Canadian trends in spending on liver hospitalizations and transplants: 2004–2020
Bibliographic record
Abstract
Background: The incidence and prevalence of liver disease are increasing and contribute to significant morbidity and mortality. In Canada, more than 3 million people live with liver diseases, accounting for approximately 2% of all hospitalizations. However, it remains unclear how much liver hospitalizations cost the Canadian health care system. Thus, this study estimates the cost of liver-related hospitalization across Canada. Methods: We conducted a population-based, retrospective study using acute inpatient admission data for liver-related hospitalizations obtained from the Canadian Institute for Health Information. We calculated the total and the average nominal spending for liver hospitalizations nationally from April 1, 2004, to March 31, 2020, based on fiscal year (FY). In addition, we stratified the average liver hospitalization spending based on age and sex group. Results: Canada spent $947 million on liver-related hospitalizations in FY2019, a 145% growth in spending from FY2004. The average liver disease-related hospitalization was estimated to be $17,506 in FY2019. Within the sub-group analysis, the age group <30 showed the highest average cost per hospitalization at $21,776; however, there was no significant difference in cost between males and females. Across the different provinces in FY2019, Alberta experienced the highest average spending per hospitalization at $23,150, whereas Ontario had the lowest spending at $15,712. Conclusions: Liver-related hospitalizations are associated with high spending that is increasing nationally with variations across provinces and territories. Our results are of great use for economic evaluations of novel interventions in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".