Using Cross-Sectional Data to Identify and Quantify the Relative Importance of Factors Associated with and Leading to Food Insecurity

International Journal of Environmental Research and Public Health

Martin Caraher¹  Alison Daly²  ColinW. Binns²  ChristinaM. Pollard²  DeborahA. Kerr²  Michael Phillips³ 

[1] Centre for Food Policy, City University of London, Northampton Square, London EC1V 0HB, UK;Faculty of Health Science, School of Public Health, Curtin University, GPO Box U1987, Perth 6845, Western Australia, Australia;Harry Perkins Institute for Medical Research, University of Western Australia, Perth 6009, Western Australia, Australia;

关键词: food insecurity;    monitoring;    surveillance;    determinants;    path diagram;   

DOI  :  10.3390/ijerph15122620

来源: DOAJ

【 摘 要 】

Australian governments routinely monitor population household food insecurity (FI) using a single measure—‘running out of food at least once in the previous year’. To better inform public health planning, a synthesis of the determinants and how they influence and modify each other in relation to FI was conducted. The analysis used data from the Health & Wellbeing Surveillance System cross-sectional dataset. Weighted means and multivariable weighted logistic regression described and modelled factors involved in FI. The analysis showed the direction and strength of the factors and a path diagram was constructed to illustrate these. The results showed that perceived income, independent of actual income was a strong mediator on the path to FI as were obesity, smoking and other indicators of health status. Eating out three or more times a week and eating no vegetables more strongly followed FI than preceded it. The analysis identified a range of factors and demonstrated the complex and interactive nature of them. Further analysis using propensity score weighted methods to control for covariates identified hypothetical causal links for investigation. These results can be used as a proof of concept to assist public health planning.

【 授权许可】

Unknown