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 of COVID-19 policies is complicated by additional challenges related to infectious disease dynamics and a multiplicity of interventions. The methods needed for policy-level impact evaluation are not often used or taught in epidemiology, and they differ in important ways that may not be obvious. Methodological complications of policy evaluations can make it difficult for decision-makers and researchers to synthesize and evaluate the strength of the evidence in COVID-19 health policy papers. Here we 1) introduce the basic suite of policy-impact evaluation designs for observational data, including cross-sectional analyses, pre-/post- analyses, interrupted time-series analysis, and difference-in-differences analysis; 2) demonstrate key ways in which the requirements and assumptions underlying these designs are often violated in the context of COVID-19; and 3) provide decision-makers and reviewers with a conceptual and graphical guide to identifying these key violations. Our overall goal is to help epidemiologists, policy-makers, journal editors, journalists, researchers, and other research consumers understand and weigh the strengths and limitations of evidence.
Keywords: coronavirus disease 2019; difference-in-differences analysis; impact evaluation; interrupted time-series analysis; nonpharmaceutical interventions; policy.
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