Smoking Prevalence and COVID-19 in Europe
Bibliographic record
Abstract
INTRODUCTION: This ecological study investigates the association between smoking prevalence and COVID-19 occurrence and mortality in 38 European nations as of May 30, 2020. METHODS: Data were collected from Our World in Data. Regression analysis was conducted to adjust for potential confounding factors such as economic activity (gross domestic product), the rate of COVID-19 testing, and the stringency of COVID-19 control policies. RESULTS: There was a statistically significant negative association between smoking prevalence and the prevalence of COVID-19 across the 38 European nations after controlling for confounding factors (p = 0.001). A strong association was found between the prevalence of COVID-19 per million people and economic activity (p = 0.002) and the rate of COVID-19 testing (p = 0.0006). Nations with stricter policy enactment showed fewer COVID-19 cases per million people, but the association was not significant (p = 0.122). Delaying policy enactment was associated with a greater prevalence of COVID-19 (p = 0.0535). Evidence of a direct association between smoking prevalence and COVID-19 mortality was not found (p = 0.626). There was a strong positive association between COVID-19 mortality rate and the prevalence of COVID-19 cases (p < 0.0001) as well as the proportion of the population over 65 years of age (p = 0.0034) and a negative association with the rate of COVID-19 testing (p = 0.0023). CONCLUSIONS: We found a negative association between smoking prevalence and COVID-19 occurrence at the population level in 38 European countries. This association may not imply a true or causal relationship, and smoking is not advocated as a prevention or treatment of COVID-19. IMPLICATIONS: Given the evidence of this ecological study, and of several other studies that found an underrepresentation of smoking prevalence in hospitalized cases, it may be worth examining, in laboratory experiments and controlled human trials, if nicotine offers any protection against COVID-19. Most importantly, to date, no study, including this one, supports the view that smoking acts as a treatment intervention or prophylaxis to reduce the impact or ameliorate the negative health impacts of COVID-19.
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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 | 0.000 |
| 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".