Neonatal Care in Low- and Middle-Income Countries: A Fresh Look
Bibliographic record
Abstract
Despite 2 decades of policy focus on newborn survival with the first ever national target included in the Sustainable Development Goals for 2030, the rate of reduction in neonatal mortality (first 28 days after birth) has ignored acceleration for child deaths under 5 years. There are still over 2 million neonatal deaths annually, mostly in low- and middle-income countries (LMICs) [1]. Additionally, almost 2 million stillbirths are estimated to occur each year, with almost half during the intrapartum period [2], and these deaths have less attention and even less funding from donors or in research [3, 4].In the decade since the launch of Every Newborn Action Plan [5] and the technical basis for various interventions and packaging [6], more LMICs have committed to implementation in health systems, especially for high-impact care for small and sick newborns. However, there remains a gap between policy and programmatic scale up, with few countries having national coverage of neonatal care units [7]. Additionally, advances have been made in identifying evidence-based interventions to address various risk factors that lead to small vulnerable newborn births including maternal undernutrition [8]. The highest risks for newborns occur in countries facing a range of contextual challenges such as armed conflicts [9, 10] and climate change [11], which further increase the fragility of health systems.In this supplement of 13 papers [12‒24], investigators from the SickKids Centre for Global Child Health (Toronto) and the Aga Khan University (Pakistan) summarize findings from extensive reviews of various interventions spanning pregnancy, childbirth, and the postnatal period to synthesize findings of relevance to LMICs (Table 1). The interventions were reviewed using standardized methods and cover the widest range to date of interventions addressing neonatal mortality and morbidity in LMICs. The findings provide up-to-date evidence to inform policy and decision making for health care and public health professionals.A legitimate question can be asked as to why focus on LMICs. Why not use the evidence available from studies in high income settings? The authors provide strong arguments in support of using evidence from intervention trials in LMICs contexts, especially those reflecting studies in large populations considering “real life” scenarios of implementing interventions in rural or other settings facing differing risk factors, such as high rates of maternal undernutrition or obesity, adolescent births, and limited health care provision. These real contextual differences could well determine the effective coverage of various interventions [25]. To illustrate, studies using chlorhexidine for cord care [26] or emollient therapy for newborn infants [27] yield different results according to the environmental risks associated with infections. For some interventions, there are simply insufficient trials from LMICs to enable a robust evaluation of the evidence, and hence global evidence could be used as a starting point. However, this supplement takes an important step forward for interventions where there is ample evidence of benefit or lack thereof from LMICs, such as the important relatively low-cost maternity care interventions and those related to immediate care after birth. A comparable approach has been taken for assessing maternal nutritional interventions of relevance to LMICs [28].The content of this supplement was chosen by Zulfiqar Bhutta. Joy Lawn and Peter Waiswa have served as Guest Editors and the overall Editorial responsibility was taken care of by Ola D. Saugstad. Karger has been extremely helpful in this process and the costs have been covered by Belinda and Bill Gates Foundation. We are most grateful to numerous reviewers and advisors who have thoroughly reviewed this body of work and provided useful inputs and course correction. We recognize the myriad areas still left uncovered related to newborn health and survival which future research and other series need to address. These include strategies to improve prevention of developmental deficits in very preterm infants [29], notably prevention of intraventricular haemorrhage [30], pulmonary surfactant delivery strategies in neonatal respiratory distress syndrome [31], respiratory outcomes of ventilation [32], and screening for complications arising from newborn special care such as retinopathy of prematurity [33]. Here, given the urgency for improved survival by 2030, we focused on reduction of mortality but do underscore that several of these interventions also impact on developmental outcomes.We have previously called for accelerating progress in reducing newborn deaths as a cornerstone for reaching the SDGs for health and also development [34]. This compilation for key evidence-informed interventions is an important step in that direction.The authors have no conflict of interest to declare.This project was funded by Belinda and Bill Gates Foundation.Z.A.B. drafted the editorial and led the technical work for the reviews. J.E.L., P.W., and O.D.S. oversaw the peer review process for the papers and reviewed the editorial content.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".