National trends in prescription drug expenditures and projections for 2019
Bibliographic record
Abstract
PURPOSE: Historical trends and factors likely to influence future pharmaceutical expenditures are discussed, and projections are made for drug spending in 2019 in nonfederal hospitals, clinics, and overall (all sectors). METHODS: Drug expenditure data through calendar year 2018 were obtained from the IQVIA National Sales Perspectives database and analyzed. New drug approvals, patent expirations, and other factors that may influence drug spending in hospitals and clinics in 2019 were also reviewed. Expenditure projections for 2019 for nonfederal hospitals, clinics, and overall (all sectors) were made through a combination of quantitative analyses and expert opinion. RESULTS: U.S. prescription sales in calendar year 2018 totaled $476.2 billion, a 5.5% increase from 2017 spending. The top 3 drugs by expenditures were adalimumab ($19.1 billion), insulin glargine ($9.3 billion), and etanercept ($8.0 billion). Prescription expenditures in nonfederal hospitals totaled $35.8 billion, a 4.8% increase from 2017. Expenditures in clinics in 2018 increased by 13.0% to $80.5 billion. The increase in spending in nonfederal hospitals was largely driven by new products and increased utilization of existing products. The list of the top 25 drugs by expenditures in nonfederal hospitals and clinics was dominated by specialty drugs. CONCLUSION: We predict continued moderate growth of 4-6% in overall drug expenditures (across the entire U.S. market). We expect the clinic sector to continue to experience high (11-13%) growth in drug spending in 2019. Finally, for nonfederal hospitals we anticipate growth in the range of 3-5%. These estimates are at the national level. Health-system pharmacy leaders should carefully examine local drug utilization patterns to determine their own organization's anticipated spending in 2019.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.006 | 0.002 |
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".