National trends in prescription drug expenditures and projections for 2020
Bibliographic record
Abstract
PURPOSE: To report historical patterns of pharmaceutical expenditures, to identify factors that may influence future spending, and to predict growth in drug spending in 2020 in the United States, with a focus on the nonfederal hospital and clinic sectors. METHODS: Historical patterns were assessed by examining data on drug purchases from manufacturers using the IQVIA National Sales Perspectives database. Factors that may influence drug spending in hospitals and clinics in 2020 were reviewed, including new drug approvals, patent expirations, and potential new policies or legislation. Focused analyses were conducted for specialty drugs, biosimilars, and diabetes medications. For nonfederal hospitals, clinics, and overall (all sectors), estimates of growth of pharmaceutical expenditures in 2020 were based on a combination of quantitative analyses and expert opinion. RESULTS: In 2019, overall US pharmaceutical expenditures grew 5.4% compared to 2018, for a total of $507.9 billion. This increase was driven to similar degrees by prices, utilization, and new drugs. Adalimumab was the top drug in US expenditures in 2019, followed by apixaban and insulin glargine. Drug expenditures were $36.9 billion (a 1.5% increase from 2018) and $90.3 billion (an 11.8% increase from 2018) in nonfederal hospitals and clinics, respectively. In clinics, growth was driven by new products and increased utilization, whereas in hospitals growth was driven by new products and price increases. Several new drugs that will likely influence spending are expected to be approved in 2020. Specialty and cancer drugs will continue to drive expenditures. CONCLUSION: For 2020 we expect overall prescription drug spending to rise by 4.0% to 6.0%, whereas in clinics and hospitals we anticipate increases of 9.0% to 11.0% and 2.0% to 4.0%, respectively, compared to 2019. These national estimates of future pharmaceutical expenditure growth may not be representative of any particular health system because of the myriad of local factors that influence actual spending.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".