Modelling lifetime QALYs and health care costs from different drinking patterns over time: a Markov model
Bibliographic record
Abstract
The negative health consequences of alcohol use and its treatment account for significant health care expenditure worldwide. Long-term modelling techniques are developed in this paper to establish a link between drinking patterns, health consequences and alcohol treatment effectiveness and cost-effectiveness. The overall change in health related quality and quantity of life which results from changes in health-related behaviour is estimated. Specifically, a probabilistic lifetime Markov model is presented where alcohol consumption in grams of alcohol per day and drinking history are used for the categorization of patients into four Markov states. Utility weights are assigned to each drinking state using EQ-5D scores. Mortality and morbidity estimates are state, gender and age specific, and are alcohol-related and non-alcohol-related. The methodology is tested in a case study. This represents a major development in the techniques traditionally used in alcohol economic models, in which short-term costs and outcomes are assessed, omitting potential longer term cost savings and improvements in health related quality of life. Assumptions and implications of the approach are discussed.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".