Spatial prediction of malaria prevalence in an endemic area of Bangladesh

Haque, U, Soares Magalhães, RJS, Reid, HL, Clements, ACA, Ahmed, SM, Islam, A, Yamamoto, T, Haque, R and Glass, GE (2010) Spatial prediction of malaria prevalence in an endemic area of Bangladesh. Malaria Journal, 9 1: 120-1-120-10. doi:10.1186/1475-2875-9-120

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Author Haque, U
Soares Magalhães, RJS
Reid, HL
Clements, ACA
Ahmed, SM
Islam, A
Yamamoto, T
Haque, R
Glass, GE
Title Spatial prediction of malaria prevalence in an endemic area of Bangladesh
Journal name Malaria Journal   Check publisher's open access policy
ISSN 1475-2875
Publication date 2010-05-09
Sub-type Article (original research)
DOI 10.1186/1475-2875-9-120
Open Access Status DOI
Volume 9
Issue 1
Start page 120-1
End page 120-10
Total pages 10
Place of publication London, United Kingdom
Publisher BioMed Central
Collection year 2011
Language eng
Subject 1108 Medical Microbiology
Formatted abstract
Background: Malaria 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%).

Methods: A 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).

Results: Predicted 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.

Conclusion: A 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.
© 2010 Haque et al; licensee BioMed Central Ltd
Keyword Schistosomiasis
Risk factor
Plasmodium falciparum
Malaria falciparum
Geographic information systems (GIS)
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article number 120

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2011 Collection
School of Public Health Publications
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Citation counts: TR Web of Science Citation Count  Cited 16 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 17 times in Scopus Article | Citations
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Created: Sun, 20 Jun 2010, 00:03:37 EST