spatially explicit burden estimates of malaria in tanzania bayesian geostatistical modeling of the malaria indicator survey data空间显式负担估计疟疾在坦桑尼亚的贝叶斯地理统计模型疟疾指标调查数据.pdf

Spatially Explicit Burden Estimates of Malaria in Tanzania: Bayesian Geostatistical Modeling of the Malaria Indicator Survey Data 1 2 1 1 Laura Gosoniu , Amina Msengwa , Christian Lengeler , Penelope Vounatsou * 1 Department of Public Health and Epidemiology, Swiss Tropical and Public Health Institute, University of Basel, Switzerland, 2 Department of statistics, University of Dar es Salaam, Dar es Salaam, Tanzania Abstract A national HIV/AIDS and malaria parasitological survey was carried out in Tanzania in 2007–2008. In this study the parasitological data were analyzed: i) to identify climatic/environmental, socio-economic and interventions factors associated with child malaria risk and ii) to produce a contemporary, high spatial resolution parasitaemia risk map of the country. Bayesian geostatistical models were fitted to assess the association between parasitaemia risk and its determinants. Bayesian kriging was employed to predict malaria risk at unsampled locations across Tanzania and to obtain the uncertainty associated with the predictions. Markov chain Monte Carlo (MCMC) simulation methods were employed for model fit and prediction. Parasitaemia risk estimates were linked to population data and the number of infected children at province level was calculated. Model validation indicated a high predictive ability of the geostatistical model, with 60.00% of the test locations within the 95% credible interval. The results indicate that older children are significantly more likely to test positive for malaria compared with younger children and living in urban areas and better-off households reduces the risk of infection. However, none of the environ