title: Clinical algorithms for malaria diagnosis lack utility among people of different age groups.
abstract: We conducted a study to determine whether clinical algorithms would be useful in malaria diagnosis among people living in an area of moderate malaria transmission within Kilifi District in Kenya. A total of 1602 people of all age groups participated. We took smears and recorded clinical signs and symptoms (prompted or spontaneous) of all those presenting to the study clinic with a history of fever. A malaria case was defined as a person presenting to the clinic with a history of fever and concurrent parasitaemia. A set of clinical signs and symptoms (algorithms) with the highest sensitivity and specificity for diagnosing a malaria case was selected for the age groups </=5 years, 6-14 years and >/=15 years. These age-optimized derived algorithms were able to identify about 66% of the cases among those <15 years of age but only 23% of cases among adults. Were these algorithms to be used as a basis for a decision on treatment among those presenting to the clinic, 16% of children </=5 years, 44% of those 6-14 years of age and 66% of the adults who had a history of fever and parasitaemia >/=5000 parasites/microl of blood would be sent home without treatment. Clinical algorithms therefore appear to have little utility in malaria diagnosis, performing even worse in the older age groups, where avoiding unnecessary use of anti-malarials would make more drugs available to the really needy population of children under 5 years of age.
authors.affiliation: Kenya Medical Research Institute, CGMRC/Wellcome Trust Collaborative Program, Kilifi, Kenya. tmwangi@kilifi.mimcom.net
authors.forename: Tabitha W
authors.initials: TW
authors.lastname: Mwangi
authors.forename: Mahfudh
authors.initials: M
authors.lastname: Mohammed
authors.forename: Hiza
authors.initials: H
authors.lastname: Dayo
authors.forename: Robert W
authors.initials: RW
authors.lastname: Snow
authors.forename: Kevin
authors.initials: K
authors.lastname: Marsh
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000465:Algorithms; D000962:Antimalarials / Q000627:therapeutic use; D002648:Child; D002675:Child, Preschool; D019353:Endemic Diseases; D005334:Fever / Q000209:etiology; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007630:Kenya / Q000453:epidemiology; D008288:Malaria / Q000175:diagnosis* / Q000188:drug therapy / Q000453:epidemiology; D018512:Parasitemia / Q000175:diagnosis; D011159:Population Surveillance / Q000379:methods; D017410:Practice Guidelines as Topic; D012680:Sensitivity and Specificity
chemical_list: D000962:Antimalarials