Publicly Funded Oral Chronic Hepatitis B Treatment Patterns in Ontario over 16 Years: An Ecologic Study
Bibliographic record
Abstract
BACKGROUND: Reimbursement for the use of hepatitis B virus (HBV) treatments has not been previously reported for public payers. OBJECTIVE: To describe the number of users and total cost of HBV treatments over the last 16 years among residents of Ontario, Canada, who were covered by the public drug program. METHODS: We conducted a repeated cross-sectional study for HBV treatments reimbursed by the public drug program in Ontario from January 1, 2000, to December 31, 2015. We projected total spending to 2020 based on current utilization trends. RESULTS: HBV drug users per year increased 30-fold, from 132 users in 2000 to 4,035 users in 2015. Total spending on HBV treatments increased 150-fold, from $136,368 annually in 2000 to $21.0 million in 2015. The spending on HBV agents is projected to increase by 65%, with an estimated drug cost of $34.6 million by 2020. CONCLUSIONS: Although not reimbursed as first-line therapy, tenofovir disoproxil fumarate has become the most commonly reimbursed HBV treatment and was associated with an increase in HBV treatment use and total spending. Results of this study found that rapid growth of HBV treatments led to a sustained increase in spending for public payers in Ontario. DISCLOSURES: This study was funded by grants from the Ontario Ministry of Health and Long-Term Care (MOHLTC) and Ontario Strategy for Patient-Orientated Research (SPOR) Support Unit, which is supported by the Canadian Institutes of Health Research and the Province of Ontario. This study was also supported by the Institute for Clinical Evaluative Sciences (ICES), a non-profit research institute sponsored by the Ontario MOHLTC. The opinions, results, and conclusions reported in this article are those of the authors and are independent from the funding sources. No endorsement by ICES or the Ontario MOHLTC is intended or should be inferred. Parts of this material are based on data and information compiled and provided by the Canadian Institute for Health Information (CIHI). However, the analyses, conclusions, opinions and statements expressed herein are those of the authors and not necessarily those of CIHI. Mamdani has received honoraria from Boehringer Ingelheim, Pfizer, Bristol-Myers Squibb, and Bayer. Janssen has received research support, consulting, and/or speaking fees from Gilead, Roche, Merck, AbbVie, Bristol-Myers Squibb, Arbutus, Janssen, and MedImmune. No other authors have any conflicts of interest to declare.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".