Factors associated with malaria microscopy diagnostic performance following a pilot quality-assurance programme in health facilities in malaria low-transmission areas of Kenya, 2014.

BACKGROUND: Malaria accounts for ~21% of outpatient visits annually in Kenya; prompt and accurate malaria diagnosis is critical to ensure proper treatment. In 2013, formal malaria microscopy refresher training for microscopists and a pilot quality-assurance (QA) programme for malaria diagnostics were independently implemented to improve malaria microscopy diagnosis in malaria low-transmission areas of Kenya. A study was conducted to identify factors associated with malaria microscopy performance in the same areas. METHODS: From March to April 2014, a cross-sectional survey was conducted in 42 public health facilities; 21 were QA-pilot facilities. In each facility, 18 malaria thick blood slides archived during January-February 2014 were selected by simple random sampling. Each malaria slide was re-examined by two expert microscopists masked to health-facility results. Expert results were used as the reference for microscopy performance measures. Logistic regression with specific random effects modelling was performed to identify factors associated with accurate malaria microscopy diagnosis. RESULTS: Of 756 malaria slides collected, 204 (27%) were read as positive by health-facility microscopists and 103 (14%) as positive by experts. Overall, 93% of slide results from QA-pilot facilities were concordant with expert reference compared to 77% in non-QA pilot facilities (p < 0.001). Recently trained microscopists in QA-pilot facilities performed better on microscopy performance measures with 97% sensitivity and 100% specificity compared to those in non-QA pilot facilities (69% sensitivity; 93% specificity; p < 0.01). The overall inter-reader agreement between QA-pilot facilities and experts was κ = 0.80 (95% CI 0.74-0.88) compared to κ = 0.35 (95% CI 0.24-0.46) between non-QA pilot facilities and experts (p < 0.001). In adjusted multivariable logistic regression analysis, recent microscopy refresher training (prevalence ratio [PR] = 13.8; 95% CI 4.6-41.4), ≥5 years of work experience (PR = 3.8; 95% CI 1.5-9.9), and pilot QA programme participation (PR = 4.3; 95% CI 1.0-11.0) were significantly associated with accurate malaria diagnosis. CONCLUSIONS: Microscopists who had recently completed refresher training and worked in a QA-pilot facility performed the best overall. The QA programme and formal microscopy refresher training should be systematically implemented together to improve parasitological diagnosis of malaria by microscopy in Kenya.

Field evaluation of diagnostic performance of malaria rapid diagnostic tests in western Kenya.

BACKGROUND: Malaria continues to be a major burden in the endemic regions of Kenya. Health outcomes associated with case management are dependent on the use of appropriate diagnostic methods. Rapid diagnostic tests (RDTs) have provided an important tool to help implement the WHO recommended parasite-based diagnosis in regions where expert microscopy is not available. One of the questions that must be answered when implementing RDTs is whether these tests are useful in a specific endemic region, as well as the most appropriate RDT to use. Data on the sensitivity and specificity of RDT test kits is important information to help guide test selection by national malaria control programmes. METHODS: This study evaluated the diagnostic performance of RDTs including First Response (FR), CareStart (CS), SD Bioline (SD), and Binax Now (BN). The performance of these malaria kits was compared to microscopy, the gold standard, for the detection of malaria parasites. The malaria RDTs were also compared to PCR which is a more sensitive reference test. Five-hundred participants were included in the study through community screening (50 %) and testing suspected malaria cases referred from health facilities. RESULTS: Of the 500 participants recruited, 33 % were malaria positive by microscopy while 51.2 % were positive by PCR. Compared to microscopy, the sensitivity of eight RDTs to detect malaria parasites was 90.3-94.8 %, the specificity was 73.3-79.3 %, the positive predictive value was 62.2-68.8 %, and the negative predictive value was 94.3-96.8 %. Compared to PCR, the sensitivity of the RDTs to detect malaria parasites was 71.1-75.4 %, the specificity was 80.3-84.4 %, the positive predictive value was 80.3-83.3 %, and the negative predictive value was 73.7-76.1 %. The RDTs had a moderate measure of agreement with both microscopy (>80.1 %) and PCR (>77.6 %) with a κ > 0.6. CONCLUSION: The performance of the evaluated RDTs using field samples was moderate; hence they can significantly improve the quality of malaria case management in endemic regions in Kenya by ensuring appropriate treatment of malaria positive individuals and avoiding indiscriminate use of anti-malarial drugs for parasite negative patients.