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.