title: Detection of severe protein-energy malnutrition by nurses in The Gambia.
abstract: OBJECTIVE
  To test whether nurses can use the WHO integrated management of childhood illness (IMCI) nutrition algorithm to identify reliably severe protein-energy malnutrition in children.
  
  
  METHODS
  Nurses were trained to identify severe protein-energy malnutrition using IMCI training materials. They identified visible severe wasting and bipedal oedema, and categorised weight-for-age using a growth chart, in consecutive children attending outpatient clinics. Their findings were compared with weight for height Z (WHZ) score, bipedal oedema assessed by a trained observer, and weight-for-age Z score.
  
  
  RESULTS
  A total of 352 children were recruited, of whom 34 (9.7%) were severely wasted (WHZ score <-3) and 18 (5.1%) had bipedal oedema. In the detection of severe wasting, the nurses' assessments showed 56% sensitivity, 95% specificity, and 56% positive predictive value (PPV), and for bipedal oedema 22%, 99%, and 57% respectively. Overall, the nurses identified only half of 50 children with severe wasting and/or bipedal oedema and wrongly identified a further 13 children as severely malnourished. Plotting weight for age by the nurses showed 62% sensitivity, 99% specificity, and 89% PPV for the detection of children with very low weight.
  
  
  CONCLUSIONS
  Severe malnutrition was both under-diagnosed and wrongly diagnosed by nurses trained in the use of the IMCI nutrition algorithm in a clinic setting in The Gambia. These guidelines for health workers and the training materials, particularly with respect to calculation of age, need further development to improve the detection of malnourished children.
authors.affiliation: Royal Victoria Hospital, Banjul, The Gambia. Clare.Hamer@bristol.ac.uk
authors.forename: C
authors.initials: C
authors.lastname: Hamer
authors.forename: K
authors.initials: K
authors.lastname: Kvatum
authors.forename: D
authors.initials: D
authors.lastname: Jeffries
authors.forename: S
authors.initials: S
authors.lastname: Allen
mesh_terms: D000465:Algorithms; D002675:Child, Preschool; D002983:Clinical Competence; D003906:Developing Countries; D003951:Diagnostic Errors; D005260:Female; D005714:Gambia; D006801:Humans; D007223:Infant; D008297:Male; D009733:Nursing Diagnosis; D011502:Protein-Energy Malnutrition / Q000175:diagnosis*; D012680:Sensitivity and Specificity