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Record W3040353355 · doi:10.1093/ntr/ntaa121

Smoking Prevalence and COVID-19 in Europe

2020· article· en· W3040353355 on OpenAlexaff
Panagiotis Tsigaris, Jaime A. Teixeira da Silva

Bibliographic record

VenueNicotine & Tobacco Research · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsConfoundingCoronavirus disease 2019 (COVID-19)MedicineDemographyPopulationMortality rateSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PrevalenceTobacco controlEnvironmental healthPublic healthInternal medicineDisease

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.264
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.264
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.248
GPT teacher head0.516
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations83
Published2020
Admission routes1
Has abstractyes

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