title: Predictive risk mapping of schistosomiasis in Brazil using Bayesian geostatistical models.
abstract: Schistosomiasis is one of the most common parasitic diseases in tropical and subtropical areas, including Brazil. A national control programme was initiated in Brazil in the mid-1970s and proved successful in terms of morbidity control, as the number of cases with hepato-splenic involvement was reduced significantly. To consolidate control and move towards elimination, there is a need for reliable maps on the spatial distribution of schistosomiasis, so that interventions can target communities at highest risk. The purpose of this study was to map the distribution of Schistosoma mansoni in Brazil. We utilized readily available prevalence data from the national schistosomiasis control programme for the years 2005-2009, derived remotely sensed climatic and environmental data and obtained socioeconomic data from various sources. Data were collated into a geographical information system and Bayesian geostatistical models were developed. Model-based maps identified important risk factors related to the transmission of S. mansoni and confirmed that environmental variables are closely associated with indices of poverty. Our smoothed predictive risk map, including uncertainty, highlights priority areas for intervention, namely the northern parts of North and Southeast regions and the eastern part of Northeast region. Our predictive risk map provides a useful tool for to strengthen existing surveillance-response mechanisms.
authors.affiliation: Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, P.O. Box, CH-4002 Basel, Switzerland; University of Basel, P.O. Box, CH-4003 Basel, Switzerland; Coordenação Geral de Hanseníase e Doenças em Eliminação, Secretaria de Vigilância em Saúde-MS, SCS Qd 4 Bloco A Ed. Principal 3° andar, CEP:70304-000 Brasília, DF, Brazil.
authors.forename: Ronaldo G C
authors.initials: RG
authors.lastname: Scholte
authors.affiliation: Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, P.O. Box, CH-4002 Basel, Switzerland; University of Basel, P.O. Box, CH-4003 Basel, Switzerland.
authors.forename: Laura
authors.initials: L
authors.lastname: Gosoniu
authors.affiliation: Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803, USA.
authors.forename: John B
authors.initials: JB
authors.lastname: Malone
authors.affiliation: Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, P.O. Box, CH-4002 Basel, Switzerland; University of Basel, P.O. Box, CH-4003 Basel, Switzerland.
authors.forename: Frédérique
authors.initials: F
authors.lastname: Chammartin
authors.affiliation: Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, P.O. Box, CH-4002 Basel, Switzerland; University of Basel, P.O. Box, CH-4003 Basel, Switzerland.
authors.forename: Jürg
authors.initials: J
authors.lastname: Utzinger
authors.affiliation: Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, P.O. Box, CH-4002 Basel, Switzerland; University of Basel, P.O. Box, CH-4003 Basel, Switzerland. Electronic address: penelope.vounatsou@unibas.ch.
authors.forename: Penelope
authors.initials: P
authors.lastname: Vounatsou
mesh_terms: D000818:Animals; D001938:Brazil / Q000453:epidemiology; D003140:Communicable Disease Control / Q000379:methods; D006801:Humans; D015995:Prevalence; D018570:Risk Assessment; D012550:Schistosoma mansoni / Q000302:isolation & purification*; D012552:Schistosomiasis / Q000453:epidemiology*; D012959:Socioeconomic Factors; D019371:Topography, Medical
keywords: Bayesian modelling; Brazil; Geostatistics; Predictive risk mapping; Schistosoma mansoni; Schistosomiasis