Economic Evaluation of Weight Loss ... - Wiley Online Library

Public Health and Public Policy

Economic Evaluation of Weight Loss Interventions in Overweight and Obese Women Larissa Roux,* Karen M. Kuntz,† Cam Donaldson,*‡ and Sue J. Goldie†

Abstract ROUX, LARISSA, KAREN M. KUNTZ, CAM DONALDSON, AND SUE J. GOLDIE. Economic evaluation of weight loss interventions in overweight and obese women. Obesity. 2006;14:1093–1106. Objective: To conduct a clinical and economic evaluation of outpatient weight loss strategies in overweight and obese adult U.S. women. Research Methods and Procedures: This study was a lifetime cost-use analysis from a societal perspective, using a first-order Monte Carlo simulation. Strategies included routine primary care and varying combinations of diet, exercise, behavior modification, and/or pharmacotherapy. Primary data were collected to assess program costs and obesity-related quality of life. Other data were obtained from clinical trials, population-based surveys, and other published literature. This was a simulated cohort of healthy 35-year-old overweight and obese women in the United States. Results: For overweight and obese women, a three-component intervention of diet, exercise, and behavior modification cost $12,600 per quality-adjusted life year gained compared with routine care. All other strategies were either less effective and more costly or less effective and less costeffective compared with the next best alternative. Results were most influenced by obesity-related effects on quality of life and the probabilities of weight loss maintenance. Discussion: A multidisciplinary weight loss program consisting of diet, exercise, and behavior modification provides

Received for review March 1, 2005. Accepted in final form March 20, 2006. The costs of publication of this article were defrayed, in part, by the payment of page charges. This article must, therefore, be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. *Department of Community Health Sciences, University of Calgary, Calgary, Canada; †Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts; and ‡Department of Economics and Centre for Health Services Research, Centre for Health Services Research and Business School (Economics), University of Newcastle upon Tyne, Newcastle upon Tyne, United Kingdom. Address correspondence to Larissa Roux, Division of Nutrition and Physical Activity, Centers for Disease Control and Prevention, 5612 Elm Street, Vancouver, British Columbia V6N-1A4, Canada. E-mail: [email protected] Copyright © 2006 NAASO

good value for money, but more research is required to confirm the impacts of such programs on quality of life and the likelihood of long-term weight loss maintenance. Key words: cost-effectiveness, weight loss interventions, obesity treatments, economic evaluation, Monte Carlo simulation

Introduction Obesity has been classified as both an epidemic and as one of our greatest public health threats (1). From 1960 to 2000, the prevalence of excess weight (BMI ⱖ 25 kg/m2) increased from 44% to 64.5% of the adult U.S. population, and the prevalence of obesity (BMI ⱖ 30 kg/m2) doubled from 13% to 30.5% (1– 4). Although these trends were observed in both sexes and across all races, ethnicities, and educational levels, a recent study documented marked ethnic-based differences in rates of weight accumulated in young U.S. adults (particularly high rates in African-American and Hispanic women), with later birth cohorts experiencing earlier onset of obesity (5). In 1995, the total medical cost attributable to obesity in the United States was estimated at 99 billion dollars, and by 2000, that figure had increased to $117 billion (6,7). Alarming upward obesityrelated epidemiological and cost trends have also been observed in the United Kingdom (8). Obesity impacts the pathogenesis of a broad spectrum of illnesses by mechanical effects, as well as through metabolic and endocrine pathways (9), having been linked to increased mortality and chronic morbidity from hypertension, diabetes, sleep apnea, depression, and certain kinds of cancer (1,10 –16). Among the most serious public health consequences of obesity is type 2 diabetes and its associated complications, such as coronary heart disease (CHD)1 (1,10,13,14,17–20). Even a 10% reduction in BMI is considered to be clinically significant in reducing the risks of these sequala (21). Current non-surgical interventions for weight loss include dietary advice, physical activity, behavior modification, and

1 Nonstandard abbreviations: CHD, coronary heart disease; QALY, quality-adjusted life year.

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pharmacotherapy. These options can be difficult to adhere to, may require significant lifestyle modifications, and can be costly (22,23). To date, despite significant investment in such interventions, and indications by several governments to allocate more resources to them (7,8,10,24,25), the most efficient of these strategies remains unknown. Furthermore, clinical studies include relatively short-term follow-up and rely only on intermediate outcomes. Given such paucity of data and the need for governments to act now, decision analytic techniques offer an important set of tools to obtain a best estimate of the effectiveness and efficiency of alternative intervention strategies (26)—in this case, of several strategies to reduce BMI in overweight and obese women. Decision analysis is particularly useful where there is uncertainty about future costs and benefits; estimates of overall costs and benefits can be produced that take such uncertainty into account, and the sensitivity of results to assumptions can be tested, thus highlighting where the future research will have best pay-offs in terms of more precision. Also, a clear advantage of decision analysis over other evaluation methods is that several interventions can be evaluated within one model, representing the realistic situation with which many policy makers are faced in assessing obesity-reduction strategies. Using a decision analytic framework, this study applies the best available epidemiological and intervention data related to obesity and its management to an economic analysis, in the first effort to determine from a societal perspective which of several non-surgical weight loss strategies is the most economically efficient, relative to routine care.

Research Methods and Procedures A first-order Monte Carlo model (Data Professional for Windows; TreeAge Software; Williamstown, MA) was developed to simulate the natural history of a hypothetical cohort of 10,000 otherwise healthy, non-pregnant 35-yearold overweight and obese women (Figure 1). Because mortality rates for adult persons ⬍35 years of age are extremely low relative to those ⬎35 years of age (27), 35 years of age was used in this study as a baseline age from which women in the model cohort were followed. The model was used to project lifetime costs and gains in both life-years and quality-adjusted life years associated with routine primary care and alternative weight loss interventions, consisting of diet, exercise, behavior modification, and/or pharmacotherapy. After the reference case recommendations of the Panel on Cost-Effectiveness in Health and Medicine, the analytic perspective used was societal, and future costs and benefits were discounted at an annual rate of 3% (28). Future costs are reported in U.S. 2001 dollars. The relative performance of alternative strategies was assessed using a ratio of the additional cost of one program divided by additional quality-adjusted life years (QALYs) gained relative to that of the next most costly alternative (after eliminating dominated 1094


Figure 1: Schematic of the model. Women enter the model one at a time at age 35, free from known CHD. Each year, a woman’s BMI level predicts the risk of developing hypertension, type 2 diabetes, or hypercholesterolemia, which, in turn, predicts her risk of CHD.

options). A number of one-way sensitivity analyses were conducted within a Monte Carlo framework to determine the robustness of the final results. Such analyses were performed on all uncertain parameters. Particular attention was paid to parameters at the front end of the model (because these were assumed to be potentially most influential) and to those parameters for which data were obtained through primary data collection. Strategies The evaluated weight loss interventions each consisted of a 6-month intervention followed by a 6-month maintenance program. We elected to focus on interventions for which there were data available from well-designed randomized trials (Appendix, available online at the Obesity website, www.obesityresearch.org). These included 1) diet only; 2) diet and pharmacotherapy; 3) diet and exercise; and 4) diet, exercise, and behavior modification. Before any weight loss intervention, a woman was modeled to undergo a comprehensive screening protocol that included laboratory and diagnostic tests (e.g., serum lipid profile, electrocardiogram) and measurement of several an-


thropometric and physiological parameters (e.g., BMI and blood pressure determinations). The dietary component, in all modeled strategies but routine care, was defined as the reduction in caloric intake necessary to achieve a 10% weight loss under the supervision of a dietitian, in accordance with the American Heart Association guidelines (29,30). For strategies incorporating exercise, the exercise protocol was modeled to consist of three 45-minute structured exercise sessions per week of moderate intensity, led by a certified instructor, and two sessions per month to review clinical progress with an exercise therapist. The behavioral modification strategy was modeled to consist of a 1-hour cognitive therapy counseling session led by a psychologist every other week. The strategy that incorporated pharmacotherapy was modeled to consist of 120 mg of a lipase inhibitor (Orlistat, Hoffman-La Roche Ltd, Basel, Switzerland) prescribed three times a day during the 6-month weight loss intervention and one half this daily dose for the subsequent 6-month maintenance phase. In all strategies, women who successfully completed the 6-month weight loss intervention were subsequently modeled to enter a 6-month maintenance program. Women who were either unable to lose weight or maintain successful weight loss were assumed to remain at their age-adjusted original BMI. Model The model uses a state-transition framework, wherein the natural history of obesity in a cohort of hypothetical women is characterized as a sequence of annual transitions from one “health state” to another. Health states are chosen to describe a woman’s current health, relevant history, quality of life, and resource use patterns. The analysis is implemented as a Monte Carlo simulation, meaning that a random number generator and a set of estimated probabilities are used to determine the sequence of state-to-state clinical pathways a given woman will follow. The “first order” nature of the Monte Carlo simulation captures the variability of individual input parameters defining each woman’s medical profile as she enters the model (31). A point estimate for each parameter that defines her profile and that will drive her transition probabilities through the model was randomly drawn from a particular distribution at the model’s onset. At the initiation of the analysis, a 35-year-old overweight or obese woman, free from known CHD, enters the model. Her BMI is randomly chosen from a uniform distribution of BMIs ⬎24.9 kg/m2 (median BMI ⫽ 30 kg/m2) based on data from the Third National Health and Nutrition Examination Survey (32). In the absence of any intervention, a woman proceeds through the model starting at her original BMI, which is annually adjusted for age-related increases in BMI (33). Significant intervention-attributable weight loss is defined as a 10% BMI reduction after the completion of a 6-month weight loss program. Short-term success was defined as maintenance of a reduced BMI post-intervention

for at least 6 months. Long-term success was defined as maintenance of a reduced BMI for at least 5 years after the weight loss intervention. The probability of long-term maintenance of weight loss depends on the degree of lifestyle modification (34)—we assumed a probability of long-term maintenance of 20% with programs requiring behavior change (e.g., exercise) and 10% with programs without lifestyle adjustments (given weight loss success at 1 year), based on study findings from a small body of literature emerging to address long-term weight loss maintenance (35–39). It is assumed that women who successfully maintain their weight loss for at least 5 years remain at their post-intervention BMI for the remainder of their lifetime, based on evidence that has suggested that the chances of long-term success greatly increase for successful maintainers who have maintained a weight loss for 2 to 5 years (40). Each year, women are subject to age- and BMI-specific obesity-related disease complications. These complications are assumed to be lifelong and predispose affected women to a greater risk of CHD. We assumed that a woman who achieves only short-term success accrues the clinical benefits associated with a lower BMI until she regains weight. Women who do not lose weight or who lose weight but do not maintain the weight loss are subject to age-adjusted cardiovascular disease risks based on their original BMI. Each woman’s clinical course was tracked individually from the time of entry into the model until death, and a running tally was maintained of all clinical events. On a woman’s death, summary statistics (e.g., quality-adjusted survival and total lifetime costs) were recorded, and a new woman enters the model. The process was repeated until a total of 10,000 women passed through the model, at which point overall performance measures such as average life expectancy, QALYs, and costs were computed. A sample size of 10,000 women was necessary to reduce sampling error below the effect sizes of interest. Clinical Data Table 1 shows selected parameter estimates and plausible ranges used in the base case, derived from a comprehensive review of the published literature between 1985 and 2001. Preference was given to prospective, randomized trials with well-described intervention protocols, interventions with durations ⱖ3 months, and larger sample sizes. We identified 75 such trials, and each was reviewed independently by the principal author and a research assistant. We excluded studies that 1) did not provide baseline and post-intervention weight and height data with standard deviations, from which change in BMI could not be directly calculated; 2) evaluated unconventional interventions and non-aerobic exercise protocols; 3) included only men or children; and 4) focused on women with established comorbidities. Because the final selection of 21 studies (41– 62) still differed considerably in both design and methodology, we chose not to OBESITY Vol. 14 No. 6 June 2006

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Table 1. Model variables: base case values and ranges used in sensitivity analyses Variable Change in BMI (kg/m2) in compliant women, post-intervention* Routine care Diet only Diet and exercise Diet, exercise, behavior modification Diet and pharmacotherapy Probability of program compliance Routine care Diet only Diet and exercise Diet, exercise, behavior modification Diet and pharmacotherapy Probability of 10% weight loss at 6 months Routine care Diet only Diet and exercise Diet, exercise, behavior modification Diet and pharmacotherapy Probability of weight loss maintenance at one year Routine care Diet only Diet and exercise Diet, exercise, behavior modification Diet and pharmacotherapy Probability of long-term weight loss maintenance (at 5 years) Programs with lifestyle modification† Programs not incorporating lifestyle modification‡ Disease-specific quality weights Obesity§ 10% weight loss§ CHD Type 2 diabetes Mortality rates Annual all-cause rate in women CHD-specific rate in first year after diagnosis Annual CHD-specific rate in subsequent years Direct medical per participant program costs (6-month) (US$)¶ Routine care Diet only Diet and exercise

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Base case

Ranges

0.26 ⫺1.98 ⫺2.55 ⫺3.11 ⫺4.55

⫺0.60 to ⫹0.46 ⫺6.66 to ⫺1.02 ⫺3.92 to ⫺1.17 ⫺6.15 to ⫺2.31 ⫺5.94 to ⫺3.65

Sources 42–44,46,48,50–53,55,59

41,42,48,103 1 0.84 0.86 0.90 0.69

0.4 to 1.0 0.4 to 1.0 0.5 to 1.0 0.3 to 1.0

0.05 0.26 0.68 0.95 0.96

0.00 to 0.1 0.02 to 1.0 0.03 to 1.0 0.70 to 1.0 0.80 to 1.0 48,104–106

0.5 0.15 0.55 0.67 0.37

0.0 to 0.5 0.2 to 0.9 0.0 to 1.0 0.0 to 1.0

0.2

0.0 to 1.0

0.1

0.0 to 0.5

0.87 0.93 0.75 (age-adjusted) 0.75 (age-adjusted)

0.81 to 0.93 0.90 to 0.99 0.60 to 0.95 0.6 to 0.95

83 83

Age-specific 0.096 0.026

0.5x to 2.0x 0.5x to 2.0x

107 108 108

35–39

68,69,108 700 2150 2750

350 to 1050 1075 to 3225 1375 to 4125


Table 1. (continued) Variable

Base case

Ranges

Diet, exercise, behavior modification Diet and pharmacotherapy Direct non-medical and time-related per participant program costs (6-month) (US$)储 Routine care Diet only Diet and exercise Diet, exercise, behavior modification Diet and pharmacotherapy 6-month maintenance per-participant program costs (US$) Programs incorporating any combination of diet, exercise, and behavior modification Program incorporating pharmacotherapy Routine care program Cost of drug (Orlistat) per pill (US$) Annual comorbidity treatment cost (US$) Hypertension Hypercholesterolemia Type 2 diabetes CHD—first year CHD—subsequent years CHD—fatal US national average wage rates ($/h) Across all occupations Minimum wage Domestic childcare services Light cleaning duties Avg cost for personal urban travel, U.S. ($/mile) Discount rate for costs and benefits (%)

3040 2820

1520 to 4560 1410 to 4230

0 120 630 630 120

60 to 180 315 to 945 315 to 945 60 to 180

150 360 0 1.32 616.62 176.34 Age-specific 10,850 1710 3665

Sources

109

0.00 to 3.00

70 71,72

0.5x to 2.0x 0.5x to 2.0x 0.5x to 2.0x 0.5x to 2.0x 0.5x to 2.0x 0.5x to 2.0x 81

14.07 5.15 8.74 8.21 0.52 3

0.5x to 2.0x 0.5x to 2.0x 0.5x to 2.0x 0.5x to 2.0x 0.5x to 2.0x 0 to 10

80 28

CHD, coronary heart disease. * Program-specific ranges for change in BMI obtained from highest and lowest post-intervention weight loss estimates from 21 randomized controlled trials (see Appendix, available online at the Obesity web site, www.obesityresearch.org). † Strategy of diet, exercise, and behavior modification. ‡ Strategies without a behavioral modification component. § Estimated from an urban sample (n ⫽ 100) of women (mean age of 49 years and mean BMI of 31.8 kg/m2) enrolled in community weight management programs. Average percent reductions in life expectancy that study subjects were willing to give up to achieve weight loss were elicited, both for a sustained BMI reduction that would render subjects the average weight for their height (resulting in a quality weight for obesity ⫽ 0.87) and for a 10% reduction in BMI (derived quality weight ⫽ 0.93) through an imaginary treatment (a single pill taken once only, which was free of charge and side effects but which would not prevent or cure health problems, nor would incur a survival benefit). These quality weights were rescaled to economic uses between death (0) and perfect health (1), using methods previously described by Harris and Nease (110). ¶ Includes consultations (e.g., physician, nutritionist, nurse, exercise therapist, psychologist); laboratory tests, chest X-ray film, electrocardiogram and exercise stress test, and educational materials. 储 Includes costs related to dietary changes, exercise equipment, fitness monitoring devices, fitness apparel, and transportation and time costs.


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pool efficacy results. Instead, we used these studies to establish a wide plausible range for sensitivity analyses based on the lowest and highest values extrapolated. For the base case, we estimated efficacy using data from four studies, which were selected based on published criteria for randomized trial quality (63) that considered participant assignment, blinding, patient follow-up, and statistical analysis (Appendix, available online at the Obesity website, www.obesityresearch.org). We estimated a strategy-specific probability of weight loss using the following methods. Efficacy data obtained from randomized controlled trials for each strategy were used in an independent simulation to predict changes in BMI at the individual level for 1000 representative women in each strategy. Specifically, changes in BMI (⫾ standard deviation) after an intervention (i.e., efficacy), for a given intervention arm of a randomized controlled trial, served as input parameters into a random number generator, which assumed BMI changes followed a normal distribution. From these data, the random number generator calculated individual level post-intervention BMIs (from a fixed starting BMI ⫽ 30 kg/m2) for 1000 simulated women in each strategy. Based on these generated data, we determined the proportion that experienced at least a 10% BMI reduction for each strategy to reflect the probability for such a reduction in BMI. This parameter was defined as clinically significant weight loss in our model. To determine an annual age- and BMI-specific risk for CHD, we estimated the incidence of type 2 diabetes, conditional on BMI, using published data (64,65). We derived diastolic blood pressure and serum lipid values using multiple linear regression techniques, as a function of age and BMI, based on data from the Third National Health and Nutrition Examination Study (32). These data were used to create BMI-specific equations that determined risk factors such as annual diastolic blood pressure, serum lipid profiles, and presence or absence of type 2 diabetes. Finally, based on a woman’s post-intervention BMI and her profile of BMI-dependent risk factors, we calculated an annual probability of CHD (including angina pectoris, myocardial infarction, and cardiac arrest) using the Framingham 10-year CHD risk equation (66,67). We limited our study to the assessment of obesity’s consequences on CHD and its intermediaries, because the magnitude of these impacts bear the greatest societal burden, and these relationships are most clearly delineated by the available data to date. Costs and Quality of Life Microcosting techniques were used to estimate resource use associated with each weight-loss intervention. Direct medical costs (e.g., healthcare provider services, laboratory and other diagnostic tests, and drugs) were estimated using published data from the American Heart Association and the National Heart, Lung, and Blood Institute (10,30) and valued 1098


using average allowed Medicare reimbursement rates (68,69). Drug costs were obtained from the 2001 pharmaceutical pricing index, based on published average wholesale prices (70). Direct medical costs associated with obesity-related morbidity and mortality (e.g., diabetes, cardiovascular disease) included annual age- and sex-specific treatment-related costs for hypertension (diastolic blood pressure ⬎ 90 mm Hg), type 2 diabetes, and hypercholesterolemia (total cholesterol ⬎ 5.2 mM) (71). The age-specific direct medical costs associated with CHD for women represented a published weighted average of the expected management costs of non-fatal myocardial infarction, cardiac arrest, and angina pectoris (72–74). For those women who developed CHD, the costs associated with the first year after diagnosis were distinguished from those accrued in subsequent years. Annual age-specific direct healthcare costs that were not specific to obesity-related morbidity were included, as recommended by Meltzer and colleagues (75–78). Direct non-medical costs (e.g., fitness attire, travel costs, diet-related) were estimated using primary data collected from a survey of a community sample (n ⫽ 100) of female medical weight management program participants in an urban setting in Canada. To fully assess the impact of these costs, which affect both the individual and society yet are rarely covered by healthcare, resources used were carefully elicited, and their costs were estimated from this sample. These costs were anticipated to differentially contribute to overall programmatic costs across interventions. The survey elicited demographic information and cost information specific to women’s use of weight loss services, time and travel associated with program participation, and out-of-pocket expenses related to exercise, fitness clothing, and dietary practices. We used a modified version of a previously described generic United Kingdom cost and use survey (79). Women in this study had a mean age of 49 years and a mean BMI of 31.8 kg/m2. Ninety percent were white, 65% were married, and one half had at least some college education. Household annual income categories ranged from less than U.S. $25,000/yr to greater than U.S. $100,000/yr. Direct non-medical resources were valued using self-reported item prices. Self-reported mileage traveled to and from weight loss program classes and associated physician visits were used to estimate participant travel costs. Total annual distance traveled in miles was valued per participant, based on estimates from the American Automobile Association that were reported by the U.S. Bureau of Transportation (80). These per-person annual travel costs were totaled and averaged across all participants. Participant time costs were estimated. Using primary data from our sample of 100 weight loss program participants, time was valued by applying wage rates specific to their occupation. More than one half of the women (52%) worked full time. Those who worked part-time or less represented ⬃30% of the sample. We also repeated this analysis using


2001 U.S. national level average wage rate data to value time. Time lost from work or leisure activity was valued using the U.S. national average wage rate, obtained from the U.S. Bureau of Labor Statistics (81). Wage rates for domestic childcare and light duty cleaning services were used to wage time lost from performing household duties (81). All costs were updated to U.S. 2001 dollars using the medical care component of the Consumer Price Index (82). Quality weights for health states are shown in Table 1. General age-specific quality weights, derived from The Beaver Dam Outcome Study (83), were applied for women ⬎45 years of age. We adjusted the age-specific weights to reflect weight loss (using quality weights derived from our study sample described above; see Table 1) and comorbid diseases using a multiplicative function, although an additive function was explored in sensitivity analysis. Temporary decrements in quality of life attributable to the interventions were assumed to be related to the intensity of effort required to participate in a particular weight loss program and were assigned for a 6-month period. Given the minimal effort required to participate in routine care, this strategy was assumed to be associated with a quality weight ⫽ 1 (i.e., no quality-of-life decrement). However, through primary data analysis, it was observed that the intensity of the most comprehensive program of diet, exercise, and behavior modification did result in a decrement in quality of life, which was associated with a derived quality weight of 0.91. To reflect such temporary decrements for programs of intermediate intensities, interpolated quality weights between 0.91 and 1 were assigned.

Results Base Case The clinical and economic outcomes associated with each weight loss strategy are shown in Table 2. For a cohort of 10,000 otherwise healthy, overweight and obese 35-yearold women, the average undiscounted QALY gains ranged from 1.55 to 4.92 months depending on the intervention. For women receiving routine care, the discounted qualityadjusted life expectancy was 18.18 years, and lifetime costs were $121,100. The most effective and efficient strategy, a three-component intervention of diet, exercise, and behavior modification, increased quality-adjusted life expectancy to 18.43 years and cost $12,600 per QALY gained, compared with routine care. Excluding the influence of quality of life, the cost of this program rose to $60,400 per life-year gained. Each other strategy was either less effective and more costly (i.e., strongly dominated) or less effective and less costeffective (i.e., weakly dominated) compared with the next best alternative (Figure 2). Sensitivity Analyses Plausible changes in the parameters assigned to program costs, compliance with intervention, comorbidity-related

quality weights, drug costs, and mortality rate associated with CHD had minimal impact on the cost-use results (Figure 3). Results were most influenced by obesity-related effects on quality of life and the likelihood of long-term weight loss maintenance. The impact of changes in the quality of life attributable to obesity effects on body image and perceived attractiveness was greater than changes in the disease-related quality of life associated with comorbidities (e.g., type 2 diabetes). A woman’s overall probability of achieving long-term weight loss depends on multiple factors—program efficacy, compliance with intervention, and the likelihood of longterm maintenance of short-term successful weight loss. Despite varying each of these factors over wide plausible ranges, the most intensive three-component strategy was nearly always preferred to the next most effective strategy of diet and exercise. The three-component strategy would have to have either a probability of compliance ⬍70% (base case 90%), an intervention efficacy ⬍60% (base case 96%), likelihood of short-term weight loss maintenance ⬍34% (base case 67%), or the probability of long-term weight loss maintenance ⬍10% (base case 20%) for its incremental cost per QALY to be $50,000 (Figure 4). Figure 4 shows the relative impact of improvements in short- and long-term weight loss on the incremental costper-QALY ratios associated with the 3-component weight loss strategy compared to routine care. An increase in the likelihood of long-term maintenance from 20% to 40% resulted in a near 2-fold reduction in the ratio. A regimen of diet and pharmacotherapy was consistently less effective and less cost effective than the 3-component strategy of diet, exercise, and behavior modification. We conducted several threshold analyses focusing on drug costs and clinical effectiveness to identify conditions under which diet and pharmacotherapy would no longer be dominated by this 3-component strategy. Even if the drugs used in the diet and pharmacotherapy strategy were free, it was still more costly and less efficient than the 3-component strategy. When we assumed that short-term weight loss incurred in the diet and pharmacotherapy strategy was 2.5-fold higher than in the base case, a strategy of diet, exercise, and behavior modification had an incremental cost of $29,000 per QALY gained, compared with diet and pharmacotherapy. If, however, the overall probability for short-term weight loss success with diet and pharmacotherapy was 3-fold higher than the base case, the incremental cost-perQALY ratio for the 3-component strategy increased to nearly $140,000 per QALY saved (compared with diet and pharmacotherapy).

Discussion Obesity is widely regarded as a public health crisis, which does not seem to be abating (84 – 87). Understanding the OBESITY Vol. 14 No. 6 June 2006

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32.245 32.219† 32.374 32.338 32.655

43.747 43.749 43.825 43.827 43.874

Undiscounted quality-adjusted life expectancy

24.170

24.119 24.120 24.128 24.129

Discounted life expectancy

18.426‡

18.183 18.169† 18.248 18.255

Discounted quality-adjusted life expectancy

124,200

121,120 122,440 122,660 123,240

Discounted lifetime costs (US$)

60,390‡

§ § §

Cost-effectiveness ratio (US$/YLS)

12,640‡

¶ § §

Cost-effectiveness ratio (US$/QALY)*

YLS, years of life saved; QALY, quality-adjusted life year. * The difference in cost divided by the difference in quality-adjusted life expectancy for each strategy compared with the next best strategy. All strategies are assumed to be equally available. † The observed reduction in quality-adjusted life expectancy with the diet-only strategy when compared with routine care was the result of a program-specific decrement in quality of life modeled to reflect the inconvenience of dieting, which was absent from the routine care strategy. ‡ Comparator was routine care. § Strategy had a higher incremental cost-effectiveness ratio than a more effective alternative strategy and was, therefore, weakly dominated. ¶ Strategy shown cost more but was less effective than routine care and was, therefore, strongly dominated.

Routine care Diet only Diet and pharmacotherapy Diet and exercise Diet, exercise, and behavior modification

Weight loss strategy

Undiscounted life expectancy

Table 2. Costs, quality-adjusted life expectancy, and incremental cost-effectiveness of weight loss strategies in overweight and obese women at 35 years of age



Figure 2: Cost-per-QALY ratios for alternate weight loss strategies. Non-dominated strategies are shown on the efficient frontier depicted by the black line. Dominated strategies fall to the right of this line and include strategies that are both less effective and more costly than the next best strategy (i.e., strongly dominated strategies), and the less effective and less cost-effective strategies, or weakly dominated strategies, in which case a linear combination of two strategies (depicted by gray lines) results in both a lower cost and greater effectiveness than another single strategy.

well-documented adverse influences on length and quality of life has never been more relevant (88 –94). The potential lifetime impacts of curbing these negative effects have received less attention. One study by Oster et al. (95) examined the benefits of theoretically attained modest sustained weight loss, another conducted by Gregg et al. (27) explored the impacts of weight loss intention, and two meta-analyses performed by O’Meara et al. (96,97) summarized drug therapy–specific economic evaluations. To our knowledge, although behavioral, diet, exercise, and drug treatments, particularly when used in combination, are effective (88), ours is the first study to examine the long-term relative value of such conservative weight loss management approaches, using an evidence-based comparative analytic framework. For overweight and obese women, a three-component strategy of diet, exercise, and behavior modification com-

pared with routine care provided gains in quality-adjusted life expectancy. The incremental cost-effectiveness ratio for a three-component strategy of diet, exercise, and behavior modification in women was $12,600, relative to routine care. Other clinical services, such as risk reduction counseling for CHD and screening for type 2 diabetes, have been associated with estimated median costs of $74,000 and $57,000 per QALY gained, respectively (98,99). However, despite appearing to provide a reasonable return on resources invested, the “worthwhileness” of such a weight loss program would depend on the resources displaced to fund it. Gains in QALYs were on the order of months, whereas life expectancy gains were on the order of weeks. The observed reduction in QALYs with the diet-only strategy when compared with routine care was the result of a program-specific decrement in quality of life modeled to reflect

Figure 3: One-way sensitivity analyses. Ranges examined for selected model parameters are shown in brackets.


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Figure 4: Impact of short- and long-term weight loss maintenance on the incremental cost-effectiveness ratios for a weight loss strategy incorporating diet, exercise, and behavior modification relative to routine care. Dotted arrows depict the values used for the base case.

the inconvenience of dieting. Without the application of this program-specific quality decrement, the QALYs associated with dieting alone would be greater than that associated with routine care. Although program-associated decrements in quality of life also were associated with other strategies, they were diluted by the substantially greater magnitude of benefits they incurred. The QALY gains associated with the two programs incorporating exercise were likely underestimates because we did not include potential enhancements to quality of life associated with exercise that have been reported elsewhere (100). We found that the impact of quality-of-life adjustments were predominantly attributable to obesity effects on well being, rather than to effects of obesity-specific comorbidities. This is, in part, explained by the fact that all women in our model experienced obesity or were overweight, whereas only a small proportion of these women progressed to having developed obesity-related comorbities. In addition, the quality-of-life effects associated with being overweight or obese were experienced immediately on entry to the model and lasted throughout a woman’s lifetime, whereas the quality-of-life decrements associated with comorbidity, which in many instances devel1102


oped years after entry into the model, accrued farther along in her lifetime. These latter effects were disproportionately attenuated by discounting. Finally, the attractiveness of the threecomponent strategy increased substantially with a hypothetical improvement in quality of life experienced with weight loss, suggesting that an important component in the design of future weight management programs, beyond helping obese individuals to achieve a lower BMI, would be to highlight the positive consequences of weight loss on quality of life in these individuals (101). Although our results were most sensitive to the assumptions about the quality-of-life benefits associated with weight loss, they also depended on the likelihood of maintaining lost weight. Low rates of successful maintenance of weight loss remain the major weakness of obesity treatment (20). We found that even small improvements in the probabilities of short- or long-term weight loss maintenance resulted in substantially more attractive cost-per-QALY ratios. Finding novel ways, even if they are expensive, to improve the chances of successful maintenance of weight loss is a major priority and would likely be a good value for the resources invested.


Pressures may be imposed on healthcare providers to offer quick, more immediately satisfying, and less demanding interventions for weight loss, such as drug therapy, rather than strategies that require long-term changes in one’s lifestyle. Our results indicate that, from a public health perspective, drug therapy may not be the most efficient approach. Although a strategy of diet and drug therapy is associated with a marginally higher probability of weight loss at 6 months and is less costly than the 3-component strategy, relatively lower probabilities of treatment compliance and weight loss maintenance following drug therapy render it less effective and less cost effective. Even if Orlistat, prescribed for one year, were free, this strategy would not be considered efficient compared to the other strategies included in this model. Our estimates may be conservative because we did not include the potential decrements in quality of life associated with drug side effects or adverse events. The potential efficiency of including drug therapy as a second-line treatment for women who do not achieve sustained weight loss with strategies that incorporate lifestyle modification was beyond the scope of this study but is an important focus for future analyses. Our analyses have several main limitations. The choice of healthy 35-year-old women as our inception cohort, while streamlining the model in some ways, may have led to underestimation of the value of weight reduction interventions, which are likely beneficial in younger women and women with comorbidities. Information on intermediate outcomes was obtained from relatively short-term clinical studies. Assumptions were necessary to project maintenance of weight loss because few studies describe successful maintenance of weight loss beyond 1 year and strategyspecific post-intervention weight cycling. There are no published quality-of-life data on CHD or its predisposing conditions in the presence of obesity; therefore, we assumed a multiplicative relationship between the quality weights for obesity and other comorbid conditions— empiric data to inform the validity of this assumption are lacking. We assumed that obesity-related morbidity and mortality impacted on the risk of CHD through intermediaries of hypertension, hypercholesterolemia, and type 2 diabetes. However, this assumption was conservative, and if other obesityrelated comorbidities were included (e.g., cancer) (102), the cost-per-QALYs gained associated with an aggressive intervention would have been even more attractive. We recognize that the interventions may be more intensive than many currently available public programs, but at a time of consideration of options to deal with this epidemic, such interventions should be considered. Although we had the benefit of primary data collection for certain cost and use parameters, these data were derived for a small sample of women in a single urban setting, which may limit the generalization of these findings. However, parameter estimates were subject to extensive sensitivity analyses.

Our results suggest that well-designed multidisciplinary weight management programs will provide modest gains in QALYs for overweight and obese women and that a 3-component intervention consisting of diet, exercise, and counseling may represent good value for money. Long-term weight loss maintenance may seem largely improbable, but findings from the National Weight Control Registry have shown that maintenance of successful weight loss for 5 years substantially decreases the risk for subsequent weight regain (40), and our model takes account of the conditional probabilities for sustaining weight loss at 5 years being small. Greater focus on identifying determinants of weight loss maintenance and on the development and evaluation of programs specific to maintenance efforts is urgently warranted. It is anticipated that emerging therapeutic advances in obesity will facilitate these efforts and allow for refinements in modeling future impacts on welfare and costs. Finally, investments that improve the likelihood of longterm maintenance, even if costly, are likely to provide a good return in terms of health gain for population for resources invested.

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