Evidence map›Paper›PMID 33268412›Full record

ArticleBMJ open2020

Vaccination assessments using the Demographic and Health Survey, 2005-2018: a scoping review.

Luke M Shenton, Abram L Wagner, Mengdi Ji, Bradley F Carlson, Matthew L Boulton

Abstract readScoping Review
In one paragraph

Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

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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

5 authors.

Luke M Shenton *Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
Abram L Wagner *Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA awag@umich.edu.ORCID 0000-0003-4691-7802
Mengdi JiDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
Bradley F CarlsonDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
Matthew L BoultonDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.

Funding

Characterizing parental beliefs to support optimization of vaccine introductionK01AI137123 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI WAGNER, ABRAM LUTHER · 2019 to 2023
$475k
NIAID NIH HHS K01 AI137123
6 · The paper itself

Abstract

objectiveTo characterise studies which have used Demographic and Health Survey (DHS) datasets to evaluate vaccination status.

designScoping review. DATA SOURCES: Electronic databases including PubMed, EBSCOhost and POPLINE, from 2005 to 2018. STUDY SELECTION: All English studies with vaccination status as the outcome and the use of DHS data. DATA EXTRACTION: Studies were selected using a predetermined list of eligibility criteria and data were extracted independently by two authors. Data related to the study population, the outcome of interest (vaccination) and commonly seen predictors were extracted.

resultsA total of 125 articles were identified for inclusion in the review. The number of countries covered by individual studies varied widely (1-86), with the most published papers using data from India, Nigeria, Pakistan and Ethiopia. Many different definitions of full vaccination were used although the majority used a traditional schedule recommended in the WHO's Expanded Programme on Immunisation. We found studies analysed a wide variety of predictors, but the most common were maternal education, wealth, urbanicity and child's sex. Most commonly reported predictors had consistent relationships with the vaccination outcome, outside of sibling composition.

conclusionsResearchers make frequent use of the DHS dataset to describe vaccination patterns within one or more countries. A clearer idea of past use of DHS can inform the development of more rigorous studies in the future. Researchers should carefully consider whether a variable needs to be included in the multivariable model, or if there are mediating relationships across predictor variables.

Indexed as

VaccinationChildDemographyEthiopiaHumansIndiaNigeriaPakistaninternational health servicespaediatric infectious disease & immunisationpublic health

Identifiers

PMID33268412
PMCPMC7713201

What OpenQuestion holds

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LicenceCC BY-NC
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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.