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Mapping access to basic hygiene services in low- and middle-income countries: A cross-sectional five-country case study of geospatial disparities

Mapping access to basic hygiene services in low- and middle-income countries: A cross-sectional five-country case study of geospatial disparities
Mapping access to basic hygiene services in low- and middle-income countries: A cross-sectional five-country case study of geospatial disparities
Handwashing with water and soap is among the most a cost-effective interventions to improve public health. Yet billions of people globally lacking handwashing facilities with water and soap on premises, with gaps particularly found in low- and middle-income countries. Targeted efforts to expand access to basic hygiene services require data at geospatially explicit scales. Drawing on country-specific cross-sectional Demographic and Health Surveys with georeferenced hygiene data, we developed an ensemble machine learning model to predict the prevalence of basic hygiene facilities in Malawi, Nepal, Nigeria, Pakistan and Uganda. The ensemble model was based on a multiple-level stacking structure, where four predictive modelling algorithms were used to produce sub-models, and a random forest model was used to generalise the final predictions. An inverse distance weighted interpolation was incorporated in the random forest model to account for spatial autocorrelation. Local coverage and a local dissimilarity index were calculated to examine the geographic disparities in access. Our methodology produced robust outputs, as evidenced by performance evaluations (all R2 were above 0.8). Among the five study countries, Pakistan had the highest overall coverage, whilst Malawi had the poorest coverage. Apparent disparities in basic hygiene services measured by local coverage were found across geographic locations and between urban and rural settings. Nigeria had the highest level of inequalities in basic hygiene services measured by a dissimilarity index, whilst Malawi showed the least segregation between populations with and without basic hygiene services. Both educational attainment and wealth were important predictors of the geospatial distribution of basic hygiene services. By producing geospatially explicit estimates of the prevalence of handwashing facilities with water and soap, this study provides a means of identifying geographical disparities in basic hygiene services. The method and outputs can be useful tools to identify areas of low coverage and to support efficient and precise targeting of efforts to scale up access to handwashing facilities and shift social and cultural norms on handwashing.
Basic hygiene, Ensemble model, Handwashing, Machine learning, WASH, Water and soap
0143-6228
Yu, Weiyu
4cca6f0a-badb-4f1c-8b38-da29ba0b9e09
Bain, R.E.S.
bc014460-35d7-4e81-8a01-0cf114356b46
Yu, Jie
cf5a9b79-b81a-4b03-ac98-8c9ee5b49a3a
Alegana, Victor A.
17871690-1cac-4acd-9371-31c71cded2f4
Dotse-Gborgbortsi, Winfred, Worlanyo
11fe21e7-431a-442b-a8c7-6a7cb05176d9
Lin, Yi
55fa7676-aa68-4672-aa04-bb754298816a
Wright, Jim
94990ecf-f8dd-4649-84f2-b28bf272e464
Yu, Weiyu
4cca6f0a-badb-4f1c-8b38-da29ba0b9e09
Bain, R.E.S.
bc014460-35d7-4e81-8a01-0cf114356b46
Yu, Jie
cf5a9b79-b81a-4b03-ac98-8c9ee5b49a3a
Alegana, Victor A.
17871690-1cac-4acd-9371-31c71cded2f4
Dotse-Gborgbortsi, Winfred, Worlanyo
11fe21e7-431a-442b-a8c7-6a7cb05176d9
Lin, Yi
55fa7676-aa68-4672-aa04-bb754298816a
Wright, Jim
94990ecf-f8dd-4649-84f2-b28bf272e464

Yu, Weiyu, Bain, R.E.S., Yu, Jie, Alegana, Victor A., Dotse-Gborgbortsi, Winfred, Worlanyo, Lin, Yi and Wright, Jim (2021) Mapping access to basic hygiene services in low- and middle-income countries: A cross-sectional five-country case study of geospatial disparities. Applied Geography, 135, [102549]. (doi:10.1016/j.apgeog.2021.102549).

Record type: Article

Abstract

Handwashing with water and soap is among the most a cost-effective interventions to improve public health. Yet billions of people globally lacking handwashing facilities with water and soap on premises, with gaps particularly found in low- and middle-income countries. Targeted efforts to expand access to basic hygiene services require data at geospatially explicit scales. Drawing on country-specific cross-sectional Demographic and Health Surveys with georeferenced hygiene data, we developed an ensemble machine learning model to predict the prevalence of basic hygiene facilities in Malawi, Nepal, Nigeria, Pakistan and Uganda. The ensemble model was based on a multiple-level stacking structure, where four predictive modelling algorithms were used to produce sub-models, and a random forest model was used to generalise the final predictions. An inverse distance weighted interpolation was incorporated in the random forest model to account for spatial autocorrelation. Local coverage and a local dissimilarity index were calculated to examine the geographic disparities in access. Our methodology produced robust outputs, as evidenced by performance evaluations (all R2 were above 0.8). Among the five study countries, Pakistan had the highest overall coverage, whilst Malawi had the poorest coverage. Apparent disparities in basic hygiene services measured by local coverage were found across geographic locations and between urban and rural settings. Nigeria had the highest level of inequalities in basic hygiene services measured by a dissimilarity index, whilst Malawi showed the least segregation between populations with and without basic hygiene services. Both educational attainment and wealth were important predictors of the geospatial distribution of basic hygiene services. By producing geospatially explicit estimates of the prevalence of handwashing facilities with water and soap, this study provides a means of identifying geographical disparities in basic hygiene services. The method and outputs can be useful tools to identify areas of low coverage and to support efficient and precise targeting of efforts to scale up access to handwashing facilities and shift social and cultural norms on handwashing.

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Yu_etal2021 - Accepted Manuscript
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More information

Accepted/In Press date: 19 August 2021
e-pub ahead of print date: 6 September 2021
Published date: October 2021
Additional Information: Funding Information: This work was supported by the National Natural Science Foundation of China under Grant 41771449 . Victor Alegana is funded as a Wellcome Trust Training Fellow (number 211208 ). Publisher Copyright: © 2021 Elsevier Ltd Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Keywords: Basic hygiene, Ensemble model, Handwashing, Machine learning, WASH, Water and soap

Identifiers

Local EPrints ID: 451397
URI: http://eprints.soton.ac.uk/id/eprint/451397
ISSN: 0143-6228
PURE UUID: e6be1097-d87b-4454-ae9c-a0b4f6d43810
ORCID for Winfred, Worlanyo Dotse-Gborgbortsi: ORCID iD orcid.org/0000-0001-7627-1809
ORCID for Jim Wright: ORCID iD orcid.org/0000-0002-8842-2181

Catalogue record

Date deposited: 23 Sep 2021 16:40
Last modified: 26 Nov 2021 03:20

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Contributors

Author: Weiyu Yu
Author: R.E.S. Bain
Author: Jie Yu
Author: Victor A. Alegana
Author: Yi Lin
Author: Jim Wright ORCID iD

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