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      Use of Bayesian geostatistical prediction to estimate local variations in Schistosoma haematobium infection in western Africa Translated title: Utilisation de prédictions par géostatistique bayésienne pour estimer les variations locales de la prévalence des infestations par Schistosoma haematobium en Afrique occidentale Translated title: Uso de modelos geoestadísticos bayesianos para predecir las variaciones locales de la infección por Schistosoma haematobium en África occidental

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          Abstract

          OBJECTIVE: To predict the subnational spatial variation in the number of people infected with Schistosoma haematobium in Burkina Faso, Mali and the Niger prior to national control programmes. METHODS: We used field survey data sets covering a contiguous area 2750 × 850 km and including 26 790 school-age children (5-14 years old) in 418 schools. The prevalence of high- and low-intensity infection and associated 95% credible intervals (CrIs) were predicted using Bayesian geostatistical models. The number infected was determined from the predicted prevalence and the number of school-age children in each km². FINDINGS: The predicted number of school-age children with a low-intensity infection was 433 268 in Burkina Faso, 872 328 in Mali and 580 286 in the Niger. The number with a high-intensity infection was 416 009, 511 845 and 254 150 in each country, respectively. The 95% CrIs were wide: e.g. the mean number of boys aged 10-14 years infected in Mali was 140 200 (95% CrI: 6200-512 100). CONCLUSION: National aggregate estimates of infection mask important local variations: e.g. most S. haematobium infections in the Niger occur in the Niger River valley. High-intensity infection was strongly clustered in western and central Mali, north-eastern and north-western Burkina Faso and the Niger River valley in the Niger. Populations in these foci will carry the bulk of the urinary schistosomiasis burden and should be prioritized for schistosomiasis control. Uncertainties in the predicted prevalence and the numbers infected should be acknowledged by control programme planners.

          Translated abstract

          OBJECTIF: Prédire les variations spatiales au niveau infranational du nombre de personnes infestées par Schistosoma haematobium au Burkina Faso, au Mali et au Niger, avant la mise en place des programmes nationaux de lutte contre la schistosomiase. MÉTHODES: Nous avons utilisé un jeu de données d'enquête sur le terrain couvrant une zone contiguë de 2750 x 850 km et 26 790 enfants d'âge scolaire (5-14 ans), répartis dans 418 écoles. La prévalence des schistosomiases de forte et de faible intensité, ainsi que les intervalles de crédibilité à 95 % associés, ont été prédits à l'aide de modèles géostatistiques bayésiens. Le nombre de personnes infestées a été déterminé à partir de la prévalence prédite et du nombre d'enfants d'age scolaire par km². RÉSULTATS: D'après les prédictions de l'étude, le nombre d'enfants d'âge scolaire atteints d'une schistosomiase de faible intensité serait de 433 268 au Burkina Faso, de 872 328 au Mali et de 580 286 au Niger. S'agissant des enfants fortement infestés, les prédictions donnaient respectivement 416 009 cas pour le Burkina Faso, 511 845 pour le Mali et 254 150 pour le Niger. Les intervalles de crédibilité à 95 % étaient larges : par exemple, le nombre moyen de garçons de 10 à 14 ans infestés au Mali était de 140 200 (ICr à 95 % : 6200-512 100). CONCLUSION: Les estimations nationales agrégées masquent d'importantes variations locales : par exemple, la plupart des infestations par S. haematobium relevées au Niger étaient apparues dans la Vallée du Niger. Les cas d'infestation lourde étaient très fortement regroupés à l'Ouest et au centre du Mali, au Nord-est et au Nord-ouest du Burkina Faso et dans la Vallée du Niger, au Niger. Les populations de ces foyers supportent la plus grande part de la charge de schistosomiase urinaire et doivent être considérées comme prioritaires dans la lutte contre la schistosomiase. Les planificateurs de programmes de lutte contre cette maladie doivent être conscients des incertitudes qui pèsent sur les prédictions de la prévalence et du nombre de personnes infestées.

          Translated abstract

          OBJETIVO: Predecir la variación territorial subnacional del número de personas infectadas por Schistosoma haematobium en Burkina Faso, Malí y el Níger antes del inicio de los programas de control nacionales. MÉTODOS: Usamos conjuntos de datos de encuestas sobre el terreno que abarcaron en total a 26 790 niños en edad escolar (5 a 14 años) de 418 escuelas repartidos en una zona de 2750 x 850 km. Mediante modelos geoestadísticos bayesianos se predijeron la prevalencia de infección de alta y baja intensidad y los correspondientes intervalos de credibilidad (ICr) del 95%. El número de personas infectadas se determinó a partir de la prevalencia predicha y del número de niños en edad escolar por km². RESULTADOS: Las predicciones sobre el número de niños en edad escolar con infección de baja intensidad fueron de 433 268 en Burkina Faso, 872 328 en Malí y 580 286 en el Níger. El número de casos de infección de alta intensidad fue de 416 009, 511 845 y 254 150, respectivamente. Los ICr95% fueron amplios: p.ej., la media de muchachos de 10 a 14 años infectados en Malí fue de 140 200 (ICr95%: 6200-512 100). CONCLUSIÓN: Las estimaciones totales nacionales de infección ocultan variaciones locales importantes: p.ej., en el Níger la mayoría de las infecciones por S. haematobium se dan en el valle del Río Níger. La infección de alta intensidad se concentra marcadamente en el centro y oeste de Malí, las zonas nororiental y noroccidental de Burkina Faso y el valle del Río Níger en el Níger. Las poblaciones de esos focos soportarán la mayor carga de esquistosomiasis urinaria y deberían ser un objetivo prioritario de la lucha contra la esquistosomiasis. Los planificadores de los programas de control deben tener en cuenta la incertidumbre asociada a la prevalencia predicha y las cifras de personas infectadas.

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          Most cited references54

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          Schistosomiasis and water resources development: systematic review, meta-analysis, and estimates of people at risk.

          An estimated 779 million people are at risk of schistosomiasis, of whom 106 million (13.6%) live in irrigation schemes or in close proximity to large dam reservoirs. We identified 58 studies that examined the relation between water resources development projects and schistosomiasis, primarily in African settings. We present a systematic literature review and meta-analysis with the following objectives: (1) to update at-risk populations of schistosomiasis and number of people infected in endemic countries, and (2) to quantify the risk of water resources development and management on schistosomiasis. Using 35 datasets from 24 African studies, our meta-analysis showed pooled random risk ratios of 2.4 and 2.6 for urinary and intestinal schistosomiasis, respectively, among people living adjacent to dam reservoirs. The risk ratio estimate for studies evaluating the effect of irrigation on urinary schistosomiasis was in the range 0.02-7.3 (summary estimate 1.1) and that on intestinal schistosomiasis in the range 0.49-23.0 (summary estimate 4.7). Geographic stratification showed important spatial differences, idiosyncratic to the type of water resources development. We conclude that the development and management of water resources is an important risk factor for schistosomiasis, and hence strategies to mitigate negative effects should become integral parts in the planning, implementation, and operation of future water projects.
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            Model-based geostatistics

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              Bayesian spatial analysis and disease mapping: tools to enhance planning and implementation of a schistosomiasis control programme in Tanzania.

              To predict the spatial distributions of Schistosoma haematobium and S. mansoni infections to assist planning the implementation of mass distribution of praziquantel as part of an on-going national control programme in Tanzania. Bayesian geostatistical models were developed using parasitological data from 143 schools. In the S. haematobium models, although land surface temperature and rainfall were significant predictors of prevalence, they became non-significant when spatial correlation was taken into account. In the S. mansoni models, distance to water bodies and annual minimum temperature were significant predictors, even when adjusting for spatial correlation. Spatial correlation occurred over greater distances for S. haematobium than for S. mansoni. Uncertainties in predictions were examined to identify areas requiring further data collection before programme implementation. Bayesian geostatistical analysis is a powerful and statistically robust tool for identifying high prevalence areas in a heterogeneous and imperfectly known environment.
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                Author and article information

                Contributors
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                Journal
                bwho
                Bulletin of the World Health Organization
                Bull World Health Organ
                World Health Organization (Genebra )
                0042-9686
                December 2009
                : 87
                : 12
                : 921-929
                Affiliations
                [1 ] University of Queensland Australia
                [2 ] Ministère de la Santé Burkina Faso
                [3 ] Ministère de la Santé Publique et de la Lutte Contre les Endémies Niger
                [4 ] Ministère de la Santé Burkina Faso
                [5 ] Institut National de Recherche en Santé Publique Mali
                [6 ] Imperial College United Kingdom
                [7 ] Queensland University of Technology Australia
                [8 ] London School of Hygiene and Tropical Medicine United Kingdom
                Article
                S0042-96862009001200012
                cffbf90f-7a2c-42e4-9823-d18500b4620b

                http://creativecommons.org/licenses/by/4.0/

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                SciELO Public Health

                Self URI (journal page): http://www.scielosp.org/scielo.php?script=sci_serial&pid=0042-9686&lng=en
                Categories
                Health Policy & Services

                Public health
                Public health

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