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Skin diseases are common worldwide, though prevalence rates in rural areas are difficult to estimate, and are primarily based on hospital studies rather than community-based studies. Primary health care providers in rural areas often lack sufficient knowledge about skin diseases, which contributes to poor skin management and subsequently causes considerable morbidity. This study looked at the performance of first-line health care providers in the management of common skin disease, using an algorithmic approach with a flowchart with diagnostic steps. As a reference standard, two dermatologists independently validated the diagnoses and treatment choices made by the providers. The performance of the algorithm was calculated in terms of the sensitivity, specificity, positive predictive value (PPV), and negative predictive value for each skin disease of the algorithm. A total of 19 patent medicine vendors and 12 traditional healers from Kano State in Nigeria diagnosed 4,147 patients with suspected skin symptoms. The most common skin disease was tinea capitis (59.2%), and it was found predominantly among boys below 15 years of age. Together, patent medicine vendors and traditional healers had 82% of the cases correctly diagnosed, and in 82% they prescribed the correct treatment. The sensitivities varied for each skin disease from 94.8% for tinea capitis to 7.1% for contact dermatitis. The specificities varied between 87.0% and 98.6%. Except for tinea capitis, lower PPVs were found for the various skin diseases when compared to earlier studies. In spite of the observed low sensitivities and low PPVs for several diseases, the algorithm seems to offer an improvement in management of common skin diseases at the peripheral level. With adaptations in training, further refinement of the algorithm and refresher training, predictive values and sensitivities can be increased.

research and reports in tropical medicine

First-line health care provider performance in the management of common skin diseases using an algorithmic approach as a diagnostic tool in Kano State, Nigeria