Andrew I. Friedson

Clinical trial representativeness is vital for the evaluation of intervention performance and study generalizability. Current methods for evaluating representativeness are limited by the quality of the disease registry data used and may not appropriately evaluate studies aimed at precision cohorts. This study evaluates the sensitivity of existing methods for measuring study representation to differences between the population targeted by the clinical intervention and the closest available population in a patient registry. Using records for U.S.-based cancer clinical trials registered to ClinicalTrials.gov from 2017–2023 and the U.S. Cancer Statistics Public Use Database we calculated representativeness measures by comparing the demographic mix (based on sex, race, and ethnicity) in each clinical trial to the demographic mix for the same form of cancer in the U.S. Cancer Statistics database via exact Binomial tests. Then, the same tests were conducted comparing trial populations to simulated populations that were demographically different from the U.S. Cancer Statistics data by fixed percentages. The outcome of interest was whether the result of the test changed when the comparator population was different. For clinical trials reporting the sex, race, and ethnicity of participants, 24, 40, and 32 percent of studies (respectively) give different results when the difference between the registry population and simulated population is 5 percentage points. For all demographics, larger differences between the registry and the simulated populations were associated with worse metric performance. Analyses of clinical trial representativeness suffer from a large loss of accuracy in settings where treatments are targeted to demographically different subgroups. Studies of representation in the context of precision medicine should be interpreted with caution.

This study examines the impacts of summer 2020 Black Lives Matter (BLM) protests on risk avoidance behaviors. Using daily anonymized smartphone data on social mobility and an event-study approach, we find that net stay-at-home behavior increased following protest onset. This result is consistent with avoidance behaviors in response to perceived risk of COVID-19 infection and protest-related violence. Furthermore, we find that BLM protests reduced local activities that were potentially riskier for infection than outdoor protests: restaurant and bar-going and retail shopping. We conclude that risk avoidance behaviors help to explain why BLM protests did not lead to local COVID-19 spread.

Considerable health care professional (HCP) burnout happens due to work culture in medical settings, often leading to HCPs leaving the health care sector. This can lead to lower access to care and increased risk of medical error. Factors that may contribute to burnout include low satisfaction with one’s work and lack of support by health care leadership.^1,2

The Centers for Disease Control and Prevention (CDC) deem large indoor gatherings without social distancing the “highest risk” activity for COVID-19 contagion. On June 20, 2020, President Donald J. Trump held his first mass campaign rally following the US coronavirus outbreak at the indoor Bank of Oklahoma arena. In the weeks following the event, numerous high-profile national news outlets reported that the Trump rally was “more than likely” the cause of a coronavirus surge in Tulsa County based on time series data. This study is the first to rigorously explore the impacts of this event on social distancing and COVID-19 spread. First, using data from SafeGraph Inc, we show that while non-resident visits to census block groups hosting the Trump event grew by approximately 25 percent, there was no decline in net stay-at-home behavior in Tulsa County, reflecting important offsetting behavioral effects. Then, using data on COVID-19 cases from the CDC and a synthetic control design, we find little evidence that COVID-19 grew more rapidly in Tulsa County, its border counties, or in the state of Oklahoma than each’s estimated counterfactual during the five-week post-treatment period we observe. Difference-in-differences estimates further provide no evidence that COVID-19 rates grew faster in counties that drew relatively larger shares of residents to the event. We conclude that offsetting risk-related behavioral responses to the rally—including voluntary closures of restaurants and bars in downtown Tulsa, increases in stay-at-home behavior, displacement of usual activities of weekend inflows, and smaller-than-expected crowd attendance—may be important mechanisms.

In the United States, the COVID-19 vaccination rate slowed from a peak of 3.6 million vaccinations per day during the week of April 5, 2021, to fewer than 2 million vaccinations per day by the week of May 3, 2021. To boost vaccine uptake, 19 states announced large cash lotteries by July 1, 2021, that were tied to COVID-19 vaccination.

Conventional wisdom often holds that the healthcare sector fares better than other sectors during economic downturns. However, little research has examined the relationship between local economic conditions and healthcare employment. Understanding how the healthcare sector responds to economic conditions is important for policymakers seeking to ensure an adequate supply of healthcare workers, as well as for those directing displaced workers into new jobs. We examine the impact of macroeconomic conditions on both the healthcare labor market and the pipeline of healthcare workers receiving healthcare degrees during 2005–2017 (the pre-COVID era). Our results indicate that the healthcare sector is stable across past business cycles. If anything, when areas experience more severe local economic downturns, healthcare employment increases. Much remains unknown about how the healthcare sector will fare during the current recession. Our study represents an important backdrop as policymakers consider ways to sustain the healthcare sector during current economic and public health turbulence.

A shelter‐in‐place order (SIPO) is one of the most restrictive non‐pharmaceutical interventions designed to curb the spread of COVID‐19. On March 19, 2020, California Governor Gavin Newsom issued the first statewide SIPO in the United States. The order closed non‐essential businesses and required residents to shelter in place for all but essential activities such as grocery shopping, retrieving prescriptions from a pharmacy, or caring for relatives. This study is the first in the economics literature to estimate the effect of a statewide SIPO on public health. Using daily state‐level coronavirus data and a synthetic control research design, we find that California's statewide SIPO reduced COVID‐19 cases by 160.9 to 194.7 per 100,000 population by April 20, one month following the order. We further find that California's SIPO led to as many as 1,566 fewer COVID‐19 deaths during this period. Back‐of‐the‐envelope calculations suggest that there were about 649 to 703 job losses per life saved, and about 14 to16 job losses per case averted during this post‐treatment period.

This study explores the impact of Shelter‐in‐Place Orders (SIPOs) on health, with attention to heterogeneity in their impacts. First, using daily state‐level social distancing data, we document that adoption of a SIPO was associated with a 9%–10% increase in the rate at which state residents remained in their homes full‐time. Using daily state‐level coronavirus case data, we find that approximately 3 weeks following the adoption of a SIPO, cumulative COVID‐19 cases fell by approximately 53.5%. However, this average effect masks important heterogeneity across states‐early adopters and high population density states appear to reap larger benefits from their SIPOs. (JEL H75, I12, I18)