Abstract
Abstract
Aim: To identify key health condition correlates of food insecurity in Australia using nationally representative data.
Methods: This cross-sectional study used 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.
Results: Prevalence of food insecurity ranged from 3-9% depending on the measure. 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.
Conclusions: 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.
Aim: To identify key health condition correlates of food insecurity in Australia using nationally representative data.
Methods: This cross-sectional study used 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.
Results: Prevalence of food insecurity ranged from 3-9% depending on the measure. 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.
Conclusions: 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.
| Original language | English |
|---|---|
| Journal | Nutrition and Dietetics |
| Publication status | Accepted/In press - 9 Sept 2024 |