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Effect of Malaria Case Investigation on Malaria Case Reporting Accuracy in Nigeria

Abstract

Malaria remains a major public health concern in Nigeria and continues to place a substantial burden on healthcare systems, particularly in areas with persistent transmission. Accurate and complete malaria case reporting is essential for disease surveillance, monitoring transmission patterns, planning control interventions, allocating resources, evaluating malaria programmes, and supporting timely public health responses. However, malaria surveillance data may be affected by incomplete documentation, inaccurate diagnosis or classification, delayed reporting, duplicate records, inconsistent case definitions, inadequate data verification, and limited capacity for routine surveillance activities. Malaria case investigation provides an opportunity to verify reported cases, review patient and laboratory information, identify inconsistencies, confirm case classifications, and strengthen the quality of malaria surveillance data. Effective case investigation may therefore improve the accuracy and reliability of malaria case reporting in Nigeria. Against this background, this study investigates the effect of malaria case investigation on malaria case reporting accuracy in Nigeria. The study will be anchored on the Donabedian Model of Healthcare Quality, Systems Theory, and the Health Information System Performance Framework. The Donabedian Model explains healthcare quality through structure, process, and outcome, with malaria case investigation representing an important surveillance process and reporting accuracy serving as a measurable outcome. Systems Theory views malaria surveillance as an interconnected system involving healthcare facilities, laboratories, surveillance officers, healthcare workers, reporting platforms, public health authorities, and communities, where weaknesses in one component may affect the quality of the entire reporting system. The Health Information System Performance Framework emphasizes the importance of data inputs, information processes, data quality, information products, and the use of information for decision-making. Collectively, these theoretical perspectives provide a suitable framework for explaining how malaria case investigation may influence the accuracy of malaria case reporting in Nigeria. The study will adopt a quantitative quasi-experimental or analytical comparative research design. The study population will comprise malaria case records from selected healthcare facilities and surveillance records maintained by relevant health authorities, as well as healthcare workers and surveillance officers involved in malaria diagnosis, documentation, investigation, and reporting. A multistage sampling technique will be used to select states, local government areas, healthcare facilities, laboratories, malaria surveillance units, and eligible malaria case records. Malaria case investigation will be assessed using indicators such as proportion of reported cases investigated, completeness of case investigation forms, verification of patient information, laboratory result verification, review of diagnosis and case classification, investigation timeliness, identification of duplicate records, follow-up of suspected cases, data verification procedures, supervisory review, and feedback to reporting facilities. Malaria case reporting accuracy will be assessed using indicators such as consistency between source documents and reported cases, accuracy of case classification, completeness of demographic information, consistency of laboratory results, accuracy of reporting dates, absence of duplicate cases, completeness of treatment information, consistency between facility registers and surveillance databases, and proportion of verified reports requiring correction. Data will be collected using structured questionnaires, malaria case investigation forms, laboratory registers, outpatient registers, malaria treatment records, surveillance reporting forms, electronic surveillance databases, supervisory reports, and relevant programme documents. Descriptive statistics will be used to summarize malaria case characteristics, investigation activities, reporting practices, and data-quality indicators. Inferential statistical techniques, including chi-square tests, t-tests, correlation analysis, and logistic or multiple regression analysis where appropriate, will be used to determine the effect of malaria case investigation on reporting accuracy. Where a quasi-experimental design is adopted, reporting accuracy before and after implementation of strengthened case investigation procedures may be compared, or facilities implementing systematic case investigation may be compared with similar facilities using routine reporting procedures. Diagnostic tests will also be conducted to assess the reliability, validity, and robustness of the findings. The study is expected to find that effective malaria case investigation has a significant positive effect on malaria case reporting accuracy in Nigeria. Healthcare facilities and surveillance systems implementing systematic case investigation are expected to demonstrate more accurate, complete, consistent, and verifiable malaria reports than facilities with limited case investigation activities. Verification of patient and laboratory information may reduce misclassification, duplicate reporting, incomplete entries, and inconsistencies between source documents and submitted surveillance reports. Timely investigation and feedback may also improve healthcare workers' compliance with reporting requirements and strengthen routine data-quality practices. Accurate malaria reporting may subsequently support better estimation of disease burden, identification of transmission patterns, allocation of resources, and planning of malaria control interventions. However, inadequate numbers of surveillance personnel, poor documentation practices, limited access to laboratory services, weak electronic reporting infrastructure, delayed feedback, inadequate training, high workloads, and insufficient supervisory support may limit the effectiveness of malaria case investigation. The study therefore expects systematic, timely, adequately staffed, and well-supervised malaria case investigation to contribute significantly to improved malaria case reporting accuracy in Nigeria. The study is expected to contribute to the literature on malaria case investigation, malaria surveillance, case reporting accuracy, health information systems, disease surveillance, data quality, malaria control, epidemiological monitoring, and public health in Nigeria. The findings will provide useful information to the Federal Ministry of Health and Social Welfare, Nigeria Centre for Disease Control and Prevention, National Malaria Elimination Programme, state ministries of health, state epidemiology units, local government health authorities, healthcare facilities, laboratory personnel, surveillance officers, development partners, and policymakers regarding strategies for improving malaria surveillance systems. The study will also provide evidence-based recommendations for strengthening malaria case investigation procedures, improving healthcare worker and surveillance officer training, standardizing case verification, strengthening laboratory-data integration, improving electronic reporting systems, conducting routine data-quality audits, providing timely feedback to reporting facilities, and strengthening supervision to improve the accuracy and reliability of malaria surveillance data across Nigeria.

Keywords: Malaria case investigation, malaria case reporting accuracy, malaria surveillance, disease surveillance, data quality, case verification, health information systems, malaria reporting, malaria control, Nigeria, public health.