Evidence map›Paper›PMID 32122718›Full record

ArticleVaccine2020

Geospatial variation in measles vaccine coverage through routine and campaign strategies in Nigeria: Analysis of recent household surveys.

C Edson Utazi, John Wagai, Oliver Pannell, Felicity T Cutts, Dale A Rhoda, Matthew J Ferrari, Boubacar Dieng, Joseph Oteri, M Carolina Danovaro-Holliday, Adeyemi Adeniran and 1 more

Abstract read
In one paragraph

Article in Vaccine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing 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

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

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Smoothed model-assisted small area estimation of proportions.The Canadian journal of statistics = Revue canadienne de statistique · 2024
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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.

C Edson UtaziWorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK; Southampton Statistical Sciences Research Institute, University of Southampton, Southampton SO17 1BJ, UK. Electronic address: c.e.utazi@soton.ac.uk.
John WagaiWorld Health Organization Consultant, Abuja, Nigeria.
Oliver PannellWorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK.
Felicity T CuttsDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK.
Dale A RhodaBiostat Global Consulting, Worthington, OH, USA.
Matthew J FerrariCenter for Infectious Disease Dynamics, The Pennsylvania State University, State College, PA, 16802, USA.
Boubacar DiengGAVI Alliance, Abuja, Nigeria.
Joseph OteriNational Primary Health Care Development Agency, Abuja, Nigeria.
M Carolina Danovaro-HollidayWorld Health Organization, Geneva, Switzerland.
Adeyemi AdeniranNational Bureau of Statistics, Abuja, Nigeria.
Andrew J TatemWorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK.

Funding

Wellcome TrustWorld Health Organization 001
6 · The paper itself

Abstract

Measles vaccination campaigns are conducted regularly in many low- and middle-income countries to boost measles control efforts and accelerate progress towards elimination. National and sometimes first-level administrative division campaign coverage may be estimated through post-campaign coverage surveys (PCCS). However, these large-area estimates mask significant geographic inequities in coverage at more granular levels. Here, we undertake a geospatial analysis of the Nigeria 2017-18 PCCS data to produce coverage estimates at 1 × 1 km resolution and the district level using binomial spatial regression models built on a suite of geospatial covariates and implemented in a Bayesian framework via the INLA-SPDE approach. We investigate the individual and combined performance of the campaign and routine immunization (RI) by mapping various indicators of coverage for children aged 9-59 months. Additionally, we compare estimated coverage before the campaign at 1 × 1 km and the district level with predicted coverage maps produced using other surveys conducted in 2013 and 2016-17. Coverage during the campaign was generally higher and more homogeneous than RI coverage but geospatial differences in the campaign's reach of previously unvaccinated children are shown. Persistent areas of low coverage highlight the need for improved RI performance. The results can help to guide the conduct of future campaigns, improve vaccination monitoring and measles elimination efforts. Moreover, the approaches used here can be readily extended to other countries.

Indexed as

MeaslesVaccination CoverageBayes TheoremChild, PreschoolGeographyHumansImmunization ProgramsInfantMeasles VaccineNigeriaSpatial AnalysisMeasles VaccineGeospatial analysisMeasles vaccinePost-campaign coverage surveyRoutine immunizationSupplementary immunization activities

Identifiers

PMID32122718
PMCPMC7079337

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.