The Benefits of Risk Factor Prevention in Americans Aged 51 Years and Older

Dana P. Goldman, PhD; Yuhui Zheng, PhD; Federico Girosi, PhD; Pierre-Carl Michaud, PhD; S. Jay Olshansky, PhD; David Cutler, PhD; John W. Rowe, MD

Disclosures

Am J Public Health. 2009;99(11):2096 

In This Article

Methods

We examined whether prevention will consume more health care resources, and if it does, how much value it generates for society. We focused our cost–benefit analysis on the potential benefits successful prevention strategies might generate. The health benefits of prevention are difficult to measure because treatment simultaneously extends life and changes the relative prevalence of fatal and nonfatal disabling diseases, thereby yielding complicated spending patterns. As a way to understand these competing risks, we developed a dynamic microsimulation model (the Future Elderly Model [FEM]) to track cohorts over time to project their health status and economic outcomes under various prevention scenarios. The FEM has been used to assess the financial risk from new medical technologies for Medicare,[4] the costs of obesity in older Americans,[5] trends in disability,[6] the future costs of cancer,[7] and the health and economic value of preventing disease after age 65 years.[8]

Data and Outcomes

We extended the model to a much larger cohort from the Health and Retirement Study (HRS),[9] a biennial survey of Americans aged 51 years and older that began in 1992. We supplemented the HRS with data from the Medicare Current Beneficiary Survey[10] and Medical Expenditure Panel Survey[11] to model medical spending and quality of life. (A detailed description of the model is available in Appendix A as a supplement to the online version of this article at https://www.ajph.org; here we describe the most salient details.)

The core of the FEM was a health module designed to predict the future health and functional status of each individual from his or her current state, accounting for a broad set of risk factors. Health conditions were derived from survey questions about heart disease (acute myocardial infarction, coronary heart disease, angina, congestive heart failure, or other heart problems), cancer (except skin cancer), chronic bronchitis or emphysema, diabetes, high blood pressure or hypertension, and stroke or transient ischemic attack. Functional status was measured by limitations in activities of daily living (ADLs), instrumental activities of daily living (IADLs), and nursing home residency. The ADLs measure was based on a battery of questions assessing difficulty dressing, eating, bathing or showering; getting into and out of a chair; and walking. For IADLs, respondents were asked if they had any difficulty using the phone, managing money, and taking medicine. From the responses to these questions, we constructed a hierarchical measure of physical functioning: no limitations, limited in at least 1 IADL, limited in 1 or 2 ADLs, and limited in 3 or more ADLs. Whether a person lived in a nursing home was included as a binary measure.

Both functional status and the likelihood of developing a health condition depended on several key risk factors: age, gender, education, race/ethnicity, obesity (body mass index [BMI; defined as weight in kilograms divided by height in meters squared] ≥ 30 kg/m2), overweight (BMI=25–29 kg/m2), ever smoking, current smoking, and functional status and health conditions. All health conditions were treated as absorbing; that is, once a person had an illness, he or she was assumed to have it forever, consistent with the way the survey questions were asked. Transitions into and out of functional states and obesity status were allowed. We modeled all health conditions, functional states, and risk factors with first-order Markov processes that controlled for baseline unobserved factors in a battery of baseline health variables. These were effective controls, according to goodness-of-fit tests (see Appendix A, available as an online supplement).

A cost module linked a person's current state—demographics, economic status, current health, risk factors, and functional status—to medical spending. These estimates were based on pooled weighted least squares regressions of total health care spending on risk factors, self-reported conditions, and functional status, with spending inflated to constant 2004 dollars derived from the medical component of the consumer price index published by the US Bureau of Labor Statistics.[12] We used the 2002 to 2004 Medical Expenditure Panel Survey[11] (n=13942) for regressions for persons younger than 65 years, and the 2002 to 2004 Medicare Current Beneficiary Survey[10] (n=29523) for regressions for those aged 65 years and older. In the baseline scenario, this spending estimate could be interpreted as the resources consumed by the individual according to the way medicine is currently practiced in the United States.

To value the health benefits of prevention to society, we predicted quality-adjusted life-years (QALYs) with the EQ-5D, a widely used health-related quality-of-life index. The EQ-5D instrument included 5 questions regarding the extent of problems in mobility, self-care, daily activities, pain, and anxiety and depression; it has been widely used in both Europe and the United States.[13,14] We used the 2001 Medical Expenditure Panel Survey[11] to estimate a linear model fitting EQ-5D scores as a function of 6 chronic conditions and functional status (details available on request). We used this model to predict a QALY measure for all persons in the simulation in every year by their simulated health and functional status.

With the FEM, we simulated outcomes for a representative cohort of respondents aged 51 or 52 years from the 2004 HRS (n=1028). In each year, the spending module predicted medical expenditures over the next 2 years by each individual's current state. We then used the health module to predict who would survive to 2006 and to predict the obesity status, disease, and functional state of the surviving population, as well as a QALY for that year. The spending module was then used to predict that period's health care resource use. We repeated the simulation until everyone in the 2004 cohort would have died. For each scenario, we conducted the simulation 10 times and averaged the outcomes. We implemented scenarios by changing the transition probabilities in the health module and then rerunning the model. Primary outcomes were life expectancy, quality-adjusted life expectancy, and lifetime medical spending. All costs and QALYs were discounted by a 3% annual discount rate as suggested by Gold et al.[15]

To estimate the net benefits, we valued the health improvement (measured by QALY-adjusted life expectancy) minus additional lifetime medical spending. For these calculations, we compared outcomes to the status quo and assumed that each QALY was worth $100000.

Scenarios

We considered 4 types of interventions: treatment for diabetes, hypertension, obesity, and smoking (Table A in Appendix B, available as a supplement to the online version of this article at https://www.ajph.org). The scenarios were designed to estimate the potential benefits from the development of an efficacious prevention regimen. We modeled these as lifetime prevention of diabetes, hypertension, obesity, or smoking at various efficacy levels. A 10% scenario assumed prevention would be successful for 10% of the at-risk population; other scenarios considered 25%, 50%, and 100% efficacy.

The 100% scenario—although certainly not feasible—was useful for predicting the maximum health and longevity benefit that might be achieved by independently eliminating smoking, hypertension, diabetes, and obesity. This percentage is of policy significance as society grapples with issues such as which diseases would be most valuable to eradicate. All interventions applied only to the population aged 51 years and older because of data limitations.

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