Predictive risk mapping of schistosomiasis in Brazil using Bayesian geostatistical models

Scholte, Ronaldo Guilherme Carvalho; Gosoniu, Laura; Malone, John B.; Chammartin, Frédérique; Utzinger, Jürg; Vounatsou, Penelope

doi:10.1016/j.actatropica.2013.12.007

Cited by 64 publications

(46 citation statements)

References 41 publications

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“…In these areas, there are favourable environmental conditions for the existence of the intermediate host, B. glabrata, in addition to poor living conditions and inadequate sanitation and housing conditions (Resendes et al, 2005). The relationships among socio-economic and environmental factors, and the distribution of intermediate hosts, in the risk of schistosomiasis transmission have been verified in studies in Brazil and other endemic countries (Bavia et al, 1999;Brooker, 2007;Guimarães et al, 2008;Schur et al, 2013;Scholte et al, 2014). In Brazil, the future transposition of the biggest river in the Northeast region (São Francisco River), planned to start in 2016, may contribute to the emergence of new disease outbreaks through the dispersion of intermediate hosts to previously non-endemic areas.…”

Section: Discussionmentioning

confidence: 94%

Trends in schistosomiasis-related mortality in Brazil, 2000–2011

Martins-Melo

Pinheiro

Ramos

et al. 2014

International Journal for Parasitology

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Section: Discussionmentioning

confidence: 94%

Trends in schistosomiasis-related mortality in Brazil, 2000–2011

Martins-Melo

Pinheiro

Ramos

et al. 2014

International Journal for Parasitology

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“…Our study also revealed that a diffLST was correlated with the prevalence of S. mansoni infection. Several epidemiological studies using Bayesian geospatial, Gaussian and Poisson modelling documented that LSTDay was negatively associated with S. mansoni, while LSTNight was positively associated with the prevalence of S. mansoni (Hu et al, 2013a(Hu et al, , 2013bSchur et al, 2013;Scholte et al, 2014). In contrast, previous geostatistical analysis from the Tonkpi region found no relationship between S. mansoni infection and diffLST (Raso et al, 2005).…”

Section: Discussionmentioning

confidence: 43%

The spatial distribution of Schistosoma mansoni infection in four regions of western Côte d’Ivoire

et al. 2015

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Schistosomiasis poses a considerable public health burden in subSaharan Africa and a sound understanding of the spatial distribution facilitates to better target control interventions. The objectives of this study were i) to assess the prevalence of Schistosoma mansoni among school-aged children in four regions of western Côte d'Ivoire; ii) to determine demographic, climatic and environmental factors that influence the distribution of S. mansoni; and iii) to map and predict the distribution of S. mansoni in non-sampled locations. Parasitological surveys were carried out in 264 schools from June to December 2011. In each school, we aimed to examine 50 children for S. mansoni infection using duplicate Kato-Katz thick smears. Schools were georeferenced using a hand-held global positioning system receiver. Demographic data were obtained from readily available school lists, while climatic and environmental data were extracted from open-access remote sensing databases. Multivariable, binary non-spatial models and a Bayesian geostatistical logistic regression model were used to identify demographic, climatic and environmental risk factors for S. mansoni infection. Risk maps were developed based on observed S. mansoni prevalences and using Bayesian geostatistical models to predict prevalences at non-sampled locations. Overall, 12,462 children provided a sufficiently large stool sample to perform at least one Kato-Katz thick smear. The observed overall prevalence of S. mansoni infection was 39.9%, ranging from 0 to 100% at the unit of the school. Bayesian geostatistical analysis revealed that age, sex, altitude and difference between land surface temperature at day and night were significantly associated with S. mansoni infection. The S. mansoni risk map presented here is being been used by the national schistosomiasis control programme for spatial targeting of praziquantel and other interventions.

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“…Data collection also influenced the results when there was a lack of spatial and laboratory sampled data in areas where the presence of infection was suspected to be high [66,72–74,80,100,103–105,111,125,130–132]. This could be due to inaccurate and missing reports [131], lack of people’s participation [132] and limited access to geographical areas [81].…”

Section: Discussionmentioning

confidence: 99%

Mapping Soil Transmitted Helminths and Schistosomiasis under Uncertainty: A Systematic Review and Critical Appraisal of Evidence

et al. 2016

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BackgroundSpatial modelling of STH and schistosomiasis epidemiology is now commonplace. Spatial epidemiological studies help inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration; however, limited attention has been given to propagated uncertainties, their interpretation, and consequences for the mapped values. Using currently published literature on the spatial epidemiology of helminth infections we identified: (1) the main uncertainty sources, their definition and quantification and (2) how uncertainty is informative for STH programme managers and scientists working in this domain.Methodology/Principal FindingsWe performed a systematic literature search using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) protocol. We searched Web of Knowledge and PubMed using a combination of uncertainty, geographic and disease terms. A total of 73 papers fulfilled the inclusion criteria for the systematic review. Only 9% of the studies did not address any element of uncertainty, while 91% of studies quantified uncertainty in the predicted morbidity indicators and 23% of studies mapped it. In addition, 57% of the studies quantified uncertainty in the regression coefficients but only 7% incorporated it in the regression response variable (morbidity indicator). Fifty percent of the studies discussed uncertainty in the covariates but did not quantify it. Uncertainty was mostly defined as precision, and quantified using credible intervals by means of Bayesian approaches.Conclusion/SignificanceNone of the studies considered adequately all sources of uncertainties. We highlighted the need for uncertainty in the morbidity indicator and predictor variable to be incorporated into the modelling framework. Study design and spatial support require further attention and uncertainty associated with Earth observation data should be quantified. Finally, more attention should be given to mapping and interpreting uncertainty, since they are relevant to inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration.

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Predictive risk mapping of schistosomiasis in Brazil using Bayesian geostatistical models

Cited by 64 publications

References 41 publications

Trends in schistosomiasis-related mortality in Brazil, 2000–2011

Trends in schistosomiasis-related mortality in Brazil, 2000–2011

The spatial distribution of Schistosoma mansoni infection in four regions of western Côte d’Ivoire

Mapping Soil Transmitted Helminths and Schistosomiasis under Uncertainty: A Systematic Review and Critical Appraisal of Evidence

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