Trends in Prescription Drug Utilization and Spending for the Department of Defense, 2002–2007
Bibliographic record
Abstract
OBJECTIVE: Examine trends in U.S. Department of Defense (DoD) outpatient drug spending and utilization between 2002 and 2007. METHODS: We analyzed pharmacy claims data from the U.S. Military Health System (MHS), using a cross-sectional analysis at the prescription and patient-year level and measuring utilization in 30-day equivalent prescriptions and expenditures in dollars. RESULTS: Pharmaceutical spending more than doubled in DoD, from $3 billion in FY02 to $6.5 billion in FY07. The largest increase occurred in the DoD community pharmacy network, where utilization grew from 6 million 30-day equivalent prescriptions in the first quarter of FY02 to more than 16 million in the last quarter of FY07. The smallest increase in annual spending occurred in FY07 (5.5%), down from a high of 27.5% in FY03. CONCLUSIONS: The MHS has experienced rapid growth in pharmaceutical spending since FY02. However, there are signs that growth in pharmaceutical spending may be slowing.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".