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Record W4407133189 · doi:10.1159/000543956

Neonatal Care in Low- and Middle-Income Countries: A Fresh Look

2025· editorial· en· W4407133189 on OpenAlexaffabout
Ola Didrik Saugstad, Joy E Lawn, Peter Waiswa, Zulfiqar A Bhutta

Bibliographic record

VenueNeonatology · 2025
Typeeditorial
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsHospital for Sick Children
FundersBill and Melinda Gates Foundation
KeywordsLow and middle income countriesMedicineNeonatal mortalityPediatricsIntensive care medicineEnvironmental healthDeveloping countryInfant mortalityEconomicsEconomic growthPopulation

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.089
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.264
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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