Associations of Socioeconomic Status and Rurality With New-Onset Cardiovascular Disease in Cancer Survivors: A Population-Based Analysis
Bibliographic record
Abstract
PURPOSE: Patients with cancer are predisposed to develop new-onset cardiovascular disease (CVD). We aimed to assess if rural residence and low socioeconomic status modify such a risk. METHODS: Patients diagnosed with solid organ cancers without any baseline CVD and on a follow-up of at least 1 year in a large Canadian province from 2004 to 2017 were identified using the population-based registry. We performed logistic regression analyses to examine the associations of rural residence and low socioeconomic status with the development of CVD. RESULTS: We identified 81,418 patients eligible for the analysis. The median age was 62 years, and 54.3% were women. At a median follow-up of 68 months, 29.4% were diagnosed with new CVD. The median time from cancer diagnosis to CVD diagnosis was 29 months. Rural patients (32.3% v 28.5%; P < .001) and those with low income (30.4% v 25.9%; P < .001) or low educational attainment (30.7% v 27.6%; P < .001) experienced higher rates of CVD. After adjusting for baseline factors and treatment, rural residence (odds ratio [OR], 1.07; 95% CI, 1.04 to 1.11; P < .001), low income (OR, 1.17; 95% CI, 1.12 to 1.21; P < .001), and low education (OR, 1.08; 95% CI, 1.04 to 1.11; P < .001) continued to be associated with higher odds of CVD. A multivariate Cox regression model showed that patients with low socioeconomic status were more likely to die, but patients residing rurally were not. CONCLUSION: Despite universal health care, marginalized populations experience different CVD risk profiles that should be considered when operationalizing lifestyle modification strategies and cardiac surveillance programs for the growing number of cancer survivors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".