Risk Factors for Misuse of Prescribed Opioids: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
STUDY OBJECTIVE: Increasing opioid prescribing has been linked to an epidemic of opioid misuse. Our objective is to synthesize the available evidence about patient-, prescriber-, medication-, and system-level risk factors for developing misuse among patients prescribed opioids for noncancer pain. METHODS: We performed a systematic search of the scientific and gray literature for studies reporting on risk factors for prescription opioid misuse. Two reviewers independently reviewed titles, abstracts, and full texts; extracted data; and assessed study quality. We excluded studies with greater than 50% cancer patients, palliative patients, and illicit opioid initiation. When possible, we synthesized the effect sizes of dichotomous risk factors and their associations with opioid misuse, using inverse-variance random-effects meta-analysis. We calculated the mean difference between opioid misusers and nonmisusers for continuous risk factors. When studies lacked homogeneity, we synthesized their results qualitatively. RESULTS: Of 9,629 studies, 65 met our inclusion criteria. Among patients with outpatient opioid prescriptions, the following factors were associated with the development of misuse: any current or previous substance use (odds ratio [OR] 3.55; 95% confidence interval [CI] 2.62 to 4.82), any mental health diagnosis (OR 2.45; 95% CI 1.91 to 3.15), younger age (OR 2.19; 95% CI 1.81 to 2.64), and male sex (OR 1.23; 95% CI 1.10 to 1.36). CONCLUSION: Although clinicians should endeavor to offer alternative pain management strategies to all patients, those who are younger, are male patients, and report a history of or current substance use or mental health diagnoses were associated with a greater risk of developing opioid misuse. Clinicians should consider prioritizing alternative pain management strategies for these higher-risk patients.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.028 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".