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

Abdullah H. Baqui, Shams E. Arifeen, Heather E. Rosen, Ishtiaq Mannan, Syed M. Rahman, Arif Billah Al-Mahmud, Daniel Hossain, Milan K. Das, Nazma Begum, Saifuddin Ahmed, Mathuram Santosham, Robert E. Black, Gary L. Darmstadt

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)1448-1456
Number of pages9
JournalTropical Medicine and International Health
Volume14
Issue number12
DOIs
StatePublished - Dec 2009

Keywords

  • Bangladesh
  • Community health workers
  • Newborn assessment
  • Newborn health
  • Newborn illness
  • Validation

ASJC Scopus subject areas

  • Parasitology
  • Public Health, Environmental and Occupational Health
  • Infectious Diseases

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