The prominence of smoking‐related mortality among individuals with alcohol‐ or drug‐use disorders
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Even though individuals with substance-use disorders have a high prevalence of tobacco smoking, surprisingly little is known about smoking-related mortality in these populations. The current retrospective cohort study aims to address this gap. DESIGN AND METHODS: The study sample included cohorts of individuals hospitalised in California between 1990 and 2005 with alcohol- (n = 509 422), cocaine- (n = 35 276), opioid- (n = 53 172), marijuana- (n = 15 995) or methamphetamine-use (n = 36 717) disorders. Death records were linked to inpatient data. Age-, race- and sex-adjusted standardised mortality ratios (SMR) were generated for 19 smoking-related causes of death. RESULTS: Smoking-related conditions comprised 49% (79 188/163 191) of total deaths in the alcohol, 40% (1412/3570) in the cocaine, 39% (4285/11 091) in the opioid, 42% (554/1332) in the methamphetamine and 36% (1122/3095) in the marijuana cohorts. The SMRs for all smoking-linked diseases were: alcohol, 3.57 (95% confidence interval [CI] = 3.55 to 3.58); cocaine, 2.40 (95% CI = 2.39 to 2.41); opioid, 4.26 (95% CI = 4.24 to 4.27); marijuana, 3.73 (95% CI = 3.71 to 3.74); and methamphetamine, 2.58 (95% CI = 2.57 to 2.59). The SMRs for almost all of the 19 cause-specific smoking-related outcomes were elevated across cohorts. DISCUSSION AND CONCLUSIONS: Given the current findings, addressing tobacco smoking among persons with substance-use disorders should be a critical concern, especially given the heavy smoking-related mortality burden and the currently limited attention devoted to smoking in these populations. [Callaghan RC, Gatley JM, Sykes J, Taylor L. The prominence of smoking-related mortality among individuals with alcohol- or drug-use disorders. Drug Alcohol Rev 2018;37:97-105].
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".