Pre- and post-pandemic comparisons in cardiovascular markers: a population-based study
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic, starting in 2020, raised concerns about potential long-term health impacts, including its effects on cardiovascular health and related biomarkers. This study part of the Bus Santé in Geneva, Switzerland, compared cardiovascular and metabolic profiles pre- (2016-2019) and post-pandemic (2023-2024) among individuals aged 30-75. Methods: Participants completed questionnaires and underwent a clinical visit, including a physical examination and fasting blood test to assess lipid and glycemic profiles. Linear regression was used to estimate results including mean systolic and diastolic blood pressure, cholesterol, and glycemic profiles, after adjusting for age, sex, smoking, and socioeconomic status. Quantile regression models were used to estimate median values. Results: A total of 4,558 participants were included. The study observed modest declines in mean glucose, cholesterol, HDL, and LDL levels post-pandemic, with stable blood pressure. The prevalence and treatment rates of diabetes, hypertension, and dyslipidemia remained consistent. Unawareness of these conditions was stable. Conclusion: Despite initial fears of a pandemic-induced health debt, results indicate healthy cardiovascular profiles post-pandemic, likely driven by improved lifestyle behaviors. This study highlights the importance of monitoring of cardiovascular health and suggests that lifestyle improvements may offset potential adverse pandemic effects in developed nations like Switzerland.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".