Alcohol, tobacco and health care costs: a population-wide cohort study (n = 606 947 patients) of current drinkers based on medical and administrative health records from Catalonia
Bibliographic record
Abstract
Background: Most cost of illness studies are based on models where information on exposure is combined with risk information from meta-analyses, and the resulting attributable fractions are applied to the number of cases. Methods: This study presents data on alcohol and tobacco use for 2011 and 2012 obtained from a routine medical practice in Catalonia of 606 947 patients, 18 years of age and older, as compared with health care costs for 2013 (all costs from the public health care system: primary health care visits, hospital admissions, laboratory and medical tests, outpatient visits to specialists, emergency department visits and pharmacy expenses). Quasi-Poisson regressions were used to assess the association between alcohol consumption and smoking status and health care costs (adjusted for age and socio-economic status). Results: Resulting health care costs per person per year amounted to 1290 Euros in 2013, and were 20.1% higher for men than for women. Sex, alcohol consumption, tobacco use and socio-economic status were all associated with health care costs. In particular, alcohol consumption had a positive dose-response association with health care costs. Similarly, both smokers and former smokers had higher health care costs than did people who never smoked. Conclusions: Alcohol and tobacco use had modest and large impacts respectively on health care costs, confirming the results of previous ecological modelling analyses. Reductions of alcohol consumption and smoking through public policies and via early identification and brief interventions would likely be associated with reductions in health care costs.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".