Decomposition Analysis of Spending and Price Trends for Biologic Antirheumatic Drugs in Medicare and Medicaid
Bibliographic record
Abstract
OBJECTIVE: Billions of public dollars are spent each year on biologic disease-modifying antirheumatic drugs (DMARDs), but the drivers of recent increases in biologic DMARD spending are unclear. This study was undertaken to characterize changes in total spending and unit prices for biologic DMARDs in Medicare and Medicaid programs and quantified the major sources of these spending increases. METHODS: We accessed drug spending data from years 2012-2016, covering all Medicare Part B (fee-for-service), Medicare Part D, and Medicaid enrollees. After calculating 5-year changes in total spending and unit prices for each biologic DMARD as well as in aggregate, we performed standard decomposition analyses to isolate 4 sources of spending growth: drug prices, uptake (number of recipients), treatment intensity (mean number of doses per claim), and treatment duration (annual number of claims per recipient), both excluding and including time-varying rebates. RESULTS: From 2012 to 2016, annual spending on public-payer claims for the 10 biologic DMARDs included in this study more than doubled ($3.8 billion to $8.6 billion), with median drug price increases of 51% in Medicare Part D (mean 54%) and 8% in Medicare Part B (mean 21%). With adjustment for general inflation, unit price increases alone accounted for 57% of the 5-year, $3.0 billion spending increase in Part D, while 37% of the spending increase was from increased uptake. Accounting for time-varying rebates, prices were still responsible for 54% of increased spending. Unit prices and spending were lower under Medicaid than under Medicare Part D, though temporal trends and contributors were similar. CONCLUSION: Postmarket drug price changes alone account for the majority of the recent spending growth in biologic DMARDs. Policy interventions targeting price increases, particularly those under Medicare Part D plans, may help mitigate financial burdens for public payers and biologic DMARD recipients.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".