From test result to surveillance record: A narrative review of malaria RDT data quality in African health information systems

Purpose Malaria rapid diagnostic tests (RDTs) generate information used for clinical care and routine surveillance. However, surveillance reliability depends not only on whether a test correctly identifies infection but also on whether the observed result is recorded, aggregated, transmitted, and interpreted accurately. This review aimed to examine weaknesses in the pathway from malaria RDT results to routine surveillance data and identify practical approaches to improving data quality. Methods A structured narrative search of PubMed, Google Scholar, and World Health Organization resources was conducted for English-language publications issued from January 2010 to 10 September 2026. Search terms combined “malaria,” “rapid diagnostic test,” “routine health information system,” “health management information system,” “HMIS,” “DHIS2,” “data quality,” “recording,” “reporting,” “completeness,” “concordance,” and “timeliness.” Empirical studies from African settings were prioritized. Non-African evidence was included only when it offered potentially transferable implementation lessons. Findings were synthesized according to the stages of the data pathway and the dimensions of data quality examined. Results Evidence from Kenya, Senegal, Uganda, and a multicountry African evaluation identified incomplete reporting, discordance between source registers and DHIS2, aggregation errors, ambiguous zero and missing values, inconsistent indicator definitions, and limited feedback to healthcare facilities. Missing counts of people tested remove the denominator needed to interpret test positivity and can make changes in testing practices appear to be changes in malaria transmission. Standardized indicators, routine source verification, supportive supervision, and interoperable offline-capable digital tools may improve data quality, although their effects vary by setting. Automated RDT readers are promising but require further evaluation of feasibility, equity, governance, and sustainability. Conclusion Malaria surveillance depends on both the validity of the original diagnostic result and the integrity of the subsequent information pathway. Data-quality interventions should be selected according to the point of failure, supported by local evidence, and evaluated before large-scale implementation.