Benjamin D. Sommers

Changes in Self-reported Insurance Coverage, Access to Care, and Health Under the Affordable Care Act

IMPORTANCE The Affordable Care Act (ACA) completed its second open enrollment period in February 2015. Assessing the laws effects has major policy implications. OBJECTIVES To estimate national changes in self-reported coverage, access to care, and health during the ACAs first 2 open enrollment periods and to assess differences between low-income adults in states that expanded Medicaid and in states that did not expand Medicaid. DESIGN, SETTING, AND PARTICIPANTS Analysis of the 2012-2015 Gallup-Healthways Well-Being Index, a daily national telephone survey. Using multivariable regression to adjust for pre-ACA trends and sociodemographics, we examined changes in outcomes for the nonelderly US adult population aged 18 through 64 years (n = 507,055) since the first open enrollment period began in October 2013. Linear regressions were used to model each outcome as a function of a linear monthly time trend and quarterly indicators. Then, pre-ACA (January 2012-September 2013) and post-ACA (January 2014-March 2015) changes for adults with incomes below 138% of the poverty level in Medicaid expansion states (n = 48,905 among 28 states and Washington, DC) vs nonexpansion states (n = 37,283 among 22 states) were compared using a differences-in-differences approach. EXPOSURES Beginning of the ACAs first open enrollment period (October 2013). MAIN OUTCOMES AND MEASURES Self-reported rates of being uninsured, lacking a personal physician, lacking easy access to medicine, inability to afford needed care, overall health status, and health-related activity limitations. RESULTS Among the 507,055 adults in this survey, pre-ACA trends were significantly worsening for all outcomes. Compared with the pre-ACA trends, by the first quarter of 2015, the adjusted proportions who were uninsured decreased by 7.9 percentage points (95% CI, -9.1 to -6.7); who lacked a personal physician, -3.5 percentage points (95% CI, -4.8 to -2.2); who lacked easy access to medicine, -2.4 percentage points (95% CI, -3.3 to -1.5); who were unable to afford care, -5.5 percentage points (95% CI, -6.7 to -4.2); who reported fair/poor health, -3.4 percentage points (95% CI, -4.6 to -2.2); and the percentage of days with activities limited by health, -1.7 percentage points (95% CI, -2.4 to -0.9). Coverage changes were largest among minorities; for example, the decrease in the uninsured rate was larger among Latino adults (-11.9 percentage points [95% CI, -15.3 to -8.5]) than white adults (-6.1 percentage points [95% CI, -7.3 to -4.8]). Medicaid expansion was associated with significant reductions among low-income adults in the uninsured rate (differences-in-differences estimate, -5.2 percentage points [95% CI, -7.9 to -2.6]), lacking a personal physician (-1.8 percentage points [95% CI, -3.4 to -0.3]), and difficulty accessing medicine (-2.2 percentage points [95% CI, -3.8 to -0.7]). CONCLUSIONS AND RELEVANCE The ACAs first 2 open enrollment periods were associated with significantly improved trends in self-reported coverage, access to primary care and medications, affordability, and health. Low-income adults in states that expanded Medicaid reported significant gains in insurance coverage and access compared with adults in states that did not expand Medicaid.

Obesity and Presenteeism: The Impact of Body Mass Index on Workplace Productivity

Objective: To examine whether obesity is associated with increased presenteeism (health-related limitations at work). Methods: Randomly selected manufacturing employees (n = 341) were assessed via height and weight measures, demographic survey, wage data, and the Work Limitations Questionnaire. The Work Limitations Questionnaire measures productivity on four dimensions. Analyses of variance and analyses of covariance were computed to identify productivity differences based on body mass index (BMI). Results: Moderately or extremely obese workers (BMI ≥35) experienced the greatest health-related work limitations, specifically regarding time needed to complete tasks and ability to perform physical job demands. These workers experienced a 4.2% health-related loss in productivity, 1.18% more than all other employees, which equates to an additional

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

506 annually in lost productivity per worker. Conclusions: The relationship between BMI and presenteeism is characterized by a threshold effect, where extremely or moderately obese workers are significantly less productive than mildly obese workers.

The Affordable Care Act and Insurance Coverage for Young Adults

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 Reform and Changes in Health Insurance Coverage in 2014

overdose and may reveal changes in prescribing practices that are shaping the evolving epidemic. Like Yokell et al, we have also noted—both in our own clinical practice and in that of colleagues—that real-time access to prescription databases for health professionals facilitates a patient-centered approach to addressing opioid abuse. In addition, we have encountered physicians managing chronic opioid therapy who are more comfortable checking a prescription monitoring program report than mandating urine drug screening, which requires awkward patient confrontation and can result in disruption of the patient-physician alliance. Additionally, we have found that when prescription monitoring program queries suggest opioid misuse or abuse, a physician in an office or emergency department has an ideal opportunity to address the issue and refer a patient to either community,outpatient, or inpatient treatmentoptions.Anecdotally, we have learned of several instances in which physicianspresentedpatientswith theirownresults fromaprescriptionmonitoringprogramsearch, leadingthepatient tothennot onlyrequest rehabilitationbut tobe takentoan inpatient treatment facility directly from the physician’s office. As clinicians, our goal will always be to appropriately treat a patient’s pain but also to ensure the patient’s safety and well-being and to facilitate an acknowledgment and redress of potentially dangerous behaviors.

Changes in health and medical spending among young adults under health reform.

On the basis of data from the Gallup–Healthways Well-Being Index, a survey of a national sample of adults, the authors estimate that the rate of uninsured nonelderly adults declined by about 5 percentage points after the Affordable Care Acts initial open-enrollment period.

Decision analysis using individual patient preferences to determine optimal treatment for localized prostate cancer.

Changes in Health and Medical Spending Among Young Adults Under Health Reform Beginning September 23, 2010, the Affordable Care Act allowed young adults to be covered under their parents’ plans until 26 years of age. This dependent coverage provision increased insurance coverage and access among young adults.1,2 However, the association between implementation of the provision and medical spending, health care use, and overall health is unknown.

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

Selecting treatment for clinically localized prostate cancer remains an ongoing challenge. Previous decision analyses focused on a hypothetical patient with average preferences, but preferences differ for clinically similar patients, implying that their optimal therapies may also differ.

The Impact Of State Policies On ACA Applications And Enrollment Among Low-Income Adults In Arkansas, Kentucky, And Texas

The authors report their analysis of the highest quality research over the past decade examining the effects of health insurance on health and conclude that insurance coverage increases access to care and improves health outcomes.

Stuck between Health and Immigration Reform — Care for Undocumented Immigrants

States are taking variable approaches to the Affordable Care Act (ACA) Medicaid expansion, Marketplace design, enrollment outreach, and application assistance. We surveyed nearly 3,000 low-income adults in late 2014 to compare experiences in three states with markedly different policies: Kentucky, which expanded Medicaid, created a successful state Marketplace, and supported outreach efforts; Arkansas, which enacted the private option and a federal-state partnership Marketplace, but with legislative limitations on outreach; and Texas, which did not expand Medicaid and passed restrictions on navigators. We found that application rates, successful enrollment, and positive experiences with the ACA were highest in Kentucky, followed by Arkansas, with Texas performing worst. Limited awareness remains a critical barrier: Fewer than half of adults had heard some or a lot about the coverage expansions. Application assistance from navigators and others was the strongest predictor of enrollment, while Latino applicants were less likely than others to successfully enroll. Twice as many respondents felt that the ACA had helped them as hurt them (although the majority reported no direct impact), and advertising was strongly associated with perceptions of the law. State policy choices appeared to have had major impacts on enrollment experiences among low-income adults and their perceptions of the ACA.