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Under the current guidelines of presumptive treatment of all children with reported fever, the risk of over-prescribing antimalarial drugs and missing other important causes of fever, like acute respiratory tract infection (ARI), is substantial. Clinical algorithms have been shown to be useful in diagnosing malaria, but often with differing results, due to regional variations. We set out to explore the clinical features associated with malaria compared with other febrile illnesses and specifically severe malaria with ARI in children under five in Pemba. Two hundred and seven children aged six months to five years presenting to a hospital clinic with fever were studied in Pemba. Clinical findings were related to the presence of malaria parasitaemia. Malaria accounted for 67.7% of the febrile episodes investigated. Five symptoms and signs, including pallor, drowsiness, splenomegaly, fever duration and no chest crackles, could accurately predict a case of malaria with a sensitivity of 91.3% and specificity of 53% and positive predictive value of 80.3%. Several clinical features were found to differentiate severe malaria from ARI. These results confirm that clinical algorithms can increase the diagnostic accuracy of malaria, although not sufficiently to replace microscopy, and by promoting the use of clinical skills other treatable causes of febrile illnesses may be identified. These findings could have implications in optimizing treatment and malaria control in children on Pemba.

Original publication




Journal article


Tropical Doctor


SAGE Publications

Publication Date





78 - 81