The risk of losing health insurance in the United States is large, and remained so after the Affordable Care Act.

Health insurance coverage in the United States is highly uncertain. In the post-Affordable Care Act (ACA), pre-COVID United States, we estimate that while 12.5% of individuals under 65 are uninsured at a point in time, twice as many-one in four-are uninsured at some point over a 2-y period. Moreover, the risk of losing insurance remained virtually unchanged with the introduction of the landmark ACA. Risk of insurance loss is particularly high for those with health insurance through Medicaid or private exchanges; they have a 20% chance of losing coverage at some point over a 2-y period, compared to 8.5% for those with employer-provided coverage. Those who lose insurance can experience prolonged periods without coverage; about half are still uninsured 6 mo later, and almost one-quarter are uninsured for the subsequent 2 y. These facts suggest that research and policy attention should focus not only on the "headline number" of the share of the population uninsured at a point in time, but also on the stability and certainty (or lack thereof) of being insured.

Beyond Causality: Additional Benefits of Randomized Controlled Trials for Improving Health Care Delivery.

Policy Points Policymakers at federal and state agencies, health systems, payers, and providers need rigorous evidence for strategies to improve health care delivery and population health. This is all the more urgent now, during the COVID-19 pandemic and its aftermath, especially among low-income communities and communities of color. Randomized controlled trials (RCTs) are known for their ability to produce credible causal impact estimates, which is why they are used to evaluate the safety and efficacy of drugs and, increasingly, to evaluate health care delivery and policy. But RCTs provide other benefits, allowing policymakers and researchers to: 1) design studies to answer the question they want to answer, 2) test theory and mechanisms to help enrich understanding beyond the results of a single study, 3) examine potentially subtle, indirect effects of a program or policy, and 4) collaborate closely to generate policy-relevant findings. Illustrating each of these points with examples of recent RCTs in health care, we demonstrate how policymakers can utilize RCTs to solve pressing challenges.

Racial Disparities In Excess All-Cause Mortality During The Early COVID-19 Pandemic Varied Substantially Across States.

The impact of the coronavirus disease 2019 (COVID-19) pandemic has been starkly unequal across race and ethnicity. We examined the geographic variation in excess all-cause mortality by race and ethnicity to better understand the impact of the pandemic. We used individual-level administrative data on the US population between January 2011 and April 2020 to estimate the geographic variation in excess all-cause mortality by race and Hispanic origin. All-cause mortality allows a better understanding of the overall impact of the pandemic than mortality attributable to COVID-19 directly. Nationwide, adjusted excess all-cause mortality during that period was 6.8 per 10,000 for Black people, 4.3 for Hispanic people, 2.7 for Asian people, and 1.5 for White people. Nationwide averages mask substantial geographic variation. For example, despite similar excess White mortality, Michigan and Louisiana had markedly different excess Black mortality, as did Pennsylvania compared with Rhode Island. Wisconsin experienced no significant White excess mortality but had significant Black excess mortality. Further work understanding the causes of geographic variation in racial and ethnic disparities-the relevant roles of social and environmental factors relative to comorbidities and of the direct and indirect health effects of the pandemic-is crucial for effective policy making.

Initial economic damage from the COVID-19 pandemic in the United States is more widespread across ages and geographies than initial mortality impacts.

The economic and mortality impacts of the COVID-19 pandemic have been widely discussed, but there is limited evidence on their relationship across demographic and geographic groups. We use publicly available monthly data from January 2011 through April 2020 on all-cause death counts from the Centers for Disease Control and Prevention and employment from the Current Population Survey to estimate excess all-cause mortality and employment displacement in April 2020 in the United States. We report results nationally and separately by state and by age group. Nationally, excess all-cause mortality was 2.4 per 10,000 individuals (about 30% higher than reported COVID deaths in April) and employment displacement was 9.9 per 100 individuals. Across age groups 25 y and older, excess mortality was negatively correlated with economic damage; excess mortality was largest among the oldest (individuals 85 y and over: 39.0 per 10,000), while employment displacement was largest among the youngest (individuals 25 to 44 y: 11.6 per 100 individuals). Across states, employment displacement was positively correlated with excess mortality (correlation = 0.29). However, mortality was highly concentrated geographically, with the top two states (New York and New Jersey) each experiencing over 10 excess deaths per 10,000 and accounting for about half of national excess mortality. By contrast, employment displacement was more geographically spread, with the states with the largest point estimates (Nevada and Michigan) each experiencing over 16 percentage points employment displacement but accounting for only 7% of the national displacement. These results suggest that policy responses may differentially affect generations and geographies.