The Influence of Urbanization on the Patterns of Hepatocellular Carcinoma Mortality From 1999 to 2020
Bibliographic record
Abstract
Background: Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related fatalities despite early diagnosis and treatment progress, creating a significant public health issue in the United States. 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 HCC mortality patterns and death locations from 1999 to 2020. The objective was to analyze trends in HCC mortality across different population groups, considering the impact of urbanicity. Methods: In this study, death certificate data obtained from the CDC WONDER database were utilized to investigate the trends in HCC mortality and location of death between 1999 and 2020. The annual percent change (APC) method was applied to estimate the average annual rate of change during the specified timeframe for the relevant health outcome. Furthermore, including data on the location of death and geographic areas allowed us to gain deeper insights into the patterns and characteristics of HCC and its impact on different regions. Results: Between 1999 and 2020, there were 184,073 reported deaths attributed to HCC, and data on the location of death were available for all cases. Most deaths occurred during inpatient admissions (34.93%) or at home (41.19%). The study also found that the highest age-adjusted mortality rate (AAMR) for HCC was observed among male patients, particularly among those identified as Asian or Pacific Islander. Variations in AAMR were determined based on the level of urbanization or rurality of the area, with higher rates observed in more densely populated and urbanized regions. In contrast, less urbanized and populated areas experienced a profound increase in AAMR over the past two decades. Conclusion: The HCC-related AAMRs have worsened over time for most ethnic groups, except for Asian or Pacific Islanders, which showed a reduction in APC despite having the worst AAMR. Although rural and less densely populated areas have substantially increased AAMR over the past two decades, more urbanized areas continued to have higher AAMR rates.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".