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Open Access Highly Accessed Research

The role of age, ethnicity and environmental factors in modulating malaria risk in Rajasthali, Bangladesh

Ubydul Haque12*, Ricardo J Soares Magalhães3, Dipak Mitra1, Korine N Kolivras4, Wolf-Peter Schmidt5, Rashidul Haque1 and Gregory E Glass6

Author Affiliations

1 International Center for Diarrhoeal Disease Research Bangladesh, 68 Shaheed Tajuddin Ahmed Sharani, Mohakhali, Dhaka 1212, Bangladesh

2 Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Ås, Norway

3 School of Population Health, University of Queensland, Herston, Queensland, Australia

4 Department of Geography, Virginia Tech, Blacksburg, VA 24061, USA

5 Environmental Health Group, Disease Control & Vector Biology Unit, London School of Hygiene and Tropical Medicine, London, UK

6 Department of Molecular Microbiology and Immunology, John Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA

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Malaria Journal 2011, 10:367  doi:10.1186/1475-2875-10-367

Published: 15 December 2011

Abstract

Background

Malaria is endemic in the Rajasthali region of the Chittagong Hill Tracts in Bangladesh and the Rajasthali region is the most endemic area of Bangladesh. Quantifying the role of environmental and socio-economic factors in the local spatial patterns of malaria endemicity can contribute to successful malaria control and elimination. This study aimed to investigate the role of environmental factors on malaria risk in Rajasthali and to quantify the geographical clustering in malaria risk unaccounted by these factors.

Method

A total of 4,200 (78.9%; N = 5,322) households were targeted in Rajasthali in July, 2009, and 1,400 individuals were screened using a rapid diagnostic test (Falci-vax). These data were linked to environmental and socio-economic data in a geographical information system. To describe the association between environmental factors and malaria risk, a generalized linear mixed model approach was utilized. The study investigated the role of environmental factors on malaria risk by calculating their population-attributable fractions (PAF), and used residual semivariograms to quantify the geographical clustering in malaria risk unaccounted by these factors.

Results

Overall malaria prevalence was 11.7%. Out of 5,322 households, 44.12% households were living in areas with malaria prevalence of ≥ 10%. The results from statistical analysis showed that age, ethnicity, proximity to forest, household density, and elevation were significantly and positively correlated with the malaria risk and PAF estimation. The highest PAF of malaria prevalence was 47.7% for third tertile (n = 467) of forest cover, 17.6% for second tertile (n = 467) of forest cover and 19.9% for household density >1,000.

Conclusion

Targeting of malaria health interventions at small spatial scales in Bangladesh should consider the social and socio-economic risk factors identified as well as alternative methods for improving equity of access to interventions across whole communities.