Evidence map›Paper›PMID 33501457›Full record

ArticlemedRxiv : the preprint server for health sciences2021

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

Noah A Haber, Emma Clarke-Deelder, Avi Feller, Emily R Smith, Joshua Salomon, Benjamin MacCormack-Gelles, Elizabeth M Stone, Clara Bolster-Foucault, Jamie R Daw, Laura A Hatfield and 14 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

24 authors.

Noah A HaberMeta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA.
Emma Clarke-DeelderDepartment of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
Avi FellerGoldman School of Public Policy, UC Berkeley, Berkeley, CA, USA.
Emily R SmithDepartment of Global Health, Milken Institute School of Public Health, George Washington University, Washington, D.C, USA.
Joshua SalomonCenter for Health Policy and Center for Primary Care and Outcomes Research, Stanford University, Stanford, CA, USA.
Benjamin MacCormack-GellesDepartment of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
Elizabeth M StoneDepartment of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Clara Bolster-FoucaultEpidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada.
Jamie R DawHealth Policy and Management, Columbia University Mailman School of Public Health, New York, NY, USA.
Laura A HatfieldDepartment of Health Care Policy, Harvard Medical School, Boston, MA, USAr.
Carrie E FryDepartment of Health Policy, Vanderbilt University, Nashville, TN, USA.
Christopher B BoyerDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Eli Ben-MichaelInstitute for Quantitative Social Science, Harvard University, Cambridge, MA, USA.
Caroline M JoyceEpidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada.
Beth S LinasDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Ian SchmidDepartment of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Eric H AuSchool of Public Health, University of Sydney, Sydney, Australia.
Sarah E WietenMeta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA.
Brooke A JarrettDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Cathrine AxforsMeta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA.
Van Thu NguyenMeta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA.
Beth Ann GriffinRAND Corporation, Arlington, VA, USA.
Alyssa BilinskiInterfaculty Initiative in Health Policy, Harvard Graduate School of Arts and Sciences, Cambridge, MA, USA.
Elizabeth A StuartDepartment of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

Funding

Optimal Methods for Estimating Policy Effect Heterogeneity in Opioid Policy ResearchP50DA046351 · NIDA · RAND CORPORATION · PI Evan David Peet · 2018 to 2026
$20.8M
CVD Epidemiology Training Program in Behavior, the Environment and Global HealthT32HL098048 · NHLBI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Goodarz Danaei, ERIC B RIMM · 2009 to 2026
$6.9M
Integrating data for causal inference in behavioral healthT32MH122357 · NIMH · JOHNS HOPKINS UNIVERSITY · PI Rashelle Jean Musci, Elizabeth A. Stuart · 2020 to 2026
$1.8M
Mental Health Services and Systems Training ProgramT32MH109436 · NIMH · JOHNS HOPKINS UNIVERSITY · PI Matthew Eisenberg, Elizabeth A. Stuart · 2017 to 2026
$1.7M
The Role of an Artificially Intelligent Chatbot in Social Support, Antiretroviral Adherence, and Depressive Symptoms among Young Adults Living with HIV in South AfricaF31MH121128 · NIMH · JOHNS HOPKINS UNIVERSITY · PI JARRETT, BROOKE · 2020 to 2021
$92k
NHLBI NIH HHS T32 HL098048NIDA NIH HHS P50 DA046351NIMH NIH HHS F31 MH121128NIMH NIH HHS T32 MH109436NIMH NIH HHS T32 MH122357
6 · The paper itself

Abstract

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. This study systematically reviewed the strength of evidence in the published COVID-19 policy impact evaluation literature.

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 November 26, 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, assessing what impact evaluation method was used, graphical display of outcomes data, functional form for the outcomes, timing between policy and impact, concurrent changes to the outcomes, and an overall rating.

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. The majority (n=23/36) of studies in our sample examined the impact of stay-at-home requirements. 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-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, 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. DISCUSSION: The reviewed literature directly evaluating the impact of COVID-19 policies largely failed to meet key design criteria for inference of sufficient rigor to be actionable by policymakers. This was largely driven by the circumstances under which policies were passed making it difficult to attribute changes in COVID-19 outcomes to particular policies. 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.

Identifiers

PMID33501457
PMCPMC7836129

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.