Validation of an Integrated Management of Childhood Illness algorithm for managing common skin conditions in Fiji.
OBJECTIVE
To assess the sensitivity of an Integrated Management of Childhood Illness (IMCI) algorithm to detect common skin conditions in children in Fiji.


METHODS
We collected data from the assessments of children aged between 2 months and 5 years who presented to one of two health clinics. Every child was assessed by a nurse trained in the use of the IMCI algorithm and also an expert paediatrician. We used a kappa statistic to measure agreement between the nurse/algorithm assessment method and the paediatrician's diagnosis.


RESULTS
High sensitivity for identifying skin problems (sensitivity: 98.7%; 95% confidence interval, CI: 95.5-99.9) was found for the algorithm applied by IMCI-trained nurses, who were able to identify the one child with a severe skin infection and all three children with periorbital cellulitis. Sensitivity was high for the classification of abscess/cellulitis (sensitivity: 95%; 95% CI: 75.1-99.9) and infected scabies (sensitivity: 89.1%; 95% CI: 77.8-95.9), but lower for identification of impetigo, fungal infection and, in particular, non-infected scabies.


CONCLUSIONS
The IMCI skin algorithm is a robust tool that should be incorporated into the IMCI after some modifications relating to scabies and impetigo. Its use by primary health-care workers will reduce the burden of skin diseases in children in Fiji through improved case identification and management. The algorithm should be considered in other countries where skin diseases in children are a priority, particularly in the Pacific region.
Steer
Andrew C
AC
Centre for International Child Health, University of Melbourne, Vic., 3052, Australia. andrew.steer@rch.org.au
Tikoduadua
Lisi V
LV
Manalac
Emmalita M
EM
Colquhoun
Samantha
S
Carapetis
Jonathan R
JR
Maclennan
Carolyn
C
D000465:Algorithms; D002675:Child, Preschool; D005260:Female; D005367:Fiji; D006801:Humans; D007223:Infant; D008297:Male; D012871:Skin Diseases / Q000175:diagnosis* / Q000451:nursing / Q000503:physiopathology