Trends in cardiovascular mortality of cancer patients in the US over two decades 1999–2019
Bibliographic record
Abstract
Abstract Background Cancer is the second most common cause of death globally after cardiovascular (CV) disease. The present study sought to compare the trends in CV mortality between patients with and without cancer in the US over two decades (1999 to 2019), stratified by sociodemographic factors such as age, sex and geographical location. Methods In this retrospective study, the number of deaths, crude- and age-adjusted mortality rates between January 1, 1999, and December 31, 2019, were obtained from the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) data set Results We examined a total of 53,422,612 deaths between 1999 and 2019; of which 33.4% were defined as CV mortality and 25.6% had malignancy. During this period, among patients with cancer, the age-adjusted mortality rate dropped by 52%. (Vs, 38% in patients with no malignancy). CV mortality was highest in patients with gastrointestinal and prostate malignancy where CV mortality accounts together for over 40% of all CV mortality in patients with cancer in 1999 and 33.6% in 2019. The age-adjusted CV mortality rate (per 100,000 people) of patients with GI and prostate cancer nearly halved over twenty years from 2.7 to 1.0 and 2.5 to 1.0. The CV age-adjusted mortality rate dropped more significantly among patients with gastrointestinal, breast, and prostate malignancy than among patients with hematological malignancy (59–63% vs. 41%. We observed that crude CV mortality rates amongst patients with cancer declined over the study period in all age groups but was more prominent among patients over 65 years old than those aged 55–64 and under 55 (51%-55% Vs. 41%, 25%, respectively).Similar reductions in mortality in men and women (54% and 53% reduction) were observed During the study period the decline in cardiovascular mortality was more prominent in metro areas which led to lower age adjusted CV mortality in Metro compared to non-Metro areas (5.7–6.3 vs 7.2). The decline in age adjusted CV mortality in patients with cancer differed significantly in different states Conclusions In our temporal analysis we show a 50% decline in CV mortality in the US over two decades in both male and female patients with cancer, that has exceeded the reduction in CV mortality seen in the non-cancer population. The greatest reductions in CV mortality were observed in patients with GI, breast and prostate malignancies, those residing in metro areas and in patients aged 65 and over. Funding Acknowledgement Type of funding sources: None. CV death among cancer patients
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".