Evidence map›Paper›PMID 34999529›Full record

ArticleSocial science & medicine (1982)2022

Women, men and COVID-19.

Yeva Aleksanyan, Jason P Weinman

Abstract read
In one paragraph

Article in Social science & medicine (1982), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Observational
  6. Article
  7. Observational
  8. Article
  9. The impact of Covid-19 in Chhattisgarh, India: A descriptive study.Journal of family medicine and primary care · 2024
    Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Enhanced surveillance of hospitalised COVID-19 patients in Europe: I-MOVE-COVID-19 surveillance network, February 2020 to December 2021.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2023
    Article
  15. Article
  16. Article
  17. Article
  18. Article
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

2 authors.

Yeva AleksanyanDepartment of Economics, Colorado State University, Campus 1771, Colorado State University, Fort Collins, CO, 80523 1771, USA. Electronic address: aleksanyanyeva@gmail.com.
Jason P WeinmanDepartment of Radiology, University of Colorado Anschutz Medical Campus and Children's Hospital, Colorado 13123 East 16th Avenue, Box B-125, Aurora, CO, 80045, USA. Electronic address: jason.weinman@childrenscolorado.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere is often gender bias in access and provision of care. Women fall through the cracks of the healthcare system due to gender-biased norms and poorer socioeconomic status.

methodsThis study uses COVID-19 sex-disaggregated data from 133 countries. Using bootstrapping and imputation methods and heteroscedastic linear regression model, it investigates the effect of biological factors and gender norms on reported differences in male and female COVID-19 case and death rates.

resultsGender norms are significant factors explaining such differences. Countries, where women experience more discrimination in families and have less access to resources, education and finance, report larger differences between male and female rates of COVID-19 cases and deaths.

conclusionWomen's lower access to healthcare due to social norms, financial and non-financial barriers may affect women's testing for COVID-19 and access to adequate care, and result in underreported female cases and deaths from COVID-19.

Indexed as

COVID-19COVID-19 TestingFemaleHumansMaleSARS-CoV-2SexismSocial NormsSocioeconomic FactorsCOVID-19GenderGender biasHealth disparitiesInequalityNorms

Identifiers

PMID34999529
PMCPMC8732878

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.