Incidence and Determinants of COVID-19 Among People Who Smoke (2018–2021): Findings From the ITC EUREST-PLUS Spain Surveys
Bibliographic record
Abstract
OBJECTIVE: To estimate the cumulative incidence of COVID-19 and its determinants among a nationally representative sample of adults from Spain who smoke. METHODS: This is a prospective cohort study that uses data from two waves (Wave 2 in 2018 and Wave 3 in 2021) of the ITC EUREST-PLUS Spain Survey. At baseline (Wave 1 in 2016), all respondents were adults (aged ≥18) who smoked. In total, 1008 respondents participated in Wave 2, and 570 out of 888 eligible participants were followed up in Wave 3 (64.2%). We estimated the cumulative incidence and the relative risk of COVID-19 (RR) and 95% confidence intervals (CI) during follow-up using self-reported information on sociodemographic, smoking-related and health-related characteristics and identified associated factors using multivariable Poisson models with robust variance adjusted for the independent variables. RESULTS: The overall cumulative incidence of self-reported COVID-19 was 5.9% (95% CI: 3.9-8.0%), with no significant differences between males (6.3%; 95% CI: 3.6-9.0%) and females (5.6%; 95% CI: 3.2-8.0%). After adjusting for age, sex, and educational level, COVID-19 incidence was positively associated with moderate nicotine dependence (RR: 2.37; 95% CI: 1.04-5.40) and negatively associated with having a partner who smoked (RR: 0.12; 95% CI: 0.03-0.42), and having friends but not a partner who smoked (RR: 0.28; 95% CI: 0.14-0.56). CONCLUSION: The correlates of having had COVID-19 among people who smoke should be considered when tailoring information and targeted non-pharmacological preventive measures.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".