Abstract 11750: Disparities Among Covid-19 Mortality in Patients With Concurrent Cancer and Cardiovascular Disease in 2020: A Nationwide Analysis
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic has highlighted important socioeconomic health disparities in the United States. Both cardiovascular disease (CVD) and cancer are associated with worse COVID-19 outcomes. We describe racial or ethnic, sex, and rural disparities in mortality rates from COVID-19 in the US cardio-oncology (C-O) population with concurrent cancer and CVD. Methods: Using the multiple causes of death data files within CDC WONDER, we assessed survival outcomes from March to December 2020 (most intense for COVID-19 deaths) by race or ethnicity, sex, and rural status. C-O populations were defined as having ICD 10 codes C00-C97 for “malignant neoplasms” and ICD 10 codes I10-I15 for “hypertensive heart disease”, I20-I25 for “ischemic heart disease” I30-I51 for “other forms of heart disease” encompassing valvular disorders, cardiac arrythmias, myocarditis, and heart failure & cardiomyopathies, or I70 for peripheral atherosclerosis listed as contributing causes of death. In this population, those with ICD 10 code U07.1 for “COVID-19” listed as an underlying cause of death were included. Age-adjusted mortality rates (AAMR) were calculated based on contemporary US population standards. Results: Overall, there were 6,311 deaths from COVID-19 among 16,282 all-cause deaths in the C-O population (AAMR 5.17 deaths/1,000,000 person-years; 95% CI 5.04-5.29). These deaths comprised 1.80% of all COVID-19 deaths (total 350,831) in the study period. Mortality rates were higher in men than women (AAMR 7.35 vs 3.44 deaths/1,000,000; HR 2.13, p<0.0001). Among racial or ethnic groups, mortality rates were highest among African Americans (AAMR 9.31 deaths/1,000,000; 95% CI 8.75-9.88), and higher among Hispanic than non-Hispanic Americans (AAMR 7.10 vs 4.86 deaths/1,000,000; HR 1.46, p<0.0001). Mortality rates were higher in rural areas than metropolitan areas (crude mortality rate 9.15 vs 6.20 deaths/1,000,000; HR 1.48, p <0.001). Conclusion: Among cancer patients with concurrent CVD, COVID-19 deaths were disproportionately higher in male, African American, Hispanic, and rural patients. Further research is needed to understand socioeconomic factors underpinning these disparities and design interventions to improve patient outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".