ANOTHER EXAMPLE OF AN ILLICIT CIGARETTE MARKET: A STUDY OF PSYCHIATRIC PATIENTS IN TORONTO, ONTARIO
Bibliographic record
Abstract
Tobacco taxation is a key mechanism for reducing smoking consumption and prevalence in the general population. Few studies, however, have acknowledged the disproportionately heavy tobacco tax burden placed upon some groups—usually poor, marginalized populations—in the drive for population-based public health goals.1 Shelley et al.2 recently examined the relations between a large tax increase in New York State and the development of a pervasive, illicit cigarette market in a low-income minority community and described the financial burden of smoking among the poor who had not quit. Our letter extends these qualitative findings in 2 ways: by examining similar issues in a different marginalized, low-income population—psychiatric patients in one of Canada’s largest psychiatric hospitals—and by quantifying the relative magnitude of illicit cigarette consumption in this population. Approximately 60% to 80% of people with schizophrenia and other severe mental illnesses smoke cigarettes3,4—a rate of roughly 4 times higher than that of the general population in Canada.5 Smoking plays an important role in the significantly higher rates of coronary heart disease morbidity and mortality found among people with severe mental illnesses, and coronary heart disease screening and smoking-cessation programs are much needed for this population.6 Our study involved the collection of cigarette butts from 3 sites in Toronto, Ontario: a 436-bed inpatient psychiatric hospital (where 70–75% of the patients have a primary diagnosis of schizophrenia), an addiction and mental health research and outpatient facility, and a large general hospital. In addition, a garbage audit was performed at the inpatient psychiatric facility to extract cigarette packages from 1 week’s worth of garbage. The collected cigarette butts were then sorted according to their filter-tip logos. In Ontario, an unbranded cigarette filter almost always indicates an illicit brand. The inpatient psychiatric hospital had a dramatically higher rate of “unbranded” cigarette butts: 54% versus 16% at the research facility and 6% at the general hospital site (Figure 1 ▶). The garbage audit resulted in the extraction of 320 cigarette packages. Approximately 80% of the packages were from illicit tobacco brands—a rate dramatically higher than that found in a recent, city-wide garbage audit in Toronto.7 FIGURE 1— Patterns of branding on cigarette butts collected at an inpatient psychiatric hospital (n = 1288 butts), an addiction and mental health research and outpatient treatment facility (n = 1271 butts), and a general hospital (n = 1784 butts). Similar to the findings of Shelley et al., our study demonstrated that cigarette taxation policies appear to place a disproportionate burden on some marginalized groups. As a result, it is important to assess and ensure principles of taxation equity for such populations,8 especially given that higher cigarette prices may paradoxically increase the availability of cheap (but illicit) cigarettes and undermine smoking-related interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".