Abstract

BackgroundPlasmodium falciparum morbid and fatal risks are considerably higher in areas supporting parasite prevalence ≥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).MethodsAn 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 ≥25%) and three classes of FCS (0, >0 – <0.75 and ≥0.75) were tested using the kappa (k) statistic and examined as continuous variables to define better levels of agreement.ResultsTwo 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 ≥25%) class with 42% of surveys represented by an FCS <0.75. Reducing the FCS value criterion to ≥0.6 improved the classification of stable, endemic parasite ratio surveys. Zero values of FCS were not adequate discriminators of zero parasite prevalence.ConclusionUsing 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.

Highlights

  • Plasmodium falciparum morbid and fatal risks are considerably higher in areas supporting parasite prevalence ≥25%, when compared with low transmission areas supporting parasite prevalence below 25%

  • The most widely cited, contemporary continental resolution map of Plasmodium falciparum transmission distribution for Africa was developed by the Mapping Malaria Risk in Africa (MARA) collaboration [5]; http:// www.mara.org.za

  • The Malaria Risk in Africa collaboration (MARA) model used a fuzzy membership approach, assigning 5 × 5 km areas to a suitability estimate for stable P. falciparum transmission based upon simple rainfall and temperature determinants of the parasite's sporogonic development and mosquito survival

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Summary

Introduction

Plasmodium falciparum morbid and fatal risks are considerably higher in areas supporting parasite prevalence ≥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). The most widely cited, contemporary continental resolution map of Plasmodium falciparum transmission distribution for Africa was developed by the Mapping Malaria Risk in Africa (MARA) collaboration [5]; http:// www.mara.org.za. It has formed the basis for several reports by the Roll Back Malaria partnership [8,9] and was used during several recent estimates of the pan-African public health malaria burden to identify population's atrisk [10,11,12]. The validity of these assumptions and the likely margins of error are examined by comparing MARA climate suitability values with empirical P. falciparum parasite prevalence survey data in Kenya

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