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

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.