PharmacoEconomics DOI 10.1007/s40273-015-0284-9
SHORT COMMUNICATION
The Effect of Obesity and Chronic Conditions on Medicare Spending, 1987–2011 Lindsay Allen1 • Ken Thorpe1 • Peter Joski1
Ó Springer International Publishing Switzerland 2015
Abstract Background Slowing the growth in Medicare expenditure is a key policy goal. Rising chronic disease prevalence is responsible for much of this growth. Objective The first goal of this study is to estimate the percentage of Medicare spending growth that is attributable to increasing disease prevalence rates of diabetes, hyperlipidaemia, hypertension and heart disease. Second, we estimate how much of this prevalence-related spending growth is attributable to rising obesity rates. Methods We employ spending decomposition equations to estimate the percentage of Medicare spending growth that is attributable to rising chronic disease prevalence, and we use two-part models to estimate the portion of prevalence-related spending that is potentially due to obesity. Results For our four conditions of interest, growing disease prevalence accounted for between 13.6 % (in heart disease) and 58.9 % (in hyperlipidaemia) of Medicare expenditure growth. Up to 17.0 % (in diabetes) of the expenditure growth due to prevalence increases may be attributable to obesity and therefore may be modifiable. Conclusions Rising obesity rates contribute to chronic disease prevalence, which, in turn, can lead to higher Medicare expenditures. To slow the growth in spending,
policy makers should consider targeting obesity, using approaches such as improving pharmacotherapy coverage and providing intensive care coordination services to Medicare enrollees.
Key Points for Decision Makers Medicare spending is growing considerably each year, contributing to the overall rise in US health care spending. A substantial portion of the rising expenditure is due to increasing rates of chronic disease in Medicare enrollees, which, in turn, can be traced back to obesity. Curbing obesity rates is therefore an important component of reducing Medicare expenditure in the USA. To this end, policy makers should consider providing pharmacotherapy coverage and intensive care coordination services to Medicare enrollees.
1 Introduction Electronic supplementary material The online version of this article (doi:10.1007/s40273-015-0284-9) contains supplementary material, which is available to authorized users. & Lindsay Allen [email protected] 1
Emory University, 1518 Clifton Rd, Atlanta, GA 30322, USA
Medicare spending grew by 3.4 % in 2013, reaching $585.7 billion, contributing considerably to the 3.6 % annual rise in overall national health expenditure in the USA [1]. Slowing this growth in Medicare expenditure has become a particularly salient public policy goal, with several proposals earning attention in policy circles. For any of these plans to yield effective results, policy makers must
L. Allen et al.
have a clear understanding of which factors are driving rising expenditures. One established reason for the long-term growth in Medicare spending is the increase in the prevalence of chronic diseases among the senior population [2]. Previous estimates placed the percentage of Medicare expenditure growth attributable to increased prevalence at 77.7 %— more than double that among the privately insured population (33.5 %) [3]. Given the well-known link between obesity and chronic conditions such as diabetes and heart disease, a sizable proportion of rising Medicare expenditures may be directly traced back to rising obesity rates in the aging US population. According to recent estimates by the Department of Health and Human Services, 28 % of US individuals aged 60 years or older are obese, placing both the current and the next generations of Medicare enrollees at risk for several expensive chronic conditions [4]. In this paper, we estimate the percentage of Medicare spending growth that is potentially attributable to rising obesity rates for four highly prevalent chronic conditions: diabetes, hypertension, hyperlipidaemia and heart disease. To the extent that obesity is a modifiable risk factor for these conditions, our results will help inform policy makers about the value of interventions that directly target obesity. We briefly review the therapeutic options for obesity that are currently covered by Medicare, and we propose coverage of additional approaches that have been empirically demonstrated to be effective.
2 Methods The data for these analyses come from two nationally representative surveys of the non-institutionalized US population: the 1987 National Medical Expenditure Survey (NMES) and the 2011 Medical Expenditure Panel Survey (MEPS) [5]. The NMES was conducted only once, in 1987, and served as a precursor to the MEPS, which began in 1996 and is repeated with a new cross-sectional sample of households every year. Both the NMES and the MEPS collected information on participants at the family and individual levels, using a large-scale survey approach. The information provided by participants in both the NMES and MEPS is supplemented by data from their medical providers and their employers, which allow us to work with actual health care expenditures.
In the first part of our analysis, which expands on previous work [3], we perform a three-way decomposition of health care spending growth over time for four major chronic conditions (diabetes, hypertension, hyperlipidaemia and heart disease). We measure expenditure by linking each selfreported medical encounter to a maximum of four disease states, using diagnosis codes. Using the Agency for Healthcare Research and Quality (AHRQ) Clinical Classifications Software for the International Classification of Diseases Ninth Revision (ICD-9), we collapse the ICD-9 codes into broader disease categories [6]. For every physician interaction, outpatient care visit, inpatient stay, emergency care encounter, home-health service or prescription medication, we use payment information for the event to calculate individual-level spending per condition per encounter. Because 1987 spending data are reported in charges, we adjust them to payments using the methods of Zuvekas and Cohen [7]. To avoid double-counting expenditures, we distribute total encounter spending equally across all conditions associated with the visit, if more than one unrelated disease state is noted. By aggregating spending information across all individuals who report having a particular disease state, we obtain yearly spending per condition. We limit our sample population to individuals enrolled in Medicare for 6 months or more at the time of the survey. A small number of these individuals are under the age of 65 years and receive disability benefits from Medicare; we control for age in our models for this reason. For each condition, we estimate the proportion of total health care spending growth that is attributable to increasing treated disease prevalence, rising spending per treated case and expanding Medicare enrollment (i.e. population growth). For each of these individual elements, we calculate a counterfactual per capita spending level that represents how changes in the factor alone (e.g. treated prevalence) would have affected 2011 spending, had factors (spending per case and population growth) other than the one in question remained constant. We then compare this counterfactual spending growth estimate with actual spending growth for each of the factors in turn, allowing us to isolate the fraction of spending growth attributable to each element. The following decomposition equations (where ‘Pop.’ represents the number of Medicare enrollees, ‘Prev’ represents prevalence rate) illustrate our analytic strategy for computing the three-way decomposition, using diabetes as an example:
The Effect of Obesity and Chronic Conditions
% of Spending growth due to prevalence ¼
½ð2011 Diabetes Prev: 1987 Diabetes Prev:Þ 2011 Spending per case 1987 Pop: 2011 Total diabetes spending 1987 Total diabetes spending
% of Spending growth due to cost per case ¼
½ð2011 Spending per case 1987 Spending per caseÞ 1987 Diabetes Prev: 1987 Pop: 2011 Total diabetes spending 1987 Total diabetes spending
% of Spending growth due to population growth ¼
½ð2011 Pop: 1987 Pop:Þ 2011 Diabetes Prev: 2011 Spending per case 2011 Total diabetes spending 1987 Total diabetes spending
For example, to determine the percentage of spending growth due to the rising rate of people in Medicare who have diabetes, we multiply the difference between the 1987 and 2011 diabetes prevalence rates by the 2011 spending per case and by the 1987 population, so that we hold costs and population constant and can isolate the effect of only the change in prevalence. Taking this as the numerator over a denominator of the change in total diabetes spending, we are able to estimate the portion of spending growth that is attributable to rising diabetes prevalence only. To isolate the effect of rising spending per case, we estimate the difference in spending per case, then multiply that by the 1987 diabetes prevalence and population size. When we compare it with the denominator of change in total diabetes spending, we obtain the proportion of spending growth that is attributable only to rising spending per case, holding other factors constant. Finally, the population growth equation multiplies the difference in population size between both years by current prevalence rates and spending per case, all over the change in diabetes spending, to obtain the relative contribution of population growth to rising diabetes spending. In our second analysis, we estimate the percentage of condition-specific spending growth that is due to increasing obesity rates. For this analysis, we make use of a two-part regression model. Two-part models are popular in the health care expenditure literature because many individuals in the population have zero health care spending in a given year [8]. To address this econometrically problematic ‘zero spike’, we divide our estimation into two stages. In the first stage, we run a logistic regression model, obtaining the probability of an individual having any health care expenditure, with indicators for each body mass index (BMI) category [underweight \18.5 kg/m2; normal 18.5 to \25 kg/m2 (reference group); overweight 25 to \30 kg/m2; obese C30 kg/m2] serving as our primary independent variables of interest. In the second stage, we use a generalized linear model with a log-link—a method recommended by Buntin and Zaslavsky [8] for modelling expenditure data in the Medicare population—to estimate
the effect of each of these BMI categories on expenditure level. By multiplying the results of the two stages together, we obtain total predicted spending for everyone in the sample, for each condition. We repeat this two-step process for each of the four disease conditions, and we run this set of four models twice, with 1987 data and then with 2011 data, for a total of eight sets of results [See the Appendix in the Electronic Supplementary Material for model specification and first- and second-stage results]. For each BMI category that is not the normal category (i.e. 18.5 to \25 kg/m2), we obtain the ratio of its predicted spending level to that of the normal BMI category. For each BMI category, we multiply the current spending ratio (using a ratio of 1.0 for those in the normal BMI category) by predicted normal BMI spending and apply each resulting expenditure amount to the distribution of BMI that existed in 1987. This calculation yields a counterfactual estimate of what predicted spending would be in 2011, had the BMI distribution from 1987 not changed but other factors (e.g. spending ratios) had. For each condition, the difference between the counterfactual predicted expenditure level and actual predicted expenditure, divided by the actual predicted expenditure level, yields the obesity-attributable percentage of health care expenditure growth. The covariates for the first and second stages are the same and include female sex; age; education (less than high school, high school graduate, some college, college degree); race/ethnicity (Hispanic, non-Hispanic black, other); income as a percentage of the federal poverty level (\100, 100–199, 200–399, C400 %); marital status (married, not married); family size and an indicator for whether a respondent reported being the head of a household; and region of the country (Midwest, South, West, Northeast). To estimate the portion of condition-specific, prevalence-related total spending growth that can be specifically linked to obesity, we multiply the percentage of total condition spending growth attributable to rising prevalence by the portion of spending growth that is attributable to obesity. The resulting percentage can be thought of as the fraction of total condition-specific spending growth that
L. Allen et al. Table 1 Descriptive statistics for individuals with selected chronic conditions enrolled in Medicare for C6 months in 1987 and 2011 Characteristic
1987 Diabetes
2011 Hypertension
Hyperlipidaemia
Heart disease
Diabetes
Hypertension
Hyperlipidaemia
Heart disease
Sample size (n)
597
1788
133
1260
1090
2655
2114
1172
Underweight [\18.5 kg/m2] (%)
2.2
3.0
1.8
5.4
0.5
1.7
1.1
2.3
Normal [18.5 to \25 kg/m2] (%)
30.0
37.0
48.9
45.8
15.4
27.0
25.6
27.4
Overweight [25 to \30 kg/m2] (%)
42.1
41.3
38.4
35.3
32.0
35.9
37.8
36.1
Obese [C30 kg/m2] (%)
25.6
18.7
10.9
13.6
52.1
35.4
35.5
34.2
Table 2 Contribution of rising prevalence, spending per case, and population growth to rising expenditures for selected chronic conditions in individuals enrolled in Medicare for six or more months, 1987–2011 Factors associated with total spending growth
Diabetes
Hypertension
Hyperlipidaemia
Treated prevalence (%)
39.8
36.6
58.9
13.6
8.7
6.2
0.7
11.2
51.5
57.2
40.4
75.2
Rising spending per case (%) Population growth (%)
could be potentially avoided through the reduction of obesity levels. All analyses were run in Stata 13 software using survey estimation commands to adjust for the complex survey designs of the MEPS and NMES. All dollar estimates are displayed in 2011 US dollars, which were calculated using the gross domestic product personal consumption deflator [9].
3 Results Table 1 summarizes the distribution of BMI categories for each condition of interest. In 1987, the vast majority of individuals with diabetes or cardiovascular conditions fell into the normal or overweight BMI categories. By 2011, this distribution had shifted, with most individuals with these conditions being classified as overweight or obese. Table 2 shows that the majority of the increase in spending growth from 1987 to 2011 for the conditions of interest is due to population growth, holding prevalence rates and spending per case constant. Rising spending per case, on the other hand, appears to play a much less important role in rising spending levels. A sizable portion of spending growth is due to rising chronic disease prevalence, which is the focal point of this paper. Almost 40 % of Medicare diabetes expenditure is attributable to the growing rate of individuals who have diabetes. Large percentages of the expenditure growth for hypertension (36.6 %) and hyperlipidaemia (58.9 %) are also caused by rising treated prevalence. For heart disease, this number is smaller (13.6 %) yet not insignificant.
Heart disease
One potential reason for the smaller relative contribution of treated prevalence rates to heart disease spending growth has to do with the definition of disease we use in this study. In our data source, individuals are asked if they have ever been diagnosed with a certain condition by a physician.1 Diabetes, hypertension and hyperlipidaemia are more often diagnosed in primary care settings, whereas heart disease frequently goes undetected until it causes a cardiovascular event. Therefore, population growth might appear to be contributing disproportionately to spending increases for hypertension. A second possible reason is that age-adjusted heart disease prevalence rates are falling in the Medicare population (and across all other age groups) [10], whereas the prevalence of diabetes is growing [11], rates of hypertension are steady [12] and screenings for hyperlipidaemia are growing more common [13]. The results in the second row of Table 2 suggest that rising treatment costs are generally not contributing meaningfully to expenditure growth. In contrast, the last row of the table shows that population growth is responsible for a large portion of expenditure growth. Holding prevalence at a constant rate and treatment costs steady, we find that just about half of Medicare diabetes expenditure can be explained by population increases. In contrast, markedly less (8.7 %) of the expenditure increase is attributable to higher treatment costs for diabetes. This pattern holds for the remaining disease states, as well. 1
This is why we use the phrase ‘treated [or diagnosed] prevalence’ and not ‘overall prevalence’. To estimate overall prevalence, we would have had to also capture the rates of undiagnosed prevalence in the population.
The Effect of Obesity and Chronic Conditions Table 3 Percentage of spending growth attributable to obesity-related prevalence increases for selected chronic conditions in individuals enrolled in Medicare for six or more months, 1987–2011 Diabetes
Hypertension
Hyperlipidaemia
Heart disease
Percentage of all spending growth attributable to treated prevalence (from Table 2)
39.8
36.6
58.9
13.6
Percentage of all spending growth attributable to obesity
42.7
18.2
8.6
13.6
Percentage of prevalence-related spending growth attributable to obesity
17.0
5.7
5.1
1.8
The second row of Table 3 displays the results from the second part of our analyses—the degree to which rising obesity rates have contributed to rising health expenditure for each condition. We estimate that 42.7 % of the increase in spending for diabetes is attributed to the shifting distribution of BMI levels in the USA. Obesity accounts for meaningful, though smaller, fractions of hypertension (18.2 %), hyperlipidaemia (8.6 %) and heart disease (13.6 %) spending increases. Since we are able to estimate the percentage of condition-specific spending growth that is related to rising treated prevalence, and the percentage of condition-specific spending growth that is due to obesity, we can estimate the percentage of condition-specific, prevalence-related spending growth that can be traced back to obesity. In the last row of Table 3, we estimate that 17.0 % of total spending growth for the treatment of diabetes in the Medicare population is due to a prevalence increase that is directly attributable to rising obesity. Similarly, obesity alone may account for small but economically meaningful percentages of the increase in total spending that is due to the rising prevalence rates of hypertension, hyperlipidaemia and heart disease in the Medicare population. These numbers represent the portion of disease-specific spending that is potentially avoidable through the reduction of obesity rates in the USA.
4 Discussion In this paper, we have estimated that sizable portions of condition-specific Medicare spending growth might be eliminated by reducing obesity rates and, successively, chronic disease prevalence. In previous work, we projected the effect that rising obesity prevalence will have on younger individuals, which will, in turn, affect Medicare spending as the population ages [14]. If obesity continues to increase at its current rate, we estimated that in 2018, the USA will spend almost $350 billion on health care costs that are directly attributable to obesity alone. This number would drop to about $150 billion if obesity rates were held at their current level [15]. In addition to the conditions studied in this paper, obesity is associated with some cancers, cognitive and functional declines, and increased nursing home
admission—all of which amplify the impact of obesity on Medicare expenditures [16]. Therefore, slowing the growth of obesity in the current and future Medicare populations represents a key priority for policy makers. Currently, Medicare provides coverage for two forms of obesity: intensive behaviour therapy (IBT) for individuals with a BMI of 30 kg/m2 or more [17] and bariatric surgery for some morbidly obese individuals [18]. Each of these interventions is characterized by some limitations: IBT may be lacking in feasibility and efficacy [19, 20], while bariatric surgery comes with surgical risks and high costs [21]. An effective, non-invasive alternative to these two interventions is pharmacotherapy, which can be delivered alone or in conjunction with behavioural lifestyle interventions [22–25]. However, at this time, Medicare does not provide coverage for pharmaceutical interventions for obesity, despite the availability of at least seven US Food and Drug Administration–approved weight-loss drugs [26]. Providing coverage for obesity-related drug therapy could be an effective mechanism for reducing obesity rates in the Medicare population. Our results highlight the interrelationship between obesity, chronic conditions and health care spending in the Medicare population: rising obesity rates increase the prevalence of chronic conditions, which, in turn, raises treatment expenditures. In the other direction, the presence of chronic conditions might also amplify obesity rates, if those conditions prevent individuals from maintaining a healthy weight. Given that the disease states we examined in this paper tend to co-occur, any obesity-related policy efforts to address curbing health expenditure growth and to improve health in the USA should also prioritize the management of multiple chronic conditions. Currently, Medicare has no such care coordination infrastructure in place. One proposed solution to this unmet need is the addition of a new benefit option for individuals enrolled in Medicare. Termed ‘Medicare Integrate’, this alternative to Medicare Advantage would emphasize evidence-based care coordination [27]. Enrollees would have access to medication management assistance, transitional care from the inpatient to the outpatient setting and broad access to both health coaches and nurse care coordinators. In addition to improving health outcomes, this programme has the potential to save costs in the long run.
L. Allen et al.
Our methodological approach should be taken in the context of its limitations. First, our data are cross-sectional, allowing us only a snapshot of data in time. Future research could apply a similar framework to panel data to examine dynamic year-to-year changes in the relationship between obesity, prevalence and spending. Second, we estimate the percentage of spending due to treated prevalence separately from that due to obesity, and we make the assumption that we can combine these results into an estimate of conditionspecific, obesity-related spending growth. This approach likely introduces some additional error into our estimates.
5 Conclusion In the Medicare population, rising obesity rates appear to be responsible for a meaningful portion of growing chronic disease prevalence. These conditions, in turn, are responsible for much of the recent rise in health care expenditure. To combat the rise of obesity, Medicare policy makers might consider the potential benefit of providing pharmacotherapy coverage and intensive care coordination to its members. Acknowledgments The authors thank the Robert Wood Johnson Foundation for its generous support of this work, and the anonymous peer reviewers for their helpful comments. Any errors are the responsibility of the authors. Conflict of interest
None.
Author contributions As part of KT’s larger Robert Wood Johnson Foundation project, KT and LA collaborated on this paper’s concept and design. LA drafted the manuscript. PJ performed the statistical analyses. KT is the guarantor for the overall content.
References 1. Centers for Medicare & Medicaid Services. NHE fact sheet. 2014. http://www.cms.gov/Research-Statistics-Data-and-Systems/ Statistics-Trends-and-Reports/NationalHealthExpendData/NHEFact-Sheet.html. Accessed 20 Apr 2015. 2. Thorpe KE, Howard DH. The rise in spending among Medicare beneficiaries: the role of chronic disease prevalence and changes in treatment intensity. Health Aff. 2006;25(5):w378–88. 3. Thorpe KE. Treated disease prevalence and spending per treated case drove most of the growth in health care spending in 1987–2009. Health Aff. 2013;32(5):851–8. 4. Administration on Aging, Administration for Community Living, US Department of Health and Human Services. A profile of older Americans. 2012. http://www.aoa.gov/Aging_Statistics/Profile/ 2012/docs/2012profile.pdf. Accessed 20 Apr 2015. 5. Agency for Health Care Research and Quality. Medical Expenditure Panel Survey: survey background. 2009. http://meps.ahrq. gov/mepsweb/about_meps/survey_back.jsp. Accessed 23 Apr 2014.
6. Healthcare Cost and Utilization Project. Clinical Classifications Software (CCS) for ICD-9-CM. 2015. www.hcup-us.ahrq.gov/ toolssoftware/ccs/ccs.jsp. Accessed 20 Apr 2015. 7. Zuvekas SH, Cohen JW. A guide to comparing health care expenditures in the 1996 MEPS to the 1987 NMES. Inquiry. 2002;39(1):76–86. 8. Buntin MB, Zaslavsky AM. Too much ado about two-part models and transformation: comparing methods of modeling Medicare expenditures. J Health Econ. 2004;23(3):525–42. 9. Williamson SH. What was the US GDP then? MeasuringWorth.com. 2014. http://www.measuringworth.org/usgdp/. Accessed 20 Apr 2015. 10. Centers for Disease Control and Prevention. Prevalence of coronary heart disease—United States, 2006–2010. 2014. http:// www.cdc.gov/mmwr/preview/mmwrhtml/mm6040a1.htm. Accessed 20 Apr 2015. 11. Centers for Disease Control and Prevention. Rate per 100 of civilian, noninstitutionalized population with diagnosed diabetes, by age, United States, 1980–2011. 2014. http://www.cdc.gov/ diabetes/statistics/prev/national/figbyage.htm. Accessed 20 Apr 2015. 12. Yoon SS, Ostchega Y, Louis T. Recent trends in the prevalence of high blood pressure and its treatment and control, 1999–2008. NCHS Data Brief. 2010;48:1–8. 13. Kuklina EV, Carroll MD, Shaw KM, Hirsch R. Trends in high LDL cholesterol, cholesterol-lowering medication use, and dietary saturated-fat intake: United States, 1976–2010. NCHS Data Brief. 2013;117:1–8. 14. Thorpe KE, Florence CS, Howard DH, Joski P. The impact of obesity on rising medical spending. Health Aff (Millwood). 2004;Suppl Web Exclusives:W4-480-6. 15. Thorpe KE. The future costs of obesity: national and state estimates of the impact of obesity on direct health care expenses. Washington, DC: United Health Foundation and American Public Health Association; 2009. 16. Felix HC, West DS. Effectiveness of weight loss interventions for obese older adults. Am J Health Promot. 2013;27(3):191–9. 17. Centers for Medicare & Medicaid Services. Decision memo for intensive behavioral therapy for obesity (CAG-00423N). Baltimore: Centers for Medicare and Medicaid Services; 2011. http:// www.cms.gov/medicare-coverage-database/details/nca-decisionmemo.aspx?&NcaName=Intensive%20Behavioral%20Therapy% 20for%20Obesity&bc=ACAAAAAAIAAA&NCAId=253&. Accessed 20 Apr 2015. 18. Centers for Medicare & Medicaid Services. Decision memo for bariatric surgery for the treatment of morbid obesity (CAG00250R). Baltimore: Centers for Medicare and Medicaid Services; 2006. http://www.cms.gov/medicare-coverage-database/details/ nca-decision-memo.aspx?NCAId=160&NcaName=Bariatric? Surgery?for?the?Treatment?of?Morbid?Obesity?%281st? Recon%29&bc=ACAAAAAAEAAA&. Accessed 20 Apr 2015. 19. Pagoto SL, Pbert L, Emmons K. The Society of Behavioral Medicine position statement on the CMS decision memo on intensive behavior therapy for obesity. Transl Behav Med. 2012;2(4):381–3. 20. Carvajal R, Wadden TA, Tsai AG, Peck K, Moran CH. Managing obesity in primary care practice: a narrative review. Ann N York Acad Sci. 2013;1281:191–206. 21. Flum DR, Kwon S, MacLeod K, et al. The use, safety and cost of bariatric surgery before and after Medicare’s national coverage decision. Ann Surg. 2011;254(6):860–5. 22. Wadden TA, Berkowitz RI, Womble LG, et al. Randomized trial of lifestyle modification and pharmacotherapy for obesity. N Engl J Med. 2005;353(20):2111–20.
The Effect of Obesity and Chronic Conditions 23. Ackermann RT, Marrero DG. Adapting the Diabetes Prevention Program lifestyle intervention for delivery in the community: the YMCA model. Diabetes Educ. 2007;33(1):69, 74–5, 77–8. 24. Thorpe KE, Yang Z. Enrolling people with prediabetes ages 60–64 in a proven weight loss program could save Medicare $7 billion or more. Health Aff. 2011;30(9):1673–9. 25. Bennett GG, Steinberg DM, Stoute C, et al. Electronic health (eHealth) interventions for weight management among racial/
ethnic minority adults: a systematic review. Obes Rev. 2014;15(Suppl 4):146–58. 26. Yanovski SZ, Yanovski JA. Long-term drug treatment for obesity: a systematic and clinical review. JAMA. 2014;311(1):74–86. 27. Thorpe KE. Medicare integrate: a new benefit option for Medicare beneficiaries. Health Aff Blog. 2013. http://healthaffairs.org/ blog/2013/07/23/medicare-integrate-a-new-benefit-option-formedicare-beneficiaries/.
SHORT COMMUNICATION
The Effect of Obesity and Chronic Conditions on Medicare Spending, 1987–2011 Lindsay Allen1 • Ken Thorpe1 • Peter Joski1
Ó Springer International Publishing Switzerland 2015
Abstract Background Slowing the growth in Medicare expenditure is a key policy goal. Rising chronic disease prevalence is responsible for much of this growth. Objective The first goal of this study is to estimate the percentage of Medicare spending growth that is attributable to increasing disease prevalence rates of diabetes, hyperlipidaemia, hypertension and heart disease. Second, we estimate how much of this prevalence-related spending growth is attributable to rising obesity rates. Methods We employ spending decomposition equations to estimate the percentage of Medicare spending growth that is attributable to rising chronic disease prevalence, and we use two-part models to estimate the portion of prevalence-related spending that is potentially due to obesity. Results For our four conditions of interest, growing disease prevalence accounted for between 13.6 % (in heart disease) and 58.9 % (in hyperlipidaemia) of Medicare expenditure growth. Up to 17.0 % (in diabetes) of the expenditure growth due to prevalence increases may be attributable to obesity and therefore may be modifiable. Conclusions Rising obesity rates contribute to chronic disease prevalence, which, in turn, can lead to higher Medicare expenditures. To slow the growth in spending,
policy makers should consider targeting obesity, using approaches such as improving pharmacotherapy coverage and providing intensive care coordination services to Medicare enrollees.
Key Points for Decision Makers Medicare spending is growing considerably each year, contributing to the overall rise in US health care spending. A substantial portion of the rising expenditure is due to increasing rates of chronic disease in Medicare enrollees, which, in turn, can be traced back to obesity. Curbing obesity rates is therefore an important component of reducing Medicare expenditure in the USA. To this end, policy makers should consider providing pharmacotherapy coverage and intensive care coordination services to Medicare enrollees.
1 Introduction Electronic supplementary material The online version of this article (doi:10.1007/s40273-015-0284-9) contains supplementary material, which is available to authorized users. & Lindsay Allen [email protected] 1
Emory University, 1518 Clifton Rd, Atlanta, GA 30322, USA
Medicare spending grew by 3.4 % in 2013, reaching $585.7 billion, contributing considerably to the 3.6 % annual rise in overall national health expenditure in the USA [1]. Slowing this growth in Medicare expenditure has become a particularly salient public policy goal, with several proposals earning attention in policy circles. For any of these plans to yield effective results, policy makers must
L. Allen et al.
have a clear understanding of which factors are driving rising expenditures. One established reason for the long-term growth in Medicare spending is the increase in the prevalence of chronic diseases among the senior population [2]. Previous estimates placed the percentage of Medicare expenditure growth attributable to increased prevalence at 77.7 %— more than double that among the privately insured population (33.5 %) [3]. Given the well-known link between obesity and chronic conditions such as diabetes and heart disease, a sizable proportion of rising Medicare expenditures may be directly traced back to rising obesity rates in the aging US population. According to recent estimates by the Department of Health and Human Services, 28 % of US individuals aged 60 years or older are obese, placing both the current and the next generations of Medicare enrollees at risk for several expensive chronic conditions [4]. In this paper, we estimate the percentage of Medicare spending growth that is potentially attributable to rising obesity rates for four highly prevalent chronic conditions: diabetes, hypertension, hyperlipidaemia and heart disease. To the extent that obesity is a modifiable risk factor for these conditions, our results will help inform policy makers about the value of interventions that directly target obesity. We briefly review the therapeutic options for obesity that are currently covered by Medicare, and we propose coverage of additional approaches that have been empirically demonstrated to be effective.
2 Methods The data for these analyses come from two nationally representative surveys of the non-institutionalized US population: the 1987 National Medical Expenditure Survey (NMES) and the 2011 Medical Expenditure Panel Survey (MEPS) [5]. The NMES was conducted only once, in 1987, and served as a precursor to the MEPS, which began in 1996 and is repeated with a new cross-sectional sample of households every year. Both the NMES and the MEPS collected information on participants at the family and individual levels, using a large-scale survey approach. The information provided by participants in both the NMES and MEPS is supplemented by data from their medical providers and their employers, which allow us to work with actual health care expenditures.
In the first part of our analysis, which expands on previous work [3], we perform a three-way decomposition of health care spending growth over time for four major chronic conditions (diabetes, hypertension, hyperlipidaemia and heart disease). We measure expenditure by linking each selfreported medical encounter to a maximum of four disease states, using diagnosis codes. Using the Agency for Healthcare Research and Quality (AHRQ) Clinical Classifications Software for the International Classification of Diseases Ninth Revision (ICD-9), we collapse the ICD-9 codes into broader disease categories [6]. For every physician interaction, outpatient care visit, inpatient stay, emergency care encounter, home-health service or prescription medication, we use payment information for the event to calculate individual-level spending per condition per encounter. Because 1987 spending data are reported in charges, we adjust them to payments using the methods of Zuvekas and Cohen [7]. To avoid double-counting expenditures, we distribute total encounter spending equally across all conditions associated with the visit, if more than one unrelated disease state is noted. By aggregating spending information across all individuals who report having a particular disease state, we obtain yearly spending per condition. We limit our sample population to individuals enrolled in Medicare for 6 months or more at the time of the survey. A small number of these individuals are under the age of 65 years and receive disability benefits from Medicare; we control for age in our models for this reason. For each condition, we estimate the proportion of total health care spending growth that is attributable to increasing treated disease prevalence, rising spending per treated case and expanding Medicare enrollment (i.e. population growth). For each of these individual elements, we calculate a counterfactual per capita spending level that represents how changes in the factor alone (e.g. treated prevalence) would have affected 2011 spending, had factors (spending per case and population growth) other than the one in question remained constant. We then compare this counterfactual spending growth estimate with actual spending growth for each of the factors in turn, allowing us to isolate the fraction of spending growth attributable to each element. The following decomposition equations (where ‘Pop.’ represents the number of Medicare enrollees, ‘Prev’ represents prevalence rate) illustrate our analytic strategy for computing the three-way decomposition, using diabetes as an example:
The Effect of Obesity and Chronic Conditions
% of Spending growth due to prevalence ¼
½ð2011 Diabetes Prev: 1987 Diabetes Prev:Þ 2011 Spending per case 1987 Pop: 2011 Total diabetes spending 1987 Total diabetes spending
% of Spending growth due to cost per case ¼
½ð2011 Spending per case 1987 Spending per caseÞ 1987 Diabetes Prev: 1987 Pop: 2011 Total diabetes spending 1987 Total diabetes spending
% of Spending growth due to population growth ¼
½ð2011 Pop: 1987 Pop:Þ 2011 Diabetes Prev: 2011 Spending per case 2011 Total diabetes spending 1987 Total diabetes spending
For example, to determine the percentage of spending growth due to the rising rate of people in Medicare who have diabetes, we multiply the difference between the 1987 and 2011 diabetes prevalence rates by the 2011 spending per case and by the 1987 population, so that we hold costs and population constant and can isolate the effect of only the change in prevalence. Taking this as the numerator over a denominator of the change in total diabetes spending, we are able to estimate the portion of spending growth that is attributable to rising diabetes prevalence only. To isolate the effect of rising spending per case, we estimate the difference in spending per case, then multiply that by the 1987 diabetes prevalence and population size. When we compare it with the denominator of change in total diabetes spending, we obtain the proportion of spending growth that is attributable only to rising spending per case, holding other factors constant. Finally, the population growth equation multiplies the difference in population size between both years by current prevalence rates and spending per case, all over the change in diabetes spending, to obtain the relative contribution of population growth to rising diabetes spending. In our second analysis, we estimate the percentage of condition-specific spending growth that is due to increasing obesity rates. For this analysis, we make use of a two-part regression model. Two-part models are popular in the health care expenditure literature because many individuals in the population have zero health care spending in a given year [8]. To address this econometrically problematic ‘zero spike’, we divide our estimation into two stages. In the first stage, we run a logistic regression model, obtaining the probability of an individual having any health care expenditure, with indicators for each body mass index (BMI) category [underweight \18.5 kg/m2; normal 18.5 to \25 kg/m2 (reference group); overweight 25 to \30 kg/m2; obese C30 kg/m2] serving as our primary independent variables of interest. In the second stage, we use a generalized linear model with a log-link—a method recommended by Buntin and Zaslavsky [8] for modelling expenditure data in the Medicare population—to estimate
the effect of each of these BMI categories on expenditure level. By multiplying the results of the two stages together, we obtain total predicted spending for everyone in the sample, for each condition. We repeat this two-step process for each of the four disease conditions, and we run this set of four models twice, with 1987 data and then with 2011 data, for a total of eight sets of results [See the Appendix in the Electronic Supplementary Material for model specification and first- and second-stage results]. For each BMI category that is not the normal category (i.e. 18.5 to \25 kg/m2), we obtain the ratio of its predicted spending level to that of the normal BMI category. For each BMI category, we multiply the current spending ratio (using a ratio of 1.0 for those in the normal BMI category) by predicted normal BMI spending and apply each resulting expenditure amount to the distribution of BMI that existed in 1987. This calculation yields a counterfactual estimate of what predicted spending would be in 2011, had the BMI distribution from 1987 not changed but other factors (e.g. spending ratios) had. For each condition, the difference between the counterfactual predicted expenditure level and actual predicted expenditure, divided by the actual predicted expenditure level, yields the obesity-attributable percentage of health care expenditure growth. The covariates for the first and second stages are the same and include female sex; age; education (less than high school, high school graduate, some college, college degree); race/ethnicity (Hispanic, non-Hispanic black, other); income as a percentage of the federal poverty level (\100, 100–199, 200–399, C400 %); marital status (married, not married); family size and an indicator for whether a respondent reported being the head of a household; and region of the country (Midwest, South, West, Northeast). To estimate the portion of condition-specific, prevalence-related total spending growth that can be specifically linked to obesity, we multiply the percentage of total condition spending growth attributable to rising prevalence by the portion of spending growth that is attributable to obesity. The resulting percentage can be thought of as the fraction of total condition-specific spending growth that
L. Allen et al. Table 1 Descriptive statistics for individuals with selected chronic conditions enrolled in Medicare for C6 months in 1987 and 2011 Characteristic
1987 Diabetes
2011 Hypertension
Hyperlipidaemia
Heart disease
Diabetes
Hypertension
Hyperlipidaemia
Heart disease
Sample size (n)
597
1788
133
1260
1090
2655
2114
1172
Underweight [\18.5 kg/m2] (%)
2.2
3.0
1.8
5.4
0.5
1.7
1.1
2.3
Normal [18.5 to \25 kg/m2] (%)
30.0
37.0
48.9
45.8
15.4
27.0
25.6
27.4
Overweight [25 to \30 kg/m2] (%)
42.1
41.3
38.4
35.3
32.0
35.9
37.8
36.1
Obese [C30 kg/m2] (%)
25.6
18.7
10.9
13.6
52.1
35.4
35.5
34.2
Table 2 Contribution of rising prevalence, spending per case, and population growth to rising expenditures for selected chronic conditions in individuals enrolled in Medicare for six or more months, 1987–2011 Factors associated with total spending growth
Diabetes
Hypertension
Hyperlipidaemia
Treated prevalence (%)
39.8
36.6
58.9
13.6
8.7
6.2
0.7
11.2
51.5
57.2
40.4
75.2
Rising spending per case (%) Population growth (%)
could be potentially avoided through the reduction of obesity levels. All analyses were run in Stata 13 software using survey estimation commands to adjust for the complex survey designs of the MEPS and NMES. All dollar estimates are displayed in 2011 US dollars, which were calculated using the gross domestic product personal consumption deflator [9].
3 Results Table 1 summarizes the distribution of BMI categories for each condition of interest. In 1987, the vast majority of individuals with diabetes or cardiovascular conditions fell into the normal or overweight BMI categories. By 2011, this distribution had shifted, with most individuals with these conditions being classified as overweight or obese. Table 2 shows that the majority of the increase in spending growth from 1987 to 2011 for the conditions of interest is due to population growth, holding prevalence rates and spending per case constant. Rising spending per case, on the other hand, appears to play a much less important role in rising spending levels. A sizable portion of spending growth is due to rising chronic disease prevalence, which is the focal point of this paper. Almost 40 % of Medicare diabetes expenditure is attributable to the growing rate of individuals who have diabetes. Large percentages of the expenditure growth for hypertension (36.6 %) and hyperlipidaemia (58.9 %) are also caused by rising treated prevalence. For heart disease, this number is smaller (13.6 %) yet not insignificant.
Heart disease
One potential reason for the smaller relative contribution of treated prevalence rates to heart disease spending growth has to do with the definition of disease we use in this study. In our data source, individuals are asked if they have ever been diagnosed with a certain condition by a physician.1 Diabetes, hypertension and hyperlipidaemia are more often diagnosed in primary care settings, whereas heart disease frequently goes undetected until it causes a cardiovascular event. Therefore, population growth might appear to be contributing disproportionately to spending increases for hypertension. A second possible reason is that age-adjusted heart disease prevalence rates are falling in the Medicare population (and across all other age groups) [10], whereas the prevalence of diabetes is growing [11], rates of hypertension are steady [12] and screenings for hyperlipidaemia are growing more common [13]. The results in the second row of Table 2 suggest that rising treatment costs are generally not contributing meaningfully to expenditure growth. In contrast, the last row of the table shows that population growth is responsible for a large portion of expenditure growth. Holding prevalence at a constant rate and treatment costs steady, we find that just about half of Medicare diabetes expenditure can be explained by population increases. In contrast, markedly less (8.7 %) of the expenditure increase is attributable to higher treatment costs for diabetes. This pattern holds for the remaining disease states, as well. 1
This is why we use the phrase ‘treated [or diagnosed] prevalence’ and not ‘overall prevalence’. To estimate overall prevalence, we would have had to also capture the rates of undiagnosed prevalence in the population.
The Effect of Obesity and Chronic Conditions Table 3 Percentage of spending growth attributable to obesity-related prevalence increases for selected chronic conditions in individuals enrolled in Medicare for six or more months, 1987–2011 Diabetes
Hypertension
Hyperlipidaemia
Heart disease
Percentage of all spending growth attributable to treated prevalence (from Table 2)
39.8
36.6
58.9
13.6
Percentage of all spending growth attributable to obesity
42.7
18.2
8.6
13.6
Percentage of prevalence-related spending growth attributable to obesity
17.0
5.7
5.1
1.8
The second row of Table 3 displays the results from the second part of our analyses—the degree to which rising obesity rates have contributed to rising health expenditure for each condition. We estimate that 42.7 % of the increase in spending for diabetes is attributed to the shifting distribution of BMI levels in the USA. Obesity accounts for meaningful, though smaller, fractions of hypertension (18.2 %), hyperlipidaemia (8.6 %) and heart disease (13.6 %) spending increases. Since we are able to estimate the percentage of condition-specific spending growth that is related to rising treated prevalence, and the percentage of condition-specific spending growth that is due to obesity, we can estimate the percentage of condition-specific, prevalence-related spending growth that can be traced back to obesity. In the last row of Table 3, we estimate that 17.0 % of total spending growth for the treatment of diabetes in the Medicare population is due to a prevalence increase that is directly attributable to rising obesity. Similarly, obesity alone may account for small but economically meaningful percentages of the increase in total spending that is due to the rising prevalence rates of hypertension, hyperlipidaemia and heart disease in the Medicare population. These numbers represent the portion of disease-specific spending that is potentially avoidable through the reduction of obesity rates in the USA.
4 Discussion In this paper, we have estimated that sizable portions of condition-specific Medicare spending growth might be eliminated by reducing obesity rates and, successively, chronic disease prevalence. In previous work, we projected the effect that rising obesity prevalence will have on younger individuals, which will, in turn, affect Medicare spending as the population ages [14]. If obesity continues to increase at its current rate, we estimated that in 2018, the USA will spend almost $350 billion on health care costs that are directly attributable to obesity alone. This number would drop to about $150 billion if obesity rates were held at their current level [15]. In addition to the conditions studied in this paper, obesity is associated with some cancers, cognitive and functional declines, and increased nursing home
admission—all of which amplify the impact of obesity on Medicare expenditures [16]. Therefore, slowing the growth of obesity in the current and future Medicare populations represents a key priority for policy makers. Currently, Medicare provides coverage for two forms of obesity: intensive behaviour therapy (IBT) for individuals with a BMI of 30 kg/m2 or more [17] and bariatric surgery for some morbidly obese individuals [18]. Each of these interventions is characterized by some limitations: IBT may be lacking in feasibility and efficacy [19, 20], while bariatric surgery comes with surgical risks and high costs [21]. An effective, non-invasive alternative to these two interventions is pharmacotherapy, which can be delivered alone or in conjunction with behavioural lifestyle interventions [22–25]. However, at this time, Medicare does not provide coverage for pharmaceutical interventions for obesity, despite the availability of at least seven US Food and Drug Administration–approved weight-loss drugs [26]. Providing coverage for obesity-related drug therapy could be an effective mechanism for reducing obesity rates in the Medicare population. Our results highlight the interrelationship between obesity, chronic conditions and health care spending in the Medicare population: rising obesity rates increase the prevalence of chronic conditions, which, in turn, raises treatment expenditures. In the other direction, the presence of chronic conditions might also amplify obesity rates, if those conditions prevent individuals from maintaining a healthy weight. Given that the disease states we examined in this paper tend to co-occur, any obesity-related policy efforts to address curbing health expenditure growth and to improve health in the USA should also prioritize the management of multiple chronic conditions. Currently, Medicare has no such care coordination infrastructure in place. One proposed solution to this unmet need is the addition of a new benefit option for individuals enrolled in Medicare. Termed ‘Medicare Integrate’, this alternative to Medicare Advantage would emphasize evidence-based care coordination [27]. Enrollees would have access to medication management assistance, transitional care from the inpatient to the outpatient setting and broad access to both health coaches and nurse care coordinators. In addition to improving health outcomes, this programme has the potential to save costs in the long run.
L. Allen et al.
Our methodological approach should be taken in the context of its limitations. First, our data are cross-sectional, allowing us only a snapshot of data in time. Future research could apply a similar framework to panel data to examine dynamic year-to-year changes in the relationship between obesity, prevalence and spending. Second, we estimate the percentage of spending due to treated prevalence separately from that due to obesity, and we make the assumption that we can combine these results into an estimate of conditionspecific, obesity-related spending growth. This approach likely introduces some additional error into our estimates.
5 Conclusion In the Medicare population, rising obesity rates appear to be responsible for a meaningful portion of growing chronic disease prevalence. These conditions, in turn, are responsible for much of the recent rise in health care expenditure. To combat the rise of obesity, Medicare policy makers might consider the potential benefit of providing pharmacotherapy coverage and intensive care coordination to its members. Acknowledgments The authors thank the Robert Wood Johnson Foundation for its generous support of this work, and the anonymous peer reviewers for their helpful comments. Any errors are the responsibility of the authors. Conflict of interest
None.
Author contributions As part of KT’s larger Robert Wood Johnson Foundation project, KT and LA collaborated on this paper’s concept and design. LA drafted the manuscript. PJ performed the statistical analyses. KT is the guarantor for the overall content.
References 1. Centers for Medicare & Medicaid Services. NHE fact sheet. 2014. http://www.cms.gov/Research-Statistics-Data-and-Systems/ Statistics-Trends-and-Reports/NationalHealthExpendData/NHEFact-Sheet.html. Accessed 20 Apr 2015. 2. Thorpe KE, Howard DH. The rise in spending among Medicare beneficiaries: the role of chronic disease prevalence and changes in treatment intensity. Health Aff. 2006;25(5):w378–88. 3. Thorpe KE. Treated disease prevalence and spending per treated case drove most of the growth in health care spending in 1987–2009. Health Aff. 2013;32(5):851–8. 4. Administration on Aging, Administration for Community Living, US Department of Health and Human Services. A profile of older Americans. 2012. http://www.aoa.gov/Aging_Statistics/Profile/ 2012/docs/2012profile.pdf. Accessed 20 Apr 2015. 5. Agency for Health Care Research and Quality. Medical Expenditure Panel Survey: survey background. 2009. http://meps.ahrq. gov/mepsweb/about_meps/survey_back.jsp. Accessed 23 Apr 2014.
6. Healthcare Cost and Utilization Project. Clinical Classifications Software (CCS) for ICD-9-CM. 2015. www.hcup-us.ahrq.gov/ toolssoftware/ccs/ccs.jsp. Accessed 20 Apr 2015. 7. Zuvekas SH, Cohen JW. A guide to comparing health care expenditures in the 1996 MEPS to the 1987 NMES. Inquiry. 2002;39(1):76–86. 8. Buntin MB, Zaslavsky AM. Too much ado about two-part models and transformation: comparing methods of modeling Medicare expenditures. J Health Econ. 2004;23(3):525–42. 9. Williamson SH. What was the US GDP then? MeasuringWorth.com. 2014. http://www.measuringworth.org/usgdp/. Accessed 20 Apr 2015. 10. Centers for Disease Control and Prevention. Prevalence of coronary heart disease—United States, 2006–2010. 2014. http:// www.cdc.gov/mmwr/preview/mmwrhtml/mm6040a1.htm. Accessed 20 Apr 2015. 11. Centers for Disease Control and Prevention. Rate per 100 of civilian, noninstitutionalized population with diagnosed diabetes, by age, United States, 1980–2011. 2014. http://www.cdc.gov/ diabetes/statistics/prev/national/figbyage.htm. Accessed 20 Apr 2015. 12. Yoon SS, Ostchega Y, Louis T. Recent trends in the prevalence of high blood pressure and its treatment and control, 1999–2008. NCHS Data Brief. 2010;48:1–8. 13. Kuklina EV, Carroll MD, Shaw KM, Hirsch R. Trends in high LDL cholesterol, cholesterol-lowering medication use, and dietary saturated-fat intake: United States, 1976–2010. NCHS Data Brief. 2013;117:1–8. 14. Thorpe KE, Florence CS, Howard DH, Joski P. The impact of obesity on rising medical spending. Health Aff (Millwood). 2004;Suppl Web Exclusives:W4-480-6. 15. Thorpe KE. The future costs of obesity: national and state estimates of the impact of obesity on direct health care expenses. Washington, DC: United Health Foundation and American Public Health Association; 2009. 16. Felix HC, West DS. Effectiveness of weight loss interventions for obese older adults. Am J Health Promot. 2013;27(3):191–9. 17. Centers for Medicare & Medicaid Services. Decision memo for intensive behavioral therapy for obesity (CAG-00423N). Baltimore: Centers for Medicare and Medicaid Services; 2011. http:// www.cms.gov/medicare-coverage-database/details/nca-decisionmemo.aspx?&NcaName=Intensive%20Behavioral%20Therapy% 20for%20Obesity&bc=ACAAAAAAIAAA&NCAId=253&. Accessed 20 Apr 2015. 18. Centers for Medicare & Medicaid Services. Decision memo for bariatric surgery for the treatment of morbid obesity (CAG00250R). Baltimore: Centers for Medicare and Medicaid Services; 2006. http://www.cms.gov/medicare-coverage-database/details/ nca-decision-memo.aspx?NCAId=160&NcaName=Bariatric? Surgery?for?the?Treatment?of?Morbid?Obesity?%281st? Recon%29&bc=ACAAAAAAEAAA&. Accessed 20 Apr 2015. 19. Pagoto SL, Pbert L, Emmons K. The Society of Behavioral Medicine position statement on the CMS decision memo on intensive behavior therapy for obesity. Transl Behav Med. 2012;2(4):381–3. 20. Carvajal R, Wadden TA, Tsai AG, Peck K, Moran CH. Managing obesity in primary care practice: a narrative review. Ann N York Acad Sci. 2013;1281:191–206. 21. Flum DR, Kwon S, MacLeod K, et al. The use, safety and cost of bariatric surgery before and after Medicare’s national coverage decision. Ann Surg. 2011;254(6):860–5. 22. Wadden TA, Berkowitz RI, Womble LG, et al. Randomized trial of lifestyle modification and pharmacotherapy for obesity. N Engl J Med. 2005;353(20):2111–20.
The Effect of Obesity and Chronic Conditions 23. Ackermann RT, Marrero DG. Adapting the Diabetes Prevention Program lifestyle intervention for delivery in the community: the YMCA model. Diabetes Educ. 2007;33(1):69, 74–5, 77–8. 24. Thorpe KE, Yang Z. Enrolling people with prediabetes ages 60–64 in a proven weight loss program could save Medicare $7 billion or more. Health Aff. 2011;30(9):1673–9. 25. Bennett GG, Steinberg DM, Stoute C, et al. Electronic health (eHealth) interventions for weight management among racial/
ethnic minority adults: a systematic review. Obes Rev. 2014;15(Suppl 4):146–58. 26. Yanovski SZ, Yanovski JA. Long-term drug treatment for obesity: a systematic and clinical review. JAMA. 2014;311(1):74–86. 27. Thorpe KE. Medicare integrate: a new benefit option for Medicare beneficiaries. Health Aff Blog. 2013. http://healthaffairs.org/ blog/2013/07/23/medicare-integrate-a-new-benefit-option-formedicare-beneficiaries/.
