PDF(Pubmed)
Abstract：
BACKGROUND: Food insecurity significantly impacts children\'s health, affecting their development across cognitive, physical, and socio-emotional dimensions. This study explores the impact of food insecurity among children aged 6 months to 5 years, focusing on nutrient intake and its relationship with various forms of malnutrition.
METHODS: Utilizing machine learning algorithms, this study analyzed data from 819 children in the West Bank to investigate sociodemographic and health factors associated with food insecurity and its effects on nutritional status. The average age of the children was 33 months, with 52% boys and 48% girls.
RESULTS: The analysis revealed that 18.1% of children faced food insecurity, with household education, family income, locality, district, and age emerging as significant determinants. Children from food-insecure environments exhibited lower average weight, height, and mid-upper arm circumference compared to their food-secure counterparts, indicating a direct correlation between food insecurity and reduced nutritional and growth metrics. Moreover, the machine learning models observed vitamin B1 as a key indicator of all forms of malnutrition, alongside vitamin K1, vitamin A, and zinc. Specific nutrients like choline in the \"underweight\" category and carbohydrates in the \"wasting\" category were identified as unique nutritional priorities.
CONCLUSIONS: This study provides insights into the differential risks for growth issues among children, offering valuable information for targeted interventions and policymaking.
METHODS: Utilizing machine learning algorithms, this study analyzed data from 819 children in the West Bank to investigate sociodemographic and health factors associated with food insecurity and its effects on nutritional status. The average age of the children was 33 months, with 52% boys and 48% girls.
RESULTS: The analysis revealed that 18.1% of children faced food insecurity, with household education, family income, locality, district, and age emerging as significant determinants. Children from food-insecure environments exhibited lower average weight, height, and mid-upper arm circumference compared to their food-secure counterparts, indicating a direct correlation between food insecurity and reduced nutritional and growth metrics. Moreover, the machine learning models observed vitamin B1 as a key indicator of all forms of malnutrition, alongside vitamin K1, vitamin A, and zinc. Specific nutrients like choline in the \"underweight\" category and carbohydrates in the \"wasting\" category were identified as unique nutritional priorities.
CONCLUSIONS: This study provides insights into the differential risks for growth issues among children, offering valuable information for targeted interventions and policymaking.
摘要：
背景：粮食不安全严重影响儿童的健康，影响他们的认知发展，物理，和社会情感维度。这项研究探讨了6个月至5岁儿童粮食不安全的影响，关注营养摄入及其与各种形式营养不良的关系。
方法：利用机器学习算法，本研究分析了约旦河西岸819名儿童的数据,以调查与粮食不安全相关的社会人口统计学和健康因素及其对营养状况的影响.孩子的平均年龄是33个月,52％的男孩和48％的女孩。
结果：分析显示，18.1％的儿童面临粮食不安全，家庭教育，家庭收入,局部性,区,和年龄成为重要的决定因素。来自食物不安全环境的儿童表现出更低的平均体重，高度,与粮食安全的同行相比，上臂中周围，表明粮食不安全与营养和生长指标下降之间存在直接关联。此外，机器学习模型观察到维生素B1是所有形式营养不良的关键指标，与维生素K1，维生素A，和锌。特定营养素，如“体重不足”类别中的胆碱和“浪费”类别中的碳水化合物被确定为独特的营养优先事项。
结论：这项研究为儿童成长问题的不同风险提供了见解，为有针对性的干预和决策提供有价值的信息。
方法：利用机器学习算法，本研究分析了约旦河西岸819名儿童的数据,以调查与粮食不安全相关的社会人口统计学和健康因素及其对营养状况的影响.孩子的平均年龄是33个月,52％的男孩和48％的女孩。
结果：分析显示，18.1％的儿童面临粮食不安全，家庭教育，家庭收入,局部性,区,和年龄成为重要的决定因素。来自食物不安全环境的儿童表现出更低的平均体重，高度,与粮食安全的同行相比，上臂中周围，表明粮食不安全与营养和生长指标下降之间存在直接关联。此外，机器学习模型观察到维生素B1是所有形式营养不良的关键指标，与维生素K1，维生素A，和锌。特定营养素，如“体重不足”类别中的胆碱和“浪费”类别中的碳水化合物被确定为独特的营养优先事项。
结论：这项研究为儿童成长问题的不同风险提供了见解，为有针对性的干预和决策提供有价值的信息。
