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metadata.dc.type: Artigo de Periódico
Título : Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models
Otros títulos : Geospatial Health
Autor : Scholte, Ronaldo G. C.
Schur, Nadine
Bavia, Maria Emília
Carvalho Filho, Edgar Marcelino de
Chammartin, Frédérique
Utzinger, Jürg
Vounatsou, Penelope
metadata.dc.creator: Scholte, Ronaldo G. C.
Schur, Nadine
Bavia, Maria Emília
Carvalho Filho, Edgar Marcelino de
Chammartin, Frédérique
Utzinger, Jürg
Vounatsou, Penelope
Resumen : Soil-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.
Palabras clave : Bayesian modelling
Geographical information system
Remote sensing
Soil-transmitted helminths
Variable selection
Brazil
metadata.dc.rights: Acesso Aberto
URI : http://repositorio.ufba.br/ri/handle/ri/17652
Fecha de publicación : 2013
Aparece en las colecciones: Artigo Publicado em Periódico (Faculdade de Medicina)

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