Is the Availability of Biosimilar Adalimumab Associated with Budget Savings? A Difference-in-Difference Analysis of 14 Countries
Bibliographic record
Abstract
The aim was to assess the influence of the presence of biosimilar adalimumab on adalimumab budget savings in 14 high- and upper-middle-income countries. This study analyzed Multinational Integrated Data Analysis System (MIDAS)-IQVIA data from the fourth quarter (Q4) of 2018 to the Q4 of 2019, comparing adalimumab expenditure (in United States dollars) and consumption (in standard units [SU]) across 14 countries (Australia, Austria, Brazil, Canada, France, Germany, Italy, Japan, Korea, Singapore, South Africa, Spain, Sweden, and Taiwan). The countries were divided into two groups based on the availability of adalimumab biosimilars during the study period. A difference-in-difference design was employed to analyze the groups, focusing on changes from Q4 2018 to Q4 2019. Additionally, changes in adalimumab expenditure were decomposed into price, quantity, and drug mix during the study period. Among countries with adalimumab biosimilars, there was a significant decrease in expenditure (− $371.0 per gross domestic product per capita; p = 0.03) over four quarters, while the consumption significantly increased (1.0 SU per 1000 population; p = 0.02). This was consistent with visual observations and differed from countries without adalimumab biosimilar. Sensitivity analysis with a narrowed list of countries (12 high-income countries) showed a consistent trend. Adalimumab expenditure decreased by 14% during the study period in countries where adalimumab biosimilars were available, mainly due to the price changes ( P t = 0.85; − 15%) and the drug-mix effect ( ε t = 0.88; − 12%). Yet, adalimumab expenditure ( E t = 1.04; +4%) changed in a quantity-dependent manner ( Q t = 1.06; +6%) in countries where adalimumab biosimilars were absent. The availability of biosimilars was associated with a decrease in adalimumab expenditure without compromising the consumption of adalimumab.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.010 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".