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

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


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.

Publication types

  • Comparative Study
  • Randomized Controlled Trial
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Validation Study

MeSH terms

  • Adolescent
  • Adult
  • Algorithms*
  • Bangladesh
  • Community Health Workers / education
  • Community Health Workers / standards*
  • Female
  • Health Surveys
  • Humans
  • Infant, Newborn
  • Infant, Newborn, Diseases / diagnosis*
  • Middle Aged
  • Neonatal Screening / standards*
  • Nursing Assessment / methods
  • Sensitivity and Specificity
  • Young Adult