Quitting behaviors and cessation assistance used among smokers with anxiety or depression: Findings among six countries of the EUREST-PLUS ITC Europe Surveys
Bibliographic record
Abstract
Introduction The current study explores quitting behaviours and use of cessation assistance among adult tobacco users with probable anxiety or depression (PAD) and in six European (EU) Member States (MS). Material and Methods The EUREST-PLUS ITC Wave 1 Europe Survey was conducted with a nationally representative cross-sectional sample of 6,011 adult cigarette smokers from six European Union (EU) Member States (MS) (Germany, Greece, Hungary, Poland, Romania, Spain) in 2016. Results Our study found that one in five smokers sampled from six EU MS had a diagnosis, treatment or positive screen for anxiety or depression, with rates of PAD varying between EU MS. Results of the multivariable logistic regression analysis showed that respondents with PAD were more likely to have made a quit attempt in the last 12 months (AOR 1.75; 95%CI 1.45-2.11), compared to respondents without PAD. Among those respondents with PAD who used support the most frequently reported quit method was prescription-based quit smoking pharmacotherapy (15.4%) followed by e-cigarettes (13.7%) and NRT (11.3%). Person-to-person behavioral support (i.e. local quit services, face-to-face advice from a doctor or other health care professional, telephone or quitline services) was reported significantly more frequently among respondents with PAD compared to those without PAD. Conclusions Given both pharmacological and non-pharmacological quit smoking aids have been shown to be safe, acceptable and effective for people with and without mental illness it is important that their use be promoted among smokers with anxiety and depression alongside behavioral counseling. Our findings support the need for interventions targeting health care professionals in providing smoking cessation assistance among this population of smokers. Acknowledgements EUREST-PLUS is a Horizon2020 project conducted by researchers throughout Europe from both the six participating countries as well as other institution partners within Europe and abroad. Partnering organizations include the European Network on Smoking Prevention (Belgium), Kings College London (United Kingdom), German Cancer Research Centre (Germany), University of Maastricht (The Netherlands), University of Athens (Greece), Aer Pur Romania (Romania), European Respiratory Society (Switzerland), the University of Waterloo (Canada), the Catalan Institute of Oncology (Catalonia, Spain), Smoking or Health Hungarian Foundation (Hungary), Health Promotion Foundation (Poland), University of Crete (Greece), and Kantar Public Brussels (Belgium). Funding The EUREST-PLUS Project takes place with the financial support of the European Commission, Horizon 2020 HCO-6-2015 program (EUREST-PLUS: 681109; C. Vardavas) and the University of Waterloo (GT. Fong). Additional support was provided to the University of Waterloo by the Canadian Institutes of Health Research (FDN-148477). GT. Fong was supported by a Senior Investigator Grant from the Ontario Institute for Cancer Research. E. Fernández is partly supported by Ministry of Universities and Research, Government of Catalonia (2017SGR139) and by the Instituto Carlos III and co-funded by the European Regional Development Fund (FEDER) (INT16/00211 and INT17/00103), Government of Spain.
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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 | 0.003 | 0.003 |
| 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 | 0.000 |
| 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".