James N. Weinstein’s research while affiliated with Microsoft and other places

Objectives To determine whether life expectancy (LE) changes between 2000 and 2019 were associated with race, rural status, local economic prosperity, and changes in local economic prosperity, at the county level. Methods Between 12/1/22 and 2/28/23, we conducted a retrospective analysis of 2000 and 2019 data from 3,123 United States counties. For Total, White, and Black populations, we compared LE changes for counties across the rural-urban continuum, the local economic prosperity continuum, and for counties in which local economic prosperity dramatically improved or declined. Results In both years, overall, across the rural-urban continuum, and for all studied populations, LE decreased with each progression from the most to least prosperous quintile (all p < 0.001); improving county prosperity between 2000–2019 was associated with greater LE gains (p < 0.001 for all). Conclusion At the county level, race, rurality, and local economic distress were all associated with LE; improvements in local economic conditions were associated with accelerated LE. Policymakers should appreciate the health externalities of investing in areas experiencing poor economic prosperity if their goal is to improve population health.

While rural-urban disparities in health and health outcomes have been demonstrated, because of their impact on (and intervenability to improve) health and health outcomes, we sought to examine cross-sectional and longitudinal inequities in health, clinical care, health behaviors, and social determinants of health (SDOH) between rural and non-rural counties in the pre-pandemic era (2015 to 2019), and to present a Health Equity Dashboard that can be used by policymakers and researchers to facilitate examining such disparities. Therefore, using data obtained from 2015–2022 County Health Rankings datasets, we used analysis of variance to examine differences in 33 county level attributes between rural and non-rural counties, calculated the change in values for each measure between 2015 and 2019, determined whether rural-urban disparities had widened, and used those data to create a Health Equity Dashboard that displays county-level individual measures or compilations of them. We followed STROBE guidelines in writing the manuscript. We found that rural counties overwhelmingly had worse measures of SDOH at the county level. With few exceptions, the measures we examined were getting worse between 2015 and 2019 in all counties, relatively more so in rural counties, resulting in the widening of rural-urban disparities in these measures. When rural-urban gaps narrowed, it tended to be in measures wherein rural counties were outperforming urban ones in the earlier period. In conclusion, our findings highlight the need for policymakers to prioritize rural settings for interventions designed to improve health outcomes, likely through improving health behaviors, clinical care, social and environmental factors, and physical environment attributes. Visualization tools can help guide policymakers and researchers with grounded information, communicate necessary data to engage relevant stakeholders, and track SDOH changes and health outcomes over time.

Background Socioeconomic status has long been associated with population health and health outcomes. While ameliorating social determinants of health may improve health, identifying and targeting areas where feasible interventions are most needed would help improve health equity. We sought to identify inequities in health and social determinants of health (SDOH) associated with local economic distress at the county-level. Methods For 3,131 counties in the 50 US states and Washington, DC (wherein approximately 325,711,203 people lived in 2019), we conducted a retrospective analysis of county-level data collected from County Health Rankings in two periods (centering around 2015 and 2019). We used ANOVA to compare thirty-three measures across five health and SDOH domains (Health Outcomes, Clinical Care, Health Behaviors, Physical Environment, and Social and Economic Factors) that were available in both periods, changes in measures between periods, and ratios of measures for the least to most prosperous counties across county-level prosperity quintiles, based on the Economic Innovation Group’s 2015–2019 Distressed Community Index Scores. Results With seven exceptions, in both periods, we found a worsening of values with each progression from more to less prosperous counties, with least prosperous counties having the worst values (ANOVA p < 0.001 for all measures). Between 2015 and 2019, all except six measures progressively worsened when comparing higher to lower prosperity quintiles, and gaps between the least and most prosperous counties generally widened. Conclusions In the late 2010s, the least prosperous US counties overwhelmingly had worse values in measures of Health Outcomes, Clinical Care, Health Behaviors, the Physical Environment, and Social and Economic Factors than more prosperous counties. Between 2015 and 2019, for most measures, inequities between the least and most prosperous counties widened. Our findings suggest that local economic prosperity may serve as a proxy for health and SDOH status of the community. Policymakers and leaders in public and private sectors might use long-term, targeted economic stimuli in low prosperity counties to generate local, community health benefits for vulnerable populations. Doing so could sustainably improve health; not doing so will continue to generate poor health outcomes and ever-widening economic disparities.

Background: While overall Medicare Part C (Medicare Advantage) enrollment has grown more rapidly than fee-for-service Medicare enrollment, changes in the growth and characteristics of different enrollee populations have not been examined. Objectives: For 2011-2019, to compare changes in the growth and characteristics of younger (age younger than 65) and older (age 65 and older) Medicare beneficiaries enrolled in Medicare Part A only, Medicare Parts A & B, and Medicare Part C. Research design: This was a retrospective, observational study. Subjects: Medicare beneficiaries who were alive and enrolled in Medicare Part A only, Medicare Parts A & B, or Medicare Part C on June 30 of each year and in no other plan that year. Measures: For each plan type and age group the numbers and mean ages of enrollees and the proportion of enrollees who were: black, female, concurrently enrolled in Medicaid, and (for older enrollees), whose initial reason for eligibility was old age and survivors' benefits. Results: Between 2011 and 2019, Medicare Part C experienced rapid expansions of 85.0% among older and 109.5% among younger enrollees. Part C enrollees were increasingly likely to be dually enrolled in Medicaid, Black and, among younger enrollees, female. Conclusions: Trends in demographic characteristics and changes in policy and growth in employer group plan offerings will likely continue to impact health care service utilization and costs in the Medicare population. Particularly as Medicare expansion to younger age groups is considered, future research should explore disparities in risk scores and care equity, quality, and costs across different Medicare enrollment options.