Abstract

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.

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.

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.