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Accuracy; Completeness; Data; Research; Timeliness; mHealth
The World Health Organization’s Global Malaria Program implemented a multi-country study to assess the entomological and epidemiological impact of long-lasting insecticide treated nets (LLINs) and indoor residual spray (IRS). Study activities included home visits to perform a LLIN survey and malaria testing of children in the household. In Kenya, Fionet™, a technology for automated malaria Rapid Diagnostic Test (mRDT) processing and interpretation at point-of-care, was evaluated against traditional paper-based methods and manual mRDT processing. Objective: To measure and compare the accuracy of diagnosis, completeness, and timeliness of data transmission between a digital mobile solution (Fionet™) and a paper-based system. Methodology: A randomised cluster sampling design of two cohorts: 1) an Active Infection Detection cohort, and 2) an Active Case Detection cohort was undertaken between November 2013 and April 2014. Community Health Workers (CHWs) visited rural households to: 1) measure malaria prevalence in children under the age of five using mRDTs, and 2) survey the use and physical status of LLINs in the household. Ten clusters were randomly assigned to Fionet™ to perform automated testing, interpretation, and survey data capture. Fionet™ transmitted all the tests and survey data to a cloud-based database. Results: A total of 1770 households were visited, 437 children tested, and 742 LLINs inspected. Fionet™ significantly improved the quality of data gathered; a two-fold increase in adherence to study protocols using Fionet™ resulted in more accurate data, data completeness was 10 times higher than with paper-based collection methods, and 87% of data were available in less than one day. Fionet™ significantly improved data quality and management, which enhanced the health system’s ability to meet the research objectives. This technology can help ensure accurate, complete, and timely availability of data. Future studies should incorporate mobile technologies such as Fionet™ to improve RDT based diagnostics of malaria and data quality.