Costos de atención médica de las enfermedades atribuibles al consumo de tabaco en América: revisión de la literatura
Bibliographic record
Abstract
The objective of this study was to compile information from published scientific literature about health care costs attributable to tobacco consumption and evaluate the different methodological strategies used in calculating estimations. Sources included MedLINE, bibliographical references from books published by the World Bank, the World Health Organization, the Panamerican Health Organization, the Interdisciplinary Health Research Group of Canada, as well as technical documentation used by the state of Minnesota, United States of America, in litigation against the tobacco industry. All of the studies published about this issue over the last 25 years or more were included. Information was obtained with respect to the study population, the cost perspective, the type of analysis used for estimating health care costs and methodology for attributing costs to tobacco consumption. In addition, comments with regard to the relevant findings and the limitations of each of the studies were added. Annual health care costs attributable to tobacco use vary between 6 and 14% of personal health expenses. In the period between the first publication and today, progress has been seen in the methodology used for calculating estimations, not only from the epidemiological perspective which improves the accuracy of the attribution of costs to risk factors, but from the economic perspective which broadens the estimation of costs from a social perspective. It is concluded that tobacco consumption leads to high health care costs, involves a cost to employers due to productivity losses and worker disability, and represents a high social cost resulting from the occurrence of premature deaths in the 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.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.028 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".