BACKGROUND: Eating disorders (EDs) are known as chronic mental disorders that can cause adverse physical and mental effects and affect different age groups, including children, and disrupt their growth and development. Based on this, the aim of this research is to determine the global prevalence of EDs in children. METHODS: To conduct this research, each of the databases PubMed, Scopus, Web of science, Embase, ScienceDirect and Google Scholar search engine were systematically searched using relevant keywords ("prevalence,""outbreak,""eating disorder,""feeding disorder,""eating problem,""appetite disorder,"to find all the studies that refer to the prevalence of EDs in children until July 23, 2024 to obtain and perform further evaluations. After data extraction, their analysis was done by Comprehensive Meta-Analysis software (Version 2); Random effects model was used for analysis and I2 index was also used to check the heterogeneity of studies. RESULTS: Based on the global prevalence of EDs in children based on meta-analysis was 1% (95% CI: 0.6-1.6); Subgroup analysis based on the study of eating disorders in children showed that the prevalence of pica among children is 2.1% (95% CI: 1-4.6), the prevalence of binge eating disorder is 1% (95% CI: 0.6-1.8), and the prevalence of anorexia nervosa and Bulimia nervosa among children is 0.6% (95% CI: 0.01-34.4) and 0.1% (95% CI: 0-0.4), respectively. CONCLUSION: Considering the importance of nutrition in children and the report of EDs among them, healthcare workers and medical personnel must pay attention to this category of disorders by timely informing parents to reduce the complications caused by it.
Nader Salari, Pegah Heidarian, Mohammad Javad Tarrahi et al.
BACKGROUND: Childhood malnutrition and morbidity remain significant public health challenges in Ethiopia, highlighting the need to assess the risk factors contributing to these issues for effective prevention and control strategies. Thus, this study aims to investigate the underlying risk factors by employing a structural equation model to analyze malnutrition as a mediator in the relationship between selected factors and morbidity. METHODS: The study utilized data from the 2016 Ethiopia Demographic and Health Survey and a sample of 8,560 under-five children were considered. The structural equation model was used to examine the association between child malnutrition, morbidity, and potential risk factors. The structural equation model makes it possible to analyze malnutrition as a mediator of the association between selected risk factors and morbidity. RESULTS: Out of the 8,560 sampled children, 12.80% were wasted, 34.75% were stunted, 23.91% were underweight, 13.9% had fever, 11.2% had diarrhea, and 59.7% had anemia. Birth interval, wealth index household, place of delivery, size of child at birth, number of children, and socioeconomic condition had a significant direct effect on childhood malnutrition and morbidity. The time to get water, toilet facility, and child is a twin variables had direct effects on childhood malnutrition and had no significant direct effects on childhood morbidity. Time to get water, birth interval, toilet facility, wealth index of household, child is a twin, place of delivery, size of child, and number of children exhibited an indirect effect on morbidity through malnutrition. CONCLUSIONS: The study revealed that there was a high prevalence of malnutrition and morbidity among under-five children in Ethiopia. Time to get water in min, place of delivery, size of child, and number of children showed a significant indirect and total effect on morbidity through malnutrition and socioeconomic conditions showed a significant total effect on morbidity via malnutrition. Implementing and extending programs such as community-based nutrition interventions for early childhood is critical, as early malnutrition showed long-term effects on growth and immunity, particularly in the regions of Affar, Dire Dawa, Gambela, Harari, Amhara, and Somali.
BACKGROUND: We have previously shown that an integrated intervention package delivered during preconception, pregnancy and early childhood substantially reduces low birth weight and stunting at 24 months of age compared with routine care. Now we conduct a benefit-cost analysis to estimate the return on investment of this integrated approach in India. This increases the policy relevance of trial results, given the low investment in healthcare in India. METHODS: We used data from 13 500 participants in the Women and Infant Integrated Interventions for Growth Study (WINGS). Integrated delivery of healthcare, nutrition, water, sanitation and hygiene (WaSH), and psychosocial care interventions during preconception period, or pregnancy and early childhood, or both (full package), was compared with routine care. We converted reduction in mortality, morbidity and increase in productivity to monetary values and calculated the benefit-cost ratio. We used primary and secondary trial health outcomes from the WINGS trial to calculate benefits, and we collected costs prospectively during the trial. Uncertainty was explored in a one-way sensitivity analysis. We applied a discount rate of 3% per annum to both costs and benefits, considering the purchasing power parity (PPP) of US dollars in 2021. RESULTS: Every dollar invested returned 6.1$ PPP for interventions during preconception, 9.9$ PPP for pregnancy and early childhood interventions and 3.7$ PPP for the full package of interventions compared with routine care in the base case scenario. The return to investment was positive (>4.6$ PPP per 1$ PPP invested) for pregnancy and early childhood interventions in all scenarios of the sensitivity analysis. The net monetary benefits of the interventions ranged between 7364 and 25 917$ PPP. CONCLUSION: Our results suggest that integrated and concurrent delivery of healthcare, nutrition, WaSH and psychosocial care interventions during pregnancy and early childhood yield positive economic returns.
Tarun Shankar Choudhary, Sarmila Mazumder, Sunita Taneja et al.
Although the adverse health effects of consuming ultra-processed foods (UPFs) are well-documented, limited evidence exists on their impact during lactation. This study examined the association between maternal UPF consumption, feeding practices, and malnutrition in breastfed infants. A cross-sectional analysis was conducted with 111 mother-child pairs up to 150 days postpartum. Infant feeding practices were assessed using WHO indicators, and malnutrition was evaluated by length-for-age and BMI-for-age. Maternal dietary intake was estimated using two 24 h recalls, and UPF consumption was classified by the Nova classification. Dyads were grouped based on the highest UPF consumption quartile, and adjusted binary logistic regression was applied. UPFs accounted for 26% of the maternal diet on average. While 71.2% of infants were exclusively breastfed, one-third were overweight, and 11.7% were stunted. High maternal UPF consumption (>32% of energy) was associated with increased odds of malnutrition by BMI-for-age (wasting or overweight) (OR 3.38; 95% CI: 1.29-8.83) and stunting (OR 3.89; 95% CI: 1.04-14.58). Feeding practices showed no association. These findings highlight that maternal UPF consumption is associated with malnutrition odds in breastfed infants in the population assessed, emphasizing the need for dietary guidance during lactation to improve infant health outcomes.
Juliana Morais de Sousa, Danielle Soares Bezerra, Lara Virginia Pessoa de Lima et al.
BACKGROUND: Child undernutrition remains a pressing global concern, with Pakistan among the most affected countries in South Asia. Despite recognition of its multifaceted causes, the relative importance of key risk factors remains insufficiently understood. This study investigates the relative significance of associated risk factors among preschool-age children in Punjab, Pakistan's most populous province. METHODS: Using data from the 2018 Punjab Multiple Indicator Cluster Survey (MICS), this study analyses 38,495 children aged 0-59 months. Binary logistic regression models were employed to estimate the effects of 25 risk factors on stunting, underweight, and wasting, selected through a review of literature. The analysis was conducted for the full sample (children aged 0-59 months) and two age sub-groups: infants aged 0-5 months and children aged 6-35 months, selected to reflect key developmental stages. Environmentally modifiable risk factors were ranked by effect size to identify high-priority intervention areas. RESULTS: Five key predictors were consistently associated with stunting and underweight: household poverty, low maternal education, short birth intervals, underage motherhood, and low birth weight (LBW). For wasting, significant determinants included LBW, low maternal education, incomplete vaccination, and recent fever. Among children aged 6-35 months, inadequate dietary diversity was a major contributor, while in infants (0-5 months), lack of exclusive breastfeeding and unassisted delivery were most influential. Box plot analysis revealed that older children face higher and more variable undernutrition risks, whereas infants benefit from protective factors such as breastfeeding and neonatal care-highlighting the importance of age-specific interventions. CONCLUSIONS: The findings underscore the urgent need for integrated, age-adapted strategies that address both structural and behavioural determinants of child undernutrition. Effective interventions must combine poverty reduction, maternal education, and healthcare access with targeted nutrition programs for infants and toddlers. These insights offer a critical evidence base to inform policies aimed at improving child nutrition and health in Punjab and similar contexts.
Malnutrition remains one of the most pressing global health challenges, particularly in developing countries like Bangladesh, where it continues to significantly impact child health and contribute to chronic illness and high child mortality. Despite the potential of machine learning to improve malnutrition predictions, research in this area remains limited in the country. This study utilizes Bangladesh Demographic and Health Survey (BDHS) 2022 data to identify and quantify key determinants of under-five malnutrition (underweight, wasting, stunting) and evaluates various machine learning models for predicting malnutrition. By addressing a critical gap, this research provides deeper insights into the root causes of malnutrition in Bangladesh. Descriptive statistics were conducted to summarize the key characteristics of the dataset. Boruta algorithm was employed to identify important features related to malnutrition which were then used to evaluate several machine learning models, including K-Nearest Neighbors (KNN), Neural Networks (NN), Classification and Regression Trees (CART), XGBoost (XGBM), Support Vector Machines (SVM), and Random Forest (RF), in addition to the traditional logistic regression (LR) model. The best-performing model was selected to identify the most important factors contributing to malnutrition. The significance of these variables was further assessed using Feature Importance plot (Based on Gini Importance) and Shapley Additive Explanation (SHAP) values. Model performance was evaluated through various metrics, including accuracy, 95% Confidence Interval (CI), Cohen's kappa, sensitivity, specificity, F1 score and precision. The study examined a cohort of 7,910 children, reporting prevalence rates of 19% for stunting, 8% for wasting, and 17% for underweight. The Boruta algorithm identified 18 confirmed features for stunting, 22 for wasting, and 19 for underweight. For stunting, the Random Forest (RF) model outperformed other methods with an accuracy of 64.19%, 95% CI of (0.623, 0.666), Cohen's kappa of 0.158, sensitivity of 56.25%, specificity of 66.00%, F1 score of 0.750 and precision of 0.60. In wasting prediction, RF achieved the highest accuracy at 76.68%, 95% CI of (0.743, 0.787), Cohen's kappa of 0.049, sensitivity of 27.22%, specificity of 80.98%, F1 score of 0.865 and precision of 0.810. Similarly, for underweight, RF demonstrated superior performance with an accuracy of 68.18%, 95% CI of (0.662, 0.703), Cohen's kappa of 0.130, sensitivity of 43.02%, specificity of 73.48%, F1 score of 0.792 and precision of 0.735. Across all malnutrition types, the RF model consistently outperformed traditional logistic regression (LR) and other ML techniques in terms of accuracy, sensitivity, specificity, and other performance metrics. For stunting, key predictors identified in both the Shapley and Gini importance plots included mother's education, father's occupation, place of delivery, wealth index, birth order, and toilet facility; for wasting, significant predictors were antenatal care, unmet family planning, mother's BMI, birth interval, father's occupation, and television ownership; and for underweight, important factors included father's occupation, mother's education, child's age, birth order, wealth index, and place of delivery. This study highlights the effectiveness of Random Forest (RF) in predicting malnutrition outcomes-stunting, wasting, and underweight-using key features identified by the Boruta algorithm. While RF demonstrates moderate performance in predicting stunting and underweight, it shows strong predictive ability for wasting. This underscores RF's potential in guiding targeted interventions for wasting, though further improvements are needed for stunting and underweight predictions. Moreover, the study identifies key contributors for each malnutrition outcome. By pinpointing these determinants, the study provides actionable insights for designing targeted interventions to combat malnutrition more effectively. These findings align with the global development agenda, particularly Sustainable Development Goal (SDG) 2: Zero Hunger and SDG 3: Good Health and Well-being, reinforcing efforts to reduce malnutrition and improve child health outcomes in Bangladesh.
Tanzila Tamanna, Shohel Mahmud, Nahid Salma et al.
In low- and middle-income countries, undernutrition often coexists with rising obesity, creating a double burden of malnutrition (DBM). Our study employs a mathematical approach to examine how maternal socioeconomic status (SES) impacts child growth in Iranian children from infancy to age two, offering new insights into strategies for tackling both undernutrition and obesity. We used data from the PERSIAN Birth Cohort. SES was divided into quintiles using multiple correspondence analysis (MCA), and child growth was assessed with eight indicators. Missing data were handled via multiple imputation (MI). SES impacts on growth were analyzed using Generalized Estimating Equations (GEE), and BMI-Z was predicted from WAZ and HAZ through linear regression by SES and age. We explored obesity risk by comparing HAZ-to-WAZ ratios from GEE models with WAZ-to-HAZ beta ratios from regressions. Sensitivity analyses compared MI with complete-case analyses (CCA). 7169 neonates were assessed at 2, 4, 6, 12, and 24 months. SES increasingly improved WAZ with age, preventing underweight in higher SES groups. SES consistently improved HAZ, significantly enhancing stunting prevention in higher SES groups. The impact on BMI-Z increased with age, peaking in Very High SES, which shows a significant rise in obesity risk by 24 months. Comparing ratios from GEE and regression models, we found that GEE ratios were lower where BMI-Z increased. Sensitivity analysis confirmed MI and CCA consistency. Addressing DBM requires understanding how imbalanced increases in HAZ and WAZ heighten obesity risk. Future research should focus on targeted interventions to manage DBM effectively.
Golnaz Rezaeizadeh, Mohammad Ali Mansournia, Maryam Sharafkhah et al.
Undernutrition affects thousands of children under five years old worldwide, and various factors are related to its onset, among which we highlight enteric infections and gastrointestinal barrier dysfunction. The cycle of intestinal infections and undernutrition has long-term consequences, such as cognitive deficits, poor growth, and metabolic diseases in adulthood. This review explores factors linked to childhood undernutrition, focusing on intestinal infections and markers of intestinal permeability that affect child development. This narrative review was conducted using Medline/PubMed, Web of Science, and Scopus, from July 2024 to March 2025. Studies involving children under five years old and addressing undernutrition, intestinal infections, or intestinal permeability markers were included. Exclusion criteria comprised studies without therapeutic focus, and books, case reports, or academic theses. No language restrictions were applied, and registration on global platforms was not required. Overall, the studies reported a close relationship between enteric pathogens, diarrheal and non-diarrheal stools, and undernutrition. Among the pathogens most frequently found in the feces of malnourished children were Shigella, enterotoxigenic Escherichia coli, enteroaggregative E. coli (EAEC), and Cryptosporidium. The studies also showed the relationship between gastrointestinal barrier function and undernutrition, with the deterioration of nutrient absorption and, consequently, repercussions on development, linear growth, and weight in children. Although the studies analyzed had different designs and heterogeneity in the age range of the studied children, it was possible to observe the relationship between the infection/undernutrition cycle. Future studies should optimize personalized nutrient-based therapies, assess long-term effects on gut health and growth, and explore the gut microbiome's role in enteric infection susceptibility and undernutrition.
Maria Clara da Cruz Carvalho, Samilly Albuquerque Ribeiro, Lélia Sales de Sousa et al.
Malnutrition remains a significant challenge in low- and middle-income countries, contributing to stunting, wasting, and micronutrient deficiencies. This study investigated the effect of locally formulated complementary foods on anthropometric parameters and micronutrient status in children aged 6 to 23 months. A randomized controlled trial involving 40 infants explored the effects of experimental diets, including Maize-Soybean, Maize-Soybean-Termite flour, Maize-Soybean-Fishbone powder, and Maize-Soybean-Termite-Fishbone-Pawpaw-Pumpkin (MaSoTFPP), and a control diet on children over a period of 90 days. Anthropometric measurements of weight, height, and mid-upper arm circumference (MUAC) as well as biochemical analyses of blood samples were carried out. Dietary intake was monitored over the 90-day feeding trial period. The data obtained were analyzed using Statistical Product for Service Solution, version 23. Significance was set at P<0.05. The findings revealed significant improvements in the anthropometric parameters, particularly in the MaSoTFPP group, which exhibited the largest percentage increase in height and MUAC. The children fed with the experimental diets demonstrated enhanced serum hemoglobin, iron, and zinc levels, which increased most significantly in the MaSoTFPP group (by 308%, 264%, and 58%, respectively). The study underscores the potential of incorporating locally available food crops in community-based management of acute malnutrition and supports the World Health Organization's recognition of fortified staple foods in improving child growth and development.
Ngozi Mercy Nnam, Peace Nwanneka Ani, Ngozi Elizabeth Chukwuemeka
BACKGROUND: Undernutrition is a global health concern, particularly in low-income countries, and can manifest as wasting, stunting and/or underweight. In Yemen, the recent armed conflict is likely to have significantly increased the incidence of undernutrition in young children, particularly those living in communities that were already experiencing socioeconomic and health disparities. AIM: To explore the risk factors associated with wasting, stunting and underweight in children aged between six months and five years living in one district of Yemen. METHOD: A cross-sectional study was conducted using a two-part questionnaire to collect anthropometric measurements and sociodemographic characteristics of children enrolled on an outpatient programme for undernourished children at three healthcare facilities in the Jiblah district, and of their families and households. RESULTS: Of the 120 children included in the study, 58 (48%) had wasting, 27 (23%) had stunting and 35 (29%) were underweight. Risk factors for undernutrition included being under the age of three years, being a girl, having had a low birthweight, having siblings under the age of five years, having a mother who is a housewife, living in a household relying on unimproved sources of drinking water, living in a rural area, and living in a low-income household. CONCLUSION: The nutritional status of children in Yemen is under continuing threat. The identification of risk factors for undernutrition may increase awareness of the issue and influence the policy decisions of the international community.
Noman Al-Hatemi, Ebraheem Al-Nawd, Laith Alosoufe et al.
