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%).

Although health care billing claims data have been widely used to study health care use, spending, and policy changes, their use in the study of infectious disease has been limited. Other data sources, including from the Centers for Disease Control and Prevention (CDC), have provided timelier reporting to outbreak experts. However, given the scope of SARS-CoV-2—the causative agent responsible for the novel coronavirus disease 2019 (COVID-19) pandemic—and the multidimensional impact of the crisis on the health care system, analyses relying on health care claims data have begun to appear. Claims-based COVID-19 studies have a role, but it is critical to understand the limitations of these data. We are concerned that many weaknesses are not recognized by those familiar with other forms of patient-level data. Below, we examine several major considerations and make suggestions about where claims data may be best leveraged to inform policy and decision making.

Epidemiological modeling has emerged as a crucial tool to help decision-makers combat COVID-19, with calls for non-pharmaceutical interventions such as stay-at-home orders and the wearing of masks. But those models have become ubiquitous and part of the public lexicon — so Nirav Shah and Jason Wang write that they should follow an impact-oriented approach.

Stanford Health Policy’s Joshua Salomon, a professor of medicine and senior fellow at the Freeman Spogli Institute for International Studies, and colleagues developed a mathematical model to examine the potential for contact tracing to reduce the spread of the coronavirus. They modeled contact tracing programs in the context of relaxed physical distancing under different assumptions for case detection, tracing coverage and the extent to which contact tracing can lead to effective quarantine and isolation.

In a recent perspective published by the New England Journal of Medicine(NEJM), Stanford Law student Alexandra Daniels analyzed a growing body of federal litigation brought by prisoners with the hepatitis C virus (HCV) who are seeking access to treatment for their condition. With co-author and mentor, Law Professor David Studdert — also a professor of medicine at Stanford Health Policy — Daniels documented the dire public health problem of HCV in prisons.