Assessing the prevalence of non-medical prescription opioid use in the Canadian general adult population: evidence of large variation depending on survey questions used
Bibliographic record
Abstract
BACKGROUND: Morbidity and mortality related to Prescription Opioid Analgesics (POAs) have been rising sharply in North America. Non-Medical Prescription Opioid Use (NMPOU) in the general population is a key indicator of POA-related harm, yet the role of question item design for best NMPOU prevalence estimates in general population surveys is unclear, and existing NMPOU survey data for Canada are limited. METHODS: We tested the impact of different NMPOU question items by comparing an item in the 2008 and 2009 (N = 2,017) samples of the CAMH Monitor surveys - an Ontario adult general population survey - with a newly developed item used in the 2010 (N = 2,015) samples of the Centre for Addiction and Mental Health (CAMH) Monitor surveys. To control for a potential difference in the population demographics between surveys, we adjusted for gender, age, region, income, prescription opioid use, cigarette smoking, weekly binge drinking, cannabis use in the past three months, and psychological distress in our analyses. RESULTS: The prevalence of NMPOU as measured by the 2008 and 2009 CAMH monitor (2.0% [95% CI: 1.2% to 2.8%]) was significantly different when compared to the prevalence of NMPOU as measured by the 2010 CAMH monitor (7.7% [95% CI: 6.3% to 9.2%]) (p < 0.001). This difference was also found when stratifying our analysis by sex (p < 0.001) and when adjusting for all potential confounding covariates. CONCLUSION: It is highly unlikely that the extensive NMPOU prevalence differences observed from the different survey items reflect an actual increase of NMPOU or changes in NMPOU determinants, but rather point to measurement effects. It appears that we currently do not have accurate estimates of NMPOU in the Canadian general population, even though these estimates are needed to guide and implement targeted interventions. Given the current substantial morbidity and mortality impact of NMPOU, there is an urgent need to systematically develop, validate and standardize NMPOU items for future general population surveys in Canada.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".