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Year : 2014  |  Volume : 55  |  Issue : 6  |  Page : 480-485

Predictors of malaria in febrile children in Sokoto, Nigeria

1 Department of Family Medicine, Usmanu Danfodiyo University Teaching Hospital, Sokoto, Nigeria
2 Department of Family Medicine, Jos University Teaching Hospital, Jos, Nigeria
3 Department of Paediatrics, Usmanu Danfodiyo University Teaching Hospital, Sokoto, Nigeria
4 Department of Biological Sciences, Kebbi State University of Science and Technology, Aliero, Nigeria
5 Department of Family Medicine, Mayo Clinic, Rochester, Minnesota, USA

Correspondence Address:
Sanjay Singh
Department of Family Medicine, Usmanu Danfodiyo University Teaching Hospital, P.M.B. 2370, Sokoto
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0300-1652.144701

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Background: Presumptive diagnosis of malaria is widespread, even where microscopy is available. As fever is very nonspecific, this often leads to over diagnosis, drug wastage and loss of opportunity to consider alternative causes of fever, hence the need to improve on the clinical diagnosis of malaria. Materials and Methods: In a prospective cross-sectional comparative study, we examined 45 potential predictors of uncomplicated malaria in 800 febrile children (0-12 years) in Sokoto, Nigeria. We developed a clinical algorithm for malaria diagnosis and compared it with a validated algorithm, Olaleye's model. Results: Malaria was confirmed in 445 (56%). In univariate analysis, 13 clinical variables were associated with malaria. In multivariate analysis, vomiting (odds ratio, OR 2.6), temperature ≥ 38.5°C (OR 2.2), myalgia (OR 1.8), weakness (OR 1.9), throat pain (OR 1.8) and absence of lung crepitations (OR 5.6) were independently associated with malaria. In children over age 3 years, any 3 predictors had a sensitivity of 82% and specificity of 47% for malaria. An Olaleye score ≥ 5 had a sensitivity of 62% and a specificity of 51%. Conclusion: In hyperendemic areas, the sensitivity of our algorithm may permit presumptive diagnosis of malaria in children. Algorithm positive cases can be presumptively treated, and negative cases can undergo parasitological testing to determine need for treatment.

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