Carga do tabagismo no Brasil e benefício potencial do aumento de impostos sobre os cigarros para a economia e para a redução de mortes e adoecimento
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The prevalence of smoking in Brazil has decreased considerably in recent decades, but the country still has a high burden of disease associated with this risk factor. The study aimed to estimate the burden of mortality, morbidity, and costs for society associated with smoking in 2015 and the potential impact on health outcomes and the economy based on price increases for cigarettes through taxes. Two models were developed: the first is a mathematical model based on a probabilistic microsimulation of thousands of individuals using hypothetical cohorts that considered the natural history, costs, and quality of life of these individuals. The second is a tax model applied to estimate the economic benefit and health outcomes in different price increase scenarios in 10 years. Smoking was responsible for 156,337 deaths, 4.2 million years of potential life lost, 229,071 acute myocardial infarctions, 59,509 strokes, and 77,500 cancer diagnoses. The total cost was BRL 56.9 billion (USD 14.7 billion), with 70% corresponding to the direct cost associated with healthcare and the rest to indirect cost due to lost productivity from premature death and disability. A 50% increase in cigarette prices would avoid 136,482 deaths, 507,451 cases of cardiovascular diseases, 64,382 cases of cancer, and 100,365 cases of stroke. The estimated economic benefit would be BRL 97.9 billion (USD 25.5 billion). In conclusion, the burden of disease and economic losses associated with smoking is high in Brazil, and tax increases are capable of averting deaths, illness, and costs to society.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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 it