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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".