Spatial prediction of malaria prevalence in an endemic area of Bangladesh
<p>Abstract</p> <p>Background</p> <p>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%).</p&...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2010-05-01
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| Series: | Malaria Journal |
| Online Access: | http://www.malariajournal.com/content/9/1/120 |
| _version_ | 1828216704923074560 |
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| author | Islam Akramul Yamamoto Taro Ahmed Syed Clements Archie CA Reid Heidi L Magalhães Ricardo Haque Ubydul Haque Rashidul Glass Gregory E |
| author_facet | Islam Akramul Yamamoto Taro Ahmed Syed Clements Archie CA Reid Heidi L Magalhães Ricardo Haque Ubydul Haque Rashidul Glass Gregory E |
| author_sort | Islam Akramul |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>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%).</p> <p>Methods</p> <p>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).</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p> |
| first_indexed | 2024-04-12T15:36:04Z |
| format | Article |
| id | doaj.art-ca5ceb720e8f4b3280db6715d243dee5 |
| institution | Directory Open Access Journal |
| issn | 1475-2875 |
| language | English |
| last_indexed | 2024-04-12T15:36:04Z |
| publishDate | 2010-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Malaria Journal |
| spelling | doaj.art-ca5ceb720e8f4b3280db6715d243dee52022-12-22T03:26:57ZengBMCMalaria Journal1475-28752010-05-019112010.1186/1475-2875-9-120Spatial prediction of malaria prevalence in an endemic area of BangladeshIslam AkramulYamamoto TaroAhmed SyedClements Archie CAReid Heidi LMagalhães RicardoHaque UbydulHaque RashidulGlass Gregory E<p>Abstract</p> <p>Background</p> <p>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%).</p> <p>Methods</p> <p>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).</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p>http://www.malariajournal.com/content/9/1/120 |
| spellingShingle | Islam Akramul Yamamoto Taro Ahmed Syed Clements Archie CA Reid Heidi L Magalhães Ricardo Haque Ubydul Haque Rashidul Glass Gregory E Spatial prediction of malaria prevalence in an endemic area of Bangladesh Malaria Journal |
| title | Spatial prediction of malaria prevalence in an endemic area of Bangladesh |
| title_full | Spatial prediction of malaria prevalence in an endemic area of Bangladesh |
| title_fullStr | Spatial prediction of malaria prevalence in an endemic area of Bangladesh |
| title_full_unstemmed | Spatial prediction of malaria prevalence in an endemic area of Bangladesh |
| title_short | Spatial prediction of malaria prevalence in an endemic area of Bangladesh |
| title_sort | spatial prediction of malaria prevalence in an endemic area of bangladesh |
| url | http://www.malariajournal.com/content/9/1/120 |
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