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PLoS One. 2017 May 2;12(5):e0173314. doi: 10.1371/journal.pone.0173314. eCollection 2017.

Predictive value of clinical and laboratory features for the main febrile diseases in children living in Tanzania: A prospective observational study.

Author information

Department of Ambulatory Care and Community Medicine, University of Lausanne, Lausanne, Switzerland.
University of Barcelona, Barcelona, Spain.
Amana Hospital, Dar es Salaam, United Republic of Tanzania.
St-Francis Hospital, Ifakara, United Republic of Tanzania.
Ilala Municipal Council, Dar es Salaam, United Republic of Tanzania.
Laboratory of Virology, Division of Infectious Diseases, University Hospital of Geneva, and Faculty of Medicine, University of Geneva, Geneva, Switzerland.
Infectious Diseases Service, Lausanne University Hospital, Lausanne, Switzerland.
Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland.



To construct evidence-based guidelines for management of febrile illness, it is essential to identify clinical predictors for the main causes of fever, either to diagnose the disease when no laboratory test is available or to better target testing when a test is available. The objective was to investigate clinical predictors of several diseases in a cohort of febrile children attending outpatient clinics in Tanzania, whose diagnoses have been established after extensive clinical and laboratory workup.


From April to December 2008, 1005 consecutive children aged 2 months to 10 years with temperature ≥38°C attending two outpatient clinics in Dar es Salaam were included. Demographic characteristics, symptoms and signs, comorbidities, full blood count and liver enzyme level were investigated by bi- and multi-variate analyses (Chan, et al., 2008). To evaluate accuracy of combined predictors to construct algorithms, classification and regression tree (CART) analyses were also performed.


62 variables were studied. Between 4 and 15 significant predictors to rule in (aLR+>1) or rule out (aLR+<1) the disease were found in the multivariate analysis for the 7 more frequent outcomes. For malaria, the strongest predictor was temperature ≥40°C (aLR+8.4, 95%CI 4.7-15), for typhoid abdominal tenderness (5.9,2.5-11), for urinary tract infection (UTI) age ≥3 years (0.20,0-0.50), for radiological pneumonia abnormal chest auscultation (4.3,2.8-6.1), for acute HHV6 infection dehydration (0.18,0-0.75), for bacterial disease (any type) chest indrawing (19,8.2-60) and for viral disease (any type) jaundice (0.28,0.16-0.41). Other clinically relevant and easy to assess predictors were also found: malaria could be ruled in by recent travel, typhoid by jaundice, radiological pneumonia by very fast breathing and UTI by fever duration of ≥4 days. The CART model for malaria included temperature, travel, jaundice and hepatomegaly (sensitivity 80%, specificity 64%); typhoid: age ≥2 years, jaundice, abdominal tenderness and adenopathy (46%,93%); UTI: age <2 years, temperature ≥40°C, low weight and pale nails (20%,96%); radiological pneumonia: very fast breathing, chest indrawing and leukocytosis (38%,97%); acute HHV6 infection: less than 2 years old, (no) dehydration, (no) jaundice and (no) rash (86%,51%); bacterial disease: chest indrawing, chronic condition, temperature ≥39.7°c and fever duration >3 days (45%,83%); viral disease: runny nose, cough and age <2 years (68%,76%).


A better understanding of the relative performance of these predictors might be of great help for clinicians to be able to better decide when to test, treat, refer or simply observe a sick child, in order to decrease morbidity and mortality, but also to avoid unnecessary antimicrobial prescription. These predictors have been used to construct a new algorithm for the management of childhood illnesses called ALMANACH.

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