With vaccines against SARS-CoV-2, the virus that causes Covid-19, on the near-term horizon, U.S. policymakers are focusing on how to ensure that Americans get vaccinated. This challenge has been compounded by reports that White House officials are exerting undue influence over the agencies that would ordinarily lead such efforts, the Food and Drug Administration and the Centers for Disease Control and Prevention.

Few issues in the policy response to the coronavirus disease 2019 (COVID-19) pandemic have inspired as impassioned debate as school reopening. There is broad agreement that school closures involve heavy burdens on students, parents, and the economy, with profound equity implications, but also that the risk of outbreaks cannot be eliminated even in a partial reopening scenario with in-school precautions. Consensus largely ends there, however: the approaches states and localities have taken to integrating these concerns into school reopening plans are highly variable.

On August 17, 2020, the Los Angeles Unified School District launched a program to test more than 700,000 students and staff for SARS-CoV-2. The district is paying a private contractor to provide next-day, early-morning results for as many as 40,000 tests daily. As of October 4, a total of 34,833 people had been tested at 42 sites. The program is notable not only because it’s ambitious, but also because it’s unusual: testing is conspicuously absent from school reopening plans in many other districts. Typically, exhaustive attention has instead focused on physical distancing, face coverings, hygiene, staggering of schedules, and cohorting (dividing students into small, fixed groups). Although the Centers for Disease Control and Prevention (CDC), the American Academy of Pediatrics, the National Academies of Sciences, Engineering, and Medicine, and state officials have urged schools to prepare for Covid-19 cases, they have offered strikingly little substantive guidance on testing. Immediate attention to improving testing access and response planning is essential to the successful reopening of schools.

Abstract

The distribution of health care payments to insurance plans has substantial consequences for social policy. Risk adjustment formulas predict spending in health insurance markets in order to provide fair benefits and health care coverage for all enrollees, regardless of their health status. Unfortunately, current risk adjustment formulas are known to underpredict spending for specific groups of enrollees leading to undercompensated payments to health insurers. This incentivizes insurers to design their plans such that individuals in undercompensated groups will be less likely to enroll, impacting access to health care for these groups. To improve risk adjustment formulas for undercompensated groups, we expand on concepts from the statistics, computer science, and health economics literature to develop new fair regression methods for continuous outcomes by building fairness considerations directly into the objective function. We additionally propose a novel measure of fairness while asserting that a suite of metrics is necessary in order to evaluate risk adjustment formulas more fully. Our data application using the IBM MarketScan Research Databases and simulation studies demonstrates that these new fair regression methods may lead to massive improvements in group fairness (eg, 98%) with only small reductions in overall fit (eg, 4%).