Evidence map›Paper›PMID 42449275›Full record

ArticleBMC public health2026

A machine learning approach to identify key socio-economic and demographic factors associated with neonatal mortality in Ethiopia.

Filippos Admasu, Mahlet Sewinet

Abstract read
In one paragraph

Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Filippos Admasu *Department of Statistics, College of Natural and Computational Sciences, Addis Ababa University, Addis Ababa, Ethiopia.
Mahlet Sewinet *IT Security Management Directorate, Bunna Bank S.C, Addis Ababa, Ethiopia. mahisewinet@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite global progress, neonatal mortality remains a significant challenge in Ethiopia, with rates higher than the global average. Identifying key determinants is important for developing targeted interventions. This study aims to comprehensively examine socio-economic and demographic factors influencing neonatal mortality in Ethiopia.

methodsData from the 2019 Ethiopia Mini-Demographic and Health Survey was analyzed. Binary logistic regression, machine learning models (logistic regression, random forests, and support vector machines), and propensity score matching were used to identify determinants of neonatal mortality and estimate the causal effect of early childbearing age.

resultsThe study population included 22,829 children, with 867 (4%) experiencing neonatal death. Significant determinants identified included primary education (AOR = 1.72, 95% CI: 1.15-2.56), large household size (AOR = 1.78, 95% CI: 1.51-2.11), female household head (AOR = 1.25, 95% CI: 1.02-1.53), and early childbearing age (AOR = 1.28, 95% CI: 1.11-1.47). First or second birth order (AOR = 0.76, 95% CI: 0.65-0.88) and Muslim religion (AOR = 0.78, 95% CI: 0.66-0.92) were protective factors. Notably, machine learning algorithms identified additional factors such as maternal age and wealth index. Additionally, propensity score matching showed that giving birth before age 20 was associated with a 2% absolute increase in neonatal mortality risk (ATT = -0.02, 95% CI: -0.03 to -0.002) compared with mothers aged 20 years or older.

conclusionThis study identified key socio-economic and demographic determinants of neonatal mortality in Ethiopia including maternal education, household characteristics, and obstetric factors. Addressing these interrelated factors through comprehensive strategies involving education, economic empowerment, family planning, and improved obstetric care is important for reducing neonatal mortality and achieving child health goals.

Indexed as

Infant MortalityMachine LearningAdolescentAdultDemographyEthiopiaFemaleHealth SurveysHumansInfantInfant, NewbornMaleRisk FactorsSociodemographic FactorsSocioeconomic FactorsYoung AdultCausal inferenceDeterminantsEthiopiaMachine learningNeonatal mortality

Identifiers

PMID42449275
PMCPMC13647735

What OpenQuestion holds

Textmetadata
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.