A Budget Impact Analysis of the Introduction of Erythropoiesis Stimulating Agent Subsequent Entry Biologics for the Treatment of Anemia of Chronic Kidney Disease in Canada
Bibliographic record
Abstract
BACKGROUND: In Europe, epoetin subsequent entry biologics (SEBs) have been in use since 2007. Canadian patents of erythropoietin stimulating agents are expiring in 2014, therefore it is predicted that epoetin SEBs will penetrate the Canadian market in the near future. OBJECTIVE: To estimate the economic impact and costs offsets associated with the uptake and use of one or more epoetin SEBs in Canada for the treatment of anemia in chronic kidney disease. DESIGN: A Canada-wide epidemiological-based budget impact analysis was conducted to estimate cost outcomes under two scenarios: with and without the availability of epoetin SEB. SETTING: The analysis was conducted from the perspective of the Canadian healthcare payer, over a 5-year time horizon from 2015 to 2019. PATIENTS: Patients included in the model were those with chronic kidney disease stages 3 to 5, who have renal anemia and require treatment with erythropoietin stimulating agents. METHODS: Only direct medical costs pertaining to drug acquisition of currently available erythropoietin stimulating agents in Canada were incorporated in the model. Cost of epoetin SEBs, market shares, uptake rates, and other model inputs were estimated from published sources or databases. No discounting of future costs was applied. RESULTS: Based on our analysis, under market phenomena similar to those seen in the Europe and without considering potential switching from originator epoetin to epoetin SEB, we could expect that Canadian adoption of epoetin SEBs could result in $35 million (2013 CAD, year 1) to $50 million (year 5) cost savings annually, with cumulative savings of $221 million after 5 years. In one-way sensitivity analyses, model variables with substantial impact on cost savings were the prevalence of chronic kidney disease and epoetin SEB uptake rates. LIMITATIONS: We did not take into account costs associated with overhead, administration, or adverse events. CONCLUSION: The advent of SEBs represents an opportunity to relieve financial pressure in our healthcare system. Under the assumption that the efficacy and safety of epoetin SEBs are non-inferior to originator products, we have shown that the adoption of epoetin SEBs can lead to cost savings for the Canadian payer.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".