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J Glob Health. 2017 Dec;7(2):021201. doi: 10.7189/jogh.07.021201.

Development and validation of a simplified algorithm for neonatal gestational age assessment - protocol for the Alliance for Maternal Newborn Health Improvement (AMANHI) prospective cohort study.

Author information

1
AMANHI Gestational Age Study Group, Bangladesh (Sylhet).
2
AMANHI Gestational Age Study Group, Ghana.
3
AMANHI Gestational Age Study Group, Pakistan (Karachi).
4
AMANHI Gestational Age Study Group, Tanzania (Pemba).
5
AMANHI Gestational Age Study Group, Zambia.
6
Brigham & Women's Hospital, Massachusetts General Hospital, Boston, Massachusetts, USA.
7
World Health Organization (MCA/MRD), Geneva, Switzerland.

Abstract

Objective:

The objective of the Alliance for Maternal and Newborn Health Improvement (AMANHI) gestational age study is to develop and validate a programmatically feasible and simple approach to accurately assess gestational age of babies after they are born. The study will provide accurate, population-based rates of preterm birth in different settings and quantify the risks of neonatal mortality and morbidity by gestational age and birth weight in five South Asian and sub-Saharan African sites.

Methods:

This study used on-going population-based cohort studies to recruit pregnant women early in pregnancy (<20 weeks) for a dating ultrasound scan. Implementation is harmonised across sites in Ghana, Tanzania, Zambia, Bangladesh and Pakistan with uniform protocols and standard operating procedures. Women whose pregnancies are confirmed to be between 8 to 19 completed weeks of gestation are enrolled into the study. These women are followed up to collect socio-demographic and morbidity data during the pregnancy. When they deliver, trained research assistants visit women within 72 hours to assess the baby for gestational maturity. They assess for neuromuscular and physical characteristics selected from the Ballard and Dubowitz maturation assessment scales. They also measure newborn anthropometry and assess feeding maturity of the babies. Computer machine learning techniques will be used to identify the most parsimonious group of signs that correctly predict gestational age compared to the early ultrasound date (the gold standard). This gestational age will be used to categorize babies into term, late preterm and early preterm groups. Further, the ultrasound-based gestational age will be used to calculate population-based rates of preterm birth.

Importance of the study:

The AMANHI gestational age study will make substantial contribution to improve identification of preterm babies by frontline health workers in low- and middle- income countries using simple evaluations. The study will provide accurate preterm birth estimates. This new information will be crucial to planning and delivery of interventions for improving preterm birth outcomes, particularly in South Asia and sub-Saharan Africa.

PMID:
29163937
PMCID:
PMC5665676
DOI:
10.7189/jogh.07.021201
[Indexed for MEDLINE]
Free PMC Article

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