Factors Associated With Prescription Opioid Use Among Community-Dwelling Older Adults
Bibliographic record
Abstract
Abstract Opioid use is a growing concern in North America, particularly among older adults. Despite the opioid crisis and the aging population, few studies have evaluated the factors associated with opioid use among older adults. Our sample includes 1657 people aged ≥65 years recruited in primary care clinics from 2011 to 2013 in the Montérégie region of Québec and participating in the “Étude sur la Santé des Aînés” ESA-Services study, a longitudinal study on aging and health service use. The presence of chronic diseases was identified through self-reported health survey data linked to health administrative data. Opioid prescriptions were identified using the provincial pharmaceutical drug registry for those covered under the public drug insurance plan. Logistic regression analyses were conducted to examine the factors associated with opioid use over a 4-year period. 31.9% of participants used opioids. Factors associated with opioid use included: female sex (OR=1.24, 95%CI: 1.01-1.53), annual household income of <$25,000 (OR=1.25, 95%CI: 1.01-1.55), level of social support (OR=0.85, 95%CI: 0.73-0.99), and presence of pain/discomfort (OR=1.66, 95%CI: 1.34-2.04). Further, participants with ≥3 chronic physical conditions also reporting anxiety and/or depression were 3.63 (95%CI: 1.83-7.18) times more likely to use an opioid than those with 0-2 chronic physical conditions and no common mental disorder. Moreover, those with moderate, high, and very high psychological distress were more likely to use an opioid than those with a low psychological distress. Our findings suggest that, among other factors, physical and psychiatric multimorbidity is strongly associated with prescription opioid use in older adults.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".