Exploring the utility of indicators of uncomplicated malaria burden from routine health facility surveillance data in identifying and mapping high-risk areas for malaria in Uganda

Background and aim: Routine surveillance is increasingly recognised as central to multi-dimensional malaria control efforts, especially for programme planning and impact assessment. Whilst it is global strategy to transform surveillance into a core programmatic component, essential in-depth interpretation of routine surveillance data remains limited, especially in higher transmission settings. I therefore aimed to explore utility of indicators of uncomplicated malaria burden from routine health facility surveillance data in identifying and mapping high-risk areas for malaria in Uganda. Methods and data sources: To examine routine surveillance indicators of malaria burden, I first evaluated internal consistency between measures from three national reference health facilities, comparing incidence and test positivity rates over time and space. In addition, I examined impacts of control interventions on the age associated burden of malaria, stratified by endemicity and intervention. I then extended this to compare routine reporting data with concurrent community cohort incidence estimates across three sub-counties to evaluate potential sources of bias. Finally, using four years of national health management information system (HMIS)-reported confirmed malaria data in a Bayesian autoregressive analytical framework, I explored the space-time distribution of malaria, and estimated adjusted national and local HMIS-based incidence rates. Primary findings: At the health facility level, HMIS-based incidence and test positivity rates showed similar trends and predicable relationships, with reduced transmission associated with increasing age of test confirmed malaria cases. Comparison of HMIS and cohort data suggested that HMIS data could provide a relatively unbiased proxy for true incidence - especially in lower-transmission, better performing surveillance systems settings. Lastly, space-time modelling of national HMIS data revealed high-burden and high-risk areas within health facility catchments, districts, and regions, highlighting the utility of routine surveillance data in identifying programmatically relevant heterogeneities in malaria burden in Uganda. Conclusion: This thesis highlights the potential viability of routine data in evaluating endemic malaria risk with improved routine HMIS. This is shown by: similar trends of HMIS-based incidence with other measures; its unbiased relationship with community cohort incidence; and, its capacity to identify high case rate locations. To realize the potential of these data, coordinated efforts are needed towards high testing rates, complete and timely recording and reporting, and multilevel feedback within national malaria control programme systems. Further research opportunities include treatment or non-care seeking and non-reporting care alternatives impacts on surveillance-based indicators of malaria burden.