Abstract
BackgroundThe use of rapid diagnostic tests (RDTs) to diagnose malaria is common in sub-Saharan African laboratories, remote primary health facilities and in the community. Currently, there is a lack of reliable methods to ascertain health worker competency to accurately use RDTs in the testing and diagnosis of malaria. Dried tube specimens (DTS) have been shown to be a consistent and useful method for quality control of malaria RDTs, however, its application in National Quality Management programmes has been limited.MethodsA Plasmodium falciparum strain was grown in culture and harvested to create DTS of varying parasite density (0, 100, 200, 500 and 1,000 parasites/µL). Using the dried tube specimens as quality control material, a proficiency testing (PT) programme was carried out in 80 representative health centres in Togo. Health worker competency for performing malaria RDTs was assessed using five blinded DTS samples, and the DTS were tested in the same manner as a patient sample would be tested by multiple testers per health centre. ResultsAll the DTS with 100 parasites/µl and 50% of DTS with 200 parasites/µl were classified as non-reactive during the pre-PT quality control step. Therefore, data from these parasite densities were not analysed as part of the PT dataset. PT scores across all 80 facilities and 235 testers was 100% for 0 parasites/µl, 63% for 500 parasites/µl and 93% for 1,000 parasites/µl. Overall, 59% of the 80 healthcare centres that participated in the PT programme received a score of 80% or higher on a set of 0, 500 and 1,000 parasites/ µl DTS samples. Sixty percent of health workers at these centres recorded correct test results for all three samples.ConclusionsThe use of DTS for a malaria PT programme was the first of its kind ever conducted in Togo. The ease of use and stability of the DTS illustrates that they this type of samples can be considered for the assessment of staff competency. The implementation of quality management systems, refresher training and expanded PT at remote testing facilities are essential elements to improve the quality of malaria diagnosis.
Application and Effectiveness of Big Data and Artificial Intelligence in the Construction of Nursing Sensitivity Quality Indicators
