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In some countries the biological targeting of universal malaria prevention may offer optimal impact on disease and significant cost-savings compared with approaches that presume universal risk. Spatially defined data on coverage of treated nets from recent national household surveys in Kenya were used within a Bayesian geostatistical framework to predict treated net coverage nationally. When combined with the distributions of malaria risk and population an estimated 8.1 million people were not protected with treated nets in 2010 in biologically defined priority areas. After adjusting for the proportion of nets in use that were not long lasting, an estimated 5.5 to 6.3 million long-lasting treated nets would be required to achieve universal coverage in 2010 in Kenya in at-risk areas compared with 16.4 to 18.1 million nets if not restricted to areas of greatest malaria risk. In Kenya, this evidence-based approach could save the national program at least 55 million US dollars.

Original publication

DOI

10.4269/ajtmh.2010.10-0331

Type

Journal article

Journal

Am J Trop Med Hyg

Publication Date

10/2010

Volume

83

Pages

854 - 860

Keywords

Bayes Theorem, Cluster Analysis, Demography, Forecasting, Humans, Insecticide-Treated Bednets, Kenya, Malaria, Mosquito Control, Needs Assessment