Katherine Baicker

Workplace Wellness Programs Can Generate Savings

Amid soaring health spending, there is growing interest in workplace disease prevention and wellness programs to improve health and lower costs. In a critical meta-analysis of the literature on costs and savings associated with such programs, we found that medical costs fall by about

Medicaid Increases Emergency-Department Use: Evidence from Oregon's Health Insurance Experiment

3.27 for every dollar spent on wellness programs and that absenteeism costs fall by about

Geographic Variation in Health Care and the Problem of Measuring Racial Disparities

2.73 for every dollar spent. Although further exploration of the mechanisms at work and broader applicability of the findings is needed, this return on investment suggests that the wider adoption of such programs could prove beneficial for budgets and productivity as well as health outcomes.

Changes in Mortality After Massachusetts Health Care Reform: A Quasi-experimental Study

Health Economy? The intensity of arguments over social science issues often seems inversely correlated with the quantity of experimental evidence. Taubman et al. (p. 263, published online 2 January; see the Policy Forum by Fisman) report on the latest analysis of an ongoing controlled experiment—the Oregon Health Insurance Experiment—that seeks to identify and quantify the effects of extending health insurance coverage to a low-income adult population. A substantial increase was observed in visits to the emergency departments of hospitals, corresponding to approximately 120 U.S. dollars per year more in hospital costs. Expanding health coverage of low-income adults can result in increased usage of hospital emergency departments. In 2008, Oregon initiated a limited expansion of a Medicaid program for uninsured, low-income adults, drawing names from a waiting list by lottery. This lottery created a rare opportunity to study the effects of Medicaid coverage by using a randomized controlled design. By using the randomization provided by the lottery and emergency-department records from Portland-area hospitals, we studied the emergency department use of about 25,000 lottery participants over about 18 months after the lottery. We found that Medicaid coverage significantly increases overall emergency use by 0.41 visits per person, or 40% relative to an average of 1.02 visits per person in the control group. We found increases in emergency-department visits across a broad range of types of visits, conditions, and subgroups, including increases in visits for conditions that may be most readily treatable in primary care settings.

Geographic variation in the quality of prescribing.

In its study of racial and ethnic disparities in health care, the Institute of Medicine (IOM) concluded that there were large and significant disparities in the quality and quantity of health care received by minority groups in the United States. This article shows that where a patient lives can itself have a large impact on the level and quality of health care the patient receives. Since black or Hispanic populations tend to live in different areas from non-Hispanic white populations, location matters in the measurement and interpretation of health (and health care) disparities. There is wide variation in racial disparities across geographic lines: some areas have substantial disparities, while others have equal treatment. Furthermore, there is no consistent pattern of disparities: some areas may have a wide disparity in one treatment but no disparity in another. The problem of differences in quality of care across regions, as opposed to racial disparities in care, should remain the target of policy makers, as reducing quality disparities would play a major role in improving the health care received by all Americans and by minority Americans in particular.

Comparing local and regional variation in health care spending.

Context After passage of a 2006 law that expanded health insurance coverage, studies have found many changes in health and health care, but none has reported changes in mortality. Contribution This study found that when Massachusetts counties were compared with similar counties in other states, all-cause and health careamenable mortality decreased after Massachusetts passed the law. Caution The study design cannot rule out the effects of unidentified confounders and thus cannot establish cause and effect. Implication The association between more insurance coverage and fewer deaths reported here is consistent with other evidence that expanding insurance coverage can improve health. The Editors Massachusetts passed comprehensive health care reform in 2006 with the goal of near-universal coverage. The lawwhich expanded Medicaid, offered subsidized private insurance, and created an individual mandatewas a model for the Affordable Care Act (1). Thus, understanding the effects of the Massachusetts law has important policy implications. Previous research documents that the Massachusetts reform succeeded in expanding health insurance among adults aged 19 to 64 years by 3 to 8 percentage points (15). Studies also indicate improvements in access to care (68), self-reported physical and mental health (9), use of preventive services (2, 10), and functional status (1, 11). However, there has been no evidence on the laws effect on mortality. Previous research on the effect of health insurance on mortality is mixed. Some observational studies suggest as much as a 40% increased risk for death for uninsured versus insured adults (12, 13), and an analysis of Medicaid expansion to low-income adults detected a 6% decrease in statewide mortality (14). Other studies, including 2 randomized trials of insurance expansion, found little or no effect on mortality (1517). Our studys objective was to examine the changes in mortality associated with the Massachusetts reform. We hypothesized that the reform reduced mortality, particularly from causes potentially treatable with timely care (such as cardiovascular disease, infections, and cancer), and that larger changes occurred among groups likely to benefit from the lawpreviously uninsured adults and those with higher prereform mortality rates. Methods Study Design Our study used a quasi-experimental prepost design with a control group and compared average mortality in Massachusetts before and after reform to mortality changes over the same period for similar populations in states without reforms (also known as a differences-in-differences analysis [18]). Our preferred specification used propensity score methods to create a control group of counties in nonreform states that best matched the distribution of prereform characteristics in Massachusetts counties (19, 20). The Massachusetts law had several components: Medicaid expansion starting in July 2006, subsidized private plans for adults with incomes less than 100% of the federal poverty level in October 2006, and expanded coverage subsidies for adults with incomes up to 300% of the federal poverty level in January 2007. It included an individual mandate effective for the 2007 tax year and minimum creditable coverage insurance standards (21). We defined the postreform period as 2007 to 2010, with 2006 omitted as a transitional year (although we included 2006 in sensitivity analyses). The prereform period was 2001 to 2005. Data Our data came primarily from the Centers for Disease Control and Preventions Compressed Mortality File, which provides county-specific annual mortality rates stratified by age, sex, and race (22). For confidentiality, the publicly available data set suppresses death counts for cells with fewer than 10 deaths. We obtained access to the nonsuppressed data set under agreement with the Centers for Disease Control and Prevention. Our sample was adults aged 20 to 64 years, the reforms primary target group (with 19-year-olds excluded because persons aged 15 to 19 years are grouped together in the data set). In addition to age, sex, and race, our estimates were adjusted for year-specific county-level poverty rates, median income, unemployment, and the percentage of Latino persons in the population (all from the Area Resource File [ARF] [23]). Subgroup analyses used prereform county-level uninsured rates from the U.S. Census Bureaus 2005 Small Area Health Insurance Estimates (24). We also analyzed measures of coverage, health care access, and self-reported health status from 2 nationally representative household surveys: the Centers for Disease Control and Preventions Behavioral Risk Factor Surveillance System (BRFSS) and the Census Bureaus Current Population Survey (CPS). These data sets have been used previously to examine the effect of the Massachusetts reform on coverage and access (24, 8, 9, 25). We present independent estimates using methods analogous to our mortality analysis to provide additional context for our results. For these data sources, we were able to include 19-year-olds, so the sample contains all adults aged 19 to 64 years. This project used preexisting deidentified data and was deemed exempt from review by the Harvard Institutional Review Board. The project received no external funding. Outcome Measures Our primary outcome was all-cause mortality. Our secondary outcome was mortality amenable to health care, adapted from previous research (2629), to focus on deaths related to conditions that are more likely to be preventable or treatable with timely care, including heart disease, stroke, cancer, infections, and other conditions (30). Table 1 of the Supplement lists the diagnosis codes from the International Classification of Diseases, 10th Revision, used in this definition and a more restrictive alternate definition tested in a sensitivity analysis. Supplement. Supplementary Material Additional outcomes were health insurance from the CPS and self-reported health (excellent or very good vs. good, fair, or poor) and access-to-care measures (cost-related delays in care, lack of a usual source of care, and absence of a preventive visit in the past year) from the BRFSS. Statistical Analysis Annual county-level death counts based on age, sex, and race were the unit of observation for the mortality analysis. Table 1 describes the analytic sample, which contains information on the number of counties; states; age-, sex-, and race-specific county-level cells; and population per year. Table 1. Analytic Sample Our regression models estimated the average annual prepost change in mortality for age-, sex-, and race-specific cells in Massachusetts counties relative to comparison counties in nonreform states (31). The study contained 5 years of prereform data (2001 to 2005) and 4 years of postreform data (2007 to 2010). Given that our outcome variable is number of deaths in each cell, our multivariate regression analyses fitted a generalized linear model using a negative binomial distribution and log link, with cell population as the exposure variable. We adjusted our analyses for race, sex, age, state, year, and economic factors (unemployment rate, poverty rate, and median income) specific to the county year (Supplement). Robust SEs were clustered at the state level to account for serial autocorrelation and for the state-level nature of the policy intervention (18), which is standard in population-based policy analyses (14, 3237). Sensitivity analyses included the pooling of annual data into prereform and postreform periods to remove potential autocorrelation, an interrupted time series model, adding 2006 (the implementation year) to our postreform data, and county-level clustering of SEs. We also tested a linear model using death rate per 100000 adults as the outcome to provide simple estimates of absolute change and results similar to prior research (14). Cells were weighted by population size to yield representative estimates. Secondary analyses used individual-level information from the BRFSS and CPS on coverage, access, and health status and were adjusted for age, sex, race/ethnicity, employment, household income, year, and state. For these binary outcomes, we used a generalized linear model with a logit link and predicted probabilities to describe the magnitude of absolute changes (38). Selection of Control Group For the mortality analysis, we used propensity scores to define a control group of counties in nonreform states that were most similar to prereform Massachusetts counties. We estimated propensity scores with a population-weighted logistic regression model using age distribution, sex, race/ethnicity, poverty rate, median income, unemployment, uninsured rate, and baseline annual mortality as predictors (Table 2 of the Supplement). The quartile of counties with the highest propensity scores, indicating the closest match to the overall population of Massachusetts 14 counties, was used as the control group in the mortality analysis. This approach yielded excellent balance on key features between Massachusetts and our control group (Table 2) and provided adequate sample sizes for subgroup analyses. We also tested a more traditional propensity scoreregression adjustment method and a 2:1 nearest-neighbor propensity scorematching approach, which yielded similar overall results (Supplement). Table 2. Summary Statistics for Study Sample Before Reform Identifying a control group with similar mortality trends in counties not in Massachusetts is the key to our approach (20). We tested for differences in the prereform mortality trends for 2001 to 2006 between Massachusetts and the control group using linear and quadratic time trends interacted with an indicator variable for Massachusetts. We repeated this test for the entire U.S. population. For the analysis of coverage, access, and self-reported health in the CPS and BRFSS, we compared Massachusetts with the other New England states (Maine, Vermont, New Hampshir

Health Insurance Coverage and Take‐Up: Lessons from Behavioral Economics

To assess U.S. geographic variation in the management of medication in elderly patients, the authors examined performance on two quality measures: the use of medications considered to be high-risk for the elderly and potentially harmful drug–disease interactions.

The specter of financial armageddon--health care and federal debt in the United States.

BACKGROUND Wide geographic variation in health care spending has generated both concern about inefficiency and policy debate about geographic-based payment reform. Evidence regarding variation has focused on hospital referral regions (HRRs), which incorporate numerous local hospital service areas (HSAs). If there is substantial variation across local areas within HRRs, then policies focusing on HRRs may be poorly targeted. METHODS Using prescription drug and medical claims data from a 5% random sample of Medicare beneficiaries from 2006 through 2009, we compared variation in health care spending and utilization among 306 HRRs and 3436 HSAs. We adjusted for beneficiary-level demographic characteristics, insurance status, and clinical characteristics. RESULTS There was substantial local variation in health care (drug and nondrug) utilization and spending. Furthermore, many of the low-spending HSAs were located in high-spending HRRs, and many of the high-spending HSAs were in low-spending HRRs. For drug spending, only 50.7% of the HSAs located within the borders of the highest-spending quintile of HRRs were in the highest-spending quintile of HSAs; conversely, only 51.5% of the highest-spending HSAs were located within the borders of the highest-spending HRRs. Similar patterns were observed for nondrug spending. CONCLUSIONS The effectiveness of payment reforms in reducing overutilization while maintaining access to high-quality care depends on the effectiveness of targeting. Our analysis suggests that HRR-based policies may be too crudely targeted to promote the best use of health care resources. (Funded by the Institute of Medicine and others.).

Health Insurance Coverage and Health — What the Recent Evidence Tells Us

CONTEXT Millions of uninsured Americans ostensibly have insurance available to them-many at very low cost-but do not take it up. Traditional economic analysis is based on the premise that these are rational decisions, but it is hard to reconcile observed enrollment patterns with this view. The policy prescriptions that the traditional model generates may thus fail to achieve their goals. Behavioral economics, which integrates insights from psychology into economic analysis, identifies important deviations from the traditional assumptions of rationality and can thus improve our understanding of what drives health insurance take-up and improved policy design. METHODS Rather than a systematic review of the coverage literature, this article is a primer for considering issues in health insurance coverage from a behavioral economics perspective, supplementing the standard model. We present relevant evidence on decision making and insurance take-up and use it to develop a behavioral approach to both the policy problem posed by the lack of health insurance coverage and possible policy solutions to that problem. FINDINGS We found that evidence from behavioral economics can shed light on both the sources of low take-up and the efficacy of different policy levers intended to expand coverage. We then applied these insights to policy design questions for public and private insurance coverage and to the implementation of the recently enacted health reform, focusing on the use of behavioral insights to maximize the value of spending on coverage. CONCLUSIONS We concluded that the success of health insurance coverage reform depends crucially on understanding the behavioral barriers to take-up. The take-up process is likely governed by psychology as much as economics, and public resources can likely be used much more effectively with behaviorally informed policy design.

The Productivity of Physician Specialization: Evidence from the Medicare Program

Projections of growing federal debt largely reflect anticipated increases in health care spending. Michael Chernew and colleagues explain the basics of deficits and debt and their implications for health care reform.