Evidence map›Paper›PMID 34756754›Full record

ArticleAmerican journal of preventive medicine2022

Governor's Party, Policies, and COVID-19 Outcomes: Further Evidence of an Effect.

Olga Shvetsova, Andrei Zhirnov, Frank R Giannelli, Michael A Catalano, Olivia Catalano

Abstract read
In one paragraph

Article in American journal of preventive medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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  12. Homelessness, Politics, and Policy: Predicting Spatial Variation in COVID-19 Cases and Deaths.International journal of environmental research and public health · 2023
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Olga ShvetsovaDepartment of Political Science, Harpur College of Arts and Sciences, Binghamton University, Binghamton, New York. Electronic address: shvetso@binghamton.edu.
Andrei ZhirnovDepartment of Politics, College of Social Sciences and International Studies, University of Exeter, Exeter, United Kingdom.
Frank R GiannelliRutgers School of Health Professions, Rutgers The State University of New Jersey, Newark, New Jersey.
Michael A CatalanoDepartment of Political Science, Harpur College of Arts and Sciences, Binghamton University, Binghamton, New York.
Olivia CatalanoIndependent Researcher, Binghamton, New York.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThis study connects the aggregate strength of public health policies taken in response to the COVID-19 pandemic in the U.S. states to the governors' party affiliations and to state-level outcomes. Understanding the relationship between politics and public health measures can better prepare American communities for what to expect from their governments in a future crisis and encourage advocacy for delegating public health decisions to medical professionals.

methodsThe public health Protective Policy Index captures the strength of policy response to COVID-19 at the state level. The authors estimated a Bayesian model that links the rate of disease spread to Protective Policy Index. The model also accounted for the possible state-specific undercounting of cases and controls for state population density, poverty, number of physicians, cardiovascular disease, asthma, smoking, obesity, age, racial composition, and urbanization. A Bayesian linear model with natural splines of time was employed to link the dynamics of Protective Policy Index to governors' party affiliations.

resultsA 10-percentage point decrease in Protective Policy Index was associated with an 8% increase in the expected number of new cases. Between late March and November 2020 and at the state-specific peaks of the pandemic, the Protective Policy Index in the states with Democratic governors was about 10‒percentage points higher than in the states with Republican governors.

conclusionsPublic health measures were stricter in the Democrat-led states, and stricter public health measures were associated with a slower growth of COVID-19 cases. The apparent politicization of public health measures suggests that public health decision making by health professionals rather than by political incumbents could be beneficial.

Indexed as

COVID-19Bayes TheoremHumansPandemicsPoliticsPublic PolicySARS-CoV-2United States

Identifiers

PMID34756754
PMCPMC8502787

What OpenQuestion holds

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Registered trials

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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.