Evidence map›Paper›PMID 41314735›Full record

ArticleSpatial and spatio-temporal epidemiology2025

Spatially varying relationships between birth registration and influencing factors in Kenya, using a suite of geographically weighted regressions.

Bibian N Robert, Peter M Macharia, M Naser Lessani, Viola Chepkurui, Joseph Kamau, Robert W Snow, Zhenlong Li, Emelda A Okiro

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Article in Spatial and spatio-temporal epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Bibian N RobertPopulation & Health Impact Surveillance Group, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya; Geoinformation and Big Data Research Laboratory, Department of Geography, The Pennsylvania State University, University Park, PA 16802, USA. Electronic address: bibianrobert@gmail.com.
Peter M MachariaPopulation & Health Impact Surveillance Group, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya; Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium. Electronic address: pmacharia@itg.be.
M Naser LessaniGeoinformation and Big Data Research Laboratory, Department of Geography, The Pennsylvania State University, University Park, PA 16802, USA. Electronic address: mlessani@psu.edu.
Viola ChepkuruiPopulation & Health Impact Surveillance Group, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya.
Joseph KamauDepartment of Civil Registration Services, Nairobi, Kenya.
Robert W SnowPopulation & Health Impact Surveillance Group, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK. Electronic address: rsnow@kemri-wellcome.org.
Zhenlong LiGeoinformation and Big Data Research Laboratory, Department of Geography, The Pennsylvania State University, University Park, PA 16802, USA. Electronic address: zhenlong@psu.edu.
Emelda A OkiroPopulation & Health Impact Surveillance Group, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK. Electronic address: eokiro@kemri-wellcome.org.

Funding

Wellcome Trust 203077Wellcome Trust 212176Wellcome Trust 224272
6 · The paper itself

Abstract

backgroundEveryone deserves legal recognition, yet millions of children remain unregistered, with the majority (87%) residing in sub-Saharan Africa and southern Asia. Despite global efforts to improve birth registration coverage, sub-national disparities persist. Across Kenya's 47 counties, birth registration completeness rates varies from nearly 100% to as low as 12.2%, suggesting local contextual factors are important. This study explores the influence of contextual factors on the spatially heterogeneous rates of birth registration in Kenya.

methodsWe utilized data from the 2022 Kenya Demographic and Health Survey. The association between registered births and its determinants (child factors, health care indicators, maternal, household and geographical factors) was assessed at the cluster level (villages) using four regression models: ordinary least square (OLS) and spatial local regression using Geographically Weighted Regression (GWR-single spatial scale for all predictors), Multiscale GWR (MGWR-each predictor operates at different spatial scale) and Similarity GWR (SGWR-single spatial scale for all predictors) models. Best-fit models were assessed using adjusted R

resultsA total of 1673 survey clusters were analysed. MGWR was the best-fitting model (AICc = 14,870.57, adjusted R

conclusionDeterminants of birth registration vary spatially at different geographical scales, necessitating context-specific targeted strategies to boost registration coverage across diverse areas and populations.

Indexed as

Birth CertificatesRegistriesAdultFemaleHumansKenyaMaleSocioeconomic FactorsSpatial RegressionBirth registrationCivil registration and vital statistics systemDemographic and health surveyKenyaMGWR

Identifiers

PMID41314735
PMCPMC7618552

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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.