A Probabilistic and Data-Driven Approach to Childhood Under nutrition Transitions

Getnet, Bogale

dc.contributor.author	Getnet, Bogale
dc.date.accessioned	2026-08-18T06:08:52Z
dc.date.available	2026-08-18T06:08:52Z
dc.date.issued	2026-06
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/16991
dc.description.abstract	Child undernutrition remains a persistent public health challenge in Ethiopia, contributing to significant developmental, health, and economic burdens. Despite national commitments to reduce its prevalence, rates of stunting, underweight, and wasting remain high, particularly among children in rural and low-income households. Previous studies have often examined undernutrition in isolation, without accounting for its concurrent forms or the complex pathways through which it evolves over time. This study aimed to analyze the dynamic nature of childhood undernutrition in Ethiopia, focusing on the transitions between nutritional states and the combined effects of social, economic, environmental, and maternal factors. The research also investigates which factors predict long-term nutritional outcomes and how interventions can be better targeted to high-risk populations. Methodologically, the research applied advanced statistical and computational tools to longitudinal panel data, including multistate Markov chain models for state transitions, dynamic Bayesian networks for causal exploration, machine learning, and deep learning techniques for long-term trend prediction. A sample of over 1,999 children, aged 1 to 15, was followed across five survey rounds (2002–2016), with data collected from five diverse regions in Ethiopia-capturing a broad representation of geographic and socioeconomic contexts. The findings revealed that undernutrition is frequently experienced in multiple forms simultaneously and is influenced by a constellation of interrelated factors-particularly maternal education, household wealth, and access to health services and sanitation. Results also showed that many children transitioned between undernutrition states over time, with some remaining chronically affected. Predictive models accurately identified high-risk groups and highlighted early adolescence as a critical window for intervention. These results offer valuable insights for Ethiopian health planners and policymakers, reinforcing the need for integrated, long-term, and equity-focused strategies. By uncovering dynamic patterns and causal pathways, this study contributes to both academic understanding and practical decision-making for child nutrition programs in Ethiopia and similar contexts	en_US
dc.language.iso	en_US	en_US
dc.subject	Statistics	en_US
dc.title	A Probabilistic and Data-Driven Approach to Childhood Under nutrition Transitions	en_US
dc.type	Dissartation	en_US