1921
Volume 88, Issue 4
  • ISSN: 0002-9637
  • E-ISSN: 1476-1645

Abstract

Abstract.

Malaria is endemic to Bangladesh. In this longitudinal study, we used hydrologic, topographic, and socioeconomic risk factors to explain single and multiple malaria infections at individual and household levels. Malaria incidence was determined for 1,634 households in 54 villages in 2009 and 2010. During the entire study period 21.8% of households accounted for all (n = 497) malaria cases detected; 15.4% of households had 1 case and 6.4% had ≥ 2 cases. The greatest risk factors for malaria infection were low bed net ratio per household, house construction materials (wall), and high density of houses. Hydrologic and topographic factors were not significantly associated with malaria risk. This study identifies stable malaria hotspots and risk factors that should be considered for cost-effective targeting of malaria interventions that may contribute to potential elimination of malaria in Bangladesh.

[open-access] This is an Open Access article distributed under the terms of the American Society of Tropical Medicine and Hygiene's Re-use License which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Loading

Article metrics loading...

The graphs shown below represent data from March 2017
/content/journals/10.4269/ajtmh.12-0456
2013-04-03
2019-04-26
Loading full text...

Full text loading...

/deliver/fulltext/14761645/88/4/727.html?itemId=/content/journals/10.4269/ajtmh.12-0456&mimeType=html&fmt=ahah

References

  1. Malaria and Parasite Disease Control Unit, 2008. Malaria Country Report Bangladesh. Dhaka: Ministry of Health. [Google Scholar]
  2. Haque U, Magalhaes RJ, Reid HL, Clements AC, Ahmed SM, Islam A, Yamamoto T, Haque R, Glass GE, , 2010. Spatial prediction of malaria prevalence in an endemic area of Bangladesh. Malar J 9: 120.[Crossref] [Google Scholar]
  3. Haque U, Ahmed SM, Hossain S, Huda M, Hossain A, Alam MS, Mondal D, Khan WA, Khalequzzaman M, Haque R, , 2009. Malaria prevalence in endemic districts of Bangladesh. PLoS ONE 4: e6737.[Crossref] [Google Scholar]
  4. Malaria and Parasite Disease Control Unit, 2011. Malaria Country Report Bangladesh. Dhaka: Ministry of Health. [Google Scholar]
  5. Haque U, Sunahara T, Hashizume M, Shields T, Yamamoto T, Haque R, Glass GE, , 2011. Malaria prevalence, risk factors and spatial distribution in a hilly forest area of Bangladesh. PLoS ONE 6: e18908.[Crossref] [Google Scholar]
  6. Global Fund to Fight AIDS, Tuberculosis and Malaria, 2006. Available at: http://www.theglobalfund.org/grantdocuments/6BANM_1267_0_full.pdf.
  7. Clark TD, Greenhouse B, Njama-Meya D, Nzarubara B, Maiteki-Sebuguzi C, Staedke SG, Seto E, Kamya MR, Rosenthal PJ, Dorsey G, , 2008. Factors determining the heterogeneity of malaria incidence in children in Kampala, Uganda. J Infect Dis 198: 393400.[Crossref] [Google Scholar]
  8. Syed MA, Rashidul H, Ubydul H, Awlad H, , 2009. Knowledge on the transmission, prevention and treatment of malaria among two endemic populations of Bangladesh and their health-seeking behavior. Malar J 8: 173.[Crossref] [Google Scholar]
  9. Haque U, Hashizume M, Sunahara T, Hossain S, Masud Ahmed S, Haque R, Yamamoto T, Glass GE, , 2010. Progress and challenges to control malaria in a remote area of Chittagong hill tracts, Bangladesh. Malar J 9: 156.[Crossref] [Google Scholar]
  10. Ahmed SM, Hossain S, Kabir MM, Roy S, , 2011. Free distribution of insecticidal bed nets improves possession and preferential use by households and is equitable: findings from two cross-sectional surveys in thirteen malaria endemic districts of Bangladesh. Malar J 10: 357.[Crossref] [Google Scholar]
  11. Reid H, Haque U, Clements ACA, Tatem AJ, Vallely A, Ahmed SM, Islam A, Haque R, , 2010. Mapping malaria risk in Bangladesh using Bayesian geostatistical models. Am J Trop Med Hyg 83: 861867.[Crossref] [Google Scholar]
  12. Reid H, Haque U, Roy S, Islam N, Clements AC, , 2012. Characterizing the spatial and temporal variation of malaria incidence in Bangladesh, 2007. Malar J 11: 170.[Crossref] [Google Scholar]
  13. Haque U, Huda M, Hossain A, Ahmed SM, Moniruzzaman M, Haque R, , 2009. Spatial malaria epidemiology in Bangladeshi highlands. Malar J 8: 185.[Crossref] [Google Scholar]
  14. Carter R, Mendis KN, Roberts D, , 2000. Spatial targeting of interventions against malaria. Bull World Health Organ 78: 14011411. [Google Scholar]
  15. Clennon JA, Kamanga A, Musapa M, Shiff C, Glass GE, , 2010. Identifying malaria vector breeding habitats with remote sensing data and terrain-based landscape indices in Zambia. Int J Health Geogr 9: 58.[Crossref] [Google Scholar]
  16. Cohen JM, Ernst KC, Lindblade KA, Vulule JM, John CC, Wilson ML, , 2010. Local topographic wetness indices predict household malaria risk better than land-use and land-cover in the western Kenya highlands. Malar J 9: 328.[Crossref] [Google Scholar]
  17. Cohen JM, Ernst KC, Lindblade KA, Vulule JM, John CC, Wilson ML, , 2008. Topography-derived wetness indices are associated with household-level malaria risk in two communities in the western Kenyan highlands. Malar J 7: 40.[Crossref] [Google Scholar]
  18. Moss WJ, Hamapumbu H, Kobayashi T, Shields T, Kamanga A, Clennon J, Mharakurwa S, Thuma PE, Glass G, , 2011. Use of remote sensing to identify spatial risk factors for malaria in a region of declining transmission: a cross-sectional and longitudinal community survey. Malar J 10: 163.[Crossref] [Google Scholar]
  19. Global Fund to Fight AIDS, Tuberculosis and Malaria, 2009. Available at: http://www.theglobalfund.org/grantdocuments/9BANM_1794_0_full.pdf.
  20. Kulldorff M, , 1997. A spatial scan statistic. Comm Statist Theory Methods 26: 14811496.[Crossref] [Google Scholar]
  21. Bousema T, Griffin JT, Sauerwein RW, Smith DL, Churcher TS, Takken W, Ghani A, Drakeley C, Gosling R, , 2012. Hitting hotspots: spatial targeting of malaria for control and elimination. PLoS Med 9: e1001165.[Crossref] [Google Scholar]
  22. World Health Organization, 2004. Global Strategic Framework for Integrated Vector Management. WHO/CDS/CPE/PVC/2004.10. Geneva: World Health Organization. [Google Scholar]
  23. Moonen B, Cohen JM, Snow RW, Slutsker L, Drakeley C, Smith DL, Abeyasinghe RR, Rodriguez MH, Maharaj R, Tanner M, Targett G, , 2010. Operational strategies to achieve and maintain malaria elimination. Lancet 376: 15921603.[Crossref] [Google Scholar]
  24. Von Seidlein L, Greenwood BM, , 2003. Mass administrations of antimalarial drugs. Trends Parasitol 19: 452460.[Crossref] [Google Scholar]
  25. Ahmed TU, , 1987. Checklist of the mosquitoes of Bangladesh. Mosq Syst 19: 187200. [Google Scholar]
  26. Elias M, Dewan ZA, Ahmed R, , 1982. Vectors of malaria in Bangladesh. J Prev Soc Med 1: 2028. [Google Scholar]
  27. Maheswary NP, Khan Z, Molla FR, Haq MI, , 1993. Incrimination of Anopheles annularis van der Wulp-1854 as an epidemic malaria vector in Bangladesh. SE Asian J Trop Med 24: 776778. [Google Scholar]
  28. Bashar K, Tuno N, Ahmed TU, Howlader AJ, , 2012. Blood-feeding patterns of Anopheles mosquitoes in a malaria-endemic area of Bangladesh. Parasit Vectors 5: 39.[Crossref] [Google Scholar]
  29. Overgaard HJ, Ekbom B, Suwonkerd W, Takagi M, , 2003. Effect of landscape structure on anopheline mosquito density and diversity in northern Thailand: implications for malaria transmission and control. Landscape Ecol 18: 605619.[Crossref] [Google Scholar]
  30. Rosenberg R, Andre RG, Ngampatom S, Hatz C, Burge R, , 1990. A stable, oligosymptomatic malaria focus in Thailand. Trans R Soc Trop Med Hyg 84: 1421.[Crossref] [Google Scholar]
  31. Dev V, , 1996. Anopheles minimus: its bionomics and role in the transmission of malaria in Assam, India. Bull World Health Organ 74: 6166. [Google Scholar]
  32. Rosenberg R, , 1982. Forest malaria in Bangladesh. III. Breeding habits of Anopheles dirus . Am J Trop Med Hyg 31: 192201. [Google Scholar]
  33. Bomblies A, , 2012. Modeling the role of rainfall patterns on seasonal malaria transmission. Clim Change 112: 673685.[Crossref] [Google Scholar]
  34. Shaman J, Stieglitz M, Stark C, Le Blancq S, Cane M, , 2002. Using a dynamic hydrology model to predict mosquito abundances in flood and swamp water. Emerg Infect Dis 8: 613. [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.4269/ajtmh.12-0456
Loading
/content/journals/10.4269/ajtmh.12-0456
Loading

Data & Media loading...

  • Received : 28 Jul 2012
  • Accepted : 13 Dec 2012
  • Published online : 03 Apr 2013

Most Cited This Month

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error