations for all 50 states and the District of Columbia (DC) was conducted by accessing the state regulations via the National Resource Center for Health and Safety in Child Care and Early Education, whose website



includes links to child care licensing websites for all 50 states and DC.6 The July 2009 review indicated that all websites were current. It is possible that some states have added legislation, but new regulations based on that legislation are not yet available on the child care licensing website. In addition, some cities, towns, and counties may have other licensing regulations that are different from their state’s regulations. Because of the sheer volume of possible local regulations, only the state regulations were considered. This review revealed that only New York and New Jersey had language in their licensing regulations requiring a site or building being considered for a child care center to be free from environmental hazards, and, if historic or current use of the site indicates that environmental hazards are present, testing and inspection by an appropriate local official or authority.7,8 Both New York and New Jersey enacted their regulations largely due to highly publicized incidents that occurred in the states. In New York, a concerned parent alerted the county health department that a child care center was adjacent to the Jackson Steel federal Superfund site. The child care center had been in operation for about seven years. The Jackson Steel site had manufactured metals and disposed of perchloroethylene (also know as PERC or tetrachloroethylene) next to the child care center site. Air sampling found PERC in the indoor air of the child care center to be exceeding the state indoor air guidance levels. The child care center closed voluntarily after the air sampling results were released.9 In 2006, it was discovered that a child care center in New Jersey was located in a building that once manufactured mercury-filled thermometers. The company that made the thermometers shut down operations in 1994, and the building remained vacant until 2001 when it was purchased by a local realtor. In 2004, the facility was rented to a day care operator.10 Testing in place at the time under New Jersey day care licensing regulations indicated the child care center was in compliance for lead, asbestos, and radon, which were not found at the center. Despite this compliance testing, elevated levels of mercury in indoor air and surface wipe samples were later found to be present in the center, prompting immediate closure. The children and staff were also biomonitored for urine mercury levels. At the time of the incident, there was no requirement in New Jersey that a site history be completed for a child care center property.10 Although these incidents were highly publicized, the Centers for Disease Control and Prevention and the Agency for Toxic Substances and Disease Registry (ATSDR) have been involved with other child care

centers affected by environmental contaminants that did not receive as much media attention. For example, in 2009, ATSDR assisted with an investigation of a child care center that was adjacent to a dry cleaning business in a strip mall. The city collected indoor air data as part of an initiative to evaluate air quality in structures adjacent to operating dry cleaners. Indoor air sampling of the child care center revealed that levels of PERC were a potential public health hazard to the sensitive population of the center. Recommendations were made to conduct more sampling, inform parents and workers of the findings, locate the source of contamination, and eliminate the exposure.11 Some types of sites are more likely to have environmental contamination than others. Sites that raise concerns include, but are not limited to, dry cleaners, smelters, mills, factories, gas stations, auto repair shops, landfills, U.S. Environmental Protection Agency (EPA) or state hazardous waste sites, rifle ranges, leaking underground storage tanks, and illegal drug labs. Vacant lots are also suspect areas that may be contaminated from previous use or illegal dumping activities. However, this issue is not an urban-only problem. “Pristine” land, such as former orchards, agricultural land, or buildings on agricultural land, may also be contaminated with pesticides or other chemicals. Given that there are more than 100,000 licensed child care centers in the U.S., there could be many situations similar to the ones described that have yet to be discovered.1 To address this problem, the state of Connecticut decided to take a proactive, non-regulatory approach to help make sure that child care centers are placed in the safest locations possible. SAFER Program Recognizing the potential for child care centers to be located on sites where environmental contamination could be harmful, the Connecticut Department of Public Health (CT DPH) Environmental and Occupational Health Assessment Program (EOHA) decided to partner with its Child Day Care Licensing Program in 2007 to create the Screening Assessment for Environmental Risk (SAFER) Program. EOHA was motivated to develop the SAFER Program because it wanted to prevent a Kiddie Kollege-type incident (the day care described previously that was located in the former mercury-thermometer manufacturing building)10 from happening in Connecticut. The SAFER Program is a proactive, non-regulatory approach to find child care centers on or near hazardous sites and raise awareness about safe child care center siting. EOHA chose to pursue a non-regulatory approach because



it was quicker and easier to implement than getting new regulations passed. It also required less resource investment than a regulatory approach. Additionally, EOHA had evidence that the regulated community (i.e., child day cares) would be more likely to embrace the SAFER Program if it did not involve onerous regulatory requirements that might make it more costly to open or operate a day care. For example, requiring costly environmental site assessments prior to opening a day care may prevent owners from opening a new center. A non-regulatory approach also afforded EOHA greater flexibility with its guidelines. After initial implementation of the SAFER Program, EOHA made (and continues to make) modifications to the program in response to suggestions from the child day care regulators. While a non-regulatory approach may have many advantages over a regulatory approach, it is recognized that a non-regulatory program has some important limitations. These limitations are discussed along with the program’s strengths. Connecticut has more than 1,500 licensed child care centers within the state. The SAFER Program includes both child day care centers and group day care homes. In Connecticut, a child day care center is defined as providing a program of supplementary care to more than 12 related or unrelated children outside of a private home on a regular basis. Group day care homes offer or provide a program of supplementary care (a) to no fewer than seven or more than 12 related or unrelated children on a regular basis, or (b) that meets the definition of a family day care home except that it operates in a facility other than a private family home. The SAFER Program does not include family day care homes providing care for six or fewer children. The SAFER Program was designed using three approaches to find child care centers with potential environmental concerns. The first approach looks at the location of licensed child care centers and compares those locations with the Connecticut Department of Environmental Protection’s (CT DEP’s) list of known hazardous waste sites. This cross-check procedure is currently performed manually. Soon, geographic information system (GIS) technology will be used to search for new and existing child care centers within one-eighth of a mile of known hazardous sites. The major limitation of this first approach is that the CT DEP waste sites list is not a complete database of all properties where hazardous chemicals were used, disposed of, or stored. Because the CT DEP list is not complete, the SAFER Program relies on two additional approaches, described in the following paragraphs, to identify child care centers with potential environmental concern.

The second approach uses a property history questionnaire developed for child care centers applying for new licenses. The questionnaire was designed to gather information that helps EOHA, in consultation with day care licensing staff, identify child care centers that may be located on a site that has past environmental contamination. The questionnaire asks those seeking a license to provide information about the past use of the property and buildings. Questions include whether the site was used in the past as a dry cleaner, gas station, landfill or dump, funeral home, or shooting range. Also included are guidance and resources that assist applicants with completing the property history questionnaire. Although the questionnaire is voluntary, because it is part of the child day care application package, applicants appear to be giving the questionnaire greater attention than if it were distributed separately from the application forms. Nevertheless, the voluntary nature of the questionnaire is an important limitation that must be acknowledged. Another limitation to this approach is that the property history questionnaire is not currently distributed to child care centers already in operation. The third approach consists of a newly developed inspection referral form. New child care centers in Connecticut are inspected by CT DPH staff and the local health department prior to beginning operations. Once a day care is in operation, it is inspected on a regular basis by state and local staff. The referral form helps inspectors identify property or building attributes that could signal the presence of hazardous contamination. The referral form also helps inspectors identify businesses operating next to a child care center that could adversely impact the environmental quality of the center, such as a dry cleaner or nail salon. During their regularly scheduled inspections, a child day care inspector only needs to devote a small amount of additional time to look for building and property attributes included in the referral form. EOHA provides ongoing training for its day care inspectors on how to use the new form. The form was also provided to local health departments for their use when conducting day care inspections. Though use of the inspection referral form is voluntary, there appears to be fairly widespread use of the form at the state and local level. Inspectors view the form as an important tool to help them ensure that day cares are operating in buildings and on land that is as safe as possible. Ongoing training will strive to improve usage of the form by inspectors. When the Day Care Licensing Program refers a day care center to EOHA, EOHA begins gathering and reviewing all information available for the property. Coordination with the local health department, CT



DEP, EPA, the child care center operator, the property owner, and the state’s Child Day Care Licensing Program are also a large part of follow-up activities. If additional action is needed, such as collecting environmental data, EOHA coordinates with all appropriate parties. For sites that are found to be a problem, EOHA provides recommendations to reduce exposure from environmental contaminants and uses risk communication to help families and day care workers understand the potential exposure and risks from exposure. There are no regulations specifically requiring follow-up at child day cares identified through the SAFER Program. Despite this lack of regulation, EOHA has not yet encountered difficulties in securing compliance with its recommendations to day cares. Recommendations have included soil and air sampling, and soil remediation. From September 2007 to May 2010, the SAFER Program generated 14 referrals to EOHA. Five of the referrals were for leaking underground storage tanks (LUSTs). LUSTs remain on the CT DEP hazardous waste lists even after the sites have been remediated. Therefore, these referrals were quickly resolved by coordination with the CT DEP’s LUSTs group. There were no hazards to the day cares from these former LUSTs. Two referrals were for groundwater contamination issues, but were resolved by coordination with CT DEP to be sure there were no vapor-intrusion issues from the groundwater. Vapor intrusion occurs when volatile chemicals from contaminated groundwater or soil migrate into buildings. Types of contaminants that can lead to vapor-intrusion issues include volatile and semivolatile organic compounds, mercury, radon, and hydrogen sulfide. Five additional referrals in December 2009 required site visits. These sites were referred because they were located in or near a former mill, industrial complex, or agricultural building. One site was located in a shopping center near an auto paint store. It was ultimately determined that the child care centers were not being impacted by previous contamination or nearby businesses. Two child care centers needed more complex follow up. The first was located on a former waste site and was found to have elevated levels of arsenic in the playground soil. The site was addressed with the coordination of the local health department, CT DEP, the property owner, and the state Child Day Care Licensing Program. Additional soil samples were collected and all parties worked to come up with a remedial plan. Risk communication was also used with the child care center operator and with the parents of children attending the center.

The second center was located in a former funeral home, where a day care director was planning on expanding operations into the basement where embalming procedures once occurred. In close coordination with the city’s health department, EOHA requested indoor air sampling in areas of the basement where the director planned on expanding her center. After discussions with the city’s health department and EOHA, the day care operator had not yet decided if the expansion would take place. Conclusions Given the tens of thousands of child care centers within the U.S., the possibility exists that many centers may be placed on sites or in buildings where environmental hazards could harm children or workers. The child care licensing inspections currently being conducted in most states may not identify a child care center placed on a contaminated site or in a contaminated building. When these sites come to attention, the situation can cause great stress for worried parents and workers as they fear the worst for the children’s health. The situation can generate media attention, create distrust of the licensing process and safety of child care centers, and produce public demands to keep something similar from happening in the future. States may use various approaches to address this issue, such as passing new legislation, creating new regulations under existing legislation, or creating new non-regulatory approaches. The Connecticut SAFER Program is an example of one state’s innovative, nonregulatory approach to the issue. The SAFER Program does have its limitations, and it is possible that a child care center may still slip through the system and be placed in a location with environmental contamination. However, because no new regulations were sought, EOHA was able to get the SAFER Program up and running quickly. The SAFER Program was developed collaboratively between EOHA and CT DPH’s Child Day Care Licensing Program and places little additional burden on the limited resources of day care inspectors. EOHA has provided ongoing training for inspectors and is available and accessible to respond quickly to any question or concern raised by an inspector. This has helped the program gain acceptance among the day care inspectors and makes it more likely that inspectors will continue to use the referral form. Further, the SAFER Program does not place onerous requirements on child day care owners/operators. If a day care is found to need complex follow-up, such as environmental sampling or cleanup, EOHA works closely with all parties—local health department, CT



DEP, child care center operator/owner, and day care licensing staff—to identify solutions that are health protective and cost-effective. The fact that the SAFER Program is non-regulatory has made it easier to craft creative solutions that fit each individual situation. The SAFER Program highlights how public health and environmental health professionals partnered with child care licensing professionals to help improve the siting of child care centers. As a result of the SAFER Program in Connecticut, EOHA and child care licensing staff have formed a close working relationship that previously did not exist. Using this new relationship, the child care licensing and EOHA staff at CT DPH are able to quickly address a whole range of environmental health issues at child day cares. The relationships and trust built have also allowed child care licensing inspectors to discuss issues with EOHA staff and discuss trends they are seeing in the field, such as lead contamination in artificial turf and cleaning products used in child care centers. Growing the program from a non-regulatory, nonprescriptive approach, CT DPH has the ability to adapt and improve the program as experience and data are gathered. For example, if gaps in the questionnaire are identified, EOHA and child care licensing professionals can easily modify the questions. The feedback from state inspectors can help drive how the program grows and changes over time. New technology, such as GIS capabilities, Web-based questionnaires, or use of handheld sampling instruments, can be easily integrated into the program to make it more efficient as time and resources allow. A final benefit of the program is that outreach is being conducted to inform municipalities in Connecticut about the issues surrounding the safe siting of child care centers. Decision makers at the local level, including city planners and zoning boards, have the ability to help prevent child care centers from being placed on potentially hazardous sites. This education and outreach to local planners and health department staff helps raise awareness among local authorities, who are typically most knowledgeable about sites in their municipalities that are inappropriate for a child care center. As part of its efforts to raise awareness about day care siting, CT DPH has developed a brochure that highlights the SAFER Program.12 The brochure has been made available to all municipalities, health directors, day care operators, and parents of the day care children, as well as the general public. CT DPH has also sent SAFER information via the Health Alert Network, an electronic messaging system that disseminates public health information to local health departments and other public health officials across the state.

The SAFER Program has required very few resources in the few years that it has been implemented. Licensing inspectors do not need to conduct any additional inspections, but simply look for a few specific property and building attributes while conducting their regularly scheduled inspections. The questionnaire given to license applicants has created little additional burden and no additional costs to the applicant. Given that most child care centers are already on sites without environmental contamination problems, the number of referrals to EOHA generated by the questionnaire, licensing inspections, and database comparisons has not required a great amount of EOHA staff time for follow-up activities. Despite its limitations, Connecticut’s SAFER Program is playing an important role in helping to prevent an incident such as Kiddie Kollege10 from occurring in Connecticut. It has also helped create a close working relationship between environmental health and child day care staff within CT DPH, which places the state in a better position to respond to emerging environmental health issues in child day care settings. The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention or the Agency for Toxic Substances and Disease Registry.

References 1. Department of Health and Human Services (US), Administration for Children and Families, Office of Child Care, National Child Care Information and Technical Assistance Center. United States child care statistics. March 2010 [cited 2010 Apr 4]. Available from: URL: http://nccic.acf.hhs.gov/poptopics/statistics.pdf 2. National Association for Regulatory Administration; Department of Health and Human Services (US), Administration for Children and Families, Office of Child Care, National Child Care Information and Technical Assistance Center. The 2005 child care licensing study: final report [cited 2008 May 8]. Available from: URL: http:// nara.affiniscape.com/associations/4734/files/2005%20Licensing% 20Study%20Final%20Report_Web.pdf 3. Barnett WS, Yarosz DJ. Who goes to preschool and why does it matter? National Institute for Early Education Research preschool policy brief. November 2007 [cited 2010 Nov 1]. Available from: URL: http://nieer.org/resources/policybriefs/15.pdf 4. Weinberg DH. Evidence from Census 2000 about earnings by detailed occupation for men and women. Census 2000 Special Reports. May 2004 [cited 2009 Oct 24]. Available from: URL: https://www.census.gov/prod/2004pubs/censr-15.pdf 5. American Academy of Pediatrics; American Public Health Association; National Resource Center for Health and Safety in Child Care and Early Education. Caring for our children: national health and safety performance standards: guidelines for out-of-home child care. 2nd edition. 2002 [cited 2008 Mar 15]. Available from: URL: http://nrckids.org/CFOC/PDFVersion/National%20Health%20 and%20Safety%20 Performance%20Standards.pdf 6. National Resource Center for Health and Safety in Child Care and Early Education. Individual states’ child care licensure regulations [cited 2009 Jul 15]. Available from: URL: http://nrckids.org/ STATES/states.htm 7. New Jersey Department of Environmental Protection. How to obtain a no further action letter [cited 2008 Apr 4]. Available from: URL: http://www.state.nj.us/dep/dccrequest/nfa.htm



8. New York State Office of Children and Family Services. 418-1.2 Procedures for applying for and renewing a license [cited 2010 Nov 1]. Available from: URL: http://www.ocfs.state.ny.us/main/ childcare/regs/418-1_CDCC_regs.asp#s2 9. Johnson GJ, Davis J, Schreiber J. Daycare centers and Superfund: a parent’s right to know. January 2003 [cited 2010 Nov 1]. Available from: URL: http://www.ag.ny.gov/media_center/2003/jan/ tutor_time.pdf 10. Centers for Disease Control and Prevention (US), Agency for Toxic Substances and Disease Registry. Health consultation: mercury exposure investigation using serial urine testing and medical

records review: Kiddie Kollege. 2007 [cited 2008 Apr 11]. Available from: URL: www.state.nj.us/health/eoh/cehsweb/kiddiekollege/ documents/kiddiekollegehc.pdf 11. Centers for Disease Control and Prevention (US), Agency for Toxic Substances and Disease Registry. All About Kids Daycare: record of activity health consult for air monitoring results. Atlanta: ATSDR; 2009. 12. Connecticut Department of Public Health. Child Day Care SAFER Program [cited 2010 Nov 1]. Available from: URL: http://www .ct.gov/dph/cwp/view.asp?a=3140&q=456216


Practice Articles

Promoting Active Transportation as a Partnership Between Urban Planning and Public Health: The Columbus Healthy Places Program Christine Godward Green, MCRPa Elizabeth G. Klein, PhD, MPHb

ABSTRACT Active transportation has been considered as one method to address the American obesity epidemic. To address obesity prevention through builtenvironment change, the local public health department in Columbus, Ohio, established the Columbus Healthy Places (CHP) program to formally promote active transportation in numerous aspects of community design for the city. In this article, we present a case study of the CHP program and discuss the review of city development rezoning applications as a successful strategy to link public health to urban planning. Prior to the CHP review, 7% of development applications in Columbus included active transportation components; in 2009, 64% of development applications adopted active transportation components specifically recommended by the CHP review. Active transportation recommendations generally included adding bike racks, widening or adding sidewalks, and providing sidewalk connectivity. Recommendations and lessons learned from CHP are provided.

Columbus Healthy Places Program, Columbus Public Health, Columbus, OH

a

Ohio State University, College of Public Health, Division of Health Behavior & Health Promotion, Columbus, OH

b

Address correspondence to: Elizabeth G. Klein, PhD, MPH, 174 W. 18th Ave., Columbus, OH 43210; tel. 614-292-5424; fax 614-688-3533; e-mail . ©2011 Association of Schools of Public Health


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American adults and children have decreased walking and biking activities by a third since the late 1970s,1 a factor that has been linked to the rise of obesity. Obesity is the second-leading cause of preventable death in the United States.2 Regular daily physical activity has been shown to reduce or prevent obesity as well as many of the leading causes of morbidity and mortality.3 There is growing evidence that promotion of active transportation benefits obesity prevention efforts.4 Specific community design features associated with increased walking and biking include sidewalks, parks and open space, distance to destinations, aesthetically pleasing places, multi-use paths, and bike racks.4,5 The ability of community design to foster active transportation is emerging as a tool in the fight against the American obesity epidemic. Compact community design can promote active transportation; conversely, sprawling community design can make active transportation less safe and convenient. Sprawl is characterized by streets that do not connect to one another, cul-de-sacs, long block lengths, and distant destination places. Sprawl has been shown to have a direct relationship to obesity and body mass index (BMI) and an indirect relationship to physical activity. Individuals living in communities rated higher on a sprawl index were less likely to walk, weighed more, and had a greater prevalence of hypertension compared with those living in communities rated lower on a sprawl index.6 Specifically, residents of the least sprawling communities walked 79 minutes more and weighed 6.3 pounds less than those living in the most sprawling county. Compact neighborhoods are those in which you can walk or bike to destinations and do not need a car. Compact community design is characterized by grid-pattern streets, short block lengths, and close destination places.6 Local zoning codes govern whether walking-friendly elements can be allowed in a given community and play an important role in creating a more physically active community.7 Currently the nation is facing a physical inactivity epidemic, particularly for physical activity in the daily American life. Only 15% of children walk to school,8 and adults walk only one of every 10 trips within one mile—an easily walkable distance.4 Consequently, nationwide, obesity and overweight have been increasing in both adults and children.2 The city of Columbus, Ohio, has identified similar, unhealthy trends in both physical activity and obesity: 59% of the adult population is obese or overweight, and 38% of third-graders are overweight.9,10 These alarming data are compounded by the fact that only about 50% of the adult population meets the physical activity guidelines11 of 150 minutes per week of

moderate-intensity aerobic activity,3 which includes sessions as short as 10 minutes in length. The Alliance for Biking & Walking’s 2010 Benchmarking Report showed the majority of Columbus residents did not walk and bike to work or for overall trips.12 For most of the indicators presented in this report, Columbus’ rates were lower than the average. For instance, in the 50 metropolitan areas surveyed, 4.8% of people walked to work, compared with only 2.7% for Columbus residents.12 When including all trips, the 50 metropolitan areas’ average was 11.0% for walking trips and 0.9% for biking trips; Columbus fell below this average, with 8.2% of residents walking and 0.3% biking for all trips. Active transportation activities, such as walking to and from public transportation, have been shown to help physically inactive populations achieve recommended levels of physical activity.13 Community design is varied in Columbus. Shopping centers, schools, and houses in older neighborhoods were built with compact community design. In these neighborhoods, walking and biking are easier and safer. In 1950, the city began to annex land around the city. The land became part of the city, and the city grew from 42 square miles in 1950 to 227 square miles in 2009 (Personal communication, Kevin Wheeler, Columbus Department of Development Planning Division, May 2010). Farm fields still exist within the city limits. Like many other American cities that grew significantly after 1950, community design in Columbus changed to sprawling. As personal cars became a more dominant part of life, many streets did not have sidewalks; likewise, there are no sidewalk connections or bike racks in these neighborhoods. As noted previously, such community design elements may be a barrier to daily physical activity, which may be associated with rising obesity rates. The poor compliance with physical activity recommendations and adult obesity rates in the city motivated Columbus Public Health to invest in broad-based strategies to address these problems in the local community. While physical activity recommendations11 can be reached by using leisure-time physical activity or active transportation, the focus of this research is on the use of active transportation to increase daily minutes of physical activity. This article describes the partnership between urban planning and public health in the development of the Columbus Healthy Places (CHP) program to promote active transportation. IMPETUS FOR CREATING CHP Several key events occurred over time that set the stage for Columbus Public Health and the Columbus


Active Transportation as a Public Health/Urban Planning Partnership  43

community to be ready for a program addressing community design. A local Mobilizing for Action through Planning and Partnerships (MAPP) process had been underway for some time, involving key stakeholders and agencies.14 MAPP is a community-driven strategy planning process for improving community health. Columbus Public Health initiated the MAPP process, as it supported the need for regular tracking and reporting of health indicators, including obesity and physical activity. In 2004, Columbus Public Health began reporting on a concise set of indicators, including the percentage of adults who are obese and overweight, percentage of adults meeting the Surgeon General’s physical activity recommendations, and percentage of third-graders categorized as overweight. These indicators were selected because they covered broad audiences and could provide valid data, and because Columbus Public Health believed it would have the ability to effect some change on them. Around the same time, Columbus Public Health recognized the link between public health and the built environment. Smart Growth principles, such as selecting a location close to a residential neighborhood with sidewalks and public transportation access, were used in decision making for the new health department building in 2001. Smart Growth refers to a national movement that restores community and focuses on walkable neighborhoods with open space and compact community design.15 In 2005, the Columbus health commissioner attended a National Association of County and City Health Officials/American Planning Association seminar that solidified the idea for a program that could focus on healthier community design. The CHP program was created to address active transportation in the community design processes for Columbus. A retired urban planner for the city worked part-time to research the program objectives and create the program work plan. The CHP program was officially launched in the fall of 2006 when Columbus Public Health staffed the program with a full-time urban planner as its coordinator. THE CHP PROGRAM The mission of CHP is to (1) establish development policies and practices to reduce negative health impacts and (2) create places that foster physical activity as part of everyday life in the city of Columbus. CHP aims to teach city employees and the community to voluntarily use community design that incorporates active transportation infrastructure and to change the built environment. It permanently alters the com-

munity design to make walking and biking safer, thus increasing opportunities for physical activity. To achieve the mission, the CHP coordinator first needed to learn the current level of interest within city divisions and the community for improved walking and biking infrastructure. Recent neighborhood and area plans were used as a tool to gauge community interest in walking and biking infrastructure. Neighborhood and area plans are documents that direct development for the next 10 to 20 years in small neighborhoods and larger sections of the city, respectively. All plans from 2004 to 2006 called for improved walking and biking infrastructure, which provided evidence of community support for built-environment changes. Another strategy to provide evidence for community neighborhood support for active transportation elements came from the input obtained from community members during neighborhood walk audits. CHP conducted 10 walk audits during the first year of the program. Residents walked the neighborhood and gave input on preferred locations to walk or bike, and whether they felt safe doing so, as well as locations perceived as unsafe or not preferable to walk or bike. This community input helped to inform the walking and biking infrastructure to be included in community design decisions. In addition to community input, neighborhood walk audits also result in neighborhood walking maps. Maps encourage active transportation by highlighting safe routes and neighborhood destinations places, as illustrated in Figure 1. Maps are distributed to community agencies and members at no cost. Further, maps have been created in conjunction with the Division of Mobility Options, the City of Columbus Department of Public Service division responsible for walking and cycling infrastructure, for the purpose of assessing mobility barriers encountered during the walk audit as a part of their community mobility plans. Recognizing that built-environment decisions are made in various departments, CHP has created successful partnerships with several agencies inside city government and in the private sector. A description of the agencies and partner activities are provided in Figure 2. Relationships were cultivated through meetings with each agency. Directors were the first point of contact. Along with an explanation of CHP, a request was made for the most appropriate agency contact. All agencies were very receptive to the CHP collaboration. The agencies welcomed the additional support for walking and biking infrastructure from the health perspective and hoped it would help to advance their work. The collaboration is unique for each agency and



Figure 1. Example of a walking map in the Driving Park neighborhood of Columbus, Ohio

based on the agency procedures. For example, the Department of Development Planning Division has a specific process for collecting information and writing neighborhood and area plans. CHP is able to review the first draft of the plan and hear public comment at the third public meeting. The CHP coordinator then works directly with the planner to incorporate any active transportation recommendations into the plans. A similar process occurs with the aforementioned Division of Mobility Options for the community mobility plans. For both of these agencies, CHP has officially supported policy changes that improve walking and biking infrastructure through letters of support. As an example of how CHP has successfully partnered with other city departments, CHP’s work with the city’s Department of Development Building Services Division is thoroughly examined in the next section. We present a case study—the review of development applications during rezoning reviews—as a successful strategy to link public health to urban planning. DEVELOPMENT APPLICATION REVIEW PROCESS Whenever new development in the city is proposed, either in farm fields or in redeveloping an older part of

the city that currently does not have active transportation infrastructure, it creates an opportunity to increase walking and biking through infrastructure. The CHP program wanted to review development applications and request walking and biking infrastructure, such as bike racks, sidewalk connections, and wider sidewalks. After consultation with the city’s Building Services Division, the division responsible for building permits and new development, CHP was given permission to comment on any application during monthly rezoning reviews. A precedent was set for Columbus Public Health to review applications when an industrial or chemical development was proposed. While CHP had permission to make recommendations to any rezoning application, the recommended changes would not be mandatory. A developer is required to go through the rezoning process when the current land use does not permit the proposed development. For example, if the current land use is residential and the proposed development is a shopping center, a rezoning is required. A uniform review process was created for every type of development application. Separate review is conducted for residential and non-residential applications, and recommendations for each type are shown in Figures 3 and 4, respectively. Residential applications



are subdivided into single-family and multifamily developments. Non-residential applications include commercial, office, industrial, and manufacturing uses. For all residential areas (Figure 3), walking and biking features are recommended, including 5-foot-wide sidewalks, connection to the existing sidewalk system, and connection to any adjacent developments and bus stops, where no property line conflicts are present. Bike racks are requested for multifamily developments and at parks included in single-family developments. Because Columbus has a Parkland Dedication Ordinance, which requires parkland be set aside for residential developments, CHP does not include parkland as a recommendation. Non-residential land uses are evaluated by using trip generators, as shown in Figure 4. Trip generators are defined for this purpose as developments within half a mile that would attract pedestrians or cyclists, including shopping centers, employment centers, bus stops, schools, parks, libraries, neighborhoods, and grocery stores. When recommending trip generators, it is also important to consider that some employees or customers may have limited choices for travel and, therefore, must walk and bike out of necessity rather than choice. If no trip generators are present or only a bus stop is within half a mile of the development, 5-foot sidewalks

and bike racks are recommended. More recommendations are made as the number of trip generators increases or if the development is closer to an existing environment conducive to walking and biking. In addition to the recommendations previously listed for residential housing, additional recommendations for non-residential development may include adding signage at the entrance and exit of the parking lot to alert drivers to pedestrians, adding bike racks, and expanding sidewalk areas to bridge the public sidewalk to the front door of the business. Concerns outside of Columbus Public Health’s purview were addressed prior to CHP officially commenting on rezoning applications. Two major issues had to be considered: the development community and the right-of-way. First, the development community is the economic development engine of the city, and the group expressed concern with extra processes, regulations, and costs. Many of the recommended walking and biking infrastructure changes are low-cost, such as adding a bike rack for $200. However, other recommendations, such as wider or longer sidewalks, can be substantially more costly for a developer. Initial apprehension was voiced in one meeting with city staff and the development community about CHP comments; however, the Building Services Division staff reiterated to the development community that all city

Figure 2. Columbus Healthy Places program role in partnering with relevant local agencies, 2007–2009, Columbus, Ohio Partner agency

Collaborative activities

Outcomes

Department of Development Building Services Division

Reviews development applications for active transportation features

• 45 out of 70 applications reviewed included active living features

Department of Development Planning Division

Assists in the development of neighborhood plans Supports neighborhood plans for adoption

• Seven letters to City Council supporting plan adoption • Four letters of support for zoning code changes • Board of Health Resolution to support parking code changes

Supports code changes that require active transportation Department of Public Service Division of Mobility Options

Participates in community mobility plans Supports zoning code changes that require active transportation

• Participated in three community mobility planning process • Three walk audits were used in community mobility plans • Two letters of support for zoning code changes

Recreation and Parks Department

Supports the expansion and protection of open spaces, parks, and wetlands through comments in rezoning review

Not applicable (outcomes not recorded by Columbus Healthy Places)

Community

Performs neighborhood walk audits and creates walking maps Teaches “Creating Safer, Healthier Neighborhoods” with a member of the Columbus Police Department

• 23 community walk audits and neighborhood walking maps • Classes held twice a year with 19 people attending in 2009



epartments are able to comment on rezoning proposd als. Since the implementation of the CHP rezoning review, no concerns or issues have been voiced. The right-of-way refers to the street plus the land adjacent to the street that is owned by the city; therefore, many walking and biking infrastructure elements fall within the right-of-way. The Department of Public Service is responsible for the right-of-way and has full control over infrastructure because infrastructure in the right-of-way is required to conform to state guidelines. CHP is able to recommend active transportation features above and beyond local zoning codes, but recommendations must be congruent with state guidelines. Further, out of respect for the Department of Public Service, CHP only includes comments that are in agreement with what Public Service permits in the right-of-way. For this reason, several meetings were held with Public Service before CHP began to comment and if there were any questions about a recommendation, the Department of Public Service was contacted. To address possible concerns of the development community, CHP chose to participate in an existing process, referred to as the rezoning staff review. This is the process by which all city departments have the opportunity to make recommendations for changes, referred to as “comments” on rezoning proposals. The first step of this process begins when the resulting comments are sent to a developer in one concise package. The purpose of the comments is to give a brief explanation as a mechanism to inform developers regarding the active transportation recommendations,

the number of trip generators within half a mile, and the relationship between the recommended features and their purpose in increasing physical activity. The developer can choose to call the CHP coordinator to discuss the recommendations. The outcome of the phone call—that is, whether or not active transportation features will be included—is reported to the Building Services staff to ensure the decision is carried out. (CHP does not have authority to approve or disapprove an application; therefore, it is voluntary for a developer to incorporate recommendations into the design.) By choosing to participate in the existing process of rezoning staff review, the CHP program is limited to recommendations after a proposal is submitted and is not involved in the initial design phase. Once the developer has the opportunity to address the recommendations from the rezoning staff review, the second step of the process is for the Development Commission to review the application. The Development Commission is a citizen advisory body that recommends approval or disapproval of the application to the Columbus City Council. The Development Commission receives CHP’s comments and can then choose to ask the developer to include active transportation features per CHP request. The third and final step of the rezoning application approval process is City Council. To change the zoning of a piece of land, the City Council must pass legislation. Columbus Public Health works closely with its appointed City Council member, the Health and Human Development Committee Chair. Due to her

Figure 3. Columbus Healthy Places program recommended active transportation features: residential use

No trip generator: • Bike racks • 5-ft sidewalks

Urban area

• 5-ft sidewalks • new sidewalks connect to existing • connect to uses within ¼ to ½ mile including bus stop • connectivity within development • bike racks • crosswalks • parking lot: pedestrian signage

Multifamily

Single family

Suburban area

• 5-ft sidewalks • new sidewalks connect to existing • connect to uses within ¼ to ½ mile including bus stop • connectivity within development • bike racks • crosswalks • parking lot: pedestrian signage • walking paths

• 5-ft sidewalks • new sidewalks connect to existing • connections to uses within ¼ to ½ mile • connectivity within development • crosswalks • walking paths • bike racks at complexes

ft 5 foot



Figure 4. Columbus Healthy Places program recommended active transportation features: non-residential use

No trip generator: • Bike racks • 5-ft sidewalks

Pedestrian Street Classesa 1–3

Bus stop within ½ mile

Bus stop plus at least one trip generator connected within ½ mile

• connection from bus stop/sidewalk to building • bike racks • 5-ft sidewalks

• 8- to 12-ft sidewalks • bike racks • parking lot: pedestrian signage • connection to public sidewalk • connect development internally

Pedestrian Street Classesa 4–5

• connection from bus stop/sidewalk to building • bike racks • 5-ft sidewalks

Pedestrian street class is designated by the Columbus Department of Public Service and classifies the demand for pedestrian travel along a segment of the street.

a

ft 5 foot

interest in the program, the Chair requested that walking and biking infrastructure comments be included in the City Council legislative packet. Prior to this request, comments were not seen by City Council. If the rezoning application accepted walking and biking infrastructure, the legislation language including this information is read at City Council. If the rezoning application did not accept walking and biking features, that is noted in an attachment to the legislation. OUTCOMES SINCE THE ADOPTION OF CHP Since its inception, CHP has been involved in the review of 70 rezoning applications. Of these, 64% have adopted CHP recommendations for active transportation features; that is, 45 of 70 applications voluntarily adopted one or more of the active transportation elements not required by the zoning code. The most commonly accepted recommendations have been bike racks (82%); connections to the existing sidewalk or adjacent property, such as another business or library (26%); and 5-foot sidewalks (18%). (Because multiple recommendations may be accepted, percentages can exceed 100%.) From 2007 to 2008, only one application included active transportation features before CHP recommendations; in 2009, that number increased to four applications. This equates to 7% (n55) of applications with active transportation elements prior to the CHP review. One example of the rezoning process supporting policy change is the new parking code passed in May 2010. Under the new code, parking lots must incor-

porate walking and biking infrastructure features, including sidewalk connections from the street to the front door, trees for shade, and bike racks. Data from CHP provided evidence for the use of these features, demonstrating that sidewalk connections and bike racks were already being voluntarily included in development. Additionally, because of the work of CHP and in partnership with the Planning and Building Services divisions, the Board of Health passed a Resolution of Support for the new parking code because of its permanent effect on community design. DISCUSSION CHP has invested in the concept that having a walking and biking infrastructure changes community design and the environment in which people live. The program has taken a social-ecological approach to address physical activity by implementing environmental change and working toward systems and policy change. This approach is in line with “The Community Guide— Promoting Physical Activity: Environmental and Policy Approaches,” which recognizes that environmental and policy approaches can increase physical activity.16 With 64% of new developments voluntarily including walking and biking infrastructure, CHP staff believe this moderate rate of adoption demonstrates feasibility for the use of policy to require this infrastructure be included in new development applications. While tracking changes within development applications due to CHP comments is clearly feasible, it is very difficult to quantify any change in the number



of people walking and biking associated with these environmental changes. Ideally, a large-scale evaluation could be conducted to assess these questions; as demonstrated in community-level evaluation of similar programs to promote physical activity, such as the Safe Routes to School program, it is possible to conduct impact evaluation with sufficient resources.17 A key to the success of the program was being able to find an existing process in which to participate. Creating the uniform application review process added transparency and fairness and made the recommendations as objective as possible. The uniform application review process also allowed the Department of Public Service to review the right-of-way recommendations and make changes as necessary. In efforts to succeed within the existing review process, CHP linked with nontraditional partners, such as the Planning and Building Services divisions. Wisconsin’s Comprehensive Planning Law of 1999 serves as a useful example for policy battles over land-use changes, but also the potential for broader promotion of active transportation and active living.18 One limiting factor to the use of the existing process is that a portion of new development does not go through the rezoning process. A rezoning is not required when a proposed development is located in the proper zoning classification—for example, if a shopping center is proposed for land already zoned commercial. For these developments, there is no process for staff review and, therefore, walking and biking infrastructure is not being requested. As a next step of the CHP program, strategies need to be developed to incorporate active transportation into this portion of development. CHP educates the nontraditional urban-planning partners on the linkage between their work and public health. In the same respect, CHP has linked existing public health programs to city departments so that the work of policy and systems change is more widely spread. As a result of this connection, the Creating Healthy Communities program, which addresses the major risk factors of chronic disease, had a 2010 objective to partner with the city’s Planning Division to add two active-living features and/or community gardens to a neighborhood plan, both of which were achieved. The recently formed Institute for Active Living has an interdepartmental team with a focus on changing the built environment through collaboration among city departments.

CONCLUSIONS AND RECOMMENDATIONS Given the concerns regarding the national obesity epidemic, programs emphasizing community design to promote physical activity are an underused resource in metropolitan areas. Now in its fourth year, CHP is successfully promoting active transportation through change to the built environment. Other metropolitan areas are encouraged to consider the adoption of a program such as CHP as a valuable investment in community-based obesity prevention. For other communities that may consider the establishment of a program similar to CHP, we offer four recommendations for success. First, establish good relationships with partner agencies; efforts to communicate the value of the program to the agency and community may be needed to convince reluctant individuals and agencies. Second, program maintenance involves staying in regular contact with individuals at each agency to proactively address concerns or issues as they emerge. Communication through e-mail or regular meetings can facilitate discussion to share problems or successes. Third, health agencies will need to be prepared to defend the public health role in development and built-environment decisions, as the adoption of a novel program such as CHP may meet with resistance from individuals and agencies. Fourth, an urban planner is the key liaison between public health and planning, and the appropriate individual must be fluent in both cultures to be successful. With the increasing recognition of the public health/planning connection, more students of urban planning are exposed to the health effects of the built environment. In the case of CHP, the urban planner had a strong background in neighborhood-level work. While the planner had not taken a public health approach, she understood the issues of low-income and minority communities, which she was able to apply to the new public health role. At Ohio State University, the ties between the College of Public Health and the City and Regional Planning departments continue to strengthen, and collaborative activities are undertaken to promote this type of interdisciplinary training. Continued interdisciplinary communication and cross-fertilization between these two fields may help to prepare students to create the next generation of programs and research in this area. Community design elements that promote active transportation should be considered as an opportunity to address sedentary lifestyle and increase physical activity. Programs such as CHP that promote positive change in the built environment can contribute to environ-



mental and infrastructure changes to promote public health through active living in our communities. REFERENCES 1. Hu P, Reuscher T. Summary of travel trends: 2001 National Household Travel Survey. Washington: Department of Transportation, Federal Highway Administration (US); 2004. Also available from: URL: http://nhts.ornl.gov/2001/pub/STT.pdf [cited 2010 Sep 15]. 2. Centers for Disease Control and Prevention (US). U.S. obesity trends, trends by state, 1985–2008. Atlanta: CDC; 2009. Also available from: URL: http://www.cdc.gov/obesity/data/trends.html [cited 2010 Sep 15]. 3. Prevalence of self-reported physically active adults—United States, 2007. MMWR Morb Mortal Wkly Rep 2008;57(48):1297-300. 4. Active Living Research, Robert Wood Johnson Foundation. Active transportation: making the link from transportation to physical activity and obesity. Research Brief, Summer 2009 [cited 2010 Sep 16]. Available from: URL: http://www.activelivingresearch.org/ files/ALR_Brief_ActiveTransportation.pdf 5. Goodell S, Williams CH; Robert Wood Johnson Foundation. The built environment and physical activity: what is the relationship? The Synthesis Project, Policy Brief No. 11, April 2007 [cited 2010 Sep 15]. Available from: URL: http://www.rwjf.org/files/research/ no11policybrief.pdf 6. Ewing R, Schmid T, Killingsworth R, Zlot A, Raudenbush S. Relationship between urban sprawl and physical activity, obesity, and morbidity. Am J Health Promot 2003;18:47-57. 7. Schilling J, Linton LS. The public health roots of zoning: in search of active living’s legal genealogy. Am J Prev Med 2005;28(2 Suppl 2):96-104. 8. National Center for Safe Routes to School. Safe routes to school travel data: a look at baseline results from parent surveys and student travel tallies, January 2010 [cited 2010 Sep 15]. Available from: URL: http://www.saferoutesinfo.org/resources/collateral/ SRTS_baseline_data_report.pdf

9. Columbus Public Health, Office of Assessment and Surveillance. 2005 Franklin County Community Health Risk Assessment. Columbus (OH): Columbus Public Health; 2005. Also available from: URL: http://publichealth.columbus.gov/uploadedFiles/Webtxt. pdf [cited 2010 Sep 14]. 10. Ohio Department of Health, Division of Family and Community Health Services, School and Adolescent Health Section. Report on body mass index of Ohio’s third graders, 2004–2005. Columbus (OH): Ohio Department of Health; 2006. 11. Department of Health and Human Services (US). Physical activity guidelines for Americans: Physical Activity Guidelines Advisory Committee report, 2008. Washington: HHS; 2008. Also available from: URL: http://www.health.gov/PAGuidelines/Report [cited 2010 Oct 14]. 12. Alliance for Biking & Walking. Bicycling and walking in the United States: 2010 benchmarking report. Washington: Alliance for Biking & Walking; 2010. 13. Besser LM, Dannenberg AL. Walking to public transit: steps to help meet physical activity recommendations. Am J Prev Med 2005;29:273-80. 14. National Association of County & City Health Officials. Mobilizing for action through planning and partnerships (MAPP) field guide. Washington: NACCHO; 2001. 15. Smart Growth Network. About Smart Growth [cited 2010 Sep 14]. Available from: URL: http://www.smartgrowth.org/about/default. asp 16. Centers for Disease Control and Prevention (US), Task Force on Community Preventive Services. Guide to community preventive services: promoting physical activity: environmental and policy approaches [cited 2010 Oct 14]. Available from: URL: http://www. thecommunityguide.org/pa/environmental-policy/index.html 17. Boarnet MG, Anderson CL, Day K, McMillan T, Alfonzo M. Evaluation of the California Safe Routes to School legislation: urban form changes and children’s active transportation to school. Am J Prev Med 2005;28:134-40. 18. Schilling J, Keyes SD. The promise of Wisconsin’s 1999 Comprehensive Planning Law: land-use policy reforms to support active living. J Health Polit Policy Law 2008;33:455-96.


Practice Articles

Animal Sentinels for Environmental and Public Health

John S. Reif, DVM, MSca

ABSTRACT Studies of the effects of environmental exposures on domestic and wild animals can corroborate or inform epidemiologic studies in humans. Animals may be sensitive indicators of environmental hazards and provide an early warning system for public health intervention, as exemplified by the iconic canary in the coal mine. This article illustrates the application of animal sentinel research to elucidate the effects of exposure to traditional and emerging contaminants on human health. Focusing on environmental issues at the forefront of current public health research, the article describes exposures to community air pollution, environmental tobacco smoke, and pesticides and associations with cancer, reproductive outcomes, and infectious diseases. Finally, it covers the role of marine mammals in monitoring the health of the oceans and humans.

Colorado State University, Department of Environmental and Radiological Health Sciences, Fort Collins, CO

a

Address correspondence to: John S. Reif, DVM, MSc, Colorado State University, Department of Environmental and Radiological Health Sciences, Environmental Health 1681, Fort Collins, CO 80523; tel. 970-491-6074; fax 970-491-2940; e-mail . ©2011 Association of Schools of Public Health

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Animal Sentinels for Environmental and Public Health  51

The concept that animals may serve as sentinels of environmental hazards that have implications for public health is not novel. The familiar image of the canary in the coal mine remains relevant in the 21st century. An article appearing in a 1914 issue of the Journal of Industrial and Engineering Chemistry provides a simple description of the concept: Birds and mice may be used to detect carbon monoxide, because they are much more sensitive to the poisonous action of the gas than are men. Experiments by the Bureau of Mines show that canaries should be used in preference to mice, sparrows, or pigeons, because canaries are more sensitive to the gas. Rabbits, chickens, guinea pigs, or dogs, although useful for exploration work in mines, should be used only when birds or mice are unobtainable, and then, cautiously, because of their greater resistance to carbon monoxide poisoning. . . . Breathing apparatus must be used where birds show signs of distress, and, for this reason, birds are of great value in enabling rescue parties to use breathing apparatus to best advantage.1

Just as miners carried caged canaries during the early part of the 20th century to warn of high levels of carbon monoxide or other impurities in the air, pet, agricultural, and wild animals have been used to assess and predict the effects of environmental contamination in human populations. In a modern example, canaries were used after the sarin release into the Tokyo subway by a terrorist cult in 1995 to detect the gas at the cult’s compounds.2 Pet animals share the environment and are exposed to many of the same agents as their human companions. Children may be exposed through similar pathways, such as household dust. Animals suffer a similar spectrum of disease as humans and, therefore, may be sensitive indicators of environmental hazards and provide an early warning system for public health intervention. Several historical examples illustrate animals’ usefulness as predictors of human illness. In the 1870s, fattened cattle experienced high mortality at a stock show in London’s Smithfield market associated with a dense industrial fog—a precursor to the air pollution episodes typified by the infamous London Fog of 1952, during which thousands of residents died.3 In the 1950s, recognition of neurobehavioral disturbances in the cat population of Minamata, Japan, preceded a severe episode of neurologic disease among local residents caused by consumption of seafood contaminated with methylmercury.4 Sediments, shellfish, and fish in Minamata Bay became contaminated with mercuric chloride as the result of effluent discharges from a chemical plant. The ataxic “dancing cats of Minamata” were a warning sign. Unfortunately, it was not recognized in

time to prevent the human epidemic. In 1962, cases of lead poisoning in cattle and horses living in the vicinity of a smelter alerted the Minnesota state health department to conduct surveillance for lead exposure in the local human populations.5 This article addresses the use of animal sentinels as models for epidemiologic studies of human diseases and environmental exposures. Observational studies of spontaneous animal disease in populations can provide additional insights not available from laboratory-based studies of experimental animals.6 The advantages of using animals as sentinels or comparative models of human disease accrue in part from their relative freedom from concurrent exposures, bias due to confounding, and, to some extent, exposure misclassification. In studies of humans, the influences of cigarette smoking, alcohol, or occupational exposures may obscure an effect of community exposure to environmental hazards. Further, the relatively short latent periods for cancer and other disorders in animals compared with those for humans create an advantage in studying spontaneous diseases in animal models.7 The accuracy of exposure assessment is a major challenge in environmental epidemiology. The restricted daily mobility and lower frequency of migration over an animal’s shorter lifespan contribute to the likelihood that exposure assessment can be conducted more accurately in studies of animal diseases.7 Much of the work involving the use of sentinels to identify environmental hazards has focused on cancers in pet animals, particularly dogs, which share the environment intimately with humans.7,8 Spontaneous canine neoplasms provide useful models for studying the health effects of environmental hazards. Many canine cancers resemble those in humans in biological behavior, pathologic features, proportional morbidity, and recognized risk factors. A classic example of a canine cancer sentinel is the study of mesothelioma by Glickman et al.9 The authors identified exposure to asbestos through the activities of the owner at work or through a hobby as a significant risk factor accounting for most of the cases. Chrysotile asbestos bodies were identified in lung tissue. There also was a significant association with the use of flea repellants, some of which contained asbestos-like fibers. The findings illustrated the usefulness of epidemiologic research to identify environmental health hazards for humans who share the environment with their pets. Thus, the diagnosis of canine mesothelioma provides an early warning system for the human disease and constitutes a true sentinel event. This article describes pet and wildlife animal sentinels. These models increase understanding of health effects associated with exposures to environmental hazards relevant to public health.



AIR POLLUTION An early reference to the use of pet dogs to study lung cancer risk associated with urban air pollution is attributed to V. S. Rosinov, a Russian scientist who claimed that the incidence of lung cancer among dogs in larger cities was higher than that in dogs from rural areas.10 In the mid-1960s, researchers began exploring the effects of exposure to urban air in Philadelphia. Radiographic screening tools, including modified photo-fluorography originally used for detection of pulmonary tuberculosis, were developed for identification of canine lung cancer and other disorders.11 Hospital data were used to explore an urban-rural gradient for respiratory tract neoplasia in a case-control study of dogs with primary pulmonary carcinoma, carcinoma of the nasal passages and paranasal sinuses, and carcinoma of the tonsil.12 The environment was divided into urban and rural segments based on atmospheric pollution data for the city of Philadelphia and the locations of major industries. No elevations in risk for an urban residence were found in the distribution of lung cancer or nasal cancer cases compared with gastrointestinal cancer controls or the total hospital population. However, a significant urban association was noted for carcinoma of the tonsil (odds ratio [OR] 5 3.3; 95% confidence interval [CI] 1.6, 6.9). Interestingly, an association between carcinoma of the tonsil and “town-kept” dogs was described in London as early as 1939.13 Radiographic techniques were also used to assess the prevalence of non-specific chronic pulmonary disease (CPD) in urban and rural dogs screened at veterinary teaching hospitals in Ithaca, New York, Boston, and Philadelphia.14 Radiographs obtained during routine clinical workups were graded for evidence of CPD without knowledge of the animal’s age or residence, and those with obvious disease process, such as pneumonia, were excluded. In Philadelphia, the prevalence of CPD was significantly higher in older dogs living in the urban area.14 In an analysis of 1,892 dogs from the three hospitals, the prevalence of CPD was significantly higher in older dogs from the more heavily polluted zones of Boston and Philadelphia compared with dogs from the referent area.15 These early studies supported the hypothesis that an urban factor, likely related to ambient air pollution, was associated with the development of pulmonary disease in this animal model. Approximately 30 years later, investigators studied the lungs of dogs from Mexico City and less polluted areas of the country and found structural lung changes, including mononuclear cell infiltrates, smooth muscle hyperplasia, peri-bronchiolar fibrosis, and vascular lesions, that represented an inflammatory response resulting

from chronic exposure to particulates and ozone.16 The pathologic changes described in the lungs of Mexico City dogs were consistent with the radiographic abnormalities identified in Philadelphia dogs and with lung lesions found in dogs from that population.17 More recently, these investigators studied urban dogs from Mexico City and found histologic evidence of neuroinflammation and an increased abundance of messenger ribonucleic acid from two inflammatory genes in the brains of the dogs. The findings were correlated with decrements in performance on psychometric tests in children similarly exposed to ambient air pollution.18 ENVIRONMENTAL TOBACCO SMOKE The first evidence that exposure to environmental (secondhand) tobacco smoke (ETS) may cause lung cancer was based on a cohort study of Japanese women that showed higher mortality rates among women married to smoking husbands.19 Since then, multiple studies have confirmed that exposure to ETS is associated with an approximate doubling of lung cancer risk among exposed household residents.20 The role of household exposure to ETS as a risk factor for cancer in pet dogs was first explored in 1992.21 Lung cancer cases (n551) and controls with other forms of cancer were obtained from two veterinary teaching hospitals. Exposure assessment included the number of smokers in the household, the amount smoked by each, and the proportion of time spent indoors by the pet. A non-significant relationship was found for exposure to a smoker in the home (OR51.6; 95% CI 0.6, 3.7) after controlling for confounding. Evidence of a dose-response with number of smokers, packs smoked per day, or an exposure index that included the time the dog spent indoors was not found. However, the dog’s skull shape modified the effect: in breeds with short and medium-length noses, the risk increased (OR52.4; 95% CI 0.7, 7.8). Although results from this small study were inconclusive, it demonstrated the feasibility of using pets to examine the potential effect of household exposures. A second hospital-based study (103 cases, 378 controls) tested the hypothesis that exposure to ETS increases risk for nasal and sinus cancer—a more common form of cancer in dogs.22 Exposure to ETS was evaluated by determining the number of smokers in the household, the number of packs smoked per day at home by each smoker, the number of years that each person smoked during the dog’s lifetime, and the proportion of time spent indoors by the dog. The adjusted OR for exposure to ETS was 1.1 (95% CI 0.7, 1.8). However, skull shape was again found to exert a strong modifying effect. Among long-nosed dogs, the



OR for a smoker in the house was 2.0 (95% CI 1.0, 4.1). A step-wise increase in the ORs across strata of total packs smoked and total indoor exposure to ETS was found for this group, with a risk of 2.5 (95% CI 1.1, 5.7) for the highest stratum.22 Urinary cotinine, a metabolite of nicotine, was used as a biomarker to validate questionnaire responses for ETS exposure in 111 dogs and was highly correlated with the exposure metrics used in the case-control studies (Unpublished data, Reif et al., Colorado State University, Department of Environmental and Radiological Health Sciences, 1996). Bertone-Johnson et al.23 later confirmed the effects of canine household exposure to ETS in a study that found concentrations of urinary cotinine were significantly related to the number of cigarettes smoked by household members and to short nose length in exposed dogs. Exposure to ETS has also been associated with an increased risk of malignant lymphoma in cats,24 but the findings have not been confirmed in further studies. Collectively, these studies support the hypothesis that exposure to ETS in the home increases risk for respiratory tract cancer in dogs. From a public health perspective, smoking cessation can be recommended as a measure to reduce the incidence of canine, as well as human, respiratory tract disorders and may provide additional motivation for concerned pet owners to quit. PESTICIDES Canine malignant lymphoma Exposures of pet dogs to pesticides have been associated with increased risk for malignant lymphoma and testicular and bladder cancer.25 Canine malignant lymphoma (CML) is a common cancer of dogs and a model for non-Hodgkin’s lymphoma (NHL) in humans. Exposure to pesticides has been suggested as an explanation for the increased risk for hematopoietic cancers such as NHL among farmers in the United States, Sweden, and New Zealand.26 Several studies have found associations with exposure to phenoxyacid herbicides such as 2,4-dichlorophenoxyacetic acid (2,4-D)—used extensively in agricultural, public, and residential settings to control the growth of broadleaf weeds27,28—and with other classes of pesticides.29 A large, hospital-based case-control study assessed the risk of dogs developing CML from exposure to lawn herbicides.30 Dogs with CML were 30% more likely to have lived in a home where the owners had applied 2,4-D or employed a commercial lawn care company to treat their yard. The risk rose to a twofold excess when the owners reported four or more herbicide applications yearly; a statistically significant trend was found

for the number of applications. These findings were supported by a biomonitoring study that determined the extent to which dogs absorb and excrete 2,4-D in urine after contact with lawn herbicides.31 Dogs living in and around residences with recent 2,4-D treatments were shown to absorb and excrete measurable amounts of the herbicide through normal activities and behaviors associated with lawn contact. Canine testicular cancer Pesticide exposures have also been associated with an increased risk of testicular cancer in a series of studies of U.S. military working dogs that served in Vietnam with their handlers.32,33 Increased rates of testicular cancer and testicular dysfunction among dogs that served in Vietnam compared with those that had remained in the United States led investigators to hypothesize that exposures to pesticides including picloram, malathion, and the phenoxyacid components of Agent Orange (2,4-D and 2,4,5-trichlorophenoxyacetic acid) could have been responsible for the selective increase in the incidence of testicular cancer.32 However, exposures to therapeutic agents used to prevent erlichiosis and malathion used to prevent tick infestation could not be ruled out as contributing factors. In a follow-up study, investigators evaluated the service records of the Vietnam dogs to determine the zones where they were deployed, but were unable to accurately document exposures to chemical defoliants.33 Identification of an increased risk for seminoma among military working dogs raised the possibility that this finding could be an indicator of increased risk among soldiers who served in Vietnam. Researchers conducted a case-control study to determine whether Vietnam veterans experienced an increased risk for testicular cancer. While Vietnam service was reported more frequently among cases of testicular cancer than among age-matched controls, the investigators were unable to determine whether the increased risk was attributable to pesticide exposures.34 Canine bladder cancer The risk of human bladder cancer is strongly associated with occupational exposures to chemicals and cigarette smoking. Canine bladder cancer may serve as a sentinel for chemical exposures in general and, more specifically, for exposure to pesticides in the home.7 An ecologic study of dogs from 13 veterinary teaching hospitals found the proportional morbidity rates for canine bladder cancer to be associated with the county’s level of industrial activity. Bladder cancer mortality rates showed a similar correlation with industrial activity in the same counties, suggesting that



a common exposure to environmental carcinogens might be responsible.35 Several case-control studies have explored associations between canine bladder cancer and pesticide exposures. A study investigating exposures to ETS, household chemicals, and pesticides found a significant association for the use of topical insecticides in the form of shampoos and dips.36 The adjusted risk increased with the number of flea and tick dips, and rose to 4.2 (95% CI 1.4, 12.7) for more than two applications per year. The analysis suggested that other compounds found in flea and tick products could contribute to the increased bladder cancer risk. Petroleum distillates and organic solvents including benzene, toluene, and xylene are among the inert ingredients used as carriers and are recognized carcinogens that could represent a hazard for exposed people.7,36 Further investigations of the role of pesticides in canine bladder cancer were carried out in Scottish terriers, a high-risk breed with approximately 18 times the risk for bladder cancer compared with mixed-breed dogs.37 In a case-control study of 83 Scottish terriers with bladder cancer and a similar number of breedmatched controls, the risk of bladder cancer was significantly higher among dogs exposed to lawns or gardens treated with herbicides or insecticides (OR57.2; 95% CI 2.2, 24.1), including phenoxy herbicides (OR54.4; 95% CI 1.7, 11.2), but not among dogs exposed to lawns or gardens treated with insecticides alone.37 The use of high-risk, genetically susceptible breeds permits evaluation of gene-environment interactions and holds promise for elucidation of cancer induction mechanisms in the sentinel animal. Recently, no association was found between lifetime exposure to chlorinated drinking water and bladder cancer in dogs.38 Chlorination disinfection byproducts have been suggested as a cause of bladder cancer in humans with long-term exposure.39 OCEANS AND HUMAN HEALTH A robust effort to identify and study animal sentinels of human health in the world’s oceans has developed over the past 50 years. The effects of exposures to xenobiotics in the marine environment may be expressed at multiple trophic levels of the ecosystem; however, attention is focused on marine mammals for several reasons.40 Marine mammals have relatively long lifespans that permit the expression of chronic diseases including cancer, abnormalities in growth and development, and reproductive failure. As apex predators, marine mammals feed at or near the top of the food chain. As the result of biomagnification, the levels of

anthropogenic contaminants found in marine mammal tissues are typically high, often higher than those found in humans. Further, the subdermal blubber layer provides a repository for lipophilic contaminants, particularly organohalogen compounds.40 Finally, the application of clinical examination procedures and hematological biochemical, immunological, and microbiological techniques, combined with pathological examination, has led to the development of health assessment methods at the individual and population levels.41,42 With these tools in hand, investigators have begun to unravel the relationships between exposure to environmental chemicals and disease endpoints in marine mammal sentinels. Organochlorine compounds and heavy metals The initial steps in studying the effects of environmental pollution on marine mammals were directed at exposure assessment. Investigators worldwide analyzed the levels of heavy metals and organochlorine compounds in blubber and other tissues from dead animals found stranded on beaches or from live-caught animals. As reviewed by O’Shea and Tanabe,43 the accumulation of data regarding levels of chemical residues in the tissues of marine mammals expanded dramatically during the latter half of the 20th century. By the end of the 1960s, high concentrations of mercury, chlorinated pesticides including dichlorodiphenyltrichloroethane (DDT) and its metabolites, polychlorinated biphenyls (PCBs), and other contaminants had been widely documented in pinnipeds and cetaceans. Multiple studies establishing exposures to marine pollutants were published in the 1980s and 1990s, and attempts at incorporating biomarkers were initiated. This period also saw the first attempt to reproduce adverse effects on reproductive success experimentally by feeding fish containing high concentrations of PCBs to captive harbor seals.44 A similar effect on reproductive success was reported subsequently in a population of bottlenose dolphins maintained by the U.S. Navy. In this observational study, higher concentrations of DDT metabolites and the sum of 10 PCB congeners were found in blubber of female dolphins whose calves were stillborn or died within the first 12 days of life compared with dolphins whose calves survived for six months or longer.45 Effects on the immune system, infectious diseases, and cancer In the 1990s, evidence began to accumulate that exposure to organochlorines led to a decrease in immunocompetence in highly exposed pinnipeds.46,47 These findings led to the hypothesis that exposure to organochlorines may be a co-factor in the extensive mortality



experienced by pinnipeds and cetaceans infected with novel strains of morbilliviruses that emerged during the late 1980s and early 1990s.48,49 The hypothesis has been difficult to confirm since the morbillivirus itself is a potent immunosuppressive agent. Further links between exposure to immunosuppressive agents in the marine environment and infectious diseases were found in a study in which exposure to PCB congeners was compared between harbor porpoises that died from infectious diseases and those that died from physical trauma.50 A significant incremental increase in risk was found, corresponding to a 2% increase in risk of infectious disease mortality for each 1-milogram/kilogram increase in blubber PCBs. Exposure to pollutants in the marine environment has also been linked to an increased risk of cancer in exposed marine mammal populations. The observation that beluga whales inhabiting the St. Lawrence estuary (SLE) had an unusually high prevalence of neoplasia compared with the relatively low (rare) prevalence in cetaceans, generally, and in belugas from the Arctic, specifically,51 led to a series of studies to address the hypothesis. SLE beluga whales live in a heavily polluted environment that receives discharges from the industries, such as aluminum smelters, along the coastline of the estuary.52 Adducts to benzo[a]pyrene, a carcinogenic polycyclic aromatic hydrocarbon, were demonstrated in SLE belugas, but not in belugas from the Arctic, and high concentrations of PCBs were found in tissues of SLE belugas.53 A heterogeneous group of tumors that included intestinal and gastric adenocarcinomas, mammary gland adenocarcinomas, ovarian granulosa cell tumors, lymphosarcoma, and bladder carcinoma was reported, consistent with the hypothesis that exposure to polycyclic aromatic hydrocarbons may be involved in the etiology of cancer in these animals. If the hypothesis is correct, the findings have important implications for contiguous human populations.52 A second cancer cluster in marine mammals was reported in adult California sea lions stranded along the California coast. From 1979 to 1994, 66 of 370 (18%) sea lions examined were found to be affected with a poorly differentiated carcinoma of urogenital origin.54 Further investigation revealed that the lesions arose from genital epithelium in males and females and that the lesions contained deoxyribonucleic acid (DNA) sequences of a novel gammaherpesvirus.55 The concentrations of PCBs and DDT compounds were higher than those found in seals that died of other causes.56 This unique sentinel neoplasm appears to arise from an interaction between a herpesvirus and environmental contaminants that may act as immu-

nosuppressive agents or from direct DNA damage or tumor promotion.55,56 Emerging contaminants Recently, attention has shifted to a group of persistent organic chemicals termed “emerging contaminants,” which include hydroxylated PCBs, polybrominated diphenyl ethers, and perfluorinated compounds. Evidence of toxicity in laboratory animals and widespread exposure among humans has raised concern regarding potential health effects of these and other contaminants found in the marine environment. Recent evidence of high exposure levels in marine mammal populations has also appeared. Studies of bottlenose dolphin populations along the eastern coast of the U.S. show that this sentinel species is highly exposed to brominated flame retardants and their hydroxlyated analogs.57 Similarly, perfluorinated compounds including perfluorooctane sulfonate were found ubiquitously in dolphins from the Gulf of Mexico and the western Atlantic Ocean.58 The sampling sites included estuarine environments in the Indian River Lagoon, Florida, and in the vicinity of Charleston, South Carolina. Extensive health assessments have been conducted on these coastal dolphin populations since 2003, under the Bottlenose Dolphin Health and Risk Assessment Project, aimed at investigating associations between dolphin health and environmental contaminants.42,59 To provide comparative data on health effects and inform human risk assessment, studies of biochemical and endocrine markers, immune function, target organ dysfunction, and health status are in progress. CONCLUSION Exciting opportunities exist for exploring marine mammal sentinels of ocean and human health through interdisciplinary research utilizing modern approaches to characterize their effects. The findings from investigations on the effects of environmental pollution on sentinel animals are relevant to human health and may lead to public health interventions and policy initiatives. REFERENCES 1. Burrell G, Seibert F. Experiments with small animals and carbon monoxide. J Ind Eng Chem 1914;6:241-4. 2. National Research Council of the National Academies. Sensor systems for biological agent attacks: protecting buildings and military bases. Washington: The National Academies Press; 2005. 3. National Research Council. Animals as sentinels of environmental health hazards. Washington: National Academy Press; 1991. 4. Tsuchiya K. The discovery of the causal agent of Minamata disease. Am J Ind Med 1992;21:275-80.



5. Hammond PB, Aronson AL. Lead poisoning in cattle and horses in the vicinity of a smelter. Ann N Y Acad Sci 1964;111:595-611. 6. Bukowski JA, Wartenberg D. An alternative approach for investigating the carcinogenicity of indoor air pollution: pets as sentinels of environmental cancer risk. Environ Health Perspect 1997;105:1312-9. 7. Kelsey JL, Moore AS, Glickman LT. Epidemiologic studies of risk factors for cancer in pet dogs. Epidemiol Rev 1998;20:204-17. 8. Reif JS. The epidemiology of cancer in pet animals. In: Withrow SJ, Vail DM, editors. Withrow & MacEwen’s small animal clinical oncology. 4th ed. St. Louis: Saunders Elsevier; 2006. p. 68-76. 9. Glickman LT, Domanski LM, Maguire TG, Dubielzig RR, Churg A. Mesothelioma in pet dogs associated with exposure of their owners to asbestos. Environ Res 1983;32:305-13. 10. Leake CD. Lung cancer in dogs (letter to the editor). JAMA 1960;173:85-6. 11. Cohen D, Reif JS, Rhodes WH. Epidemiologic studies of lung cancer in dogs. In: Severi L, editor. Lung tumours in animals. Proceedings of the Third Quadrennial International Conference on Cancer; 1965 Jun 24–29; Perugia, Italy. Perugia (Italy): University of Perugia; 1966. p. 165-80. 12. Reif JS, Cohen D. The environmental distribution of canine respiratory tract neoplasms. Arch Environ Health 1971;22:136-40. 13. Ragland WL 3rd, Gorham JR. Tonsillar carcinoma in rural dogs. Nature 1967;214:925-6. 14. Reif JS, Cohen D. Canine pulmonary disease. II. Retrospective radiographic analysis of pulmonary disease in rural and urban dogs. Arch Environ Health 1970;20:684-9. 15. Reif JS, Cohen D. Canine pulmonary disease: a spontaneous model for environmental epidemiology. In: The National Research Council. Animals as monitors of environmental pollutants. Washington: National Academy of Sciences; 1979. p. 241-50. 16. Calderón-Garcidueñas L, Mora-Tiscareño A, Fordham LA, Chung CJ, García R, Osnaya N, et al. Canines as sentinel species for assessing chronic exposures to air pollutants: part 1. Respiratory pathology. Toxicol Sci 2001;61:342-55. 17. Reif JS, Rhodes WH, Cohen D. Canine pulmonary disease and the urban environment. I. The validity of radiographic examination for estimating the prevalence of pulmonary disease. Arch Environ Health 1970;20:676-83. 18. Calderón-Garcidueñas L, Mora-Tiscareño A, Ontiveros E, GómezGarza G, Barragán-Mejía G, Broadway J, et al. Air pollution, cognitive deficits and brain abnormalities: a pilot study with children and dogs. Brain and Cogn 2008;68:117-27. 19. Hirayama T. Cancer mortality in nonsmoking women with smoking husbands based on a large-scale cohort study in Japan. Prev Med 1984;13:680-90. 20. Environmental Protection Agency (US), Office of Research and Development, Office of Health and Environmental Assessment. Respiratory health effects of passive smoking: lung cancer and other disorders. Washington: EPA; 1992. 21. Reif JS, Dunn K, Ogilvie GK, Harris CK. Passive smoking and canine lung cancer risk. Am J Epidemiol 1992;135:234-9. 22. Reif JS, Bruns C, Lower KS. Cancer of the nasal cavity and paranasal sinuses and exposure to environmental tobacco smoke in pet dogs. Am J Epidemiol 1998;147:488-92. 23. Bertone-Johnson ER, Procter-Gray E, Gollenberg AL, Ryan MB, Barber LG. Environmental tobacco smoke and canine urinary cotinine level. Environ Res 2008;106:361-4. 24. Bertone ER, Snyder LA, Moore AS. Environmental tobacco smoke and risk of malignant lymphoma in pet cats. Am J Epidemiol 2002;156:268-73. 25. Reif JS. Companion dogs as sentinels for the carcinogenic effects of pesticides. Pesticides, People and Nature 1999;1:7-14. 26. Blair A, Zahm SH, Pearce NE, Heineman EF, Fraumeni JF Jr. Clues to cancer etiology from studies of farmers. Scand J Work Environ Health 1992;18:209-15. 27. Hoar SK, Blair A, Holmes FF, Boysen C, Robel RJ, Hoover R, et al. Agricultural herbicide use and risk of lymphoma and soft-tissue sarcoma. JAMA 1986;256:1141-7. 28. Zahm SH, Weisenburger DD, Babbitt PA, Saal RC, Vaught JB, Cantor KP, et al. A case-control study of non-Hodgkin’s lymphoma and the herbicide 2,4 dichlorophenoxyacetic acid (2,4-D) in eastern Nebraska. Epidemiology 1990;1:349-56. 29. McDuffie HH, Pahwa P, McLaughlin JR, Spinelli JJ, Fincham S,

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Dosman JA, et al. Non-Hodgkin’s lymphoma and specific pesticide exposures in men: cross-Canada study of pesticides and health. Cancer Epidemiol Biomarkers Prev 2001;10:1155-63. Hayes HM, Tarone RE, Cantor KP, Jessen CR, McCurnin DM, Richardson RC. Case-control study of canine malignant lymphoma: positive association with dog owner’s use of 2,4-dichlorophenoxyacetic acid herbicides. J Natl Cancer Inst 1991;83:1226-31. Reynolds PM, Reif JS, Ramsdell HS, Tessari JD. Canine exposure to herbicide-treated lawns and urinary excretion of 2,4-dichlorophenoxyacetic acid. Cancer Epidemiol Biomarkers Prev 1994;3:233-7. Hayes HM, Tarone RE, Casey HW, Huxsoll DL. Excess of seminomas observed in Vietnam service U.S. military working dogs. J Natl Cancer Inst 1990;82:1042-6. Hayes HM, Tarone RE, Casey HW. A cohort study of the effects of Vietnam service on testicular pathology of U.S. military working dogs. Mil Med 1995;160:248-55. Tarone RE, Hayes HM, Hoover RN, Rosenthal JF, Brown LM, Pottern LM, et al. Service in Vietnam and risk of testicular cancer. J Natl Cancer Inst 1991;83:1497-9. Hayes HM Jr, Hoover R, Tarone RE. Bladder cancer in pet dogs: a sentinel for environmental cancer? Am J Epidemiol 1981;114: 229-33. Glickman LT, Shofer FS, McKee LJ, Reif JS, Goldschmidt MH. An epidemiologic study of insecticide exposure, obesity, and risk of bladder cancer in household dogs. J Toxicol Environ Health 1989;28:407-14. Glickman LT, Raghavan M, Knapp DW, Bonney PL, Dawson MH. Herbicide exposure and the risk of transitional cell carcinoma of the urinary bladder in Scottish Terriers. J Am Vet Med Assoc 2004;224:1290-7. Backer LC, Coss AM, Wolkin AF, Flanders WD, Reif JS. Evaluation of associations between lifetime exposure to drinking water disinfection by-products and bladder cancer in dogs. J Am Vet Med Assoc 2008;232:1663-8. Villanueva CM, Cantor KP, Grimalt JO, Malats N, Silverman D, Tardon A, et al. Bladder cancer and exposure to water disinfection by-products through ingestion, bathing, showering, and swimming in pools. Am J Epidemiol 2007;165:148-56. Reddy ML, Dierauf LA, Gulland FMD. Marine mammals as sentinels of ocean health. In: Dierauf LA, Gulland FMD, editors. CRC handbook of marine mammal medicine. 2nd ed. Boca Raton (FL): CRC Press; 2001. p. 3-13. Wells RS, Rhinehart HL, Hansen LJ, Sweeney JC, Townsend FI, Stone R, et al. Bottlenose dolphins as marine ecosystem sentinels: developing a health monitoring system. Eco Health 2004;1:246-54. Reif JS, Fair PA, Adams J, Joseph B, Kilpatrick DS, Sanchez R, et al. Evaluation and comparison of the health status of Atlantic bottlenose dolphins from the Indian River Lagoon, Florida, and Charleston, South Carolina. J Am Vet Med Assoc 2008;233:299307. O’Shea TJ, Tanabe S. Persistent ocean contaminants and marine mammals: a retrospective overview. In: O’Shea TJ, Reeves RR, Long AK, editors. Marine mammals and persistent ocean contaminants. Proceedings of the Marine Mammal Commission Workshop; 1998 Oct 12–15; Keystone, Colorado. Bethesda (MD): Marine Mammal Commission; 1999. p. 87-92. Reijnders PJ. Reproductive failure in common seals feeding on fish from polluted coastal waters. Nature 1986;324:456-7. Reddy ML, Reif JS, Bachand A, Ridgeway SH. Opportunities for using Navy marine mammals to explore associations between organochlorine contaminants and unfavorable effects on reproduction. Sci Total Environ 2001;274:171-82. Ross PS, De Swart RL, Reijnders PJ, Van Loveren H, Vos JG, Osterhaus AD. Contaminant-related suppression of delayed-type hypersensitivity and antibody responses in harbor seals fed herring from the Baltic Sea. Environ Health Perspect 1995;103:162-7. De Swart RL, Ross PS, Vos JG, Osterhaus AD. Impaired immunity in harbour seals (Phoca vitulina) exposed to bioaccumulated environmental contaminants: review of a long-term feeding study. Environ Health Perspect 1996;104 Suppl 4:823-8. Hall AJ, Law RJ, Wells DE, Harwood J, Ross HM, Kennedy S, et al. Organochlorine levels in common seals (Phoca vitulina) which were victims and survivors of the 1988 phocine distemper epizootic. Sci Total Environ 1992;115:145-62.



49. Aguilar A, Borrell A. Abnormally high polychlorinated biphenyl levels in striped dolphins (Stenella coeruleoalba) affected by the 1990–1992 Mediterranean epizootic. Sci Total Environ 1994;154:237-47. 50. Hall AJ, Hugunin K, Deaville R, Law RJ, Allchin CR, Jepson PD. The risk of infection from polychlorinated biphenyl exposure in the harbor porpoise (Phocoena phocoena): a case-control approach. Environ Health Perspect 2006;114:704-11. 51. De Guise S, Lagacé A, Béland P. Tumors in St. Lawrence beluga whales (Delphinapterus leucas). Vet Pathol 1994;31:444-9. 52. Martineau D, Lemberger K, Dallaire A, Labelle P, Lipscomb TP, Michel P, et al. Cancer in wildlife, a case study: beluga from the St. Lawrence estuary, Québec, Canada. Environ Health Perspect 2002;110:285-92. 53. Martineau D, De Guise S, Fournier M, Shugart L, Girard C, Lagacé A, et al. Pathology and toxicology of beluga whales from the St. Lawrence estuary, Québec, Canada. Past, present and future. Sci Total Environ 1994;154:201-15. 54. Gulland FM, Trupkiewicz JG, Spraker TR, Lowenstine LJ. Metastatic carcinoma of probable transitional cell origin in 66 free-living California sea lions (Zalophus californianus), 1979 to 1994. J Wildl Dis 1996;32:250-8.

55. Lipscomb TP, Scott DP, Garber RL, Krafft AE, Tsai MM, Lichy JH, et al. Common metastatic carcinoma of California sea lions (Zalophus californianus): evidence of genital origin and association with novel gammaherpesvirus. Vet Pathol 2000;37;609-17. 56. Yitalo GM, Stein JE, Home T, Johnson LL, Tilbury KL, Hall AJ et al. The role of organochlorines in cancer-associated mortality in California sea lions (Zalophus californianus). Mar Pollut Bull 2005;50:30-9. 57. Fair PA, Mitchum G, Hulsey TC, Adams J, Zolman E, McFee W, et al. Polybrominated diphenyl ethers (PBDEs) in blubber of free-ranging bottlenose dolphins (Tursiops truncatus) from two southeast Atlantic estuarine areas. Arch Environ Contam Toxicol 2007;53:483-94. 58. Houde M, Wells RS, Fair PA, Bossart GD, Hohn AA, Rowles TK, et al. Polyfluoroalkyl compounds in free-ranging bottlenose dolphins (Tursiops truncatus) from the Gulf of Mexico and the Atlantic Ocean. Environ Sci Technol 2005;39:6591-8. 59. Fair PA, Adams J, Mitchum G, Hulsey TC, Reif JS, Houde M, et al. Contaminant blubber burdens in Atlantic bottlenose dolphins (Tursiops truncatus) from two southeast U.S. estuarine areas: concentrations and patterns of PCBs, pesticides, PBDEs, PFCs, and PAHs. Sci Total Environ 2010;408:1577-97.


Practice Articles

The 2009 National Environmental Public Health Conference: One Model for Planning Green and Healthy Conferences

Perri Zeitz Ruckart, MPHa Cory Moore, MPHa,b Deborah Burgin, PhDa Maggie Kelly Byrne, BAb

ABSTRACT The Centers for Disease Control and Prevention’s National Center for Environmental Health and the Agency for Toxic Substances and Disease Registry committed to making their 2009 National Environmental Public Health Conference a model for green and healthy conferences. The conference included increased opportunities for physical activity, both as part of conference events and for transportation to the conference. In addition, conference meals were healthy and sustainably sourced. The conference also implemented intuitive, accessible recycling; online scheduling and evaluation to minimize hard-copy materials; and the purchase of carbon offsets to reduce the unwanted environmental impact of the conference. Public health professionals have an opportunity and obligation to support healthy behaviors at their events and to serve as leaders in this area. Facilitating healthy and sustainable choices is in alignment with goals for both public health and broader social issues—such as environmental quality—that have a direct bearing on public health.

Centers for Disease Control and Prevention, Agency for Toxic Substances and Disease Registry, Atlanta, GA

a

Centers for Disease Control and Prevention, National Center for Environmental Health, Atlanta, GA

a

Address correspondence to: Perri Zeitz Ruckart, MPH, Centers for Disease Control and Prevention, Agency for Toxic Substances and Disease Registry, 4770 Buford Hwy., MS F57, Atlanta, GA 30341; tel. 770-488-3808; fax 770-488-7187; e-mail .

58 


2009 NEPHC: A Model for Planning Green and Healthy Conferences  59

The Centers for Disease Control and Prevention’s (CDC’s) National Center for Environmental Health (NCEH) and the Agency for Toxic Substances and Disease Registry (ATSDR) committed to making their 2009 National Environmental Public Health Conference (NEPHC) a model for green and healthy conferences. The 2009 conference was the first NEPHC to incorporate green and healthy initiatives. The commitment to make the NEPHC as healthy and sustainable as possible fit naturally with the conference theme of “Healthy People in a Healthy Environment” and with the disciplinary focus of environmental public health itself, which exists at the intersection of human health and the health of the environment. This commitment also supports broader public health goals beyond the specialty of environmental health, such as the Department of Health and Human Services’ 2010 Strategic Sustainability Performance Plan.1 With more than twothirds of Americans obese or overweight,2 public health professionals have both an opportunity and obligation to facilitate healthy choices at public health meetings and events. The 2009 NEPHC included increased opportunities for physical activity and meals that were healthy and sustainably sourced. The conference also implemented the following elements to reduce the unwanted environmental impact of the conference and promote health and wellness: intuitive, accessible recycling; online scheduling and evaluation to minimize hard-copy materials; encouraging the use of public transportation; and purchasing carbon offsets. The commitment to support a healthy and sustainable conference was evident at the very top levels of the organization and among members of the various teams working to organize the NEPHC. A planning subcommittee was established to identify and champion green and healthy options and provide technical assistance to other NEPHC planning committees, the hotel, and the meeting planning contractors to support this initiative. Public health partner organizations also were integral to ensuring the success of this endeavor. Partner input was obtained early in the planning process, which facilitated additional ideas on how to achieve the vision for a green and healthy conference and also increased buy-in for likely changes to the traditional conference format. The NEPHC built upon the ideas put forth by other organizations, such as the Environmental Protection Agency (EPA), in generating ideas for ways to “green” the conference.3–5 As part of their efforts to maximize healthy and environmentally sustainable options, the NEPHC organizers recognized that such choices often have the perception of being less convenient. When faced

with these challenges, conference organizers devoted time and resources to identify or create a healthy and environmentally sustainable choice by making it the easier, more convenient, and/or default choice. Changing the context in which decisions are made has more impact than relying on individual efforts.6 The NEPHC organizers considered opportunities to promote green and healthy choices when selecting the meeting venue, setting the conference schedule, making food and other purchasing decisions for the conference, and pursuing pollution reduction opportunities (Figure 1). GREEN AND HEALTHY PRACTICES Site selection Site selection is one of the most important choices that a meeting planner can make. From the outset, meeting organizers placed a priority on finding a venue that was easily accessible via public transit and that shared the NEPHC Planning Committee’s commitment to healthy and sustainable practices, such as those exemplified by the Green Hotel Initiative developed by the Coalition for Environmentally Responsible Economies.7 The hotel selected had won awards for its policies and practices in supporting environmental sustainability and had demonstrated leadership by reducing water usage and wastes both in guestrooms and behindthe-scenes hotel operations. The venue also offered a 24-hour fitness center for hotel guests. The NEPHC organizers selected a hotel that was easily accessible by public transportation and then promoted the use of this option by providing a walking map prominently displayed in the conference booklet and by posting transit directions as the first transportation option on the conference website.8 These measures provided another opportunity to positively impact both the environment and health. Green and healthy purchasing choices Conferences incorporate many purchasing decisions that have implications for improved public health and pollution reduction, such as decisions about food. In addition to providing healthier fare, such as vegetarian choices and organic items, venue staff obtained commitments from food vendors to purchase locally grown foods as much as possible. This practice is in alignment with strategies outlined in the Common Community Measures for Obesity Prevention Project, which recommends increasing availability of healthy foods, including through mechanisms to purchase food from local farms.9 Food was served on china with metal



Figure 1. Checklist for planning a “green” and healthy meeting Select a meeting venue with demonstrated leadership in green and healthy initiatives • Location on a major public transportation line • Within walking distance to restaurants and other attractions • Energy and water conservation efforts • Single stream recycling • Healthy local, sustainable restaurant menus • Composting • Eco-friendly construction and landscaping • 24-hour fitness center • Smoke-free facility Incorporate green and healthy purchasing decisions • Organic, healthy, and locally produced seasonal food • Serve food and beverages from large, reusable containers rather than individually packaged servings • Purchase carbon offsets • Conference giveaway chosen during preconference registration includes choice of organic cotton bag or reusable aluminum water bottle or attendees could choose to take nothing Facilitate intuitive and accessible recycling • Recycling bins for paper, plastic, glass, and aluminum that are larger and more numerous than waste bins • Collect conference name tags and conference bags for recycling at end of conference Reduce amount of paper waste generated • Online systems for registration, abstract submission, and evaluation • Free wireless Internet access and computer kiosks during the conference • On-site guides to answer attendee questions • Print on alternative fiber (bagasse and bamboo) and post-consumer fiber when hard-copy materials are necessary Encourage exhibitors to support a green and healthy exhibition • Healthy snacks instead of candy • Electronic media to display information • Organic, biodegradable, recycled and/or recyclable promotional items • Minimize shipping materials to the event and use recyclable packing materials when shipping is necessary • Select paper products made from post-consumer recycled content • Send information to attendees electronically after the conference Provide opportunities for attendees to be physically active • Morning yoga sessions and mobile workshops

flatware or on compostable plates, and condiments were served in larger containers, not as individually packaged servings, to minimize solid waste. Waste reduction Organizers took several steps to reduce or eliminate waste generated by the conference. The organizers made recycling opportunities readily apparent and accessible to conference participants to make recycling the default choice over traditional disposal. Recycling bins for paper, plastic, glass, and aluminum were larger and more numerous than waste bins, and signs indicated where to deposit recycled goods. Similarly, organizers reduced the amount of hard-copy materials by offering a conference book containing minimal printed information (maps, session titles and tracks, welcome letter, and schedule of activities) on alternative fiber and post-consumer fiber. The NEPHC Planning Committee realized that transitioning away from paperbased mechanisms involved publicizing creative, convenient alternatives. Free wireless access was provided

for conference participants to access the conference schedule as well as to take notes on their laptops during a presentation. In addition, four computer kiosks with printers were available to enable attendees to access the Internet at any time and to access the online schedule, even if they did not bring their own laptops. Additionally, the NEPHC Planning Committee provided helpful guides to answer attendee questions. Nearly 50 college student volunteers were trained as ambassadors and attended the conference for free in exchange for their services. Conference exhibitors were also encouraged to promote health and wellness and further reduce the environmental footprint of the conference when planning their exhibits. Incorporating physical activity into conference events Nearly two-thirds of Americans are overweight or obese,2 which increases risk for a range of public health problems, including coronary heart disease, type 2 diabetes, some cancers, stroke, and other problems.10



In addition to selecting a site with a fitness center and within walking distance of public transportation and destinations, the NEPHC organizers also arranged for free morning yoga classes for conference participants. Offering yoga provided another way to help attendees obtain their recommended physical activity for the day,11 something often hard to do while traveling. In addition, opportunities for physical activity were included in the conference through the innovative use of mobile workshops that provided attendees the opportunity to leave the meeting venue and experience a local public health issue through first-hand experience. Organizing these workshops provided opportunities to build partnerships between federal agency staff and representatives of local organizations, such as local governments, nonprofit organizations, and universities, and provided conference attendees with opportunities to learn and network in a different environment. TRANSFERABILITY Since the 2009 NEPHC, members of that conference Planning Committee have undertaken several activities to transfer the lessons learned about how to improve the health benefits and environmental sustainability of conferences, including developing a checklist for other organizations to use in planning a conference

with the health of the public and environment in mind (Figure 1). Members of the 2009 NEPHC Organizing Committee established a new workgroup within CDC’s Go Green, Get Healthy Initiative to share best practices within CDC/ATSDR on how to improve the environmental health of government meetings and conferences. The 2009 NEPHC organizers also provide technical assistance to other CDC offices, as well as to state-based public health organizations, and teach courses on the topic at Health and Human Services University. These examples illustrate how the 2009 NEPHC is serving as a model and leader for other public health organizations. IMPACT It is difficult to directly measure the health and environmental benefits of the 2009 NEPHC. However, the healthy and sustainable practices employed by this conference did have a positive impact on the overall health and environment of the attendees. Evaluation data from conference attendees show that they supported the health and sustainability efforts made by the NEPHC Planning Committee (Figure 2). The majority of respondents rated the following attributes as excellent: accessibility of public transportation (n5187/312); accessibility of recycling bins (n5107/141); and sustainable and healthy food options at sponsored events

Figure 2. Participant ratings of the 2009 NEPHC initiatives

NEPHC 5 National Environmental Public Health Conference



(n593/143). Additionally, more than 80% of the respondents felt the condensed conference program provided adequate information on daily schedules, hotel map, etc. (total n5312); 65% of the attendees preferred not having a full conference program (total n5311); and almost 70% of the attendees used the free wireless Internet service provided during the conference (total n5133). Of the 1,220 people who attended the conference, approximately 25% of the attendees recycled their name badges, and 14% of the attendees chose not to take a conference giveaway. While there is room for improvement, these numbers represent a baseline for which to compare the health and environmental impact of future NEPHCs. While some negative environmental impacts did occur from some attendees coming to the conference via air or automobile, the NEPHC organizers sought to minimize the environmental impact of other actions associated with the conference. Positive environmental impacts included energy savings, pollution reduction, and conservation of natural resources by recycling;12 purchasing carbon offsets for those traveling to the conference;13 reducing printed materials;14 and promoting the use of refillable aluminum bottles and glasses from water pitchers instead of offering plastic bottled beverages during conference breaks and lunches.14,15 The 2009 conference used 1.4 trees to provide minimal printed information and used online tools for managing most aspects of the conference. As a result of these improvements, more than 11 trees were saved compared with the 2006 NEPHC.16 Additionally, because alternative and post-consumer fibers were used, no virgin paper and, therefore, no “new” trees were used to print conference books. Using public transportation to get to the conference and mobile workshops provided opportunities for both physical activity and reduced carbon emissions compared with alternatives. Morning yoga sessions were attended by approximately 35 people each day they were offered and helped attendees meet the adult 2008 Physical Activity Guidelines for Americans of engaging in muscle-strengthening activities on two or more days a week.11 Benefits of physical activity include a reduced risk of cardiovascular disease and some cancers, strengthened bones and muscles, and improved mental health.10 Although the 2009 NEPHC was unable to collect information on the number of people who used public transportation, we suggest that future NEPHC evaluations collect this information so that increases in the use of public transportation can be measured.

CONCLUSION A major priority of the 2009 NEPHC was to incorporate “green” and healthy planning and sustainability when making decisions about the conference. Tangible benefits of the conference included healthier food and exercise options, the purchase of carbon offsets, significant decreases in printed materials compared with the 2006 conference, and increased reliance on public transportation for travel to the hotel, airport, and mobile workshops. Furthermore, the healthy and sustainable measures undertaken by the 2009 NEPHC planning committee contributed to energy savings, pollution reduction, and conservation of natural resources, all of which help lessen global warming and protect human health from climate change. While it is difficult to document a direct reduction in health risk as a result of one meeting, it is nonetheless also clear that facilitating healthy and sustainable choices is in alignment with public health goals and contributes to achieving goals for broader social issues—such as environmental quality—that also have a direct bearing on public health. Organizers for the 2009 NEPHC demonstrated that public health meetings can—and should—be implemented with these goals in mind. The authors thank Julie Fishman for reviewing the manuscript. The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention or the Agency for Toxic Substances and Disease Registry.

REFERENCES 1. Department of Health and Human Services (US). 2010 strategic sustainability performance plan [cited 2010 Sep 30]. Available from: URL: http://www.hhs.gov/about/2010_hhs_sustainability_plan .pdf 2. Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999–2008. JAMA 2010;303:235-41. 3. Environmental Protection Agency (US). Green meetings [cited 2010 Sep 30]. Available from: URL: http://www.epa.gov/opptintr/ greenmeetings 4. National Recycling Coalition. Green meetings policy [cited 2010 Sep 30]. Available from: URL: http://www.resourcesaver.org/file/ toolmanager/CustomO16C45F42045.pdf 5. Oceans Blue Foundation, Green Meeting Industry Council. Bluegreen meetings [cited 2010 Sep 30]. Available from: URL: http:// www.bluegreenmeetings.org/index.htm 6. Frieden TR. A framework for public health action: the health impact pyramid. Am J Public Health 2010;100:590-5. 7. Coalition for Environmentally Responsible Economies. Green hotel initiative: best practice survey [cited 2010 Sep 28]. Available from: URL: http://www.ceres.org//Page.aspx?pid5761 8. Centers for Disease Control and Prevention (US), Agency for Toxic Substances and Disease Registry. 2009 National Environmental Public Health Conference [cited 2010 May 5]. Available from: URL: http://team-psa.com/2009nephc/main.asp



9. Khan LK, Sobush K, Keener D, Goodman K, Lowry A, Kakietek J, et al. Recommended community strategies and measurements to prevent obesity in the United States. MMWR Recomm Rep 2009;58(RR-7):1-26. 10. Department of Health and Human Services, Office of the Surgeon General (US). The Surgeon General’s vision for a healthy and fit nation. 2010 [cited 2010 Jul 16]. Available from: URL: http://www .surgeongeneral.gov/library/obesityvision/index.html 11. Department of Health and Human Services (US). 2008 physical activity guidelines for Americans [cited 2010 Jul 14]. Available from: URL: http://www.health.gov/paguidelines 12. Environmental Protection Agency (US). Puzzled about recycling’s value? Look beyond the bin [cited 2010 Jul 16]. Available from: URL: http://www.epa.gov/wastes/conserve/downloads/benefits .pdf

13. Carbonfund.org. Project types [cited 2010 Apr 20]. Available from: URL: http://www.carbonfund.org/projects 14. BlueSkyModel.org. Pounds of CO2 per pound of stuff [cited 2010 May 5]. Available from: URL: http://www.stewartmarion.com/ carbon-footprint/html/carbon-footprint-stuff.html 15. International Bottled Water Association. Bottled water facts and drinking water safety management through direct mail bottled water education campaign [cited 2011 Jan 13]. Available from: URL: http://www.idswater.com/water/us/IBWA/bottled_water_ facts/15_0/g_supplier_2.html 16. Conservatree. Trees into paper [cited 2010 May 5]. Available from: URL: http://www.conservatree.com/learn/EnviroIssues/TreeStats .shtml


Research Articles

Health Outcomes and Green Renovation of Affordable Housing

Jill Breysse, MHSa David E. Jacobs, PhDa William Weber, MArchb Sherry Dixon, PhDa Carol Kawecki, MA, RNa Susan Aceti, MSWa Jorge Lopez, BSc

ABSTRACT Objective. This study sought to determine whether renovating low-income housing using “green” and healthy principles improved resident health and building performance. Methods. We investigated resident health and building performance outcomes at baseline and one year after the rehabilitation of low-income housing using Enterprise Green Communities green specifications, which improve ventilation; reduce moisture, mold, pests, and radon; and use sustainable building products and other healthy housing features. We assessed participant health via questionnaire, provided Healthy Homes training to all participants, and measured ventilation, carbon dioxide, and radon. Results. Adults reported statistically significant improvements in overall health, asthma, and non-asthma respiratory problems. Adults also reported that their children’s overall health improved, with significant improvements in non-asthma respiratory problems. Post-renovation building performance testing indicated that the building envelope was tightened and local exhaust fans performed well. New mechanical ventilation was installed (compared with no ventilation previously), with fresh air being supplied at 70% of the American Society of Heating, Refrigerating, and Air-Conditioning Engineers standard. Radon was ,2 picocuries per liter of air following mitigation, and the annual average indoor carbon dioxide level was 982 parts per million. Energy use was reduced by 45% over the one-year post-renovation period. Conclusions. We found significant health improvements following low-income housing renovation that complied with green standards. All green building standards should include health requirements. Collaboration of housing, public health, and environmental health professionals through integrated design holds promise for improved health, quality of life, building operation, and energy conservation.

National Center for Healthy Housing, Columbia, MD

a

University of Minnesota Center for Sustainable Building Research, College of Design, Minneapolis, MN

b

Southwest Minnesota Housing Partnership, Slayton, MN

c

Address correspondence to: Jill Breysse, MHS, National Center for Healthy Housing, 10320 Little Patuxent Pkwy., Ste. 500, Columbia, MD 21044; tel. 443-539-4155; fax 443-539-4150; e-mail . ©2011 Association of Schools of Public Health

64 


Health and Green Renovation of Affordable Housing  65

Low-income families are more likely to encounter environmental health and safety hazards in their homes and communities and are, therefore, disproportionately affected by environmental diseases.1,2 Low-income children are eight times more likely to suffer from lead poisoning,3 and childhood asthma rates are higher in low-income communities.4 Housing affects health directly and indirectly, and the burden of housing-related diseases and injuries is substantial.5 Physical, chemical, and biological exposures in the home that produce adverse health outcomes and associated housing interventions have been reviewed elsewhere.6–9 Data are needed to elucidate the complex links between health, buildings, and communities to enable building owners, community planners, and others to more confidently implement health-based housing interventions. This study sought to determine whether renovating low-income housing using green and healthy principles improved resident health and building performance. Several different “green” rating systems have appeared recently, including Enterprise Green Communities Criteria,10 U.S. Environmental Protection Agency’s (EPA’s) Energy Star Plus Indoor Air Program (Indoor airPLUS),11 and Leadership in Energy and Environmental Design (LEED).12 While such systems may improve health, evidence to support this claim is sparse. The definition of “green” construction is fluid and developing. These leading green building systems differ greatly with regard to whether health aspects of green are required or optional. For example, the Enterprise Green Communities standards used in the reno-

vation described in this study include several required health-related specifications, while LEED only provides a certain number of optional points for health items. Although two earlier studies demonstrated significant respiratory health improvements in new green construction,13,14 this is the first study to investigate whether renovating low-income housing using green principles improves resident health. METHODS A three-building, 60-unit apartment complex in southwest Minnesota underwent substantial green renovation in 2006–2007, complying with the voluntary Enterprise Green Communities Criteria, 10 which cover eight renovation areas: integrated design process, location and neighborhood fabric, site, water conservation, energy conservation, materials and resources, healthy living environment, and operations and management. Of the housing improvements conducted, eight were health-related (Figure 1). We administered a structured health interview to assess self-reported health status of participating adults and children. The health interview was adapted from the Centers for Disease Control and Prevention’s (CDC’s) annual National Health Interview Survey,15 CDC’s Behavioral Risk Factor Surveillance System,16 and the National Institute of Environmental Health Sciences’ National Survey of Lead and Allergens in Housing (sponsored in conjunction with the U.S. Department of Housing and Urban Development [HUD]).17

Figure 1. Health-related green housing rehabilitation features: Green Housing Renovation Study, Minnesota, 2006–2008 Green housing feature

Rehabiliation action

Ventilation and fresh air supply

Comply with ASHRAE Standard 62.2. Install air-handling units in each apartment, with ducted supplies into bedroom and living space and central return at the unit. Duct fresh air from exterior to systems return duct.

Off-gassing

Use low-VOC adhesives, finishes, and paints.

Radon

Conduct testing and mitigate if indicated.

Pest control

Conduct integrated pest management.

Tobacco smoke

No smoking in common areas

Mold control

No carpets in wet rooms

Moisture control

Install kitchen and bath exhaust fans.

Other green features

Energy: Install geothermal heating and cooling system; install high-performance (U-value 0.32) windows; add insulation to exterior walls (existing R-value 11 plus 7.5 added) and to roof assembly (R-value 48). Water: Replace water fixtures in kitchen and bathroom, install dual-flush toilets, and install low-water clothes washers.

ASHRAE 5 American Society of Heating, Refrigerating, and Air-Conditioning Engineers VOC 5 volatile organic compound


66  Research Articles

Interviews were administered one to four months after residents moved into renovated apartments and were repeated 12 to 18 months later. We assessed prerenovation health status through respondent recall at this interview, which included questions concerning housing characteristics, demographics, cleaning practices, smoking history, respiratory health, and physical injury. One adult per dwelling was interviewed and provided information on his or her health and the health of other resident adults and children. Study data collection began in November 2006 and was completed in September 2008. Time 0 (T0) refers to the residents’ recall of conditions in their old homes prior to renovation; time 1 (T1) refers to the first study visit, which occurred an average of 67 days after people moved into the renovated apartments; and time 2 (T2) refers to the follow-up visit approximately 12 to 18 months later. Following each interview, residents received training on maintaining a healthy home using a HUD booklet focusing on methods of keeping housing dry, clean, pest-free, safe, contaminant-free, ventilated, and maintained.18 Building performance testing was conducted in the third renovated building (the only one that was vacant after renovation was complete but before re-occupancy) to compare it with design criteria and building standards. The renovated air-handling system was designed to supply outdoor air to individual apartments, instead of the pre-renovation system, which supplied outdoor air only through unplanned building envelope leakage. Airflow was measured with the air handler running continuously. Total building shell leakage was measured using a calibrated blower door, which was also used to test two apartments. Inlet airflow was tested during operation of the air handler, bathroom exhaust fans, and kitchen exhaust fans. Kitchen and bathroom exhaust fan airflows were compared with design specifications. Duct return airflows and duct leakage from the total air-handling system were evaluated using a duct blaster. Interstitial pressures were measured between rooms within individual units, with interior doors closed if rooms were positively pressured and opened if neutral or negatively pressured relative to the main living space. Pressure tests were conducted to determine if return air within living areas was adequate. We placed three-day radon test dosimeters in 25 locations in the three buildings before renovation. After renovation but before any radon mitigation, alpha-track long-term (approximately 90-day) radon dosimeters were placed in 17 locations in two buildings. After radon mitigation in two buildings, 90-day dosimeters were placed in 26 locations. Using Onset® HOBO

data loggers (Onset Computer Corp., Bourne, Massachusetts), carbon dioxide (CO2) levels were tracked in the living space of four units for approximately 12 months post-renovation. Funding limitations prevented testing more units. CO2 data were retrieved quarterly and compared with the commonly used indoor air quality guideline of 1,000 parts per million (ppm) to determine if fresh air ventilation was adequate.19–21 We analyzed utility bills to determine overall energy use and carbon gas emissions before renovation and one year after renovation. Total annual energy use (measured in kilo British thermal units per year [kBTU/yr]) was divided by the square footage (ft2) of the conditioned space and heating degree days (HDD) to compensate for yearly weather fluctuations (kBTU/ HDD/ft2/yr). When utility bill data were missing for individual apartments, we estimated energy use by using average energy use for the same unit type. For interview questions that could be answered either yes or no, we used McNemar’s test to test the hypothesis that the percent of people answering yes to a question was different at two specific times. When all people had the same responses at both times, the p-value could be calculated. The binomial test of proportion was used to test the hypothesis that the proportion of respondents with better health was different from the proportion reporting worse health. For questions that could be answered with a multiple list of options representing some order of intensity (e.g., whether general health was “very good/excellent,” “good,” or “fair/poor”), we used the Cochran-Mantel-Haenszel row mean score test for ordinal variables to test the hypothesis that the means at two specific times were significantly different. When comparing interview data at two different time periods, we first matched data for both participants and apartments. Statistical significance was defined as p,0.05 and marginal significance as 0.05#p,0.1. We did not control for multiple testing. Results Thirty-one (31) of the 54 occupied units (57%) were enrolled in the study. Due to differing start times for building renovation and the study, residents could not be interviewed before renovation, so baseline data were based on recall. At the initial visit, residents had lived in their newly renovated apartments less than one month to approximately four months. Housing data for T0 and T1 were gathered for 31 units. Interviewed adults from 29 apartments provided T0 and T1 health data about themselves, 21 other adults, and 30 children younger than 18 years of age residing in these apartments. Fifteen (52%) of the 29 apartments that provided T0



and T1 health data had children. Residents in 18 of the 31 units agreed to participate in the T2 follow-up visit, a retention rate of 58%. The 18 interviewed adults provided T2 data for themselves, six other adults, and 17 children living in these apartments. Nine (50%) of the 18 apartments had children at T2. Of the 17 children, two were 17 years old at the T1 visit and 18 years old at the T2 visit. Demographic data Study participants were largely immigrants of minority race/ethnicity and all low-income (Table 1). Less

than half (43%) of the adults but most (82%) of the children were born in the U.S. Most adults were either white (37%) or African (not African American) (35%). Mean annual household income was $29,000. There were no statistically significant differences in resident demographics between T1 and T2 (all p.0.1). Adult health More adults reported better vs. worse overall health at T1 compared with T0 (34% vs. 7%; p50.042; Table 2). Adult health status was better at T2 than T1 (p50.052). Sixty-two percent reported that adult health was very

Table 1. Demographic data of participants: Green Housing Renovation Study, Minnesota, 2006–2008 Initial interview (T1)

Follow-up interview (T2)

Characteristic

Na

Result

Nb

Result

Born in the U.S. (n [percent]) Adults Children

49 27

21 (43) 22 (82)

24 17

11 (46) 14 (82)

Age (in years) (mean) Adults Children

50 29

24 17

44 6

Highest level of education (median) Adults Children

49 13

10th grade 7th grade

24 8

12th gradec 6th grade

Female gender (n [percent]) Adults Children

50 30

29 (58) 11 (37)

24 16

16 (67) 7 (44)

Race/ethnicity (n [percent]) Adults White/Hispanic White/non-Hispanic African Black/African American American Indian/Hispanic American Indian/non-Hispanic Some other race/don’t know/Hispanic Children White/Hispanic White/non-Hispanic African Black/African American/Hispanic Black/African American/non-Hispanic American Indian/Hispanic Some other race/don’t know/Hispanic Number of people living in each apartment (mean)

39 6

49

22 5 13 17 4 1 1 8

(10) (27) (35) (8) (2) (2) (16)

1 2 9 2 11 1 3

(3) (7) (31) (7) (38) (3) (10)

29

2 8 7 2 0 0 3

(9) (36) (32) (9) (0) (0) (14)

1 2 2 0 9 1 2

(6) (12) (12) (0) (53) (6) (12)

17

31

3

18

2

Length of time in renovated home at T1 (months) (range)

30

1–3

16

14–29

Lived elsewhere 12 months prior to T1 (n [percent])

21

8 (38)

8

$29,000

Annual household income (median)

NA

NA

7

$28,000

Although some T1 data were reported for 50 adults and 30 children in 31 homes, some values were missing.

a

b

Although some T2 data were reported for 24 adults and 17 children in 18 homes, some values were missing.

Two residents that were children at T1 were adults at T2.

c

T1 5 time 1 (refers to the first study visit, which occurred an average of 67 days after people moved into the renovated apartments) T2 5 time 2 (refers to the follow-up visit approximately 12 to 18 months after people moved into the renovated apartments) NA 5 not applicable



Table 2. General health data for adults and children: Green Housing Renovation Study, Minnesota, 2006–2008 T1 Characteristic

N

Health comparison: interviewed adultc Better Same Worse

29

Health comparison: childc Better Same Worse

30

General health status: adult Very good or excellent Good Fair or poor

21

General health status: child Very good or excellent Good Fair or poor

17

Count (percent)

T2 P-valuea

N

0.042

18

10 (34) 17 (59) 2 (7)

P-valuea

P-valueb

0.786

NA

0.358

NA

NA

0.052

NA

0.206

5 (28) 9 (50) 4 (22) 0.476

15

7 (23) 19 (63) 4 (13)

5 (33) 8 (52) 2 (13) NA

21

7 (33) 10 (48) 4 (19)

13 (62) 5 (24) 3 (14) NA

9 (53) 6 (35) 2 (12)

Count (percent)

17 11 (65) 6 (35) 0 (0)

P-value from the binomial test of proportion that the percent with better health is greater than the percent with worse

a

b

P-value from the Cochran-Mantel-Haenszel row mean score test (for ordinal variables) that there was a difference in the means at T1 and T2

At T1 respondents were asked about changes from time 0. At T2 respondents were asked about changes from T1.

c

T1 5 time 1 (refers to the first study visit, which occurred an average of 67 days after people moved into the renovated apartments) T2 5 time 2 (refers to the follow-up visit approximately 12 to 18 months after people moved into the renovated apartments) NA 5 not applicable

good/excellent at T2, compared with 33% at T1. Those reporting health status as fair/poor decreased from 19% at T1 to 14% at T2. The percentage of adults reporting several specific health problems significantly improved from T0 to T1 (Figure 2) for asthma (p50.046) and non-asthma respiratory problems (p50.030). Improvements in reports of non-asthma respiratory problems (p50.025) remained significant from T0 to T2 (Figure 3). For adults, non-asthma respiratory problems included emphysema, hay fever, sinusitis, and chronic bronchitis. Child health More children had better overall health vs. worse health at T1 than T0, although the difference was not significant (23% vs. 13%; p50.476; Table 2). Reports of general child health status did not significantly change when comparing T1 to T2 (p50.206). The percentage of children with non-asthma respiratory problems improved from 33% to 15% between T0 and T1 (p50.025) (Figure 2). For children, non-asthma respiratory problems included hay fever, sinusitis, chronic bronchitis, ear infections, and respiratory allergies. At T0 and T1, only 7% of children reportedly had doctor-diagnosed asthma.

Housing condition The majority of people reported that their newly renovated homes at T1 were easier to clean (p,0.001), more comfortable (p,0.001), and safer (p50.008) than their old homes at T0; their neighborhood was safer (p50.021); and their children played outside more (p50.059) (Figure 4). At T1, significantly fewer people reported that their renovated homes had a mildew odor/musty smell (p50.020) or evidence of water/dampness (p50.083) (Figure 5). At T2, only one person reported dampness, and none reported mildew odors/musty smells (Figure 6). The percentage of residents reporting a cockroach problem marginally improved at T1 compared with T0 (p50.083; Figure 5). From T0 to T1, the use of insecticides by either residents or exterminators/maintenance personnel significantly improved (p50.059 and p50.003, respectively). At T2, only two people still reported having cockroach problems, and none reported using insecticides at home (Figure 6). The percentage of residents reporting mice/rat problems in the previous 12 months decreased from T0 to T2 (p50.046). From T0 to T1, fewer people reported smoke inside their homes due to incense, cigarettes, cigars, pipes,



wood fires, or non-tobacco cigarettes (p50.025) (Figure 5). The percentage increased slightly between T0 and T2, but not significantly (p50.157) (Figure 6).

mean bathroom exhaust airflow of 68 cfm exceeded ASHRAE’s 50-cfm standard. The mean measured return airflow for tested air handlers was 346 cfm, within 10% of manufacturer-specified values. However, the mean duct leakage was 28 cfm/100ft2@25 Pa, more than 3.5 times higher than EPA’s Indoor airPLUS new construction criterion of 6 cfm/100ft2@25 Pa.11 The bedrooms were under positive pressure with the ventilation system on, contrary to design specifications.

Building performance testing and indoor CO2 Blower door testing showed building air leakage was 0.38 cubic feet per minute per square foot at 50 pascals (cfm/ft2@50 Pa) (Table 3), higher than the current standard for Minnesota single family housing of 0.24 cfm/ft2@50 Pa.22 The mean leakage in individual units was 3.7 times greater than that for the building. Mean fresh air supply rates to individual units were 21 cfm and 29 cfm for two- and three-bedroom units, respectively, approximately 70% of the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) 62.2 standards of 30 cfm and 39 cfm, respectively.23 The mean annual level of CO2 was 982 ppm, slightly less than the 1,000 ppm guideline. The mean kitchen exhaust fan high-speed airflow of 84 cfm was slightly below ASHRAE’s 100-cfm standard, but the

Radon Of the 25 areas having pre-renovation short-term radon tests, seven had radon levels equal to or greater than the EPA’s 4-picocuries-per-liter (pCi/L) action level (Table 4).24 Because the renovation included sealing of all basement foundation cracks, radon mitigation was not included in the original renovation. But postrenovation long-term measurements conducted before radon mitigation yielded two of 17 tested areas with levels greater than 4 pCi/L; therefore, radon mitigation

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Figure 2. Change in reports of specific adult (n=49) and child (n=27) health issues, pre-renovation (T0) vs. immediate post-renovation (T1): Green Housing Renovation Study, Minnesota, 2006–2008

For adults, non-asthma respiratory problems include emphysema, hay fever, sinusitis, and chronic bronchitis.

a

b

For children, non-asthma respiratory problems include hay fever, sinusitis, chronic bronchitis, ear infections, and respiratory allergies.

T0 5 time 0 (refers to the residents’ recall of conditions in their old home prior to renovation) T1 5 time 1 (refers to the first study visit, which occurred an average of 67 days after people moved into the renovated apartments) NA 5 not applicable



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(p

0.

5

N

31

A)

7)

Figure 3. Change in reports of specific adult (n=22) and child (n=13) health issues, pre-renovation (T0) vs. one year post-renovation (T2): Green Housing Renovation Study, Minnesota, 2006–2008

For adults, non-asthma respiratory problems include emphysema, hay fever, sinusitis, and chronic bronchitis.

a

b

For children, non-asthma respiratory problems include hay fever, sinusitis, chronic bronchitis, ear infections, and respiratory allergies.

T0 5 time 0 (refers to the residents’ recall of conditions in their old home prior to renovation) T2 5 time 2 (refers to the follow-up visit approximately 12 to 18 months after people moved into the renovated apartments) NA 5 not applicable

Figure 4. Changes in reports of general housing condition (better vs. worse), pre-renovation (T0) vs. immediate post-renovation (T1): Green Housing Renovation Study, Minnesota, 2006–2008

T0 5 time 0 (refers to the residents’ recall of conditions in their old home prior to renovation) T1 5 time 1 (refers to the first study visit, which occurred an average of 67 days after people moved into the renovated apartments)



Percent reporting housing condition

Figure 5. Changes in reports of specific housing conditions, pre-renovation (T0) to immediate post-renovation (T1): Green Housing Renovation Study, Minnesota, 2006–2008

T0 5 time 0 (refers to the residents’ recall of conditions in their old home prior to renovation) T1 5 time 1 (refers to the first study visit, which occurred an average of 67 days after people moved into the renovated apartments)

Figure 6. Changes in specific housing conditions, pre-renovation (T0) to one year post-renovation (T2): Green Housing Renovation Study, Minnesota, 2006–2008

T0 5 time 0 (refers to the residents’ recall of conditions in their old home prior to renovation) T2 5 time 2 (refers to the follow-up visit approximately 12 to 18 months after people moved into the renovated apartments)



Table 3. Building ventilation performance results: Green Housing Renovation Study, Minnesota, 2006–2008 Ventilation system Building shell leakage (cfm/ft2@50 Pa) (n51) Unit leakage (cfm/ft2@50 Pa) (n52) Ratio of building leakage to unit leakage Carbon dioxide (ppm)

Minimum

Maximum

Mean

Design criterion

Design criterion reference

NA

0.38

NA

0.24

Minnesota Single-Family Housing Standard

1.33

1.44

1.39

NA

NA

3.5

3.8

3.7

NA 1,000

NA Indoor Air Quality benchmark

253

2,499

982

Fresh-air supply (cfm) Two-bedroom unit (n514) Three-bedroom unit (n510)

0 12

36 46

21 29

Kitchen exhaust (cfm) (n523)

32

151

84

100

ASHRAE 62.2

Bathroom exhaust (cfm) (n524)

52

90

68

50

ASHRAE 62.2

Duct leakage (cfm/100ft2@25 Pa) (n512)

22

32

28

6

EPA 2007 Manufacturer data sheet specifications

30a 39a

ASHRAE 62.2 ASHRAE 62.2

Duct return airflow (cfm) (n512)

215

460

346

Within 10% of manufacturer data sheets

Within-unit interstitial pressure for all tested bedrooms (Pa) (n558)

1.0

15.4

6.1

NA

NA

Within-unit interstitial pressure for bedrooms without air handlers in living room (Pa) (n546)

1.5

15.4

7.1

NA

NA

ASHRAE 62.2 ventilation rate is calculated according to the following equation: (number of bedrooms  1)  (7.5 cfm)  (square feet  0.01 cfm).

a

cfm 5 cubic feet per minute ft2 5 square foot Pa 5 Pascal NA 5 not applicable ppm 5 parts per million ASHRAE 5 American Society of Heating, Refrigerating, and Air-Conditioning Engineers EPA 5 Environmental Protection Agency

was added. The mitigation utilized the existing foundation drain tile and sub-slab plumbing along with fan-powered exterior vertical stacks to vent soil gas outdoors. Post-mitigation long-term average radon levels were 0.7 pCi/L, with no test results exceeding 4 pCi/L. Energy use The annual complex-wide post-renovation combined electric/natural gas energy use was 5.05 BTU/HDD/ ft2/yr compared with 9.76 BTU/HDD/ft2/yr for prerenovation, a 48% reduction. Without considering HDD, the total combined one-year post-renovation electric and natural gas energy use was 39.3 kBTU/ ft2/yr (Figure 7; October 2007 through September 2008). This result, while well below the pre-renovation energy use of 72.4 kBTU/ ft2/yr, was 56% higher than the Architecture 2030 Challenge benchmark of 25.4 kBTU/ft2/yr set for U.S. Midwest residential projects

of five or more units.25 Differentiation of energy use intensity by end use could not be determined due to overlapping end use from the same sources. Electricity use combines household use with all heating, cooling, and ventilation energy. DISCUSSION Primary drivers of the green building movement are improved energy conservation and reduced carbon emissions. In the 1970s, some of the first energy efficiency efforts yielded major indoor air-quality problems.26 By contrast, this study yielded improvements in energy efficiency, health, and indoor environmental quality. However, retrofitting ventilation systems in buildings that previously relied solely on building leakage for fresh air supply is difficult. Even the most well-designed renovation needs post-renovation commissioning, including building performance testing.



Table 4. Radon testing results (pCi/L): Green Housing Renovation Study, Minnesota, 2006–2008 Test result

Minimum

Maximum

Average

N (percent) of results 4 pCi/La

Short-term test results, pre-construction (n525)

1.0

6.7

3.1

7 (28)

Long-term test results, post-construction/pre-mitigation (n517)

0.6

4.5

2.2

2 (12)

Long-term test results, post-construction and post-mitigation (n526)

0.3

2.2

0.7

0 (0)

4 pCi/L 5 Environmental Protection Agency action level

a

pCi/L 5 picocuries per liter

Study data suggest that had the buildings achieved the full ASHRAE-specified fresh air rates, average CO2 levels, which were only barely below the benchmark of 1,000 ppm, may have been lower. Results also suggest that the benefits of improved housing for low-income households likely include significant health cost reductions and could contribute to health-care cost containment, not to mention reduced suffering from avoidable illnesses. Limitations This study had several limitations. First, it was difficult to discern whether health improvements were due to the nature of “green” renovation vs. “normal” renovation. In Minnesota and other jurisdictions, most federal- and state-assisted renovations are mandated “green,” making this distinction less important. Another limitation was that one adult answered the health questions for children and other adults in the apartment, potentially introducing bias. Cultural differences between interviewers and interviewees may have caused misunderstanding of some questions.

While 14 households required no translation services, the rest did. Self-reported health at two points in time may be subject to recall bias and uncertainty, especially because, due to the timing of study funding, residents interviewed had to recall pre-renovation health and housing information just after renovation was complete. Although evidence indicates that recall reports were reasonably well-correlated with actual health,27 future studies should endeavor to find funds for rehabilitation and research simultaneously so that baseline data can be gathered before renovation begins. For the building performance testing, the fact that fresh-air delivery was lower than design specifications was likely due to complex pressure differentials, making air more likely to be drawn from adjacent units than from outside. Although the building shell was made tighter, interior walls were not sealed during renovation, potentially causing increased odor or smoke migration between apartments, a common postrenovation resident complaint. Duct leakage was high because the contractor failed to seal each duct joint. These findings led to retrofits, which may have cost

Energy consumption (kBtu/sq ft/yr)

Figure 7. One-year post-intervention total building energy consumption—electricity and natural gas combined: Green Housing Renovation Study, Minnesota, 2006–2008

kBTU/sq ft/yr 5 kilo British thermal unit per square foot per year


2030 Challenge benchmark: 25.4 kBTU/sq ft/yr


less if implemented during the original rehabilitation. Some ventilation systems did not perform to design specifications, likely due to limited equipment choices/ sizing options, budgetary constraints, and existing building characteristics. Ventilation design could be improved by using a secondary energy-efficient fan in the fresh-air duct to improve airflow volume and an intermittent timer-cycling thermostat to ensure regular fresh-air delivery. The high positive pressures within each unit could drive air and moisture into exterior walls or ceiling assemblies, potentially causing future deterioration and mold problems. In each unit, the design team installed a transfer grill linking bedrooms to the living spaces, creating a return path and equalizing pressure. Future projects with central air return options should include jump-ducting or transfer grills with light and acoustic dampening grills. At the design stage, no protocols existed for conducting radon tests and mitigation in multifamily buildings. The American Association of Radon Scientists and Technologists is working to develop protocols for multifamily testing and mitigation. Minnesota has adopted mandatory radon construction techniques for single-family new construction, and Minnesota Housing requires radon-resistant construction for new multifamily housing in EPA Radon Zone 1. Had radon mitigation been included in the original renovation instead of retrofitting, costs would have been reduced. Overall, building performance results demonstrate that sufficient planning at the design stage and immediate post-renovation testing are essential to ensure that building ventilation works as intended. Building performance testing results underscore the need for early formation of an integrated design team (including architecture, engineering, environment, public health, and other expertise) to yield more cost-effective results. The statistical strength of future studies could be improved by larger enrollment and the use of both medical data and self-reported data to evaluate health outcomes. Although it would have been preferable to account for the lack of independence of health reports between residents in the same apartment, this was not possible due to the small study sample size. Initial interviews indicated that participants were generally in good health at the start of the study, possibly indicating that healthier people agreed to participate. A larger sample size and a baseline population in poorer health may have shown additional health improvements.

CONCLUSIONS The green renovation produced improvements in resident health, particularly adults, whose overall health, asthma, and non-asthma respiratory problems significantly improved during the one- to four-month follow-up period. Adults’ non-asthma respiratory problems also improved one year post-renovation. Children’s non-asthma respiratory problems improved during the one- to four-month and one-year follow-up periods. Exposures to radon declined, children played outside more, and security and ventilation were improved. Energy use declined dramatically. The renovations yielded improved housing conditions, making homes easier to clean, more comfortable, and safer both inside the apartment and in the community. There were fewer moisture and dampness issues, little or no pest problems, and less smoke indoors. All green housing standards should include health-related requirements. Integrated design teams could yield more sustainable, energy-efficient, and healthy housing. The authors acknowledge the following project partners for their contributions to this study: the building residents and property managers, Southwest Minnesota Housing Partnership, Minnesota Green Communities, Greater Minnesota Housing Fund, University of Minnesota Center for Sustainable Building Research, and National Center for Healthy Housing. Study funding was provided by the Blue Cross Blue Shield of Minnesota Foundation, the U.S. Environmental Protection Agency, and Enterprise Community Partners.

REFERENCES 1. Jacobs DE. Environmental health disparities in housing. Am J Public Health. In press 2011. 2. Krieger J, Higgins DL. Housing and health: time again for public health action. Am J Public Health 2002;92:758-68. 3. Blood lead levels—United States, 1999–2002. MMWR Morb Mortal Wkly Rep 2005;54(20):513-6. 4. Mannino DM, Homa DM, Akinbami LJ, Moorman JE, Gwynn C, Redd SC. Surveillance for asthma—United States, 1980–1999. MMWR Surveill Summ 2002;51(1):1-13. 5. World Health Organization. Report of the WHO technical workshop on quantifying disease from inadequate housing, Bonn, Germany, 28–30 November 2005. Geneva: WHO; 2006. 6. Jacobs DE, Brown MJ, Baeder A, Sucosky MS, Margolis S, Hershovitz J, et al. A systematic review of housing interventions and health: introduction, methods, and summary findings. J Public Health Manag Pract 2010;16(5 Suppl):S5-10. 7. Krieger J, Jacobs DE, Ashley PJ, Baeder A, Chew GL, Dearborn D, et al. Housing interventions and control of asthma-related indoor biologic agents: a review of the evidence. J Public Health Manag Pract 2010;16(5 Suppl):S11-20. 8. Sandel M, Baeder A, Bradman A, Hughes J, Mitchell C, Shaughnessy R, et al. Housing interventions and control of health-related chemical agents: a review of the evidence. J Public Health Manag Pract 2010;16(5 Suppl):S24-33. 9. DiGuiseppi C, Jacobs DE, Phelan KJ, Mickalide AD, Ormandy D. Housing interventions and control of injury-related structural deficiencies: a review of the evidence. J Public Health Manag Pract 2010;16(5 Suppl):S34-43.



10. Enterprise Community Partners. Enterprise Green Communities Criteria 2005 [cited 2009 Nov 30]. Available from: URL: http:// www.greencommunitiesonline.org/tools/criteria/index.asp 11. Environmental Protection Agency (US). Indoor airPLUS construction specifications [cited 2009 Nov 30]. Available from: URL: http:// www.epa.gov/indoorairplus/construction_specifications.html 12. U.S. Green Building Council, Leadership for Environment and Energy Design (LEED). LEED rating systems [cited 2009 Nov 30]. Available from: URL: http://www.usgbc.org/DisplayPage. aspx?CMSPageID5222 13. Krieger J, Takaro TK, Song L, Beaudet N, Edwards K. A randomized controlled trial of asthma self-management support comparing clinic-based nurses and in-home community health workers: the Seattle-King County Healthy Homes II Project. Arch Pediatr Adolesc Med 2009;163:141-9. 14. Leech JA, Raizenne M, Gusdorf J. Health in occupants of energy efficient new homes. Indoor Air 2004;14:169-73. 15. National Center for Health Statistics (US). 2006 National Health Interview Survey questionnaire [cited 2011 Jan 18]. Available from: URL: ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/ Survey_Questionnaires/NHIS/2006/English 16. Centers for Disease Control and Prevention (US). Behavioral Risk Factor Surveillance System [cited 2010 Jan 6]. Available from: URL: http://www.cdc.gov/BRFSS 17. National Institute of Environmental Health Sciences. National Survey of Lead and Allergens in Housing (NSLAH) [cited 2011 Jan 18]. Available from: URL: http://www.niehs.nih.gov/research/ clinical/join/studies/riskassess/nslah.cfm 18. Department of Housing and Urban Development (US). Help yourself to a healthy home: protect your children’s health. Washington: HUD, Department of Agriculture (US) (in conjunction with the Cooperative State Research Education and Extension Service); 2001.

19. Shendell DG, Prill R, Fisk WJ, Apte MG, Blake D, Faulkner D. Associations between classroom CO2 concentrations and student attendance in Washington and Idaho. Indoor Air 2004;14:333-41. 20. Erdmann CA, Apte MG. Mucous membrane and lower respiratory building related symptoms in relation to indoor carbon dioxide concentrations in the 100-building BASE dataset. Indoor Air 2004;14 Suppl 8:127-34. 21. Bartlett K, Martinez M, Bert J. Modeling of occupant-generated CO2 dynamics in naturally ventilated classrooms. J. Occup Environ Hyg 2004;1:139-48. 22. ASTM International. ASTM International Standard E1677-95: standard specification for an air retarder (AB) material or system for low-rise framed building walls, 1995. As incorporated by reference in the State of Minnesota. Residential Energy Code: codes adopted by reference. St. Paul (MN): Minnesota Office of the Revisor of Statutes; 2005. 23. American Society of Heating, Refrigerating, and Air-Conditioning Engineers. ASHRAE Standard 62.2-2007: ventilation and acceptable indoor air quality in low-rise residential buildings. Atlanta: ASHRAE; 2007. 24. Environmental Protection Agency (US). A citizen’s guide to radon: the guide to protecting yourself and your family from radon. Washington: EPA Indoor Environments Division; 2009. 25. Architecture 2030. 2030 challenge targets: U.S. residential regional averages [cited 2009 Dec 15]. Available from: URL: http://www .architecture2030.org/downloads/2030_Challenge_Targets_Res_ Regional.pdf 26. Sundell J. On the history of indoor air quality and health. Indoor Air 2004;14 Suppl 7:51-8. 27. Miilunpalo S, Vuori I, Oja P, Pasanen M, Urponen H. Self-rated health status as a health measure: the predictive value of self-reported health status on the use of physician services and on mortality in the working-age population. J Clin Epidemiol 1997;50:517-28.


Research Articles

Primary Prevention of Lead Exposure: The Philadelphia Lead Safe Homes Study

Carla Campbell, MD, MSa,b Mary Tran, MPHc Edward Gracely, PhDb,d Naomi Starkey, MPH, CPH, CHESe Hans Kersten, MDd,f Peter Palermo, MSg Nancy Rothman, MEd, MSN, EdDe Laura Line, MSe Tine Hansen-Turton, MGA, JDe

ABSTRACT Objective. Lead exposure in children can lead to neuropsychological impairment. This study tested whether primary prevention interventions in the newborn period prevent elevated blood lead levels (BLLs). Methods. The Philadelphia Lead Safe Homes (LSH) Study offered parental education, home evaluation, and lead remediation to the families of urban newborns. Households were randomized to a standard lead education group or maintenance education group. We conducted home visits at baseline, six months, and 12 months. To compare BLLs, we identified a matched comparison group. Results. We enrolled and randomized 314 newborns in the intervention component; 110 completed the study. There were few significant differences between the randomized groups. In the combined intervention groups, positive results on visual inspection declined from baseline to 12 months (97.0% to 90.6%, p0.007). At baseline, 36.9% of homes were above the U.S. Environmental Protection Agency’s lead dust standard, compared with 26.9% at 12 months (p0.032), mainly due to a drop in windowsill dust levels. Both groups showed a significant increase in parental scores on a lead education test. Children in the intervention and matched control groups had similar geometric mean initial BLLs (2.6 vs. 2.7, p0.477), but a significantly higher percentage of children in the intervention group had an initial blood lead screening compared with those in the matched group (88.9% vs. 84.4%, p0.032). Conclusions. A study of primary prevention of lead exposure showed a higher blood lead screening rate for the combined intervention groups and mean BLLs at one year of age not statistically different from the comparison group. Most homes had lead hazards. Lead education significantly increased knowledge.

The Children’s Hospital of Philadelphia, Philadelphia, PA

a

Drexel University School of Public Health, Philadelphia, PA

b

University of California, Davis, School of Medicine, Sacramento, CA

c

Drexel University College of Medicine, Philadelphia, PA

d

National Nursing Centers Consortium, Philadelphia, PA

e

St. Christopher’s Hospital for Children, Philadelphia, PA

f

Philadelphia Department of Public Health, Childhood Lead Poisoning Prevention Program, Philadelphia, PA

g

Address correspondence to: Carla Campbell, MD, MS, Drexel University School of Public Health, Department of Environmental and Occupational Health, Mailstop 1034, Bellet Building, Room 1124, 245 N. 15th St., Philadelphia, PA 19102; tel. 215-762-4379; fax 215-762-8846; e-mail . ©2011 Association of Schools of Public Health

76 


The Philadelphia Lead Safe Homes Study  77

Poor housing conditions have been associated with adverse health outcomes for many years. This relationship has been well documented for lead poisoning.1–5 An emphasis on prevention of lead exposure and elevated blood lead levels (BLLs) (level 10 micrograms per deciliter [µg/dL]) has been driven by studies documenting the adverse effects, including neuropsychological impairment, in U.S. children with lower BLLs, including levels 10 µg/dL.6–13 Contact with deteriorating lead-based paint (LBP) and leadcontaminated dust and soil is currently the primary source of lead exposure for U.S. children.14 Primary prevention of lead exposure and lead poisoning has been a policy priority for both the Centers for Disease Control and Prevention (CDC) and the U.S. Department of Housing and Urban Development (HUD) for several years. One CDC publication focused entirely on strategies for a housing-based approach to primary prevention of lead poisoning1 recommended screening of high-risk housing (by home evaluation) and children (through blood lead testing). A more recent publication recommended primary prevention of both housing-based lead hazards and other sources of lead.9,15 Primary prevention interventions, such as lead hazard control (LHC) of a property before a child is poisoned, cleaning, or educational interventions, could be simple ways to reduce or prevent lead exposure. A few studies have examined this question but were unable to show the benefits. For example, Lanphear16 et al. undertook a randomized controlled trial of lead dust control in 275 urban children, followed from six months until 24 months of age, to evaluate the effectiveness of dust control in preventing or limiting elevation of BLLs. The authors found no differences in BLL or dust levels between the intervention and control groups. There was also no difference in BLLs in a follow-up study at 48 months.17 Another study of primary prevention by Dugbatey et al.18 randomized low-income inner-city pregnant women into full case management, partial case management, and control groups. The children’s BLLs were collected every six months. In the analysis, there were no significant differences in the BLLs of children in the three groups, refuting the hypothesis that the full case management group would have lower levels. Dugbatey et al. also reported on the difficulty of obtaining data in this population.18 A Cochrane Collaborative review19 examined 12 studies to determine the efficacy of household interventions in preventing or decreasing subject children’s exposure to lead. These studies, including those utilizing primary and secondary prevention, were broken down into the

following categories of intervention: educational only, environmental (interior dust control or soil abatement) only, and a combination of the two. Several studies16,20 began when the children were younger than one year of age and showed mean baseline BLLs of 10 µg/dL. The analysis found that neither educational nor environmental interventions alone effectively reduced BLLs or floor dust levels; however, the authors noted that, with a follow-up period of 12 months, the relatively long half-life of lead in blood might have biased the change in BLL that was observed toward a null effect. Three of the 12 studies used a combination of education and dust control, similar to the Lead Safe Homes (LSH) Study described in this article, but could not be analyzed through meta-analysis due to differences in data collection. The Philadelphia LSH Study was designed to test the efficacy of educational and environmental interventions in a cohort of urban newborns and their families during the first year of life. It was modeled on the Philadelphia Lead Safe Babies program, a primary prevention program for children younger than one year of age.21 Relatively few studies have looked at intensive interventions during the newborn age, when, presumably, a child’s BLLs have not yet been elevated from postnatal lead exposure. The study intended to prevent the typical peaking of BLLs that occurs between 12 and 36 months of age, due to persistence of hand-to-mouth activity, when more mobile children have greater access to LBP hazards.22 Our study provided environmental evaluation and remediation twice (at baseline and 12 months), as well as detailed lead exposure prevention education. The study also examined the effect of education regarding proper home maintenance, which addressed recommendations of the HUD Task Force report that parents, rental property owners, and homeowners become educated about LBP hazards and lead safe work practices to avoid lead hazards in their homes.23 Methods Study population The Philadelphia LSH Study was a randomized trial that offered environmental education, evaluation, and remediation, as needed, to the families and homes of high-risk newborn children. It was conducted by workers from the Philadelphia Department of Public Health, the Children’s Hospital of Philadelphia, the National Nursing Centers Consortium, St. Christopher’s Hospital for Children, and Drexel University, whose institutional review boards approved the study, which was funded through a HUD Lead Technical Studies grant.



We recruited study children from urban outpatient practices located in low-income neighborhoods of Philadelphia, where the prevalence of children with elevated BLLs is higher than average. After the outreach workers explained the study and obtained informed consent from the child’s caregiver, the study coordinator selected the next card in the random sequence to randomize that family to receive either standard lead-poisoning prevention education (standard education group; hereafter, SEG) or standard education with additional extensive education regarding essential maintenance practices for keeping a home in lead safe condition (maintenance education group; hereafter, MEG). The outreach workers reviewed the MEG educational points with the families at each study visit. The additional education was compiled into a 22-page handbook, which included information on the problems regarding older homes and LBP hazards and a series of tips for families, such as preventing damage to paint, looking out for peeling or chipping paint, completing the maintenance diary each month, reporting problems with LBP hazards in their homes, working safely with LBP (this involved a list of explicit actions to take to maintain safety), and a list of “dos” and “don’ts” for household cleaning. The handbook was created using information from booklets published by the U.S. Environmental Protection Agency (EPA), HUD, CDC, and the Maine Department of Environmental Protection. We randomized blocks using computer-generated random numbers. The outreach workers educated participating families during the baseline, six-month, and 12-month home visits. Parents were given cleaning materials and supplies, and workers reinforced the prevention education (including specific cleaning instructions) during study visits. (Workers conducted the six-month educational intervention by phone for study families unable to arrange a home visit.) The study staff and health department staff (arranging LHC work) followed a detailed protocol for attempting to reach families lost to follow-up, including multiple phone and mail contacts, visits to the last known address, and contact with the subject’s primary care provider. We recruited the study children from outpatient practices of The Children’s Hospital of Philadelphia, St. Christopher’s Hospital for Children, and several nurse-managed health centers participating in the National Nursing Centers Consortium. A comparison group, with a 2:1 match, was identified from The Children’s Hospital of Philadelphia clinical database, and controls were matched by age, census tract, racial/

ethnic background, and gender. Eight children could not be matched on racial/ethnic background but were matched for the other characteristics. We created the comparison group to compare the BLLs of children receiving one of the study interventions with those who had received the community standard for prevention of elevated BLLs, such as information from the child’s health-care provider during clinical visits. We utilized an electronic recruitment tool that was managed by the Pediatric Research Consortium for The Children’s Hospital of Philadelphia practices. Families were asked if someone could call them about the study, and a list of interested parents was regularly relayed to study staff. Other children were identified through wall posters and direct referral from health-care providers. Eligible children resided in Philadelphia County, spoke either English or Spanish, had a home that was judged to be in a condition enabling remediation (in stable condition), and did not have a history of elevated BLLs. The outreach worker team included a bilingual worker, and all documents were translated into Spanish. We excluded families if they had participated in the Lead Safe Babies program or received services from the Childhood Lead Poisoning Prevention Program of the Philadelphia Department of Public Health for other children in the family. The study team formed a community advisory board, which comprised representatives from the targeted community, and met regularly following recommendations detailed in the National Research Council’s 2005 report on housing-related health hazards involving children.24 Parental knowledge assessment All families were administered pre- and posttests with both standard lead and maintenance questions on the first home visit (before and after the education was given), and the standard lead test was repeated at six and 12 months. The maintenance posttest was also administered to MEG families at these visits. The study utilized the shortened version of the Chicago Lead Knowledge Test,25 modified by Hans Kersten.26 The test evaluates parental knowledge regarding lead exposure prevention. Test-retest reliability of the full Chicago Lead Knowledge Test was 0.96 but has not been assessed for the shortened version. The SEG test had 14 questions, and possible scores ranged from 1 to 14, with one point scored for each correctly answered question. The MEG test was similar, except it had only 10 questions, with a possible score ranging from 1 to 10.



Quality control measures Per HUD specifications, we formulated a quality assurance plan detailing the protocols and quality assurance procedures to be used for specimen (lead dust) and data collection. BLLs were drawn by each subject’s primary care provider, and results were reported to study staff. Collection of study data Data verification procedures were in place to ensure accuracy of data collection and data entry. The study manager entered data into a Microsoft® Excel database. The study manager and principal investigator routinely performed quality assurance evaluations on selected study charts to identify errors in data entry. Following completion of data cleaning and quality assurance procedures, the data was imported into a statistical software program, SPSS® version 18,27 for analysis. Home evaluation The outreach workers systematically evaluated each subject’s residence by looking at the condition of paint (intact, fair, or poor) and testing for lead dust levels at baseline and at 12 months. Areas judged to be fair had a paint defect or visible dust on 10 square feet (sq ft) of exterior surfaces, 2 sq ft of interior surfaces, or less than 10% of small surfaces. Areas were labeled as poor if these limits were exceeded. Homes were reassessed whenever a subject moved. Collection of lead dust specimens Study personnel trained by a certified risk assessor, in accordance with EPA and HUD protocols, uniformly collected the dust wipe specimens for home evaluation. Field audits were performed. They took samples from two floor areas and one windowsill area, where the infant was likely to spend the most time during the first year. Measured areas of the floor (1 sq ft) and windowsill (variable area) were sampled with a standard pre-wetted towelette. Field blank and spiked specimens were submitted regularly. Specimens were initially analyzed by the International Asbestos Testing Laboratory of Mount Laurel, New Jersey, and then by EMSL of Westmont, New Jersey, both of which are National Lead Laboratory Accreditation Program-certified laboratories for dust wipe analysis. Clearance dust wipes, including a full set of 13 wipes, were collected after environmental remediation, per HUD protocol, with homes re-cleaned until all clearance levels were below EPA standards.

Home remediation/LHC work We offered LHC work for homes that had either an elevation of any of the three lead dust levels above EPA standards (40 µg/sq ft for floors and 250 µg/sq ft for windowsills) or visual evaluation results showing at least one area in poor or at least two areas in fair condition at either the baseline or 12-month home evaluation. A small number of homes meeting criteria for referral were not referred, due to minimal areas of concern on visual inspection and the high numbers of homes meeting criteria for this work. Once referred, health department abatement staff evaluated the property and specified the LHC work, including paint stabilization and replacement of deteriorated building components when needed, repainting, and specialized lead dust cleaning (known as a Superclean). Services were rendered by Pennsylvania-certified Phildelphia Public Health Department abatement staff members or lead abatement contractors, per HUD guidelines.28 All homes receiving remediation also received a Superclean. The MEG families were also asked to assess their homes monthly for needed repair or maintenance work, such as deterioration of the paint or presence of water leaks, by use of a special diary, and these families were referred to Childhood Lead Poisoning Prevention Program staff in the same manner. During contacts with the families by phone at one, three, and nine months from enrollment and in person during home visits at six and 12 months, the outreach workers questioned MEG families about identification of new areas noted to need repair or maintenance work. The results of the environmental evaluation and dust wipe testing were reported within several weeks by letter to the parent/guardian and the property owner. For houses with an identified lead problem, the letter indicated that the home would need to be further checked and some type of remediation work would be required, which could be provided by study staff at no charge. BLLs Blood lead testing (in most cases, venous) was carried out by the subject’s primary care provider and employed the services of five different laboratories, all of which participated in at least one proficiency program for BLL analysis. All of the study children’s providers were encouraged to do blood lead screening of their patients using the high-risk protocol recommended by the Philadelphia Department of Public Health at 9–12 months, 15–18 months, 2 years, and 3 years of age. The Children’s Hospital of Philadelphia clinics caring for the control children had access to



general information and posted recommendations, but did not receive a specific screening protocol. Periodic cleaning summary Outreach workers assessed cleaning activity by questioning the parent at the baseline, six-month, and 12-month home visits. We tabulated results for wet dusting and mopping of the subject child’s bedroom, living room, dining room, bathroom, and hallway areas and broke them into the following categories of cleaning frequency: low (quarterly), moderate (bimonthly or monthly), or high (daily or weekly). Statistical analysis Study Question 1 hypothesized that study interventions would result in lower BLLs in the LSH Study cohort when compared with a group of children whose parents did not receive these interventions. We compared geometric mean BLLs (due to non-normal distribution) of the study children and a comparison group using a two-tailed t-test. Sample size calculations estimated, with an average (pooled) standard deviation of 2.5, that 256 children per group would provide 80% power to detect a mean difference of 0.6 µg/dL in BLLs. Study Question 2 hypothesized that at least 15% fewer MEG homes would have home evaluation results meeting criteria for remediation at 12 months, compared with SEG homes, using Chi-square comparison. This analysis required a sample size of 128 per group (for both the SEG and MEG) to compare remediation rates of 30% vs. 45%, to achieve 80% power in a one-tailed test. Study Question 3 involved calculating descriptive statistics of multiple housing variables over time. Outcomes included whether the households met criteria for home evaluation failure, referral for remediation work, cleaning frequency, and cost of remediation. We performed two different analyses to compare percentages regarding housing characteristics. A Chi-square test compared the percentages for all children with data at each visit, as if they were two independent groups. This approach maximized the sample size, since only one-third of the sample had data at 12 months. We performed a McNemar’s test on the study children with data at both visits. This approach compares subjects to themselves, providing a cleaner comparison, but excludes most of the baseline data. Study Question 4 determined significant predictors of BLL by using bivariate tests such as Mann-Whitney U tests and t-tests (for dichotomous variables), analysis of variance (multiple groups), and correlation coefficients (numeric variables). Key child, family and household, home assessment, study arm, and test-score characteristics were evaluated.

Study Question 5 hypothesized that parents receiving lead education would increase their general lead knowledge at baseline and retain this over time, and the MEG would score higher on the standard lead test than the SEG. We used two-tailed Wilcoxon tests to determine changes from baseline within groups and Mann-Whitney U tests to compare groups at each visit. A Spearman correlation was used to test for association (hypothesized to be negative) between parental knowledge at 12 months and 12-month BLLs. This correlation was assessed separately for the two groups. With 128 children per group, the study had adequate power (80%) to detect correlations as small as 0.3. Results Cohort characteristics A total of 314 newborn children were enrolled and randomized to the SEG (n160) and MEG (n154) arms; 110 (SEG: n51; MEG: n59) completed the 12-month study. Twenty newborns were formally withdrawn from the study. Demographic characteristics of the group are displayed in Table 1 for each study arm and the entire cohort. The cohort was predominantly African American (82.5%) and low-income; 85.6% received either Medicaid or state Children’s Health Insurance Program (CHIP) insurance. Families utilized an average of 2.3 poverty assistance programs, from a list of six programs, with no significant differences between groups. Very few reported lead exposure from hobbies (4.8%), while 39.8% of parents had ever worked in construction, auto mechanics, battery plants, or other jobs potentially associated with exposure. Fourteen homes (4.5%) reported a non-study child being diagnosed with an elevated BLL, and 10 homes (3.2%) reported a history of chelation therapy for the parent, his/her partner, or a non-study child. Twenty-seven study mothers (8.6%) were born outside of the United States, compared with 36 study fathers (11.5%); they were predominantly from Latin American, Caribbean, and African countries (data not shown). Table 2 displays characteristics of the study children’s housing, also without significant differences between the two groups. Most of the cohort consisted of renters (55.0%), with 10.7% owning their home and 26.9% living in a home owned by a family member. Regarding housing age, 34.2% said their home was built prior to 1978, and 13.5% said it probably was; only 8.7% reported a home built after 1978. There were no significant differences for any of the characteristics displayed in Tables 1 and 2, when families that completed the 12-month visit were compared with those who did not.



BLL results The first study question hypothesized that study interventions would result in lower BLLs in study children when compared with a group whose parents did not

receive the LSH interventions. The initial BLLs (drawn around one year of age) are displayed in Table 3. (BLLs drawn around 2 years of age will be compared at a later date.) Geometric mean BLLs were 2.6 and

Table 1. Demographic and socioeconomic characteristics of the Philadelphia Lead Safe Homes Study cohort Characteristic

All N (percent)

SEG N (percent)

MEG N (percent)

Total children

314

160

154

Total households

310

159

151

Gender Male Female

164 (52.2) 150 (47.8)

82 (51.2) 78 (48.8)

Race/ethnicity African American White or Caucasian Asian Hispanic Other Two or more categories

259 18 3 12 10 21

(82.5) (5.7) (1.0) (3.8) (0.3) (6.7)

129 9 1 5 1 11

(80.6) (5.6) (0.6) (9.6) (0.6) (6.9)

130 9 2 3 0 10

(84.4) (5.8) (1.3) (1.9) (0.0) (6.5)

0.51

Annual household income $10,000 $10,000–$20,000 $20,000–$30,000 $30,000–$40,000 $40,000 Not sure No answer

97 53 37 23 24 70 6

(31.3) (17.1) (11.9) (7.4) (7.7) (22.6) (1.9)

46 30 20 12 13 36 2

(28.9) (18.9) (12.6) (7.5) (8.2) (22.6) (1.3)

51 23 17 11 11 34 4

(33.8) (15.2) (11.3) (7.3) (7.3) (22.5) (2.6)

0.91

82 (53.2) 72 (46.8)

P-valuea

0.72

Health insurance Medicaid or CHIP Other None

262 (85.6) 30 (9.8) 14 (4.6)

134 (84.3) 17 (10.7) 8 (5.0)

128 (87.1) 13 (8.8) 6 (4.1)

0.49

Use of low-income programs TANF/welfare WIC Food stamps Section 8 housing PHA SSDI Mean number of programs (SD) Range

173 (55.8) 277 (89.4) 172 (55.5) 26 (8.4) 30 (9.7) 34 (11.0) 2.3 (1.2) 0–5

87 (54.7) 144 (90.6) 82 (51.6) 14 (8.8) 12 (7.5) 14 (8.8) 2.2 (1.2) 0–5

86 (57.0) 133 (88.1) 90 (59.6) 12 (7.9) 18 (11.9) 20 (13.2) 2.4 (1.3) 0–5

0.69 0.48 0.16 0.79 0.19 0.21 0.26b

$461 $524 $28–$2,300

$446 $500 $30–$2,000

$476 $550 $28–$2,300

0.17c

Monthly rent Mean Median Range

P-values reported are two-tailed and refer to significance of Chi-square tests comparing SEG and MEG study arms.

a

b

Two-tailed p-value for parametric t-test comparing SEG and MEG study arm means (skewness statistic: 0.15)

Two-tailed p-value for Mann-Whitney U test comparing SEG and MEG study arm means

c

SEG  standard education group MEG  maintenance education group CHIP  Children’s Health Insurance Program TANF  Temporary Assistance for Needy Families WIC  Special Supplemental Nutrition Program for Women, Infants, and Children PHA  Philadelphia Housing Authority SSDI  Social Security Disability Insurance SD  standard deviation



Table 2. Home characteristics of the Philadelphia Lead Safe Homes Study cohort Characteristic Total households

All N (percent)

SEG N (percent)

MEG N (percent)

310

159

151

P-valuea

Homeowner Self Landlord Family member Other

33 170 83 23

(10.7) (55.0) (26.9) (7.4)

17 86 47 9

(10.7) (54.1) (29.6) (5.7)

16 84 36 14

(10.7) (56.0) (24.0) (9.3)

0.51

Housing built before 1978 Yes Probably Not sure No

106 42 134 27

(34.2) (13.5) (43.2) (8.7)

54 23 68 13

(34.0) (14.5) (42.8) (8.2)

52 19 66 14

(34.4) (12.6) (43.7) (9.3)

0.97

Repair/remodeling in past two years No Yes Not sure

161 (51.9) 142 (45.8) 7 (2.3)

84 (52.8) 72 (45.3) 3 (1.9)

77 (51.0) 70 (46.4) 4 (2.6)

0.85

Repair/remodeling ever done by self No Yes

244 (78.7) 64 (20.6)

124 (78.0) 34 (21.4)

120 (79.5) 30 (19.9)

0.11

P-values refer to significance of Chi-square tests comparing SEG and MEG study arms.

a

SEG  standard education group MEG  maintenance education group

Table 3. Comparison of the study cohort and control group BLL values: Philadelphia Lead Safe Homes Study Characteristic Total children: N

LSH Study group

Control group

314

628

Children with a recorded BLL value: N (percent) 279 (88.9) Children for whom a venous specimen was taken: N (percent) Mean age at draw (in months) (SD)

530 (84.4)

11.0 (3.2)

11.8 (3.9)

10.0

10.3

Median BLL (μg/dL)

2.7

2.6

Geometric mean BLL (μg/dL)

2.6

2.7

0.4–41.9

0.4–32.5

P-values are one-tailed significance for Chi-square test.

a

b

0.032a

260/268 (97.0) 472/530 (89.2) 0.001a

Median age at draw (in months)

Range of BLLs (μg/dL)

P-value

P-values are two-tailed significance for Mann-Whitney U test.

P-values are two-tailed significance for unpaired t-test.

c

BLL  blood lead level LSH  Lead Safe Homes SD  standard deviation μg/dL  microgram per deciliter

0.013b

0.477c

2.7 for the LSH cohort and comparison group and not significantly different (p0.477). With the final number of BLL results for each group, we did not have the power to detect any differences smaller than 0.6 between the group means. The control group was significantly older than the LSH cohort: 11.8 months of age (standard deviation [SD]  3.9) vs. 11.0 months of age (SD3.2), p0.013. Lead screening rates were high: 279 (88.9%) of LSH children had a first BLL taken by around one year of age compared with 530 (84.4%) in the matched comparison group (p0.032). BLLs by study arm were not significantly different, with geometric means of 2.6 for the SEG and 2.7 for the MEG (p0.680) (data not shown). We explored predictors of higher BLLs through bivariate analyses. Independent variables with significant correlations included older age at first blood draw (rs0.257, p0.010); dust wipe levels at baseline and 12 months (p0.003 for both time periods by unpaired t-test); and needing a referral for LHC work at 12 months (p0.002 by unpaired t-test). As none of these variables was surprising as a predictor, we determined that a multivariate analysis would be of limited value. Housing results Housing results are summarized in Table 4 and Figure 1.



Visual inspection results. Ninety-seven percent (n 294/303) of homes had positive results on visual inspection at baseline vs. 90.6% (n96/106) at 12 months (p0.007 by Chi-square test). For the 105 homes with evaluations at both time points, a higher percentage had positive results on home evaluation at baseline than at 12 months (n103, 98.1% vs. n95, 90.5%; p0.021 by McNemar’s test). The percentages for each study arm were not significantly different. Lead dust results. For lead dust results, 36.9% of homes (n113) were positive (above the EPA standard) at baseline, compared with 26.9% (n28) at 12 months (p0.032 by Chi-square test). The floor dust wipe results for the LSH cohort changed from 20.3% positive (n62) to 17.3% positive (n18) (p0.26 by Chisquare test), whereas the window dust results decreased from 26.5% positive (n81) to 15.1% positive (n16) (p0.011 by Chi-square test). There were no significant differences between the two study arms (Figure 1). For houses with data at both times (n106), there was a significant change in the percent with any dust wipe level above EPA standards from 39.4% (n41) at baseline to 26.9% (n28) at 12 months (p0.031 by McNemar’s test). For houses with data at both times,

there were no significant changes in percent for floor or sill dust levels above EPA standards. Met criteria for remediation. Ninety-eight percent (n300) of homes at baseline met criteria for remediation vs. 89% (n98) at 12 months (p0.001 by Chisquare test). A significantly higher proportion of homes having complete housing data at both time points met criteria for remediation at baseline (n108/110, 98.2%) compared with 12 months (n98/110, 89.1%) (p0.006 by McNemar’s test). Study arms did not differ in proportions of homes meeting criteria for remediation at baseline and at 12 months, but we did not have an adequate number in each group at 12 months (n128 per group) to detect a difference of 15% in remediation needs. As discussed previously, not all homes that fit the criteria were referred for remediation. Referred for remediation. We referred 89.5% (n274) of homes for LHC work at baseline, compared with 57.4% (n58) at 12 months (p0.001 by Chi-square test). Of 101 homes with data at baseline and 12 months, 93 (92.1%) were referred for LHC work at baseline vs. 58 (57.4%) at 12 months (p0.001 by McNemar’s test). At baseline, a significantly higher percentage of MEG

Table 4. Home evaluation data summary: Philadelphia Lead Safe Homes Study Characteristic

All N (percenta)

SEG N (percenta)

MEG N (percenta)

P-valueb

Baseline home evaluation Maximum available households Any positive dust wipe result (n306)c

306

157

149

113 (36.9)

61 (38.9)

52 (34.9)

0.47

Positive visual inspection (n303)

294 (97.0)

148 (96.1)

146 (98.0)

0.53

Met criteria for LHC workd (n306)c

300 (98.0)

152 (96.8)

148 (99.3)

0.24

Referred for LHC work (n306)c

274 (89.5)

135 (86.0)

139 (93.3)

0.04

110

51

59

c

12-month home evaluation Maximum available households Any positive dust wipe result (n104)

28 (26.9)

11 (23.9)

17 (29.3)

Positive visual inspection (n106)c

96 (90.6)

43 (89.6)

53 (91.4)

0.50

Met criteria for LHC workd (n110)c

98 (89.1)

44 (86.3)

54 (91.5)

0.38

Referred for LHC work (n101)c

58 (57.4)

24 (53.3)

34 (60.7)

0.46

78 (28.2)

42 (30.7)

36 (25.7)

0.36

c

Completed LHC work/all referred e

0.54

Percentages are based on the number of households for which data were available for each variable.

a

b

P-values refer to significance of Chi-square tests comparing SEG and MEG study arms.

Number of households for which data were available for this variable

c

A home evaluation result was interpreted as positive if either of the floor dust wipe samples was at 40 micrograms/square foot (μg/sq ft) or higher; the window sample was at 250 μg/sq ft or higher; and/or the visual inspection showed two or more areas rated fair or one or more areas rated poor.

d

All referred homes: n277; all referred SEG homes: n137; all referred MEG homes: n140

e

SEG  standard education group MEG  maintenance education group LHC  lead hazard control



Percent

Figure 1. Percentage of homes with a positive dust wipe samplea at baseline and 12 months, by study arm: Philadelphia Lead Safe Homes Study

Windows

Floors

Positive floor dust wipe sample 40 micrograms/square foot (μg/sq ft); positive window dust wipe sample 250 μg/sq ft

a

SEG  standard education group MEG  maintenance education group

families (93.3%, n139) than SEG families (86.0%, n135) (p0.04) were referred for LHC work; the difference was not significant at 12 months. Home condition improved and the need for remediation work decreased at 12 months. A total of 78 homes (28.2%) were remediated over the course of the study; with a mean of five months, a median of four months, and a range of two to 15 months from the time of referral to the work completion date. Of those receiving LHC work before the 12-month visit, half passed the home evaluation at 12 months. BLLs at one year of age for children having LHC work prior to the first blood test did not differ significantly from those of children whose homes were referred for work but did not receive it. The mean cost of remediation work was $4,971.80 (range of $1,886.60 to $10,973.00) for the SEG and $5,918.37 (range of $1,000.00 to $12,224.00) for the MEG (p0.174); median costs were $4,656.00 for the SEG and $5,512.10 for the MEG. There were no significant differences in cleaning levels between the SEG and MEG, except that a higher percentage of SEG families (44%, n31) mopped their dining rooms, compared with the MEG families (23%, n13)

(p0.04). The entire cohort improved their cleaning activity over the course of the study. Higher posttest scores were associated with increased dusting activity of kitchens and basements at six and 12 months in both groups. Twenty of the MEG families identified a need for maintenance work. Of these, eight had the work completed; it was not completed in situations where the family was not cooperative with these attempts. Parental knowledge acquisition The combined groups’ standard education test scores rose during the baseline visit from a pretest mean of 6 to a posttest mean of 12 (p0.001) (Figure 2 and Table 5). Subsequent comparisons indicated retention of most of the material. A higher percentage performed well on questions about lead exposure pathways, but not as well on questions about good nutrition. Median scores were not significantly different between arms (Table 5). The MEG parents achieved significant gains in knowledge on a separate maintenance test over the study course, with median scores and interquartile ranges of 8 (7–9) for the pretest; 9 (8–10) for posttest 1; 9 (8–9) for posttest 2; and 9 (8–9) for posttest 3 (all



Figure 2. Box-and-whisker plots of Standard Education Test median scores, by study arm: Philadelphia Lead Safe Homes Study

were significant at p0.05 when compared with the pretest by Wilcoxon test) (data not shown). A negative association between increased parental knowledge score and a child’s BLL at one year of age was hypothesized, with 80% power to detect a correlation of 0.35, with 50 children per group. Study results showed non-

significant, but positive, Spearman correlation coefficients for the SEG (rs0.181, p0.223, n47) and MEG (rs0.103, p0.454, n55). Therefore, the study did not demonstrate an impact of parental knowledge on the children’s first BLLs at one year of age.



Discussion The Philadelphia LSH Study was a randomized controlled trial of primary prevention interventions during the first year of life for 314 urban newborns. We did not find significant differences in initial BLLs (drawn around one year of age) between study children and a matched comparison group of children, nor were there significant differences in BLLs of children in the SEG vs. those in the MEG. Although both the study and comparison groups had high blood-lead screening rates around one year of age (88.9% and 84.4%), a significantly higher percentage of LSH Study children were screened. We found that most study homes had lead hazards at baseline, with some decrease in lead dust levels for floors (non-significant) and windowsills (significant) by the end of the study. The study documented parental acquisition of knowledge about lead exposure prevention, which was retained during the year-long study. The MEG did not improve on most measures, when compared with the SEG. In comparing results with those of previous studies, results were similar to the Lanphear et al. 1999 study,16 with no significant differences in BLLs between intervention and control groups; however, our study demonstrated significant decreases in lead dust levels. Our geometric mean BLLs were very low (2.6 µg/dL for the LSH cohort and 2.7 µg/dL for the comparison group), and the mean ages of draw were 11.0 months for the study cohort and 11.8 months for the comparison group. This young age might account for lower levels, as the typical pattern in children’s BLLs is an increase around one year of age, with a peak between 18 and 36 months of age.22 These low values may reflect the decrease in geometric mean BLLs nationally (1.9

µg/dL for children aged 1–5 years with data from the 1991–2004 National Health and Nutrition Examination Survey)29 and in Philadelphia (3.0 µg/dL in 2008) (Personal communication, Peter Palermo, Philadelphia Department of Public Health, Childhood Lead Poisoning Prevention Program, August 2009). As mean levels decline, differences in population means will be harder to demonstrate. Differences at 2 years of age may be greater due to the factors described previously. We intend to collect, analyze, and report on the 24-month BLL data once it becomes available. Dugbatey et al. experienced problems similar to our study regarding retention and follow-up, such as difficulty in finding clients due to changes in address and phone numbers and non-compliance with study visits.18 Some declined study participation due to concerns about lack of approval by family members or eviction by landlords. Dugbatey et al. concluded that the competing needs of survival in poverty made concern about a child’s lead exposure less compelling for these mothers, especially without obvious signs of disease. Our study experienced a similar difficulty in retention, and other articles have discussed the challenges of conducting inner-city health research.30 Limitations and strengths Our study had several limitations. As noted previously, a significant part of the cohort was lost to follow-up for the six- and 12-month visits, although only 20 participants formally withdrew. Study outreach workers had difficulty reaching families due to frequent phone number changes or service suspension, as well as moves to another address. The use of disposable phones may have contributed to this intermittent phone access. Another factor affecting attrition was that the baseline

Table 5. Standard Education Test median scores, by study arm and for entire cohort: Philadelphia Lead Safe Homes Study SEG MEG Test N N

SEG Median score (IQR)

MEG Median score (IQR) P-valuea

Combined group Median score (IQR)

Pretest (baseline)

159

151

6 (4–7)

6 (4–7)

0.739

Posttest 1 (baseline)

159

150

12 (11–13)

12 (11–13)

0.624

12 (11–13)b

6 (4–7)

Posttest 2 (six months)

75

61

10 (8–11)

10 (9–11)

0.830

10 (9–11)b

Posttest 3 (12 months)

49

59

10 (9–12)

10 (8–12)

0.903

10 (9–12)b

Two-tailed p-value by Mann-Whitney U test comparing MEG and SEG study arms

a

b

p18 years by gender, age, race/ethnicity, income, education, and region—HealthStyles survey, United States, 2005a Generator safe in open garage (n54,647)b Characteristic Total Genderd Male Female Age (in years)d 18–34 35–64 $65 Race/ethnicityd White, non-Hispanic Black, non-Hispanic Other, non-Hispanic Hispanic Annual household incomed ,$25,000 $$25,000 Educatione #High school Some college $College graduate Regiond Northeast Midwest South West

Generator safe in basement (n54,634)c

Percent agree

Percent uncertain

Percent agree

Percent uncertain

25.6

37.7

9.0

34.1

29.4 21.9

32.5 42.7

10.1 7.8

30.5 37.6

23.5 25.5 30.2

42.4 36.3 32.7

8.9 9.1 8.8

39.2 32.7 28.6

27.9 18.7 23.7 19.6

36.3 44.0 41.3 34.6

8.6 11.0 8.8 9.9

31.6 42.3 41.6 33.4

28.2 24.6

39.3 37.1

11.2 8.1

37.0 33.1

24.7 26.3 22.9

38.1 38.2 40.0

9.3 9.8 7.0

34.2 35.2 33.2

28.9 29.7 22.3 24.1

37.3 35.6 35.5 43.8

11.9 6.7 9.2 8.6

32.9 31.3 33.6 38.9

a Note: Weighted n is different from actual number of respondents. Percent agree category includes respondents who “strongly agree” and “moderately agree.” Percent uncertain category includes those who “neither agree nor disagree.” b

Item 1: It is safe to run a generator in a garage as long as the door is open.

Item 2: It is safe to run a generator in a basement as long as a window is open.

c

d

Demographic characteristic significantly associated with percent response to survey items 1 and 2 by Chi-square test, p,0.05

Demographic characteristic significantly associated with percent response to survey item 2 by Chi-square test, p,0.05

e

to be male, older than aged 65 years, non-Hispanic white, and from the Midwest, with an annual income ,$25,000. Those who agreed with or were uncertain about the generator-in-basement item tended to be male, aged 35–64 years, non-Hispanic black, and from the Northeast, with an annual income ,$25,000 and some college. 2006 generator and CO safety items Table 2 shows the proportion of respondents who agreed with or were uncertain about generator and CO safety questions in the 2006 HeathStyles survey. In 2006, 60.9% of respondents were uncertain about running a generator in an open garage (36.0%) or agreed that it was safe (24.9%). Similarly, more than half (51.9%) of respondents reported agreement with or uncertainty about the statement, “It is safe to run a generator in a garage that is not attached to a house.”

Most respondents (69.8%) agreed that a CO detector should be used with a generator, and only a small proportion (23.3%) of respondents believed that a CO detector was not needed with a new furnace. While most respondents (63.5%) agreed with the importance of an annual inspection for fuel-burning appliances, 26.1% remained uncertain. Demographic characteristics associated with responses to the five Likert-scaled questions about CO safety in the 2006 HealthStyles are also presented in Table 2. Region was the only demographic variable significantly associated (Chi-square, p,0.05) with response to the statement about operating a generator in an open garage, with fewer respondents from the South reporting agreement and uncertainty. Race/ ethnicity and region were both significantly associated with the statement about using a CO detector with a generator. Specifically, a higher proportion of



Table 2. Attitudes toward generator use and carbon monoxide safety among adults aged >18 years by gender, age, race/ethnicity, income, education, and region—HealthStyles survey, United States, 2006a Generator safe in open garage (n54,927)b

Use CO detector with generator (n54,938)c

Generator safe in unattached garage (n54,917)d

No CO detector needed with new furnace (n55,033)e

Percent agree

Percent uncertain

Percent agree

Percent uncertain

Percent agree

24.9

36.0

69.8

22.8

19.5

32.4

8.0

26.7 23.0

34.0 38.0

70.3 69.3

21.6 23.9

21.2 17.8

29.2 35.6

Age (in years)h 18–34 35–64 $65

20.2 26.3 29.3

41.6 34.6 29.2

67.2 70.1 74.4

26.6 22.0 17.7

16.2 20.4 23.1

Race/ethnicityi White, non-Hispanic Black, non-Hispanic Other, non-Hispanic Hispanic

25.5 22.2 26.9 22.7

35.0 37.7 32.8 41.5

71.3 67.5 60.6 68.7

22.4 22.2 60.6 68.7

Annual household incomej ,$25,000 $$25,000

24.3 25.1

38.4 35.2

69.3 70.0

Education #High school Some college $College graduate

26.3 25.9 22.3

35.8 35.3 37.1

Regionk Northeast Midwest South West

26.1 29.2 22.1 23.5

35.9 36.5 33.2 40.3

Characteristic Total Genderg Male Female

Percent Percent Percent uncertain agree uncertain

Annual appliance inspection important (n55,055)f Percent agree

Percent uncertain

15.3

63.5

26.1

8.8 7.2

15.1 15.4

62.8 64.2

26.3 25.9

40.5 29.7 25.1

8.1 7.1 10.6

17.7 14.3 13.7

53.5 64.5 79.6

32.5 25.8 14.6

19.7 20.0 17.5 18.9

31.8 33.3 31.6 35.4

7.0 11.2 14.1 7.4

14.4 13.2 20.7 19.3

63.5 70.5 61.4 59.0

26.9 20.5 25.5 27.0

22.1 23.0

21.1 18.9

35.8 31.3

7.9 8.0

18.5 14.1

66.4 62.5

24.6 26.6

66.8 70.6 72.2

24.9 22.7 20.4

21.4 18.9 17.7

32.7 32.5 32.6

8.5 6.7 8.6

14.5 15.7 15.5

65.0 62.6 63.3

25.0 26.7 26.3

74.5 71.5 73.1 58.3

20.0 21.5 19.2 32.8

21.2 24.1 18.2 15.1

34.1 34.2 28.9 35.1

6.0 7.9 9.2 7.8

11.4 10.7 15.7 23.3

67.9 63.1 67.0 54.5

24.5 27.0 22.2 33.0

a Note: Weighted n is different from actual number of respondents. Percent agree category includes respondents who “strongly agree” and “moderately agree.” Percent uncertain category includes those who “neither agree nor disagree.” b

Item 1: It is safe to run a generator in a garage as long as the door is open.

Item 2: If you use a gas-powered generator, you should also use a carbon monoxide detector.

c

d

Item 3: It is safe to run a generator in a garage that is not attached to the home.

Item 4: I don’t need a carbon monoxide detector in my house if I have a new furnace.

e f

Item 5: It is important to have fuel-burning appliances inspected professionally at the beginning of each heating season.

g

Demographic characteristic significantly associated with percent response to survey item 3 by Chi-square test, p,0.05

Demographic characteristic significantly associated with percent response to survey items 3, 4, and 5 by Chi-square test, p,0.05

h

Demographic characteristic significantly associated with percent response to survey items 2, 4, and 5 by Chi-square test, p,0.05

i

Demographic characteristic significantly associated with percent response to survey items 3 and 4 by Chi-square test, p,0.05

j

Demographic characteristic significantly associated with percent response to survey items 1, 2, 3, 4, and 5 by Chi-square test, p,0.05

k


on-Hispanic white respondents and those living in the n Northeast agreed that a CO detector should be used. Likewise, a higher proportion of respondents who were women, aged 18–34 years, with an annual household income ,$25,000, and living in the Midwest agreed

with or were uncertain about the safety of operating a generator in an unattached garage. Age, race/ethnicity, income, and region were associated with the statement, “No CO detector is needed with a new furnace.” Although the majority


Attitudes about Carbon Monoxide Safety  105

of respondents reported “disagreement,” suggesting that most believe a CO detector is needed with a new furnace, those at risk (e.g., who reported agreement/ uncertainty with the item) tended to be younger (aged 18–43 years), of non-Hispanic other race/ethnicity, with a household income ,$25,000, and living in the West. Age, race/ethnicity, and region were also associated with the statement “an annual inspection for fuelburning appliances is important.” Those who agreed or were uncertain tended to be older (aged 65 years), non-Hispanic black, and living in the Northeast. Of respondents who answered the question about checking the battery in their CO detector (n55,251), more than half (51%) reported that they did not own a detector. Most of the remainder (24%) reported checking the battery when it beeps, 11% reported checking the battery every six months, and 7% reported checking it once annually; 7% could not specify how often they check. Among those who reported that they did not own a CO detector (n52,691), the majority (71%) were homeowners, while 25% reported renting, and 4% occupied a residence without rent or did not specify. DISCUSSION The results of this study indicate that a large proportion of adults in the U.S. believe that it is safe to operate a gas-powered generator in an enclosed space, such as a garage, and that most of the respondents surveyed in 2005 and 2006—the majority of whom were homeowners—did not own a CO detector. These nationally representative findings are consistent with those of previous studies based on smaller samples and suggest some barriers exist to effective public health prevention of CO poisoning that might be addressed through improved health communication. Our goal was to describe attitudes that might place the public at increased risk for poisoning or death from CO that can be targeted with public health prevention strategies. Specifically, we measured attitudes about (a) placement of gas-powered generators, (b) maintenance of fuel-burning appliances, and (c) use of CO detectors. In both survey years, more than 60% of adults believed it safe to run a generator in an open garage (or were uncertain about safe placement) and, regardless of location, the proportion reporting uncertainty about safe generator use was substantial. Extrapolated to the July 2008 U.S. population estimate,29 our study indicates that more than 144 million adults may be unaware of the dangers of operating a gasoline-powered generator in an enclosed space. Other smaller, community-based surveys have reported similar findings. For example, only 2.4% of community members surveyed after four

major hurricanes in Florida recognized CO poisoning as a major health concern.30,31 Taken together, our nationally representative findings and those of previous studies suggest that prevention messages may not be reaching large segments of the public and that opportunities for primary prevention of CO poisoning remain. Notably, although most respondents in 2006 were aware of the need to use a CO detector with a generator and a new furnace, fewer than half reported owning a CO detector. Of those respondents, most reported checking the battery only when it beeps. In other words, even for the minority who do have a CO detector, more than half effectively do not have one because the battery could dead. This finding is of concern because fuel-burning home heating systems continue to be a leading source of fatal CO poisonings each year.32 Research has indicated that more than half of all CO-related deaths could be prevented by use of a CO detector,33 and evidence is emerging that state and local policies mandating use of CO detectors in residential settings can reduce poisonings and deaths.31 However, most respondents in our study were private homeowners who would not be affected by legislation requiring CO detectors in rental properties. Regardless, secondary prevention efforts promoting CO detector use should remain an important component of CO-related interventions because CO detectors are inexpensive—usually less than $25—and widely available. Another goal of this study was to identify populations that may be at increased risk for CO poisoning. A review of CO-specific surveillance in the U.S. reported people at risk of non-fatal poisoning tend to be members of racial/ethnic minority groups and middle-aged, while those who die from CO poisoning are more likely to be non-Hispanic white or black, aged ≥65 years, and men.9 Our analysis of 2005 HealthStyles data indicated that men aged $65 years of non-Hispanic white race/ ethnicity, with an income ,$25,000 a year, and residing in the Midwest may be at greatest risk, based on their belief that using a generator in an attached garage was safe. In 2006, however, the only factor associated with attitudes was Midwest residence. Level of education was only associated with the generator-in-basement question in 2005 and was not associated with any of the 2006 questions. It is interesting to note that we found few demographic differences overall that were statistically significant and little geographic variation in attitudes, suggesting that additional extrinsic factors may influence public attitudes about CO safety.2 For example, the non-weatherproof design of portable generators or lack of a sufficiently long power cord



may cause people to keep generators indoors, despite knowledge of the dangers of CO.

Figure 2. Guidelines to prevent carbon monoxide exposurea

Implications for health messaging Unintentional CO poisoning is almost entirely preventable, given appropriate installation, use, and maintenance of fuel-burning appliances and CO detectors.1 Therefore, it is important to consider communication factors that facilitate attitude change (e.g., audience characteristics and message content) when exploring the implications of this study for health messaging.34,35 Overall, we found that a large proportion of the public remains unaware of the potential risk of CO poisoning. Together with qualitative evidence suggesting that community members may have different levels of CO awareness and knowledge,36,37 this finding supports communication strategies based on the assessment of multiple audience factors.5,38,39 Our findings also have practical implications for the content of health messaging. Assessment of baseline knowledge regarding the existence and propagation of CO remains an important component of any health-communication strategy. Generator-related messages need to emphasize the minimum safe-placement distance from a dwelling (now recognized as 25 feet40), avoidance of use in an attached garage, and avoidance of use in a basement. For furnaces and other appliances, promoting the practice of regular, professional maintenance is indicated, although the financial cost of this practice may prove to be a barrier to widespread adoption. Future health messaging could also emphasize the use of CO detectors because this is less costly, in both dollars and effort, than primary prevention behaviors and, therefore, more likely to be adopted by consumers. The analysis described in this article is the first step in the development of a targeted, national communication strategy for CO-poisoning prevention. CDC is developing this strategy to enhance public perception of the risk of CO, encourage CO detector use, and raise awareness of behaviors that can help reduce the risk of unintentional poisoning.36,37 Figure 2 shows guidelines from CDC on preventing CO exposure.

Do have your heating system, water heater, and any other gas-, oil-, or coal-burning appliances serviced by a qualified technician every year.

Limitations This study is subject to several limitations. HealthStyles data are influenced by selection bias because panel surveys solicit responses from a population willing to participate in research; data from this sample may not be representative of the attitudes and behaviors of the U.S. adult population. HeathStyles data reflect self-reported knowledge, attitudes, or behaviors measured at one point in time and are not linked to actual behaviors or health outcomes via longitudinal

Do install a battery-operated CO detector in your home and check or replace the battery when you change the time on your clocks each spring and fall. If the detector sounds, leave your home immediately and call 911. Do seek prompt medical attention if you suspect CO poisoning and are feeling dizzy, light-headed, or nauseous. Do not use a generator, charcoal grill, camp stove, or other gasoline- or charcoal-burning device inside your home, basement, or garage, or near a window. Do not run a car or truck inside a garage attached to your house, even if you leave the door open. Do not burn anything in a stove or fireplace that is not vented. Do not heat your house with a gas oven. Source: Unintentional non-fire-related carbon monoxide exposures—United States, 2001–2003. MMWR Morb Mortal Wkly Rep 2005;54(2):36-9.

a


follow-up or direct observation. Furthermore, assessment of attitudes was context-neutral due to limited survey space, CO items were presented alongside other unrelated items in HealthStyles, and item content was developed rapidly as part of a public health emergency response; these factors may have resulted in inaccurate or biased responses. Specifically, results may be subject to recall bias based on respondent characteristics (e.g., men might recall generator or detector information differently from women) or information bias due to confusion between smoke and CO detectors. CONCLUSIONS The results of this study—the first to assess public attitudes toward CO safety in the U.S. on a national level—indicate that most adults report attitudes consisting of inaccurate knowledge, beliefs, or behaviors that may place them at increased risk for unintentional CO poisoning. The proportion of adults who reported uncertainty about safe generator use was substantial, regardless of location, and most people do not own a CO detector. This finding suggests that current safety messages related to generator operation may not be reaching large segments of the public and opportunities for the primary prevention of CO poisoning remain. Public health prevention messaging should continue to focus on promoting proper generator placement, maintenance of fuel-burning appliances, and use of CO detectors. Development of a comprehensive national


Attitudes about Carbon Monoxide Safety  107

strategy for CO surveillance and communication may help identify populations at increased risk of CO exposure and prevent future poisonings. The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

REFERENCES 1. Carbon monoxide-related deaths—United States, 1999–2004. MMWR Morb Mortal Wkly Rep 2007;56(50):1309-12. 2. Raub JA, Mathieu-Nolf M, Hampson NB, Thom SR. Carbon monoxide poisoning—a public health perspective. Toxicology 2000;145:1-14. 3. Unintentional non-fire-related carbon monoxide exposures—United States, 2001–2003. MMWR Morb Mortal Wkly Rep 2005;54(2):36-9. 4. Daley WR, Smith A, Paz-Argandona E, Malilay J, McGeehin M. An outbreak of carbon monoxide poisoning after a major ice storm in Maine. J Emerg Med 2000;18:87-93. 5. Use of carbon monoxide alarms to prevent poisonings during a power outage—North Carolina, December 2002. MMWR Morb Mortal Wkly Rep 2004;53(9):189-92. 6. Van Sickle D, Chertow DS, Schulte JM, Ferdinands JM, Patel PS, Johnson DR, et al. Carbon monoxide poisoning in Florida during the 2004 hurricane season. Am J Prev Med 2007;32:340-6. 7. Hampson NB, Zmaeff JL. Carbon monoxide poisoning from portable electric generators. Am J Prev Med 2005;28:123-5. 8. Consumer Product Safety Commission (US). Memorandum to Janet Buyer from Sandra E. Inkster. Health hazard assessment of CO poisoning associated with emissions from a portable 5.5 kilowatt, gasoline-powered generator Washington: CPSC; September 21, 2004. Also available from: URL: http://www.cpsc.gov/library/ foia/foia07/brief/PortableGenerators.pdf [cited 2008 Oct 30]. 9. King ME, Mott JA. Public health surveillance for carbon monoxide in the United States: a review of national data. In: Penney DG, editor. Carbon monoxide poisoning. Boca Raton (FL): CRC Press; 2008. p. 233-50. 10. Runyan CW, Johnson RM, Yang J, Waller AE, Perkis D, Marshall SW, et al. Risk and protective factors for fires, burns, and carbon monoxide poisoning in U.S. households. Am J Prev Med 2005;28:102-8. 11. Johnson RM, Azrael D, Hemenway D. Letter to the editor. Am J Lifestyle Med 2010;4:367. 12. Daley WR, Shireley L, Gilmore R. A flood-related outbreak of carbon monoxide poisoning—Grand Forks, North Dakota. J Emerg Med 2001;21:249-53. 13. Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process 1991;50:179-211. 14. Conner M, Sherlock K, Orbell S. Psychosocial determinants of ecstasy use in young people in the UK. Brit J Health Psychol 1998;3:295-317. 15. Stead M, Tagg S, MacKintosh AM, Eadie D. Development and evaluation of a mass media Theory of Planned Behaviour intervention to reduce speeding. Health Educ Res 2005;20:36-50. 16. Jamner MS, Wolitski RJ, Corby NH, Fishbein M. Using the Theory of Planned Behavior to predict intention to use condoms among female sex workers. Psychol Health 1998;13:187-205. 17. Altherr A, Mosler HJ, Tobias R, Butera F. Attitudinal and relational factors predicting the use of solar water disinfection: a field study in Nicaragua. Health Educ Behav 2008;35:207-20. 18. Montoya T, Gurian PL, Velazquez-Angulo G, Corella-Barud V, Rojo A, Graham JP. Carbon monoxide exposure in households in Ciudad Juarez, Mexico. Int J Hyg Environ Health 2008;211:40-9. 19. Galada HC, Gurian PL, Corella-Barud V, Perez FG, VelazquezAngulo G, Flores S, et al. Applying the mental models framework to carbon monoxide in northern Mexico. Pan Am J Public Health 2009;25:242-53. 20. Mortality associated with Hurricane Katrina—Florida and Alabama, August–October, 2005. MMWR Morb Mortal Wkly Rep 2006;55(9):239-42.

21. Carbon monoxide poisoning from hurricane-associated use of portable generators—Florida, 2004. MMWR Morb Mortal Wkly Rep 2005;54(28):697-700. 22. Ourso A. A report on post-hurricane carbon monoxide poisoning in Volusia County. Epi update: a weekly publication of the Florida Department of Health, Bureau of Epidemiology, January 7, 2005 [cited 2006 Jul 11]. Available from: URL: http://www.doh.state. fl.us/disease_ctrl/epi/Epi_Updates/Epi_Weekly/01-07-05.htm 23. Altmann TK. Attitude: a concept analysis. Nurs Forum 2008;43: 144-50. 24. Consumer Product Safety Commission (US). Know carbon monoxide dangers before the power goes out [press release #07-203]. June 1, 2007 [cited 2011 Jan 3]. Available from: URL: http://www .cpsc.gov/cpscpub/prerel/prhtml07/07203.html 25. Consumer Product Safety Commission (US). Safety tips for tropical storm victims—CPSC warns of dangers at home in the aftermath of Tropical Storm Fay [press release #08-370]. August 22, 2008 [cited 2011 Jan 3]. Available from: URL: http://www.cpsc.gov/cpscpub/ prerel/prhtml08/08370.html 26. Maibach EW, Maxfield A, Ladin K, Slater MD. Translating health psychology into effective health communication: the American HealthStyles audience segmentation project. J Health Psychol 1996;1:261-77. 27. Pollard WE. Use of consumer panel survey data for public health communication planning: an evaluation of survey results. Proceedings of the Annual Meeting of the American Statistical Association, Joint Statistical Meetings, Section on Health Policy Statistics. Alexandria (VA): American Statistical Association; 2002. Also available from: URL: http://www.amstat.org/sections/srms/Proceedings/ y2002/Files/JSM2002-000768.pdf [cited 2011 Jan 3]. 28. SAS Institute, Inc. SAS®: Version 9.2. Cary (NC): SAS Institute, Inc.; 2008. 29. Census Bureau (US). Annual estimates of the resident population of the United States, regions, states, and Puerto Rico: April 1, 2000 to July 1, 2009 (NST-EST2009-01) [cited 2011 Jan 3]. Available from: URL: http://www.census.gov/popest/states/NST-ann-est.html 30. Epidemiologic assessment of the impact of four hurricanes—Florida, 2004. MMWR Morb Mortal Wkly Rep 2005;54(28):693-7. 31. Broder J, Mehrotra A, Tintinalli J. Injuries from the 2002 North Carolina ice storm, and strategies for prevention. Injury 2005;36: 21-6. 32. Consumer Product Safety Commission (US). Non-fire carbon monoxide deaths associated with the use of consumer products: 2002 annual estimates [cited 2006 Jul 11]. Available from: URL: http://www.cpsc.gov/LIBRARY/co05.pdf 33. Yoon SS, Macdonald SC, Parrish RG. Deaths from unintentional carbon monoxide poisoning and potential for prevention with carbon monoxide detectors. JAMA 1998;279:685-7. 34. Hovland CI, Janis IL, Kelley JJ. Communication and persuasion. New Haven (CT): Yale University Press; 1953. 35. Schwarz N. Attitude construction: evaluation in context. Social Cognition 2007;25:638-56. 36. Damon S. New findings in CO poisoning prevention. Paper presented at the National Environmental Public Health Conference; 2009 Oct 29; Atlanta. 37. Centers for Disease Control and Prevention (US). Air Pollution and Respiratory Health Branch: about the program [cited 2011 Jan 3]. Available from: URL: http://www.cdc.gov/nceh/airpollution/ default.htm 38. Slater MD, Kelly KJ, Thackeray R. Segmentation on a shoestring: health audience segmentation in limited-budget and local social marketing interventions. Health Promot Pract 2006;7:170-3. 39. Gulati RK, Kwan-Gett T, Hampson NB, Baer A, Shusterman D, Shandro JR, et al. Carbon monoxide epidemic among immigrant populations: King County, Washington, 2006. Am J Public Health 2009;99:1687-92. 40. Wang L, Emmerich SJ. Modeling the effects of outdoor gasoline powered generator use on indoor carbon monoxide exposures. NIST Technical Note 1637. August 2009 [cited 2011 Jan 3]. Available from: URL: http://fire.nist.gov/bfrlpubs/build09/art009.html


Research Articles

Carbon Monoxide Poisoning After an Ice Storm in Kentucky, 2009

Emily C. Lutterloh, MD, MPHa,b,c Shahed Iqbal, PhD, MBBS, MPHa,d Jacquelyn H. Clower, MPHd Henry A. Spiller, MSe Margaret A. Riggs, PhD, MPH, MSb,f Tennis J. Sugg, MPHb Kraig E. Humbaugh, MD, MPHb Betsy L. Cadwell, MSPHa Douglas A. Thoroughman, PhD, MSb,f

ABSTRACT Objectives. Carbon monoxide (CO) poisoning is a leading cause of morbidity and mortality during natural disasters. On January 26–27, 2009, a severe ice storm occurred in Kentucky, causing widespread, extended power outages and disrupting transportation and communications. After the storm, CO poisonings were reported throughout the state. The objectives of this investigation were to determine the extent of the problem, identify sources of CO poisoning, characterize cases, make recommendations to reduce morbidity and mortality, and develop prevention strategies. Methods. We obtained data from the Kentucky Regional Poison Center (KRPC), hyperbaric oxygen treatment (HBOT) facilities, and coroners. Additionally, the Kentucky Department for Public Health provided statewide emergency department (ED) and hospitalization data. Results. During the two weeks after the storm, KRPC identified 144 cases of CO poisoning; exposure sources included kerosene heaters, generators, and propane heaters. Hospitals reported 202 ED visits and 26 admissions. Twentyeight people received HBOT. Ten deaths were attributed to CO poisoning, eight of which were related to inappropriate generator location. Higher rates of CO poisoning were reported in areas with the most ice accumulation. Conclusions. Although CO poisonings are preventable, they continue to occur in postdisaster situations. Recommendations include encouraging use of CO alarms, exploring use of engineering controls on generators to decrease CO exposure, providing specific information regarding safe use and placement of CO-producing devices, and using multiple communication methods to reach people without electricity.

a Centers for Disease Control and Prevention, Epidemic Intelligence Service, Scientific Education and Professional Development Program Office, Atlanta, GA

Kentucky Department for Public Health, Frankfort, KY

b

Current affiliation: New York State Department of Health, Albany, NY

c

Centers for Disease Control and Prevention, Air Pollution and Respiratory Health Branch, Atlanta, GA

d

Kentucky Regional Poison Center, Louisville, KY

e

Centers for Disease Control and Prevention, Office of Public Health Preparedness and Response, Atlanta, GA

f

Address correspondence to: Shahed Iqbal, PhD, MBBS, MPH, Centers for Disease Control and Prevention, National Center for Environmental Health, Division of Environmental Hazards and Health Effects, Air Pollution and Respiratory Health Branch, 4770 Buford Hwy. NE, MS F-58, Atlanta, GA 30341; tel. 770-488-0787; fax 770-488-1540; e-mail .

108 


CO Poisoning After an Ice Storm in Kentucky, 2009  109

Each year in the United States, carbon monoxide (CO) poisoning is responsible for approximately 450 unintentional, non-fire-related deaths and more than 20,000 emergency department (ED) visits.1,2 It is a primary cause of morbidity and mortality after natural disasters, mainly because power outages lead to highrisk behaviors for CO poisoning, including improper placement and use of portable generators, use of alternative heating units (e.g., kerosene or propane heaters), and use of cooking devices as heating sources (e.g., charcoal grills or gas stoves).3–6 After hurricanes Katrina and Rita struck the U.S. Gulf Coast in 2005, 10 deaths and 78 nonfatal injuries were attributed to CO poisoning.4 After four major hurricanes in Florida in 2004, six CO-related deaths and 167 nonfatal CO-poisoning cases from 51 incidents were reported; portable generators were the reported source in the majority of cases.3 Outbreaks of CO poisoning after ice storms have also been reported.5–9 Simple measures, including ventilation of heating appliances, avoidance of indoor use of grills, and installation of battery-powered CO alarms can prevent unintentional CO-poisoning cases.2 Characterizing the nature of CO exposures and identifying the population at risk is crucial to developing effective prevention strategies. During January 26–27, 2009, a severe ice storm occurred in Kentucky, causing widespread, extended power outages and disrupting transportation and communications. Kentucky residents received less than one day’s warning before the storm, and 103 of Kentucky’s 120 counties were eventually declared disaster areas. Damage was particularly severe in western Kentucky, where households were without electricity for more than two weeks. Soon after the storm, CO poisonings were reported, which prompted an investigation to determine the extent of the problem, identify sources of CO poisoning in this cold-weather disaster, describe the epidemiology of cases, make recommendations to reduce morbidity and mortality, and develop prevention strategies. METHODS The definition of CO poisoning was based on Council of State and Territorial Epidemiologists (CSTE) case definitions,10 as described for each of the following data sources. Exposures The Kentucky Regional Poison Center (KRPC) provided case reports that included demographics, symptoms, and a narrative description of the exposure,

which included the source of CO. KRPC generated these reports from calls to the center originating from health-care providers and the general public. In case of multiple calls referring to the same person, KRPC staff de-duplicated records based on patient identifiers. People with confirmed CO poisoning on the basis of KRPC data had exhibited signs or symptoms of CO poisoning, had been exposed to CO, and either had a carboxyhemoglobin level 12% or elevated CO found in an air sample at the exposure site; people with probable CO poisoning had exhibited signs or symptoms and had been exposed to CO, but had no reported carboxyhemoglobin or environmental CO measurements. ED visits and hospitalizations The Kentucky Department for Public Health (KDPH), Office of Health Policy provided International Classification of Diseases, Ninth Revision (ICD-9) coded outpatient hospital visit and inpatient hospitalization data for confirmed cases (ICD-9 codes 986, E868.3, E868.8, E868.9, and E982.1). Cases were included if one or more ICD-9 codes were listed among the diagnoses. Data were collected from 108 hospitals, which included all acute care hospitals in Kentucky, with the exception of Veterans Administration hospitals and state psychiatric hospitals. All outpatient visits were assumed to be ED visits, although the possibility that some were hospital walk-in clinic visits could not be excluded. Hyperbaric oxygen treatment The two Kentucky hyperbaric oxygen treatment (HBOT) facilities that treat people with emergent CO poisoning provided HBOT data. One facility provided data through a surveillance system operated by the Centers for Disease Control and Prevention (CDC) and the Undersea and Hyperbaric Medical Society; the other facility provided data directly. For data from HBOT facilities, only probable cases were defined, requiring a history of CO exposure and receipt of HBOT. Mortality County coroners provided mortality data. Confirmed fatalities had signs of CO poisoning and either a carboxyhemoglobin level 12% or elevated CO in an air sample with an indicative source; suspected fatalities had signs of CO poisoning and an indicative source of CO. Power outages and meteorologic data The Kentucky Public Service Commission, Kentucky Municipal Utility Association, and Kentucky Association



of Electric Cooperatives provided power-outage data. Ice accumulation data were based on maps provided by the National Weather Service. We used temperature data for Bowling Green, Kentucky,11 a city near the most severely affected area. The storm affected western and central Kentucky most severely; the far southern and far eastern Kentucky counties were affected to varying but lesser degrees. Because the storm was essentially a statewide event, the analysis included the entire state. Data were collected for the period January 26–February 14, by which time power had been restored to about 95% of the approximately 770,000 customers experiencing outages (Personal communications, various personnel, Kentucky Public Service Commission, February 2009). Intentional exposures and one exposure in Indiana that had been treated in Kentucky were excluded. This investigation underwent human subjects review by CDC, was determined to represent public health problem evaluation and control rather than research, and was granted exempt status. RESULTS CO exposures KRPC logged 115 CO exposure calls, representing possible exposures of 275 people, compared with nine

calls during the same period in 2008. Illness among 144 people from 65 of the 115 calls met the case definition (105 probable and 39 confirmed). The remaining 131 potentially exposed people did not represent cases, often because they were asymptomatic family members of exposed, symptomatic people. Twenty-seven percent of the cases were in children younger than aged 18 years (Table 1). Of the 65 calls that included at least one case, 20 calls representing 43 cases were from health-care providers. The other 45 calls were from an affected person, a friend, or a family member. KRPC data included two of the 10 deaths. The most common reported exposure sources among the cases were heating devices and generators (Table 2). ED visits, hospitalizations, and HBOT Kentucky hospitals reported 202 ED visits and 26 hospital admissions for CO poisoning, compared with 11 ED visits and no admissions during the same period in 2008. The median age of ED patients was 32 years (range: six weeks to 91 years) and of admitted patients was 63 years (range: 28 to 89 years). Although 32% of people examined in an ED for CO poisoning were children, no children were admitted; 50% of admitted people were aged 64 years or older (Table 1). The number of days patients were admitted ranged from less than one day to 14 days (mean: 2.6 days; median: 2.0

Table 1. Carbon monoxide poisoning cases after an ice storm, demographics by data source—Kentucky, January 26–February 14, 2009 Characteristic Age (in years) 0–17 18–44 45–64 64 Sex Female Male

KRPC cases N (percent)

39 (27) 105 (73)b

ED visits N (percent)a

64 72 42 24

(32) (36) (21) (12)

Hospitalizations N (percent)a

0 3 10 13

(0) (12) (38) (50)

HBOT N (percent)a

6 12 6 4

(21) (43) (21) (14)

Deaths N (percent)

0 2 6 2

(0) (20) (60) (20)

84 (58) 60 (42)

119 (59) 83 (41)

19 (73) 7 (27)

16 (57) 12 (43)

3 (30) 7 (70)

Race/ethnicity Black White Hispanic Other/unknown

NAc NAc NAc NAc

25 (12) 150 (74) NAc 27 (13)

2 (8) 22 (85) NAc 2 (8)

8 16 4 0

5 4 1 0

Total

144

202

26

(29) (57) (14) (0)

28

(50) (40) (10) (0)

10

Percentages may not total 100 because of rounding.

a

b

Includes all people aged 18 years; adult ages often not collected

Data not collected

c

KRPC  Kentucky Regional Poison Center ED  emergency department HBOT  hyperbaric oxygen treatment NA  not available



Table 2. CO sources for CO-poisoning incidents and cases reported to the Kentucky Regional Poison Center—Kentucky, January 26–February 14, 2009 CO source

Incidents (n65)a N (percent)

Kerosene heater Generator Propane heater Charcoal Gas fireplace Gas oven Other Unknown

29 20 11 5 3 2 6 2

(45) (31) (17) (8) (5) (3) (9) (3)

Cases (n144)b N (percent) 63 47 30 8 7 7 15 2

(44) (33) (21) (6) (5) (5) (10) (1)

All incidents include at least one case; two sources were present in 13 (20%) incidents.

a

b

Two sources were present for 35 cases.

CO  carbon monoxide

days). Twenty-eight Kentucky residents were treated with HBOT (age range: 1 to 79 years; median: 38 years). Sixteen (57%) had been poisoned as a result of generator use. Twelve (43%) were members of racial/ ethnic minority groups (Table 1). Mortality Of 36 storm-related deaths, 10 (28%) from seven incidents were attributed to CO poisoning. These deaths included eight confirmed and two suspected cases (age range: 26 to 76 years; median age: 58 years) (Table 1). The first four deaths occurred less than three days after the beginning of the storm. Eight deaths (80%) from five incidents were associated with incorrect location of a gasoline-powered generator. The other two deaths involved a propane heater and a charcoal grill. Six (60%) decedents were members of racial/ethnic minority groups, including two (20%) immigrants. One of the immigrants had used a charcoal grill indoors; the other had used a generator indoors. Association between CO-poisoning cases, power outages, and meteorologic data The epidemiologic curves of KRPC cases (Figure 1) and ED visits (Figure 2) demonstrate a predictable pattern when viewed with power-outage and temperature data, with the largest numbers of cases occurring two to four days after the storm when temperatures were below freezing and the number of power outages remained high. Rates of ED visits for CO poisoning by Area Development District (Figure 3) also demonstrate a pattern, when viewed with ice-accumulation data, with the highest rates of ED visits occurring in the most severely affected areas of western Kentucky. The

maximum rate of ED visits was 21.5 visits per 100,000 people for one district in the most severely affected area of the state. DISCUSSION CO-poisoning cases continue to occur despite being a preventable and well-known consequence of disasterrelated power outages.4,6 After this ice storm, CO poisoning was the leading cause of storm-related deaths, surpassing other common causes such as hypothermia and cardiac events. Kerosene heaters were the most common source of CO poisoning; however, the majority of deaths and severe poisoning cases during this period were associated with generators, which is consistent with a previous study reporting more severe poisoning with generators.6 Even when generators were used outdoors, CO poisoning has occurred when the generators were placed 7 feet from the home.4 A community needs assessment conducted in severely affected areas of Kentucky after this storm determined that use of generators and alternative heating sources was common. In three surveyed areas with widespread power outages, 44% to 56% of households had used a generator since the storm, and 4% to 6% of them reported use of a generator indoors or in a garage; 35% to 43% of households had used a charcoal or gas grill, 21% to 36% of whom reported using it indoors (Unpublished data, Community Assessment for Public Health Emergency Response conducted by KDPH and CDC, February 6–9, 2009). Shelters in the surveyed areas offered services to the population for varying periods after the storm. The community needs assessment was a door-to-door survey of people in private residences, which would eliminate people who remained in shelters or who left the area to stay with family or friends. This might partially explain the high percentage of households reporting use of generators or alternative heating sources. Lack of education about CO poisoning might contribute to high-risk behaviors. After four major hurricanes in Florida in 2004, less than 50% of adult respondents involved in CO-poisoning incidents related to generator use reported having received instruction in safe operation of the generator.12 Studies have also demonstrated that people are often unaware of the dangers of operating CO-producing devices indoors or near homes.13,14 The number of CO-poisoning cases varied, as expected, with temperature, power restoration, and ice accumulation. The highest rates of CO poisoning were observed in the western part of Kentucky, which



e xperienced the most ice accumulation and, therefore, the most extensive power outages. These findings support the belief that inclement weather causing widespread power outages increases high-risk behavior among the affected population, thereby increasing the number of CO exposures. This investigation also determined that people from minority groups disproportionately suffered from severe CO poisoning. Eighteen (47%) of 38 people who died or were administered HBOT were members of racial/ethnic minority groups, whereas across Kentucky, only 10% of the population are members of racial/ethnic minority groups.15 One death and five HBOT cases among minority individuals were caused from burning charcoal indoors, which has previously been described among minority and immigrant groups and has been attributed to language and cultural factors.9,16,17 A study of CO-poisoning cases after a windstorm in Washington State in 2006 determined that all eight deaths were in minority immigrant households; six were associated with improper generator use and

two with indoor charcoal use.16 A retrospective review of CO-poisoning patients in Washington State who had been administered HBOT during a nine-year period demonstrated that the relative risk of severe CO poisoning was elevated among black and Hispanic individuals, compared with non-Hispanic white people.17 In response to the CO-poisoning cases, KDPH issued news releases and public service announcements, distributed fact sheets, and activated a person-to-person network to contact members of vulnerable groups such as the elderly, people with hearing impairments, and people living in remote areas. The Kentucky National Guard was mobilized for house-to-house welfare checks and other assistance. In coordination with CDC and a mobile telephone provider, a mass text message regarding CO poisoning was sent to Kentucky customers. Limitations One limitation of this analysis was that certain severely affected areas were without both landline and mobile telephone service for days after the storm, which

Figure 1. KRPC CO-poisoning cases, with power outages and mean daily temperature— Kentucky, January 26–February 14, 2009

KRPC 5 Kentucky Regional Poison Center CO 5 carbon monoxide



might have decreased the number of CO exposures reported to KRPC. Additionally, transportation was difficult because of icy roads and downed trees and power lines, which might have prevented people from seeking medical attention. Therefore, data presented in this article might underestimate the true number of affected people. Also, individuals might be included in more than one dataset; therefore, the numbers of COpoisoning cases from each dataset cannot be summed to provide a total. Recommendations The hierarchy of controls indicates that engineering interventions (e.g., generator emission controls and CO-alarm installation) might be more successful than other types of interventions at decreasing COpoisoning cases. Electronic fuel injection or catalytic after-treatment might decrease CO-poisoning cases from generators by reducing the level of CO in the generator exhaust.18 Two manufacturers of marine generators voluntarily incorporated catalytic converters to decrease CO poisonings on houseboats,19 and

similar measures have been suggested for portable generators.5,20 Weatherization of generators by incorporating a waterproof housing, receptacle covers, and ground-fault circuit interrupter protection might lessen the risk for electrocution and make outside generator use easier.18 Because no consensus has been reached regarding a safe distance from a home for operating a generator, CDC recommends placing the generator as far from the home as possible.4 A recent study by the National Institutes of Standards and Technology determined that generators should be placed more than 25 feet from a one-story house to avoid CO entry related to airflow patterns.21 Generators should be sheltered to prevent water damage and electrocution, and connected by using an extension cord rated for outdoor use. They may be secured with lock and chain to prevent theft. Vendors should be encouraged to offer these items along with CO alarms at the point of sale; ideally they should be displayed alongside the generators. Even with these recommendations, people might not comprehend the dangers of operating a generator

Figure 2. Emergency department visits for CO poisoning, with power outages and mean daily temperature—Kentucky, January 26–February 14, 2009




Figure 3. Emergency department visits for carbon monoxide poisoning, by Area Development District with ice accumulation—Kentucky, January 26–February 14, 2009

ED 5 emergency department in. 5 inch

in a garage, on a porch, or in a basement; therefore, unsafe practices should be specifically addressed in prevention messages. CDC recommendations for using generators and other CO-producing devices include installing a CO alarm that is battery operated or has battery backup. Prevention messages should stress that it is critical to adhere to manufacturer recommendations and provide adequate ventilation for CO-producing heaters, and should also emphasize that unconventional heating sources (e.g., charcoal briquettes or gas stoves) are unsafe. Given the lack of knowledge concerning CO poisoning and use of CO-producing devices, educational interventions might also be helpful. Evidence indicates that intensive public education before and after a storm can decrease CO poisonings, especially among minority groups.22 Because of challenges in communicating with people without electricity and because CO poisonings have been documented as early as nine hours after a storm,9 dissemination of prevention messages should begin before storms whenever possible. Messages disseminated after storms should begin immediately, because the majority of CO-poisoning cases after storms occur on days two and three,5 and should use multiple forms of media to reach people without electricity (e.g., radio, fact sheets, door-to-door campaigns, and mobile telephone text messages).

CONCLUSIONS CO-poisoning epidemics are common yet preventable causes of morbidity and mortality after disasters. Use of emission-control devices on generators might decrease severe CO poisonings. CO alarm use should be encouraged, and educational messages should focus on safe generator use and avoidance of unsafe heating and cooking practices. Vendors should be encouraged to display CO alarms and other safety equipment alongside generators. Prevention messaging should begin before storms and should include components directed toward minority populations. Forms of mass communication that do not require electricity (e.g., mass public-service text messages) might be valuable in the immediate aftermath of disasters. The authors acknowledge the following individuals and groups for their contributions to this article: Neil B. Hampson, MD, Center for Hyperbaric Medicine, Virginia Mason Medical Center in Seattle, Washington; Yanique Redwood, PhD, and Sara Vagi, PhD, Epidemic Intelligence Service officers, Centers for Disease Control and Prevention (CDC) in Atlanta, Georgia; the Kentucky ice storm Community Assessment for Public Health Emergency Response (CASPER) team; Kentucky coroners; Kentucky hyperbaric oxygen treatment facility personnel; and Kentucky Department for Public Health officials. The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of CDC.



REFERENCES 1. Carbon monoxide-related deaths—United States, 1999–2004. MMWR Morb Mortal Wkly Rep 2007;56(50):1309-12. 2. Nonfatal, unintentional, non-fire-related carbon monoxide exposures—United States, 2004–2006. MMWR Morb Mortal Wkly Rep 2008;57(33):896-9. 3. Carbon monoxide poisoning from hurricane-associated use of portable generators—Florida, 2004. MMWR Morb Mortal Wkly Rep 2005;54(28):697-700. 4. Carbon monoxide poisoning after two major hurricanes—Alabama and Texas, August–October 2005. MMWR Morb Mortal Wkly Rep 2006;55(9):236-9. 5. Hampson NB, Stock AL. Storm-related carbon monoxide poisoning: lessons learned from recent epidemics. Undersea Hyperb Med 2006;33:257-63. 6. Daley WR, Smith A, Paz-Argandona E, Malilay J, McGeehin M. An outbreak of carbon monoxide poisoning after a major ice storm in Maine. J Emerg Med 2000;18:87-93. 7. Wrenn K, Conners GP. Carbon monoxide poisoning during ice storms: a tale of two cities. J Emerg Med 1997;15:465-7. 8. Broder J, Mehrotra A, Tintinalli J. Injuries from the 2002 North Carolina ice storm, and strategies for prevention. Injury 2005;36: 21-6. 9. Houck PM, Hampson NB. Epidemic carbon monoxide poisoning following a winter storm. J Emerg Med 1997;15:469-73. 10. Council of State and Territorial Epidemiologists. Updates to 1998 case definition for acute carbon monoxide poisoning surveillance. Atlanta: CSTE; 2007. Also available from: URL: http://www.cste.org/ PS/2007ps/2007psfinal/EH/07-EH-03.pdf [cited 2009 Feb 27]. 11. Weather Underground. Weather history for Bowling Green, Kentucky. Ann Arbor (MI): Weather Underground, Inc.; 2009. Also available from: URL: http://www.wunderground.com [cited 2009 Feb 16]. 12. Van Sickle D, Chertow DS, Schulte JM, Ferdinands JM, Patel PS, Johnson DR, et al. Carbon monoxide poisoning in Florida during the 2004 hurricane season. Am J Prev Med 2007;32:340-6.

13. Greife AL, Goldenhar LM, Fruend E, Stock A, Halperin W. Carbon monoxide poisoning from gasoline-powered engines: risk perception among Midwest flood victims. Am J Public Health 1997;87: 466-7. 14. Hampson NB, Zmaeff JL. Carbon monoxide poisoning from portable electric generators. Am J Prev Med 2005;28:123-5. 15. Census Bureau (US). Estimates of the resident population by race and Hispanic origin for the United States and states: July 1, 2008 (SC-EST2008-04). Washington: Census Bureau; 2009. Also available from: URL: http://www.census.gov/popest/states/asrh/ SC-EST2008-04.html [cited 2009 Jun 30]. 16. Gulati RK, Kwan-Gett T, Hampson NB, Baer A, Shusterman D, Shandro JR, et al. Carbon monoxide epidemic among immigrant populations: King County, Washington, 2006. Am J Public Health 2009;99:1687-92. 17. Ralston JD, Hampson NB. Incidence of severe unintentional carbon monoxide poisoning differs across racial/ethnic categories. Public Health Rep 2000;115:46-51. 18. Elder J, Buyer J; Consumer Product Safety Commission (US). Memorandum to the commission regarding staff review of portable generator safety [cited 2010 Apr 14]. Available from: URL: http:// www.cpsc.gov/library/foia/foia07/brief/PortableGenerators.pdf 19. Consumer Product Safety Commission (US). Staff review of portable generator safety: briefing to the commission, October 26, 2006 [cited 2010 Oct 13]. Available from: URL: http://www.cpsc.gov/volstd/ engine/portgenstaffrev.pdf 20. Cukor J, Restuccia M. Carbon monoxide poisoning during natural disasters: the Hurricane Rita experience. J Emerg Med 2007;33: 261-4. 21. Wang L, Emmerich SJ. Modeling the effects of outdoor gasoline powered generator use on indoor carbon monoxide exposures [NIST Technical Note 1637, August 2009] [cited 2010 Mar 8]. Available from: URL: http://fire.nist.gov/bfrlpubs/build09/art009 .html 22. Lin G, Conners GP. Does public education reduce ice storm-related carbon monoxide exposure? J Emerg Med 2005;29:417-20.


Research Articles

Hazards of Illicit Methamphetamine Production and Efforts at Reduction: Data from the Hazardous Substances Emergency Events Surveillance System Natalia Melnikova, MD, PhDa Wanda Lizak Welles, PhDb Rebecca E. Wilburn, MPHb Nancy Rice, MPHc Jennifer Wu, MSa Martha Stanbury, MSPHd

ABSTRACT Objectives. Methamphetamine (meth) is a highly addictive drug of abuse that can easily be made in small illegal laboratories from household chemicals that are highly toxic and dangerous. Meth labs have been found in locations such as homes, outbuildings, motels, and cars. Its production endangers the “cook,” neighbors, responders, and the environment. This article describes surveillance data used to examine the emergence and public health impacts of illicit clandestine meth labs, as well as two states’ efforts to thwart lab operations and prevent responder injuries. Methods. We analyzed data collected from 2001 to 2008 by 18 states participating in the Agency for Toxic Substances and Disease Registry’s Hazardous Substances Emergency Events Surveillance (HSEES) Program to examine the occurrence and public health impacts of clandestine meth production. Results. HSEES data indicate that the majority of clandestine meth lab events occurred in residential areas. About 15% of meth lab events required evacuation. Nearly one-fourth of these events resulted in injuries, with 902 reported victims. Most victims (61%) were official responders, and one-third were members of the general public. Since 2004, with the implementation of local and federal laws and prevention activities, the number of meth lab events has declined. Increased education and training of first responders has led to decreased injuries among police officers, firefighters, and emergency medical personnel. Conclusions. HSEES data provided a good data source for monitoring the emergence of domestic clandestine meth production, the associated public health effects, and the results of state and federal efforts to promote actions to address the problem.

Centers for Disease Control and Prevention, Agency for Toxic Substances and Disease Registry, Division of Health Studies, Atlanta, GA

a

New York State Department of Health, Center for Environmental Health, Division of Environmental Health Assessment, Bureau of Toxic Substance Assessment, National Toxic Substance Incidents Program, Troy, NY

b

Minnesota Department of Health, Environmental Health Division, Hazardous Substances Emergency Events Surveillance Program, St. Paul, MN

c

Michigan Department of Community Health, Division of Environmental Health, Lansing, MI

d

Address correspondence to: Natalia Melnikova, MD, PhD, Centers for Disease Control and Prevention, Agency for Toxic Substances and Disease Registry, Division of Health Studies, 4770 Buford Hwy., MS F-57, Atlanta, GA 30341; tel. 770-488-3697; fax 770-488-7187; e-mail .

116 


Meth Production Hazards and Reduction Efforts  117

Methamphetamine (meth) is a psychostimulant and sympathomimetic drug, with medical uses for the treatment of narcolepsy, attention deficit disorders, and obesity. Long-term meth use has numerous adverse physical and psychological consequences.1–3 Meth is a very addictive drug with a high potential for abuse. According to the 2007 National Survey on Drug Use and Health, approximately 13 million Americans aged 12 years or older reported using meth at least once during their lifetime.4 Unlike drugs such as marijuana or heroin, which are derived from plants, meth can be synthesized in clandestine drug laboratories using a variety of easily bought chemicals that are “cooked” with ephedrine- or pseudoephedrine-containing products. The chemicals vary depending on the process, but can include anhydrous ammonia, drain cleaners, paint thinner, metallic lithium, hydrochloric or sulfuric acids, starter fluid, camping fuel, and others. Chemicals found in meth labs are hazardous and toxic. Exposure to the chemicals or by-products can damage the respiratory tract, mucous membranes, eyes, and skin. It is relatively easy for a cook to acquire the chemicals and recipes necessary to make meth.5 Labs have been found in fixed locations—homes, outbuildings, and hotel/ motel rooms—or in mobile sites, such as trunks of cars, motor homes, or moving vans. The processes used to make meth can result in fires, explosions, spills, or air releases of hazardous chemicals, putting the cook and others nearby, including children and responders, at risk of injury or death.6–9 To address the chemical and physical hazards associated with illicit clandestine labs, federal and state governments have passed legislation aimed at decreasing the number of meth labs. In 2004, many states began applying strong restrictions on sales of ephedrine- and pseudoephedrine-containing products, which are the key ingredients in meth production. In September 2006, the Federal Combat Methamphetamine Epidemic Act of 2005 restricted the retail sale of ephedrine and pseudoephedrine products nationwide.10 However, illegal meth production and abuse continue to be serious concerns within the United States and throughout the world.11 This article describes the public health impacts of clandestine meth production in 18 states that collected data on acute releases of hazardous substances. It also provides information on the actions taken in two states to control illicit meth production and prevent injuries to first responders and bystanders during clandestine lab seizures by law enforcement.

Methods From 1990 to 2009, the Hazardous Substances Emergency Events Surveillance (HSEES) Program, established by the Centers for Disease Control and Prevention and the Agency for Toxic Substances and Disease Registry (ATSDR), collected and analyzed information about acute releases of hazardous substances and threatened releases that resulted in a public health action, such as an evacuation. The goal of the program was to use the collected data to identify prevention strategies that could be implemented to reduce the frequency of these events and the associated morbidity (injury) and mortality (death) experienced by first responders, employees, and the general public. The ATSDR HSEES Program provided funding to a number of state health departments to identify and record information on spill/release events occurring in the funded states. An eligible HSEES event was defined by ATSDR protocol as any uncontrolled or illegal release or threatened release of one or more hazardous substance(s) (excluding releases of petroleum) in a quantity sufficient to require removal, cleanup, or neutralization according to federal, state, or local law. A clandestine drug lab incident was included in the HSEES system if there was an acute release of a hazardous substance (i.e., the lab was in operation [“cooking”] within 72 hours of the lab seizure by law enforcement). Incidents without a known chemical release within the 72-hour period, but with a public health action, such as an evacuation, were also included. States participating in the HSEES Program identified events from a variety of sources, including state environmental conservation or protection agencies, police and fire departments, poison control centers, federal databases (i.e., U.S. Coast Guard National Response Center and U.S. Department of Transportation Hazardous Material Information Resource System), hospitals, local media, and others. Some states routinely received reports from law enforcement on meth lab seizures. Collected information was entered into a standardized, secure Web-based system maintained by ATSDR. Information recorded included the event location, the responsible party, the types and quantities of chemical(s) involved, primary and secondary causes of the release, the number of individuals injured as a result of the release, the types of injuries, the number of people decontaminated, and the number of people evacuated or sheltered in place. We analyzed retrospective data, 2001–2008 (the latest complete year of data), from the HSEES system to identify trends in illegal clandestine meth lab events and in the public health consequences (e.g., morbidity,



mortality, and evacuations). The analysis included data collected by the 18 state health departments that participated in the HSEES Program at any time from 2001 to 2008. Eleven states (Colorado, Iowa, Louisiana, Minnesota, New York, North Carolina, Oregon, Texas, Utah, Washington, and Wisconsin) collected information during the entire period. Seven states participated at various times during the period (New Jersey: 2001–2008, excluding 2006; Alabama and Mississippi: 2001–2003; Florida and Michigan: 2005–2008; Missouri: 2001–2005; and Rhode Island: 2001). Results Overall, 3.6% (n52,373) of the total HSEES events (n566,588) reported from 2001 to 2008 were meth related (Table 1). The highest percentage of meth lab events were reported in 2003 (n5524 events, 5.8% of all 2003 HSEES events) and 2004 (n5442 events, 5.7% of all 2004 HSEES events). The percentage of reported meth events decreased in 2005, when states began applying sales restrictions on ephedrine products. The percentage further decreased in 2006, the year the Federal Combat Methamphetamine Epidemic Act became effective nationwide.10 The majority of events (n52,102, 88.6%) occurred at fixed facilities (e.g., hotels, apartments, or sheds) (Figure 1). The remainder of the events (n5271, 11.4%) were related to transportation (e.g., mobile

Table 1. All HSEES events and clandestine meth lab events, by year: ATSDR HSEES database, 2001–2008 Year 2001 2002 2003 2004 2005 2006 2007 2008 Total

HSEES events N 8,978 9,014 9,105 7,744 8,603 7,267 7,947 7,930 66,588

Meth events N

Meth events Percent

297 423 524 442 300 180 102 105 2,373

3.3 4.7 5.8 5.7 3.5 2.5 1.3 1.3 3.6

HSEES 5 Hazardous Substances Emergency Events Surveillance ATSDR 5 Agency for Toxic Substances and Disease Registry meth 5 methamphetamine

labs in cars or motor homes, or meth lab chemicals being transported). It is difficult to assign a meaning to the changing percentages over time, because the number of participating states varied from 11 to 18 during this period. More than 85.6% (n52,032) of illegal clandestine meth lab events occurred within one-fourth of a mile of a residence. The general land use immediately surrounding an event recorded in the HSEES system can be characterized by as many as two descriptors. The general land use immediately surrounding the reported meth lab locations was identified as residential in 68.3%

Percent

Figure 1. Proportion of fixed-facility and transportation-related methamphetamine lab events, by year: ATSDR HSEES database, 2001–2008

Year

ATSDR 5 Agency for Toxic Substances and Disease Registry HSEES 5 Hazardous Substances Emergency Events Surveillance



Percent

Figure 2. Proportion of methamphetamine lab events by type of area in the vicinity of the event, by year: ATSDR HSEES database, 2001–2008

Year Residential

Commercial

Agricultural/undeveloped

Industrial

Recreational

Other

ATSDR 5 Agency for Toxic Substances and Disease Registry HSEES 5 Hazardous Substances Emergency Events Surveillance

of events (n51,621), commercial in 28.9% of events (n5686), agricultural or undeveloped in 28.5% of events (n5676), industrial in 2.7% of events (n564), recreational in 1.0% of events (n524), and other in 2.7% of events (n564). The total (n53,135) is greater than the total number of events because more than one area could be reported per event. Meth labs have been most commonly found in residential areas, with an upward and progressive trend in the percentage of events in these areas from 2001 to 2008 (Figure 2). In recent years, a decreasing percentage of meth labs have been discovered in agricultural or undeveloped areas and in commercial areas. Because residential areas can have higher population densities, and chemicals in meth labs can volatilize, explode, or catch fire, people in homes near the vicinity of a meth lab are potentially at risk. Meth lab events required evacuations more than twice as often as all HSEES events during the 2001–2008 surveillance period (14.5% [n5343] of meth lab events compared with 6.5% [n54,339] of all HSEES events). In the HSEES data, the highest percentage of evacuations was ordered in response to meth events in 2008 (30.5%, n532), and the lowest in 2005 (8.3%, n525), but the actual number of events with an evacuation was relatively stable. The 2001–2008 clandestine meth lab events resulted in the evacuation of 3,596 people. The greatest number of people (n51,210) evacuated was in 2004, which had the second-highest number

of events (n5442). The lowest number of people (n5108) evacuated was in 2008, which had 105 events, the second-lowest number reported. Analysis of all HSEES events, 2001–2008, showed that 9.0% of the events resulted in reported victims. Analysis of all meth events during the same interval showed that nearly one-fourth (22.8%, n5541, range from 37.0% in 2001 to 10.6% in 2006) resulted in victims. Victims were defined as people who suffered at least one adverse health effect or who died in association with the clandestine drug lab chemical incident. Of the 16,474 HSEES event victims reported from 2001 to 2008, 902 (5.5%) were related to clandestine meth lab events. Most often, the victims were treated at the scene by emergency medical personnel (6.8%, n561) or they were observed and reported by an official even though the victim did not seek medical treatment (42.9%, n5387). The percentage of all victims in the HSEES database who did not seek medical treatment when officials observed symptoms was 6.5%, which is significantly different (p50.001) when compared with victims in meth events. In meth lab events, about onethird of victims (33.9%, n5306) were treated at the hospital and released; 10.8% of victims (n597) were admitted to a hospital for further treatment; 2.3% of victims (n521) received treatment from a private physician; and 2% of victims (n518) died. Thirteen deaths occurred at the scene or upon arrival at the hospital,



three deaths occurred after arrival at the hospital, and two deaths occurred at unknown times. When these data are compared with all victims recorded in the HSEES system, the more severe medical outcomes of being admitted to a hospital or dying are consistent: 11.2% of all victims were admitted to the hospital, and 2.5% of all victims died. The most frequently reported symptoms or health effects were respiratory irritation (53.8%), headache (34.0%), burns (15.4%), and eye irritation (10.5%) (Table 2). Sixty-one percent (n5552) of victims were official responders to the incident, including police officers (55.1%, n5497), firefighters (5.9%, n553), emergency medical services (EMS) personnel (0.2%, n52), and unspecified responders (0.2%, n52). One-third of victims (33.5%, n5302) were general public, and 5.1% (n546) were employees. The percentage of meth lab events with victims in the HSEES system consistently declined from 36.6% (n5110) in 2001 to 11.4% (n512) in 2008, with no deaths reported in 2008. The percentage of police officers among victims decreased from 49.7% in 2001 to 35.3% in 2008, with a high of 70.9% in 2004 and a low of 24.2% in 2007 (Figure 3). The percentage of firefighters and other official responders among victims declined slightly from 8.9% in 2001 to 5.9% in 2008, and peaked at 11.l% in 2005. During the same period, the percentage of general public among victims increased from 29.1% (n552) to 47.1% (n58), with a high of 72.3% (n524) in 2007.

Table 2. Frequency of injury types reported by victims associated with methamphetamine lab events: ATSDR HSEES database, 2001–2008 Injury typea

N

Victims Percent

Respiratory irritation

485

53.8

Headache

307

34.0

Chemical burns

139

15.4

Eye irritation

95

10.5

Gastrointestinal problems

75

8.3

Dizziness/CNS effects

67

7.4

Trauma

34

3.8

Skin irritation

32

3.5

Thermal burns

26

2.9

Shortness of breath

18

2.0

Other

16

1.8

a The HSEES system allows for as many as seven injury types to be reported for each victim.

ATSDR 5 Agency for Toxic Substances and Disease Registry HSEES 5 Hazardous Substances Emergency Events Surveillance CNS 5 central nervous system

HSEES Program states affected by the meth lab epidemic used their HSEES data to target public health interventions. The outreach activities included creating responder, public, and worker education and awareness; educating santitation workers and law

Percent

Figure 3. Proportion of responder and general-public victims associated with methamphetamine lab events, by year: ATSDR HSEES database, 2001–2008

Year

ATSDR 5 Agency for Toxic Substances and Disease Registry HSEES 5 Hazardous Substances Emergency Events Surveillance EMTs 5 emergency medical technicians



e nforcement personnel about meth lab refuse dangers; and participating in state policy advisory groups. Examples of public health and legislative efforts to fight the meth lab epidemic in two states, Minnesota and New York, follow. Minnesota After observing emerging meth lab trends in the state from 1997 to 2000, the Minnesota HSEES Program conferred with HSEES programs in Iowa and Missouri. Data from these programs indicated that meth-related activity was moving north rapidly.12 Using HSEES data and non-HSEES data in support of action, the Minnesota Department of Health obtained state funds in 2001 for a Meth Lab Program (MLP). The priority focus of the MLP was on education related to public health and safety hazards. As the MLP endeavored to educate responders and the public on meth hazards, meth activity continued to increase, but the number of related victims, especially first responders, declined. In January 2005, near the beginning of the legislative session, Meth Day at the Capitol was held at the Minnesota State Capitol. Many different groups, such as government agencies, law enforcement agencies, treatment providers, and community organizations, developed presentations, displays, posters, and videos aimed at informing Minnesota lawmakers and the general public about meth activity and the associated community hazards. In conjunction with data from other sources, HSEES data helped to show how meth activity had spread extensively in the state. The event assisted in further increasing public awareness of the issue. A bill that limited access to pseudoephedrine and ammonia was passed by the Minnesota Legislature, signed by Governor Pawlenty in May 2005, and enacted in July 2005.13 Data analyses showed that, after implementation of the meth lab laws in July 2005, the number of newly discovered meth labs declined almost threefold: from 95 in January–June 2005 to 33 in July–December 2005. New York Clandestine drug labs, primarily meth labs, were first identified in New York State (NYS) in the 1980s and then virtually disappeared until 2001. As other states were already dealing with the problems of clandestine meth labs, NYS HSEES Program staff sought to learn from their experiences by conducting extensive research and contacting key personnel in those states. They learned that the response to clandestine meth labs needed to be multi-agency and that awareness training was needed by everyone who might respond to a clandestine meth lab. To address the immediate

need and raise awareness about and recognition of clandestine meth labs and the associated hazards, NYS HSEES program staff helped facilitate awareness seminars that targeted all agencies and groups that could be involved in clandestine drug lab identification and response throughout NYS. During development of the multi-agency presentations, law enforcement identified a need for an easy-toread reference card. This need led to the development of a visor card with relevant technical (visual indicators of a lab, products commonly found, and potential hazards) and contact information that was distributed to all law enforcement personnel in NYS. To increase awareness among public health officials and decrease their chance of injury, NYS HSEES Program staff developed guidance in 2003 about the physical dangers of responding to odor complaints that may originate from a clandestine meth lab. This odor-guidance document was distributed to all county environmental health officials and to state environmental health staff in regional and district offices. NYS HSEES Program staff made a presentation at the NYS Department of Health’s Environmental Health Directors Fall Conference in 2003 and conducted a video-cast for state and local health department staff. NYS HSEES Program staff also participated in the preparation of a report that focused on the effectiveness of three chemical deterrents that could be added to agricultural anhydrous ammonia to prevent its subsequent use in the production of meth. NYS HSEES Program staff provided data and testimony to the New York State Commission of Investigations that wrote “Methamphetamine Use and Manufacture,” released in 2005.14 NYS HSEES Program staff also provided information to the New York City Attorney General’s Office for the report, “New York State Law Enforcement Council—2005 Legislative Priorities.”15 Following release of the reports, a comprehensive bill to combat meth labs was drafted, passed, and enacted into law in 2005.16 This legislation restricted sales of pseudoephedrine and created and/or increased penalties for crimes associated with the clandestine manufacture of meth. After the law was passed in 2005, the number of identified clandestine meth labs in NYS decreased to fewer than 20 per year in 2007 and 2008. Discussion HSEES data show a surge and then a decline of meth labs during the past decade, with fewer new labs reported after the implementation of state and federal laws. Because illegal production of meth often involves use of volatile chemicals and makeshift equipment,



these sites can be extremely dangerous and pose a health threat to the cooks, responders, and the public. Fires, explosions, spills, and volatilized chemicals are hazards at meth lab sites and extremely dangerous for humans and the environment.12 These hazards are reflected in the type of injuries reported to the HSEES system. More than half of the victims in HSEES events reported respiratory irritation, approximately one-third of the victims reported headaches, and 15.4% reported chemical burns. The HSEES data indicate the danger of these sites: about 14.5% of HSEES-qualifying meth lab incidents required evacuations compared with 6.5% of all HSEES events, and nearly one-fourth (22.9%) of these incidents resulted in injuries compared with about 9.0% of all HSEES incidents.17 Meth labs clearly pose a greater threat to responders than to other populations. Of the 902 victims, more than 60% were responders: police officers, firefighters, and EMS personnel. For all HSEES incidents, less than 10% of victims were responders. Many of these labs (68.3%) were found in residential areas. Members of the general public who were at risk in meth lab events included vulnerable populations, such as children and the elderly. About one-third of victims in meth lab events were members of the general public, similar to the percentage of general public victims in all HSEES incidents.18 These statistics demonstrate the risk that meth labs pose to responders and to the communities in which meth labs are found. After 2004, the percentage of injuries to responders, especially police officers, generally declined, while the percentage of injuries to the general public continued to rise. This trend in injured-responder data might be related to several factors, such as the implementation of local, state, and federal regulations that helped to reduce the number of labs; increased awareness and training for responders; the development of established protocols that promoted caution and the use of personal protective equipment, such as self-contained breathing apparatuses and chemical-resistant clothing, during a meth lab response; and changes in meth lab composition. First, state and federal laws restricting access to pseudoephedrine and other precursors to meth appear to have been effective in reducing the number of labs overall during the time period. This reduction in meth lab numbers most likely helped to reduce the number of injuries sustained by responders. Second, as described in the information from Minnesota and New York, efforts to educate responders about best practices for responding to meth labs began in 2001, when the meth labs were clearly a growing problem. These efforts involved many agencies and organizations and were initially concentrated on ensur-

ing that the first responders to arrive on scene, often police officers, would recognize the hazard and take precautions. This increase in awareness and knowledge might have contributed to reducing injuries to responders. Law enforcement officials continue to identify clandestine meth labs on a regular basis. Actions to decrease the number of labs and the resulting morbidity and mortality have been somewhat effective to date, but responders and the public must be aware that these labs still exist and, therefore, the hazards still exist. Meth users will continuously look for “new and improved” methods for making meth. Also, meth is the most common drug manufactured in clandestine labs but is not the only drug manufactured in these clandestine settings. Data on clandestine drug lab incidents will help to identify changes in the drugs being made, in the frequency of these incidents, and in the hazards involved. These data can be used to revise response protocols and training for responders and to educate environmental health and public health professionals who address issues of decontamination and chemical exposure. Limitations There are limitations to the data that were collected through the HSEES system. First, reporting to the HSEES system is not mandated, and states rely on other mandated reporting sources. Meth lab details are particularly difficult to obtain because of the confidentiality surrounding pending legal actions. The requirements for reporting clandestine meth labs differed among the participating states, leading to variations in the capture and availability of meth lab data by state. Staff from the participating states also saw that many clandestine meth lab incidents, although reported within the states, did not meet the HSEES case definition because they were not acute events involving a release within the 72-hour period. For these reasons, the HSEES data do not provide a complete characterization of the magnitude of the clandestine meth lab problem in the participating states. It also is not known how well the available data reflect trends in states that did not participate in the HSEES Program. Data from the U.S. Drug Enforcement Administration (DEA) indicate that there are large regional differences in the numbers of clandestine meth labs, with states in the mid- and southeastern sections of the country generally reporting higher meth lab discoveries.19 These limitations in the data may be compounded by the changing number of participating states during the eight-year data-collection period, which might have affected the trends in meth lab incidents collected by the HSEES system.



Conclusions Despite these limitations, HSEES data served a valuable role in initial identification of the surge in clandestine meth labs and continued to be a tool to document meth lab trends and the associated risks in the past decade. As the prevention activities in the states of Minnesota and New York indicate, the HSEES data allowed state health departments to identify an emerging problem relatively early. The recognition of the growing numbers and potential health risks of these clandestine meth labs provided evidence that helped to garner support and secure funds for actions that addressed the problem. The sustained pressure from law enforcement, public health, citizen groups, and others, along with state- and federal-directed chemical restrictions, has likely contributed to continuing decreases of domestic clandestine meth production and associated injuries in the United States over the past several years. As described in this article, a strong decline in meth lab activities was seen following enactment of meth-related legislation in Minnesota and New York.12–15 However, DEA data show that this problem has not disappeared and could be on the rise again, with 7,485 labs reportedly seized nationally in 2008 and 10,064 labs seized in 2009.20 To protect public health, surveillance of clandestine drug labs and other chemical incidents should continue. This surveillance will yield data that can be used to support public health efforts and legislation as trends change. The Hazardous Substances Emergency Events Surveillance Program represents the collaborative effort of many people whose cooperation is gratefully acknowledged. The authors thank Rita B. Messing, PhD; Noreen Hughes, MS; Jenny K. Ehrlich, MPH; and their other partners in the state health departments who worked hard to collect the data and perform the program activities reported in this article. The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention or the Agency for Toxic Substances and Disease Registry.

3. 4.

5.

6.

7.

8. 9. 10. 11.

12. 13. 14.

15. 16. 17. 18.

19.

References 1. Thompson PM, Hayashi KM, Simon SL, Geaga JA, Hong MS, Sui Y, et al. Structural abnormalities in the brains of human subjects who use methamphetamine. J Neurosci 2004;24:6028-36. 2. Simon SL, Dean AC, Cordova X, Monterosso JR, London ED. Methamphetamine dependence and neuropsychological functioning:

20.

evaluating change during early abstinence. J Stud Alcohol Drugs 2010;71:335-44. Henry BL, Minassian A, Perry W. Effect of methamphetamine dependence on everyday functional ability. Addict Behav 2010;35:593-8. Department of Health and Human Services (US), Substance Abuse and Mental Health Services Administration, Office of Applied Studies. 2007 national survey on drug use and health: national results. Appendix B: statistical methods and measurements [cited 2008 Dec 30]. Available from: URL: http://www.oas.samhsa.gov/ NSDUH/2k7NSDUH/AppB.htm Public health consequences among first responders to emergency events associated with illicit methamphetamine laboratories— selected states, 1996–1999. MMWR Morb Mortal Wkly Rep 2000;49(45):1021-4. Witter RZ, Martyny JW, Mueller K, Gottschall B, Newman LS. Symptoms experienced by law enforcement personnel during methamphetamine lab investigations. J Occup Environ Hyg 2007;4:895902. Martyny JW, Arbuckle SL, McCammon CS, Esswein EJ, Erb N. Chemical exposures associated with clandestine methamphetamine laboratories [cited 2010 Dec 14]. Available from: URL: http://www. nationaljewish.org/pdf/chemical_exposures.pdf Horton DK, Berkowitz Z, Kaye WE. The acute health consequences to children exposed to hazardous substances used in illicit methamphetamine production, 1996–2001. J Child Health 2003;1:99-108. Acute public health consequences of methamphetamine laboratories—16 states, January 2000–June 2004. MMWR Morb Mortal Wkly Rep 2005;54(14):356-9. The Combat Methamphetamine Epidemic Act of 2005. Pub. L. No. 109-177, Sec. 701-756, 120 Stat. 192, 256-77 (March 9, 2006). European Monitoring Centre for Drugs and Drug Addiction; Europol. Methamphetamine: a European Union perspective in the global context. 2009 [cited 2010 Dec 14]. Available from: URL: http://www.europol.europa.eu/publications/Joint_publications_on_illicit_drugs/Methamphetamine.pdf Anhydrous ammonia thefts and releases associated with illicit methamphetamine production—16 States, January 2000–June 2004. MMWR Morb Mortal Wkly Rep 2005;54(14):359-61. Minnesota Statutes 2010, Sect. 35.051, 152.02, 152.0262, and 152.136. New York State Commission of Investigation. Methamphetamine use and manufacture. Final report. January 2005 [cited Dec 14 2010]. Available from: URL: http://www.nyslec.com/pdfs/111804_finalreport.pdf New York State Law Enforcement Council. 2005 legislative priorities [cited 2010 Dec 14]. Available from: URL: http://nyslec.org/ pdfs/2005priorities.pdf Laws of New York, 2005, Chapter 394: Methamphetamine [cited 2010 Dec 14]. Available from: URL: http://criminaljustice.state .ny.us/legalservices/ch_394_meth.htm Drug Enforcement Administration (US). Environmental impacts of methamphetamine [cited 2009 Mar 6]. Available from: URL: http://www.usdoj.gov/dea/concern/meth_environment.html Centers for Disease Control and Prevention (US), Agency for Toxic Substances and Disease Registry, Hazardous Substances Emergency Events Surveillance Program. Biennial report 2007–2008 [cited 2010 Dec 14]. Available from: URL: http://www.atsdr.cdc.gov// HS/HSEES/annual2008.html Scott MS, Dedel K. Clandestine methamphetamine labs, 2nd ed. Washington: Department of Justice (US), Office of Community Oriented Policing Services; 2006. Also available from: URL: http:// www.cops.usdoj.gov/ric/ResourceDetail.aspx?RID529 [cited 2010 May 21]. Drug Enforcement Administration (US). Maps of methamphetamine incidents [cited 2010 Sep 21]. Available from: URL: http:// www.justice.gov/dea/concern/map_lab_seizures.html


Research Articles

Healthy Workplaces: The Effects of Nature Contact at Work on Employee Stress and Health

Erin Largo-Wight, PhDa W. William Chen, PhD, CHESb Virginia Dodd, PhD, MPHb Robert Weiler, PhD, MPHb

ABSTRACT Objectives. Cultivating healthy workplaces is a critical aspect of comprehensive worksite health promotion. The influence of healthy workplace exposures on employee health outcomes warrants research attention. To date, it is unknown if nature contact in the workplace is related to employee stress and health. This study was designed to examine the effects of nature contact experienced at work on employee stress and health. Methods. Office staff at a southeastern university (n5503, 30% response rate) participated in the cross-sectional study. We used a 16-item workplace environment questionnaire, the Nature Contact Questionnaire, to comprehensively measure, for the first time, nature contact at work. The Perceived Stress Questionnaire and 13 established health and behavioral items assessed the dependent variables, general perceived stress, stress-related health behaviors, and stress-related health outcomes. Results. There was a significant, negative association between nature contact and stress and nature contact and general health complaints. The results indicate that as workday nature contact increased, perceived stress and generalized health complaints decreased. Conclusions. The findings suggest that nature contact is a healthy workplace exposure. Increasing nature contact at work may offer a simple populationbased approach to enhance workplace health promotion efforts. Future researchers should test the efficacy of nature-contact workplace stress interventions.

University of North Florida, Brooks College of Health, Department of Public Health, Jacksonville, FL

a

University of Florida, College of Health and Human Performance, Department of Health Education and Behavior, Gainesville, FL

b

Address correspondence to: Erin Largo-Wight, PhD, University of North Florida, Brooks College of Health, Department of Public Health, 1 UNF Drive, Jacksonville, FL 32224; tel. 904-620-2037; fax 904-620-1035; e-mail . ©2011 Association of Schools of Public Health

124 


Effects of Nature Contact on Employee Stress and Health  125

The work environment contributes to employee health. A sick environment can threaten health through biological and psychological pathways. Biologically, indoor air pollutants and toxins may cause illness, such as the Sick Building Syndrome.1 Psychologically, office environments typified with crowding and noise contribute to chronic stress.2,3 Conversely, office environments can be created to enhance employee health. Healthy exposures include the following: availability of healthy behavioral options (e.g., healthy food choices), enhanced and optimized safety, environmental sustainability and stewardship, and the opportunity for nature contact at work.4–8 The healthy workplace consists of these healthful exposures and is free of the negative ones. Effective, comprehensive worksite health promotion programs (WHPPs) aim to foster a healthy workplace. It is now widely believed that worksite health promotion should go beyond education and focus on individual behavior change and also include environmental modifications. Environmental modifications are physical changes or interventions to the workplace environment. Engbers et al. conducted a systematic review of 13 randomized controlled trials (RCTs) with environmental interventions at work entitled “Worksite Health Promotion Programs with Environmental Changes.”4 The Working Healthy Project, for example, was a study of more than 2,000 employees that showed how environmental modifications, such as food labeling on vending machines and at restaurants and a red-line route to promote lunchtime walking, resulted in a significant increase in fruit and vegetable consumption and physical activity at 2.5 years follow-up.9 Environmental modifications are especially important components of WHPPs because they support and enable health and behavioral outcomes. One way that the workplace may be modified to promote health is through the purposeful use of nature contact.10 Nature contact is a component of all healthy places and the focus of this workplace study. Everyday nature contact is exposure to the outdoors or outdoor-like elements in the places people live, work, and play.5 At work, nature contact may be achieved by adding an indoor office plant or taking a work break outdoors. To date, a handful of workplace studies have suggested that nature contact experienced at work or in an office setting may be health promoting. For example, previous work or office findings suggest that relaxing outdoors,11 indoor office plants,12 and office window views7 were related to less stress. Although these findings support the notion that nature contact is a component of a healthy workplace, studies are few and limited.

There are important nature-contact and health findings in other populations and other settings. These findings, although not directly related to work, may inform future worksite studies and practice. These findings help point to possible forms of nature contact that may represent healthy exposures at work and warrant future research. For example, a nature-contact intervention of gardening reduced stress in a study of breast cancer patients.13 Other less active forms of outdoor exposure, such as spending passive time in an urban park, have also been associated with less stress among random samples of city dwellers.14 Indoor exposure to plants, natural lighting, fish tanks, and a view from the window have been previously associated with less stress among many populations.15–19 In addition, exposure to abstract representations of nature experienced indoors, such as recorded nature sounds or photographed images, has been associated with decreased stress and stress-related outcomes.20 A study demonstrated that a “nature therapy” intervention consisting of two forms of nature contact—a nature mural printed on a hospital bedside curtain and a nature CD playing—resulted in significantly less perceived pain and stress during a bronchoscopy procedure.21 In summary, these studies suggest that the following forms of nature contact were health-promoting among a variety of populations and settings: window view, natural light, fish tanks, live or artificial plants, listening to recorded nature sounds on a CD, nature photography or art, and outdoor breaks or lunch.5 These nature-contact findings may inform future work studies by pointing to these forms of nature contact that may be healthful in the workplace as well. The theoretical question “How does nature contact promote health?” has previously been explored. In a nutshell, nature contact reduces stress. Biological researchers point to an evolutionary explanation for this phenomenon. The biophilia hypothesis contends that natural elements are calming for people today because of the linkage to survival in the past (just as common fears—such as snakes, spiders, and heights— are rooted in the past and related to survival):22,23 “Throughout human existence, human biology has been embedded in the natural environment. Those who could smell the water, find the plants, follow the animals, and recognize the safe havens must have enjoyed survival advantages.”23 Psychological researchers have studied the brain and stress response after exposure to nature contact. This work has led to two potential mechanisms to explain how natural elements reduce stress in people today. Environments with natural elements either (1) restore stress-fatigued cognitive resources to enhance coping abilities24 or (2)



stimulate underutilized portions of the “old” brain, which balance the concentrated stimulation and relieve exhausted portions of the brain25 to reduce stress. PURPOSE Stress-reducing work environments represent an important focus of research and practice. Stress not only influences mental health and quality of life, it also increases the likelihood of chronic diseases, such as heart disease and cancer.26–28 Stress and related health consequences are more prevalent in the U.S. today than in the past, and work is attributed as a major cause.29 According to the demand-control model and previous findings, occupational positions with low decision latitude and high psychological demands, such as office staff, suffer most from stress.30,31 In addition to having high-stress jobs, office staff are a priority public health population because they represent 70% of the U.S. workforce.32 In this study, we examine workplace environments and stress among office staff. This study was designed to (1) describe the influence of nature contact at work on perceived stress and stress-related health and behavioral outcomes and (2) inform public health promotion. Although there is evidence that nature contact is health-promoting in many populations and varied settings, there are few findings on nature contact at work and health among office staff, a priority public health population.7 To date, it is unknown if regular contact with nature in the workplace is associated with perceived stress levels of office staff. Understanding and designing healthy workplaces is important and offers a promising and population-based approach to reduce stress and related health outcomes among working Americans.5,7 METHODS Participants We invited a census of office staff at a southeastern university (n51,622) to participate in the study. The group included 13 job codes of full-time, mostly deskbound office staff, such as secretaries and office clerks. Electronic informed consent was obtained from all participants; participation was anonymous and voluntary. Procedures We used a cross-sectional, Web-based survey design to collect data. We sent an e-mail invitation along with the Web link to access the online survey to the census. The participants took approximately 10–15 minutes to complete the online survey. We utilized a Web-based survey because it was cost-efficient, environmentally

sound, practical, and had the potential to reach the study’s population.33 Five previously identified strategies34 were used to minimize potential disadvantages of Web-based surveys, such as non-response error and low response rate: (1) e-mails were personalized by addressing each participant by name; (2) informed consent to participate in the study was obtained by clicking “next” on the online survey; (3) personal questions about income, age, and marital status were located at the end of the survey; (4) two follow-up, reminder e-mails were sent three and five days after the initial e-mail invitation to office staff who had not yet participated; and (5) participants in the study were eligible for a nominal incentive. Instruments We measured nature contact at work using a 16-item scale, the Nature Contact Questionnaire (NCQ). We measured total score and three subscales—outdoor, indoor, and indirect nature contact. The outdoor-nature-contact subscale measured the employees’ outdoor exposure at work—for example, “the weekly frequency of work breaks outdoors.” The indoor-nature-contact subscale measured employees’ exposure to natural elements within the office space, such as view from a window, natural light, and live plants. An example was “the number of live plants in the office.” The indirectnature-contact subscale measured employees’ exposure to abstract representations of natural elements in the office, such as photographs of natural landscapes and recorded nature sounds. An example was “percentage of time per week listening to recorded nature sounds on CD.” The range of possible total scores was 16 to 96. We used a continuous Likert scale to quantify the response options and included percentage of time exposed to the item (0%, 1%–20%, 21%–40%, 41%–60%, 61%–80%, and 81%–100%), frequency of contact with the item (N/A, 0, 1, 2, 3, 4, and 5 or more), and number of contact items (0, 1, 2, 3, 4, and 5 or more). We established content validity (expert panel), construct validity (Kaiser-Meyer-Olkin 5 0.68), internal consistency (alpha 5 0.63), and test-retest reliability (r50.84). The NCQ and psychometric properties were reported in detail elsewhere.35 We measured stress using the Perceived Stress Questionnaire (PSQ). The PSQ consists of 30 items, such as “you have too many things to do,” “you feel lonely or isolated,” and “you find yourself in situations of conflict.” The range of possible total scores was 30 to 120. The reported test-retest reliability of the PSQ was r50.82 and the internal reliability was alpha 5 0.92. The PSQ psychometric properties also were reported in detail elsewhere.36



We measured health and health behaviors using 13 items drawn from the Behavioral Risk Factor Surveillance System (BRFSS),37 National Quality Institute,38 Centers for Disease Control and Prevention,39 and previous studies.40,41 We measured self-reported health, the number of days in the past month influenced by poor health, and behavioral items—including cigarette smoking and preventive behaviors—using BRFSS historical questions.37 Alcohol and coffee consumption were measured with items similar to the BRFSS historical questions but modified based on previous research.40 We measured frequency of moderate and vigorous physical activity using items from a national healthy workplace questionnaire.38 Lastly, we measured diet with two items related to fruit and vegetable consumption based on previous research41 and defined a serving using governmental guidelines from the 5 A Day for Better Health Program.39

Table 1. Relationships between nature contact at work and stress, general health, and number of days health prevented activities among office staff Dependent variables

Nature contact total

Stress total General health Days health poor

20.14a 20.14a 0.01

Outdoor nature contact subtotal


20.17b 20.17b 0.04

Indoor nature contact subtotal


0.04 20.10c 20.03

Indirect nature contact subtotal


20.08 0.00 20.01

p,0.01

a

b

Data analysis We used SPSS® version 1642 to analyze the data. All survey responses were numerically coded, and totals and subtotals were calculated for both survey instruments (PSQ and NCQ). We used multiple regression analysis to determine which of the health and behavioral items were stress related. To explore the relationship between nature contact and health and the relationships among forms of nature contact, we conducted bivariate correlation analyses and independent t-test analyses. RESULTS Demographics The majority of the participants were women (92.9%) and white (82.5%). The mean age of the participants was 42 years, with a standard deviation of 12 years. Approximately half of the participants attended some college or technical school (47.5%), reported earning $25,001–$35,000 per year (49.5%), and reported being married (54.4%). The response rate was about 30% (n5503). Nature contact at work and employee health First, we determined which of the health and behavioral survey items were stress related. To determine the stress-related variables, we employed a multiple regression analysis with the PSQ stress total as the dependent variable and the 13 health and behavior survey items as independent variables. Data analysis revealed that “general health” self-rating (poor to excellent) and “number of days in the past 30 days that health prevented from doing usual activities” were significant predictors of stress and, thus, represented

r

Independent variables

p,0.001 (two-tailed)

p,0.05

c

the stress-related variables in this study. None of the health behavior items, such as smoking and physical activity, was a statistically significant predictor of stress. As a result, we included the PSQ stress total and the two stress-related health variables as dependent variables in remaining analyses. We used Pearson product bivariate correlations to examine the relationship between nature contact at work and the three study variables (stress, general health, and number of days health prevented activities). Higher nature-contact scores represented more nature contact at work, and lower perceived stress and health scores represented less stress and fewer health concerns (Table 1). The correlations were interpreted based on the strength of the association. We conducted t-test analyses to further examine the patterns of association between nature contact and health. High and low nature-contact scores were dummy-coded as 1 and 2, respectively. The highnature-contact group was one standard deviation above the mean and the low-nature-contact group was one standard deviation below the mean. We ran analyses to compare the highest and lowest nature-contact groups. The high-total-nature-contact group and the high-outdoor-nature-contact group had significantly less stress and better general health than the related low groups. There was no statistically significant difference between high and low nature contact for the number of days one missed normal activities over the last month for any measure of nature contact. Table 2



provides a summary of the influence of high vs. low nature contact on total stress score. DISCUSSION The purpose of this study was to examine the influence of nature contact at work on stress and health among office staff, a priority public health population that has not been well studied.11 The findings from this study were consistent with previous findings in other settings and the primary theoretical explanations.5,24–25 Employees with more nature contact at work reported significantly less perceived stress and stress-related health complaints. These findings suggest that nature contact at work may constitute a healthy workplace exposure. It is important to understand healthy workplace exposures. It is now widely believed that cultivating healthy workplaces is an important component of comprehensive WHPPs.4 This study’s main findings suggest that nature contact at work, as in other settings, is associated with stress reduction among employees. These findings, in the context of the larger body of literature, suggest that the purposeful use of nature contact at work may reduce employee stress.5 Creating, enhancing, or promoting the use of outdoor break areas, for example, may be one way for health promotion practitioners to cultivate a healthy workplace with nature-contact exposures.10 Future research should build off of these cross-sectional findings and assess if environmental (nature-contact) interventions at work result in stress reduction among employees. Although the effect size was small to moderate, the findings were statistically significant and important. The findings are particularly important because increasing nature-contact exposure at work may be an inexpensive and practical way to enhance worksite health promo-

tion efforts. In contrast to other factors that influence perceived stress, such as social support, job demands, and relaxation skills,28 enhancing nature contact at work is a relatively simple approach. Adding indoor plants, opening blinds, or going outside for a work break instead of to the break room, for example, are straightforward ways to increase healthy exposures at work to combat stress and promote health. Enhancing coping or social support, on the other hand, likely involves more time, effort, and resources. Maller et al. and others recognized that “contact with nature may provide an effective population-wide strategy.”43 These findings are also important because this was the first known study to measure nature contact comprehensively at work or in any setting. Other studies have examined the influence of one form of nature contact (e.g., the number of indoor plants) on stress or health. Researchers have previously pointed to three forms of nature contact important for child development that were similar to the forms measured and analyzed in this study,44 but this is the first known study to measure all known health-promoting forms of nature contact. The findings from this study also allowed the first-ever quantitative comparisons between forms of nature contact. Kuo emphasized the need to study nature contact comprehensively to determine “which forms or doses of nature enhance effectiveness and which do not.”45 In this study, findings suggest that the forms of nature contact may matter. The most direct nature contact—outdoor nature contact—had the strongest association with stress reduction and health. The frequency of employees’ outdoor exposure at work had the strongest negative correlation to stress and health complaints, whereas the least direct form of nature contact—indirect nature contact—resulted in the least health benefits. Employees’ exposure to

Table 2. Relationships between perceived stress and high vs. low nature contact at work among office staff Independent variables

N

M

SD

t-score

Low nature contact—total High nature contact—total

41 60

67.3 60.5

16.2 16.2

2.1a

Low nature contact—outdoor subscale High nature contact—outdoor subscale

85 58

68.0 59.2

17.8 15.7

3.1b

Low nature contact—indoor subscale High nature contact—indoor subscale

131 52

65.4 63.1

16.1 17.1

0.8

Low nature contact—indirect subscale High nature contact—indirect subscale

84 46

66.1 60.1

16.5 16.6

2.1a

M 5 mean SD 5 standard deviation p,0.05

a

b

p,0.01 (two-tailed)



nature photography or nature sounds in the office, for example, had the weakest negative correlation to stress and health complaints. These novel findings will help health promotion practitioners begin to prioritize efforts. These findings are important for shaping workplace stress interventions and may suggest that taking an outdoor “booster break,”46 for example, would be more important than displaying nature photography or a live plant in the office. Future research should build off of these cross-sectional findings and compare environmental (nature-contact) interventions at work to best inform practice. Limitations Although the findings from our study and other studies suggest that nature contact may be helpful to reduce employee stress, future research should be conducted. An important limitation of our study was that participants consisted of office staff from one university. This limits generalizability to larger populations. Future research should examine other working populations. Another limitation of our study was the lack of causal relationships. Like all cross-sectional studies, the findings from this study cannot infer causation. Nature contact did not cause stress reduction in this study. Future studies should examine the efficacy of workplace nature-contact interventions, such as the outdoor booster break, on employee stress among varying populations of employees. Intervention research could also be employed to better compare the forms (outdoor, indoor, and indirect) of nature contact on stress. Ideally, future intervention research should employ a RCT design in an applied workplace setting with several follow-ups to best inform recommendations for practice. CONCLUSIONS Creating environments with natural elements to reduce stress is both intuitive and scientific. Office windows, vacation destinations, and real-estate costs worldwide suggest that people everywhere value nature contact (and will pay more for it).5,17,47–50 This phenomenon has also been well studied. The main theoretical perspectives suggest that natural elements are calming for people today because of the linkage to survival in the past.5,24,25 A recent review entitled “Cultivating Healthy Places and Communities: Evidenced-Based Nature Contact Recommendations”10 summarized the nature-contact literature as it related to human health. The article points to 12 research-based health promotion recommendations, with the assumption that “environments

can be protected, created, reconfigured, or regulated to prevent, eliminate, or mitigate [stress].”51 The recommendations include the following: advocate for the preservation of pristine wilderness; incorporate wooded parks/green space in community design; maintain healing gardens; cultivate and landscape grounds for outdoor viewing; welcome animals inside; provide a plethora of indoor potted plants within view; light rooms with bright, natural sunlight; provide a clear view of nature outside; allow outside air and sounds in; display nature photography and realistic nature art; watch nature on TV or videos; and listen to recorded nature sounds.10 Our study’s findings support the notion that many of these recommendations may also apply to the workplace environment. These findings, together with the previous studies and the evidenced-based recommendations, suggest that nature contact may be fostered through environmental modifications to reduce employee stress. The concept of “wellness by design”15 in the workplace may be achieved, in part, through the purposeful use of nature contact. This study was supported by the Society for Public Health Education (SOPHE)/Agency for Toxic Substances and Disease Registry (ATSDR) student fellowship in environmental health promotion. The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of SOPHE or ATSDR.

REFERENCES 1. Samet JM, Spengler JD. Indoor environments and health: moving into the 21st century. Am J Public Health 2003;93:1489-93. 2. Brennan A, Chugh JS, Kline T. Traditional versus open office design: a longitudinal field study. Environ Behav 2002;34:279-99. 3. Raffaello M, Maass A. Chronic exposure to noise in industry: the effects on satisfaction, stress symptoms, and company attachment. Environ Behav 2002;34:651-71. 4. Engbers LH, van Poppel MN, Chin A Paw MJ, van Mechelen W. Worksite health promotion programs with environmental changes: a systematic review. Am J Prev Med 2005;29:61-70. 5. Frumkin H. Beyond toxicity: human health and the natural environment. Am J Prev Med 2001;20:234-40. 6. Frumkin H, McMichael AJ. Climate change and public health: thinking, communicating, acting. Am J Prev Med 2008;35:403-10. 7. Kaplan R. The role of nature in the context of the workplace. Landscape Urban Plan 1993;26:193-201. 8. Srinivasan S, O’Fallon LR, Dearry A. Creating healthy communities, healthy homes, healthy people: initiating a research agenda on the built environment and public health. Am J Public Health 2003;93:1446-50. 9. Emmons KM, Linnan JA, Shadel WG, Marcus B, Abrams DB. The Working Healthy Project: a worksite health-promotion trial targeting physical activity, diet, and smoking. J Occup Environ Med 1999;41:545-55. 10. Largo-Wight E. Cultivating healthy places and communities: evidenced-based nature contact recommendations. Int J Environ Health Res 2011;21:41-61. 11. Trenberth L, Dewe P, Walkey F. Leisure and its role as a strategy for coping with work stress. Int J Stress Manag 1999;6:89-103. 12. Larson L, Adams J, Deal B, Kweon BS, Tyler E. Plants in the workplace: the effects of plant density on productivity, attitudes, and perceptions. Environ Behav 1998;30:261-81.



13. Cimprich B. Development of an intervention to restore attention in cancer patients. Cancer Nurs 1993;16:83-92. 14. Grahn P, Stigsdotter UA. Landscape planning and stress. Urban For Urban Green 2003;2:1-18. 15. Ulrich RS. Wellness by design: psychologically supportive patient surroundings. Group Pract J 1991;40:10-9. 16. Dijkstra K, Pieterse ME, Pruyn A. Stress-reducing effects of indoor plants in the built healthcare environment: the mediating role of perceived attractiveness. Prev Med 2008;47:279-83. 17. Kaplan R. The nature of the view from home: psychological benefits. Environ Behav 2001;33:507-42. 18. Leather P, Beale D, Santos A, Watts J, Lee L. Outcomes of environmental appraisal of different hospital waiting areas. Environ Behav 2003;35:842-69. 19. Shibata S, Suzuki N. Effects of the foliage plant on task performance and mood. J Environ Psychol 2002;22:265-72. 20. Felsten G. Where to take a study break on the college campus: an attention restoration theory perspective. J Environ Psychol 2009;29:160-7. 21. Diette GB, Lechtzin N, Haponik E, Devrotes A, Rubin HR. Distraction therapy with nature sights and sounds reduces pain during flexible bronchoscopy: a complementary approach to routine analgesia. Chest 2003;123:941-8. 22. Buss DM. Evolutionary psychology: a new paradigm for psychological science. Psychol Inq 1995;6:1-30. 23. Wilson EO. Biophilia: the human bond with other species. Cambridge (MA): Harvard University Press; 1984. 24. Kaplan S. The restorative benefits of nature: toward an integrative framework. J Environ Psychol 1995;15:169-82. 25. Ulrich RS, Simons RF, Losito BD, Fiorito E. Stress recovery during exposure to natural and urban environments. J Environ Psychol 1991;11:201-30. 26. Lazarus RS, Folkman S. Stress, appraisal, and coping. New York: Springer; 1984. 27. Cohen S, Frank E, Doye WJ, Dkoner DP, Rabin BS, Gwaltney JM Jr. Types of stressors that increase susceptibility to the common cold in healthy adults. Health Psychol 1998;17:214-23. 28. Karren KJ, Hafen BQ, Smith NL, Frandsen KJ. Mind/body health: the effects of attitudes, emotions and relationships. 2nd ed. San Francisco: Benjamin Cummings; 2002. 29. Horan AP. An effective workplace stress management intervention: Chicken Soup for the Soul at Work Employee Groups. Work 2002;18:3-13. 30. Mausner-Dorsch H, Eaton WW. Psychological work environment and depression: epidemiologic assessment of the demand-control model. Am J Public Health 2000;9:1765-70. 31. Melchior M, Krieger N, Kawachi I, Berkman LF, Niedhammer I, Goldberg M. Work factors and occupational class disparities in sickness absence: findings from the GAZEL cohort study. Am J Public Health 2005;95:1206-12. 32. Mendell MJ, Fisk WJ, Kreiss K, Levin H, Alexander D, Cain WS, et al. Improving the health of workers in indoor environments: priority research needs for a national occupational research agenda. Am J Public Health 2002;92:1430-40.

33. Daley EM, McDermott RJ, McCormack Brown KR, Kittleson MJ. Conducting web-based survey research: a lesson in Internet designs. Am J Health Behav 2003;27:116-24. 34. Dillman DA. Mail and Internet surveys: the tailored design method. 2nd ed. New York: John Wiley & Sons, Inc.; 2000. 35. Largo-Wight E, Chen W, Dodd V, Weiler R. The Nature Contact Questionnaire: a measure of healthy workplace exposure. Work. In press 2011. 36. Levenstein S, Prantera C, Varvo V, Scribano ML, Berto E, Luzi C, et al. Development of the Perceived Stress Questionnaire: a new tool for psychosomatic research. J Psychosom Res 1993;37:19-32. 37. Centers for Disease Control and Prevention (US). Behavioral Risk Factor Surveillance System—historical questions [cited 2010 Dec 13]. Available from: URL: http://apps.nccd.cdc.gov/brfssQuest 38. National Quality Institute. Health in the workplace employee questionnaire. Investing in comprehensive workplace health promotion. Toronto: National Quality Institute; 2001. 39. Centers for Disease Control and Prevention (US), National Fruit and Vegetable Program. What counts as a cup? [cited 2010 Dec 13]. Available from: URL: http://www.fruitsandveggiesmatter.gov/ what/examples.html 40. Conway TL, Vickers RR Jr, Ward HW, Rahe RH. Occupational stress and variation in cigarette, coffee, and alcohol consumption. J Health Soc Behav 1981;22:155-65. 41. Berrigan D, Dodd K, Troiano RP, Krebs-Smith SM, Barbash RB. Patterns of health behavior in U.S. adults. Prev Med 2003;36:615-23. 42. SPSS, Inc. SPSS®: Version 16. Chicago: SPSS, Inc.; 2001. 43. Maller C, Townsend M, Pryor A, Brown P, St Leger L. Healthy nature healthy people: “contact with nature” as an upstream health promotion intervention for populations. Health Promot Int 2006;21:45-54. 44. Kellert SR. Experiencing nature: affective, cognitive, and evaluative development in children. In: Kahn PH Jr, Kellert SR, editors. Children and nature: psychological, sociocultural, and evolutionary investigations. Cambridge (MA): The MIT Press; 2002. p. 117-51. 45. Kuo FE. Coping with poverty: impacts of environment and attention in the inner city. Environ Behav 2001;33:5-34. 46. Taylor WC. Transforming work breaks to promote health. Am J Prev Med 2005;29:461-5. 47. Ulrich RS, Simons RF, Miles MA. Effects of environmental simulations and television on blood donor stress. J Archit Plan Res 2003;20:38-47. 48. Northridge ME, Sclar ED, Biswas P. Sorting out the connections between the built environment and health: a conceptual framework for navigating pathways and planning healthy cities. J Urban Health 2003;80:556-68. 49. Regan CL, Horn SA. To nature or not to nature: associations between environmental preferences, mood states and demographic factors. J Environ Psychol 2005;25:57-66. 50. Parsons R. The potential influences of environmental perception on human health. J Environ Psychol 1991;11:1-23. 51. Hartig T. Guest editor’s introduction. Environ Behav 2001;33:475-9.


Research Articles

Residential Light and Risk for Depression and Falls: Results from the LARES Study of Eight European Cities

Mary Jean Brown, ScD, RNa David E. Jacobs, PhDb

ABSTRACT Objectives. We examined the relationship between self-reported inadequate residential natural light and risk for depression or falls among adults aged 18 years or older. Methods. Generalized estimating equations were used to calculate the odds of depression or falls in participants with self-reported inadequate natural residential light vs. those reporting adequate light (n6,017) using data from the World Health Organization’s Large Analysis and Review of European Housing and Health Survey, a large cross-sectional study of housing and health in representative populations from eight European cities. Results. Participants reporting inadequate natural light in their dwellings were 1.4 times (95% confidence interval [CI] 1.2,1.7) as likely to report depression and 1.5 times (95% CI 1.2, 1.9) as likely to report a fall compared with those satisfied with their dwelling’s light. After adjustment for major confounders, the likelihood of depression changed slightly, while the likelihood of a fall increased to 2.5 (95% CI 1.5, 4.2). Conclusion. Self-reported inadequate light in housing is independently associated with depression and falls. Increasing light in housing, a relatively inexpensive intervention, may improve two distinct health conditions.

a Centers for Disease Control and Prevention, National Center for Environmental Health, Division of Emergency and Environmental Health Services, Atlanta, GA

National Center for Healthy Housing, Columbia, MD

b

Address correspondence to: Mary Jean Brown, ScD, RN, Centers for Disease Control and Prevention, Division of Emergency and Environmental Health Services, National Center for Environmental Health, 4770 Buford Hwy. NE, MS F-46, Atlanta, GA 30341; tel. 770-488-7492; fax 770-488-3635; e-mail .


 131


In 2000, the World Health Organization (WHO) estimated that depression (unipolar depressive disorders) caused 4.4% of the disability adjusted life years (DALYs) worldwide and an estimated 12% of the total life years lived with disability.1 A multinational study estimated that approximately 7% of Europeans suffered from major depression that substantially impaired their working or social lives; the prevalence of major depression ranged from 3.8% in Germany to 9.9% in the United Kingdom.2 Injuries are also a leading cause of the global burden of disease. In 2000, WHO ranked falls as 15th among leading causes of disease burden—accounting for an estimated 3.4 million DALYs—in adults aged 30–44 years.3 Among the high-income countries included in that study, falls ranked as the 13th and 14th leading causes of morbidity for people aged 15–44 years.3 Together, depression and falls constitute major portions of the global burden of disease. However, WHO recently concluded that insufficient evidence exists regarding light in housing and its relationship to mental and other health effects.4 Falls and depression may have a commonality related to inadequate residential light, but the evidence to date has been insufficient to establish the link. The relationship between lack of light and depression has been well documented, and the evidence that light is a potent neurobiological agent seems clear.5,6 The role of light as a major synchronizer of circadian rhythms has been established for alertness, plasma melatonin, body temperature, and sleep/wakefulness.7,8 Light therapy has been used to treat seasonal affective disorder (SAD) since the 1980s, when Rosenthal et al. found that artificial light was effective in treating the disorder.6 The light intensity of 2,500–10,000 lumens per meter squared (lux) used during therapy is much brighter than normal indoor light, which is usually 300–500 lux, but not as bright as summer sunlight, which can be as bright as 100,000 lux.9 A consensus has been reached concerning the efficacy of light to treat seasonal depression, based on independent studies from around the world that show an average decrease of 20%–25% in depressive symptoms.10 Depressive symptoms are determined using both observer rating scales, such as the Hamilton Depression Rating Scale, and self-assessment of symptoms.11 Few studies have compared artificial with natural light. However, in a study conducted in Switzerland, researchers compared the use of low-intensity artificial light, defined as half an hour of artificial light at 2,800 lux, with one hour of outdoor light.12 The study concluded that outdoor light was more effective than artificial light, with outdoor light causing a 50% reduc-

tion in depressive symptoms. A statistically significant reduction of 25% in depressive symptoms, as measured by the doctor-administered Hamilton Depressive Rating Scale, occurred in the group receiving the low-dose artificial light, although self-reported depressive symptoms did not improve for this group. In another study, low levels of light increased the likelihood of depression when depressed patients reportedly were exposed to 40% less moderate light (100 to 1,000 lux per day), compared with a non-depressed control group.13 Light therapy results in a rapid decrease in depressive symptoms, and few researchers have followed participants over long periods. However, in the few studies that followed patients for longer than one week, positive response rates increased with duration of the light intervention. The salutary effect of light has been most extensively studied in relationship to seasonal depression, although studies of light’s effect on individuals with nonseasonal depression, late luteal phase dysphoric disorder, and bulimia nervosa also have shown promise.14 Three main hypotheses have been proposed: (1) light’s effect on circadian phase shift, (2) light’s effect on the major monoamine transmitters, and (3) an individual’s genetic vulnerability. However, the causal pathway for depression is undoubtedly complex, as shown by (1) the conflicting results of different studies, (2) the independent effects of light and standard antidepressant pharmacotherapy, (3) the mediation of the relationship between light and depression by whether daily behavior followed a predictable pattern, and (4) evidence of reduced retinal contrast perception in depressed compared with non-depressed individuals.13–15 We undertook this study in part to determine if there is an association between self-reported adequacy of natural light in housing and depression. In the United States, falls are a significant cause of home injuries across all age groups; an estimated 5.6 million nonfatal falls required medical attention in 1999.16 Risk factors for falls among the people aged 65 years and older have been well-studied and include arthritis, foot problems, medications, and cognitive and motor impairment.17 Environmental hazards do not seem to be strong predictors for risk of falls among the elderly, and results of environmental mitigation have been disappointing.18 The prevalence of falls among adults aged 18 years and older is similar to the prevalence in both older and younger people. In 1998 in the U.S., it was estimated that 38% of nonfatal, unintentional fall injuries occurring at home were among people aged 25 to 64 years.16 To date, no studies have examined housing factors that predict falls in this age group, and more


Residential Light and Risk for Depression and Falls, 8 European Cities  133

detailed studies of precisely which housing factors are most predictive of falls are needed. Lack of adequate natural light may be one such housing factor, as poor light can prevent individuals from seeing hazards for tripping and falling in their environment. In this article, we describe the association between self-reported natural residential light and the risk for depression and serious nonfatal falls among study participants aged 18 years and older in the Large Analysis and Review of European Housing and Health Status (LARES) survey.19 METHODS Housing and health survey From 2002 to 2003, WHO, with funding from the German Federal Ministry of Health, conducted the LARES study, a cross-sectional survey to improve knowledge of the impact of housing on the physical well-being and mental health of residents. Eight European cities participated in the survey: Vilnius, Lithuania; Geneva, Switzerland; Forli, Italy; Bonn, Germany; Ferreira do Alentejo, Portugal; Budapest, Hungary; Bratislava, Slovakia; and Angers, France. The dataset, based on 290 questions with 1,095 items, included information on the condition of 3,373 dwellings and the health status of 8,519 inhabitants. The mean response rate for all cities was 44.2% of the eligible sample of households. Forli and Ferreira had the lowest participation rate (34%) and Angers the highest (48%). The sample in each city was randomly generated from resident registries, the local tax registry, or the national health insurance registry.20 LARES used two questionnaires: an inhabitant questionnaire that described the residents’ perception of their dwelling and a health questionnaire for each inhabitant to report on his or her health status. In addition, an inspection of the dwelling was completed by a trained inspector. The methodology of this survey has been described in more detail elsewhere.19,21 Health assessment In this study, we used LARES data from health questionnaires completed by each individual resident to identify those residents aged 18 years and older who reported a fall, doctor-diagnosed depression, or three to four cardinal symptoms of depression within the past year. Symptoms of depression include self-reported sleep disturbance, lack of interest in activities, low selfesteem, and loss of appetite for two weeks or longer. This measure is highly correlated with the Diagnostic and Statistical Manual of Mental Disorders criteria for major depression.22 Participants with doctor-diagnosed

depression but without reported symptoms were included in the group with depression. We also used the health questionnaire data to identify those residents who reported a fall within the last year and the housing element (e.g., stairs) that was related to the fall. The inspection report provided information on whether the dwelling was a single-family home or part of a multifamily dwelling, as well as the presence and condition of interior and exterior stairs. Light assessment We used the questionnaire to identify those residents who reported either “turning on a light even on bright days because the natural light is not sufficient” or “missing daylight” in the last year, both of which are indicators of natural light in the residence. We then compared this group with residents who reported that their light was adequate. Residents who responded that they did not know if they turned on the lights (n70, including three respondents with depressive symptoms) or if they missed daylight (n54, including nine with depressive symptoms) and residents whose questionnaires were incomplete (n19) were excluded from the analysis because their responses could not be classified reliably. Because no physical measurements of light were included in LARES, our analysis did not include a dose-response assessment of light intensity on health outcomes. Factors associated with falls Participants who reported a fall within the last year were also asked about household elements and furnishings that were involved in the fall, such as stairs, kitchen utensils, pets, or toys. Participants could select more than one household element from this list. Statistical analyses Statistical analyses were performed using SAS® version 9.02.23 We analyzed data for LARES participants aged 18 years or older. A bivariate logistic regression model was fitted to determine the odds that people who missed daylight or turned the light on during the day were either more likely to have depressive symptoms, to fall, or both. Because multiple residents were surveyed in the same dwelling in some instances, we used a generalized estimating equations (GEE) approach, with the robust (“sandwich”) variance estimator to account for possible clustering at the building level. We used a GEE variation, the alternating logistic regression method, that used odds ratios (ORs) because these are more appropriate measures of association between dichotomous outcomes.



Confounders We created categorical variables for variables suggested by the literature as important predictors of falls and depression, and we calculated unadjusted ORs for the exposure and outcomes of interest as well as for factors thought to confound this association. For example, we included city of residence, which would account for latitude and unmeasured cultural influences; participant characteristics such as age by decade, health status (good/excellent vs. poor/fair), disability (yes vs. no), health insurance (public/none vs. some private), low vs. high/middle income, marital status, and education level; and housing characteristics such as neighborhood and dwelling satisfaction, housing type, and tenancy. The final multivariate models adjusted for potential confounders, including gender, alcohol use, employment status, health status, education, health insurance, and income, all of which were found to be significant predictors of risk at the bivariate level using the GEE alternating logistic regression method just described. City of residence was forced into the model, but there was no correlation between city of residence, turning lights on during the day, and risk for depression or falls. City of residence also acted as a proxy for differences in the amount of sunshine per day by geographic location. RESULTS Of the 6,017 people meeting the study inclusion criteria as described, 784 (13.0%) were depressed, reporting either doctor-diagnosed depression or three or more of the cardinal symptoms of depression, and 450 (7.5%) reported a fall within the last year (Table 1); 131 (2.2%) participants reported both conditions (data not shown). In the 3,076 houses where more than one participant was interviewed, only the participants in one dwelling disagreed on whether the light in the dwelling was adequate. Depression Of those participants who met our definition of depression, the lowest percentage lived in Bonn (8.0%; n46) and the highest percentage lived in Ferreira (28.7%; n198) (Table 1). Compared with participants who did not report depression, those with depression were more likely to be female, be in poor health, be handicapped, feel dissatisfied with their dwelling, drink more than four alcoholic beverages a day or abstain from alcohol, be aged 70 years or older, live in Vilnius or Ferreira, have public or no health insurance, and work less than full time (Table 1). Participants with depression were more likely to

report inadequate light in their dwelling compared with those who did not have depressive symptoms (OR1.4; 95% confidence interval [CI] 1.2, 1.7) (Table 2). The odds of reporting inadequate light among participants with doctor-diagnosed depression was 1.4 (95% CI 1.1, 1.7) for residents with inadequate vs. adequate residential light; participants with three or more major symptoms of depression were 1.6 times (95% CI 1.3, 1.9) as likely to report inadequate light compared with participants who reported adequate light (data not shown). The association between light and depression remained statistically significant even after controlling for major confounders, including gender, health status, education, marital status, self-reported health status, handicap, age, and city of residence. In the controlled model, the estimated OR of depression, given self-reported inadequate residential light, decreased slightly from the unadjusted OR of 1.4 to 1.3 (95% CI 1.1, 1.6) (Figure). Falls Compared with participants who did not report a fall in the last year, those who reported falling were more likely to be female; be in poor health; be divorced, widowed, or separated; be older than 70 years of age; have low income; have a self-reported handicap; live in Bonn or Ferreira; and work less than full time. Of those participants who reported falling, the fewest lived in Geneva (5.2%, n25) and the most lived in Ferreira (12.8%, n89) (Table 1). Compared with participants who were not depressed, participants with depression were more likely to also report a fall (OR3.1; 95% CI 2.4, 4.1; n80), although the variance of depression explains less than 2% of the variation in falls. Participants who fell were also more likely to report inadequate light than those who did not fall (OR1.5; 95% CI 1.2, 1.9) (Table 2). Of the 13 housing factors listed as related to a fall, most (more than 48%) were related to structural factors such as stairs (Table 3). Although 667 (16.4%) of study participants who lived in buildings with outside staircases reported that the lighting on the exterior staircase was inadequate, broken, or nonexistent, people who lived in dwellings with exterior staircases were no more likely to suffer a fall than those who lived in buildings without exterior staircases (OR1.2; 95% CI 0.7, 2.2). However, people in buildings with inside stairs were more likely to report a fall than people without inside stairs (OR1.7; 95% CI 1.4, 2.1). Participants who reported difficulty with stairs were more than three times as likely to fall compared with those who reported no difficulty with stairs. However,



Table 1. Characteristics of the LARES study sample, eight European cities, 2002–2003

Characteristic

Study population N (percent)

Participants with depression N (percent of study population)

Participants with falls N (percent of study population)

City Angers, France Bonn, Germany Bratislava, Slovakia Budapest, Hungary Ferreira do Alentejo, Portugal Forli, Italy Geneva, Switzerland Vilnius, Lithuania

633 580 693 795 698 831 483 1,304

(10.6) (9.7) (11.2) (13.2) (11.8) (14.0) (8.0) (21.5)

60 46 75 111 198 80 44 170

(9.7) (8.0) (10.9) (14.0) (28.7) (9.8) (9.2) (13.1)

51 61 51 60 89 40 25 73

(8.0) (10.5) (7.4) (7.6) (12.8) (4.8) (5.2) (5.6)

Age (in years) 18–29 30–39 40–49 50–59 60–69 70

1,401 1,048 1,105 1,001 761 701

(23.3) (17.4) (18.4) (16.6) (12.7) (11.7)

112 98 158 147 113 156

(8.1) (9.4) (14.3) (14.8) (15.1) (22.8)

101 73 61 55 60 100

(7.2) (7.0) (5.5) (5.5) (7.9) (14.3)

Gender Male Female

2,777 (46.2) 3,240 (53.9)

229 (8.7) 555 (16.7)

152 (5.5) 298 (9.2)

Marital status Married Separated/divorced/widowed Single

4,052 (67.3) 750 (12.5) 1,215 (20.2)

497 (12.4) 179 (24.2) 108 (9.0)

264 (6.5) 90 (12.0) 96 (7.9)

Education Primary or less Secondary Post-secondary

1,202 (20.0) 3,117 (51.8) 1,698 (28.2)

288 (24.3) 377 (12.2) 119 (7.0)

130 (10.8) 207 (6.6) 113 (6.7)

Alcohol use Abstainer Former user Occasional user 1–2 drinks per day 3–4 drinks per day 4 drinks per day

1,315 253 3,786 510 111 42

266 68 360 56 17 24

133 26 248 29 7 7

Employment Full time Part time or unemployed

2,649 (44.0) 3,368 (56.0)

229 (8.7) 555 (16.7)

131 (5.0) 319 (9.5)

Self-reported health Good/excellent Fair or poor

3,376 (56.0) 2,641 (44.1)

171 (5.1) 613 (23.5)

186 (5.5) 264 (10.0)

Health insurance Public or none At least some private

4,372 (73.0) 1,645 (27.3)

644 (14.8) 140 (8.6)

338 (7.7) 112 (6.8)

Self-reported handicap Yes No

693 (11.5) 5,324 (88.5)

195 (28.7) 589 (11.1)

110 (15.9) 340 (6.4)

Difficulty with stairsa Yes No

718 (11.9) 5,298 (88.1)

248 (35.1) 536 (10.2)

135 (18.8) 315 (6.0)

Economic status Low income Middle income or higher

2,042 (34.0) 3,975 (66.0)

362 (17.9) 422 (10.7)

185 (9.1) 265 (6.7)

(22.0) (4.2) (62.7) (8.6) (1.9) (0.7)

(20.5) (27.0) (9.6) (11.1) (15.5) (41.5)

(10.1) (10.3) (6.6) (5.7) (6.3) (16.7)

continued on p. 136 Public Health Reports / 2011 Supplement 1 / Volume 126


Table 1 (continued). Characteristics of the LARES study sample, eight European cities, 2002–2003

Study population N (percent)

Participants with depression N (percent of study population)

Participants with falls N (percent of study population)

Dwelling satisfaction Satisfied Dissatisfied

5,390 (90.0) 627 (10.4)

635 (11.9) 149 (23.9)

387 (7.2) 63 (10.1)

Neighborhood satisfaction Satisfied Dissatisfied

5,590 (93.0) 427 (7.1)

709 (12.8) 75 (17.7)

406 (7.3) 44 (10.3)

Residential crowding None Moderate Severe

1,791 (30.0) 3,211 (53.4) 1,015 (16.9)

244 (13.8) 381 (11.9) 159 (15.8)

145 (8.1) 235 (7.3) 70 (6.9)

Light Inadequate Adequate

2,083 (34.6) 3,934 (65.4)

327 (15.8) 457 (11.7)

197 (9.5) 253 (6.4)

Housing type Single family Multifamily

1,883 (31.3) 4,134 (68.7)

302 (16.2) 482 (11.7)

187 (9.9) 263 (6.4)

Tenancy Owner occupied Rental Total

4,439 (76.1) 1,555 (25.9) 6,017 (100.0)

587 (13.3) 193 (12.6) 784 (13.0)

320 (7.2) 127 (8.2) 450 (7.5)

Characteristic

Data not available for one respondent, so percentages based on n6,016

a

LARES  Large Analysis and Review of European Housing and Health Status

among people reporting a fall, the absolute number of people who fell and who also reported no difficulty with stairs (n315) was nearly 2.5 times the number of those who fell and who reported difficulty with stairs (n135) (Table 1). Only 170 (11%) of the participants who reported difficulty with stairs lived in buildings with interior staircases, which suggests that they were more likely to have chosen to live in dwellings where they needed to use stairs less frequently. The multivariate logistic analysis controlled for major confounders including gender, health status, education, alcohol consumption, satisfaction with residence, difficulty with stairs, presence of interior stairs, depression, and city of residence. In this model, the odds of a fall increased from the unadjusted OR of 1.4 to 2.5 times greater for people who reported inadequate light compared with those who reported adequate light (95% CI 1.5, 4.2) (Figure). DISCUSSION We found a 6% prevalence of doctor-diagnosed depression in the eight cities that made up the LARES sample, which is similar to that cited previously.2 Another 7% of participants from the eight cities reported suffering

from three or more of the cardinal symptoms of depression. More than one-third of the LARES participants with depression reported inadequate natural light in their dwellings. Our findings indicate that self-reported inadequate residential light is associated with risk for depression, independent of other confounders known to increase risk (such as gender, education level, or city of residence). We also found that falls were more likely to occur in homes where residents reported inadequate natural light, even after controlling for other major predictors of falls. The rate of falls identified in the LARES study was somewhat higher than the 2.1% overall rate of nonfatal falls that required medical treatment in the U.S. population reported in 1998. Fatalities from falls appear to be increasing in the U.S., rising 29.2% from 1999 to 2004.24 Comparison with previous studies The pathogensis of depression is not well understood. However, bright-light therapy is efficacious in treating SAD and other forms of depression. A meta-analysis of 20 randomized controlled trials of light therapy for mood disorders found a significant decrease in depression severity in patients undergoing bright-light treatment.25 Among depressed patients receiving stan-



Table 2. Unadjusted odds ratios and housing and health characteristics among adults reporting falls and depression: LARES Study, eight European cities, 2002–2003 Odds ratio among those reporting falls (95% CI)

Odds ratio among those reporting depression (95% CI)

Light Inadequate residential light vs. adequate light

1.5 (1.2, 1.9)

1.4 (1.2, 1.7)

Health Poor health status vs. good health status

1.9 (1.5, 2.3)

5.5 (4.6, 6.7)

Neighborhood Satisfied with neighborhood vs. dissatisfied

0.7 (0.5, 1.0)

0.7 (0.5, 0.9)

Dwelling Satisfied with dwelling vs. dissatisfied

0.7 (0.5, 1.0)

0.4 (0.3, 0.5)

Insurance Some private health insurance vs. public only or none

0.9 (0.7, 1.1)

0.6 (0.5, 0.7)

Income Middle or high income vs. low income

1.4 (1.2, 1.8)

1.9 (1.6, 2.2)

Marital status Divorced/widowed/separated vs. married Single vs. married

1.9 (1.5, 2.5) 1.1 (0.8, 1.4)

2.5 (2.0, 3.0) 0.6 (0.5, 0.7)

Education Secondary education vs. primary or none More than secondary education vs. primary or none

0.8 (0.6, 1.0) 0.9 (0.7, 1.1)

0.8 (0.7, 1.0) 0.4 (0.4, 0.5)

Alcohol intake Social drinker vs. abstainers 2–3 drinks per day vs. abstainers 4 drinks per day vs. abstainers

0.7 (0.6, 0.9) 0.7 (0.4, 1.0) 1.1 (0.6, 2.1)

0.5 (0.4, 0.5) 0.8 (0.6, 1.1) 1.8 (1.2, 2.7)

Employment Part time or unemployed vs. full time

2.0 (1.6, 2.4)

2.0 (1.7, 2.4)

Gender Female vs. male

1.8 (1.5, 2.1)

1.8 (1.5, 2.0)

Age (in years) 30–39 vs. other adult ages 40–49 vs. other adult ages 50–59 vs. other adult ages 60–69 vs. other adult ages 70 vs. other adult ages

0.9 0.7 0.7 1.1 2.4

(0.7, (0.5, (0.5, (0.8, (1.9,

1.2) 0.9) 0.9) 1.4) 3.0)

0.7 1.1 1.2 1.2 2.2

(0.5, (0.9, (1.0, (1.0, (1.8,

0.8) 1.4) 1.5) 1.5) 2.7)

City Angers vs. other cities Bonn vs. other cities Bratislava vs. other cities Budapest vs. other cities Ferreira vs. other cities Forli vs. other cities Geneva vs. other cities Vilnius vs. other cities

1.1 1.5 1.0 1.0 2.0 0.6 0.7 0.7

(0.8, (1.2, (0.7, (0.8, (1.5, (0.4, (0.4, (0.5,

1.5) 2.1) 1.4) 1.4) 2.6) 0.8) 1.0) 0.9)

0.7 0.6 0.8 1.1 3.3 0.7 0.7 1.0

(0.5, (0.4, (0.6, (0.9, (2.6, (0.5, (0.5, (0.8,

1.0) 0.8) 1.1) 1.4) 4.0) 0.9) 0.9) 1.2)

Residential crowding Moderate crowding vs. none Severe crowding vs. none

1.0 (0.8, 1.2) 0.9 (0.6, 1.2)

0.8 (0.7, 0.9) 1.3 (1.0, 1.5)

Housing type Single family vs. multifamily

0.6 (0.5, 0.8)

0.7 (0.6, 0.8)

Rental property No vs. yes

1.2 (0.9, 1.5)

0.9 (0.8, 1.1)

Handicapped Yes vs. no

2.7 (2.2, 3.5)

3.2 (2.6, 3.8)

Characteristic

LARES  Large Analysis and Review of European Housing and Health Status CI  confidence interval



Table 3. Housing elements and furnishings involved in falls: LARES Study, eight European cities, 2002–2003 Housing factor

Number involved in falls (n671)a N (percent)

Structural elements (e.g., stairs or cracks in flooring)

225 (48.6)

Electric equipment (e.g., tripping on electrical cords)

23 (5.0)

Water/sanitary system (e.g., slipping on wet surface)

15 (3.2)

Heating/cooling equipment

31 (6.7)

Kitchen equipment

56 (16.6)

Knives/silverware Furniture/furnishings

104 (22.5) 87 (18.8)

Washing products

9 (1.9)

Gas/fumes

9 (1.9)

Food items (e.g., slipping on spilled food on floors)

6 (1.3)

Animals/pets

8 (1.7)

Toys Other

9 (1.9) 89 (19.2)

Participants could select more than one housing factor involved in the fall. a


dard antidepressant medication, application of bright light resulted in a greater improvement in symptoms for patients receiving both therapies.26 Even among healthy volunteers, application of bright-light exposure resulted in increased vitality and decreased depressive symptoms.27 When exposure to the light was stopped, vitality decreased and depressive symptoms returned to baseline levels within two weeks. There also is evidence from human and animal models that those with depression have reduced contrast gain regardless of medication use.15 Taken together, these studies suggest that depression may operate along multiple pathways; that light may act through a pathway not affected by pharmacotherapy; and that physiologic differences in retinal contrast gain may be unchanged despite changes in depressive symptoms as a result of either light, medication, or both. In this study, we found a relationship between self-report of inadequate natural light and depression. However, further study is needed to determine whether increasing natural light reduces symptoms despite any physiologic differences in contrast processing. Limitations The LARES dataset does not allow for adjustment of potentially important differences in individual behaviors that may improve or limit exposure to natural light,

Figure. Odds ratios for falls and depression among adults with inadequate residential light: LARES Study, eight European cities, 2002–2003

a

a Falls adjusted for health, income, marital status, alcohol consumption, employment, gender, age, city, multifamily dwelling, handicap, and difficulty with stairs. Depression adjusted for health, dwelling satisfaction, insurance type, income, marital status, education, alcohol consumption, employment status, gender, age, city, multifamily dwelling, handicap, and residential crowding.




including working outdoors, vitamin D consumption, or other factors. Prospective studies are needed that are explicitly designed to elucidate the impact, if any, of these factors. The results of this study also may have been affected by misclassification bias in either the exposure of interest (self-report of adequate light) or the health outcomes (falls and depression). The adequacy of residential light was determined using a qualitative measure collected during in-person interviews rather than actual light measurements. This may have introduced some misclassification in that artificial light was sufficient in some units. Nonetheless, in models controlling for major predictors of falls or depression, including age, alcohol consumption, handicap, and general health status, self-reported inadequate natural light was an independent predictor of both conditions. The data collection instrument has not been evaluated for external validity. However, given that there was only one case out of 3,076 where more than one participant living in the same dwelling disagreed on the adequacy of light, we are confident of the internal validity of the data. We measured depression with a validated index of depressive symptoms that correlate well with the criteria in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition.22,28 Because this index relies on self-report of symptoms, it is likely that it results in some misclassification. However, doctor-diagnosed depression showed a very similar association with exposure to inadequate light, suggesting that the effect of misclassification is small. These cross-sectional data cannot establish conclusive causality, and it is possible that participants who met our definition of depression may have been more likely to consider the light in their homes to be inadequate. Although our self-reported adequacy of light does not permit comparisons between light in different residences, given that there was general agreement of the adequacy of light within households where more than one person was interviewed, the evidence from this large population-based survey suggests that selfreported inadequate natural light may contribute to at least two important health conditions—depression and injury from falls—typically viewed as unrelated to each other. This analysis emphasizes the value of large, crosssectional population surveys that measure both health and housing conditions; however, such surveys are exceedingly rare. For example, in the U.S., two large population surveys are conducted that measure housing and health separately: the American Housing Survey (AHS) and the National Health Interview Survey (NHIS). Yet, AHS does not record health information,

and NHIS does not record housing data, which makes identifying housing and health connections unnecessarily difficult. Future surveys should link housing and health data in a single survey such as LARES to enable identification of housing factors that either contribute to or cause adverse health conditions, or contribute to or cause improved health. Such surveys can also play an important role in identifying promising longitudinal trials and other means of investigation that assess the effects of interventions and elucidate in a robust way causal pathways. Based on the LARES analysis presented in this article, a longitudinal trial that determines whether improved light decreases depression and prevents injuries is clearly needed. CONCLUSION Inadequate light was associated with risk for depression and falls, both of which contribute substantially to the global burden of disease. This association remained statistically significant after controlling for confounding variables. Given the magnitude of the problem and the inexpensive nature of the intervention, further investigation is needed. Such studies should determine whether either improved window placement and construction, which if it occurs during the design phase is not cost prohibitive, or increased exposure to sunlight by planned outdoor activities reduces the risk of depression and falls in people who report inadequate residential light. To help prevent depression and falls, housing codes and inspection systems should routinely assess whether residents report that the light in their dwelling is adequate. The World Health Organization (WHO) coordinated the Large Analysis and Review of European Housing and Health Status study with funding from the German Federal Ministry of Health. Staff time for the authors’ work was provided by the U.S. Centers for Disease Control and Prevention. All applicable European requirements regarding human subjects protection were met by WHO.

REFERENCES 1. Ustun TB, Ayuso-Mateos JL, Chatterji S, Mathers C, Murray CJ. Global burden of depressive disorders in the year 2000. Br J Psychiatry 2004;184:386-92. 2. Lepine JP, Gastpar M, Mendlewicz J, Tylee A. Depression in the community: the first pan-European study DEPRES (Depression Research in European Society). Int Clin Psychopharmacol 1997;12:19-29. 3. Peden M, McGee K, Krug E, editors. Injury: a leading cause of the global burden of disease, 2000. Geneva: World Health Organization; 2002. 4. World Health Organization. Report on the WHO technical meeting on quantifying disease from inadequate housing, Bonn, Germany, 28–30 November, 2005. Geneva: WHO; 2006. 5. Terman M, Terman JS . Light therapy for seasonal and nonseasonal depression: efficacy, protocol, safety, and side effects. CNS Spectr 2005;10:647-63.



6. Rosenthal NE, Sacks DA, Gillin JC, Lewy AJ, Goodwin FK, Davenport Y, et al. Seasonal affective disorder. A description of the syndrome and preliminary findings with light therapy. Arch Gen Psychiatry 1984;41:72-80. 7. Czeisler CA. The effect of light on the human circadian pacemaker. In: Chadwick DJ, Ackrill K, editors. Circadian clocks and their adjustment [Ciba Foundation Symposium 183]. London: John Wiley & Sons Ltd.; 1995. p. 254-302. Quoted in: Cajochen C, Krauchi K, Danilenko KV, Wirz-Justice A. Evening administration of melatonin and bright light: interactions on the EEG during sleep and wakefulness. J Sleep Res 1998;7:145-57. 8. Cajochen C, Munch M, Kobialka S, Krauchi K, Steiner R, Oelhafen P, et al. High sensitivity of human melatonin, alertness, thermoregulation, and heart rate to short wavelength light. J Clin Endocrinol Metab 2005;90:1311-6. 9. Light Therapy Products. Information: how to choose a light box [cited 2011 Jan 10]. Available from: URL: http://www.lighttherapyproducts .com/how_to_choose_a_light_box.aspx 10. Wirz-Justice A, Benedetti F, Berger M, Lam RW, Martiny K, Terman M, et al. Chronotherapeutics (light and wake therapy) in affective disorders. Psychol Med 2005;35:939-44. 11. Martiny K. Adjunctive bright light in non-seasonal major depression. Acta Psychiatr Scand Suppl 2004;425:7-28. 12. Wirz-Justice A, Graw P, Krauchi K, Sarrafzadeh A, English J, Arendt J, et al. “Natural” light treatment of seasonal affective disorder. J Affect Disord 1996;37:109-20. 13. Haynes PL, Ancoli-Israel S, McQuaid J. Illuminating the impact of habitual behaviors in depression. Chronobiol Int 2005;22:279-97. 14. Lam RW, Levitan RD. Pathophysiology of seasonal affective disorder: a review. J Psychiatry Neurosci 2000;25:469-80. 15. Bubl E, Kern E, Ebert D, Bach M, Tebartz van Elst L. Seeing gray when feeling blue? Depression can be measured in the eye of the diseased. Biol Psychiatry 2010;68:205-8. 16. Runyan CW, Perkis D, Marshall SW, Johnson RM, Coyne-Beasley T, Waller AE, et al. Unintentional injuries in the home in the United States, part II: morbidity. Am J Prev Med 2005;28:80-7.

17. Close JC. Prevention of falls in older people. Disabil Rehabil 2005;27:1061-71. 18. Gill TM, Williams CS, Tinetti ME. Environmental hazards and the risk of nonsyncopal falls in the homes of community-living older persons. Med Care 2000;38:1174-83. 19. World Health Organization. Housing and health: the large analysis and review of European housing and health status (LARES) project [cited 2011 Jan 10]. Available from: URL: http://www.euro.who.int/ en/what-we-do/health-topics/environmental-health/Housing-andhealth/activities/the-large-analysis-and-review-of-european-housingand-health-status-lares-project 20. World Health Organization. LARES: data on the representativeness of the LARES samples. Geneva: WHO; 2004. 21. Bonnefoy X, Braubach M, Davidson M, Robbel N. A pan-European housing and health survey: description and evaluation of methods and approaches. Int J Environment Pollution 2006;30:363-83. 22. Brody DS, Hahn SR, Spitzer RL, Kroenke K, Linzer M, deGruy FV, Williams JB. Identifying patients with depression in the primary care setting: a more efficient method. Arch Internal Med 1998;158:246975. 23. SAS Institute, Inc. SAS®: Version 9.02. Cary (NC): SAS Institute, Inc.; 2008. 24. Bergen G, Chen LH, Warner M, Fingerhut LA. Injury in the United States: 2007 chartbook. Hyattsville (MD): National Center for Health Statistics (US); 2008. 25. Golden RN, Gaynes BN, Ekstrom RD, Hamer RM, Jacobsen FM, Suppes T, et al. The efficacy of light therapy in the treatment of mood disorders: a review and meta-analysis of the evidence. Am J Psychiatry 2005;162:656-62. 26. Kripke DF. Light treatment for nonseasonal depression: speed, efficacy, and combined treatment. J Affect Disord 1998;49:109-17. 27. Partonen T, Lonnqvist J. Bright light improves vitality and alleviates distress in healthy people. J Affect Disord 2000;57:55-61. 28. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 4th ed. Washington: American Psychiatric Association; 2000.


Research Articles

Poverty, Sprawl, and Restaurant Types Influence Body Mass Index of Residents in California Counties

Jennifer Gregson, PhD, MPHa

ABSTRACT Objectives. This article examines the relationships between structural poverty (the proportion of people in a county living at 130% of the federal poverty level [FPL]), urban sprawl, and three types of restaurants (grouped as fast food, chain full service, and independent full service) in explaining body mass index (BMI) of individuals. Methods. Relationships were tested with two-tiered hierarchical models. Individual-level data, including the outcome variable of calculated BMI, were from the 2005, 2006, and 2007 California Behavioral Risk Factor Surveillance Survey (n14,205). County-level data (n33) were compiled from three sources. The 2000 U.S. Census provided the proportion of county residents living at 130% of FPL and county demographic descriptors. The sprawl index used came from the Smart Growth America Project. Fast-food, full-service chain, and full-service independently owned restaurants as proportions of the total retail food environment were constructed from a commercially available market research database from 2004. Results. In the analysis, county-level demographic characteristics lost significance and poverty had a consistent, robust association on BMI (p0.001). Sprawl demonstrated an additional, complementary association to county poverty (p0.001). Independent restaurants had a large, negative association to BMI (p0.001). The coefficients for chain and fast-food restaurants were large and positive (p0.001), indicating that as the proportion of these restaurants in a county increases, so does BMI. Conclusions. This study demonstrates the important role of county poverty and urban sprawl toward understanding environmental influences on BMI. Using three categories of restaurants demonstrates different associations of fullservice chain and independent restaurants, which are often combined in other research.

a University of California, Davis, Department of Sociology, Davis, CA (current affiliation: California Department of Public Health, Network for a Healthy California, Sacramento, CA)

Address correspondence to: Jennifer Gregson, PhD, MPH, California Department of Public Health, Policy, Planning, and Evaluation Section, PO Box 997377, MS 7204, Sacramento, CA 95899-7377; tel. 916-449-5455; fax 916-449-5414; e-mail . ©2011 Association of Schools of Public Health


 141


Structural arguments to explain the prevalence of population overweight often focus on the food environment, such as retail stores where grocery items are purchased and, to a lesser degree, restaurants where prepared food is purchased.1 However, restaurants and the food environment are only part of an environmental-level, structural explanation. Population overweight has increased for all population segments, but rates in lower-income groups are disproportionately higher,2 likely indicating that disadvantage from poverty is a contributing factor. Urban sprawl has also been empirically linked to weight status of the people living in sprawling areas.3 This article aims to untangle the relationships between county poverty, urban sprawl, and three types of restaurants toward explaining body mass index (BMI), and to assess the contributions of each of the three factors. It examines these factors in the 33 most populous of California’s 58 counties, which accounts for 97% of the state’s population. BACKGROUND Presence of restaurants Although restaurants are a large part of the built food environment, they are far less studied as part of a structural explanation for BMI than the retail components of the food environment. There is plausible theoretical argument that restaurant food is higher in fat and calories than foods prepared at home,4 and, thus, it may contribute to weight gain. Research on restaurants typically distinguishes between fast-food and full-service establishments. Results linking the presence of both varieties to population BMI are mixed, but more conclusive for fast food. The association between eating fast food and weight gain among individuals is consistently found in the literature, as concluded by Larson et al.,1 thereby establishing a link between individual consumption behavior and BMI. The structural argument about the presence of fast food as a component of the food environment and the influence on individuals is far less clear. Structural or environmental-level studies of smaller geographic areas such as census tracts or blocks do not generally detect significant relationships between the presence of fast-food stores and BMI of the residents.5,6 However, national studies of fast food grouped at the county level7 and between states8 are able to detect population-wide patterns. Structural relationships between obesity and the presence of full-service restaurants are more ambiguous, and one study actually found an inverse relationship between the presence of full-service restaurants and BMI.7 Three other studies demonstrated no

relationship to full-service restaurants,9–11 though only one of these was conducted with adults.11 This body of research is limited because there are few studies, and the knowledge base is emerging. However, this research is also limited by possible measurement issues, because chain restaurants that are full service are not distinguished from independently owned restaurants. Grouping these types of restaurants together assumes that they are similar. But anecdotally, there is little reason to assume that chain restaurants and independent restaurants are similar. Chain restaurants have a corporate organizational structure and, as such, have more purchasing power; they aim for consistency in their food and standardize preparation in company manuals, using approved vendors chain-wide, and, in some cases, in off-site preparation kitchens; and they have more political and legal resources for land use and store placement than an independent restaurant. Independently owned restaurants are small businesses and have more autonomy to respond to customer requests and employ locally based marketing practices. It should not be assumed that restaurant chains have the same theoretical and empirical relationships as independently owned restaurants to BMI. There is a dearth of theoretical and empirical distinctions between these types of restaurants in the food environment literature; one intent of this study was to determine if chain and independent restaurants indeed have a different relationship to BMI. A conclusive relationship between full-service restaurant presence in general and BMI has not been consistently demonstrated in past research and may continue to evolve as methods and definitions, such as separate categories for independent restaurants, continue to emerge. Furthermore, from the perspective of a structural argument, the type and placement of restaurants may also be influenced by other structural factors, county poverty, and urban sprawl. County poverty Poverty is more than just an individual’s economic status. When experienced by large numbers of people, poverty is a social condition and has social processes embedded within it, such as residential segregation. Structural poverty may well contribute to social and physical environments that exacerbate obesity. Kreiger argued that including poverty is an essential component of understanding disease etiology in general, and that “economic deprivation is present, and it matters.”12 She further maintained that the poor have worse health because of social injustice, and not because the poor are deficient in some way or because poor health interferes with gaining economic prosperity.


Effect of Poverty, Sprawl, and Restaurant Type on BMI in California  143

A review of income inequality (when a small percent of the population owns a disproportionately large amount of the resources) and health found that in 70% of the studies, when a society has a high degree of income inequality, that society has worse population health.13 Areas with higher inequality do not have worse health outcomes simply because there are more poor people.13 County poverty is more than the aggregation of people with low incomes. Rather, the processes of social selection and the sorting of people inherent in residential income segregation,14 and the ensuing concentration of poverty, can influence the amount of resources in a community and the quality and types of businesses choosing to be located there. When the poor are segregated from the wealthy into specific areas, segregating the poor can contribute to conditions that result in higher BMI. The process of separating rich from poor in residential settlement is deliberate, evidenced by residential zoning policies intended to maintain property values of single-family homes,15 and placement of food stores follows residential settlement patterns.16 Using the degree of county poverty as an explanation of the food environment addresses a structural approach and frames it in terms of privilege and disadvantage. However, structural poverty has rarely been considered with the presence of restaurants or other environmental explanations, such as sprawling urban form. Sprawl and physical activity in the environment Sprawl and land use are increasingly being identified as a cause of health problems.15,17 The report “Measuring the Health Effects of Sprawl”3 demonstrated that county-level sprawl was associated with higher BMI, less walking, and higher risk of hypertension. The premise of sprawl as a contributing factor to BMI is that activity structured into daily life in a traditional city form, such as walking or biking for errands and commuting (utilitarian physical activity, as opposed to leisure-time physical activity), is being minimized by environmental forces that emphasize sprawling urban form and automobile dependence,15,18 resulting in weight gain. Sprawl was included in this analysis to control for effects of utilitarian physical activity that may influence BMI, and also to control for sprawl itself, which may be related to restaurant presence or county poverty. Studies examining store and restaurant placement generally do not account for the effects of sprawl; they are treated as two separate research topics. In two recent comprehensive literature reviews about BMI and eating environments, which dedicated a significant amount of discussion to environmental factors, none of the articles accounted for sprawl,1,19

despite sprawl’s empirical association with BMI. Thus, testing the relationships of restaurants in the built food environment to BMI should adjust for county poverty to reflect disadvantage in restaurant placement decisions and should adjust for sprawl to control for physical activity and restaurant placement associated with sprawling areas. METHODS The goal of this analysis was to create a model for each type of restaurant to clarify the relationship between a restaurant type and BMI, as well as to account for relationships with county poverty and sprawl. This research question had a nested structure; it addressed individual behaviors (BMI) that exist within and are influenced by a larger, constructed environment (county poverty, sprawl, and proportional presence of restaurants). Two-tiered hierarchical models were employed. At each level, the model demonstrated the relationship of variables within a level and how variables in a more specific level (e.g., individuals or, more generally, level-1) are influenced by variables in other broader levels (e.g., environment and, more generally, level-2). The analytical approach focuses the statistical explanation on the main effects of the environmental variables on individuals. In this study, the data were from individuals in California counties; thus, the datasets were geographically linked by county. County-level measures of poverty, sprawl, and restaurant types were regressed on individual-level measures of demographic characteristics and BMI. Individual data were from the 2005, 2006, and 2007 California Behavioral Risk Factor Surveillance System (BRFSS)20 (n14,205, Table 1) and included BMI as the outcome variable and basic sampling characteristics of the respondents. The data were weighted using the BRFSS sampling weights, modeling other similarly structured studies,7,21,22 and the weights were normalized. BMI has many accepted influential factors and correlations with individual characteristics. Increased BMI is associated with age, gender, race, and income in individuals in surveillance surveys;23 thus, these variables are used for the individual-level model. Each respondent can be identified by the county of residence, making it possible to link micro-level data with macro-level data. The county dataset was compiled from multiple sources. The 2000 U.S. Census provided demographic data, used as control variables, and the proportion of the population living at 130% of the federal poverty line (FPL) as a measure of income inequality. This is the line of demarcation that the federal government



Table 1. Characteristics of BRFSS respondents and California counties, of counties included in analysis of influence of poverty, sprawl, and restaurant type on BMI

Individual data (n14,205), unweighted

Characteristic Mean SD

Mini- mum

Maximum

BMI Age Gender Female Male Race/ethnicity White Black Hispanic Other (ref.) Annual income $15,000 $15,000–$49,999 $50,000 (ref.)

26.90 50.38

5.58 16.88

9.30 18.00

71.00 98.00

0.60 0.40

0.51 0.49

0.00 0.00

1.00 1.00

0.65 0.04 0.23 0.08

0.48 0.21 0.42 0.27

0.00 0.00 0.00 0.00

1.00 1.00 1.00 1.00

0.14 0.36 0.50

0.35 0.48 0.50

0.00 0.00 0.00

1.00 1.00 1.00

County data (n33)

Characteristic Mean SD Sprawl index Proportion living at 130% of federal poverty level Proportion Hispanic Proportion white Proportion black Proportion Asian (ref.) Proportion male Proportion aged 45 years Proportion aged 18–44 years (ref.) Proportion fast-food restaurants Proportion independently owned restaurants Proportion chain restaurants

Mini- mum

Maximum

0.00

1.00

1.15

4.35

0.19 0.29 0.57 0.05 0.08 0.50

0.08 0.16 0.17 0.04 0.07 0.02

0.09 0.06 0.22 0.00 0.02 0.49

0.34 0.78 0.88 0.15 0.31 0.57

0.45

0.05

0.34

0.55

0.55

0.05

0.45

0.66

0.18

0.04

0.06

0.25

0.42

0.07

0.27

0.61

0.04

0.01

0.02

0.07

BRFSS  Behavioral Risk Factor Surveillance System BMI  body mass index SD  standard deviation ref.  reference

uses for Food Stamp eligibility, free school lunch eligibility, and other safety-net programs. County-level data on restaurants were from the commercial market research database of Dun & Bradstreet24 that includes a comprehensive listing of all California food retail establishments. Restaurants are categorized

by fast food, chains with six or more that are not fast food, and independently operated restaurants that are not fast food. These restaurant variables are presented as proportions of the total retail food outlets in the county. The denominator was created by summing the total count of supermarkets (large chains), grocery stores (small and independent chains), fast food, convenience stores, chain restaurants, independent restaurants, fruit and vegetable markets, delis, independent and chain coffee stores, bakeries, other single-item vendors such as butcher shops, co-ops, commissaries, ice cream stores, and all other food stores. The sprawl index was provided by the Smart Growth America Study.3,25 The index was constructed so that more compact areas would have a larger index and more sprawling areas would have a smaller index. California data were extracted from the national dataset and ranked on a national scale. An index comprised only of California counties would change the score, but not the order in which counties are ranked. However, the sprawl index required some measures that were not available for the least populated counties; these counties did not have an index score and were not included in the analysis. The Figure shows a list of all the included and excluded counties. The 33 included counties accounted for 97% of the state’s population. The models were built in an iterative process, first including demographic control variables (which became nonsignificant) and then introducing predictor variables representing components of the posed research question. Final models included only significant predictor variables. Multicollinearity between the county-level variables in the model was tested using the variance inflation factor (Table 2), for which scores greater than 10 are considered high. County poverty was introduced first, as a theoretically pervasive explanatory variable for BMI. Sprawl was then introduced to adjust for relationships between what is theorized to be utilitarian physical activity and BMI.3,26 Each restaurant type, in no particular order, was then included in a model, adjusting for county poverty and sprawl, and thereby demonstrating the contributions of each predictor to a relationship with BMI. In all models, the individual-level variables remained the same. The analysis was conducted with HLM version 6.06.27 Individual variables were group centered, and county-level predictor variables were grand-mean centered. RESULTS A significant null model indicated that there was adequate variance among counties to test multilevel models. The grand mean for BMI in California from



Figure. California counties excluded and included from the analysis of the influence of poverty, sprawl, and restaurant type on body mass index Excluded Alpine Amador Calaveras Colusa Del Norte Glenn Humboldt Inyo Lake Lassen Madera Mariposa Mendocino Modoc Mono Nevada Plumas San Benito Sierra Siskiyou Sutter Tehama Trinity Tuolumne Yuba

Included Alameda Butte Contra Costa El Dorado Fresno Imperial Kern Kings Los Angeles Marin Merced Monterey Napa Orange Placer Riverside Sacramento San Bernardino San Diego San Francisco San Joaquin San Luis Obispo San Mateo Santa Barbara Santa Clara Santa Cruz Shasta Solano Sonoma Stanislaus Tulare Ventura Yolo

2005–2007 was 26.909, with a standard error (SE) of 0.184. The 95% confidence interval for the mean of county means for BMI for the 33 counties was between 26.548 and 27.270. BMI is defined as “normal” at 18.5–24.9, “overweight” at 25.0–29.9, and “obese” at 30.0.28 The variance of the county means around the grand mean (mean of county means) is 1.032. To gauge the magnitude of the variation among counties for BMI, a plausible values range was calculated with a 95% probability, yielding a range of 25.898 to 27.920. The Chi-square test for the null model shows the test has a value of 352.023 with 32 degrees of freedom and p0.001, indicating that significant variation does exist among counties in the BMI of residents. The interclass correlation was used to calculate the specific amount of variance in BMI between counties. The total amount of county-level variance that can be explained is 3.43%.

Table 2. Testing for multicollinearity between environmental-level predictor variables using the VIF in an analysis of the influence of poverty, sprawl, and restaurant type on body mass index of residents in 33 California counties Variable

VIF in model Final model 4

Final model 5

Final model 6

1.177 1.182 1.097 NAa NAa

1.357 1.178 NAa 1.398 NAa

1.578 1.532 NAa NAa 2.171

County poverty Sprawl Chain restaurants Fast-food restaurants Independent restaurants

Not all variable conbinations were tested.

a

VIF  variance inflation factor NA  not applicable

Control variables The analysis first demonstrated variation in socioeconomic characteristics of people and the counties in which they live and how these influence BMI. Individual variables typically associated with BMI and county demographic variables were included in this base model, and it served as the foundation for future model iterations. Poverty, sprawl, and stores were added to the level-2 models in ensuing iterations, and nonsignificant level-2 variables from preceding models were dropped. The level-1 model was unchanged through the analysis because these variables are standard epidemiologic controls. Table 3 describes the individual- and county-level associations with BMI. County-level demographic variables for race/ethnicity were statistically significant, while those for gender and age were not (Table 3, Model 1). All individual variables were statistically significant except for annual income $15,000. Individual data were expected to be highly significant because there is a genetic component to BMI, as with many health outcomes, and because the behaviors that affect BMI— eating and exercise—are individual actions. Recall, though, that the proposed analysis focused on the role of the county-level variance. In all, county demographics and individual demographics explained slightly more than a third (39%) of the level-2 variance, indicating a partial explanation. But with nearly 60% of the level-2 variance unexplained, there remains room for additional theories about what influences BMI. County poverty The next iteration of models tested county poverty. When county poverty was tested with race/ethnicity variables, two of the three lost significance, and so the


Coeff.

P-value

27.067

0.001

0.133

SE

8.343

27.036

Coeff.

0.001 2.268

0.001 0.128

P-value SE

0.013

8.941

27.036

Coeff.

0.001 0.004

0.001 2.329

0.001 0.128

P-value SE

0.014

7.845

27.043

Coeff.

SE

0.01

0.004

0.001 1.910

0.001 0.111

P-value

Model 4: County poverty, sprawl, and chain restaurants

0.010

5.835

27.036

Coeff.

0.05

0.01

0.001

0.005

1.850

0.101

P-value SE

Model 5: County poverty, sprawl, and fast-food restaurants

47.3 238.094 0.001

38.7 229.889 0.001

NS 0.178

0.544

NS 0.425 0.05

0.001 0.223 0.001 0.382 0.001 0.469

0.001 0.004 0.001 0.122

0.633

NS 0.178

1.701 3.319 2.983

0.223 0.382 0.470

0.001 0.001 0.001

NS 0.424 0.05

0.034 0.927

57.7 127.127 0.001

0.437

NS 0.178

0.001 0.228 0.001 0.386 0.001 0.474

0.001 0.004 0.001 0.122

NS 0.425 0.05

1.664 3.293 2.957

0.034 0.929

71.2 113.911 0.001

0.297

NS 0.178

0.001 0.223 0.001 0.381 0.001 0.470

0.001 0.004 0.001 0.122

NS 0.426 0.05

1.698 3.316 2.980

0.034 0.926


NS  not significant

FPL  federal poverty level

SE  standard error

coeff.  coefficient

a

Two of three race categories lost significance when tested with county poverty; the third lost significance when tested with sprawl. The reduced model is presented.

Model fit Variance component, level-2 Percent of level-2 variance explained Chi-square

Annual income $15,000 $15,000–$49,999 $50,000 (reference)

a a

a

0.004 0.122

1.395 1.785 3.576 NS NS

0.001 0.001

7.805 0.001 5.375 0.01 9.907 0.01 NS NS

Individual predictors (fixed effects) Age 0.034 Male 0.929 Race/ethnicity White 1.697 Black 3.314 Hispanic 2.983 Other (reference)

Proportion Hispanic White Black Male Aged 45 years

0.001 0.001 0.001

0.001 0.001

NS 0.178

0.223 0.382 0.468

0.004 0.122

76.4 100.221 0.001

0.243

NS 0.426 0.05

1.699 3.313 2.987

0.034 0.926

Proportion chain restaurants 21.337 0.05 9.396 Proportion fast-food restaurants 8.620 0.01 2.872 Proportion independently owned restaurants

Sprawl

Proportion 130% FPL

Intercept

County-level predictors (fixed effects)

Model 2: Model 3: County poverty, County poverty Model 1: Base reduced and sprawl

Table 3. County poverty and sprawl, as well as proportions of independent, chain, and fast-food restaurants in a county, predict body mass index of individuals in an analysis of 33 California counties

0.05

74.7 101.872

0.261

NS

2.003

0.001

NS 0.178

0.001 0.222 0.001 0.382 0.001 0.469

0.001 0.004 0.001 0.122

0.001 1.557

NS 0.426 0.05

1.700 3.315 2.981

0.034 0.928

8.254

SE

0.001 0.091

P-value

NS

4.127

27.052

Coeff.

Model 6: County poverty, sprawl, and independently owned restaurants



race/ethnicity variables were dropped. County poverty had a positive association with BMI (Table 3, Model 2, coefficient 8.3, p0.001), and the model explained 47% of the variance. Thus, the association of county racial/ethnic characteristics that initially appeared was actually explained by county poverty, which is a robust predictive variable. Sprawl County poverty was next tested with sprawl. Both county poverty and sprawl had significant associations with BMI (Table 3, Model 3). The sprawl index had a significant coefficient (0.013, p0.001) in the expected direction to indicate that more sprawl is associated with higher BMI. The sprawl index is structured so that more compact counties will have a higher score and more sprawling ones will have a lower score. County poverty remained significant (p0.001) and retained the magnitude of the coefficient (8.9), which indicated that sprawl was measuring a different, additional process than county poverty. The overall model explained 58% of the county-level variance. Restaurants The proportion of chain restaurants (Table 3, Model 4) was a significant predictor of BMI, even when controlling for county poverty and sprawl. The coefficient for chain restaurants was large and positive (coefficient 21.337, p0.05), indicating that as the proportion of chain restaurants in a county increased, so did BMI. In this case, the relationship of county poverty was not greatly influenced, and sprawl, poverty, and chain restaurants were all associated with BMI. The overall model was significant at p0.001 and explained 71% of the county-level variance. Like chain restaurants, the proportion of fast-food restaurants (Table 3, Model 5) was significantly associated with BMI after controlling for county poverty and sprawl. The coefficient for fast-food restaurants was positive (coefficient 8.62, p0.01), indicating that as the proportion of fast-food restaurants in a county increased, so did BMI. In this case, the effect of county poverty was slightly reduced to a coefficient of 5.83, down from 8.94 in the model with only county poverty and sprawl. Sprawl was of a similar magnitude. This finding indicates that some of the associations of county poverty were expressed through fast-food restaurants, but that sprawl, poverty, and fast-food restaurants were all associated with BMI. The overall model was significant at p0.001 and explained 76% of the county-level variance. Independently owned restaurants (Table 3, Model 6) had a large, negative association to BMI (coefficient

8.254, p0.001), meaning that as the proportion of independent restaurants in a county increased, the BMI decreased. In this model, the presence of independent restaurants absorbed the associations of sprawl, which lost significance. County poverty lost magnitude, dropping from 8.9 in the model without the restaurants to 4.1 when the restaurants were included. The overall relationship of independent restaurants to BMI was different from the other two restaurant categories. County poverty retained a significant relationship, so the inverse relationship with independent restaurants was not acting as a proxy for county poverty, but some of the associations between county poverty and BMI are expressed through independent restaurants. Overall, the model had a Chi-square of p0.001 and explained 75% of the county-level variance. DISCUSSION County poverty Of the causal variables of interest, county poverty was consistently significant with a positive relationship to BMI in all models. This evidence supports the argument that BMI is, in part, associated with of the degree of structural poverty, defined in this study as the proportion of people in a county living at 130% of FPL. This finding is an empirical example of theoretical assertions that the societal relationships that perpetuate poverty and inequality are strong causal forces.12–14,29 It is also noteworthy that individual annual income of $15,000 was not a significant level-1 predictor of BMI in the models, and especially not in the restaurant models. This demonstrates how poverty is a structural problem and has implications beyond that of individual characteristics. However, very few published studies exist to which comparisons about the role of poverty on BMI can be made. This finding reiterates the importance of including county poverty in other studies and reviews about BMI and the food environment,1,19 which, as a body of research, include measures of structural poverty only rarely. Sprawl Sprawl has an association to BMI in addition to what county poverty contributes. The magnitude of the county poverty coefficient changed little with the addition of sprawl, and more variance in the overall model was explained when sprawl was included, so these variables appear to be measuring different processes that are complementary in their associations to BMI. As a methodological note, the sprawl measure appears to function similarly in this current study as it did when it was used in other studies.26



Restaurants Drawing comparisons from the findings of this analysis to the published literature is limited because “restaurants” are often grouped by only fast-food and full-service (or non-fast-food) varieties. In this study, there is added specificity from separating independently owned and operated full-service restaurants from chain full-service restaurants, for which very different relationships to BMI were demonstrated. Categorizing restaurants in three groups clarifies different relationships for each restaurant type and suggests that measurement differences may account for some inconsistencies in the published literature that combined all full-service restaurants. Thus, the findings in this current study inform the measurement of restaurants as they are used in environmental-level studies about the role of various types of restaurants and the relationships to adult BMI. Fast food. The association between fast food and BMI was consistent with other research. Finding this relationship after controlling for poverty and sprawl was a unique contribution. Mehta and Chang7 used national BRFSS data grouped by county and controlled for population size and median household income, which are components of how measures of sprawl and poverty are derived, but essentially different measures. They used the number of fast-food restaurants per 100,000 people as the store measure (restaurant density),7 rather than a proportion of the total built food environment. Their model explained 18% of the level-2 variance, compared with 24.3% in this analysis. The overall results of both research approaches indicated the same relationship, but including sprawl and poverty in the analysis explained additional county-level variance. While the results from this study are consistent with those of Mehta and Chang7 and Maddock,8 it should be noted that studies conducted in regions smaller than counties report mixed results.5,6,11 The link between an individual’s consumption of fast food and BMI is much more established than the link between an individual’s exposure or proximity to fast food and BMI. Chain and independent restaurants. Published literature groups chain restaurants and independent restaurants together. For example, Mehta and Chang conducted a study similar to this one, but they grouped restaurants as “full service,” which included chain and independently owned restaurants. In that study, restaurant density was associated with a lower risk of obesity.7 Inagami et al. also grouped restaurants in a multilevel study, but found that a higher concentration of restaurants was associated with higher BMI.30 This current

research examined the separate effects of chain and independent restaurants. Chain restaurants had a very strong, positive relationship to BMI after controlling for sprawl and poverty. Understanding the association between chain restaurants and BMI may become more important as menu-labeling laws, which target chain restaurants,31 are proposed and implemented. Contrary to fast-food and chain restaurants, independent restaurants had a negative relationship to BMI that absorbed some effects of sprawl and poverty, and were more related to land use and structural economic conditions. Mehta and Chang7 used a ratio of full-service to fast-service restaurants, and a county with a higher density of full-service restaurants was associated with lower BMI. This finding was similar to the finding that counties with more independent restaurants had lower BMI, but it would be contrary to the finding for chain restaurants, which are also full service. In California, independent restaurants comprise approximately 42% of the food environment, and chain restaurants 4% (Table 1). Thus, research that has not differentiated the two types of full-service restaurant could have been measuring the proportionally larger presence of independent restaurants, which could obscure the opposite effects from chain restaurants. Limitations The outcome variable for this research project was BMI, and although significant relationships were demonstrated, there is a long causal pathway from the presence of restaurants, sprawl, and poverty to BMI. This study contributes to a structural explanation of BMI by including a series of generally understudied causal variables. The predictor variables for restaurants were constructed as a proportion of the total food environment. This method was chosen so that the total food environment would be represented in the measure, reflecting the environmental structure. Other previous work used a retail food environment index, though not specifically linked to a health outcome,32 but this work made a composition index familiar and intellectually accessible to health professionals. A limitation to the construction of measures in this study, however, is that the three restaurant variables are inherently correlated. The relationships between county poverty, sprawl, and restaurant type are delineated, but not the compounded relationship of all restaurant types to BMI. This research included only 33 of California’s 58 counties. The counties that were included are the largest, most urbanized in the state, while the excluded counties were more rural with smaller populations. The population of the included counties represented 97%



of the state’s population, but the findings should be applied only to the counties that were actually included, and the findings do not generally inform large rural areas. The overall results of this work may provide useful ideas for the excluded counties and, in particular, for the larger cities in those counties. CONCLUSIONS This study demonstrated environmental associations of poverty and sprawl on BMI for people living in larger, generally urban California counties. County poverty had a robust, large, positive effect on BMI. Sprawl also contributed to the structural explanation. Both of these factors were somewhat reduced when restaurants were added to an explanation. Of particular importance is that independently owned and operated restaurants were associated with reduced BMI, while chain restaurants and fast-food restaurants had strong, positive associations with increased BMI. These variables explained 71% to 76% of the county-level variance. Thus, restaurants, sprawl, and county poverty contribute to a comprehensive structural explanation of BMI, and these factors should be included in future research on this topic. This study was conducted as dissertation research at the University of California, Davis, Department of Sociology. The Network for a Healthy California, where the author was concurrently employed, provided the use of Dun & Bradstreet data. The results are the findings of the author and do not necessarily reflect opinions of the California Department of Public Health.

REFERENCES 1. Larson NI, Storey MT, Nelson MC. Neighborhood environments: disparities in access to healthy foods in the US. Am J Prev Med 2009;36:74-81. 2. Centers for Disease Control and Prevention (US). Behavioral Risk Factor Surveillance System [cited 2005 Dec 15]. Available from: URL: http://www.cdc.gov/brfss/pubs/index.htm 3. McCann B, Ewing R. Measuring the health effects of sprawl: a national analysis of physical activity, obesity and chronic disease. Washington: Smart Growth America, Surface Transportation Policy Project; 2003. 4. Bowman SA, Vinyard BT. Fast food consumption of US adults: impact on energy and nutrient intakes and overweight status. J Am Coll Nutr 2004;23:163-8. 5. Rundle A, Neckerman KM, Freeman L, Lovasi GS, Purciel M, Quinn J, et al. Neighborhood food environment and walkability predict obesity in New York City. Environ Health Perspect 2009;117:442-7. 6. Wang MC, Kim S, Gonzalez AA, MacLeod KE, Winkleby MA. Socioeconomic and food-related physical characteristics of the neighbourhood environment are associated with body mass index. J Epidemiol Community Health 2007;61:491-8. 7. Mehta NK, Chang VW. Weight status and restaurant availability: a multilevel analysis. Am J Prev Med 2008;34:127-33. 8. Maddock J. The relationship between obesity and the prevalence of fast food restaurants: state-level analysis. Am J Health Promot 2004; 19:137-43.

9. Sturm R, Datar A. Body mass index in elementary school children, metropolitan area food prices and food outlet density. Public Health 2005;119:1059-68. 10. Powell LM, Auld MC, Chaloupka FJ, O’Malley PM, Johnston LD. Access to fast food and food prices: relationship with fruit and vegetable consumption and overweight among adolescents. Adv Health Econ Health Serv Res 2007;17:23-48. 11. Jeffery RW, Baxter J, McGuire M, Linde J. Are fast food restaurants an environmental risk factor for obesity? Int J Behav Nutr Phys Act 2006;3:2. 12. Kreiger N. Why epidemiologists cannot afford to ignore poverty. Epidemiology 2007;18:658-63. 13. Wilkinson RG, Pickett KE. Income inequality and population health: a review and explanation of the evidence. Soc Sci Med 2006; 62:1768-84. 14. Mayer SE, Sarin A. Some mechanisms linking economic inequality and infant mortality. Soc Sci Med 2005;60:439-55. 15. Frank LD, Engelke PO, Schmid TL. Health and community design: the impact of the built environment on physical activity. Washington: Island Press; 2003. 16. Weinberg Z, Epstein MS. Factors affecting supermarket development in inner cities. In: No place to shop: challenges and opportunities facing the development of supermarkets in urban America. Washington: Public Voice for Food and Health Policy; 1996. p. 13-36. 17. Frumkin H. Urban sprawl and public health. Public Health Rep 2002;117:201-17. 18. Frumkin H, Frank L, Jackson R. Urban sprawl and public health: designing, planning and building for healthy communities. Washington: Island Press; 2004. 19. Story M, Kaphingst KM, Robinson-O’Brien R, Glanz K. Creating healthy food and eating environments: policy and environmental approaches. Annu Rev Public Health 2008;29:253-72. 20. California Department of Public Health, Chronic Disease Surveillance and Research Branch, Survey Research Group Section. California Behavioral Risk Factor Surveillance System [cited 2008 Nov 14]. Available from: URL: http://www.surveyresearchgroup .com/sub.php?page=data 21. Chang VW. Racial residential segregation and weight status among US adults. Soc Sci Med 2006;63:1289-303. 22. Chang VW, Christakis NA. Income inequality and weight status in US metropolitan areas. Soc Sci Med 2005;61:83-96. 23. California Department of Health Care Services, California Department of Public Health. California food guide [cited 2010 Jul 30]. Available from: URL: http://www.cafoodguide.ca.gov 24. Dun & Bradstreet, Inc. California food retailers, downloadable data. Short Hills (NJ): Dun & Bradstreet, Inc.; 2004. 25. Ewing R, Pendall R, Chen D. Measuring sprawl and its impact. Washington: Smart Growth America; 2003. 26. Ewing R, Schmid T, Killingsworth R, Zlot A, Raudenbush S. Relationship between urban sprawl and physical activity, obesity, and morbidity. Am J Health Promot 2003;18:47-57. 27. Scientific Software International, Inc. HLM: Version 6.06. Lincoln wood (IL): SSI, Inc.; 2004–2009. 28. Department of Health and Human Services (US), National Institutes of Health, National Heart Lung and Blood Institute. The practical guide: identification, evaluation, and treatment of overweight and obesity in adults [cited 2009 Mar 4]. Available from: URL: http:// www.nhlbi.nih.gov/guidelines/obesity/practgde.htm 29. Wilkinson RG, Pickett KE. Income inequality and social dysfunction. Annu Rev Sociol 2009;35:493-511. 30. Inagami S, Cohen DA, Brown AF, Asch SM. Body mass index, neighborhood fast food and restaurant concentration, and car ownership. J Urban Health 2009;86:683-95. 31. Center for Science in the Public Interest. Menu labeling [cited 2010 Nov 3]. Available from: URL: http://www.cspinet.org/ menulabeling 32. California Center for Public Health Advocacy. Detailed methodology, searching for healthy food: the food landscape in California cities and counties [cited 2010 Oct 21]. Available from: URL: http:// www.publichealthadvocacy.org/RFEI/expanded%20methods.pdf


Research Articles

Effects of a Television Drama about Environmental Exposure to Toxic Substances

May G. Kennedy, PhD, MPHa Elizabeth Eustis Turf, PhDb Maureen Wilson-Genderson, PhDa Kristen Wells, MPHb Grace C. Huang, MPHc,d Vicki Beck, MSc,e

ABSTRACT Objective. This study assessed short-term outcomes of viewing an episode of a prime-time television drama in which a child developed cancer after environmental exposure to an illegal pesticide. The study explored the effects among viewers of feeling transported into a narrative world. Methods. Respondents (n2,139) to a post-episode Internet panel survey were asked if they had seen the show and asked questions about their demographic information, their frequency of viewing the television show, the degree to which they had felt transported into a narrative world created by the drama, and their knowledge and beliefs about the health effects of environmental exposure. Conversations with key informants from federal agencies and advocacy groups were also held. Results. Episode viewing and narrative transportation were positively associated with knowledge of toxic exposure effects, and transported viewers reported being more likely to report an unusually high number of cancer cases to authorities. The show also appeared to have prompted a clarification of federal pesticide-testing policy. Conclusions. Entertainment Education is a promising strategy for disseminating key points of information about environmental health.

Virginia Commonwealth University, Department of Social and Behavioral Health, Richmond, VA

a

Virginia Commonwealth University, Department of Epidemiology and Community Health, Richmond, VA

a

University of Southern California, Annenberg School for Communication and Journalism, The Norman Lear Center, Hollywood, Health and Society Program, Beverly Hills, CA

c

Current affiliation: University of Southern California, Keck School of Medicine, Institute for Health Promotion and Disease Prevention Research, Los Angeles, CA

d

Current affiliation: Independent consultant, San Diego, CA

e

Address correspondence to: May G. Kennedy, PhD, MPH, Virginia Commonwealth University, P.O. Box 980149, 1112 E. Clay St., Richmond, VA 23298; tel. 804-8284548; fax 804-828-5440; e-mail . ©2011 Association of Schools of Public Health

150 


Effects of a TV Show about Toxic Exposure  151

Entertainment education (EE) is the use of a serialized drama (e.g., a soap opera) or some other entertainment format to bring about widespread, positive change in public behavior or its prerequisites, such as knowledge and attitudes. In the United States, EE often takes the form of a health-related storyline within an episode of a regularly broadcast commercial television show.1 Hollywood writers and producers have complete creative control over the content of the storyline, but they frequently consult with medical and public health experts to ensure the accuracy of the health information in their scripts.2 There is accumulating evidence that storylines resulting from these consultations can increase health knowledge, health information-seeking behavior, and conversations about health topics, as well as intentions to perform health behaviors, among mass audiences.3–8 BACKGROUND Possible mechanisms of EE effects It has long been believed that EE effects are brought about largely by observing the consequences of the behavior of a role model, a process elucidated in social cognitive theory,9 but other theories have begun to shed light on finer-grained causal mechanisms of EE.10 For example, Cohen11 pointed to the importance of character identification in vicarious learning, and recent studies have shown that character identification can moderate EE effects.12,13 An alternative to Cohen’s explanation is that, rather than identifying with fictional characters, regular viewers form parasocial relationships with them.14,15 Such relationships deepen the emotional impact of “interactions” with characters, and that may help to explain why stronger EE effects have been demonstrated among regular viewers of a show than among less frequent viewers.16,17 Unsolicited viewer letters have been cited as anecdotal support for the parasocial relationship explanation.1 Narrative transportation The transportation-imagery model (TIM)18,19 offers additional insight into why some televised narratives have a greater impact than others. Narrative transportation has been defined as the sense of being carried away by a narrative into a fictional world. TIM holds that a narrative increases knowledge, persuades an audience member to alter beliefs and attitudes, and prompts behavioral intentions to the degree that it transports. Unlike the mainly cognitive mechanisms believed to underlie rhetorical, argument-based persuasion, narrative persuasion is thought to engage both cognitive and emotional processes, and the engagement of head and heart is thought to be enjoyable. Enjoying a story

should lead an audience member to finish it and perhaps even return to it in some way (e.g., by discussing it with others), thus creating the potential to increase the exposure dosage of any embedded health messages. In the past, scholars examining the effects of emotional arousal and involvement on health-related outcomes such as knowledge, attitudes, and behaviors tended to conclude that emotion strengthens outcomes. For example, a large body of research based on parallel- or dual-process models of attitude change20,21 has shown that attitudes with both cognitive and emotional components are stronger predictors of intentions to take threat-reducing action.22,23 However, narratives may have unique persuasive power. For example, they can bring about emotional arousal regardless of whether a story is framed as truth or fiction.24 In addition, immersion in a story can move counterarguments from one’s real life out of awareness temporarily, thus rendering the counterarguments less likely to undercut the messages the narrative conveys.25 A reliable scale of transportation has been developed and used to show that written narratives are indeed more persuasive if they are transporting.18 However, a written narrative is thought to transport the reader partly by evoking vivid images; it is not clear that the TIM would apply to visual media that supply images instead of evoking them.26 An opportunity to study the relationship between narrative transportation and the effects of real-world video media arose when a popular evening television drama broadcast a storyline about the health effects of environmental exposure to toxic chemicals. Experts in environmental health had consulted with writers of the show as the script was being developed. One of the major points the experts made was that it is extremely difficult and rare to link specific toxic exposures to negative health effects in individual cases. Because this notion is counterintuitive for many, an overview of the technical challenges of confirming such a linkage is provided in the next section. Health effects of hazardous substances in the environment Negative health effects of toxic environmental exposures range from short-term symptoms, such as drowsiness, headaches, rashes, and convulsions, to serious, long-term maladies, such as growth impairment, cancer, and death. The mechanisms underlying these adverse health effects are highly complex. Whether exposure to a potentially toxic environmental agent will harm health typically depends on the amount of the substance and whether it is absorbed internally; to cause harm, the agent must be able to cross the body’s



boundary.27 The extent of health effects also depends on (a) the duration, rate, and route of exposure; (b) the health, age, family disease history, and post-exposure behaviors of the exposed individuals; and (c) the presence of and interactions with other substances also present in the environment. Cancer is among the most widely feared of these health consequences. The World Health Organization’s International Agency for Research on Cancer has identified more than 100 substances as known or probable cancer-causing agents and 300 more as suspected carcinogens.28 While cancer death rates have gone down overall in recent years, rates of several cancers hypothesized to be associated with certain environmental exposures (e.g., thyroid cancer and radiation exposure) have increased.29 Although these associations and trends are apparent at the population level, it is seldom possible to establish a statistically sound link between specific exposures to substances and individual cases of cancer. Among the methodological challenges in establishing defensible causal linkages are (a) the often decades-long latency period between exposure to an agent and the development of cancer, (b) the other exposures the patient may have had in the interim, and (c) the gaps in cancer-registry data needed to estimate background rates of disease occurrence.30 These research challenges also pertain to confirming the existence of a suspected cancer cluster (i.e., a greater-than-expected number of cancer cases in a geographic area over a certain period of time).31 State and local health departments investigate more than 1,000 reports of suspected cancer clusters each year, but fewer than 15% turn out to exceed background case levels.32 When a cluster is confirmed, further epidemiologic investigation often fails to identify the underlying cause with confidence, unless the cancer is a rare type and the exposure was intense and sustained (e.g., in occupations that require use of chemicals).33 Investigating cancer clusters is costly in terms of public health resources and credibility. Although the typical ambiguous finding presents an opportunity to educate citizens about preventable causes of cancer and effective screening methods, it frustrates community expectations.34 Mass education strategies such as EE could be used to increase knowledge about the elusiveness of definitive cause-effect linkages, and such knowledge could facilitate future communication between cluster investigators and the concerned public. The consultation In the fall of 2006, the executive producer of the NBC television show Law and Order: Special Victims Unit (L&O:

SVU ) learned of some pesticide-testing regulation issues.35 In October, writers from the show contacted Hollywood, Health and Society (HH&S), a program of The Norman Lear Center at the University of Southern California’s Annenberg School for Communication and Journalism that conducts health information outreach to the entertainment industry, to inquire about testing indoor pesticides and connections between pesticides and childhood cancers. HH&S introduced the writers to environmental health experts from the National Center for Environmental Health (NCEH) and the Agency for Toxic Substances and Disease Registry (ATSDR) at the Centers for Disease Control and Prevention and the National Cancer Institute. The experts pointed out that children exposed to hazardous chemicals are at higher risk of disease outcomes than exposed adults, but that apparent cancer clusters seldom can be distinguished statistically from background cancer rates, and that efforts to link individual cancer cases to specific environmental causes are usually unsuccessful. The director of NCEH/ATSDR also met with writers from the show in Los Angeles to provide additional information and ideas for storylines. The storyline With 13.45 million U.S. viewers, L&O: SVU was the 23rd most-watched network show in February 2007.36 An episode called “Loophole” was aired February 6, 2007. It began with detectives from the New York Police Department’s elite sex-crime unit receiving photographs of a little boy in his underwear. Initially assumed to be child pornography, the photographs turned out to be data from a chemical company’s test of an unregistered pesticide. The test involved spraying the pesticide in an apartment building; the boy developed “cancer in his blood” as a result of repeated exposure to the hazardous substance. A corrupt federal official character had been conducting sham plant inspections, and the detectives quickly uncovered evidence that incriminated the pesticide manufacturer. However, according to the storyline, there was a loophole in federal regulations that protected the company from prosecution and damage liability. Undaunted by their lack of firm regulatory ground, the detectives bluffed their way through a confrontation with the head of the company and he agreed to pay the child’s medical bills. Health information provided The plot dramatized the health effects of toxic chemical exposure on children, and included a heated exchange between the detectives and a female police department employee in a white lab coat. This medical authority



figure argued that it is rarely possible to link a case of cancer to a particular chemical exposure with a sufficient degree of certainty, but the child’s life-threatening condition was enough to convince the detectives. Several physical symptoms of toxic chemical exposure were mentioned during the show but were not depicted in action or tied integrally to the plot. Data source Nielsen Entertainment maintains a large (n23,050) Internet panel of U.S. residents who expressed interest in being surveyed about entertainment-related issues when they were recruited at a public venue. To evaluate the effects of the L&O: SVU storyline, Nielsen invited HH&S to add items to a wave of the survey already scheduled to be fielded for four weeks (February 21, 2007–March 26, 2007). Hypotheses As in previous research, we expected to find an association between knowledge and exposure to the healthrelated storyline. Consistent with TIM, we hypothesized that, among viewers, changes in knowledge, beliefs, and anticipated action would be associated with the degree of narrative transportation experienced. MethodS Participants We invited panel households with a member who had reported sometimes watching L&O: SVU and respondents to two earlier panel surveys on the topic of hazardous chemical exposure to complete an online questionnaire. The response rate was 38% (2,139/5,678). Viewing status was undetermined for 102 respondents; we excluded them from the analyses. Procedure We e-mailed eligible panel members invitations to “. . . help us learn more about what viewers like you think about health issues in your favorite prime-time shows.” We told them the anonymous survey would require five to eight minutes to complete and provided them a link to the survey. Panelists who did not respond initially were sent an e-mail reminder after 10 days.

level of education, number of children 18 years of age, and income range. L&O: SVU viewing. We asked participants how many episodes of L&O: SVU they had seen in the past year (response options were 1, 2, 3, 4, and 5 or more). We also asked, “Did you see the L&O: SVU episode about testing an unregistered pesticide in an apartment building?” If the respondent reported watching this episode, they were counted as a viewer. Participants who reported not having watched any episodes of the series in the past year were coded as non-viewers. Symptom knowledge. We asked participants, “How likely is it that someone who is exposed to toxic waste would develop the following problems: behavioral/learning problems, birth defects, difficulty breathing, cancer, neurological deficits, rash or skin irritation, and leukemia?” Responses were indicated by means of a 10-point, Likert-type scale (1  not at all likely; 10  very likely) (α0.94). Knowledge that children are vulnerable. We asked participants, “On a scale from 1 to 10, how much do you agree that children are more likely than adults to develop health problems after exposure to hazardous chemicals?” (1  strongly disagree; 10  strongly agree). Knowledge that a link is difficult to prove. We asked respondents, “On a scale from 1 to 10, how much do you agree that it is difficult to prove a link between environmental toxins and cancer cases?” (1  strongly disagree; 10  strongly agree). Protection belief. We asked respondents to rate their agreement with the following statement: “Environmental policies and regulations protect me and my family from exposure to toxic substances in the environment” (1  strongly disagree; 10  strongly agree).

Measures

Actions participants might take. We asked participants, “On a scale from 1 to 10, how likely would you be to do one of the following if you discovered an unusual number of cancer cases in your neighborhood: alert the media, call the health department/board, contact the companies that might be producing hazardous waste, contact the local school board, search state reports on toxic waste control, and call a law enforcement agency or official?” (1  not at all likely; 10  very likely) (α0.85).

Demographics. We collected demographic information from study participants because any member of the panel household may respond to an invitation to participate. Variables included respondent age, gender, race/ethnicity (coded as white and nonwhite), highest

Narrative transportation. We created a measure of narrative transportation by modifying an existing scale developed for written narratives by Green and Brock.19 Respondents indicated their degree of agreement with seven statements describing their viewing experience:



found my mind wandering while watching; show affected me emotionally; wanted to find out how the episode ended; when the show ended, I found it easy to put out of my mind; events in the show are relevant to my everyday life; could picture myself in the scene of the events in the show; and events in the show seemed realistic (1  not at all; 7  very much) (α0.72). Analysis plan We computed descriptive statistics and bivariate comparisons of viewers and non-viewers of the L&O: SVU episode “Loophole.” The mean response of the items each individual completed was used for multi-item scales. We used multiple regression to estimate (a) effects of exposure to L&O: SVU “Loophole” for the sample as a whole and (b) effects of narrative transportation among members of the viewer subsample. We adjusted all models for age, gender, education, race/ethnicity, income, and number of children aged 18 years. Analyses were performed using SAS version 9.1.37 Results Demographics of viewers and non-viewers With the exception that non-viewers had more children aged 18 years, viewers and non-viewers of L&O: SVU “Loophole” were similar demographically (Table 1). The typical respondent was a white female, 40 years of age, with an annual family income of $70,000. Bivariate contrasts between viewers and non-viewers Viewers had higher scores for all three knowledge variables than non-viewers, although the difference on the scale of symptom knowledge was significant only for a one-sided test. Viewers and non-viewers did not differ on either the protection belief item or the action likelihood scale. Regression analyses for all respondents Being a regular viewer of L&O: SVU and having seen the “Loophole” episode were independently and positively associated with knowing that it is hard to prove a link between environmental toxins and cancer cases (Table 2). Higher levels of education, being nonwhite, having more children aged 18 years, and having seen “Loophole” were positively associated with knowing that children are more vulnerable to toxic exposure. Older age and being female were positively associated with symptom knowledge. There were no significant predictors of the protection belief. Age (positive), education (negative), and number of children aged

18 years (positive) were all associated with the action likelihood scale. Regression analyses for “Loophole” viewers Among viewers, no variables tested were significantly associated with endorsement of the statement that proving a link between environmental toxins and cancer cases is difficult or the protection belief. Being nonwhite, having more children aged 18 years, and reporting higher levels of narrative transportation were positively associated with agreeing that children are more vulnerable to toxic exposure (Table 3). Narrative transportation was positively associated with scores on both the symptom knowledge and anticipated action scales. Discussion This study may be the first to have demonstrated an association between viewing a fictional televised narrative about exposure to hazardous chemicals and knowledge about the effects of toxic chemical exposure. One thing that viewers of the episode learned—that identifying cancer clusters and linking them to specific toxic agents is inherently difficult—was a central learning objective for the public health experts who served as consultants to writers of the drama. Perhaps because of deeper engagement with the characters and storylines fostered by following them over time, regular viewers of the show learned the most; this is a common finding in EE. Of three measures of knowledge, scores on the symptom knowledge scale had the weakest association with viewing the episode. One explanation for the weakness of this finding is that symptom information was provided but not actually dramatized in the storyline. For example, there was no pregnant woman in the show, but participants were asked about the connection between birth defects and toxic chemicals in the environment. The integration of health facts into the action of a fictional narrative is an EE mechanism that deserves further study. Among viewers of the show, those who felt transported by the narrative were more likely to have retained the undramatized symptom information, as well as to learn that children are more vulnerable to toxic exposures and to indicate that they would take action if they suspected a cluster of cases. These observations were consistent with the TIM. Although narrative transportation was not associated with knowing that causal linkages are elusive, there may have been a ceiling effect on this measure among episode



Table 1. Frequencies of and differences between demographic and outcome variables for viewers vs. non-viewers of the “Loophole” episode of Law and Order: Special Victims Unit

Variable

Viewers (n295) Mean (SD)

Non-viewers (n1,742) Mean (SD)

F (df1) a

Age

44.2 (11.7)

42.9 (12.0)

3.1b

Number of children aged 18 years

1.3 (0.7)

1.5 (0.8)

8.1c

Know it is difficult to prove a link between toxic exposure and a cancer cased

4.9 (2.8)

4.1 (2.5)

23.7e

Know that children are more vulnerable to health effects from toxic exposured

7.3 (2.3)

6.9 (2.4)

9.2c

Know symptoms of toxic exposure

8.0 (1.7)

7.8 (1.7)

2.9b

Believe that policies protect my family from toxic substancesd

5.5 (2.5)

5.7 (2.4)

1.9

f

Likely to take actions in response to neighborhood cancer casesf

5.9 (2.1)

6.1 (2.1)

1.3

29.2 (7.5)

NA

NA

Viewers Percent

Non-viewers Percent

Χ 2 (df)

Female gender

64

59

2.4 (1)

Nonwhite race/ethnicity

11

11

0.0 (1)

Annual household income $15,999 $16,000–$29,999 $30,000–$49,999 $50,000–$69,999 $70,000–$99,999 $100,000 Refused

1 3 10 14 23 28 21

1 2 14 15 19 28 21

6.3 (6)

Education Eighth grade Some high school High school Some college College degree Refused

1 1 5 24 44 25

1 1 6 26 39 27

4.1 (5)

Narrative transportation scaleg

Variable

Significance of F tests adjusted for unequal numbers

a

b

p0.10

p0.01

c

d

Score based on a scale of 1 to 10 where 1  strongly disagree and 10  strongly agree

p0.001

e

Score based on a scale of 1 to 10 where 1  not at all likely and 10  very likely

f

Score based on mean total of respondents’ degree of agreement (where 1  not at all and 7  very much) with seven statements describing their viewing experience: found my mind wandering while watching; show affected me emotionally; wanted to find out how the episode ended; when the show ended I found it easy to put out of my mind; events in the show are relevant to my everyday life; could picture myself in the scene of the events in the show; and events in the show seemed realistic

g

SD  standard deviation F  F statistic df  degree of freedom NA  not applicable


0.154

0.062

0.005

0.031

0.017

0.032

0.005

0.006

β

L&O: SVU  Law and Order: Special Victims Unit

SEB  standard error of b

p0.05

p0.01

b

a

1.084a

0.212

0.202

Regular L&O: SVU viewer

Viewed “Loophole” episode

0.040

Number of children aged 18 years 0.014

0.437b

0.183

0.056

Income

0.116

0.063

0.139

0.171

Gender

0.004

0.425

SEB

Race/ethnicity

0.016

0.001

B

Education

Age

Variable

Know it is difficult to prove a link between toxic exposure and a cancer case

b

0.477a

0.006

0.210b

0.040

0.373

0.149

0.156a

0.003

B

0.152

0.032

0.064

0.070

0.005

0.073

0.024

0.048

0.171 0.037

0.030

0.058

0.015

β

0.108

0.059

0.004

SEB

Know that children are more vulnerable to health effects from toxic exposure

0.076

0.086

0.062

0.053

0.089

0.516a

0.039

0.014a

B

0.135

0.129

0.043

0.025

0.016

0.019

0.032

0.046

0.017

0.153

0.073 0.116

0.022

0.107

β

0.040

0.003

SEB


0.116

0.091

0.030

0.041

0.257

0.233

0.120

0.019

B

0.199

0.189

0.064

0.038

0.172

0.109

0.059

0.005

SEB

0.016

0.014

0.010

0.024

0.033

0.047

0.045

0.099

β

Believe that policies protect my family from toxic substances

0.103

0.060

0.113b

0.022

0.110

0.159

0.113b

0.008b

B

0.170

0.057

0.034

0.154

0.097

0.053

0.004

0.356

SEB

0.016

0.010

0.044

0.015

0.016

0.036

0.048

0.047

β

Likely to take actions in response to neighborhood cancer cases

Table 2. Multiple regression of demographics, regular Law and Order: Special Victims Unit viewership, and viewing of the “Loophole” episode on knowledge, attitude toward protective policy, and estimated likelihood of taking action, for the full study sample (n=2,139)




p0.001

0.013

0.476

0.022

0.388

0.234

0.119

0.545

0.349

0.186

0.015

SEB

0.036

0.073

0.034

0.001

0.026

0.023

0.027

0.037

β

L&O: SVU  Law and Order: Special Victims Unit

SEB  standard error of b

b

a

p0.05

Narrative transportation scale

Regular L&O: SVU viewer

0.132

0.241

Race/ethnicity

0.002

0.136

Gender

Number of children 18 years of age

0.084

Income

0.009

Education

B

Age

Variable

Know it is difficult to prove a link between toxic exposure and a cancer case

0.036a

0.061

0.463a

0.016

1.103a

0.223

0.116

0.023

B

0.017

0.097

0.184

0.094

0.430

0.276

0.147

0.012

SEB

0.118

0.037

0.148

0.011

0.147

0.047

0.046

0.118

β

Know that children are more vulnerable to health effects from toxic exposure

0.054b

0.0003

0.116

0.016

0.331

0.231

0.118

0.009

B

0.051

0.014

0.061

0.067

0.066

0.070

β

0.012

0.247

0.219 0.00009

0.131

0.067

0.307

0.197

0.105

0.008

SEB


0.0001

0.067

0.337

0.002

0.564

0.070

0.225

0.025

B

0.019

0.333

0.200

0.102

0.468

0.300

0.160

0.130

SEB

0.0004

0.011

0.100

0.001

0.070

0.013

0.084

0.119

β

Believe that policies protect my family from toxic substances

0.016b

0.086

0.101

0.324

0.053

0.428

0.263

0.168

B

0.011

0.016

0.277

0.167

0.085

0.388

0.249

0.133

SEB

0.087

0.302

0.020

0.112

0.037

0.061

0.059

0.073

β

Likely to take actions in response to neighborhood cancer cases

Table 3. Multiple regression of demographics, regular viewership, and narrative transportation on knowledge, attitude toward protective policy, and estimated likelihood of taking action for viewers of the “Loophole” episode of Law and Order: Special Victims Unit



viewers. Overall, our findings support extending the TIM from its original focus on written narratives, which require a reader to generate mental images, to visual media, which supply images for a viewer to perceive and process. Given the strain on cluster investigation resources and the high proportion of inconclusive investigation findings, reinforcing the tendency to take action if one suspects a cancer cluster might be considered an unintended consequence of the EE stimulus. Offsetting this effect was the finding that, although the storyline portrayed a corrupt government official and cast a federal pesticide-testing policy in a negative light, there were no exposure or transportation-related drops in the belief that “environmental policies and regulations protect me and my family from exposure to toxic substances in the environment.” Viewers may have assumed that, like the corrupt official, the policy was a fictional device intended to add drama to the plot. It has been charged, however, that a pesticide-testing loophole really did exist.38 After the show aired, the U.S. Environmental Protection Agency posted a clarification of its policy on a Web page entitled “Response to Television Show Depicting Illegal Pesticide Human Study.”39 We regard this clear articulation of the limits of allowable human pesticide testing as a positive, policy-level outcome of EE outreach. Another higher-order effect may also be attributable to the broadcast. The storyline spurred environmental advocacy organizations to circulate an e-mail promoting the show and to organize viewing groups comprising concerned individuals and families of children who had been exposed to pesticides. The families called the national prime-time broadcast of the storyline empowering (Personal communication, Martha Arguello, MD, director, Physicians for Social Responsibility, May 2008). Limitations Survey respondents were not asked if they had accessed an informational Web page (www.cdc.gov/nceh/ clusters/faq.htm) recommended by the public health consultants and posted on the show’s Web page shortly before the broadcast, so the opportunity to study dosage and channel effects was missed. This study used a modified version of an existing scale developed for written narratives by Green and Brock18 that has demonstrated reliability and validity. The modified version was not subjected to pretesting prior to being used in this study; fortunately, the Chronbach’s alpha reliability of the modified scale proved to be acceptable. Other study limitations included use of single-item measures and a sample of volunteers from a Web

panel that was skewed toward well-educated, affluent individuals. However, the method was internally valid in that the demographic profiles of exposed and unexposed respondents were not markedly different, and major demographic variables were held constant statistically. Conclusions From a practical intervention perspective, these findings suggest that health outreach to Hollywood can facilitate the effective delivery of technical and even counterintuitive information about an important environmental health topic. Despite the fact that public health officials did not have creative control over the content of the network broadcast, the EE offering addressed some of the aims of environmental health advocates without lowering confidence in (and potentially compromising the effectiveness of) agencies that promulgate and enforce environmental regulations. From a theoretical perspective, the most interesting finding was that those who felt more transported by a video narrative were likely to have higher levels of knowledge. This may mean that they learned more from the show and were more primed for action. Future research should explore narrative factors that make all kinds of narratives more transporting. This study was supported in part by the Hollywood, Health and Society (HH&S) program of The Norman Lear Center at the University of Southern California, Annenberg School for Communication and Journalism. The findings and conclusions in this article are those of the authors and do not necessarily represent the views of HH&S. The authors thank Neal Baer, executive producer of Law and Order: Special Victims Unit, for his leadership in bringing national network television exposure to this health issue. They also thank Beverly Kingsley and Charles Green from the Centers for Disease Control and Prevention, National Center for Environmental Health for their expert consultation on the storyline. The authors thank Sandra de Castro Buffington, current HH&S director, and Sheena Nahm, former HH&S research coordinator, for supplying them with relevant information about the outreach program.

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