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.

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.

Population Health Metrics Research Consortium gold standard verbal autopsy validation study: design, implementation, and development of analysis datasets.

BACKGROUND: Verbal autopsy methods are critically important for evaluating the leading causes of death in populations without adequate vital registration systems. With a myriad of analytical and data collection approaches, it is essential to create a high quality validation dataset from different populations to evaluate comparative method performance and make recommendations for future verbal autopsy implementation. This study was undertaken to compile a set of strictly defined gold standard deaths for which verbal autopsies were collected to validate the accuracy of different methods of verbal autopsy cause of death assignment. METHODS: Data collection was implemented in six sites in four countries: Andhra Pradesh, India; Bohol, Philippines; Dar es Salaam, Tanzania; Mexico City, Mexico; Pemba Island, Tanzania; and Uttar Pradesh, India. The Population Health Metrics Research Consortium (PHMRC) developed stringent diagnostic criteria including laboratory, pathology, and medical imaging findings to identify gold standard deaths in health facilities as well as an enhanced verbal autopsy instrument based on World Health Organization (WHO) standards. A cause list was constructed based on the WHO Global Burden of Disease estimates of the leading causes of death, potential to identify unique signs and symptoms, and the likely existence of sufficient medical technology to ascertain gold standard cases. Blinded verbal autopsies were collected on all gold standard deaths. RESULTS: Over 12,000 verbal autopsies on deaths with gold standard diagnoses were collected (7,836 adults, 2,075 children, 1,629 neonates, and 1,002 stillbirths). Difficulties in finding sufficient cases to meet gold standard criteria as well as problems with misclassification for certain causes meant that the target list of causes for analysis was reduced to 34 for adults, 21 for children, and 10 for neonates, excluding stillbirths. To ensure strict independence for the validation of methods and assessment of comparative performance, 500 test-train datasets were created from the universe of cases, covering a range of cause-specific compositions. CONCLUSIONS: This unique, robust validation dataset will allow scholars to evaluate the performance of different verbal autopsy analytic methods as well as instrument design. This dataset can be used to inform the implementation of verbal autopsies to more reliably ascertain cause of death in national health information systems.