Risk Factors for Prescription Opioid Misuse in Older Adults: Findings from the National Survey on Drug Use and Health
Bibliographic record
Abstract
Abstract Prescription opioid misuse is recognized as a significant public health concern and social problem because it significantly increases the risk of both fatal and non-fatal overdose. Although a large body of research has examined risk factors for prescription opioid misuse, this research has disproportionately focused on younger adults, even though older adults are more likely to have an opioid prescription than any other age cohort. To address this gap in the literature, this study analyzed nationally representative data from the 2023 National Survey on Drug Use and Health to identify risk factors for past-year prescription opioid misuse among older adults (n = 9,762). Stepwise binary logistic regression analyses were conducted to examine risk factors for prescription opioid misuse. In the first model, which only accounted for sociodemographics, risk factors for opioid misuse were being divorced (OR = 1.60; 95% CI: 1.15-2.22) and having an annual income less than $20,000 (OR = 1.55; 95% CI: 1.01-2.39). When physical health factors were added to the model, income was no longer associated with misuse and poor self-rated health was found to be associated with opioid misuse (OR = 1.94; 95% CI: 1.44-2.63). When mental health and other substance use factors were added in the final model, self-rated health remained significantly associated with misuse (OR = 1.70; 95% CI: 1.24-2.34) and a lifetime history of depression was also found to be associated with misuse (OR = 2.06; 95% CI: 1.47-2.88). Findings from this study help inform our basic knowledge on risk factors for prescription opioid misuse, which can help inform targeted screening and intervention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".