Evidence map›Paper›PMID 37479986›Full record

ArticleBMC infectious diseases2023

Cholera and COVID-19 pandemic prevention in multiple hotspot districts of Uganda: vaccine coverage, adverse events following immunization and WASH conditions survey.

Godfrey Bwire, Annet Kisakye, Esther Amulen, John Baptist Bwanika, Joan Badebye, Christine Aanyu, Brenda Doreen Nakirya, Alfred Okello, Stephen Acellam Okello, Justine N Bukenya and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC infectious diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.1field-weighted citation impact, top 14% of its field
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, 7 citations in OpenAlex.

  1. Article
  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

11 authors at 5 institutions in 1 country.

Godfrey BwireSchool of Public Health, Makerere University, Kampala, Uganda. gbwire1@yahoo.com.ORCID http://orcid.org/0000-0002-8376-2857
Annet KisakyeWorld Health Organization, Kampala, Uganda.
Esther AmulenSchool of Public Health, Makerere University, Kampala, Uganda.
John Baptist BwanikaSchool of Public Health, Makerere University, Kampala, Uganda.
Joan BadebyeSchool of Forestry, Environmental and Geographical Sciences, Makerere University, Kampala, Uganda.
Christine AanyuSchool of Public Health, Makerere University, Kampala, Uganda.
Brenda Doreen NakiryaCommunity Concerns Uganda Initiative, Jinja, Uganda.
Alfred OkelloDepartment of Public Health, St Mary's Hospital Lacor, Gulu, Uganda.
Stephen Acellam OkelloDepartment of Health Services, Kyambogo University, Kampala, Uganda.
Justine N BukenyaSchool of Public Health, Makerere University, Kampala, Uganda.
Christopher Garimoi OrachSchool of Public Health, Makerere University, Kampala, Uganda.
Makerere University · UGKyambogo University · UGMinistry of Health · UGSt. Mary's Hospital Lacor · UGWorld Health Organization - Uganda · UG

Funding

World Health Organization 001
6 · The paper itself

Abstract

backgroundBetween March, 2020 and December, 2021 due to cholera and coronavirus disease 2019 (COVID-19) pandemics, there were 1,534 cholera cases with 14 deaths and 136,065 COVID-19 cases with 3,285 deaths reported respectively in Uganda. This study investigated mass vaccination campaigns for the prevention of the two pandemics namely: oral cholera vaccine (OCV) and COVID-19 vaccine coverage; adverse events following immunization (AEFI); barriers and enablers for the vaccine uptake and assessed water, sanitation and hygiene (WASH) conditions in the six cholera and COVID-19 hotspot districts of Uganda.

methodsA household survey was conducted between January and February, 2022 in the six cholera hotspot districts of Uganda which had recently conducted OCV mass vaccination campaigns and had ongoing COVID-19 mass vaccination campaigns. The survey randomly enrolled 900 households with 4,315 persons of whom 2,085 were above 18 years. Data were collected using a data entry application designed in KoBoToolbox and analysed using STATA version 14. Frequencies, percentages, odds ratios, means, confidence intervals and maps were generated and interpreted.

resultsThe OCV coverage for dose one and two were 85% (95% CI: 84.2-86.4) and 67% (95% CI: 65.6-68.4) respectively. Among the 4,315 OCV recipients, 2% reported mild AEFI, 0.16% reported moderate AEFI and none reported severe AEFI. The COVID-19 vaccination coverage for dose one and two were 69.8% (95% CI: 67.8-71.8) and 18.8% (95% CI: 17.1-20.5) respectively. Approximately, 23% (478/2,085) of COVID-19 vaccine recipient reported AEFI; most 94% were mild, 0.6% were moderate and 2 cases were severe. The commonest reason for missing COVID-19 vaccine was fear of the side effects. For most districts (5/6), sanitation (latrine/toilet) coverage were low at 7.4%-37.4%.

conclusionThere is high OCV coverage but low COVID-19 vaccine and sanitation coverage with high number of moderate cases of AEFI recorded due to COVID-19 vaccines. The low COVID-19 vaccine coverage could indicate vaccine hesitancy for COVID-19 vaccines. Furthermore, incorporation of WASH conditions assessment in the OCV coverage surveys is recommended for similar settings to generate data for better planning. However, more studies are required on COVID-19 vaccine hesitancy.

Indexed as

CholeraCholera VaccinesCOVID-19COVID-19 VaccinesHumansHygieneImmunizationPandemicsSanitationUgandaCholera VaccinesCOVID-19 VaccinesAdverse event following immunizationAfricaCholeraCoronavirusCOVIDPandemicSanitationUgandaVaccine coverageVaccine hesitancyWASHWater coverage

Identifiers

PMID37479986
PMCPMC10362646
OpenAlexW4385064154

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.