Smoking trends and health equity in Switzerland between 1992 and 2017: dependence of smoking prevalence on educational level and social determinants
Bibliographic record
Abstract
Background Switzerland ranks among the top three healthcare systems in the world with regards to healthcare access, suggesting a high degree of health equity. However, Switzerland has few preventive strategies against smoking abuse. The aim of this study is to clarify whether educational level and citizenship status have an influence on the prevalence of smoking in Switzerland and whether there is health inequity related to a lack of preventive strategies. Methods We based our analysis on publicly available health data published in the Swiss government's Swiss health survey (1992–2017). We compared the prevalence of smoking across the years and correlated these data with levels of educational attainment, citizenship status and age. Results A continuous significant decline in smokers is observed in the highest education group (TERT). Over time, prevalence was reduced from 29% in 1992 to 23% in 2017 ( p < 0.001). The intermediate-level educational group (SEK 2) showed smaller but also significant decline on a 0.05 sigificance level over the same period, from 31% to 29% ( p = 0.003). The lowest educational group showed a nonsignificant decline from 28% to 27% ( p = 0.6). The population who holds Swiss citizenship showed a decrease in smoking from 28% to 26% within the time frame ( p < 0.001). People without Swiss citizenship had a much higher prevalence of smokers, at 38% in 1992 and declining to 32% in 2017 ( p < 0.001). All cohorts from age 15 to age 64 have a far higher prevalence of smokers than cohorts at an older age, with the highest prevalence in the 25–34 age group. Conclusion In Switzerland, individuals with lower levels of education and non-Swiss populations are more susceptible to health risk of smoking. This is despite the existence of a high-quality healthcare system that has nevertheless failed to negated health inequities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".