A microsimulation cost–utility analysis of alcohol screening and brief intervention to reduce heavy alcohol consumption in <scp>Canada</scp>
Bibliographic record
Abstract
BACKGROUND AND AIMS: Screening and brief intervention (SBI) is a public health intervention that has been shown to be effective in reducing heavy alcohol consumption. The aim of this study is to estimate the cost-effectiveness of implementing universal alcohol SBI in primary care in Canada. DESIGN: We developed a microsimulation model of alcohol consumption and its effects on 18 alcohol-related causes of death. SETTING: The model simulates a Canadian population. PARTICIPANTS: The model simulates individuals and their alcohol consumption on a continuous scale starting from age 17 years to death. INTERVENTIONS: The reference case assumes no SBI in Canada. The base case assumes screening was conducted using the Alcohol Use Disorders Identification Test (AUDIT) at a threshold score of 8. Additional analyses included evaluating SBI using the AUDIT at threshold scores between 4 and 8 or the Derived Alcohol Use Disorders Identification Test (AUDIT-C) at threshold scores between 3 and 7. MEASUREMENTS: The model estimates the direct health-care costs, life years gained and quality-adjusted life years (QALY) gained, which are then used to estimate the incremental cost-effectiveness ratio (ICER) of SBI versus no SBI. FINDINGS: SBI with AUDIT (at a threshold score of 8) had an ICER of $8729/QALY. Our results suggest that using AUDIT thresholds between 8 and 4, inclusive, would be cost-effective for the whole population, as well as for men and women individually. Our results suggest that the AUDIT-C would be cost-effective at thresholds of 7 to 3, inclusive, for men, women and the whole population. CONCLUSIONS: In Canada, screening and brief intervention via Alcohol Use Disorders Identification Test (AUDIT) and Derived Alcohol Use Disorders Identification Test (AUDIT-C) to reduce heavy alcohol consumption appears to be cost-effective for men and women at Alcohol Use Disorders Identification Test (AUDIT) thresholds of 8 and lower and at Derived Alcohol Use Disorders Identification Test (AUDIT-C) thresholds of 7 and lower.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".