Social disparities in cardiovascular mortality of patients with cancer in the USA between 1999 and 2019
Bibliographic record
Abstract
Background: Temporal trends of the impact of social determinants on cardiovascular outcomes of cancer patients has not been previously studied. Objectives: This study examined social disparities in cardiovascular mortality of people with and without cancer in the US population between 1999 and 2019. Methods: Primary cardiovascular deaths were identified from the Multiple Cause of Death database and grouped by cancer status. The cancer cohort was subcategorized into breast, lung, prostate, colorectal, and haematological. The number of cardiovascular deaths, crude cardiovascular mortality rate, cardiovascular age-adjusted mortality rate (AAMR), and percentage change in cardiovascular AAMR were calculated by cancer status and cancer type, and stratified by sex, race, ethnicity, and urban-rural setting. Results: 17.9 million cardiovascular deaths were analysed. Of these, 572,222 occurred in patients with a record of cancer. The cancer cohort were older and included more men and White racial groups. Regardless of cancer status, cardiovascular AAMR was higher in men, rural settings, and Black or African American races. Cardiovascular AAMR declined over time, with greater reduction in those with cancer (-51.6% vs -38.3%); the greatest reductions were in colorectal (-68.4%), prostate (-60.0%), and breast (-58.8%) cancers. Sex, race, and ethnic disparities reduced over time, with greater narrowing in the cancer cohort. There was increase in urban-rural disparities, which appeared greater in those with cancer. Conclusions: While most social disparities narrowed over time, urban-rural disparities widened, with greater increase in those with cancer. Healthcare plans should incorporate strategies for reduction of health inequality equitable access to cardio-oncology services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".