Nonmedical Prescription Opioid Use and Mental Health and Pain Comorbidities: A Narrative Review
Bibliographic record
Abstract
OBJECTIVE: In North America, the prevalence of nonmedical prescription opioid use (NMPOU), and morbidity and mortality related to prescription opioid analgesics (POAs) has risen sharply. Epidemiologic studies have suggested a high prevalence of mental health and pain comorbidities in NMPOU samples. Given the potential importance for interventions, a narrative review was conducted on studies reporting data on the co-occurrence of NMPOU with mental health problems and pain symptoms in general, treatment, or special populations. METHOD: A search of MEDLINE, PubMed, PsycINFO, and Web of Science using defined search terms yielded 74 studies on NMPOU and mental health and (or) pain. Thirty-nine studies published between 1997 and 2009 were included in the review-based on the data they provided on NMPOU and mental health and pain comorbidities. RESULTS: Our review found strong associations between NMPOU and the comorbidities of interest. Associations between NMPOU and mental health were strongest for depression (OR range 1.2 to 4.3) followed by anxiety disorders (OR range 1.2 to 3.0) in general and treatment populations. The prevalence of pain ranged from 14.5% to 61.5% in general, treatment, and street drug user samples reporting NMPOU. CONCLUSIONS: The extensive associations observed between NMPOU and mental health and pain comorbidities suggest that effective preventive or treatment interventions for NMPOU must consider and attend to these comorbidities. As POAs are widely available and used in North America, POAs may increasingly be used in nonmedical ways for pain or mental health problems not effectively diagnosed or treated.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".