Food insecurity, chronic pain, and use of prescription opioids
Bibliographic record
Abstract
Chronic pain has been on the rise in recent decades in Canada. Accordingly, the use of prescription opioids (PO) in Canada increased drastically between 2005 and 2014, only starting to decrease in 2015. Both pain and PO use have serious public health repercussions, disproporionately affecting select socially disadvantaged populations. Food insecurity is a strong risk factor for mental disorders and suicidal outcomes, yet its relationship to chronic pain and PO use is largely unknown. Using two recent cycles from the population representative Canadian Community Health Survey (CCHS), we examined the association of household food insecurity status with chronic pain and PO use among Canadians 12 years and older, adjusting for health and sociodemographic characteristics. Compared to food-secure individuals, marginally, moderately, and severely food-insecure individuals had 1.31 (95% confidence interval [CI] 1.15-1.48), 1.89 (95% CI 1.71-2.08), and 3.29 (95% CI 2.90-3.74) times higher odds of experiencing chronic pain and 1.55 (95% CI 1.30-1.85), 1.77 (95% CI 1.54-2.04), and 2.65 (95% CI 2.27-3.09) times higher odds of using PO in the past year, respectively. The graded association with food insecurity severity was also found in severe pain experience and pain-induced activity limitations among chronic pain patients and, less consistently, in intensive, excess, and alternative use of PO and its acquisition through means other than medical prescription among past-year PO users. Food insecurity was a much more powerful predictor of chronic pain and PO use than other well-established social determinants of health like income and education. Policies reducing food insecurity may lower incidence of chronic pain and help contain the opioid crisis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".