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BACKGROUND: Plasmodium falciparum morbid and fatal risks are considerably higher in areas supporting parasite prevalence > or =25%, when compared with low transmission areas supporting parasite prevalence below 25%. Recent descriptions of the health impacts of malaria in Africa are based upon categorical descriptions of a climate-driven fuzzy model of suitability (FCS) for stable transmission developed by the Mapping Malaria Risk in Africa collaboration (MARA). METHODS: An electronic and national search was undertaken to identify community-based parasite prevalence surveys in Kenya. Data from these surveys were matched using ArcView 3.2 to extract spatially congruent estimates of the FCS values generated by the MARA model. Levels of agreement between three classes used during recent continental burden estimations of parasite prevalence (0%, >0-<25% and > or =25%) and three classes of FCS (0, >0-<0.75 and > or =0.75) were tested using the kappa (k) statistic and examined as continuous variables to define better levels of agreement. RESULTS: Two hundred and seventeen independent parasite prevalence surveys undertaken since 1980 were identified during the search. Overall agreement between the three classes of parasite prevalence and FCS was weak although significant (k = 0.367, p < 0.0001). The overall correlation between the FCS and the parasite ratio when considered as continuous variables was also positive (0.364, p < 0.001). The margins of error were in the stable, endemic (parasite ratio > or =25%) class with 42% of surveys represented by an FCS <0.75. Reducing the FCS value criterion to > or =0.6 improved the classification of stable, endemic parasite ratio surveys. Zero values of FCS were not adequate discriminators of zero parasite prevalence. CONCLUSION: Using the MARA model to categorically distinguish populations at differing intensities of malaria transmission in Kenya may under-represent those who are exposed to stable, endemic transmission and over-represent those at no risk. The MARA approach to defining FCS values of suitability for stable transmission represents our only contemporary continental level map of malaria in Africa but there is a need to redefine Africa's population at risk in accordance with both climatic and non-climatic determinants of P. falciparum transmission intensity to provide a more informed approach to estimating the morbid and fatal consequences of infection across the continent.

Original publication




Journal article


Malar J

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Adolescent, Child, Child, Preschool, Climate, Fuzzy Logic, Humans, Infant, Infant, Newborn, Kenya, Least-Squares Analysis, Malaria, Falciparum, Models, Biological, Prevalence, Risk Factors