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Community-based validation of assessment of newborn illnesses by trained community health workers in Sylhet district of Bangladesh.

Objectives: 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 independent...

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Published in:Tropical Medicine & International Health Vol. 14; no. 12; pp. 1448 - 1457
Main Authors: Baqui AH, Arifeen SE, Rosen HE, Mannan I, Rahman SM, Al-Mahmud AB, Hossain D, Das MK, Begum N, Ahmed S, Santosham M, Black RE, Darmstadt GL, Baqui, Abdullah H, Arifeen, Shams E, Rosen, Heather E, Mannan, Ishtiaq, Rahman, Syed M, Al-Mahmud, Arif Billah, Hossain, Daniel
Format: research Journal Article
Published: Wiley-Blackwell Dec2009
Online Access:View this record in EBSCOhost
Summary:Objectives: 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.Conclusion: Community health workers with minimal training can use a diagnostic algorithm to identify severely ill newborns with high validity.