Recent changes in trends of opioid overdose deaths in North America
Bibliographic record
Abstract
BACKGROUND: As several regulatory and environmental changes have occurred in North America, trends in overdose deaths were examined in the United States (US), Ontario and British Columbia (BC), including changes in consumption levels of prescription opioids (PO) and overdose deaths, changes in correlations between consumption levels of PO and overdose deaths and modeled differences between observed and predicted overdose deaths if no changes had occurred. METHODS: Consumption levels of PO included defined daily doses for statistical purposes per million inhabitants per day for the US and Canada (2001-2015). Overdose deaths included opioid overdose deaths for the US (2001-2017) and Ontario (2003-2017) and illicit drug overdose deaths for BC (2001-2017). The analytic techniques included structural break point analyses, Pearson product-moment correlations and multivariate Gaussian state space modeling. RESULTS: Consumption levels of PO changed in the US in 2010 and in Canada in 2012. Overdose deaths changed in the US in 2014 and in Ontario and BC in 2015. Prior to the observed changes in consumption levels of PO, there were positive correlations between consumption levels of PO and overdose deaths in the US (r = 0.99, p < 0.001) and Ontario (r = 0.92, p = 0.003). After the observed changes in consumption levels of PO, there was a negative correlation between consumption levels of PO and overdose deaths in the US (r = - 0.99, p = 0.002). Observed overdose deaths exceeded predicted overdose deaths by 5.7 (95% Confidence Interval [CI]: 4.8-6.6), 3.5 (95% CI: 3.2-3.8) and 21.8 (95% CI: 18.6-24.9) deaths per 100,000 people in the US, Ontario and BC, respectively in 2017. These excess deaths corresponded to 37.7% (95% CI: 31.9-43.6), 39.2% (95% CI: 36.3-42.1) and 72.2% (95% CI: 61.8-82.6) of observed overdose deaths in the US, Ontario and BC, respectively in 2017. CONCLUSIONS: The opioid crisis has evolved in North America, as a sizeable proportion of overdose deaths are now attributable to the several regulatory and environmental changes. These findings necessitate substance use policies to be conceptualized more broadly as well as the continued expansion of harm reduction services and types of pharmacotherapy interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".