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Neonatal-perinatal medicine fellows must achieve a meaningful accomplishment in scholarly activity as part of their training.Despite the requirement for scholarly training in fellowship, there is a vanishingly small number of MD-only physician-scientists pursuing a research-oriented career.Recent neonatal trainees have identified several factors that preclude their careers in research-focused academic neonatology, including lower pay in academic positions, inadequate training in research techniques, and the perception that individuals in research careers have a poor work-life balance. High competition for limited pediatric research funds also contributes to a diminishing pool of physician-scientists in neonatology. This small number of physician-scientists is threatened by a high rate of attrition among physicians who enter this career path. In order to prevent further declines in the number of neonatal physician-scientists, we need improvements in funding and strong intra- and cross-institutional mentorship to foster individuals interested in a career as a physician-scientist.

Additional file 1.

Additional file 1: Table S1. Day 0–1 neonatal mortality by received by care around delivery indicator. Table S2. Day 2–6 neonatal mortality by received care around delivery.

Background Determining cause of death is needed to develop strategies to reduce maternal death, stillbirth, and newborn death, especially for low-resource settings where 98% of deaths occur. Most existing classification systems are designed for high income settings where extensive testing is available. Verbal autopsy or audits, developed as an alternative, are time-intensive and not generally feasible for population-based evaluation. Furthermore, because most classification is user-dependent, reliability of classification varies over time and across settings. Thus, we sought to develop classification systems for maternal, fetal and newborn mortality based on minimal data to produce reliable cause-of-death estimates for low-resource settings. Results In six low-resource countries (India, Pakistan, Guatemala, DRC, Zambia and Kenya), we evaluated data which are collected routinely at antenatal care and delivery and could be obtained with interview, observation, or basic equipment from the mother, lay-health provider or family to inform causes of death. Using these basic data collected in a standard way, we then developed an algorithm to assign cause of death that could be computer-programmed. Causes of death for maternal (trauma, abortion, hemorrhage, infection and hypertensive disease of pregnancy), stillbirth (birth trauma, congenital anomaly, infection, asphyxia, complications of preterm birth) and neonatal death (congenital anomaly, infection, asphyxia, complications of preterm birth) are based on existing cause of death classifications, and compatible with the World Health Organization International Classification of Disease system. Conclusions Our system to assign cause of maternal, fetal and neonatal death uses basic data from family or lay-health providers to assign cause of death by an algorithm to eliminate a source of inconsistency and bias. The major strengths are consistency, transparency, and comparability across time or regions with minimal burden on the healthcare system. This system will be an important contribution to determining cause of death in low-resource settings.