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Validation of a clinical algorithm to identify neonates with severe illness during routine household visits in rural Bangladesh
  1. Gary L Darmstadt1,
  2. Abdullah H Baqui1,
  3. Yoonjoung Choi1,
  4. Sanwarul Bari2,
  5. Syed M Rahman2,
  6. Ishtiaq Mannan1,
  7. A S M Nawshad Uddin Ahmed3,
  8. Samir K Saha4,
  9. Habibur Rahman Seraji2,
  10. Radwanur Rahman2,
  11. Peter J Winch1,
  12. Stephanie Chang1,
  13. Nazma Begum2,
  14. Robert E Black1,
  15. Mathuram Santosham1,
  16. Shams El Arifeen2 for the Bangladesh Projahnmo-2 (Mirzapur) Study
  1. 1Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
  2. 2Public Health Sciences Division, ICDDR,B, Dhaka, Bangladesh
  3. 3Department of Pediatrics, Kumudini Women's Medical College, Mirzapur, Bangladesh
  4. 4Department of Microbiology, Bangladesh Institute of Child Health, Dhaka Shishu Hospital, Dhaka, Bangladesh
  1. Correspondence to Dr Gary L Darmstadt, Family Health Division, Global Health Program, Bill & Melinda Gates Foundation, PO Box 23350, Seattle, WA 98102, USA; gary.darmstadt{at}


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. no. NCT00198627.

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  • Bangladesh Projahnmo-2 (Mirzapur) Study Group A S M Nawshad Uddin Ahmed, Saifuddin Ahmed, Nabeel Ashraf Ali, Abdullah H Baqui, Nazma Begum, Robert E Black, Sanwarul Bari, Atique Iqbal Chowdhury, Gary L Darmstadt, Shams El-Arifeen, A K M Fazlul Haque, Zahid Hasan, Amnesty LeFevre, Ishtiaq Mannan, Anisur Rahman, Radwanur Rahman, Syed Moshfiqur Rahman, Taufiqur Rahman, Samir K Saha, Mathuram Santosham, Habibur Rahman Seraji, Ashrafuddin Siddik, Hugh Waters, Peter J Winch and K Zaman.

  • Competing interests None.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Ethics approval The study was approved by the Committee on Human Research at the Johns Hopkins Bloomberg School of Public Health, and the Ethical Review Committee and Research Review Committee at ICDDR,B.

  • Contributors GLD, AHB, PJW, REB, MS and SEA were primarily responsible for study design and securing funding for the study. SB, SMR, IM, ASMNUA, HRS and RR were responsible for day-to-day management of the project, including data collection. SC and NB were responsible for project data management. YC was primarily responsible for data analysis, and YC and GLD were primarily responsible for preparing the manuscript. All authors reviewed and approved the manuscript.