Evidence map›Paper›PMID 40691221›Full record

ArticleScientific reports2025

Association of COVID-19 outcomes with measures of institutional and interpersonal trust: an ecological analysis using national data from 61 countries.

Lillian Rountree, Guangyu Tong, Fan Bu, Megan Ranney, Bhramar Mukherjee

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Lillian RountreeDepartment of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Guangyu TongSection of Cardiovascular Medicine, Department of Internal Medicine Yale School of Medicine, Yale University, New Haven, CT, USA.
Fan BuDepartment of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Megan RanneyYale School of Public Health, Yale University, New Haven, CT, USA.
Bhramar MukherjeeDepartment of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, USA. bhramar.mukherjee@yale.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite the importance of human behavior in containing a disease outbreak, formal quantitative analyses examining the relationship between measures of trust and COVID-19 outcomes remain limited. We use data from Wave 7 (2017-2022) of the World Values Survey to assess the country-level relationship between trust and COVID-19 outcomes across 61 countries via clustering and regression. After adjusting for country-level confounders, our findings indicate that countries with low trust have significantly greater numbers of COVID-19 deaths (1200.6 more COVID-19 deaths per million, 95% CI [510.92, 1890.3]), significantly greater excess death (2289.1 more excess deaths per million, 95% CI [971.1, 3607.2]), and a lower vaccination rate (16.6 fewer people vaccinated per 100, 95% CI [-27.7, -5.6]) than high trust countries, suggesting a tangible impact of trust on country-level COVID-19 outcomes. We discuss differences between interpersonal and institutional trust and advocate for incorporating trust in disease modeling to better predict country-level outcomes.

Indexed as

COVID-19TrustHumansSARS-CoV-2Vaccination

Identifiers

PMID40691221
PMCPMC12280113

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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.