Studying Incidence of Malaria from Routine Health Facility Reporting to Assess Impact of Targeted Control Interventions: Transforming Surveillance for Malaria Control

Following global declines in malaria burden due to large-scale interventions, highest-burden countries, especially in sub-Saharan Africa, are most affected by stalled progress on further declines. Renewed international strategies for control interventions currently emphasize targeted approaches, supported by enhanced surveillance. To inform these targeted approaches and assess their impacts, small-scale spatial assessments of malaria risk that utilize longitudinal data are vital but need to be thoroughly evaluated to be exploited. Using geostatistical models, I will study the concurrent space-time distribution of incidence and/or risk of malaria at multiple geographical scales to assess the impacts of targeted control interventions across all regions in Uganda, following the initiation of the global high-burden to the high-impact initiative. To do this, I propose using Health Management Information System (HMIS)-based incidence of malaria, estimated at health facility catchment level. Together with environmental covariates, geo-spatial attributes data, and geo-located health facilities, I will: use AccessMod (a WHO supported tool) to estimate health services accessibility and health facility catchments; estimate monthly catchment-level incidence rates using Bayesian conditional autoregressive (CAR) models; investigate attributable impacts of targeted control interventions on incidence using counterfactual analyses; and, regularly share output risk maps with the national malaria control division (NMCD) for policy support.