Spatial prediction of malaria prevalence in an endemic area of Bangladesh

Malaria Journal

Research

Syed Masud Ahmed¹  Akramul Islam¹  Taro Yamamoto²  Gregory E Glass³  Rashidul Haque⁴  Ubydul Haque⁵  Archie CA Clements⁶  Heidi L Reid⁶  Ricardo J Soares Magalhães⁶ 

[1] BRAC, BRAC Centre, 1212, Dhaka, 75 Mohakhali, Bangladesh;Department of International Health, Institute of Tropical Medicine (NEKKEN) and the Global Center of Excellence programme, Nagasaki University, Japan;Department of Molecular Microbiology and Immunology, John Hopkins Bloomberg School of Public Health, 21205, Baltimore, MD, USA;International Center for Diarrhoeal Disease Research Bangladesh, 68 Shaheed Tajuddin Ahmed Sharani, 1212, Dhaka, Mohakhali, Bangladesh;International Center for Diarrhoeal Disease Research Bangladesh, 68 Shaheed Tajuddin Ahmed Sharani, 1212, Dhaka, Mohakhali, Bangladesh;Department of International Health, Institute of Tropical Medicine (NEKKEN) and the Global Center of Excellence programme, Nagasaki University, Japan;University of Queensland, School of Population Health, Queensland, Herston, Australia;

关键词: Malaria;    Shuttle Radar Topographic Mission;    Malaria Prevalence;    Malaria Risk;    Fragmented Forest;   

DOI  :  10.1186/1475-2875-9-120

 received in 2010-01-25, accepted in 2010-05-09,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundMalaria is a major public health burden in Southeastern Bangladesh, particularly in the Chittagong Hill Tracts region. Malaria is endemic in 13 districts of Bangladesh and the highest prevalence occurs in Khagrachari (15.47%).MethodsA risk map was developed and geographic risk factors identified using a Bayesian approach. The Bayesian geostatistical model was developed from previously identified individual and environmental covariates (p < 0.2; age, different forest types, elevation and economic status) for malaria prevalence using WinBUGS 1.4. Spatial correlation was estimated within a Bayesian framework based on a geostatistical model. The infection status (positives and negatives) was modeled using a Bernoulli distribution. Maps of the posterior distributions of predicted prevalence were developed in geographic information system (GIS).ResultsPredicted high prevalence areas were located along the north-eastern areas, and central part of the study area. Low to moderate prevalence areas were predicted in the southwestern, southeastern and central regions. Individual age and nearness to fragmented forest were associated with malaria prevalence after adjusting the spatial auto-correlation.ConclusionA Bayesian analytical approach using multiple enabling technologies (geographic information systems, global positioning systems, and remote sensing) provide a strategy to characterize spatial heterogeneity in malaria risk at a fine scale. Even in the most hyper endemic region of Bangladesh there is substantial spatial heterogeneity in risk. Areas that are predicted to be at high risk, based on the environment but that have not been reached by surveys are identified.

【 授权许可】

CC BY   
© Haque et al; licensee BioMed Central Ltd. 2010