MétaCan
Menu
Back to cohort
Record W4311021432 · doi:10.1038/s41598-022-25160-y

Determinants of undernutrition among young children in Ethiopia

2022· article· en· W4311021432 on OpenAlexaff
Biniyam Sahiledengle, Lillian Mwanri, Pammla Petrucka, Abera Kumie, Girma Beressa, Daniel Atlaw, Yohannes Tekalegn, Demisu Zenbaba, Fikreab Desta, Zinash Teferu, Debebe Wordofa, Kenbon Seyoum, Degefa Gomora, Getahun Negash, Kingsley Agho

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsWastingUnderweightMalnutritionMedicineAnthropometryConfidence intervalOdds ratioLogistic regressionDemographyPsychological interventionCross-sectional studyPediatricsMalnutrition in childrenEnvironmental healthBody mass indexOverweightInternal medicine

Abstract

fetched live from OpenAlex

Ethiopia is one of the countries in sub-Saharan Africa with the highest burden of childhood undernutrition. Despite the high burden of this scourge, little is known about the magnitude and contributing determinants to anthropometric failure among children aged 0-23 months, a period regarded as the best window of opportunity for interventions against undernutrition. This study examined factors associated with undernutrition (stunting, wasting, and underweight) among Ethiopian children aged 0-23 months. This study used a total weighted sample of 2146 children aged 0-23 months from the 2019 Ethiopian Mini Demographic and Health Survey. The data were cleaned and weighted using STATA version 14.0. Height-for-age (HFA), weight-for-height (WFH), and weight-for-age (WFA) z-scores < - 2 SD were calculated and classified as stunted, wasting, and underweight, respectively. Multilevel mixed-effects logistic regression models adjusted for cluster and survey weights were used. Adjusted odds ratio (AOR) and 95% confidence interval (CI) were estimated. Statistical significance was declared at p < 0.05. The overall weighted prevalence of stunting, wasting, and underweight respectively were 27.21% [95% CI (25.32-29.18)], 7.80% [95% CI (6.71-9.03)], and 16.44% [95% CI (14.90-18.09)] among children aged 0-23 months in Ethiopia. Female children were less likely to be associated with stunting [AOR: 0.68, 95% CI (0.54-0.86)], wasting [AOR: 0.70, 95% CI (0.51, 0.98)], and underweight [AOR: 0.64, 95% CI (0.49, 0.83)] than their male counterparts. Conversely, older children aged 12-17 months [AOR: 2.22, 95% CI (1.52, 3.23)] and 18-23 months [AOR: 4.16, 95% CI (2.75, 6.27)] were significantly at an increased odds of becoming stunted. Similarly, the likelihood of being underweight was higher in older age groups: 6-11 months [AOR: 1.74, 95% CI (1.15, 2.63)], 12-17 months [AOR: 2.13, 95% CI (1.40, 3.24)], and 18-23 months [AOR: 4.08, 95% CI (2.58, 6.44)] compared with the children younger than 6 months. Lower wealth quintile was one of the other significant determinants of stunting and underweight. The study's findings indicated that the most consistent significant risk factors for undernutrition among children aged 0-23 months are: male sex, older age groups and lower wealth quintile. These findings emphasize the importance of strengthening nutrition-specific and sensitive interventions that address the immediate and underlying drivers of childhood undernutrition in early life, as well as targeting low-income households with male children, in order for Ethiopia to meet the Sustainable Development Goals (SDGs) 1,2 and 3 by 2030.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.271
Teacher spread0.257 · 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".

Quick stats

Citations46
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueScientific ReportsSame topicChild Nutrition and Water AccessFrench-language works237,207