The Burden of Hepatitis C Virus Infection Is Growing: A Canadian Population-Based Study of Hospitalizations from 1994 to 2004
Bibliographic record
Abstract
BACKGROUND: Nearly 1% of Canadians are infected with the hepatitis C virus (HCV). Simulation analyses have suggested that HCV will place an increasing burden on the health care system as the infected population ages, but supportive clinical data are limited. OBJECTIVES: To study temporal trends in HCV-related hospitalizations and predictors of increased health care utilization from a Canadian population-based perspective. METHODS: An administrative hospitalization database from the Calgary Health Region was used to identify patients who were admitted for HCV between 1994 and 2004. The primary outcomes were liver-related HCV hospitalizations, length of stay, hospital costs and in-hospital mortality. Average annual growth rates in outcomes were calculated and subgroup analyses were conducted according to age, sex and HIV/HCV coinfection status. RESULTS: Between 1994 and 2004, there were 4002 HCV-related hospitalizations; 22% were liver-related. Liver-related hospitalizations, lengths of stay and in-hospital mortality increased approximately fourfold or an average of 15% to 18% annually (P<0.0005). Patients aged 40 to 59 years and HIV/HCV coinfected patients experienced the largest average annual growth rates (19% to 27% and 30% to 40%, respectively; P<0.0005), reflecting the accelerated natural history of HCV in these subgroups. Hospital costs for liver-related HCV hospitalizations increased by an average of 41% annually (P=0.001) between 2000 and 2004. The average annual increase in liver-related hospitalizations remained significant in a sensitivity analysis, even when 75% of HCV cases were under-reported in 1994. CONCLUSIONS: The present studies' findings confirm the growing burden of HCV on the Canadian health care system. Strategies to prevent HCV infection and maximize the dissemination and most effective use of potentially curative antiviral therapies are necessary to reduce these trends.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".