National trends in prescription drug expenditures and projections for 2021
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 2021 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 2021 were reviewed-including new drug approvals, patent expirations, and potential new policies or legislation. Focused analyses were conducted for biosimilars, cancer drugs, generics, coronavirus disease 2019 (COVID-19) pandemic influence, and specialty drugs. For nonfederal hospitals, clinics, and overall (all sectors), estimates of growth of pharmaceutical expenditures in 2021 were based on a combination of quantitative analyses and expert opinion. RESULTS: In 2020, overall pharmaceutical expenditures in the United States grew 4.9% compared to 2019, for a total of $535.3 billion. Utilization (a 2.9% increase) and new drugs (a 1.8% increase) drove this increase, with price changes having minimal influence (a 0.3% increase). Adalimumab was the top drug in 2020, followed by apixaban and insulin glargine. Drug expenditures were $35.3 billion (a 4.6% decrease) and $98.4 billion (an 8.1% increase) in nonfederal hospitals and clinics, respectively. In clinics, growth was driven by new products and increased utilization, whereas in hospitals the decrease in expenditures was driven by reduced utilization. Several new drugs that will influence spending are expected to be approved in 2021. Specialty and cancer drugs will continue to drive expenditures along with the evolution of the COVID-19 pandemic. CONCLUSION: For 2021, we expect overall prescription drug spending to rise by 4% to 6%, whereas in clinics and hospitals we anticipate increases of 7% to 9% and 3% to 5%, respectively, compared to 2020. 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".