Tropical medicine & international health : TM & IH · 2006
Bayesian spatial analysis and disease mapping: tools to enhance planning and implementation of a schistosomiasis control programme in Tanzania.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Clements Archie C A, Lwambo Nicholas J S, Blair Lynsey, Nyandindi Ursuline, Kaatano Godfrey, Kinung'hi Safari, Webster Joanne P, Fenwick Alan, Brooker Simon
Аннотация
OBJECTIVE: 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.
METHODS: Bayesian geostatistical models were developed using parasitological data from 143 schools.
RESULTS: 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.
CONCLUSION: Bayesian geostatistical analysis is a powerful and statistically robust tool for identifying high prevalence areas in a heterogeneous and imperfectly known environment.
METHODS: Bayesian geostatistical models were developed using parasitological data from 143 schools.
RESULTS: 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.
CONCLUSION: Bayesian geostatistical analysis is a powerful and statistically robust tool for identifying high prevalence areas in a heterogeneous and imperfectly known environment.