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Understanding and improving access to prompt and effective malaria treatment and care in rural Tanzania: the ACCESS Programme |
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| Authors: | Manuel W Hetzel Nelly Iteba Ahmed Makemba Christopher Mshana Christian Lengeler Brigit Obrist Alexander Schulze Rose Nathan Angel Dillip Sandra Alba Iddy Mayumana Rashid A Khatib Joseph D Njau Hassan Mshinda |
| Institution: | 1. Department of Geography, University of Liverpool, Liverpool, UK 2. Division of International Health (IHCAR), Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden 3. Liverpool School of Tropical Medicine, University of Liverpool, UK 4. Environmental Research Institute. North Highland College, UHI Millennium Institute, KW14 7JD, Thurso, UK |
| Abstract: | BackgroundMalaria is a significant public health problem in Tanzania. Approximately 16 million malaria cases are reported every year and 100,000 to 125,000 deaths occur. Although most of Tanzania is endemic to malaria, epidemics occur in the highlands, notably in Kagera, a region that was subject to widespread malaria epidemics in 1997 and 1998. This study examined the relationship between climate and malaria incidence in Kagera with the aim of determining whether seasonal forecasts may assist in predicting malaria epidemics.MethodsA regression analysis was performed on retrospective malaria and climatic data during each of the two annual malaria seasons to determine the climatic factors influencing malaria incidence. The ability of the DEMETER seasonal forecasting system in predicting the climatic anomalies associated with malaria epidemics was then assessed for each malaria season.ResultsIt was found that malaria incidence is positively correlated with rainfall during the first season (Oct-Mar) (R-squared = 0.73, p < 0.01). For the second season (Apr-Sep), high malaria incidence was associated with increased rainfall, but also with high maximum temperature during the first rainy season (multiple R-squared = 0.79, p < 0.01). The robustness of these statistical models was tested by excluding the two epidemic years from the regression analysis. DEMETER would have been unable to predict the heavy El Niño rains associated with the 1998 epidemic. Nevertheless, this epidemic could still have been predicted using the temperature forecasts alone. The 1997 epidemic could have been predicted from observed temperatures in the preceding season, but the consideration of the rainfall forecasts would have improved the temperature-only forecasts over the remaining years.ConclusionThese results demonstrate the potential of a seasonal forecasting system in the development of a malaria early warning system in Kagera region. |
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