A comparative analysis of biologics market dynamics in 12 countries: (Bio)similar and sustainability
Bibliographic record
Abstract
Objective: We sought to evaluate the relationship between biosimilar introduction and the prices and expenditures of biologics in high-income countries. Methods: This study examined IQVIA-MIDAS sales data for biologics and biosimilars from January 2018 to June 2020 across 12 high-income countries (Australia, Austria, Canada, France, Germany, Italy, Japan, Korea, Spain, Sweden, Switzerland, and the United Kingdom). We selected seven biologics that fall under the World Health Organization Anatomical Therapeutic Chemical code L (Antineoplastic and immunomodulating agents), and categorized them into two groups based on the availability of biosimilar data. Group A consisted of biologics with biosimilars-etanercept, infliximab, rituximab, and trastuzumab, while Group B comprised biologics without biosimilars, including cetuximab, nivolumab, and pembrolizumab. A descriptive analysis was conducted to investigate the association between biosimilar introduction and trends in Fisher price index and relative expenditure. In addition, two-sample t-test and Wilcoxon rank-sum test were used to assess the statistical significance of differences in growth rates between the two groups. Results: Group A exhibited a declining trend in the Fisher price index, whereas Group B remained stable across most countries, with average compound quarterly growth rates being -2.19% and -0.54%, respectively (p < 0.001). Relative expenditure trends also revealed contrasting patterns between the groups across most countries, further highlighting the differences. The average compound quarterly growth rates for relative expenditures was -1.78% for Group A and 7.66% for Group B (p < 0.001). Conclusion: The introduction of biosimilars was significantly associated with reductions in the prices and expenditures of biologics in high-income countries. This underscores the potential role of biosimilars in supporting the long-term sustainability of the healthcare system.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".