Comparison of Crude Population-Level Indicators of Opioid Use and Related Harm in New Zealand and Ontario (Canada)
Bibliographic record
Abstract
North America and select other Commonwealth jurisdictions have been experiencing unprecedented opioid epidemics characterized by excessive and persistently high levels of opioid misuse, morbidity and mortality, and related disease burden. Recent discussions have considered whether New Zealand might undergo or needs to expect a similar 'opioid crisis'. Towards further informing these considerations, we examine and compare essential, publicly available indicators of opioid utilization and harms (mortality) from New Zealand and the Canadian province of Ontario, due to the fact that both operate public health care systems in similar socio-cultural settings. We find that the two jurisdictions have featured vastly different population levels of opioid exposure, opioid consumption patterns (e.g., high-dose/long-term/high-risk prescribing) known as key predictors of adverse outcomes, and levels of opioid mortality as evidenced by concrete epidemiological indicators and data. Specifically for opioid-related death rates, these were already approximately threefold higher in Ontario compared to New Zealand based on most recent comparison data (e.g., 2012); these differentials have likely further grown more recently given major and distinct changes in population-level opioid exposure and risks, and subsequent opioid-related deaths since then in Ontario. Based on the present data and related evidence, New Zealand does not seem to need to anticipate an opioid mortality epidemic similar to that experienced in North America; however, it would be of interest to establish more comprehensive and timely surveillance of key system-level indicators of opioid use and harms as are standard in North America. As such, this inter-jurisdictional comparison makes for a case study in starkly contrasting scenarios of opioid use and harms, the drivers behind which deserve further systematic examination.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".