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Targeting Trachoma Control through Risk Mapping: The Example of Southern Sudan

BACKGROUND: Trachoma is a major cause of blindness in Southern Sudan. Its distribution has only been partially established and many communities in need of intervention have therefore not been identified or targeted. The present study aimed to develop a tool to improve targeting of survey and control...

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Autores principales: Clements, Archie C. A., Kur, Lucia W., Gatpan, Gideon, Ngondi, Jeremiah M., Emerson, Paul M., Lado, Mounir, Sabasio, Anthony, Kolaczinski, Jan H.
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2923154/
https://www.ncbi.nlm.nih.gov/pubmed/20808910
http://dx.doi.org/10.1371/journal.pntd.0000799
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author Clements, Archie C. A.
Kur, Lucia W.
Gatpan, Gideon
Ngondi, Jeremiah M.
Emerson, Paul M.
Lado, Mounir
Sabasio, Anthony
Kolaczinski, Jan H.
author_facet Clements, Archie C. A.
Kur, Lucia W.
Gatpan, Gideon
Ngondi, Jeremiah M.
Emerson, Paul M.
Lado, Mounir
Sabasio, Anthony
Kolaczinski, Jan H.
author_sort Clements, Archie C. A.
collection PubMed
description BACKGROUND: Trachoma is a major cause of blindness in Southern Sudan. Its distribution has only been partially established and many communities in need of intervention have therefore not been identified or targeted. The present study aimed to develop a tool to improve targeting of survey and control activities. METHODS/PRINCIPAL FINDINGS: A national trachoma risk map was developed using Bayesian geostatistics models, incorporating trachoma prevalence data from 112 geo-referenced communities surveyed between 2001 and 2009. Logistic regression models were developed using active trachoma (trachomatous inflammation follicular and/or trachomatous inflammation intense) in 6345 children aged 1–9 years as the outcome, and incorporating fixed effects for age, long-term average rainfall (interpolated from weather station data) and land cover (i.e. vegetation type, derived from satellite remote sensing), as well as geostatistical random effects describing spatial clustering of trachoma. The model predicted the west of the country to be at no or low trachoma risk. Trachoma clusters in the central, northern and eastern areas had a radius of 8 km after accounting for the fixed effects. CONCLUSION: In Southern Sudan, large-scale spatial variation in the risk of active trachoma infection is associated with aridity. Spatial prediction has identified likely high-risk areas to be prioritized for more data collection, potentially to be followed by intervention.
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spelling pubmed-29231542010-08-31 Targeting Trachoma Control through Risk Mapping: The Example of Southern Sudan Clements, Archie C. A. Kur, Lucia W. Gatpan, Gideon Ngondi, Jeremiah M. Emerson, Paul M. Lado, Mounir Sabasio, Anthony Kolaczinski, Jan H. PLoS Negl Trop Dis Research Article BACKGROUND: Trachoma is a major cause of blindness in Southern Sudan. Its distribution has only been partially established and many communities in need of intervention have therefore not been identified or targeted. The present study aimed to develop a tool to improve targeting of survey and control activities. METHODS/PRINCIPAL FINDINGS: A national trachoma risk map was developed using Bayesian geostatistics models, incorporating trachoma prevalence data from 112 geo-referenced communities surveyed between 2001 and 2009. Logistic regression models were developed using active trachoma (trachomatous inflammation follicular and/or trachomatous inflammation intense) in 6345 children aged 1–9 years as the outcome, and incorporating fixed effects for age, long-term average rainfall (interpolated from weather station data) and land cover (i.e. vegetation type, derived from satellite remote sensing), as well as geostatistical random effects describing spatial clustering of trachoma. The model predicted the west of the country to be at no or low trachoma risk. Trachoma clusters in the central, northern and eastern areas had a radius of 8 km after accounting for the fixed effects. CONCLUSION: In Southern Sudan, large-scale spatial variation in the risk of active trachoma infection is associated with aridity. Spatial prediction has identified likely high-risk areas to be prioritized for more data collection, potentially to be followed by intervention. Public Library of Science 2010-08-17 /pmc/articles/PMC2923154/ /pubmed/20808910 http://dx.doi.org/10.1371/journal.pntd.0000799 Text en Clements et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Clements, Archie C. A.
Kur, Lucia W.
Gatpan, Gideon
Ngondi, Jeremiah M.
Emerson, Paul M.
Lado, Mounir
Sabasio, Anthony
Kolaczinski, Jan H.
Targeting Trachoma Control through Risk Mapping: The Example of Southern Sudan
title Targeting Trachoma Control through Risk Mapping: The Example of Southern Sudan
title_full Targeting Trachoma Control through Risk Mapping: The Example of Southern Sudan
title_fullStr Targeting Trachoma Control through Risk Mapping: The Example of Southern Sudan
title_full_unstemmed Targeting Trachoma Control through Risk Mapping: The Example of Southern Sudan
title_short Targeting Trachoma Control through Risk Mapping: The Example of Southern Sudan
title_sort targeting trachoma control through risk mapping: the example of southern sudan
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2923154/
https://www.ncbi.nlm.nih.gov/pubmed/20808910
http://dx.doi.org/10.1371/journal.pntd.0000799
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