Nat Hazards (2017) 89:79–91 DOI 10.1007/s11069-017-2955-7 ORIGINAL PAPER
Association between air pollution and chronic diseases among the elderly in China Su Liu1 • Zhijun Yan1,2,3 • Yan Liu1 • Qiuju Yin1,3 Lini Kuang1
•
Received: 5 November 2016 / Accepted: 3 June 2017 / Published online: 12 June 2017 Ó Springer Science+Business Media B.V. 2017
Abstract With the development of Chinese economies, air pollution is becoming more and more serious, which greatly affects the residents’ daily life and health. Meanwhile, China’s aging population is growing rapidly and bringing a number of social problems. We used the data of CHARLS and analyzed the relationships between air pollution and chronic diseases among the elderly in China. The results showed that air pollution had significant adverse effects on the health of the elderly, especially on diabetes and heart diseases. The subgroup analysis showed that female is more sensitive to air pollution than male, while different age groups are significantly sensitive to different chronic diseases. Keywords Air pollution Health The elderly Chronic disease
1 Introduction With the fast development of economies, air pollution in China has aggravated gradually. The air quality index (AQI) of China’s cities usually exceeds the standard. Among all 161 cities monitored, only 16 cities met the national air quality standard in 2014 (Ministry of Environmental Protection of the People’s Republic of China 2015). The continuously deteriorated air pollution brought great health burden worldwide (Tarik et al. 2015). Prior epidemiologic studies have well documented the negative effects of air pollution on human
& Qiuju Yin
[email protected] 1
School of Management and Economics, Beijing Institute of Technology, Beijing, China
2
Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, China
3
Sustainable Development Research Institute for Economy and Society of Beijing, Beijing, China
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health. Higher concentrations of different air pollutants are significantly associated with higher mortality and greater hospital admissions of diseases such as cardiovascular disease, hypertension and respiratory disease (Dong et al. 2012; Hoek et al. 2013; Kim et al. 2015; Shah et al. 2013). The elderly are more susceptible to outdoor air pollution (Brook et al. 2010; Huang et al. 2012). There are lots of studies research focusing on the relationship between air pollution and the elderly’s health. For example, nitrogen dioxide (NO2) levels were significantly associated with the risk of asthma hospitalization for both first-ever admissions and people with a history of asthma (Andersen et al. 2012a). Exposure to higher levels of NO2 and particulate matter smaller than 2.5 lm (PM2.5) was related to hospitalization for community-acquired pneumonia (Neupane et al. 2010). Alexeeff et al. (2008) also identified that ozone had acute effect on lung function in the elderly. In addition, studies showed that higher concentration of air pollution raised the risk of cardiovascular diseases. A study in America concluded that exposure to PM2.5 triggered cardiovascular mortality and nonfatal events (Brook et al. 2010). Bell et al. (2009) found a statistically significant association between PM2.5 and hospitalizations for cardiovascular and respiratory diseases. Moreover, air pollution was associated with many other common chronic diseases of elderly, such as hypertension and diabetes (Andersen et al. 2012b; Coogan et al. 2012). Significant relationship between air pollution and the risk of elderly hypertension was found in Korea (Choi and Cho 2013). A study indicated that traffic-related air pollution was linked to higher prevalence odds of type 2 diabetes among elderly women (Kramer et al. 2010). Further, researchers found that traffic-related air pollution affected the cognitive function of men and women aged above 60 years old (Gatto et al. 2014; Power et al. 2011). The significant adverse effects of air pollution on different diseases were also well explored in China (Chen et al. 2004; Fang et al. 2012; Ikram et al. 2016; Liu et al. 2016), while the impact of air pollution on the elderly’s health is less explored. Dong et al. (2012) found that PM10 and NO2 were significantly associated with the respiratory diseases of the elderly in Shenyang. Chen et al. (2009) also discovered that vehicular pollution increased the cardiovascular morbidity among the elderly in Guangzhou. However, in China, the existing studies mostly focused on a particular district and small sample (Chen et al. 2009). It lacks the comprehensive study to investigate the effects of air pollution on the health of elderly from the whole country level, while air pollutants are important factors to the health of the elderly (Kan et al. 2008). Moreover, China has experienced increasing severe level of aging in the past years; it is important to study the association between air pollution and the health of the elderly in China. Most previous studies only focus on analyzing the impact of a single disease on the health of the elderly, and the comparison of different influence of single air pollutant and overall air quality on health is missing (Simoni et al. 2015; Zhou et al. 2015). Aimed to analyze the impact of air pollution on the health of the elderly in China, we studied the relationships between air pollution and chronic diseases including dyslipidemia, diabetes, heart disease and asthma of the elderly. We also investigated whether the relationship varies between different samples, such as different age or different gender. The rest of the study is organized as follows. The next section presents the data and the method. The results are given in Sect. 3. The final section discusses the contributions and limitations of this study.
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2 Materials and methods 2.1 Study population We used the data of China Health and Retirement Longitudinal Study (CHARLS) to analyze the impact of air pollution on the health of the elderly. CHARLS is conducted by the National Development Institute of Peking University, aiming at collecting the personal and family information of people above 45 years old in China and promoting the study of China’s aging population problem. CHARLS conducted a nationwide survey in 2011 and tracked the observation in 2013 and 2014, covered 28 provinces (except Hainan, Tibet, Ningxia, Taiwan, Hong Kong and Macao) and 150 counties, about 17,000 people in China. CHARLS selected samples through multistage probability sampling (Zhao et al. 2014b). First, using the probability-proportional-to-size (PPS) sampling technique, 150 countylevel units were randomly chosen from a sampling frame containing all county-level units with the exception of Tibet. The sample was stratified by region and within region by urban districts or rural counties and per capita statistics on gross domestic product (GDP). The final sample of 150 counties fell within 28 provinces. Second, using PPS method, three primary sampling units (village or community) within each county-level unit were identified. In total, 52.67% of 450 primary sampling units were rural and 47.33% were urban areas. Third, 80 households were selected from each primary sampling units. Fourth, people older than 40 in the household were chosen as subjects. More details of sampling procedure can be found in users’ guide (Zhao et al. 2013). CHARLS includes the following information: personal basic information, family structure and economic support, health status, physical measurement, medical service utilization and medical insurance, work, retirement and pension, assets, income, consumption and the basic situation of community, etc. We conducted the cross-sectional analysis based on the CHARLS data in 2013. We chose the study population of the elderly above 60 years old. After dropping observations with missing information of variables such as age and gender, the study population was left with 3907 subjects. In order to examine the influence of air quality on chronic diseases and minimize the effect of other factors except air quality, we excluded samples who reported having that chronic disease in the 2011 survey of each chronic disease.
2.2 Data Based on the report of WHO and previous studies (Bentayeb et al. 2012), four chronic diseases including dyslipidemia, diabetes or high blood, heart disease and asthma were chosen in this study. Whether an elderly have a chronic disease is defined as dependent variable, so the value of dependent variable is 0 or 1. The disease situation of the elderly was measured by CHARLS questionnaire, which includes self-report questions to ask the observation whether he/she is suffering from chronic diseases. We choose two indicators of air quality, i.e., PM10 and DAYS, as independent variables. PM10 is the ambient fine particulate matter less than 10 um in aerodynamic diameter and represents the degree of a single air pollutant. The concentration of PM10 was reported in the Environment Quality Report issued by Ministry of Environmental Protection. DAYS is defined as the number of days that have good air quality (AQI B 100), which can describe the overall air quality other than one kind of air pollutants. Our study used two different kinds of indicators to represent the air quality situation. We collected these data from
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Environment Quality Report of 116 cities, covered the cities of the final sample. The number of cities that our sample lived accounts for 40.55% of the whole country, and these cities cover almost all provinces in China. Control variables in previous studies including age, gender, education, race, living region (rural and urban), BMI (body mass index), smoking, alcohol intake were used in our analysis (Schikowski et al. 2010; Tian et al. 2015; Yan et al. 2016). Gender was classified into two levels, while 0 represents for male and 1 represents for female. Education was categorized as primary education or below, middle school and college or above education. We used two binary variables (EDU1\EDU2) to represent the three status of education. The race was divided into Han and others. A binary variable was also used to represent the race. Living region meant whether the sample lives in the urban or not (rural). BMI was calculated as dividing the weight by the squared height. Smoking and alcohol intake were binary variables that are equal to 1 when the subject had the behavior. In addition, this study also considered several factors that were closely related to the health of the elderly, such as retirement (retired and not retired), marital status (have couple and have no couple), weekly exercise and social activity (social activity), which are all obtained from the CHARLS (Dong et al. 2012). In CHARLS, physical exercise was measured by time spent on walking, moderate activities and vigorous activities in a week. In our study, physical exercise was classified into two levels, while nonexercise was 0 and otherwise 1. Social activities included communication with friends, visiting community club or providing help to family, friends and neighbors. Having social activities was marked as 1, and having no social activities was 0. Meanwhile, to control the potential effects of climate factors, we also chose some meteorological variables as control variables, including the annual average temperature (AAT) and annual precipitation (AP), which were obtained from the climate report of the cities.
2.3 Methods We employed logistic regression models to analyze the relationship between air quality and the risk of four chronic diseases among the elderly. As the dependent variable of our study, whether the elderly had a chronic disease, was a binary variable, the logistic regression model was adopted to explore the effects of independent variables on dependent variables. First, we evaluated the impacts of air quality on different chronic diseases separately. The influence of each air quality indicator, including DAYS and PM10, on each chronic disease was analyzed using the following regression model separately: pðdiseasei ¼ 1Þ ¼ b0 þ b1 logðAirqualityi Þ þ Si þ Ci þ ni ln 1 pðdiseasei ¼ 1Þ where i is the subject and diseasei represents whether the subject i suffered from the chronic diseases. diseasei is dichotomous, while diseasei = 1 refers that the subjects i suffered from a specific chronic disease and 0 otherwise. Airqualityi represents the quality exposure level of subject i. DAYS and PM10 are used as indicators of air quality and analyzed separately. That means we use two regression models to explore the effect of DAYS and PM10, respectively. Si represents the control variables related to the subject, including gender, age, race, education, living region, BMI, smoking (tobacco), alcohol intake (wine), marital status (MS), retirement (retire), weekly exercise, weekly social activity. Ci refers to the climate condition of the sample city, including the AAT and AP. ei is the error term.
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We also explored the age- and gender-specific effects of air quality on these four diseases. After grouping the samples into two groups by gender or age, we conducted the regression analysis to examine the effect of air pollution on the chronic disease risk of the elderly. We tested the statistical significance of differences between subgroups by calcuqffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ^2 Þ 1:96 SE^2 þ SE^2 , where Q ^2 are ^1 and Q ^1 Q lating the 95% confidence interval as ðQ 1 2 the estimates for the two categories, while SE^12 and SE^22 are their respective standard errors (Barnett et al. 2006; Bentayeb et al. 2010; Zhao et al. 2014a). All statistical analyses in our study were performed in EViews.
3 Results 3.1 Statistical analysis The descriptive statistics of air quality, the prevalence of chronic diseases and other control variables are shown in Tables 1, 2 and 3. A total of 3907 people were included in this study. Due to the dropping data of observation that already suffered from the chronic disease in 2011, the sample number of each chronic disease is slightly less than 3907. Table 1 shows the descriptive statistics of chronic disease prevalence. The disease that has largest number of new reported patients is heart disease, about 211 in 3907 people. In addition, 4.5% (n = 143) and 5.1% (n = 170) of the elderly are reported to have diabetes and dyslipidemia, respectively, while the prevalence of asthma is about 1.7% (n = 52) in all observations. The information of climate condition and air quality is shown in Table 2. The number of DAYS and PM10 is less than 3907, because the air quality data of some cities are not available. The annual average concentration of PM10 is 99 lg/m3 and slightly exceeds the secondary national standard of 70 lg/m3. However, it greatly exceeds the WHO standard of 20 lg/m3, which indicates the air pollution is still serious in China. From Table 3, the average education level of elderly is less than primary level (four for elementary school level, average is only 2.45). In total, 87.9% of the sample live in the countryside. About exercise and social activities, about 68.7% of the elderly have no exercise in the whole week and only 49.7% of the elderly do not have social activities. The effect of air quality on the risk of chronic diseases of the elderly is presented in Table 4. We only report the coefficients of interest for simplification. For dyslipidemia, PM10 increases the risk of dyslipidemia of the elderly by 0.44% (95% CI 0.01–0.88) with per 1% increase of the concentration, while a 1% increase in DAYS decreases the risk of dyslipidemia by 0.55% (95% CI 0.94–0.16). Meanwhile, the effects of DAYS are significantly associated with reduction of the risk of diabetes by 0.43% (95% CI 0.87–0.00). However, PM10 has no significant relationship with diabetes. Similar results are observed in the relationship between heart disease and PM10. With a 1% increase in DAYS, the risk Table 1 Descriptive statistics of four chronic diseases (2013)
N
Yes
No
Yes at 2011
Dyslipidemia
3907
170 (4.3)
3183 (81.5)
554 (14.2)
Diabetes
3907
143 (3.7)
3002 (76.8)
762 (19.5)
Heart disease
3907
211 (5.4)
3124 (80.0)
572 (14.6)
Asthma
3907
52 (1.3)
3069 (78.6)
786 (20.1)
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Table 2 Descriptive statistics of climate and air quality (2013)
N
Max.
Min.
Mean
AAT (°C)
3907
24
-1.8
15.72
AP (mm)
3907
2300
139
998
DAYS (days)
3649
365
43
292
PM10 (lg/m3)
3236
309
12.5
99
Table 3 Demographic statistics of the study population Variables
N (%) or mean ± SD
Variables
Age
67.78 – 6.63
Alcohol intake
Gender Male
1473 (37.7)
Female
2434 (6.3)
Race Han
3665 (93.8)
Other
242 (6.2)
Marital status Married
3075 (78.7)
Unmarried
832 (21.3)
Education
N (%) or mean ± SD
Yes
1026 (36.2)
No
2881 (73.7)
Region Urban
474 (12.1)
Rural
3433 (87.9)
Retirement Yes
284 (7.3)
No
3623 (92.7)
Exercise Nonexercise
2686 (68.7)
Exercise
1221 (31.3)
Less than primary school
3413 (87.4)
Middle and high school
434 (11.1)
College or above
60 (1.5)
Nonsocial activity
1943 (49.7)
23.06 – 3.52
Social activity
1963 (50.3)
BMI
Social activity
Smoking Yes
742 (19.0)
No
3162 (81.0)
Table 4 Relative risk of diseases DAYS
PM10
Dyslipidemia
-0.0055*** (-0.0094, -0.0016)
0.0044** (0.0001, 0.0088)
Diabetes
-0.0043* (-0.0087, 0.0000)
0.0020 (20.0024, 0.0065)
Heart disease
-0.0034* (-0.0066, -0.0002)
0.0023 (20.0014, 0.0060)
Asthma
20.0030 (20.0107, 0.0047)
0.0102** (0.0012, 0.0192)
Adjusted model: adjusted for age, age squares, gender, race, marital status, region, education, BMI, smoking, alcohol intake, retirement, exercise and social activity 95% confidence interval is reported in the parenthesis * 10% significance level; ** 5% significance level; *** 1% significance level
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of heart disease is significantly decreased by 0.34% (95% CI 0.66–0.02). Although DAYS is irrelevant to asthma, PM10 is significantly associated with asthma, while 1% increase of the PM10 concentration increases 1.02% (95% CI 0.12–1.92) of the asthma’s risk. The results of Table 4 show that air quality is significantly associated with the risk of chronic disease of the elderly, although the effects of DAYS and PM10 on different diseases are not the same. At the same time, we also give the whole regression result of the model of dyslipidemia in Table 5. The first column lists the variables used in the study. The second column represents the regression result of the first model, in which the dependent variable is whether the subject suffered from the dyslipidemia and the key independent variable is DAYS. The third column represents the regression result of the second model, in which the dependent variable is whether the subject suffered from the dyslipidemia and the key independent variable is PM10. As we can see from the table, DAYS and PM10 both have a significant effect on dyslipidemia. In addition, the results of control variables are shown in the table. We can find that BMI, retirement and social activities have a significant effect on the health of the elderly.
Table 5 Coefficient result of the model between air quality indicators and dyslipidemia Dyslipidemia (DAYS)
Dyslipidemia (PM10)
b0
29.5208 (9.1708)
214.003 (9.0976)
log(DAYS)/log(PM10)
20.5484 (0.2026)***
0.4401 (0.2202)**
Gender
0.3201 (0.2159)
Age
0.2192 (0.2601)
0.2784 (0.2588)
Age2
20.0016 (0.0018)
20.0019 (0.0018)
Race
0.0506 (0.3889)
0.2783 (0.4343)
Marital status
0.0750 (0.2305)
0.0998 (0.2339)
EDU1
0.1633 (0.8348)
0.3874 (0.8163)
EDU2 Smoking
0.1655 (0.2185)
0.5644 (0.8218)
0.6275 (0.8051)
-0.0916 (0.2607)
0.0098 (0.2623)
Alcohol intake
0.0820 (0.2071)
0.1615 (0.2078)
BMI
0.0802 (0.0249)***
0.0794 (0.0256)***
Region Retirement Exercise Social Annual average temperature
0.2717(0.2756) 1.1685 (0.2970)*** -0.0786(0.0937) 0.2452 (0.0799)*** -0.0869 (0.0563)
0.4611 (0.2649)* 1.0791 (0.2905)*** -0.1462 (0.0997) 0.2347 (0.0808)*** -0.0819 (0.0572)
Annual average temperature2
0.0034 (0.0028)
0.0029 (0.0028)
Annual precipitation
0.0001 (0.0007)
-0.0002 (0.0007)
Annual precipitation2
- (–)
- (–)
Adjusted model: adjusted for age, age squares, gender, race, marital status, region, education, BMI, smoking, alcohol intake, retirement, exercise and social activity, annual average temperature and annual precipitation * 10% significance level; ** 5% significance level; *** 1% significance level
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3.2 Subgroups analysis Tables 6 and 7 show the results of stratified analysis by gender and age. In the genderspecific analysis of dyslipidemia and diabetes, stronger risks of chronic diseases are only observed in the female for DAYS (-0.63%, 95% CI -1.09 to -0.17 and -0.54%, 95% CI -1.03 to -0.05). In addition, the effect of PM10 on asthma for female is 0.0112, which means that higher risk is found for female (1.12%, 95% CI 0.09–2.16). Table 7 summarizes the estimates of age-specific effects for each disease and air quality. For dyslipidemia, PM10 and DAYS have a significant impact on the elderly between 60 and 65 years old (0.67%, 95% CI 0.05–1.30 and -0.63%, 95% CI -1.12 to -0.15). However, the result of diabetes shows that age is not a significant modified factor of the relationship between air quality and the risk of diabetes in the elderly. Yet, the effect of PM10 on heart disease of the elderly above 65 years old is stronger than of the elderly below 65 years old (0.65%, 95% CI 0.11–1.19). However, the relationship between air pollution and asthma had no significant difference between the elderly age above 65 and below 65.
4 Discussion With the fast development of economies in China, the air quality is becoming one of the most concerned problems. This study focuses on the relationship between air pollution and the elderly and analyzes the effects of air pollution on the risk of the chronic diseases of the elderly. Different with prior literature (Simoni et al. 2015), we use DAYS as an indicator of air quality. The results show that the risk of the dyslipidemia, diabetes, heart diseases and asthma of the elderly increases with lower air quality, which were consistent with previous studies. Balti et al. (2014) reviewed the studies about air pollution and diabetes, and the results showed a prospective relationship between main air pollutants and the increased risk of diabetes. Wu et al. (2016) also found that PM10 has effects on asthma of adults. Moreover, the high concentration of air pollutants has a significant impact on dyslipidemia and asthma of the elderly. Andersen et al. (2012a) found long-term exposure to NO2 for up to 35 years caused the increased risk of asthma hospitalizations in an elderly cohort. The results of our study also suggest that the elderly are more sensitive than young people. Although the overall air quality has a significant impact on diabetes and heart diseases, the influence of PM10 on diabetes and heart diseases is not significant. Previous studies also found that PM10 is not so significant as other air pollutants such as NO2 or distance from road (Eze et al. 2014; Puett et al. 2011). In addition, we also analyzed the relationship between air quality and the risk of dyslipidemia of the elderly, which was ignored in previous studies (Simoni et al. 2015). Dyslipidemia is an abnormal amount of lipids in the blood. Dyslipidemia is a common disease in China, and it is a risk factor for atherosclerosis, coronary heart disease and ischemic stroke. It is important to show the association between air pollution and dyslipidemia for better prevention. In our study, the results show that both overall air quality and the concentration of PM10 have a significant impact on the risk of dyslipidemia. Consistent with previous studies, the subgroups analysis found that the effects of air pollution on the risk of chronic diseases vary with different gender. For dyslipidemia and diabetes, the female was more sensitive to DAYS compared with the male. Eze et al. (2015) found the similar results, which demonstrated that the effect of air quality on
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-0.0037 (-0.0110, 0.0037)
-0.0061 (-0.0305, 0.0190)
Heart disease
Asthma
-0.0063***(-0.0109, -0.0017)
-0.0022 (-0.0132, 0.0089)
-0.0031 (-0.0075, 0.0013)
-0.0054** (-0.0103, -0.0005)
0.0039 (-0.0030, 0.0108)
0.0104 (-0.0118, 0.0330)
0.0017 (-0.0017, 0.0051)
-0.0005 (-0.0087, 0.0079)
0.0112* (0.0009, 0.0216)
0.0020 (-0.0024, 0.0065)
0.0032 (-0.0023, 0.0087)
0.0045 (-0.0013, 0.0103)
Female
* 10% significance level; ** 5% significance level; *** 1% significance level
95% confidence interval is reported in the parenthesis
Adjusted model: adjusted for age, age squares, race, marital status, region, education, BMI, smoking, alcohol intake, retirement, exercise and social activity
-0.0027 (-0.0108, 0.0056)
-0.0025 (-0.0121, 0.0073)
Dyslipidemia
Diabetes
Male
Male
Female
PM10
DAYS
Table 6 Gender-specific relative risk of diseases associated with different air quality indicators
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–
-0.0063 (-0.0153, 0.0028)
-0.0027 (-0.0076, 0.0023)
-0.0045 (-0.0109, 0.0020)
-0.0037 (-0.0106, 0.0032)
[65
–
-0.0031 (-0.0080, 0.0019)
0.0041 (-0.0031, 0.0113)
0.0067** (0.0005, 0.0130)
B65
PM10
0.0116 (-0.0007, 0.0239)
0.0065** (0.0011, 0.0119)
0.0003 (-0.0057, 0.0064)
0.0025 (-0.0036, 0.0087)
[65
* 10% significance level; ** 5% significance level; *** 1% significance level
95% confidence intervals are reported in the parenthesis
Adjusted model: adjusted for gender, race, marital status, region, education, BMI, smoking, alcohol intake, retirement, exercise and social activity
Asthma
-0.0035
Heart disease
(-0.0095, 0.0026)
-0.0063**(-0.0112,-0.0015)
-0.0044 (-0.0105, 0.0018)
Dyslipidemia
Diabetes
B65
DAYS
Table 7 Age-specific relative risk of diseases associated with different of air quality indicators
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diabetes was more pronounced in females than in males. Although other indicators had no significant difference between female and male, PM10 was significantly associated with asthma for female. However, for dyslipidemia and diabetes, no significant difference was found between the two groups for PM10. The different results of diabetes are not special, Dijkema et al. (2011) also showed nonsignificant results between male and female. This may be due to the diseases that are affected by many factors and sensitive to different index of air pollution, and this can be explored in future research works. Moreover, different age group showed different sensitive level to air pollution. The effect of air pollution on dyslipidemia was stronger among the elderly below 65 years old. In contrast, the elderly above 65 years old were more sensitive to heart disease than the elderly below 65 years old. Ye et al. (2016) also found obvious evidence of effects on increased risk of CHD (coronary heart disease) morbidity in the elderly aged above 65 years old for PM10 (0.27, 95% CI 0.15–0.40). For different chronic diseases, the society needs to pay more attention on different groups of the elderly. Such as dyslipidemia, the elderly below 65 years old are more sensitive to air pollution and should avoid to exposure to high level of air pollution. This study contributes to the prior literature in the following aspects. First, to the best of our knowledge, this is the first study to examine the relationship between the elderly health and air pollution using a large population-based data covering many areas of China. Second, we studied the effects of air quality on different diseases of the elderly population. Some diseases, such as dyslipidemia, were hardly explored in previous studies. Third, our study is helpful to investigate the vulnerability of different groups of people and propose more suitable protection measures for different people. Our research also has limitations. Firstly, we only do cross-sectional analysis. A longitudinal analysis will be helpful to improve the credibility of our result. Secondly, we only use two indicators to represent the air quality. Since PM2.5 is becoming a more and more important index to measure the air quality, the future work can incorporate that index into the research. Thirdly, we only considered the location of the elderly in 2013, while some observations may move from one city to another one during 2011 to 2013. This movement may affect the status of air quality that exposured to the observation. Another limitation is that we did not consider other potential influencing factors, such as second-hand smoking, which may also exert an impact on the health of the elderly. Possible impacts of unmeasured factors can be explored in future researches.
5 Conclusion In this study, we explored the effects of air pollution on the health of the elderly in China. The results showed that the air pollution does have a significant negative effect on the risk of different chronic diseases such as dyslipidemia, diabetes, heart disease and asthma of the elderly. Our findings suggest that further studies on the relationship between air pollution and health should pay more attention on the elderly. Acknowledgements This work was supported by National Natural Science Foundation of China (Award#:71572013, 71272057 and 71521002), Beijing Natural Science Foundation (9152015) and Joint Development Program of Beijing Municipal Commission of Education. Compliance with ethical standards Conflict of interest The authors declare that there is no conflict of interest.
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