Community-based validation of assessment of newborn illnesses by trained community health workers in Sylhet district of Bangladesh.
OBJECTIVE
To validate trained community health workers' recognition of signs and symptoms of newborn illnesses and classification of illnesses using a clinical algorithm during routine home visits in rural Bangladesh.


METHODS
Between August 2005 and May 2006, 288 newborns were assessed independently by a community health worker and a study physician. Based on a 20-sign algorithm, sick neonates were classified as having very severe disease, possible very severe disease or no disease. The physician's assessment was considered as the gold standard.


RESULTS
Community health workers correctly classified very severe disease in newborns with a sensitivity of 91%, specificity of 95% and kappa value of 0.85 (P < 0.001). Community health workers' recognition showed a sensitivity of more than 60% and a specificity of 97-100% for almost all signs and symptoms.


CONCLUSIONS
Community health workers with minimal training can use a diagnostic algorithm to identify severely ill newborns with high validity.
Baqui
Abdullah H
AH
Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA. abaqui@jhsph.edu
Arifeen
Shams E
SE
Rosen
Heather E
HE
Mannan
Ishtiaq
I
Rahman
Syed M
SM
Al-Mahmud
Arif Billah
AB
Hossain
Daniel
D
Das
Milan K
MK
Begum
Nazma
N
Ahmed
Saifuddin
S
Santosham
Mathuram
M
Black
Robert E
RE
Darmstadt
Gary L
GL
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001459:Bangladesh; D003150:Community Health Workers / Q000193:education / Q000592:standards*; D005260:Female; D006306:Health Surveys; D006801:Humans; D007231:Infant, Newborn; D007232:Infant, Newborn, Diseases / Q000175:diagnosis*; D008875:Middle Aged; D015997:Neonatal Screening / Q000592:standards*; D009730:Nursing Assessment / Q000379:methods; D012680:Sensitivity and Specificity; D055815:Young Adult