Prevalence of pain among nonmedical prescription opioid users in substance use treatment populations: systematic review and meta-analyses.
Bibliographic record
Abstract
BACKGROUND: Prescription opioid analgesics (POA) are widely used in the pharmacotherapeutic treatment of acute and chronic pain in North America, where nonmedical prescription opioid use (NMPOU) has become a substantial public health concern in recent years. Existing epidemiological data suggest an association between NMPOU and pain problem symptoms in different populations, including samples in substance use treatment, although the extent of these correlations has not been systematically assessed. OBJECTIVE: To systematically review and meta-analyze the prevalence of pain symptoms or problems among populations reporting NMPOU in substance use treatment. STUDY DESIGN: Systematic review and meta-analyses. METHODS: A systematic review and meta-analyses were conducted for pain symptoms in substance use treatment samples reporting NMPOU within the last 30 days or at admission to treatment. Overall, 8 unique epidemiological studies were identified and included in the meta-analyses; in 7 of these samples POAs were the primary drug and/or POA dependence was reported. RESULTS: The pooled prevalence of pain in all NMPOU samples in substance use treatment was 58% (95% confidence interval [CI]: 53%-64%). The pooled prevalence of pain in the studies with POAs as the primary drug and/or POA dependence was 60% (95% CI: 52%-67%), and the prevalence of pain with "any" POA abuse (n = 2 studies) was 50% (95% CI: 40%-60%). LIMITATIONS: A small number of studies were available and included in the review; these were restricted to cross-sectional datasets only. Statistical heterogeneity was found in the meta-analytical results. CONCLUSIONS: Pain symptoms are disproportionately elevated in substance use treatment samples reporting NMPOU. Effective measures to prevent and treat NMPOU are urgently needed, although a substantive extent of NMPOU observed in this specific context may relate directly or indirectly to the presence of pain, e.g., either as an expression of ineffective pain care or as a consequence of previous POA-based interventions. At the same time, effective ways to treat and address ongoing pain issues in NMPOU samples need to be implemented, which may require ongoing opioid-based pharmacotherapeutic care aimed at both pain and dependence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".