Individual and area-level socioeconomic status, Life's Simple 7, and comorbid cardiovascular disease and cancer: a prospective analysis of the UK Biobank cohort
Bibliographic record
Abstract
OBJECTIVES: We aimed to investigate the associations of individual and area-level socioeconomic status (SES) with incident cardiovascular diseases (CVD) alone, cancer alone, and comorbid CVD and cancer, and the mediation role of cardiovascular health score in these associations. STUDY DESIGN: This was a population-based prospective cohort study. METHODS: We used data from the UK Biobank, a population-based prospective cohort study. Latent class analysis was used to create an individual-level SES index based on three indicators (household income, education level, and employment status), and the Townsend Index was defined as the area-level socioeconomic status. We used the American Heart Association's (AHA) Life's Simple 7 (smoking, body weight, physical activity, diet, blood pressure, blood glucose, and total cholesterol) to calculate the cardiovascular health score. We used Cox proportional hazard regression models to estimate the hazard ratio (HR) and 95% confidence interval (CI) adjusted for demographic, environmental, and genetic factors. RESULTS: Compared with high SES, the HRs in participants with low individual and area-level SES were 1.33 (95% confidence interval [CI] 1.29 to 1.38) and 1.24 (95% CI 1.20 to 1.29) for incident CVD, 0.96 (95% CI 0.93 to 0.99) and 0.95 (95%CI 0.92 to 0.98) for incident cancer, 1.32 (95%CI 1.24 to 1.40) and 1.15 (95%CI 1.08 to 1.22) for incident comorbid CVD and cancer, respectively. Additionally, the mediation proportion of CVD score for individual and area-level SES was 47.93% and 48.87% for incident CVD, 44.83% and 59.93% for incident comorbid CVD and cancer. The interactions between individual-level SES and CVD scores were significant on incident CVD, and comorbid CVD and cancer, and the protective associations were stronger in participants with high individual-level SES. CONCLUSIONS: Life's Simple 7 significantly mediated the associations between SES and comorbid CVD and cancer, while almost half of the associations remained unclear.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".