Using a cascade of care framework to identify gaps in access to medications for alcohol use disorder in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND AND AIMS: Despite the significant burden of alcohol use disorder (AUD) and availability of safe and effective medications for AUD (MAUD), population-level estimates of access and engagement in AUD-related care are limited. The aims of this study were to generate a cascade of care for AUD in British Columbia (BC), Canada, and to estimate the impacts of MAUD on health outcomes. DESIGN: This was a retrospective population-based cohort study using linked administrative health data. SETTING: British Columbia, Canada, 2015-2019. PARTICIPANTS: Using a 20% random sample of BC residents, we identified 7231 people with moderate-to-severe alcohol use disorder (PWAUD; overall prevalence = 0.7%). MEASUREMENTS: We developed a six-stage AUD cascade (from diagnosis to ≥6 months retention in MAUD) among PWAUD. We evaluated trends over time and estimated the impacts of access to MAUD on AUD-related hospitalizations, emergency department visits and death. FINDINGS: Between 2015 and 2019, linkage to AUD-related care decreased (from 80.4% to 46.5%). However, rates of MAUD initiation (11.4% to 24.1%) and retention for ≥1 (7.0% to 18.2%), ≥3 (1.2% to 4.3%) or ≥6 months (0.2% to 1.6%) increased significantly. In adjusted analyses, access to MAUD was associated with reduced odds of experiencing any AUD-related adverse outcomes, with longer retention in MAUD showing a trend to greater odds reduction: adjusted odds ratio (95% CI) ranging from 0.59 (0.48-0.71) for MAUD retention <1 month to 0.37 (0.21-0.67) for ≥6 months retention. CONCLUSIONS: Access to medications for alcohol use disorder among people with moderate-to-severe alcohol use disorder in British Colombia, Canada increased between 2015 and 2019; however, initiation and retention remained low. There was a trend between longer retention in medications for alcohol use disorder and greater reductions in the odds of experiencing alcohol use disorder-related adverse outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".