The Costs of Alcohol, Illegal Drugs, and Tobacco in Canada, 2002
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to estimate costs attributable to substance use and misuse in Canada in 2002. METHOD: Based on information about prevalence of exposure and risk relations for more than 80 disease categories, deaths, years of life lost, and hospitalizations attributable to substance use and misuse were estimated. In addition, substance-attributable fractions for criminal justice expenditures were derived. Indirect costs were estimated using a modified human capital approach. RESULTS: Costs of substance use and misuse totaled almost Can. $40 billion in 2002. The total cost per capita for substance use and misuse was about Can. $1,267: Can. $463 for alcohol, Can. $262 for illegal drugs, and Can. $541 for tobacco. Legal substances accounted for the vast majority of these costs (tobacco: almost 43% of total costs; alcohol: 37%). Indirect costs or productivity losses were the largest cost category (61%), followed by health care (22%) and law enforcement costs (14%). More than 40,000 people died in Canada in 2002 because of substance use and misuse: 37,209 deaths were attributable to tobacco, 4,258 were attributable to alcohol, and 1,695 were attributable to illegal drugs. A total of about 3.8 million hospital days were attributable to substance use and misuse, again mainly to tobacco. CONCLUSIONS: Substance use and misuse imposes a considerable economic toll on Canadian society and requires more preventive efforts.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".