Evidence map›Paper›PMID 37529434›Full record

ArticleFrontiers in public health2023

COVID-19 vaccine acceptance among pregnant women: a hospital-based cross-sectional study in Sudan.

Saeed M Omar, Osama S Osman, Rehana Khalil, Osama Al-Wutayd, Ishag Adam

Abstract read
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

5 authors.

Saeed M OmarFaculty of Medicine, Gadarif University, Gadarif, Sudan.
Osama S OsmanFaculty of Medicine, Gadarif University, Gadarif, Sudan.
Rehana KhalilDepartment of Family and Community Medicine, Unaizah College of Medicine and Medical Sciences, Qassim University, Unaizah, Saudi Arabia.
Osama Al-WutaydDepartment of Family and Community Medicine, Unaizah College of Medicine and Medical Sciences, Qassim University, Unaizah, Saudi Arabia.
Ishag AdamDepartment of Obstetrics and Gynecology, Unaizah College of Medicine and Medical Sciences, Qassim University, Unaizah, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Pregnancy increases the risk of developing a severe illness due to COVID-19 infection. To the best of our knowledge, no previous study has been conducted on COVID-19 vaccine acceptance among pregnant women in Sudan. Hence, this study aimed to determine COVID-19 vaccination acceptance and its predictors among pregnant women. Methods: A cross-sectional study was conducted among 623 pregnant women attending Gadarif maternity hospital in eastern Sudan through a structured questionnaire. Data were obtained on sociodemographic characteristics, obstetric and health-related characteristics, COVID-19 infection, and vaccination-related information, as well as beliefs about and acceptance of COVID-19 vaccination. Results: COVID-19 vaccine acceptance among the pregnant women was 2.7%. The vaccine acceptance was higher if their husband's education was secondary school or higher [adjusted odds ratio [AOR] 4.30, 95% confidence interval (CI) 1.11-16.65, Conclusion: Acceptance of the COVID-19 vaccination among the pregnant women was very low. Discussions with pregnant women and their husbands by health care professionals regarding the safety of COVID-19 vaccine for the mother and her baby are highly recommended.

Indexed as

COVID-19COVID-19 VaccinesCross-Sectional StudiesFemaleHospitalsHumansInfantPregnancyPregnant PeopleSudanCOVID-19 VaccinesacceptanceCOVID-19COVID-19 vaccinepregnancySudan

Identifiers

PMID37529434
PMCPMC10387537

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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.