Association of Cigarette Use and Substance Use Disorders among US Adults with and without a Recent Diagnosis of Cancer
Bibliographic record
Abstract
Background: Few studies have examined substance use disorders (SUDs) in cancer patients and it is unclear whether SUDs differentially impact cigarette smoking in patients with vs. without cancer. This study used epidemiological data to estimate current cigarette smoking prevalence and quit ratios among US adults with and without SUDs by cancer status. Methods: Data were drawn from the 2015–2018 National Survey on Drug Use and Health (n = 170,111). Weighted current smoking prevalence and quit ratios were estimated across survey years by SUDs (with vs. without) and by cancer status (with vs. without). Results: Among those with cancer, current smoking prevalence was higher for those with vs. without SUDs (47% vs. 13%, p < 0.001) and quit ratios lower for those with vs. without SUDs (45% vs. 71%, p = 0.002). A similar pattern was observed in adults without cancer, with higher smoking prevalence (56% vs. 21%, p < 0.001) and lower quit ratios (23% vs. 51%, p < 0.001) observed for those with vs. without SUDs, respectively. In adjusted logistic regressions, the SUD × cancer status interaction was not significant for smoking prevalence or quit ratios (AOR = 1.2; 95% CI: 0.7, 2.1, p = 0.56; AOR = 1.0; 95% CI: 0.5, 2.0, p = 0.91, respectively), though smoking prevalence was lower and quit ratios higher for adults with vs. without cancer (ps < 0.05). Conclusions: Among US adults with and without cancer, individuals with SUDs evidenced higher cigarette smoking and lower quit ratios than those without SUDs. Addressing SUDs and their impact on smoking cessation is critical in cancer patients with implications for improving health and treatment outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".