ArticleBMJ global health2021
Simplified models to assess newborn gestational age in low-middle income countries: findings from a multicountry, prospective cohort study.
Article in BMJ global health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Assessing the diagnostic accuracy of postnatal clinical scoring methods and foot length measurement for estimating gestational age and birthweight of newborns in low- and middle-income countries: a systematic review and meta-analysis.BMJ paediatrics open · 2024Pooled it
- Poverty during pregnancy as a health inequality: pregnant women's experiences in accessing primary health services.BMC pregnancy and childbirth · 2026Article
- Data science and artificial intelligence for maternal, newborn and child health: scoping review and thematic analysis.BMC public health · 2025Article
- Measuring ENAP interventions for small and/or sick newborns in routine health information systems: indicators and considerations from a WHO expert consultation.Journal of global health · 2025Article
- Identification of factors associated with acute malnutrition in children under 5 years and forecasting future prevalence: assessing the potential of statistical and machine learning methods.BMJ public health · 2025Article
- The use of artificial intelligence in sexual and reproductive health: a comprehensive scoping review.npj women's health · 2025Article
- Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review.Journal of medical Internet research · 2024Article
- Young Infant Mortality Associated with Preterm and Small-for-Gestational-Age Births in Rural Bangladesh: A Prospective Cohort Study.The Journal of pediatrics · 2024Article
- How Can Nutrition Research Better Reflect the Relationship Between Wasting and Stunting in Children? Learnings from the Wasting and Stunting Project.The Journal of nutrition · 2023Article
- Acceptability and Feasibility of a Low-Cost Device for Gestational Age Assessment in a Low-Resource Setting: Qualitative Study.JMIR human factors · 2022Article
- Effective Approaches to Fetal Brain Segmentation in MRI and Gestational Age Estimation by Utilizing a Multiview Deep Inception Residual Network and Radiomics.Entropy (Basel, Switzerland) · 2022Article
- Population-based rates, risk factors and consequences of preterm births in South-Asia and sub-Saharan Africa: A multi-country prospective cohort study.Journal of global health · 2022Article
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Abstract
introductionPreterm birth is the leading cause of child mortality. This study aimed to develop and validate programmatically feasible and accurate approaches to estimate newborn gestational age (GA) in low resource settings.
methodsThe WHO Alliance for Maternal and Newborn Health Improvement (AMANHI) study recruited pregnant women from population-based cohorts in five countries (Bangladesh, Ghana, Pakistan, Tanzania and Zambia). Women <20 weeks gestation by ultrasound-based dating were enrolled. Research staff assessed newborns for: (1) anthropometry, (2) neuromuscular/physical signs and (3) feeding maturity. Machine-learning techniques were used to construct ensemble models. Diagnostic accuracy was assessed by areas under the receiver operating curve (AUC) and Bland-Altman analysis.
results7428 liveborn infants were included (n=536 preterm, <37 weeks). The Ballard examination was biased compared with ultrasound dating (mean difference: +9 days) with 95% limits of agreement (LOA) -15.3 to 33.6 days (precision ±24.5 days). A model including 10 newborn characteristics (birth weight, head circumference, chest circumference, foot length, breast bud diameter, breast development, plantar creases, skin texture, ankle dorsiflexion and infant sex) estimated GA with no bias, 95% LOA ±17.3 days and an AUC=0.88 for classifying the preterm infant. A model that included last menstrual period (LMP) with the 10 characteristics had 95% LOA ±15.7 days and high diagnostic accuracy (AUC 0.91). An alternative simpler model including birth weight and LMP had 95% LOA of ±16.7 and an AUC of 0.88.
conclusionThe best machine-learning model (10 neonatal characteristics and LMP) estimated GA within ±15.7 days of early ultrasound dating. Simpler models performed reasonably well with marginal increases in prediction error. These models hold promise for newborn GA estimation when ultrasound dating is unavailable.
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