Prevalence and Severity of Food Insecurity Before and During the Coronavirus Disease 2019 Pandemic Among Adults and Youth in Australia, Canada, Chile, Mexico, the United Kingdom, and the United States.

BACKGROUND: Disruptions from the coronavirus disease 2019 (COVID-19) pandemic potentially exacerbated food insecurity among adults and youth. OBJECTIVES: The objective was to examine changes in the prevalence and severity of food insecurity among adults and youth from before (2019) to during (2020) the pandemic in multiple countries. METHODS: Repeated cross-sectional data were collected among adults aged 18-100 y (n = 63,278) in 5 countries in November to December in 2018-2020 and among youth aged 10-17 y (n = 23,107) in 6 countries in November to December in 2019 and 2020. Food insecurity in the past year was captured using the Household Food Security Survey Module and the Child Food Insecurity Experiences Scale. Changes in the prevalence and severity of food insecurity were examined using logistic and generalized logit regression models, respectively. Models included age, gender, racial-ethnic identity, and other sociodemographic characteristics associated with food insecurity to adjust for possible sample differences across waves. Models were weighted to reflect each country's population. RESULTS: Adults [adjusted OR (AOR): 1.15; 95% CI: 1.02, 1.31] and youth (AOR: 1.43; 95% CI: 1.19, 1.71) in Mexico were more likely to live in food-insecure households in 2020 compared to 2019. Adults in Australia (AOR: 0.81; 95% CI: 0.72, 0.92) and Canada (AOR: 0.87; 95% CI: 0.77, 0.99) were less likely to live in food-insecure households in 2020. Trends in severity aligned with changes in prevalence, with some exceptions. Youth in Australia (AOR: 2.24; 95% CI: 1.65, 3.02) and the United States (AOR: 1.39; 95% CI: 1.04, 1.86) were more likely to have many compared with no experiences of food insecurity in 2020 compared to 2019. There was no evidence of change among adults and youth in the remaining countries. CONCLUSIONS: Except for Mexico, few changes in food insecurity among adults and youth were observed from before to during the COVID-19 pandemic. Action is needed to support households at risk of food insecurity.

Food Security Status in Relation to Co-operative Enrolment Among University of Waterloo Undergraduate Students: A Cross-Sectional Analysis.

Purpose: Co-operative (co-op) education facilitates development of workplace competencies but may have unintended consequences for financial stability and food security. This study examined the association between co-op program enrolment and food security status among a sample of undergraduate students. Financial insufficiency and strategies to cope with it were also characterized.Methods: Cross-sectional data were collected from 167 co-op and 89 non-co-op students at the University of Waterloo from January to March 2019. Logistic regression assessed associations between co-op program enrolment and food insecurity.Results: Twenty-four percent of co-op and 39.3% of non-co-op students lived in moderately or severely food insecure households. Adjusting for confounders, the odds of living in moderately or severely food insecure households were lower among co-op students (adjusted odds ratio: 0.51, 95% CI: 0.27-0.97), though no association was observed when marginal food insecurity was included within the food insecure category. One-quarter (26.3%) of co-op students and 38.2% of non-co-op students reported financial insufficiency, which they tried to cope with by asking parents or friends for assistance or initiating paid work.Conclusions: Co-op program enrolment was weakly associated with lower odds of living in moderately or severely food insecure households, and food insecurity prevalence was high overall. Efforts are needed to alleviate food insecurity among postsecondary students.

Food insecurity, food skills, health literacy and food preparation activities among young Canadian adults: a cross-sectional analysis.

OBJECTIVE: To assess associations between household food security status and indicators of food skills, health literacy and home meal preparation, among young Canadian adults. DESIGN: Cross-sectional data were analysed using logistic regression and general linear models to assess associations between food security status and food skills, health literacy and the proportion of meals prepared at home, by gender. SETTING: Participants recruited from five Canadian cities (Vancouver (BC), Edmonton (AB), Toronto (ON), Montreal (QB) and Halifax (NS)) completed an online survey. PARTICIPANTS: 1389 men and 1340 women aged 16-30 years. RESULTS: Self-reported food skills were not associated with food security status (P > 0·05) among men or women. Compared to those with high health literacy (based on interpretation of a nutrition label), higher odds of food insecurity were observed among men (adjusted OR (AOR): 2·58, 95 % CI 1·74, 3·82 and 1·56, 95 % CI 1·07, 2·28) and women (AOR: 2·34, 95 % CI 1·48, 3·70 and 1·92, 95 % CI 1·34, 2·74) with lower health literacy. Women in food-insecure households reported preparing a lower proportion of breakfasts (ß = -0·051, 95 % CI -0·085, -0·017), lunches (ß = -0·062, 95 % CI -0·098, -0·026) and total meals at home (ß = -0·041, 95 % CI -0·065, -0·016). Men and women identifying as Black or Indigenous, reporting financial difficulty and with lower levels of education had heightened odds of experiencing food insecurity. CONCLUSIONS: Findings are consistent with other studies underscoring the financial precarity, rather than lack of food skills, associated with food insecurity. This precarity may reduce opportunities to apply health literacy and undertake meal preparation.

Advances in methods for characterizing dietary patterns: A scoping review.

There is a growing focus on better understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterize dietary patterns, such as machine learning algorithms and latent class analysis methods, may offer opportunities to measure and characterize dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterize dietary patterns. This scoping review synthesized literature from 2005-2022 applying methods not traditionally used to characterize dietary patterns, referred to as novel methods. MEDLINE, CINAHL, and Scopus were searched using keywords including machine learning, latent class analysis, and least absolute shrinkage and selection operator (LASSO). Of 5274 records identified, 24 met the inclusion criteria. Twelve of 24 articles were published since 2020. Studies were conducted across 17 countries. Nine studies used approaches that have applications in machine learning to identify dietary patterns. Fourteen studies assessed associations between dietary patterns that were characterized using novel methods and health outcomes, including cancer, cardiovascular disease, and asthma. There was wide variation in the methods applied to characterize dietary patterns and in how these methods were described. The extension of reporting guidelines and quality appraisal tools relevant to nutrition research to consider specific features of novel methods may facilitate complete and consistent reporting and enable evidence synthesis to inform policies and programs aimed at supporting healthy dietary patterns.