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Nutritional Status and Eating Patterns in Children and Adolescents: Tackling...
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Nutritional Status and Eating Patterns in Children and Adolescents: Tackling Vulnerabilities, Gender Gaps and Nutritional Disparities

A special issue of Children (ISSN 2227-9067). This special issue belongs to the section "Global Pediatric Health".

Deadline for manuscript submissions: 10 October 2025 | Viewed by 393

Special Issue Editors

Prof. Dr. Ersilia Buonomo

E-Mail Website
Guest Editor
Department of Biomedicine and Prevention, University of Rome Tor Vergata, 00133 Rome, Italy
Interests: epidemiology; global health; nutrition and public health; vaccine hesitancy
Special Issues, Collections and Topics in MDPI journals
Dr. Stefania Moramarco

E-Mail Website
Guest Editor
Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
Interests: nutrition and public health; food security; malnutrition; epidemiological surveillance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nutrition plays a fundamental role in shaping the current and future health of children and adolescents. Sustainable Development Goal (SDG) 2 aims to achieve zero hunger and eliminate all forms of malnutrition, ensuring that no one is left behind. Achieving this goal both contributes to—and requires—coordinated progress toward SDG 3, which promotes healthy lives and well-being for all, at all ages. Disparities in nutritional status continue to reflect and exacerbate broader social inequities, particularly among vulnerable populations, including in critical contexts such as conflict areas. Recent research has also highlighted gender-based disparities in nutritional outcomes, underscoring the significant connection between nutrition and SDG 5, which advocates for gender equality.

It is essential to recognize and respond to the diverse and evolving nutritional needs of children and adolescents, taking into account differences in age, gender, and socio-cultural background. A nuanced understanding of these factors is crucial to designing equitable, inclusive, and effective nutrition interventions and public health strategies.

This Special Issue welcomes original research articles, reviews, and short communications that examine the nutritional status and eating patterns of children and adolescents, with a particular interest in those that cover social vulnerabilities, gender disparities, and cultural contexts.

Prof. Dr. Ersilia Buonomo
Dr. Stefania Moramarco
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Children is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • nutritional assessment
  • dietary intake
  • food security
  • eating patterns
  • nutrition and gender disparities
  • nutritional inequalities
  • cultural dietary practices
  • vulnerable populations

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Published Papers (1 paper)

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Research

25 pages, 4400 KiB  
Article
Early Childhood Anemia in Ghana: Prevalence and Predictors Using Machine Learning Techniques
by Maryam Siddiqa, Gulzar Shah, Mahnoor Shahid Butt, Asifa Kamal and Samuel T. Opoku
Children 2025, 12(7), 924; https://doi.org/10.3390/children12070924 - 12 Jul 2025
Viewed by 246
Abstract
Background/Objectives: Early childhood anemia is a severe public health concern and the most common blood disorder worldwide, especially in emerging countries. This study examines the sources of childhood anemia in Ghana through various societal, parental, and child characteristics. Methods: This research [...] Read more.
Background/Objectives: Early childhood anemia is a severe public health concern and the most common blood disorder worldwide, especially in emerging countries. This study examines the sources of childhood anemia in Ghana through various societal, parental, and child characteristics. Methods: This research used data from the 2022 Ghana Demographic and Health Survey (GDHS-2022), which comprised 9353 children. Using STATA 13 and R 4.4.2 software, we analyzed maternal, social, and child factors using a model-building procedure, logistic regression analysis, and machine learning (ML) algorithms. The analyses comprised machine learning methods including decision trees, K-nearest neighbor (KNN), logistic regression, and random forest (RF). We used discrimination and calibration parameters to evaluate the performance of each machine learning algorithm. Results: Key predictors of childhood anemia are the father’s education, socioeconomic status, iron intake during pregnancy, the mother’s education, and the baby’s postnatal checkup within two months. With accuracy (94.74%), sensitivity (82.5%), specificity (50.78%), and AUC (86.62%), the random forest model was proven to be the most effective machine learning predictive model. The logistic regression model appeared second with accuracy (67.35%), sensitivity (76.16%), specificity (56.05%), and AUC (72.47%). Conclusions: Machine learning can accurately predict childhood anemia based on child and paternal characteristics. Focused interventions to enhance maternal health, parental education, and family economic status could reduce the prevalence of early childhood anemia and improve long-term pediatric health in Ghana. Early intervention and identifying high-risk youngsters may be made easier with the application of machine learning techniques, which will eventually lead to a healthier generation in the future. Full article
(This article belongs to the Special Issue Nutritional Status and Eating Patterns in Children and Adolescents: Tackling Vulnerabilities, Gender Gaps and Nutritional Disparities)
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