Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models

Detalhes bibliográficos
Autor(a) principal: Scholte, Ronaldo G. C.
Data de Publicação: 2013
Outros Autores: Schur, Nadine, Bavia, Maria Emília, Carvalho Filho, Edgar Marcelino de, Chammartin, Frédérique, Utzinger, Jürg, Vounatsou, Penelope
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFBA
Texto Completo: http://repositorio.ufba.br/ri/handle/ri/17652
Resumo: Texto completo: acesso restrito. p.97-110
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spelling Scholte, Ronaldo G. C.Schur, NadineBavia, Maria EmíliaCarvalho Filho, Edgar Marcelino deChammartin, FrédériqueUtzinger, JürgVounatsou, Penelope2015-05-11T14:39:30Z20131827-1987http://repositorio.ufba.br/ri/handle/ri/17652v. 8, n. 1Texto completo: acesso restrito. p.97-110Soil-transmitted helminths (Ascaris lumbricoides, Trichuris trichiura and hookworm) negatively impact the health and wellbeing of hundreds of millions of people, particularly in tropical and subtropical countries, including Brazil. Reliable maps of the spatial distribution and estimates of the number of infected people are required for the control and eventual elimination of soil-transmitted helminthiasis. We used advanced Bayesian geostatistical modelling, coupled with geographical information systems and remote sensing to visualize the distribution of the three soil-transmitted helminth species in Brazil. Remotely sensed climatic and environmental data, along with socioeconomic variables from readily available databases were employed as predictors. Our models provided mean prevalence estimates for A. lumbricoides, T. trichiura and hookworm of 15.6%, 10.1% and 2.5%, respectively. By considering infection risk and population numbers at the unit of the municipality, we estimate that 29.7 million Brazilians are infected with A. lumbricoides, 19.2 million with T. trichiura and 4.7 million with hookworm. Our model-based maps identified important risk factors related to the transmission of soiltransmitted helminths and confirm that environmental variables are closely associated with indices of poverty. Our smoothed risk maps, including uncertainty, highlight areas where soil-transmitted helminthiasis control interventions are most urgently required, namely in the North and along most of the coastal areas of Brazil. We believe that our predictive risk maps are useful for disease control managers for prioritising control interventions and for providing a tool for more efficient surveillance-response mechanisms.Submitted by Edileide Reis (leyde-landy@hotmail.com) on 2015-03-17T11:57:42Z No. of bitstreams: 1 Ronaldo G. C. Scholte.pdf: 348151 bytes, checksum: a21fa87fea3ded91743dbd0018482f1f (MD5)Approved for entry into archive by Flávia Ferreira (flaviaccf@yahoo.com.br) on 2015-05-11T14:39:30Z (GMT) No. of bitstreams: 1 Ronaldo G. C. Scholte.pdf: 348151 bytes, checksum: a21fa87fea3ded91743dbd0018482f1f (MD5)Made available in DSpace on 2015-05-11T14:39:30Z (GMT). No. of bitstreams: 1 Ronaldo G. C. 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dc.title.pt_BR.fl_str_mv Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models
dc.title.alternative.pt_BR.fl_str_mv Geospatial Health
title Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models
spellingShingle Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models
Scholte, Ronaldo G. C.
Bayesian modelling
Geographical information system
Remote sensing
Soil-transmitted helminths
Variable selection
Brazil
title_short Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models
title_full Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models
title_fullStr Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models
title_full_unstemmed Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models
title_sort Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models
author Scholte, Ronaldo G. C.
author_facet Scholte, Ronaldo G. C.
Schur, Nadine
Bavia, Maria Emília
Carvalho Filho, Edgar Marcelino de
Chammartin, Frédérique
Utzinger, Jürg
Vounatsou, Penelope
author_role author
author2 Schur, Nadine
Bavia, Maria Emília
Carvalho Filho, Edgar Marcelino de
Chammartin, Frédérique
Utzinger, Jürg
Vounatsou, Penelope
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Scholte, Ronaldo G. C.
Schur, Nadine
Bavia, Maria Emília
Carvalho Filho, Edgar Marcelino de
Chammartin, Frédérique
Utzinger, Jürg
Vounatsou, Penelope
dc.subject.por.fl_str_mv Bayesian modelling
Geographical information system
Remote sensing
Soil-transmitted helminths
Variable selection
Brazil
topic Bayesian modelling
Geographical information system
Remote sensing
Soil-transmitted helminths
Variable selection
Brazil
description Texto completo: acesso restrito. p.97-110
publishDate 2013
dc.date.issued.fl_str_mv 2013
dc.date.accessioned.fl_str_mv 2015-05-11T14:39:30Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://repositorio.ufba.br/ri/handle/ri/17652
dc.identifier.issn.none.fl_str_mv 1827-1987
dc.identifier.number.pt_BR.fl_str_mv v. 8, n. 1
identifier_str_mv 1827-1987
v. 8, n. 1
url http://repositorio.ufba.br/ri/handle/ri/17652
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.pt_BR.fl_str_mv http://dx.doi.org/10.4081/gh.2013.58
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