Arantxa Roca-Feltrer

Robust, timely, and decision-oriented surveillance, monitoring, and evaluation (SM&E) systems are fundamental to malaria control and elimination. N

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 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.

: Aggregate malaria data reporting accuracy indicates the overall quality of reported malaria surveillance data and is calculated using a routine data quality audit (RDQA) toolkit during a health facility audit. Three example scenarios are presented that highlight the limitations of the aggregate reporting accuracy methods of three malaria RDQA toolkits. A weighted absolute percentage error-based aggregate data reporting accuracy (WADRA) approach was found to resolve these limitations by using the register values as the weighting factor, enabling the detection of low-accuracy facilities that are otherwise considered high-accuracy by the current toolkits. Accordingly, country malaria programs should consider adopting the WADRA method in their RDQAs to maximize the detection of low-accuracy facilities and enhance decisions ranging from the development and implementation of corrective action plans to the prioritization and allocation of resources for data quality improvement efforts.

: Since 2015, the Zambia National Malaria Elimination Centre has conducted routine data quality audits in Central, Southern, and Western provinces, but trends in data reporting accuracy have not been examined. Routine data quality audit data collected at health facilities reporting into the monthly health management information system (HMIS) and weekly malaria rapid reporting system (MRRS) were used to measure data reporting accuracy trends from 2015 to 2022 and potential influencing factors using three data elements: outpatient department attendance and rapid diagnostic test (RDT)-tested cases for HMIS and MRRS, total confirmed cases for HMIS only, and RDT-positive cases for MRRS only. Reporting accuracies for HMIS and MRRS data elements and the percentage of facilities reporting with high accuracy (≥85%) improved over this period. Low-accuracy (<70%) health facilities were uncommon, accounting for less than 15% of facilities for HMIS and MRRS. With each successive DQA visit, the proportion of facilities with high accuracy increased from visits 1 to 8: 23% to 56% (HMIS) and 42% to 85% (MRRS). No correlation was observed between facility size or incidence and overall accuracy for HMIS and MRRS. Starting in 2017, about 40–50% of health facilities appeared to be overreporting incidence in comparison with their register-based incidence. The risk stratification determined by register-based and reported incidences matched in more than 70% of facilities. Routine data quality audits conducted between 2015 and 2022 in Central, Southern, and Western provinces showed an improvement in malaria data reporting accuracy.

: Mozambique has implemented routine data quality assessments (DQAs) to improve accuracy of health facility (HF) malaria reporting since 2019. However, despite this being a resource-intensive exercise, the impact of operational factors on DQAs has not yet been systematically investigated. This analysis aimed to provide insights into optimizing the operational delivery of routine DQAs. A two-level logistic regression model based on 1,354 DQAs conducted across 195 HFs (16 districts, November 2019–December 2022) was used to estimate the impact of relevant operational factors, namely number of DQAs received, baseline reporting accuracy, HF setting, workload, malaria transmission intensity, and the shift to digital reporting, on accurate reporting by HFs. A report was considered accurate if the deviation between number of confirmed malaria cases in reports and register books was 80% after five DQAs, whereas HFs with accurate baseline data did not improve beyond the baseline visit. Other operational factors did not significantly affect reporting accuracy. Prioritizing HFs with low baseline accuracy for more frequent DQAs (every 6 months) with at least one visit to all HFs every 3 years might optimize resource allocation in Mozambique. Similar analytic approaches can be applied in other countries to optimize resource allocations for the delivery of routine DQAs.

: Improving the visibility and global coordination of malaria surveillance and data quality improvement initiatives is required to optimize sharing of best practices, tools, and approaches and to promote efficient, effective, and equitable distribution of resources. With these aims in mind, Rollback Malaria’s Surveillance, Monitoring and Evaluation Working Group established the Surveillance Practice and Data Quality Committee in May 2021. As a priority initiative, the committee conducted a landscape analysis of implementing partners’ (IPs’) malaria surveillance–related projects. A questionnaire that included questions on current project objectives, activities, geographic scope, and lessons learned was distributed among committee members and other IPs. Three years since its inception, information has been submitted regarding 49 projects by 25 IPs and funded by 17 donors. To present and share the landscaping results, an interactive dashboard was published to the Rollback Malaria’s Global Malaria Dashboard website (endmalaria.org) in March 2021. It is the first time that multiple stakeholders have shared such information regarding surveillance projects.

: Mozambique addressed critical malaria surveillance system challenges by rolling out an integrated malaria information storage system (iMISS) at the district level in February 2021. The iMISS integrates malaria data from existing systems across thematic program areas to improve data availability and use. In seven districts, the platform was extended to health facilities (HFs), allowing HFs to access iMISS and use tablets to submit monthly malaria reports to a central database, eliminating the need for paper-based reporting to districts. A structured evaluation of the iMISS rollout to HFs was carried out in February–July 2021. The four evaluation areas were data quality (reporting rate, timeliness, and fidelity) of monthly malaria reports electronically submitted to the iMISS, adoption of the iMISS for data-informed decision-making, system maintenance, and acceptability of the iMISS among target users. All 94 HFs in the seven targeted districts were assessed. Over the 6-month period, 86.1% of reported cases on the iMISS were consistent with cases recorded in paper-based reports, allowing for up to 10% discrepancy. In addition, 69.0% of expected monthly district meetings were held, and information from iMISS was discussed during 58.6% of these meetings. Maintenance issues, mostly related to tablet access and internet connectivity, were experienced by 74.5% of HFs; 33.7% of issues were resolved within 1 month. The iMISS and electronic submission of malaria reports were well accepted by HF- and district-level users. Continued political commitment and timely execution of issue management workflows are crucial to ensure trust in the new platform and facilitate higher levels of data use.