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The Global Network Neonatal Cause of Death algorithm for low-resource settings
Garces, A. L., McClure, E. M., Perez, W., Hambidge, K. M., Krebs, N. F., Figueroa, L., Bose, C. L., Carlo, W. A., Tenge, C., Esamai, F., Goudar, S. S., Saleem, S., Patel, A. B., Chiwila, M., Chomba, E., Tshefu, A., Derman, R. J., Hibberd, P. L., Bucher, S., ... Goldenberg, R. L. (2017). The Global Network Neonatal Cause of Death algorithm for low-resource settings. Acta Paediatrica, 106(6), 904-911. https://doi.org/10.1111/apa.13805
Aim: This study estimated the causes of neonatal death using an algorithm for low-resource areas, where 98% of the world's neonatal deaths occur.
Methods: We enrolled women in India, Pakistan, Guatemala, the Democratic Republic of Congo, Kenya and Zambia from 2014 to 2016 and tracked their delivery and newborn outcomes for up to 28 days. Antenatal care and delivery symptoms were collected using a structured questionnaire, clinical observation and/or a physical examination. The Global Network Cause of Death algorithm was used to assign the cause of neonatal death, analysed by country and day of death.
Results: One-third (33.1%) of the 3068 neonatal deaths were due to suspected infection, 30.8% to prematurity, 21.2% to asphyxia, 9.5% to congenital anomalies and 5.4% did not have a cause of death assigned. Prematurity and asphyxia-related deaths were more common on the first day of life (46.7% and 52.9%, respectively), while most deaths due to infection occurred after the first day of life (86.9%). The distribution of causes was similar to global data reported by other major studies.
Conclusion: The Global Network algorithm provided a reliable cause of neonatal death in low-resource settings and can be used to inform public health strategies to reduce mortality.
