Dataset Information

Problems with Evidence Assessment in COVID-19 Health Policy Impact Evaluation (PEACHPIE): A systematic strength of methods review.

ABSTRACT:

Introduction

The impact of policies on COVID-19 outcomes is one of the most important questions of our time. Unfortunately, there are substantial concerns about the strength and quality of the literature examining policy impacts. This study systematically assessed the currently published COVID-19 policy impact literature for a checklist of study design elements and methodological issues.

Methods

We included studies that were primarily designed to estimate the quantitative impact of one or more implemented COVID-19 policies on direct SARS-CoV-2 and COVID-19 outcomes. After searching PubMed for peer-reviewed articles published on November 26 or earlier and screening, all studies were reviewed by three reviewers independently and in consensus. The review tool was based on review guidance for assessing COVID-19 health policy impact evaluation analyses, including first identifying the assumptions behind the methods used, followed by assessing graphical display of outcomes data, functional form for the outcomes, timing between policy and impact, concurrent changes to the outcomes, and an overall rating.

Results

After 102 articles were identified as potentially meeting inclusion criteria, we identified 36 published articles that evaluated the quantitative impact of COVID-19 policies on direct COVID-19 outcomes. The majority (n=23/36) of studies in our sample examined the impact of stay-at-home requirements. Nine studies were set aside due to inappropriate study design (n=8 pre/post; n=1 cross-section), and 27 articles were given a full consensus assessment. 20/27 met criteria for graphical display of data, 5/27 for functional form, 19/27 for timing between policy implementation and impact, and only 3/27 for concurrent changes to the outcomes. Only 1/27 studies passed all of the above checks, and 4/27 were rated as overall appropriate. Including the 9 studies set aside, we found that only four (or by a stricter standard, only one) of the 36 identified published and peer-reviewed health policy impact evaluation studies passed a set of key design checks for identifying the causal impact of policies on COVID-19 outcomes.

Discussion

The current literature directly evaluating the impact of COVID-19 policies largely fails to meet key design criteria for useful inference. This may be partially due to the circumstances for evaluation being particularly difficult, as well as a context with desire for rapid publication, the importance of the topic, and weak peer review processes. Importantly, weak evidence is non-informative and does not indicate how effective these policies were on COVID-19 outcomes.

SUBMITTER: Haber NA

PROVIDER: S-EPMC7836129 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Problems with Evidence Assessment in COVID-19 Health Policy Impact Evaluation: A systematic review of study design and evidence strength.

Haber Noah A NA Clarke-Deelder Emma E Feller Avi A Smith Emily R ER Salomon Joshua J MacCormack-Gelles Benjamin B Stone Elizabeth M EM Bolster-Foucault Clara C Daw Jamie R JR Hatfield Laura A LA Fry Carrie E CE Boyer Christopher B CB Ben-Michael Eli E Joyce Caroline M CM Linas Beth S BS Schmid Ian I Au Eric H EH Wieten Sarah E SE Jarrett Brooke A BA Axfors Cathrine C Nguyen Van Thu VT Griffin Beth Ann BA Bilinski Alyssa A Stuart Elizabeth A EA

medRxiv : the preprint server for health sciences 20210910

<h4>Introduction</h4>Assessing the impact of COVID-19 policy is critical for informing future policies. However, there are concerns about the overall strength of COVID-19 impact evaluation studies given the circumstances for evaluation and concerns about the publication environment. This study systematically reviewed the strength of evidence in the published COVID-19 policy impact evaluation literature.<h4>Methods</h4>We included studies that were primarily designed to estimate the quantitative ...[more]

PMID: 33501457

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Project description:IntroductionAssessing the impact of COVID-19 policy is critical for informing future policies. However, there are concerns about the overall strength of COVID-19 impact evaluation studies given the circumstances for evaluation and concerns about the publication environment.MethodsWe included studies that were primarily designed to estimate the quantitative impact of one or more implemented COVID-19 policies on direct SARS-CoV-2 and COVID-19 outcomes. After searching PubMed for peer-reviewed articles published on 26 November 2020 or earlier and screening, all studies were reviewed by three reviewers first independently and then to consensus. The review tool was based on previously developed and released review guidance for COVID-19 policy impact evaluation.ResultsAfter 102 articles were identified as potentially meeting inclusion criteria, we identified 36 published articles that evaluated the quantitative impact of COVID-19 policies on direct COVID-19 outcomes. Nine studies were set aside because the study design was considered inappropriate for COVID-19 policy impact evaluation (n=8 pre/post; n=1 cross-sectional), and 27 articles were given a full consensus assessment. 20/27 met criteria for graphical display of data, 5/27 for functional form, 19/27 for timing between policy implementation and impact, and only 3/27 for concurrent changes to the outcomes. Only 4/27 were rated as overall appropriate. Including the 9 studies set aside, reviewers found that only four of the 36 identified published and peer-reviewed health policy impact evaluation studies passed a set of key design checks for identifying the causal impact of policies on COVID-19 outcomes.DiscussionThe reviewed literature directly evaluating the impact of COVID-19 policies largely failed to meet key design criteria for inference of sufficient rigour to be actionable by policy-makers. More reliable evidence review is needed to both identify and produce policy-actionable evidence, alongside the recognition that actionable evidence is often unlikely to be feasible.