Published in PLOS One on 2026-04-22, this study is presented as an attributed evidence briefing. The excerpts below are the original researchers’ words, reproduced under their Creative Commons Attribution licence. HealthSignal has selected the abstract and, where needed, opening background paragraphs; the complete methods, results, limitations and references remain in the linked paper.
Original authors: Rewina Tilahun Gessese, Jenberu Mekurianew Kelkay, Fetlework Gubena Arage, Tigist Kifle Tsegaw, Zinabu Bekele Tadese, Meron Asmamaw Alemayehu, Eliyas Addisu Taye, Eyob Akalewold Alemu
Source: PLOS One • DOI 10.1371/journal.pone.0345000
Read the complete primary study ↗ · Creative Commons Attribution licence
From the researchers
Abstract
The prevalence of under-five stunting in Ethiopia remains above 30%, which, according to World Health Organization (WHO), constitutes a major public health concern. Stunting has long-term consequences for child growth, development, and overall health. Accurate forecasting of its prevalence is therefore essential to guide policymakers and inform targeted interventions. This study aimed to forecast the prevalence of under-five stunting in Ethiopia for the period 2025–2030 using historical data and time series modeling.
Annual under-five stunting prevalence data for Ethiopia from 2000 to 2024 were retrieved from the WHO Global Health Observatory. Time series forecasting models, including Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM), were developed and evaluated. Model performance was assessed using time series cross-validation with mean absolute error (MAE), mean absolute percentage error (MAPE), and R² as evaluation metrics. The best-performing model was applied to forecast stunting prevalence for 2025–2030.
The ETS model demonstrated the best predictive performance (MAE = 1.09, MAPE = 2.71%, R² = 0.903) and was selected for forecasting. Forecasting indicated a gradual decline in under-five stunting prevalence in Ethiopia from 33.95% in 2025 to 31.95% in 2030. The projected prevalence for 2029 is 32.35% (95% CI: 26.90–37.79%), above the national target of 19%, and the 2030 forecast remains well above the SDG target of ending all forms of malnutrition.
Under-five stunting in Ethiopia is projected to remain above national and SDG targets by 2030, indicating current nutrition efforts are insufficient; national program evaluation and evidence-based policy adjustments are recommended.
Background
Malnutrition refers to deficiencies, excesses, or imbalances in a person’s intake of energy and/or nutrients. It encompasses undernutrition (including stunting, wasting, underweight, and micronutrient deficiencies) as well as over nutrition (overweight and obesity) [1]. Among the forms of undernutrition, stunting is defined as a height-for-age Z-score below −2 standard deviations from the World Health Organization (WHO) Child Growth Standards and represents chronic nutritional deficits in children under five [2].
In 2024, about 150.2 million children under five were stunted worldwide, with most cases occurring in Asia (51%) and Africa (43%) [3]. In Ethiopia, the 2019 Ethiopia Mini Demographic and Health Survey reported that 37% of children under five were stunted, while a 2023 nationwide nutrition survey conducted by Ministry of Health and partners estimated the prevalence at 39% [4,5]. More recently, a 2024 systematic review and meta-analysis reported a pooled prevalence of 43% [6]. Overall, these findings show that the prevalence of stunting in Ethiopia ranges between 37% and 43%. All estimates exceed the WHO threshold of 30%, highlighting the very high public health significance of child stunting [2].
Reading this evidence
Use the full paper to check the study design, participant selection, comparator and outcome definitions before drawing a practical conclusion. An association does not by itself show cause and effect. A reported performance measure needs its denominator and uncertainty. A finding from a particular facility, sample or time period should retain that scope when it is discussed elsewhere.
This briefing makes the source accessible within the wider technology & industry desk. It does not assess an individual patient or certify a product. When considering relevance to another health system, examine the original authors’ account of context and limitations, then seek local implementation evidence. The related reporting below provides other studies to read alongside this one; it is a topic connection, rather than a claim that their results are directly comparable.
© 2026 Rewina Tilahun Gessese, Jenberu Mekurianew Kelkay, Fetlework Gubena Arage, Tigist Kifle Tsegaw, Zinabu Bekele Tadese, Meron Asmamaw Alemayehu, Eliyas Addisu Taye, Eyob Akalewold Alemu. Excerpts reproduced under CC BY. Selection, formatting and contextual notes by HealthSignal. The researchers are the authors of the source study, not members of HealthSignal’s staff.
Explore connected evidence
- Evidence brief: Predicting severe stunting and its determinants among under-five in Eastern African Countries: A machine learning algorithms
- Evidence brief: Machine learning-based prediction of fever among under-five children in Ethiopia: A national-level study
- Evidence brief: Assessment of trends and determinants of under-five mortality among children born to older women: Evidence from Ethiopian demographic and health surveys
