Examining the evolving landscape of liver cancer burden in the United States from 1990 to 2019
Bibliographic record
Abstract
INTRODUCTION: Liver cancer (LC) is frequently preceded by cirrhosis and poses a significant public health challenge in the United States (US). Recent decades have seen notable shifts in the epidemiological patterns of LC, yet national data guiding the optimal allocation of resources and preventive efforts remain limited. This study aims to investigate the current trends, risk factors, and outcomes of LC in the US. METHODS: This study utilized the Global Burden of Disease (GBD) dataset to collect data on the annual incident cases, deaths, Disability-Adjusted Life Years (DALYs), age-standardized incidence rates (ASIR), age-standardized death rates, and age-standardized DALY rates of primary LC and its etiologies and risk factors, between 1990 and 2019. Percentage changes in incident cases, DALYs, and deaths and the estimated annual percentage change (EAPC) in ASIR and deaths rates of LC were calculated to conduct temporal analysis. Linear regression was applied for the calculation of EAPCs. Correlations of EAPC with socio-demographic index (SDI) were separately evaluated by Pearson correlation analyses. RESULTS: We observed a marked increase in the ASIR of LC, increasing from 2.22 (95% CI: 2.15-2.27) per 100,000 people in 1990 to 5.23 (95% CI: 4.28-6.29) per 100,000 people in 2019, a percentage change of 135.4%. LC due to hepatitis C followed by alcohol use were the primary factors driving this increase. The ASIR and age-standardized death rates of LC showed a significant average annual increase of 3.0% (95% CI: 2.7-3.2) and 2.6% (95% CI: 2.5-2.8), respectively. There was a significant negative correlation between the SDI and the EAPC in ASIR (ρ = -0.40, p = 0.004) and age-standardized death rates (ρ = -0.46, p < 0.001). In 2019, drug and alcohol use, followed by elevated body mass index (BMI) were the primary risk factors for age-standardized DALY rates attributable to LC. CONCLUSION: The increased burden of LC in the US highlights the need for interventions. This is particularly important given that LC is mostly influenced by modifiable risk factors, such as drug and alcohol use, and elevated BMI. Our findings highlight the urgent need for public health interventions targeting socio-economic, lifestyle, and modifiable risk factors to mitigate the escalating burden of LC.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".