Abstract 4141272: Trends in Cancer Versus Cancer with Heart Failure Related Mortality in the United States from 1999-2020. A CDC WONDER Database Analysis
Bibliographic record
Abstract
Aims: This study aimed to analyze two decades of consecutive mortality data to investigate the association between cancer and cancer with heart failure across the United States (US), discerning patterns and disparities in mortality rates. Methods: Data were obtained from the multiple cause of death files using CDC WONDER spanning 1999 to 2020; ICD-10 codes were used to identify cancer and cancer with heart failure related deaths in adults aged ≥25. Demographic and regional distributions of mortality were analyzed. Joinpoint regression analysis was used to determine trends in age-adjusted mortality rates (AAMR) to estimate annual percentage changes (APC). Results: Between 1999 and 2020, 14,309,991 cancer-related deaths occurred in the US out of which 612,346 were associated with cancer and heart failure. The overall AAMR per 100,000 for cancer-related deaths decreased from 353.9 in 1999 to 260.9 in 2020 characterized by an annual percentage change (APC) of -1.60 spanning from 1999 to 2018, and an APC of 0.58 thereafter till 2020. AAMR per 100,000 for heart failure and cancer-related deaths decreased from 16.1 to 14.0, with varied APCs, declining from 1999 to 2013, reaching a minimum AAMR of 11 followed by a rise from 2013 to 2020. For cancer related only, men accounted for 52.7% of deaths, compared to 47.3% for women. Similarly, cancer with heart failure had mortality higher in males. Non-Hispanic (NH) White and Hispanic populations had the highest AAMRs for cancer related mortality while NH White and NH American Indian or Alaskan Native had the highest mortality in cancer with heart failure. Regional differences were observed, with the most cancer-related deaths observed in the South while the most cancer with heart failure related deaths occurred in the Midwest. State-wise stratification further supported the difference. Conclusions: Cancer-related mortality is decreasing while cancer with heart failure related mortality is increasing following initial decline. The highest AAMRs were observed for cancer related mortality among NH White population, men, people living in the South; and non-metropolitan US while cancer with heart failure had highest mortality in NH White population, men, people living in Midwest; and non-metropolitan areas. The findings underscore the need for focused interventions aimed at reducing mortality related to cancer and cancer with heart failure, particularly among vulnerable populations.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".