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.

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.

When an experienced provider opts to leave a healthcare workforce (attrition), there are significant costs, both direct and indirect. Turnover of healthcare providers is underreported and understudied, despite evidence that it negatively impacts care delivery and negatively impacts working conditions for remaining providers. In the Veterans Affairs (VA) healthcare system, attrition of women’s health primary care providers (WH-PCPs) threatens a specially trained workforce; it is unknown what factors contribute to, or protect against, their attrition.

Even before the covid-19 pandemic, virtual consultations (also called telemedicine consultations) were on the rise, with many healthcare systems advocating a digital-first approach. At the start of the pandemic, many GPs and specialists turned to video consultations to reduce patient flow through healthcare facilities and limit infectious exposures. Video and telephone consultations also enable clinicians who are well but have to self-isolate, or who fall into high risk groups and require shielding, to continue providing medical care. The scope for video consultations for long term conditions is wide and includes management of diabetes, hypertension, asthma, stroke, psychiatric illnesses, cancers, and chronic pain. Video consultations can also be used for triage and management of a wide range of acute conditions, including, for example, emergency eye care triage. This practice pointer summarises the evidence on the use of video consultations in healthcare and offers practical recommendations for video consulting in primary care and outpatient settings.

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.