Published in American journal of epidemiology
Policy responses to coronavirus disease 2019 (COVID-19), particularly those related to nonpharmaceutical interventions, are unprecedented in scale and scope. However, evaluations of policy impacts require a complex combination of circumstance, study design, data, statistics, and analysis. Beyond the issues that are faced for any policy, evaluation ...
Published in American journal of epidemiology
Epidemiologists sometimes use external sources of variation to explore highly confounded exposure-outcome relationships or exposures that cannot be randomized. These exogenous sources of variation, or natural experiments, are sometimes proposed as instrumental variables to examine the effects of given exposures on given outcomes. Previous epidemiol...
Published in American journal of epidemiology
We assessed the teratogenicity of tenofovir, a human immunodeficiency virus (HIV) drug similar to remdesivir that is currently being evaluated for the treatment of coronavirus disease 2019 (COVID-19). Using US Medicaid Analytic eXtract (MAX) claims data (2000-2014), we identified a population-based pregnancy cohort of women with HIV who filled at l...
Published in American journal of epidemiology
Clinical prediction models (CPMs) are often used to guide treatment initiation, with individuals at high risk offered treatment. This implicitly assumes that the probability quoted from a CPM represents the risk to an individual of an adverse outcome in absence of treatment. However, for a CPM to correctly target this estimand requires careful caus...
Published in American journal of epidemiology
Cardiovascular disease (CVD) risk-prediction models are used to identify high-risk individuals and guide statin initiation. However, these models are usually derived from individuals who might initiate statins during follow-up. We present a simple approach to address statin initiation to predict "statin-naive" CVD risk. We analyzed primary care dat...
Published in American journal of epidemiology
Genotype-phenotype association studies often combine phenotype data from multiple studies to increase statistical power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data-set organization. Here we describe a centralized system for phenotype...
Published in American journal of epidemiology
Statistical correction for measurement error in epidemiologic studies is possible, provided that information about the measurement error model and its parameters are available. Such information is commonly obtained from a randomly sampled internal validation sample. It is however unknown whether randomly sampling the internal validation sample is t...
Published in American journal of epidemiology
We compared three hypothetical trajectories of change in both general and COVID-19-specific anxiety during the 1st wave of the spread in the state of Israel: panic (very high anxiety, either from the outset or rapidly increasing), complacency (stable and low anxiety), and threat-sensitive (a moderate, linear increase compatible with the increase in...
Published in American journal of epidemiology
Coronavirus disease 2019 (COVID-19) is disproportionately burdening racial and ethnic minority groups in the United States. Higher risks of infection and mortality among racialized minorities are a consequence of structural racism, reflected in specific policies that date back centuries and persist today. Yet our surveillance activities do not refl...
Published in American journal of epidemiology
In 2019, the National Institutes of Health combined the Multicenter AIDS Cohort Study (MACS) and the Women's Interagency HIV Study (WIHS) into the MACS/WIHS Combined Cohort Study (MWCCS). In this paper, participants who made a study visit during October 2018-September 2019 (targeted for MWCCS enrollment) are described by human immunodeficiency viru...
