A framework for identifying cost efficiencies in malaria routine data quality audits: methodology and application in Zambia

BACKGROUND: Routine data quality audits (RDQA) are a critical activity conducted by many country malaria programs to assess the reliability of reported malaria data, but it requires significant human and financial resources. In collaboration with the Zambia National Malaria Elimination Centre, PATH's Malaria Control and Elimination Partnership in Africa (MACEPA) proposed a framework for identifying cost efficiencies in RDQAs. To demonstrate this approach, previously collected RDQA data from Zambia was used as a case study. METHODS: A systematic, replicable framework to identify cost efficiencies in malaria RDQA interventions was developed by applying health economic evaluation principles and micro-costing methods. The framework consists of four sequential stages: (1) Conduct a costing analysis to estimate costs of the RDQA interventions by estimating financial and economic costs of all RDQA phases (planning, orientation, audit, feedback); (2) Identify cost drivers of RDQA interventions through comparative analysis of total costs, cost per health facility catchment area (HFCA), and proportional contribution of each cost category, activity, and phase across multiple RDQA interventions; (3) Develop resource-optimized scenarios by adopting the lowest-cost feasible practices; (4) Evaluation of cost-efficiency of scenarios using incremental cost per additional HFCA audited compared to the base-case implementation. Nine RDQA interventions implemented by four projects in Zambia (2022-2024) covering 10 provinces, 91 districts, and 1,189 HFCAs were evaluated. RESULTS: The proposed framework estimated the average economic costs of $24,938 per intervention ($205 per HFCA), with personnel (64%) and transportation (25%) as primary cost drivers of RDQAs in Zambia. Optimized scenarios incorporating virtual orientation meetings, streamlined audit team composition, and harmonized allowances reduced total costs by 41%. Transition to digital RDQA tools projected additional savings of up to 52%. CONCLUSION: This structured four-stage framework provides a practical methodology for identifying substantial cost efficiencies in RDQA interventions while maintaining or expanding coverage and quality. The analysis has strong implications for policy makers, funders and implementers of malaria programs on optimizing resource utilization for maintaining high-quality routine malaria data. The approach can extend to other disease-specific interventions or analogous activities, such as supportive supervision visits, enabling more sustainable implementation in countries facing similar resource constraints in health programs.