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Age Decomposition of Mortality Rates Among Children Younger Than 5 Years in 47 LMICs

2025· article· en· W4408289978 on OpenAlexaff
Omar Karlsson, Thomas W. Pullum, Akhil Kumar, Rockli Kim, S. V. Subramanian

Bibliographic record

VenueJAMA Pediatrics · 2025
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineDemographyConfidence intervalCohortPediatricsMortality rateEtiologyInfant mortalityCohort studyAge groupsPopulationEnvironmental healthSurgery

Abstract

fetched live from OpenAlex

Importance: Despite a global decline in the mortality rate of children younger than 5 years (the under-5 mortality rate), neonatal deaths continue to present a substantial challenge. The etiology behind deaths varies between the early and late neonatal periods as well as at later ages. Objective: To decompose the under-5 mortality rate in 47 low- and middle-income countries into 8 age intervals, providing a comprehensive understanding of varying vulnerability across age groups. Design, Setting, and Participants: This cross-sectional study used nationally representative data from 47 Demographic and Health Surveys conducted between 2014 and 2023 in low- and middle-income countries, including 1.4 million live births. Exposure: Age in days, weeks, months, or years. Main Outcomes and Measures: The under-5 mortality rate was decomposed by age based on a life table approach, using true cohort probabilities for the early and late neonatal periods and synthetic cohort probabilities for other age intervals, to obtain deaths per 1000 live births (ie, the cohort entering the life table) for each age interval. Results: In the pooled sample of 1 448 001 live births, there were 14 576 deaths in the early neonatal period (age 0 to 6 days); 3400 in the late neonatal period (age 7-27 days); 6760 in the early postneonatal period (age 28 days to 5 months); 4912 in the late postneonatal period (age 6-11 months); and 5145, 3990, 2674, and 1640 at ages 1, 2, 3, and 4 years, respectively. The early neonatal mortality rate accounted for 21.3 (95% CI, 20.5-22.1) deaths per 1000 births from a total under-5 mortality rate of 57.7 (95% CI, 56.2-59.3) deaths per 1000 births. The early neonatal mortality rate was significantly higher than mortality at subsequent ages (eg, median [IQR] mortality rates: early neonatal period, 18.8 [14.3-23.2] deaths per 1000 births; late neonatal period, 4.7 [3.1-5.9] deaths per 1000 births) and much higher when considering the average daily mortality rate. The early neonatal mortality rate accounted for the greatest share of under-5 mortality rate in all but 2 countries. In most countries the lowest mortality rates were observed at age 3 or 4 years. The share of deaths occurring in the late postneonatal period and later was greater in countries with greater under-5 mortality rates. Conclusions and Relevance: The concentration of mortality in the first week after birth underscores a critical need for enhanced maternal and neonatal health care. Furthermore, early neonatal mortality rates should be routinely reported and included in health targets. In this study, the age of 6 months emerged as an important turning point: high-mortality countries were characterized by a greater concentration of deaths after age 6 months than countries with lower under-5 mortality rate.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.306
Teacher spread0.298 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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