Examining the relationship between food insecurity and causes of injury in Canadian adults and adolescents
Bibliographic record
Abstract
BACKGROUND: Food insecurity, as an indicator of socioeconomic disadvantages and a determinant of health, may be associated with injury by increasing risk exposure and hampering risk mitigation. We examined the association between food insecurity and common causes of injury in the general population. METHODS: Linking the Canadian Community Health Survey 2005-2017 to National Ambulatory Care Reporting System 2003-2017, this retrospective cohort study estimated incidence of injury-related emergency department (ED) visits by food insecurity status among 212,300 individuals 12 years and above in the Canadian provinces of Ontario and Alberta, adjusting for prior ED visits, lifestyle, and sociodemographic characteristics including income. RESULTS: Compared to those in food-secure households, individuals from moderately and severely food-insecure households had 1.16 (95% confidence interval [CI] 1.07-1.25) and 1.35 (95% CI 1.24-1.48) times higher incidence rate of ED visits due to injury, respectively, after confounders adjustment. The association was observed across sex and age groups. Severe food insecurity was associated with intentional injuries (adjusted rate ratio [aRR] 1.81; 95% CI 1.29-2.53) including self-harm (aRR 1.87; 95% CI 1.03-3.40) and violence (aRR 1.79; 95% CI 1.19-2.67) as well as non-intentional injuries (aRR 1.34; 95% CI 1.22-1.46) including fall (aRR 1.43; 95% CI 1.24-1.65), medical complication (aRR 1.39; 95% CI 1.06-1.82), being struck by objects (aRR 1.43; 95% CI 1.07-1.91), overexertion (aRR 1.31; 95% CI 1.04-1.66), animal bite or sting (aRR 1.60; 95% CI 1.08-2.36), skin piercing (aRR 1.80; 95% CI 1.21-2.66), and poisoning (aRR 1.65; 95% CI 1.05-2.59). Moderate food insecurity was associated with more injuries from violence (aRR 1.56; 95% CI 1.09-2.21), falls (aRR 1.22; 95% CI 1.08-1.37), being struck (aRR 1.20; 95% CI 1.01-1.43), and overexertion (aRR 1.25; 95% CI 1.04-1.50). Moderate and severe food insecurity were associated with falls on stairs and being struck in non-sports settings but not with falls on same level or being struck during sports. Food insecurity was not related to transport injuries. CONCLUSIONS: Health inequity by food insecurity status extends beyond diseases into differential risk of injury, warranting policy intervention. Researchers and policymakers need to address food insecurity as a social determinant of injury to improve health equity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".