  Policy responses to COVID-19, particularly those related to
non-pharmaceutical interventions, are unprecedented in scale and scope.
Epidemiologists are more involved in policy decisions and evidence generation
than ever before. However, policy impact evaluations always 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 lags, lack of direct observation of key outcomes, and a
multiplicity of interventions occurring on an accelerated time scale. The
methods needed for policy-level impact evaluation are not often used or taught
in epidemiology, and differ in important ways that may not be obvious. The
volume and speed, and methodological complications of policy evaluations can
make it difficult for decision-makers and researchers to synthesize and
evaluate strength of evidence in COVID-19 health policy papers.
  In this paper, we (1) introduce the basic suite of policy impact evaluation
designs 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 these
designs are often violated in the context of COVID-19, and (3) provide
decision-makers and reviewers a conceptual and graphical guide to identifying
these key violations. The overall goal of this paper is to help
epidemiologists, policy-makers, journal editors, journalists, researchers, and
other research consumers understand and weigh the strengths and limitations of
evidence that is essential to decision-making.
