Opioid prescriptions and patients’ health services utilization and cost before and during the COVID-19 pandemic: an exploratory population-based administrative data analysis
Bibliographic record
Abstract
The objective was to explore percentages of the population treated with prescribed opioids and costs of opioid-related hospitalizations and emergency department (ED) visits among individuals treated with prescription opioids and costs of all opioid-related hospitalizations and ED visits in the province (i.e., provincial costs) before and during the coronavirus disease 2019 (COVID-19) pandemic in Alberta, Canada. In administrative data, we identified individuals treated with prescription opioids and opioid-related hospitalizations and ED visits among those individuals and among all individuals in the province between 2015/16 and 2021/22 fiscal years. Services used were counted on an item-by-item basis and costed using case-mix approaches. Annually, from 9.98% (2020/21-2021/22) to 14.52% (2017/18) of the provincial population was treated with prescription opioids. Between 2015/16 and 2021/22, annual costs of opioid-related hospitalizations and ED visits among individuals treated with prescription opioids were ∼$5 and ∼$2 million, respectively. In 2020/21-2021/22, the provincial costs of opioid-related hospitalizations (∼$14 million) and ED visits (∼$7.0 million) were almost twice the costs observed in 2015/16 and immediately before the pandemic (2019/20). Our findings suggest that increases in the opioid-related utilization of inpatient and ED services between 2015/16 and 2021/22, including the drastic increases observed during the COVID-19 pandemic, were likely driven by unregulated substances.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".