Projected Healthcare System Cost Burden of Metabolic Dysfunction-Associated Steatotic Liver Disease in Canada
Bibliographic record
Abstract
Background and Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common cause of chronic liver disease worldwide. The current and projected cost of treating individuals with MASLD in Canada remains unknown. Our objective was to calculate the projected liver-specific and total health-care costs for people living with MASLD in Canada from 2020 to 2050. Methods: The health-care usage of a cohort of patients diagnosed with MASLD in Calgary, Alberta was calculated using administrative data. Liver-specific encounters were identified and the average costs per year per patient were calculated. Projected costs were calculated by multiplying the average cost per patient within each health state by the projected prevalence of each health state. Results: There were 6358 patients in the cohort. The annual average liver-specific cost per patient was $7.02 for F0/F1, $35.30 for F2, $60.46 for F3, and $72.55 for F4. The projected Canada-wide liver-specific cost was $85.5 million in 2020 and was expected to increase by $51 million by 2050. The average annual total health-care cost per patient was $397.90 for F0/F1, $781.53 for F2, $2881.84 for F3, and $1598.82 for F4. Thus, the projected Canada-wide total health-care cost was $3.76 billion in 2020 and was expected to increase by almost $2 billion by 2050. Conclusion: These estimates underscore the need for a MASLD framework that focuses on both prevention and innovative care models to change the predicted trajectory of health-care costs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".