Prevalence and Predictors of Food Insecurity among Older People in Canada
Bibliographic record
Abstract
Background: Food insecurity research has been mainly examined among young people. The root causes of food insecurity are closely linked to poverty, and social policies and income supplements, including public and private pensions, have been shown to sharply curb food insecurity into later life. However, social, economic, and political trends that are closely connected to social and health inequalities threaten to undermine the conditions that have limited food insecurity among older people until now. Exploring the prevalence and predictors of food insecurity among older people across Canada has important implications for domestic policies concerning health, healthcare, and social welfare. Methods: Data come from the Canadian Community Health Survey 2012 Annual Component (n = 14,890). Descriptive statistics and a generalized linear model approach were used to determine prevalence and estimate the associations between food insecurity—as measured by the Household Food Security Survey Module—and social, demographic, geographic, and economic factors. Results: Approximately 2.4% of older Canadians are estimated to be moderately or severely food insecure. Income was by far the strongest predictor of food insecurity (total household income <$20,000 compared to >$60,000, OR: 46.146, 95% CI: 12.523–170.041, p < 0.001). Younger older people, and those with a non-white racial background also had significantly greater odds of food insecurity (ages 75+ compared to 65–74, OR: 0.322, 95% CI: 0.212–0.419, p < 0.001; and OR: 2.429, 95% CI: 1.438–4.102, p < 0.001, respectively). Sex, home ownership, marital status, and living arrangement were all found to confound the relationship between household income and food insecurity. Prevalence of food insecurity varied between provinces and territories, and odds of food insecurity were approximately five times greater for older people living in northern Canada as compared to central Canada (OR: 5.189, 95% CI: 2.329–11.562, p < 0.001). Conclusion: Disaggregating overall prevalence of food insecurity among older people demonstrates how disparities exist among sub-groups of older people. The seemingly negligible existence of food insecurity among older people has obscured the importance, practicality, and timeliness of including this age group in research on food insecurity. The current research underscores the critical importance of an income floor in preventing food insecurity among older people, and contributes a Canadian profile of the prevalence and predictors of food insecurity among older people to the broader international literature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".