Examining the prevalence of hepatic steatosis and advanced fibrosis using non-invasive measures across Canada: A national estimate using the Canadian Health Measures Survey (CHMS) from 2009-2019
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Prevalence estimates are crucial for enhancing preparedness to prevent and manage chronic diseases. This is the first study to estimate the prevalence of hepatic steatosis and advanced fibrosis in Canada, leveraging a nationally representative survey and multiple validated non-invasive tests (NITs). MATERIALS AND METHODS: The Canadian Health Measures Survey (CHMS) is Canada's largest direct health measures survey, which collects data on sociodemographic, clinical factors, and blood chemistry. We determined steatosis using two NITs: the Hepatic Steatosis Index (HSI) and the NAFLD Ridge Score (NRS). The FIB-4 Index and NAFLD fibrosis score (NFS) were used to assess the risk of advanced fibrosis among adults with steatosis. Survey weights were incorporated to account for oversampling, survey nonresponse, and post-stratification. RESULTS: Between 2009 and 2019, 1365 children (55 % males, median age 13 (IQR: 10-15) and 4664 adults (51 % males, median age 45 (IQR: 34-62), 57 % reporting weekly alcohol consumption) were included in our study. The weighted steatosis prevalence ranged from 9 to 11 % among children to 38-48 % among adults based on the NRS and HSI, respectively. Between 86-87 % of adults with type 2 diabetes and 65-72 % with hypertension had evidence of steatosis. Overall, 1.2-2.4 % of adults with steatosis were at risk of advanced liver fibrosis. CONCLUSIONS: We estimate between 1 in 3 and 1 in 2 adults have hepatic steatosis, and 195,000-406,200 are at high risk of advanced liver fibrosis in Canada. No routine screening guidelines for liver fibrosis exist in Canada, and most patients are unaware of their condition. Prevalence studies are essential for raising awareness and advocating for the inclusion of steatotic liver disease on national public health agendas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".