Is the US Population Behaving Healthier?

In the past few decades, some measures of population risk have improved, while others have deteriorated. Understanding the health of the population requires integrating these different trends. We compare the risk factor profile of the population in the early 1970s with that of the population in the early 2000s and consider the impact of a continuation of recent trends. Despite substantial increases in obesity in the past three decades, the overall population risk profile is healthier now than it was formerly. For the population aged 25-74, the 10 year probability of death fell from 9.8 percent in 1971-75 to 8.4 percent in 1999-2002. Among the population aged 55-74, the 10 year risk of death fell from 25.7 percent to 21.7 percent. The largest contributors to these changes were the reduction in smoking and better control of blood pressure. Increased obesity increased risk, but not by as large a quantitative amount. In the future, however, increased obesity may play a larger role than continued reductions in smoking. We estimate that a continuation of trends over the past three decades to the next three decades might offset about a third of the behavioral improvements witnessed in recent years.

Nationwide Trends in Cardiovascular Disease Spending among the Elderly, 2000-2009

Background/Aims: One in every six health care dollars is spent on individuals with cardiovascular disease (CVD), and this spending will likely increase as the U.S. population ages. In order to understand the value of our substantial investment in CVD care, it is important to understand trends in CVD spending and, because value varies by patient risk, whether these trends vary across subpopulations with different CVD risk. The aim of this study was to assess national trends in CVD spending among different CVD risk subgroups. Methods: We examined trends during 2000-2009 in CVD-related and inflation-adjusted average spending using Medicare Current Beneficiary Survey data. We studied a total sample of 35,378 non-institutionalized, fee-for-Service Medicare beneficiaries 65 years or older. Analyses were conducted overall and stratified according to presence of CVD and, in those without CVD, level of CVD risk (high versus low). Results: From 2000 to 2009, among patients with CVD, overall annual spending increased by 29% (95%CI: 20-39), from $11,726 to$15,109. Medicare and out-of-pocket (OOP) spending increased by 39% (95%CI: 26-51), from $7,017 to$9,741 and by 14% (95%CI: 1-26), from $1,800 to$2,047 respectively. In individuals with high CVD risk, overall spending, Medicare, and out-of-pocket spending increased by 44% (95% CI: 12-76), from $7,204 to$10,404; by 75% (95% CI: 22-129), from $3,789 to$6,657; and by 12% (95% CI: -14-37), from $1,328 to$1,484 respectively. For those at low CVD risk, changes in spending were not statistically significant. Per capita averages in 2009 were: overall: $7,938 (SE=608), Medicare:$4,444 (SE=512), and OOP: $1,567 (SE=107). Conclusions: We observed significant increases in spending, especially among patients with pre-existing CVD and those at high CVD risk. More research is needed to investigate whether these increases in spending impacted health-related outcomes and whether different CVD subpopulations benefited differently from this spending growth

Effects of Increased Utilization of CVD Medications by Medicare Beneficiaries on Spending Vary by CVD Status

Background/Aims: To understand the value of our substantial investment in cardiovascular disease (CVD) care, it is important to understand the associations of CVD therapies and spending. The aim of this study was to assess the effect of increased utilization of CVD medications on spending among different CVD risk subgroups. Methods: We used 1999-2009 Medicare Current Beneficiary Survey data to identify 26,903 non-institutionalized, fee-for-Service 65 years or older users of angiotensin converting enzyme inhibitor (ACE), angiotensin receptor blocker (ARBs), other antihypertensives, and statins(61,741person-years). For each drug, we used generalized linear models to estimate the effect of additional prescription fills on spending (i.e. overall, Medicare, out-of-pocket); stratified according to presence of CVD and, in those without CVD, level of CVD risk (high versus low). Results: In the high CVD risk subgroup, each additional prescription fill of ACE, ARB, or statindecreased overall spending (marginal effects: -$274 (CI=-405, -143), -$139 (CI=-300, 22), and -$93 (CI=-250, 64) respectively) and Medicare spending (marginal effects: -$273 (CI=-386, -160), -$156 (CI=-314, 3), and -$160 (CI=-306, -14) respectively). Similar patterns were found in the subgroup with CVD (marginal effects of ACE, ARB, and statins on overall spending: -$184 (CI=-362, -6), -$184 (CI=-377, 8), and -$117 (CI=-235, 2); and on Medicare spending (-$232 (CI=-362, -103), -$186 (CI=-363, -9), and -$229 (CI=-328, -130)). The increased use of these drugs has the opposite effect in the low CVD risk subgroup generally. In contrast, in all 3 subgroups, each additional prescription fill of these drugs generally increased out-of-pocket spending by up to $55. Conclusions: We observed overall cost-savings associated with increased use of CVD medications among both patients with pre-existing CVD and those at high CVD risk. Eliminating or reducing copays for these drugs (i.e. value based insurance design) for such patients may improve their overall health and save money

The Health Effects of Increased CVD Medication use Varies by CVD Status of Medicare Beneficiaries

Background/Aims: Cardiovascular disease (CVD) is the leading cause of death and disability in the United States. The aim of this study was to assess the effect of increased utilization of CVD medications on MI, stroke, and all-cause mortality among different CVD risk subgroups. Methods: We used 1999-2009 Medicare Current Beneficiary Survey data to identify 26,903 non-institutionalized, fee-for-Service Medicare beneficiaries 65 years or older who were users of angiotensin converting enzyme inhibitor (ACE), angiotensin receptor blocker (ARB), other antihypertensive medications, and statin. These beneficiaries contributed a total of 61,741 person-years. For each study drug, we used logistic regression models to estimate the effect of additional prescription fills on MI, stroke, and all-cause mortality; stratified according to presence of CVD and, in those without CVD, level of CVD risk (high versus low). Results: Additional prescription fills of ACE, ARB, other antihypertensives, or statin did not affect MI occurrence among high CVD risk individuals; while in those with CVD, significant effects of ACE and statin were found: OR per 6 additional fills: 0.76 (95% CI= 0.59, 0.98) and 0.74 (CI= 0.60, 0.92) respectively. Additional drug fills did not affect stroke in either subpopulation except fills of other antihypertensives in the CVD subgroup (OR of 6 additional fills: 0.93 (CI= 0.89, 0.98). In both subgroups, an inverse relationship between increased use of the study drugs and all-cause mortality was generally found although insignificant. For those at lower CVD risk, events were generally too few to allow multivariate analyses. Conclusions: We found inverse relationships between increased use of some CVD medications; and MI, stroke, and mortality (although some were not significant) for some subpopulations but not others. Future research is needed to confirm this to justify the need to eliminate or reduce copays for these drugs for some subgroups that may benefit most from them

A Proposed Method for Monitoring U.S. Population Health: Linking Symptoms, Impairments, and Health Ratings

We propose a method of quantifying non-fatal health on a 0-1 QALY scale that details the impact of specific symptoms and impairments and is not based on ratings of counterfactual scenarios. Measures of general health status are regressed on health impairments and symptoms in different domains, using ordered probit and ordinary least squares regression. This yields estimates of their effects analogous to disutility weights, and accounts for complex non-additive relationships. Health measures used include self-rated health status on a 5-point scale, EuroQol 5D (EQ-5D) scores, and ratings of current health using a 0-100 rating scale and a time-tradeoff. Data are from the nationally representative Medical Expenditure Panel Survey (MEPS) year 2002 (N=34,615), with validation in an independent sample from MEPS 2000 (N=21,067) and among 1420 adults age 45-89 in the Beaver Dam Health Outcomes Study. Decrement weights for symptoms and impairments are used to derive estimates of overall health-related quality of life, laying the groundwork for a detailed national summary measure of health. To purchase a copy of the earlier version of this paper, please contact the Working Papers department directly at (617) 588 1405.

Value of Medical Innovation in the United States: 1960-2000

Background: The increased use of medical therapies has led to increased medical costs. To provide insight into the value of this increased spending, we compared gains in life expectancy with the increased costs of care from 1960 through 2000. Methods: We estimated life expectancy in 1960, 1970, 1980, 1990, and 2000 for four age groups. To control for the influence of nonmedical factors on survival, we assumed in our base-case analysis that 50 percent of the gains were due to medical care. We compared the adjusted increases in life expectancy with the lifetime cost of medical care in the same years. Results: From 1960 through 2000, the life expectancy for newborns increased by 6.97 years, lifetime medical spending adjusted for inflation increased by approximately $69,000, and the cost per year of life gained was$19,900. The cost increased from $7,400 per year of life gained in the 1970s to$36,300 in the 1990s. The average cost per year of life gained in 1960–2000 was approximately $31,600 at 15 years of age,$53,700 at 45 years of age, and $84,700 at 65 years of age. At 65 years of age, costs rose more rapidly than did life expectancy: the cost per year of life gained was$121,000 between 1980 and 1990 and $145,000 between 1990 and 2000. Conclusions: On average, the increases in medical spending since 1960 have provided reasonable value. However, the spending increases in medical care for the elderly since 1980 are associated with a high cost per year of life gained. The national focus on the rise in medical spending should be balanced by attention to the health benefits of this increased spending.Economic

Forecasting the Effects of Obesity and Smoking on U.S. Life Expectancy

Background: While increases in obesity over the past 30 years have adversely affected population health, there have been concomitant improvements due to reductions in smoking. Better understanding of the joint effects of these trends on longevity and quality of life will help policymakers target resources more efficiently. Methods: For each year from 2005 to 2020, we forecast life expectancy and qualityadjusted life expectancy for a representative 18 year old, assuming a continuation of past trends in smoking from the National Health Interview Survey (1978-79, 1990-91 and 2004-06), and past trends in body-mass index (BMI) from the National Health and Nutrition Examination Survey (1971-75, 1998-1994, and 2003-06). The 2003 Medical Expenditure Panel Survey was used to examine the effects of smoking and BMI on health-related quality of life. Results: The negative effects of increasing BMI overwhelmed the positive effects of declines in smoking in multiple scenarios. In the base case, increases in the remaining life expectancy of a typical 18 year old are held back by 0.71 years or 0.91 quality-adjusted years between 2005 and 2020. If all U.S. adults became normal weight non-smokers by 2020, LE is forecast to increase by 3.76 life years or 5.16 quality-adjusted years. Conclusions: If past obesity trends continue unchecked, the negative impact on U.S. population health is forecast to overtake the positive effect from declining smoking rates, which could erode the pattern of steady gains in health experienced since early in the 20th century.Economic

Measuring Health Care Costs of Individuals with Employer-Sponsored Health Insurance in the U.S.: A Comparison of Survey and Claims Data

As the core nationally representative health expenditure survey in the United States, the Medical Expenditure Panel Survey (MEPS) is increasingly being used by statistical agencies to track expenditures by disease. However, while MEPS provides a wealth of data, its small sample size precludes examination of spending on all but the most prevalent health conditions. To overcome this issue, statistical agencies have turned to other public data sources, such as Medicare and Medicaid claims data, when available. No comparable publicly available data exist for those with employer-sponsored insurance. While large proprietary claims databases may be an option, the relative accuracy of their spending estimates is not known. This study compared MEPS and MarketScan estimates of annual per person health care spending on individuals with employer-sponsored insurance coverage. Both total spending and the distribution of annual per person spending differed across the two data sources, with MEPS estimates 10 percent lower on average than estimates from MarketScan. These differences appeared to be a function of both underrepresentation of high expenditure cases and underestimation across the remaining distribution of spending.

Diffusion of published cost-utility analyses in the field of health policy and practice

OBJECTIVES: The diffusion of cost-utility analyses (CUAs) through the medical literature was examined, documenting visible patterns and determining how they correspond with expectations about the diffusion of process innovations. METHODS: This study used 539 CUAs from a registry. It includes data elements comprising year of publication, the research center in which the study was performed, the clinical area covered by the CUA, and the specific journal. Finally, each paper was assigned to a journal type that could be one of the three categories: health services research, general medicine, or clinical specialty. RESULTS: When the average number of publications is plotted against time, the plot reveals an S-shaped curve. It appears that, whereas CUAs initially were published more frequently in general medical or health services research journals, there was a clear increase in the diffusion of CUA into subspecialty journals over time. The concentration ratio for research centers as measured by the Herfindhal-Hirschman Index decreased over time. CONCLUSIONS: The spread of CUA through the medical literature follows patterns identified for the diffusion of other new technologies and processes. Future research should focus on what impact this spread has had on the practice of medicine and formulation of health policy

Cost-utility analyses in orthopaedic surgery

BACKGROUND: The rising cost of health care has increased the need for the orthopaedic community to understand and apply economic evaluations. We critically reviewed the literature on orthopaedic cost-utility analysis to determine which subspecialty areas are represented, the cost-utility ratios that have been utilized, and the quality of the present literature. METHODS: We searched the English-language medical literature published between 1976 and 2001 for orthopaedic-related cost-utility analyses in which outcomes were reported as cost per quality-adjusted life year. Two trained reviewers independently audited each article to abstract data on the methods and reporting practices used in the study as well as the cost-utility ratios derived by the analysis. RESULTS: Our search yielded thirty-seven studies, in which 116 cost-utility ratios were presented. Eleven of the studies were investigations of treatment strategies in total joint arthroplasty. Study methods varied substantially, with only five studies (14%) including four key criteria recommended by the United States Panel on Cost-Effectiveness in Health and Medicine. According to a reader-assigned measure of study quality, cost-utility analyses in orthopaedics were of lower quality than those in other areas of medicine (p = 0.04). While the number of orthopaedic studies has increased in the last decade, the quality did not improve over time and did not differ according to subspecialty area or journal type. For the majority of the interventions that were studied, the cost-utility ratio was below the commonly used threshold of $50,000 per quality-adjusted life year for acceptable cost-effectiveness. CONCLUSIONS: Because of limitations in methodology, the current body of literature on orthopaedic cost-utility analyses has a limited ability to guide policy, but it can be useful for setting priorities and guiding research. Future research with clear and transparent reporting is needed in all subspecialty areas of orthopaedic practice