Optimizing strategies for malaria surveillance and measuring the impact of contro

University Of California, San Francisco, San Francisco CA

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Abstract

Surveillance systems in malaria endemic-countries are needed to capture information on intervention coverage and changes in malaria transmission, infection, and disease. Importantly, surveillance data must be effectively communicated to policy-makers to inform future intervention strategies. However, the capacity to conduct high-quality malaria surveillance is currently inadequate in much of Africa and the existing health information system is insufficient for monitoring progress in malaria control. Data on vector behavior and transmission intensity are not routinely collected. Morbidity and mortality data collected at health facilities may be biased and are often incomplete, inaccurate, and largely rely on clinical diagnosis in the absence of laboratory confirmation. Community surveys are currently the most robust strategy for malaria surveillance, but are expensive and logistically challenging, conducted infrequently with limited geographic coverage, and not comprehensive enough to fully capture the dynamics of transmission, infection, and disease. Identifying the optimal methods of gathering reliable data for routine malaria surveillance is essential for improving our understanding of malaria epidemiology and providing an evidence base for maximizing the impact of control interventions. For this project comprehensive malaria surveillance studies will be conducted at 3 sentinel sites with widely varied epidemiology to collect data on measures of transmission intensity, infection and disease and identify optimal methods for surveillance. Surveillance activities will then be streamlined and expand to 6 sentinel sites to measure the impact of key malaria control interventions on malaria transmission, infection, and disease. Our specific aims will be: 1) to identify optimal strategies for malaria surveillance in Uganda by comparing different methodologies at multiple sites with varied transmission intensity, 2) to estimate the impact of key malaria control interventions on measures of transmission intensity, infection, and disease using surveillance data at multiple sites in Uganda, and 3) to conduct an economic evaluation of malaria control interventions to identify the optimal coverage levels and mix of interventions at multiple sites in Uganda.