Evidence map›Paper›PMID 35251462›Full record

ArticleThe Pan African medical journal2021

Adverse event following vaccine surveillance in Kaduna State, Northwestern Nigeria (January 2018 -June 2019): analysis of health facility´s records.

Sambo Godwin Ishaku, Gregory Umeh, Bulus Adzu, Anthony Onimisi, Madubu Dauda, Hadiza Aliyu Iyal, Neyu Iliyasu, Danjuma Jenom Sunday, Jeremiah Daikwo, Sannom Mildred Yates and 7 more

Abstract read
In one paragraph

Article in The Pan African medical journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. A Systematic Review on Yellow Fever Vaccine and Adverse Events.Global health, epidemiology and genomics · 2026
    Pooled it
  2. 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

17 authors.

Sambo Godwin IshakuWorld Health Organization (WHO), Kaduna State Office, Kaduna, Nigeria.
Gregory UmehWorld Health Organization (WHO), Kaduna State Office, Kaduna, Nigeria.
Bulus AdzuDepartment of Pharmacology and Toxicology, National Institute for Pharmaceutical Research and Development, Abuja, Nigeria.
Anthony OnimisiWorld Health Organization (WHO), Abuja, Nigeria.
Madubu DaudaWorld Health Organization (WHO), Kaduna State Office, Kaduna, Nigeria.
Hadiza Aliyu IyalWorld Health Organization (WHO), Kaduna State Office, Kaduna, Nigeria.
Neyu IliyasuKaduna State Primary Healthcare Board, Kaduna, Nigeria.
Danjuma Jenom SundayMinistry of Health and Social Services, Kaduna, Nigeria.
Jeremiah DaikwoMinistry of Health and Social Services, Kaduna, Nigeria.
Sannom Mildred YatesBarau Dikko Teaching Hospital, Kaduna State University, Kaduna, Nigeria.
Ibrahim Idris IbrahimWorld Health Organization (WHO), Kaduna State Office, Kaduna, Nigeria.
Lami Hajara SamailaWorld Health Organization (WHO), Kaduna State Office, Kaduna, Nigeria.
Basirat AbdullahiWorld Health Organization (WHO), Kaduna State Office, Kaduna, Nigeria.
Stephen Kadarko ParomFaculty of Pharmaceutical Sciences, Kaduna State University, Kaduna, Nigeria.
Kabir Yusuf MaiwashiNational Primary Health Care Development Agency, Abuja, Nigeria.
Fureratu ZakariWorld Health Organization (WHO), Abuja, Nigeria.
Kase Sarah NuhuDepartment of Medical Laboratory Science, Kaduna State University, Kaduna, Nigeria.

Funding

World Health Organization 001
6 · The paper itself

Abstract

introductionAdverse Events Following Immunization (AEFI) are one of the main reasons for inadequate immunization coverage in Kaduna State, and AEFI underreporting serves as a barrier to achieving goals of global pharmaco-vigilance for vaccine. The purpose of this study is to estimate the completeness of variables in the AEFI line-listing forms, calculate AEFI reporting rates by Local Government Areas & vaccine type and profile the reported cases according to their reactions.

methodswe conducted a descriptive, cross-sectional, retrospective study of primary surveillance records. We calculated AEFI reporting rates in the State and Local Government areas and AEFI Vaccine reaction rates to the various antigens. We used Binary logistic regression to determine the association between gender and vaccine reactions.

resultsseven thousand eight hundred and twenty-four (7,824) AEFI cases were reported. The completeness of variables on the filled AEFI line-list varied from 21% to 100%. The State had a high AEFI reporting rate of 9.09 per 10,000 administered doses. Fever (<38oC) was the main AEFI reaction. Severe AEFI cases accounted for only 0.89% of the total reported cases. Pentavalent vaccine was the suspect antigen responsible for the highest number of AEFI cases, with a vaccine reaction rate of 44.77 per 10,000 doses. The Zaria Local Government area had the highest AEFI reporting rate, while the Sanga Local Government area had the lowest AEFI reporting rate in the State. The difference between genders in the number of reported AEFI cases was not statistically significant (p>0.05). There were 35% higher odds of occurrence of bleeding among males than among females (aOR: 1.354; P-value: p=.012; 95% CI: 1.070-1.715; Nagelkerke-R

conclusionour study shows a higher occurrence of severe AEFI in subjects undergoing pentavalent vaccine. Thiscaused the highest incidence of AEFI. There was no significant association between gender and AEFI reactions.

Indexed as

Adverse Drug Reaction Reporting SystemsVaccinesCross-Sectional StudiesFemaleHealth FacilitiesHumansImmunizationInfantMaleNigeriaRetrospective StudiesVaccinationVaccinesImmunizationKaduna Statesurveillance

Identifiers

PMID35251462
PMCPMC8856967

What OpenQuestion holds

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LicenceCC BY
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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.