Epidemiological stratification and sub-national tailoring of malaria interventions in Liberia

Malaria is a major cause of illness and death in Liberia. Given the high burden of disease and limited resources, Liberia implemented a subnational tailoring (SNT) approach. This approach involved stakeholder engagement, data review, and advanced analytics to update transmission risk assessment, optimize intervention targeting, and revise the national operational plan.

An SNT team was established to determine intervention targeting criteria, compile and analyse relevant data sources, and stratify malaria risk and its determinants to inform geographical targeting of interventions. The analysis was performed at the district level. Data collected and reviewed included routine malaria data from health facilities, the national survey on malaria indicators, entomological data, demographic and health surveys, and modelled malaria burden metrics.

Epidemiological stratification was conducted based on modelled parasite prevalence (PfPR), incorporating results from the 2022 Malaria Indicator Survey, to inform intervention strategies. Additional indicators relevant for decision-making, such as insecticide resistance, historical malaria interventions, and access to healthcare, were also stratified. The median PfPR across the 98 health districts was 29% (SD = 4.8%), ranging from 17 to 37%. The stratification identified 84 districts as moderate transmission and 14 as high transmission, with no districts classified as low transmission. Appropriate malaria control interventions were proposed based on these strata. Findings from the SNT analysis informed the revision of the national operational plan and facilitate resource mobilization for the scale-up of dual-active nets and expanding vaccination.

This NMCP-led subnational malaria stratification for Liberia effectively informed the targeting of eight key interventions and highlighted data gaps for future refinement. This work not only provides a framework for monitoring progress and accelerating malaria burden reduction through tailored approaches but also sets the stage for continuously data-driven decision-making, emphasizing future prioritization based on projected impact, cost, and resource availability.