Trends in Colorectal Cancer Mortality in the United States, 1999 - 2020
Bibliographic record
Abstract
Background: The United States faces a significant public health issue with colorectal cancer (CRC), which remains the third leading cause of cancer-related fatalities despite early diagnosis and treatment progress. Methods: This investigation utilized death certificate data from the Centers for Disease Control and Prevention Wide-Ranging OnLine Data for Epidemiologic Research (CDC WONDER) database to investigate trends in CRC mortality and location of death from 1999 to 2020. Additionally, the study utilized the annual percent change (APC) to estimate the average annual rate of change over the specific time period for the given health outcome. Incorporating the location of death in this study served the purpose of identifying patterns related to CRC and offering valuable insights into the specific locations where deaths occurred. Results: Between 1999 and 2020, there were 1,166,158 CRC-related deaths. The age-adjusted mortality rates (AAMRs) for CRC consistently declined from 20.7 in 1999 to 12.5 in 2020. Men had higher AAMR (18.8) than women (13.4) throughout the study. Black or African American patients had the highest AAMR (21.1), followed by White (15.4), Hispanic/Latino (11.8), American Indian or Alaska native (11.4), and Asian or Pacific Islanders (10.2). The location of death varied, with 41.99% at home, 28.16% in medical facilities, 16.6% in nursing homes/long-term care facilities, 7.43% in hospices, and 5.80% at other/unknown places. Conclusion: There has been an overall improvement in AAMR among most ethnic groups, but an increase in AAMR has been observed among white individuals below the age of 55. Notably, over one-quarter of CRC-related deaths occur in medical facilities.
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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 | 0.002 | 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) | 0.002 | 0.001 |
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".