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Validation of a clinical algorithm to identify neonates with severe illness during routine household visits in rural Bangladesh

Abstract

Background To validate a clinical algorithm for community health workers (CHWs) during routine household surveillance for neonatal illness in rural Bangladesh.

Methods Surveillance was conducted in the intervention arm of a trial of newborn interventions. CHWs assessed 7587 neonates on postnatal days 0, 2, 5 and 8 and identified neonates with very severe disease (VSD) using an 11-sign algorithm. A nested prospective study was conducted to validate the algorithm (n=395). Physicians evaluated neonates to determine whether newborns with VSD needed referral. The authors calculated algorithm sensitivity and specificity in identifying (1) neonates needing referral and (2) mortality during the first 10 days of life.

Results The 11-sign algorithm had sensitivity of 50.0% (95% CI 24.7% to 75.3%) and specificity of 98.4% (96.6% to 99.4%) for identifying neonates needing referral-level care. A simplified 6-sign algorithm had sensitivity of 81.3% (54.4% to 96.0%) and specificity of 96.0% (93.6% to 97.8%) for identifying referral need and sensitivity of 58.0% (45.5% to 69.8%) and specificity of 93.2% (92.5% to 93.7%) for screening mortality. Compared to our 6-sign algorithm, the Young Infant Study 7-sign (YIS7) algorithm with minor modifications had similar sensitivity and specificity.

Conclusion Community-based surveillance for neonatal illness by CHWs using a simple 6-sign clinical algorithm is a promising strategy to effectively identify neonates at risk of mortality and needing referral to hospital. The YIS7 algorithm was also validated with high sensitivity and specificity at community level, and is recommended for routine household surveillance for newborn illness.

ClinicalTrials.gov no. NCT00198627.

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