A 30-year time trend analysis of primary liver cancer incidence in Canada, Mongolia, and the United Kingdom, (1990 - 2019)
Bibliographic record
Abstract
Background: As one of the leading causes of cancer deaths, reducing primary liver cancer incidence remains a top global health priority. Overall, the most common causes of liver cancer are chronic hepatitis B and hepatitis C viral infections, dietary alcohol abuse, and non-alcoholic fatty steatohepatitis. Countries with historically high disease burden, such as China, have implemented public health strategies to reduce primary liver cancer incidence significantly. Conversely, countries like Mongolia have not experienced the same declines despite prolonged public health intervention. Historically, Western countries have largely avoided substantial disease burden, only to see upticks in incidence rates as etiology and populations have shifted. This study examines primary liver cancer incidence in Canada and the United Kingdom, representative of low burden North American and European populations, and Mongolia, a representative of a high burden Asian population, to analyze incidence trends over 30 years and evaluate for signs of disease reduction.Methods: Data on liver cancer incidence estimates were retrieved from the Global Burden of Disease 2019, covering 30 years from 1990 to 2019. Crude and age-standardized incidence rates and a web-based Age-Period-Cohort tool were calculated to determine age, period, and cohort effects. Hypothesis testing using Wald chi-squared testing was implemented to determine the statistical significance of the time trends observed.Results: Overall, populations in Canada, Mongolia, and the United Kingdom experienced increasing age-standardized incidence rates of primary liver cancer over the 30 years. Plotted longitudinal ASIR estimates reveal that Canadian and UK populations experienced steady increases in age-standardized incidence rates. In contrast, Mongolia peaked around 2010, withdrawing slightly to finish with a net increase. All populations experienced statistically significant positive increases in net drift and period effects. Regardless of gender, all UK populations experienced increased PLC risk after the 1955 reference cohort, while Canada mostly saw statistically significant increases.Conclusion: Exacerbated by shifting causal factors, asymptomatic disease progression, and limited early-detection strategies, liver cancer incidence continues to increase globally. Surveillance must guide data-driven public awareness campaigns, promote preventative lifestyle behaviors, and reduce liver cancer incidence among populations at highest risk.
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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 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".