Evidence map›Paper›PMID 38140234›Full record

ArticleVaccines2023

Geospatial Analyses of Recent Household Surveys to Assess Changes in the Distribution of Zero-Dose Children and Their Associated Factors before and during the COVID-19 Pandemic in Nigeria.

Justice Moses K Aheto, Iyanuloluwa Deborah Olowe, Ho Man Theophilus Chan, Adachi Ekeh, Boubacar Dieng, Biyi Fafunmi, Hamidreza Setayesh, Brian Atuhaire, Jessica Crawford, Andrew J Tatem and 1 more

Abstract read
In one paragraph

Article in Vaccines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 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

11 authors.

Justice Moses K AhetoWorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK.ORCID 0000-0003-1384-2461
Iyanuloluwa Deborah OloweWorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK.
Ho Man Theophilus ChanWorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK.
Adachi EkehSydani Group, Abuja, Nigeria.
Boubacar DiengGavi, The Vaccine Alliance, Abuja, Nigeria.
Biyi FafunmiNational Bureau of Statistics, Abuja, Nigeria.
Hamidreza SetayeshGavi, The Vaccine Alliance, Geneva, Switzerland.ORCID 0000-0003-1691-0298
Brian AtuhaireGavi, The Vaccine Alliance, Geneva, Switzerland.
Jessica CrawfordGavi, The Vaccine Alliance, Geneva, Switzerland.
Andrew J TatemWorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK.
Chigozie Edson UtaziWorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK.ORCID 0000-0002-0534-5310

Funding

Gavi NA
6 · The paper itself

Abstract

The persistence of geographic inequities in vaccination coverage often evidences the presence of zero-dose and missed communities and their vulnerabilities to vaccine-preventable diseases. These inequities were exacerbated in many places during the coronavirus disease 2019 (COVID-19) pandemic, due to severe disruptions to vaccination services. Understanding changes in zero-dose prevalence and its associated risk factors in the context of the COVID-19 pandemic is, therefore, critical to designing effective strategies to reach vulnerable populations. Using data from nationally representative household surveys conducted before the COVID-19 pandemic, in 2018, and during the pandemic, in 2021, in Nigeria, we fitted Bayesian geostatistical models to map the distribution of three vaccination coverage indicators: receipt of the first dose of diphtheria-tetanus-pertussis-containing vaccine (DTP1), the first dose of measles-containing vaccine (MCV1), and any of the four basic vaccines (bacilli Calmette-Guerin (BCG), oral polio vaccine (OPV0), DTP1, and MCV1), and the corresponding zero-dose estimates independently at a 1 × 1 km resolution and the district level during both time periods. We also explored changes in the factors associated with non-vaccination at the national and regional levels using multilevel logistic regression models. Our results revealed no increases in zero-dose prevalence due to the pandemic at the national level, although considerable increases were observed in a few districts. We found substantial subnational heterogeneities in vaccination coverage and zero-dose prevalence both before and during the pandemic, showing broadly similar patterns in both time periods. Areas with relatively higher zero-dose prevalence occurred mostly in the north and a few places in the south in both time periods. We also found consistent areas of low coverage and high zero-dose prevalence using all three zero-dose indicators, revealing the areas in greatest need. At the national level, risk factors related to socioeconomic/demographic status (e.g., maternal education), maternal access to and utilization of health services, and remoteness were strongly associated with the odds of being zero dose in both time periods, while those related to communication were mostly relevant before the pandemic. These associations were also supported at the regional level, but we additionally identified risk factors specific to zero-dose children in each region; for example, communication and cross-border migration in the northwest. Our findings can help guide tailored strategies to reduce zero-dose prevalence and boost coverage levels in Nigeria.

Indexed as

Bayesian geostatistical modellingBayesian multilevel modellingcomposite coverageDemographic and Health SurveysDTP1 coverageMCV1 coverageMultiple Indicator Cluster Surveyzero-dose prevalence

Identifiers

PMID38140234
PMCPMC10747017

What OpenQuestion holds

Textmetadata
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.