Fry Jane M, Temple Jeromey B, Williams Ruth
School of Life and Medical Sciences and School of Health and Social Work, University of Hertfordshire, Hertfordshire, UK.
Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia.
Nutr Diet. 2025 Feb;82(1):64-75. doi: 10.1111/1747-0080.12907. Epub 2024 Oct 21.
This study aimed to identify key health condition correlates of food insecurity in Australia using nationally representative data.
This cross-sectional study used data from a large, nationally representative Australian survey that included questions on the dynamics of families and households, income, wealth, welfare, labour market activity (including unemployment and joblessness), life satisfaction and wellbeing. Binary logistic regression models of eight items of food insecurity measured the association between 17 health conditions and food insecurity while controlling for various demographic and socioeconomic variables. A zero-inflated negative binomial model identified correlates of the number of food insecurity problems.
Prevalence of food insecurity ranged from 3% to 9% depending on the measure analysed. Individuals experiencing blackouts, fits or loss of consciousness were 2-6 times more likely to report food insecurity than other individuals. When including control variables and incorporating other health conditions, several conditions significantly increased probability of any food insecurity: sight problems; blackouts, fits or loss of consciousness; difficulty gripping things; nervous conditions; mental illness; and chronic or recurring pain.
Detailed information on how health conditions are associated with different types of food insecurity was generated using population-representative data, 17 sets of health conditions, and eight measures of food insecurity. Understanding connections between food insecurity and health conditions allows public health professionals to create effective, targeted and holistic interventions.
本研究旨在利用具有全国代表性的数据，确定澳大利亚粮食不安全状况的关键健康状况相关因素。
这项横断面研究使用了来自一项大型全国代表性澳大利亚调查的数据，该调查包括有关家庭和住户动态、收入、财富、福利、劳动力市场活动（包括失业和无业）、生活满意度和幸福感的问题。粮食不安全八项指标的二元逻辑回归模型在控制各种人口统计学和社会经济变量的同时，测量了17种健康状况与粮食不安全之间的关联。零膨胀负二项式模型确定了粮食不安全问题数量的相关因素。
根据所分析的衡量标准，粮食不安全的患病率在3%至9%之间。经历过停电、昏厥或意识丧失的个体报告粮食不安全的可能性是其他个体的2至6倍。在纳入控制变量并纳入其他健康状况后，有几种状况显著增加了出现任何粮食不安全状况的概率：视力问题；停电、昏厥或意识丧失；抓握东西困难；神经状况；精神疾病；以及慢性或复发性疼痛。
利用具有人口代表性的数据、17组健康状况和八项粮食不安全衡量标准，生成了有关健康状况与不同类型粮食不安全之间关联的详细信息。了解粮食不安全与健康状况之间的联系，有助于公共卫生专业人员制定有效、有针对性和全面的干预措施。
