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The gold-standard approaches for gleaning statistically valid conclusions from data involve random sampling from the population. Collecting properly randomized data, however, can be challenging, so modern statistical methods, including propensity score reweighting, aim to enable valid inferences when random sampling is not feasible. We put forth an approach for making inferences based on available data from a source population that may differ in composition in unknown ways from an eventual target population. Whereas propensity scoring requires a separate estimation procedure for each different target population, we show how to build a single estimator, based on source data alone, that allows for efficient and accurate estimates on any downstream target data. We demonstrate, theoretically and empirically, that our target-independent approach to inference, which we dub “universal adaptability,” is competitive with target-specific approaches that rely on propensity scoring. Our approach builds on a surprising connection between the problem of inferences in unspecified target populations and the multicalibration problem, studied in the burgeoning field of algorithmic fairness. We show how the multicalibration framework can be employed to yield valid inferences from a single source population across a diverse set of target populations. 

Objectives. To assess the impact of the COVID-19 pandemic on mental distress in US adults. Methods. Participants were 5065 adults from the Understanding America Study, a probability-based Internet panel representative of the US adult population. The main exposure was survey completion date (March 10–16, 2020). The outcome was mental distress measured via the 4-item version of the Patient Health Questionnaire. Results. Among states with 50 or more COVID-19 cases as of March 10, each additional day was significantly associated with an 11% increase in the odds of moving up a category of distress (odds ratio = 1.11; 95% confidence interval = 1.01, 1.21; P = .02). Perceptions about the likelihood of getting infected, death from the virus, and steps taken to avoid infecting others were associated with increased mental distress in the model that included all states. Individuals with higher consumption of alcohol or cannabis or with history of depressive symptoms were at significantly higher risk for mental distress. Conclusions. These data suggest that as the COVID-19 pandemic continues, mental distress may continue to increase and should be regularly monitored. Specific populations are at high risk for mental distress, particularly those with preexisting depressive symptoms. 

Abstract Background Cross-sectional studies have found that the coronavirus disease 2019 (COVID-19) pandemic has negatively affected population-level mental health. Longitudinal studies are necessary to examine trajectories of change in mental health over time and identify sociodemographic groups at risk for persistent distress. Purpose To examine the trajectories of mental distress between March 10 and August 4, 2020, a key period during the COVID-19 pandemic. Methods Participants included 6,901 adults from the nationally representative Understanding America Study, surveyed at baseline between March 10 and 31, 2020, with nine follow-up assessments between April 1 and August 4, 2020. Mixed-effects logistic regression was used to examine the association between date and self-reported mental distress (measured with the four-item Patient Health Questionnaire) among U.S. adults overall and among sociodemographic subgroups defined by sex, age, race/ethnicity, household structure, federal poverty line, and census region. Results Compared to March 11, the odds of mental distress among U.S. adults overall were 1.84 (95% confidence interval [CI] = 1.65–2.07) times higher on April 1 and 1.92 (95% CI = 1.62–2.28) times higher on May 1; by August 1, the odds of mental distress had returned to levels comparable to March 11 (odds ratio [OR] = 0.80, 95% CI = 0.66–0.96). Females experienced a sharper increase in mental distress between March and May compared to males (females: OR = 2.29, 95% CI = 1.85–2.82; males: OR = 1.53, 95% CI = 1.15–2.02). Conclusions These findings highlight the trajectory of mental health symptoms during an unprecedented pandemic, including the identification of populations at risk for sustained mental distress. 

Abstract AimsTo examine changes in drinking behavior among United States (US) adults between March 10 and July 21, 2020, a critical period during the COVID‐19 pandemic. DesignLongitudinal, internet‐based panel survey. SettingThe Understanding America Study (UAS), a nationally representative panel of US adults age 18 or older. ParticipantsA total of 4298 US adults who reported alcohol use. MeasurementsChanges in number of reported drinking days from March 11, 2020 through July 21, 2020 in the overall sample and stratified by sex, age, race/ethnicity, household structure, poverty status, and census region. FindingsCompared with March 11, the number of drinking days per week was significantly higher on April 1 by an average of 0.36 days (95% CI = 0.30, 0.43), on May 1 by an average of 0.55 days (95% CI = 0.47, 0.63), on June 1 by an average of 0.41 days (95% CI = 0.33, 0.49), and on July 1 by an average of 0.39 days (95% CI = 0.31, 0.48). Males, White participants, and older adults reported sustained increases in drinking days, whereas female participants and individuals living under the federal poverty line had attenuated drinking days in the latter part of the study period. ConclusionsBetween March and mid‐July 2020, adults in the United States reported increases in the number of drinking days, with sustained increases observed among males, White participants, and older adults.