Use of Clinical Algorithms for Diagnosing Malaria

Several attempts have been made to identify symptoms and signs based algorithms for diagnosing malaria. In this paper, we review the results of published studies and assess the risks and benefits of this approach in different epidemiological settings. Although in areas with a low prevalence the risk of failure to treat malaria resulting from the use of algorithms was low, the reduction in the wastage of drugs was trivial. The odds of wastage of drugs increased by 1.49 (95% confidence limit 1.45-1.51) for each 10% decrease in the prevalence of malaria. In highly endemic areas the algorithms had a high risk of failure to treat malaria. The odds of failure to treat increased by 1.57 (95% confidence limit 1.50-1.65) for each 10% increase in the prevalence. Furthermore, the best clinical algorithms for diagnosing malaria were site-specific. We conclude that the accuracy of clinical algorithms for diagnosing malaria is not sufficient to determine whether antimalarial drugs should be given to children presenting with febrile illness. In highly endemic areas where laboratory support is not available, the policy of offering antimalarial drugs to all children presenting with a febrile illness recommended by the integrated child management initiative is appropriate.