Changes in health care costs before and after smoking cessation
Bibliographic record
Abstract
Previous research on health care costs among former smokers suggests that quitters incur greater health care costs for up to 4 years after cessation compared with continuing smokers. However, little is known about the relationship between health care costs and utilization in the periods before as well as after cessation. The present study used a retrospective cohort design with automated health plan and primary data to examine the health care costs and clinical experiences before and after smoking cessation among former smokers compared with a sample of continuing smokers. Subjects were a random sample of adults (aged 25 and older) whose smoking status was identified by a physician during a primary care visit to the Group Health Cooperative (GHC), a nonprofit, integrated health care delivery system in western Washington state. Total direct health care costs among former smokers began to rise in the quarter prior to cessation and were significantly greater (p < .001) than those of continuing smokers in the quarter immediately following cessation. This difference dissipated within one quarter following cessation. We replicated the postquit cost spike among former smokers found by other research and showed that this spike dissipated within the first year postquit. Smoking cessation did not result in sustained cost increases among former smokers.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".