Associations of rural residence and low socioeconomic status on the risk of new onset cardiovascular disease (CVD) in cancer patients.
Bibliographic record
Abstract
90 Background: Cancer patients are predisposed to CVD due to cancer treatments and shared risk factors (smoking/physical inactivity). We aimed to assess if rural residence and low socioeconomic status (SES) modify the risk of developing CVD. Methods: Patients diagnosed with non-metastatic solid organ cancers without baseline CVD in a large Canadian province from 2004 to 2017 were identified using the population-based registry. Postal codes were linked with Census data to determine rural residence as well as neighborhood-level income and educational attainment. Low income was defined as <46000 CAD/annum; low education was defined as a neighborhood in which <80% attended high school. Myocardial infarction, congestive heart failure, arrythmias and cerebrovascular accident constituted as CVD.We performed logistic regression analyses to examine the associations of rural residence and low SES with the development of CVD, adjusting for measured confounding variables. Results: We identified 81,275 patients diagnosed with cancer without pre-existing CVD. The median age was 62 years and 54.2% were women. The most prevalent cancer types included breast (28.6%), prostate (23.1%), and colorectal (14.9%). At a median follow-up of 68 months, 29.4% were diagnosed with new CVD. The median time from cancer diagnosis to CVD was 29 months. Rural patients (32.3 vs 28.4%,P < .001) and those with low income (30.4% vs 25.9%,P < .001) or low educational attainment (30.7% vs 27.6%,P < .001) experienced higher rates of CVD. After adjusting for baseline factors and treatment, rural residence (odds ratio[OR], 1.07; 95% confidence interval[CI], 1.04-1.11;P < .001), low income (OR,1.17;95%CI,1.12-1.21;P < .001) and low education (OR,1.08;95%CI,1.04-1.11;P < .001) continued to associate with higher odds of CVD. Further, patients with colorectal cancer were more likely to develop CVD compared with other tumors (OR,1.12;95% CI,1.04-1.16;P = .001). A multivariate Cox regression model showed that patients with low SES were more likely to die, but patients residing rurally were not. Conclusions: Approximately one-third of cancer survivors develop CVD on follow-up. Despite universal healthcare, marginalized populations experience different CVD risk profiles that should be considered when operationalizing lifestyle modification strategies and cardiac surveillance programs. [Table: see text]
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".