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Trop Med Int Health. 2009 Dec;14(12):1448-56. doi: 10.1111/j.1365-3156.2009.02397.x. Epub 2009 Oct 5.

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

Author information

  • 1Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA. abaqui@jhsph.edu

Abstract

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.

PMID:
19807901
[PubMed - indexed for MEDLINE]
PMCID:
PMC2929169
Free PMC Article
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