Evidence map›Paper›PMID 36318889›Full record

ArticleThe American journal of tropical medicine and hygiene2022

Country-Level Governance Indicators as Predictors of COVID-19 Morbidity, Mortality, and Vaccination Coverage: An Exploratory Global Analysis.

Mahdi Baghbanzadeh, Madison Smith, Juergen Pilz, M Sohel Rahman, Ajlina Karamehic-Muratovic, Ashvita Garg, Esther Annan, Uyen-Sa D T Nguyen, Nathan Schedler, Rajesh Nandy and 2 more

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Article in The American journal of tropical medicine and hygiene, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

12 authors.

Mahdi BaghbanzadehOperations, Snapp, Tehran, Iran.
Madison SmithDepartment of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, Texas.
Juergen PilzAlpen-Adria University of Klagenfurt, Klagenfurt, Austria.
M Sohel RahmanDepartment of Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, Dhaka, Bangladesh.
Ajlina Karamehic-MuratovicDepartment of Sociology and Anthropology, Saint Louis University, St. Louis, Missouri.
Ashvita GargDepartment of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, Texas.
Esther AnnanDepartment of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, Texas.
Uyen-Sa D T NguyenDepartment of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, Texas.
Nathan SchedlerDoisy College of Health Sciences, Saint Louis University, St. Louis, Missouri.
Rajesh NandyDepartment of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, Texas.
Rafiul IslamIndependent Development Practitioner, Adabor, Dhaka, Bangladesh.
Ubydul HaqueDepartment of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, Texas.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the COVID-19 pandemic continues to affect all countries across the globe, this study seeks to investigate the relationship between nations' governance, COVID-19 national data, and nation-level COVID-19 vaccination coverage. National-level governance indicators (corruption index, voice and accountability, political stability, and absence of violence/terrorism), officially reported COVID-19 national data (cases, death, and tests per one million population), and COVID-19 vaccination coverage was considered for this study to predict COVID-19 morbidity and mortality. Results indicate a strong relationship between nations' governance and officially reported COVID-19 data. Countries were grouped into three clusters using only the governance data: politically stable countries, average countries or "less corrupt countries," and corrupt countries or "more corrupt countries." The clusters were then tested for significant differences in reporting various aspects of the COVID-19 data. According to multinomial regression, countries in the cluster of politically stable nations reported significantly more deaths, tests per one million, total cases per one million, and higher vaccination coverage compared with nations both in the clusters of corrupt countries and average countries. The countries in the cluster of average nations reported more tests per one million and higher vaccination coverage than countries in the cluster of corrupt nations. Countries included in the corrupt cluster reported a lower death rate and morbidity, particularly compared with the politically stable nations cluster, a trend that can be attributed to poor governance and inaccurate COVID-19 data reporting. The epidemic evaluation indices of the COVID-19 cases demonstrate that the pandemic is still evolving on a global level.

Indexed as

COVID-19COVID-19 VaccinesHumansMorbidityPandemicsVaccination CoverageCOVID-19 Vaccines

Identifiers

PMID36318889
PMCPMC9709024

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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.