Spatial prediction of malaria prevalence in an endemic area of Bangladesh

Ubydul Haque, Ricardo Magalhães, Heidi L. Reid, Archie C.A. Clements, Syed Ahmed, Akramul Islam, Taro Yamamoto, Rashidul Haque, Gregory E. Glass

Research output: Contribution to journalArticle

25 Citations (Scopus)

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.

Original languageEnglish
Article number120
JournalMalaria Journal
Volume9
Issue number1
DOIs
StatePublished - 7 May 2010

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Bangladesh
Malaria
Geographic Information Systems
Bayes Theorem
Binomial Distribution
Geography
Public Health
Economics
Technology
Infection

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Haque, U., Magalhães, R., Reid, H. L., Clements, A. C. A., Ahmed, S., Islam, A., ... Glass, G. E. (2010). Spatial prediction of malaria prevalence in an endemic area of Bangladesh. Malaria Journal, 9(1), [120]. https://doi.org/10.1186/1475-2875-9-120
Haque, Ubydul ; Magalhães, Ricardo ; Reid, Heidi L. ; Clements, Archie C.A. ; Ahmed, Syed ; Islam, Akramul ; Yamamoto, Taro ; Haque, Rashidul ; Glass, Gregory E. / Spatial prediction of malaria prevalence in an endemic area of Bangladesh. In: Malaria Journal. 2010 ; Vol. 9, No. 1.
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Haque, U, Magalhães, R, Reid, HL, Clements, ACA, Ahmed, S, Islam, A, Yamamoto, T, Haque, R & Glass, GE 2010, 'Spatial prediction of malaria prevalence in an endemic area of Bangladesh', Malaria Journal, vol. 9, no. 1, 120. https://doi.org/10.1186/1475-2875-9-120

Spatial prediction of malaria prevalence in an endemic area of Bangladesh. / Haque, Ubydul; Magalhães, Ricardo; Reid, Heidi L.; Clements, Archie C.A.; Ahmed, Syed; Islam, Akramul; Yamamoto, Taro; Haque, Rashidul; Glass, Gregory E.

In: Malaria Journal, Vol. 9, No. 1, 120, 07.05.2010.

Research output: Contribution to journalArticle

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T1 - Spatial prediction of malaria prevalence in an endemic area of Bangladesh

AU - Haque, Ubydul

AU - Magalhães, Ricardo

AU - Reid, Heidi L.

AU - Clements, Archie C.A.

AU - Ahmed, Syed

AU - Islam, Akramul

AU - Yamamoto, Taro

AU - Haque, Rashidul

AU - Glass, Gregory E.

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N2 - 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.

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