Rapid and precise diagnosis of malaria is an essential element in effective case management and control of malaria. Malaria microscopy is used as the gold standard for malaria diagnosis, however results remain poor as positivity rate in Nigeria is consistently over 90%. The United States President’s Malaria Initiative (PMI) through the Malaria Action Program for States (MAPS) supported selected states in Nigeria to build capacity for malaria microscopy. This study demonstrates the effectiveness of in-service training on malaria microscopy amongst medical laboratory scientists.
The training was based on the World Health Organization (WHO) basic microscopy training manual. The 10-day training utilized a series of didactic lectures and examination of teaching slides using a CX 21 Olympus binocular microscope. All 108 medical laboratory scientists trained from 2012 to 2015 across five states in Nigeria supported by PMI were included in the study. Evaluation of the training using a pre-and post-test method was based on written test questions; reading photographic slide images of malaria parasites; and prepared slides.
There was a significant improvement in the mean written pre-and post-tests scores from 37.9% (95% CI 36.2–39.6%) to 70.7% (95% CI 68.4–73.1%) (p < 0.001). The mean counting post-test score improved significantly from 4.2% (95% CI 2.6–5.7%) to 27.9% (95% CI 25.3–30.5%) (p < 0.001). Mean post-test score for computer-based picture speciation test (63.0%) and picture detection test (89.2%) were significantly higher than the mean post-test score for slide reading speciation test (38.3%) and slide reading detection test (70.7%), p < 0.001 in both cases.
Parasite detection and speciation using enhanced visual imaging was significantly improved compared with using direct microscopy. Regular in-service training and provision of functional and high resolution microscopes are needed to ensure quality routine malaria microscopy.
Country: NigeriaKeywords: Capacity development | Research | Health system strengthening | Evidence generation | Malaria | Diagnosis | SDGs
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