Infant Mortality

BackgroundPeriviable infants are a highly vulnerable neonatal group, and their survival rates are considerably affected by patient-, caregiver-, and institution-level factors, exhibiting wide variability across different income countries and time periods. This study aims to systematically review the literature on the survival rates of periviable infants and compare rates among countries with varied income levels and across different time periods.MethodsComprehensive searches were conducted across MEDLINE, Embase, CENTRAL, and Web of Science. Cohort studies reporting survival outcomes by gestational age (GA) for periviable infants born between 22 + 0 and 25 + 6 weeks of gestation were considered. Paired reviewers independently extracted data and assessed the risk of bias and quality of evidence. Data pooling was achieved using random-effects meta-analyses.ResultsSixty-nine studies from 25 countries were included, covering 56,526 live births and 59,104 neonatal intensive care unit (NICU) admissions. Survival rates for infants born between 22 and 25 weeks of GA ranged from 7% (95% CI 5-10; 22 studies, n = 5,658; low certainty) to 68% (95% CI 63-72; 35 studies, n = 21,897; low certainty) when calculated using live births as the denominator, and from 30% (95% CI 25-36; 31 studies, n = 3,991; very low certainty) to 74% (95% CI 70-77; 48 studies, n = 17,664, very low certainty) for those admitted to NICUs. The survival rates improved over the two decades studied; however, stark contrasts were evident across countries with varying income levels.ConclusionAlthough the survival rates for periviable infants have improved over the past two decades, substantial disparities persist across different economic settings, highlighting global inequalities in perinatal health. Continued research and collaborative efforts are imperative to further improve the global survival and long-term outcomes of periviable infants, especially those in Low- and Middle-Income Countries.Systematic review registrationPROSPERO, CRD42022376367, available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022376367.

BackgroundMass administration of azithromycin to children 1 to 59 months of age has been shown to reduce mortality among infants and children in this age group in some areas of sub-Saharan Africa. The largest effects have appeared to be among infants younger than 12 months of age, within 3 months after treatment; this observation motivated the design of the current trial.MethodsIn this trial, we randomly assigned villages in Mali, West Africa, in a 3:4:2 ratio to receive distributions of placebo, azithromycin two times a year, or azithromycin four times a year. Infants 1 to 11 months of age received, in doses of 20 mg per kilogram of body weight, placebo every 3 months (control group); azithromycin at two quarterly visits from January through June and placebo at two quarterly visits from July through December (twice-yearly azithromycin group); or azithromycin every 3 months (quarterly azithromycin group). The primary outcome was death within 3 months after eligibility had been confirmed, analyzed in the intention-to-treat population.ResultsFrom December 2020 through December 2022, a total of 1151 villages were enrolled in the trial; 386 villages were randomly assigned to the control group, 511 to the twice-yearly azithromycin group, and 254 to the quarterly azithromycin group. Among all the villages, 149,090 infants received at least one dose of placebo or azithromycin, with a total of 82,600 person-years of follow-up; 968 deaths were recorded. Mortality was 11.9 deaths per 1000 person-years at risk in the control group, 11.8 deaths per 1000 person-years in the twice-yearly azithromycin group (incidence rate ratio, 1.00; 95% confidence interval [CI], 0.83 to 1.19), and 11.3 deaths per 1000 person-years in the quarterly azithromycin group (incidence rate ratio, 0.93; 95% CI, 0.75 to 1.15). Adverse events were rare, and the percentages of infants with adverse events were similar in the three groups. Mortality among untreated children 12 to 59 months of age was similar across groups.ConclusionsMass administration of azithromycin in Mali, limited to infants 1 to 11 months of age, did not result in lower infant or child mortality than placebo, regardless of whether azithromycin was delivered twice yearly or quarterly. (Funded by the Gates Foundation; LAKANA ClinicalTrials.gov number, NCT04424511.).

ImportanceAntenatal corticosteroids (ACS) are accepted to be most effective in reducing prematurity-related neonatal mortality and morbidity when administered 1 to 7 days before birth. However, precise data on the optimal timing of administration are scarce.ObjectiveTo investigate the association between ACS administration to birth interval as a continuous variable and neonatal outcomes among preterm neonates.Design, setting, and participantsThis national retrospective cohort study was conducted from 2018 to 2021 at level III neonatal intensive care units participating in the Canadian Neonatal Network. Participants included singleton and twin neonates born from 23 weeks 0 days' to 31 weeks 6 days' gestation. Data were analyzed from November 29, 2023, to March 8, 2024.ExposureACS administration to birth interval.Main outcomes and measuresThe primary outcome was neonatal mortality. The secondary outcome was a composite of mortality or severe neurologic injury. Associations of the ACS administration to birth interval with the study outcomes were modeled using restricted cubic splines with 5 knots.ResultsA total of 7950 neonates met the study criteria, from 7124 pregnancies (mean [SD] maternal age, 31.1 [5.7] years). Compared with individuals who received ACS, those who did not were younger and had lower rates of nulliparity, twin pregnancy, hypertension, and gestational diabetes. The overall rates of neonatal mortality and the composite outcome were 8% (670 of 7950) and 14% (1132 of 7950), respectively. ACS exposure was associated with reduced neonatal mortality as early as 2 hours after administration (adjusted risk ratio [ARR], 0.83 [95% CI, 0.70-1.00]). The reduction in mortality risk increased to a plateau 12 hours following exposure (ARR, 0.56 [95% CI, 0.40-0.78]), remained stable for the first 2 weeks following exposure, and gradually decreased after that. The association with reduced mortality was no longer observed at 4 weeks after administration (ARR, 0.82 [95% CI, 0.56-1.20]), and the ARR approached the null 5 weeks after administration (ARR, 0.99 [95% CI, 0.56-1.73]). This pattern was not affected by gestational age at birth or the number of fetuses.Conclusions and relevanceIn this cohort of neonates born from 23 weeks 0 days' gestation to 31 weeks 6 days' gestation, ACS administration was associated with a reduction in neonatal mortality as early as 2 hours after the administration of the first dose. The interval associated with the greatest reduction in neonatal mortality was between 12 hours and 14 days before birth, which was wider than the currently accepted optimal interval of 1 to 7 days. These findings may have important clinical implications for the management of pregnancies at risk of preterm birth, particularly regarding the administration of ACS even in cases in which preterm birth is imminent, and the timing of repeat courses of ACS.

BackgroundNeonatal mortality remains a major public health challenge in low- and middle-income countries (LMICs), particularly in sub-Saharan Africa, where health systems often lack effective triage mechanisms to identify and prioritize high-risk neonates. Existing clinical tools frequently fail to support timely decision-making during the critical early postnatal period. A previous machine learning (ML)-based neonatal risk prediction model developed using multicountry LMIC datasets demonstrated high predictive accuracy for neonatal mortality in the Indian context, achieving an area under the curve above 0.80. The model incorporates 11 neonatal parameters assessed from delivery through day 2 of life, with birth weight identified as the strongest predictor.ObjectiveThis study aims to evaluate the contextual feasibility, applicability, and usability of the neonatal risk predictor variables embedded in the ML model in Kenyan health care facilities to inform potential adoption aligned with national health priorities and global targets, including Sustainable Development Goal 3.2.MethodsA mixed methods feasibility study will be conducted through the real-world preimplementation of a neonatal risk assessment tool in 3 Kenyan health facilities that provide neonatal services. Qualitative and quantitative approaches will be combined to strengthen methodological rigor and enhance the credibility of the findings. The study will be implemented in 3 sequential phases. Phase 1 will involve key informant interviews to capture contextual insights on existing workflows, stakeholder perceptions, and implementation considerations. Phase 2 will comprise a 4-month intervention period during which a paper-based neonatal risk tool will be integrated into routine clinical workflows to assess feasibility, applicability, and usability. Phase 3 will involve postintervention surveys conducted longitudinally at 2 time points to evaluate outcomes, user experience, and implementation barriers.ResultsThe neonatal risk predictors are expected to demonstrate strong contextual feasibility, allowing for integration into existing triage systems with minimal workflow disruption. Their applicability is expected to enable early identification of high-risk neonates within the first 48 hours of life, thereby enabling timely clinical decision-making. Usability assessments are expected to indicate positive user experience, acceptability, and perceived usefulness among health care workers. Six participants (frontline neonatal health workers) had been recruited by the time of initial submission of the manuscript (August 2025). Data analysis is ongoing and is expected to be concluded in March 2026. Publication of study findings is expected by June 2026.ConclusionsThe study is expected to generate actionable evidence to support the translation of ML-based neonatal risk prediction into routine clinical practice in LMIC settings, thereby reducing neonatal mortality and advancing progress toward Sustainable Development Goal target 3.2.