Evidence map›Paper›PMID 42242770›Full record

ArticleBMJ (Clinical research ed.)2026

Systematic estimates of global causes of neonatal and under 5 mortality in 2000-24: secondary data analysis using bayesian multinomial logistic regression.

Jamie Perin, David Prieto-Merino, Astha Wahi, Sandipan Pramanik, Hallie Eilerts-Spinelli, Francisco Villavicencio, Tyler H McCormick, Danzhen You, Abhirup Datta, Kathleen L Strong and 2 more

Abstract read
In one paragraph

Article in BMJ (Clinical research ed.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Jamie PerinDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
David Prieto-MerinoUniversidad de Alcalá, Madrid, Spain.
Astha WahiDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Sandipan PramanikDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore USA.
Hallie Eilerts-SpinelliDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Francisco VillavicencioDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.ORCID 0000-0003-3951-7341
Tyler H McCormickDepartment of Statistics, Department of Sociology, University of Washington, Seattle, WA, USA.
Danzhen YouUNICEF Office of Strategy and Evidence - Innocenti, New York, New York, USA.
Abhirup DattaDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore USA.
Kathleen L StrongDepartment of Maternal, Newborn, Child and Adolescent Health, World Health Organization, Geneva, Switzerland.
Robert E BlackDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Li LiuDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA lliu26@jhu.edu.ORCID 0000-0003-3102-6708

Funding

Country-Specific Modeling of Verbal Autopsy Misclassification for Improved Child and Neonatal Cause-Specific Mortality Estimates at Low- and High-Resolution CausesK99HD114884 · NICHD · JOHNS HOPKINS UNIVERSITY · PI Sandipan Pramanik · 2025 to 2026
$228k
NICHD NIH HHS K99 HD114884
6 · The paper itself

Abstract

objectiveTo estimate cause specific mortality among neonates and children under 5 for 195 countries from 2000 to 2024.

designSecondary data analysis using a Bayesian multinomial logistic regression model to estimate cause specific mortality fractions. DATA SOURCES: PubMed, Embase, Web of Science, SCOPUS, Cochrane, Global Health Index Medicus, PAHO, Global Health OVID, Africa-Wide Information, IndMed, WHO Mortality Database, Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and Health and Demographic and Surveillance Systems (HDSS). INCLUSION CRITERIA: Studies in the general population reporting empirical cause specific mortality for at least two causes in the age groups of interest, with a specified method for cause ascertainment. For studies identifying causes of mortality with verbal autopsy, ≥25 deaths reported with ≤25% of these deaths with unknown cause. For vital registration, ≥80% population completeness and ≤10% deaths assigned to ill defined causes determined by the international classification of diseases, 10th revision.

resultsCause specific mortality for countries with adequate quality vital registration was estimated with their own data with minor adjustments. For countries with low mortality without adequate quality vital registration, cause specific mortality was modeled by age group and based on vital registration. For high mortality areas, cause specific mortality was modeled primarily on the basis of verbal autopsy data identified in a systematic review. Estimated cause distributions were applied to all cause mortality rates and death counts estimated by the United Nations Inter-agency Group for Child Mortality Estimation. Among 4.9 million estimated global deaths in under 5s in 2024, the most important cause of death was preterm birth complications, with 0.82 (90% uncertainty interval 0.76 to 0.88) million deaths and 6.17 (5.93 to 6.42) deaths for every 1000 live births. This was followed closely by lower respiratory infections at 0.66 (0.60 to 0.71) million deaths, intrapartum related events (0.48 (0.43 to 0.52) million), and malaria (0.45 (0.39 to 0.51) million). Analysis for trends over time showed that the decline in most causes has slowed since 2016.

conclusionWith the recent slowed pace of decline in under 5 mortality for most primary causes of death, many high mortality countries are at risk of missing the sustainable development goal targets of ≤12 neonatal deaths and ≤25 under 5 deaths per 1000 live births without acceleration. Estimates presented here can help countries to determine the most appropriate course of action to reduce under 5 mortality and achieve these targets.

Indexed as

Child MortalityGlobal HealthInfant MortalitySecondary Data AnalysisBayes TheoremCause of DeathChild, PreschoolFemaleHumansInfantInfant, NewbornLogistic Models

Identifiers

PMID42242770
PMCPMC13235706

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.