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Costs of early detection systems for epidemic malaria in highland areas of Kenya and Uganda

DOI: 10.1186/1475-2875-8-17

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An economic costing was carried out from the health service provider's perspective in both countries. Staff time for data entry and processing, as well as supervising and coordinating EDS activities at district and national levels was recorded and associated opportunity costs estimated. A threshold analysis was carried out to determine the number of DALYs or deaths that would need to be averted in order for the EDS to be considered cost-effective.The total costs of the EDS per district per year ranged between US$ 14,439 and 15,512. Salaries were identified as major cost-drivers, although their relative contribution to overall costs varied by country. Costs of relaying surveillance data between facilities and district offices (typically by hand) were also substantial. Data from Uganda indicated that 4% or more of overall costs could potentially be saved by switching to data transfer via mobile phones. Based on commonly used thresholds, 96 DALYs in Uganda and 103 DALYs in Kenya would need to be averted annually in each district for the EDS to be considered cost-effective.Results from this analysis suggest that EDS are likely to be cost-effective. Further studies that include the costs and effects of the health systems' reaction prompted by EDS will need to be undertaken in order to obtain comprehensive cost-effectiveness estimates.Despite continuing research on the epidemiology and control of malaria epidemics, little is known about the public health burden associated with these events [1]. The data that are available indicate that epidemics can cause widespread morbidity, and that epidemic-related risks of severe disease and death are relatively high across all age groups affected [2-4]. Moreover, little is known about the economic burden of epidemics, or the costs of interventions used for epidemic prevention and control. Without reliable information in this area policy makers are unable to make informed resource allocation decisions based on sound evidence [5].For


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