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Spatial Modelling of Soil-Transmitted Helminth Infections in Kenya: A Disease Control Planning Tool

BACKGROUND: Implementation of control of parasitic diseases requires accurate, contemporary maps that provide intervention recommendations at policy-relevant spatial scales. To guide control of soil transmitted helminths (STHs), maps are required of the combined prevalence of infection, indicating w...

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Autores principales: Pullan, Rachel L., Gething, Peter W., Smith, Jennifer L., Mwandawiro, Charles S., Sturrock, Hugh J. W., Gitonga, Caroline W., Hay, Simon I., Brooker, Simon
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3035671/
https://www.ncbi.nlm.nih.gov/pubmed/21347451
http://dx.doi.org/10.1371/journal.pntd.0000958
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author Pullan, Rachel L.
Gething, Peter W.
Smith, Jennifer L.
Mwandawiro, Charles S.
Sturrock, Hugh J. W.
Gitonga, Caroline W.
Hay, Simon I.
Brooker, Simon
author_facet Pullan, Rachel L.
Gething, Peter W.
Smith, Jennifer L.
Mwandawiro, Charles S.
Sturrock, Hugh J. W.
Gitonga, Caroline W.
Hay, Simon I.
Brooker, Simon
author_sort Pullan, Rachel L.
collection PubMed
description BACKGROUND: Implementation of control of parasitic diseases requires accurate, contemporary maps that provide intervention recommendations at policy-relevant spatial scales. To guide control of soil transmitted helminths (STHs), maps are required of the combined prevalence of infection, indicating where this prevalence exceeds an intervention threshold of 20%. Here we present a new approach for mapping the observed prevalence of STHs, using the example of Kenya in 2009. METHODS AND FINDINGS: Observed prevalence data for hookworm, Ascaris lumbricoides and Trichuris trichiura were assembled for 106,370 individuals from 945 cross-sectional surveys undertaken between 1974 and 2009. Ecological and climatic covariates were extracted from high-resolution satellite data and matched to survey locations. Bayesian space-time geostatistical models were developed for each species, and were used to interpolate the probability that infection prevalence exceeded the 20% threshold across the country for both 1989 and 2009. Maps for each species were integrated to estimate combined STH prevalence using the law of total probability and incorporating a correction factor to adjust for associations between species. Population census data were combined with risk models and projected to estimate the population at risk and requiring treatment in 2009. In most areas for 2009, there was high certainty that endemicity was below the 20% threshold, with areas of endemicity ≥20% located around the shores of Lake Victoria and on the coast. Comparison of the predicted distributions for 1989 and 2009 show how observed STH prevalence has gradually decreased over time. The model estimated that a total of 2.8 million school-age children live in districts which warrant mass treatment. CONCLUSIONS: Bayesian space-time geostatistical models can be used to reliably estimate the combined observed prevalence of STH and suggest that a quarter of Kenya's school-aged children live in areas of high prevalence and warrant mass treatment. As control is successful in reducing infection levels, updated models can be used to refine decision making in helminth control.
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spelling pubmed-30356712011-02-23 Spatial Modelling of Soil-Transmitted Helminth Infections in Kenya: A Disease Control Planning Tool Pullan, Rachel L. Gething, Peter W. Smith, Jennifer L. Mwandawiro, Charles S. Sturrock, Hugh J. W. Gitonga, Caroline W. Hay, Simon I. Brooker, Simon PLoS Negl Trop Dis Research Article BACKGROUND: Implementation of control of parasitic diseases requires accurate, contemporary maps that provide intervention recommendations at policy-relevant spatial scales. To guide control of soil transmitted helminths (STHs), maps are required of the combined prevalence of infection, indicating where this prevalence exceeds an intervention threshold of 20%. Here we present a new approach for mapping the observed prevalence of STHs, using the example of Kenya in 2009. METHODS AND FINDINGS: Observed prevalence data for hookworm, Ascaris lumbricoides and Trichuris trichiura were assembled for 106,370 individuals from 945 cross-sectional surveys undertaken between 1974 and 2009. Ecological and climatic covariates were extracted from high-resolution satellite data and matched to survey locations. Bayesian space-time geostatistical models were developed for each species, and were used to interpolate the probability that infection prevalence exceeded the 20% threshold across the country for both 1989 and 2009. Maps for each species were integrated to estimate combined STH prevalence using the law of total probability and incorporating a correction factor to adjust for associations between species. Population census data were combined with risk models and projected to estimate the population at risk and requiring treatment in 2009. In most areas for 2009, there was high certainty that endemicity was below the 20% threshold, with areas of endemicity ≥20% located around the shores of Lake Victoria and on the coast. Comparison of the predicted distributions for 1989 and 2009 show how observed STH prevalence has gradually decreased over time. The model estimated that a total of 2.8 million school-age children live in districts which warrant mass treatment. CONCLUSIONS: Bayesian space-time geostatistical models can be used to reliably estimate the combined observed prevalence of STH and suggest that a quarter of Kenya's school-aged children live in areas of high prevalence and warrant mass treatment. As control is successful in reducing infection levels, updated models can be used to refine decision making in helminth control. Public Library of Science 2011-02-08 /pmc/articles/PMC3035671/ /pubmed/21347451 http://dx.doi.org/10.1371/journal.pntd.0000958 Text en Pullan 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
Pullan, Rachel L.
Gething, Peter W.
Smith, Jennifer L.
Mwandawiro, Charles S.
Sturrock, Hugh J. W.
Gitonga, Caroline W.
Hay, Simon I.
Brooker, Simon
Spatial Modelling of Soil-Transmitted Helminth Infections in Kenya: A Disease Control Planning Tool
title Spatial Modelling of Soil-Transmitted Helminth Infections in Kenya: A Disease Control Planning Tool
title_full Spatial Modelling of Soil-Transmitted Helminth Infections in Kenya: A Disease Control Planning Tool
title_fullStr Spatial Modelling of Soil-Transmitted Helminth Infections in Kenya: A Disease Control Planning Tool
title_full_unstemmed Spatial Modelling of Soil-Transmitted Helminth Infections in Kenya: A Disease Control Planning Tool
title_short Spatial Modelling of Soil-Transmitted Helminth Infections in Kenya: A Disease Control Planning Tool
title_sort spatial modelling of soil-transmitted helminth infections in kenya: a disease control planning tool
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3035671/
https://www.ncbi.nlm.nih.gov/pubmed/21347451
http://dx.doi.org/10.1371/journal.pntd.0000958
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