Significant factors associated with problematic use of opioid pain relief medications among the household population, Canada, 2018.
Bibliographic record
Abstract
BACKGROUND: Reliance on the use of opioids to manage pain has increased over time, as have opioid-related morbidity and deaths. In 2018, 12.7% of Canadians reported having used opioid pain relief medications (OPRMs) in the previous year. Among these people, 9.6% had engaged in problematic use that could cause harm to their health. Though socioeconomic characteristics associated with opioid-related harms have previously been reported, population-level evidence based on administrative health data lacks important behavioural and psychosocial information. This analysis extends previous research by using modelling to report factors related to the problematic use of OPRMs for the household population aged 15 and older in Canada. DATA AND METHODS: This analysis uses responses to the 2018 Canadian Community Health Survey to identify factors that are significantly associated, after adjustment using multivariate logistic regression models, with elevated odds of problematic use of OPRMs. RESULTS: The fully adjusted model confirmed that being male, being younger (ages 20 to 24), having fair or poor mental health, having unmet needs for help with mental or emotional health or substance problems, being a smoker, or being unattached and living with others were significantly related to problematic OPRM use. INTERPRETATION: Subjective perceptions significantly related to problematic OPRM use, independent of socioeconomic circumstances, were examined in this study. While previous research based on administrative health data has contributed much to knowledge about factors associated with opioid harms, modelled results revealed that self-reported experiential factors also warrant consideration as they are significantly associated with problematic use. Having fair or poor mental health, having unmet perceived needs for help, and being unattached in terms of household arrangement relationship were related to problematic use of OPRMs, even after adjustment for socioeconomic and other health covariates. This study suggests risk profiles that could be used to inform health care providers, and strategies to support safe pain management.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".