Background: Nulliparity has been associated with lower birth weight (BW) and other adverse pregnancy outcomes, with most of the data coming from high-income countries. In this study, we examined birth weight for gestational age z-scores and neonatal (28-day) mortality in a large prospective cohort of women dated by first trimester ultrasound from multiple sites in low and middle-income countries. Methods: Pregnant women were recruited during the first trimester of pregnancy and followed through 6 weeks postpartum from Maternal Newborn Health Registry (MNHR) sites in the Democratic Republic of Congo (DRC), Guatemala, Belagavi and Nagpur, India, and Pakistan from 2017 and 2018. Data related to the pregnancy and its outcomes were collected prospectively. First trimester ultrasound was used for determination of gestational age; (BW) was obtained in grams within 48 h of delivery and later transformed to weight for age z-scores (WAZ) adjusted for gestational age using the INTERGROWTH-21st standards. Results: 15,121 women were eligible and included. Infants of nulliparous women had lower mean BWs (males: 2676 gr, females: 2587 gr, total: 2634 gr) and gestational age adjusted weight for age z-scores (males: − 0.73, females: − 0.77, total: − 0.75,) than women with one or more previous pregnancies. The largest differences were between zero and one previous pregnancies among female infants. The associations of parity with BW and z-scores remained even after adjustment for maternal age, maternal height, maternal education, antenatal care visits, hypertensive disorders, and socioeconomic status. Nulliparous women also had a significantly higher < 28-day neonatal mortality rate (27.7 per 1,000 live births) than parous women (17.2 and 20.7 for parity of 1–3 and ≥ 4 respectively). Risk of preterm birth was higher among women with ≥ 4 previous pregnancies (15.5%) compared to 11.3% for the nulliparous group and 11.8% for women with one to three previous pregnancies (p = 0.0072). Conclusions: In this large sample from diverse settings, nulliparity was independently associated with both lower BW and WAZ scores as well as higher neonatal mortality compared to multiparity.

Stillbirths account for 2.6 million deaths annually. 98% occur in low- and lower middle-income countries. Accurate classification of stillbirths in low-resource settings is challenged by poor pregnancy dating and infrequent access to electronic heart rate monitoring for both the newborn and fetus. In these settings, liveborn infants may be misclassified as stillbirths, and stillbirths may be misclassified as miscarriages. Causation is available for only 3% of stillbirths globally due to the absence of registration systems. In low-resource settings where culture and autopsy are infrequently available, clinical course is used to assign cause of stillbirth. This method may miss rare or subtle causes, as well as those with non-specific clinical presentations. Verbal autopsy is another technique for assigning cause of stillbirth when objective medical data are limited. This method requires family engagement and physician attribution of cause. As interventions to reduce stillbirths in LMICs are increasingly implemented, attention to accurate classification and assignment of causes of stillbirth are critical to charting progress.

There is a substantial gap in our understanding of resuscitation practices following Helping Babies Breathe (HBB) training. We sought to address this gap through an analysis of observed resuscitations following HBB 2nd edition training in the Democratic Republic of the Congo. This is a secondary analysis of a clinical trial evaluating the effect of resuscitation training and electronic heart rate monitoring on stillbirths. We included in-born, liveborn neonates ≥28 weeks gestation whose resuscitation care was directly observed and documented. For the 2592 births observed, providers dried/stimulated before suctioning in 97% of cases and suctioned before ventilating in 100%. Only 19.7% of newborns not breathing well by 60 s (s) after birth ever received ventilation. Providers initiated ventilation at a median 347 s (>five minutes) after birth; no cases were initiated within the Golden Minute. During 81 resuscitations involving ventilation, stimulation and suction both delayed and interrupted ventilation with a median 132 s spent drying/stimulating and 98 s suctioning. This study demonstrates that HBB-trained providers followed the correct order of resuscitation steps. Providers frequently failed to initiate ventilation. When ventilation was initiated, it was delayed and interrupted by stimulation and suctioning. Innovative strategies targeting early and continuous ventilation are needed to maximize the impact of HBB.

One third of all neonatal deaths are caused by intrapartum-related events, resulting in neonatal respiratory depression (i.e., failure to breathe at birth). Evidence-based resuscitation with stimulation, airway clearance, and positive pressure ventilation reduces mortality from respiratory depression. Improving adherence to evidence-based resuscitation is vital to preventing neonatal deaths caused by respiratory depression. Standard resuscitation training programs, combined with frequent simulation practice, have not reached their life-saving potential due to ongoing gaps in bedside performance. Complex neonatal resuscitations, such as those involving positive pressure ventilation, are relatively uncommon for any given resuscitation provider, making consistent clinical practice an unrealistic solution for improving performance. This review discusses strategies to allow every birth to act as a learning event within the context of both high- and low-resource settings. We review strategies that involve clinical-decision support during newborn resuscitation, including the visual display of a resuscitation algorithm, peer-to-peer support, expert coaching, and automated guidance. We also review strategies that involve post-event reflection after newborn resuscitation, including delivery room checklists, audits, and debriefing. Strategies that make every birth a learning event have the potential to close performance gaps in newborn resuscitation that remain after training and frequent simulation practice, and they should be prioritized for further development and evaluation.

Ninety percent of intrapartum-related neonatal deaths are attributable to respiratory depression, with the vast majority of these deaths occurring in low- and lower-middle–income countries. Neonatal resuscitation training with Helping Babies Breathe (HBB) decreases mortality from respiratory depression. Cardiorespiratory monitoring in conjunction with HBB can provide valuable resuscitation feedback for both training and bedside purposes. In this article, we discuss 3 innovations that couple cardiorespiratory monitoring with HBB: NeoNatalie Live, the Augmented Infant Resuscitator, and NeoBeat. NeoNatalie Live is a high-fidelity manikin that facilitates bag mask ventilation training through case scenarios of varying difficulty. The Augmented Infant Resuscitator is added in-line between a face mask and ventilation bag during bag mask ventilation training to provide users with real-time feedback on ventilation quality. NeoBeat is a battery-operated heart rate meter that digitally displays the newborn heart rate during bedside resuscitations. For each innovation, we review details of the device, implementation in the field, and areas for further research. Using early experience implementing these devices, we suggest building blocks for effective translation of training into improved care. We also highlight general challenges in implementation of devices in facilities in low- and lower-middle–income countries including considerations for training, adaptations to existing workflow, and integration into the ecosystem. Although the devices highlighted in this article hold promise, more data are needed to understand their impact on newborn outcomes.

Background: Low birth weight (LBW, < 2500 g) infants are at significant risk for death and disability. Improving outcomes for LBW infants requires access to advanced neonatal care, which is a limited resource in low- and middle-income countries (LMICs). Predictive modeling might be useful in LMICs to identify mothers at high-risk of delivering a LBW infant to facilitate referral to centers capable of treating these infants. Methods: We developed predictive models for LBW using the NICHD Global Network for Women’s and Children’s Health Research Maternal and Newborn Health Registry. This registry enrolled pregnant women from research sites in the Democratic Republic of the Congo, Zambia, Kenya, Guatemala, India (2 sites: Belagavi, Nagpur), Pakistan, and Bangladesh between January 2017 – December 2020. We tested five predictive models: decision tree, random forest, logistic regression, K-nearest neighbor and support vector machine. Results: We report a rate of LBW of 13.8% among the eight Global Network sites from 2017–2020, with a range of 3.8% (Kenya) and approximately 20% (in each Asian site). Of the five models tested, the logistic regression model performed best with an area under the curve of 0.72, an accuracy of 61% and a recall of 72%. All of the top performing models identified clinical site, maternal weight, hypertensive disorders, severe antepartum hemorrhage and antenatal care as key variables in predicting LBW. Conclusions: Predictive modeling can identify women at high risk for delivering a LBW infant with good sensitivity using clinical variables available prior to delivery in LMICs. Such modeling is the first step in the development of a clinical decision support tool to assist providers in decision-making regarding referral of these women prior to delivery. Consistent referral of women at high-risk for delivering a LBW infant could have extensive public health consequences in LMICs by directing limited resources for advanced neonatal care to the infants at highest risk.