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Validation of a clinical algorithm to identify neonates with severe illness during routine household visits in rural
Gary L Darmstadt1, Abdullah H Baqui, Yoonjoung Choi
1Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA. gary.darmstadt@gatesfoundation.org
Insights
A simplified 6-sign algorithm effectively identifies newborns needing urgent care in rural Bangladesh. This community health worker-led approach shows promise for reducing neonatal mortality and improving healthcare access.
Area of Science:
- Global Health
- Pediatrics
- Public Health
Background:
- Neonatal illness poses a significant threat in rural settings.
- Community Health Workers (CHWs) are crucial for healthcare delivery in underserved areas.
- Accurate identification of sick newborns is vital for timely intervention.
Purpose of the Study:
- To validate a clinical algorithm for CHWs in identifying neonatal illness during household surveillance.
- To assess the algorithm's effectiveness in detecting neonates requiring referral and those at risk of mortality.
- To compare the performance of different algorithms for neonatal illness detection.
Main Methods:
- A 6-sign algorithm was developed and tested by CHWs on 7587 neonates in Bangladesh.
- A nested study (n=395) validated the algorithm's sensitivity and specificity.
- Physician evaluations determined referral needs and mortality outcomes.
Main Results:
- The 6-sign algorithm demonstrated high sensitivity (81.3%) and specificity (96.0%) for identifying neonates needing referral.
- The algorithm showed moderate sensitivity (58.0%) and high specificity (93.2%) for screening mortality.
- The Young Infant Study 7-sign (YIS7) algorithm also performed well at the community level.
Conclusions:
- A simple 6-sign algorithm is a promising tool for CHW-led surveillance of neonatal illness.
- This strategy can effectively identify at-risk neonates needing hospital referral and reduce mortality.
- The validated YIS7 algorithm is also recommended for routine newborn illness surveillance.
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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