Abstract
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.
Highlights
Priority setting and resource allocation for disease control requires rational decisions on the geographic distribution of the populations at risk, the control interventions most appropriate to meet their health needs, and the existing levels of intervention coverage within these areas
The spatial distribution of the combined survey data is presented in Figure 1 and shows that observed insecticide-treated nets (ITN)/long-lasting insecticidal nets (LLIN) coverage among all ages was highest in the western, coastal, and central regions of the country
From the modeled ITN/LLIN coverage shown in Figure 2A, the maximum posterior predicted mean coverage ranged from 3% to 66%, with ITN coverage in the northern part of the country generally less than 10%
Summary
Priority setting and resource allocation for disease control requires rational decisions on the geographic distribution of the populations at risk, the control interventions most appropriate to meet their health needs, and the existing levels of intervention coverage within these areas. Rational decision making for malaria is hampered because the disease burden is greatest in predominantly low income countries coincidentally plagued by weak health information and planning systems.[1] Recent advances in high-resolution digital maps of malaria risk[2] and population distribution[3] provide new opportunities to identify populations at risk to guide global and regional malaria resource allocation.[4,5] effective national resource allocation requires higher resolution malaria risk and population mapping congruent with equivalent maps of existing levels of intervention coverage. Rather it is presumed to be the protection of all age groups living in areas where the risk of infection merits wide scale distribution of ITN and is more cost-efficient than other methods of vector control
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