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Policy responses to COVID-19, particularly those related tonon-pharmaceutical interventions, are unprecedented in scale and scope.Epidemiologists are more involved in policy decisions and evidence generationthan ever before. However, policy impact evaluations always require a complexcombination of circumstance, study design, data, statistics, and analysis.Beyond the issues that are faced for any policy, evaluation of COVID-19policies is complicated by additional challenges related to infectious diseasedynamics and lags, lack of direct observation of key outcomes, and amultiplicity of interventions occurring on an accelerated time scale. Themethods needed for policy-level impact evaluation are not often used or taughtin epidemiology, and differ in important ways that may not be obvious. Thevolume and speed, and methodological complications of policy evaluations canmake it difficult for decision-makers and researchers to synthesize andevaluate strength of evidence in COVID-19 health policy papers.  In this paper, we (1) introduce the basic suite of policy impact evaluationdesigns for observational data, including cross-sectional analyses, pre/post,interrupted time-series, and difference-in-differences analysis, (2)demonstrate key ways in which the requirements and assumptions underlying thesedesigns are often violated in the context of COVID-19, and (3) providedecision-makers and reviewers a conceptual and graphical guide to identifyingthese key violations. The overall goal of this paper is to helpepidemiologists, policy-makers, journal editors, journalists, researchers, andother research consumers understand and weigh the strengths and limitations ofevidence that is essential to decision-making.

COVID-19 Policy Impact Evaluation: A guide to common design issues