Global cirrhosis prevalence trends and attributable risk factors–an ecological study using data from 1990–2019
Bibliographic record
Abstract
BACKGROUND AND AIMS: Cirrhosis is a major public health issue worldwide with significant morbidity and mortality. We aimed to explore the time series associations between varying levels of risk factors and cirrhosis prevalence and predict the cirrhosis prevalence under alternative scenarios to consolidate evidence for further intervention plans. METHODS: We collected data of cirrhosis and its risk factors from 1990 to 2019 across 178 countries and used a generalized linear mixed model to explore the time series associations between cirrhosis and risk factors. We simulated scenarios with varying levels of risk factors and investigated benefits gained from the control of risk factors compared with the status quo. RESULTS: The global cirrhosis prevalence varied geographically, with the highest observed in East and Southeast Asia, mainly due to high hepatitis prevalence. Our study revealed that each 1% increase in prevalence of hepatitis B and C, cirrhosis prevalence would correspondingly increase 0.028% and 0.288%. There would be approximately 392.15 million fewer cirrhosis patients if the goals of a 65% reduction in prevalence of hepatitis and a 10% reduction in alcohol consumption were achieved. CONCLUSIONS: Given that cirrhosis prevalence has different risk factors depending on geography, it is important to identify an appropriate set of interventions for cirrhosis that are adapted to the epidemiological situation in a specific country. Interventions targeting hepatitis may have a significant impact on global cirrhosis prevalence, therefore, the adoption of specific interventions for hepatitis in high-burden regions and high-risk groups is warranted to lower the global burden of cirrhosis.
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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 | 0.002 | 0.004 |
| 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) | 0.001 | 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".