Burden of food insecurity in older adults from diverse global settings: policy recommendations
Bibliographic record
Abstract
Abstract Food insecurity prevalence is highest in low and middle-income countries, yet there is a dearth of research on the burden in older adults in these settings. Food insecurity has long-term consequences for the health of older persons. We examined cross-sectional food security data from 1,482 participants in the 2016 wave of the longitudinal International Mobility in Aging Study (IMIAS) conducted in Canada, Albania, Colombia, and Brazil. These are community samples between 68 and 79 years. Food security was assessed with the Latin American and Caribbean Household Food Security Scale and recoded to yes/no. Covariates of interest included sex, site, income, living arrangement, and education. Descriptive statistics, with tests of statistical significance, were used. Responses to scale items varied from 10% of participants reporting worry about running out of food and being unable to eat healthy foods to 2% reporting not eating for a whole day or having to beg for food. Food insecurity in the sample was 17%. Few Canadian respondents (<5%) were food insecure, compared to 30% in Tirana, 28% in Manizales, and 18% in Natal. Better educated and income sufficient respondents were significantly more food secure than lesser educated, lower income ones. Respondents living with spouses were significantly less likely to be food insecure than those living alone or in other arrangements (e.g. with children). The exception was Tirana; 25% of those in other arrangements were food insecure compared to 32% living with a spouse and 44% alone. Food insecurity did not differ significantly by age or sex. Site, income, living arrangement, and education were all associated with food insecurity status. Study findings contribute to a significant gap in literature about food security in older adults. Because food insecurity in older adults leads to negative health outcomes, results suggest specific interventions to improve health and reduce burden on healthcare systems is needed for elderly. Key messages Food insecurity has severe health consequences for elderly and location, income, education, and living arrangement contributes to health inequalities in this population across diverse settings. Little to no research has been done on food insecurity in elderly outside of North America and study findings contribute to significant gap in research in this population across global settings.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".