NAFLD and Alcohol‐Associated Liver Disease Will Be Responsible for Almost All New Diagnoses of Cirrhosis in Canada by 2040
Bibliographic record
Abstract
BACKGROUND AND AIMS: Mortality secondary to cirrhosis in North America is increasing. We describe the incidence of cirrhosis stratified by birth cohort and cirrhosis etiology and project disease burden to 2040. APPROACH AND RESULTS: This is a retrospective cohort study in Ontario, Canada, using population-based administrative health care data. Individuals with incident cirrhosis (2000-2017) were identified, and etiology was defined as HCV, HBV, NAFLD, alcohol-associated liver disease (ALD), or autoimmune liver disease/other using validated case definitions. Annual age/sex-adjusted cirrhosis incidence rate per 100,000 person-years was calculated with incidence projection to 2040 using age-period-cohort modeling along with average annual percent change (AAPC) in cirrhosis incidence stratified by birth cohort and etiology. In total, 159,549 incident cases of cirrhosis were identified. Incidence increased by 26% with an AAPC of 2%/year (95% CI, 1.6-2.4; P < 0.001). The largest increases were for HCV (AAPC, 4.1%/year; 95% CI, 2.6-5.7; P < 0.001) and NAFLD (AAPC, 3.3%/year; 95% CI, 2.6-4.1%; P < 0.001). ALD and HCV cirrhosis in those born >1980 increased by 11.6%/year (95% CI, 9.3-13.9; P < 0.001) and 9.5%/year (95% CI, 6.2-13.0; P < 0.001), respectively. However, by 2040, cirrhosis incidence is projected to continue to increase, driven mostly by NAFLD, especially in postmenopausal women, and ALD in individuals born >1980. CONCLUSIONS: Cirrhosis incidence will continue to increase over the next two decades secondary to NAFLD with a worrisome rapid rise in ALD cirrhosis among young adults. Public education, policy, and intervention targeting NAFLD risk factors and alcohol use in young adults are urgently needed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".