Relation between opioid-related harms and socioeconomic inequalities in Ontario: a population-based descriptive study
Bibliographic record
Abstract
BACKGROUND: Negative health outcomes associated with the use of both prescribed and nonprescribed opioids are increasingly prevalent. We examined long-term trends in opioid-related harms in Ontario across a set of 6 indicators and the relation between harms and neighbourhood income in 2016. METHODS: We examined rates of neonatal abstinence syndrome, opioid poisoning (fatal and nonfatal) and nonpoisoning opioid-related events from 2003 to 2016 in Ontario using population-based health administrative databases. We conducted descriptive analyses for harm indicators across neighbourhood income quintiles in 2016 (2015 for death). We examined social inequalities in opioid-related harms on both relative (prevalence ratio) and absolute (potential rate reduction) scales. RESULTS: Rates of opioid-related harms increased dramatically between 2003 and 2016. In 2016, neonatal abstinence syndrome and opioid poisoning and nonpoisoning events showed a strong social gradient, with harm rates being lowest in higher-income neighbourhoods and highest in lower-income neighbourhoods. Prevalence ratios for the lowest-income neighbourhoods compared to the highest-income neighbourhoods ranged from 2.36 (95% confidence interval [CI] 2.15-2.58) for emergency department visits for opioid poisoning to 3.70 (95% CI 2.62-5.23) for neonatal abstinence syndrome. Potential rate reductions for opioid-related harms ranged from 34.8% (95% CI 29.1-40.1) to 49.9% (95% CI 36.7-60.5), which suggests that at least one-third of all harmful events could be prevented if all neighbourhoods had the same socioeconomic profile as the highest-income neighbourhoods. INTERPRETATION: Rates of opioid-related harms increased in Ontario between 2003 and 2016, and people in lower-income neighbourhoods experienced substantially higher rates of opioid-related harms than those in higher-income neighbourhoods. This finding can inform planning for opioid-related public health interventions with consideration of health equity.
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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 | 0.000 |
| 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".