Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models
Soil-transmitted helminths (<em>Ascaris lumbricoides</em>, <em>Trichuris trichiura</em> 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 spati...
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doaj-7a42e440d12f41c1b4d999b0139d758f2020-11-25T03:34:51ZengPAGEPress PublicationsGeospatial Health1827-19871970-70962013-11-01819711010.4081/gh.2013.5858Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical modelsRonaldo G. C. Scholte0Nadine Schur1Maria E. Bavia2Edgar M. Carvalho3Frédérique Chammartin4Jürg Utzinger5Penelope Vounatsou6Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland; Laboratory of Helmintology and Medical Malacology - René Rachou Research Center/Fiocruz, Belo HorizonteDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, BaselLaboratory of Monitoring Diseases by Geographic Information System, School of Veterinary Medicine, Federal University of Bahia, SalvadorDepartment of Preventive Medicine, Federal University of Bahia, SalvadorDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, BaselDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, BaselDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, BaselSoil-transmitted helminths (<em>Ascaris lumbricoides</em>, <em>Trichuris trichiura</em> 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 <em>A. lumbricoides</em>, <em>T. trichiura</em> 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 <em>A. lumbricoides</em>, 19.2 million with <em>T. trichiura</em> 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.http://www.geospatialhealth.net/index.php/gh/article/view/58Bayesian modelling, geographical information system, remote sensing, soil-transmitted helminths, variable selection, Brazil. |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ronaldo G. C. Scholte Nadine Schur Maria E. Bavia Edgar M. Carvalho Frédérique Chammartin Jürg Utzinger Penelope Vounatsou |
spellingShingle |
Ronaldo G. C. Scholte Nadine Schur Maria E. Bavia Edgar M. Carvalho Frédérique Chammartin Jürg Utzinger Penelope Vounatsou Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models Geospatial Health Bayesian modelling, geographical information system, remote sensing, soil-transmitted helminths, variable selection, Brazil. |
author_facet |
Ronaldo G. C. Scholte Nadine Schur Maria E. Bavia Edgar M. Carvalho Frédérique Chammartin Jürg Utzinger Penelope Vounatsou |
author_sort |
Ronaldo G. C. Scholte |
title |
Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models |
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 |
publisher |
PAGEPress Publications |
series |
Geospatial Health |
issn |
1827-1987 1970-7096 |
publishDate |
2013-11-01 |
description |
Soil-transmitted helminths (<em>Ascaris lumbricoides</em>, <em>Trichuris trichiura</em> 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 <em>A. lumbricoides</em>, <em>T. trichiura</em> 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 <em>A. lumbricoides</em>, 19.2 million with <em>T. trichiura</em> 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. |
topic |
Bayesian modelling, geographical information system, remote sensing, soil-transmitted helminths, variable selection, Brazil. |
url |
http://www.geospatialhealth.net/index.php/gh/article/view/58 |
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