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Risk Factors of Malnutrition among In-School Children and Adolescents in Developing Countries: A Scoping Review.

Mustapha AmoaduSusanna Aba AbrahamAbdul Karim AdamsWilliam Akoto-BuabengPaul ObengJohn Elvis Hagan
Published in: Children (Basel, Switzerland) (2024)
Malnutrition among in-school children is a complex issue influenced by socio-economic, environmental, and health-related factors, posing significant challenges to their well-being and educational trajectories in developing countries. This review synthesized evidence on the multifaceted aspects of child malnutrition within the educational setting in developing countries. This review followed the six steps outlined by Arksey and O'Malley's framework. Four main databases (PubMed, CENTRAL, JSTOR, and Scopus) were searched. Additional searches were conducted in WHO Library, ProQuest, HINARI, Google Scholar, and Google. Reference lists of eligible papers were checked. This review found that low family income, varying family sizes, parental employment status, and educational levels significantly impact malnutrition among in-school children and adolescents. Environmental elements, including rural/urban residence, household sanitation, and living conditions, also influence malnutrition. In addition, nutrition knowledge, dietary habits, nutrient deficiencies, physical activity, and prevalent health conditions compound the risk of malnutrition. This study underscores the extensive health impact of malnutrition on general health, specific nutrient deficiencies, fetal/maternal health concerns, and overall morbidity. Also, malnutrition affects school performance and attendance, impacting cognitive abilities, and academic achievements. Addressing these challenges requires comprehensive policy actions aligned with Sustainable Development Goals, emphasizing poverty alleviation, health literacy, and gender equity.
Keyphrases
  • mental health
  • physical activity
  • healthcare
  • public health
  • health information
  • risk factors
  • human health
  • body mass index
  • health promotion
  • pregnant women
  • machine learning
  • big data
  • birth weight