Association Between Prescription Opioid Use and Mortality in Community-Dwelling Older Adults
Bibliographic record
Abstract
Abstract Prescription opioid use is concerning among older adults. Yet, few studies have examined the impact of opioid use on mortality by considering multimorbidity. Our sample includes 1586 older adults aged ≥65 recruited in primary care from 2011-2013 in a large health administrative region in Quebec and participating in the ESA-Services study, a longitudinal study on aging and health service use. An opioid prescription delivered in the 3 years prior to the baseline interview was identified using the provincial pharmaceutical drug registry. Mortality was ascertained from the vital statistics registry until 2015. The presence of chronic diseases was based on self-reported and physician diagnostic codes in health administrative databases. Physical multimorbidity was defined as ≥3 chronic physical conditions from either source. Physical/psychiatric multimorbidity was defined as ≥3 chronic physical conditions and ≥1 common mental disorder from either source. Logistic regression analyses were conducted to examine the association between opioid use and mortality, controlling for sociodemographic factors. Interactions were tested for opioid use and multimorbidity. Older adults with physical multimorbidity using opioids were 1.76 (95%CI: 1.02-3.03) times more likely to die than those not using opioids. Those with physical/psychiatric multimorbidity using opioids were 2.27 (95%CI: 1.26-4.09) times more likely to die than those not using opioids. Older age, male sex, and single marital status significantly increased the risk of mortality. Overall, opioid use increases the risk of death in older adults with multimorbidity. The presence of mental disorders further increases the risk of death in older adults with physical multimorbidity using opioids.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".