Principal components analysis of drug expenditure and utilisation trends for major therapeutic classes in US Medicaid programmes
Bibliographic record
Abstract
OBJECTIVE: Drug expenditures have been increasing much faster than spending on other medical services and have become burdensome for state Medicaid programmes. The purpose of this study was to analyse these trends across major therapeutic classes and to identify their similarities and differences. METHODS: Using national claims data from the Centers for Medicare & Medicaid Services for 1991 quarter 1 through to 2004 quarter 4, expenditures and prescriptions were aggregated across all drugs in 64 different therapeutic classes, providing 128 (64 x 2) different time series, most of length 56 quarters. Principal components analysis (PCA) was then applied to the data. RESULTS: PCA revealed three principal components that accounted for 90% (92%) of total variation in Medicaid drug expenditure (utilisation) patterns. The first principal component (PC1), explaining 66% (67%) of the variation, is an exponential-like upward trend; PC2, explaining 17% (14%) of the variation, represents an increasing-then-decreasing pattern; and PC3, explaining 7% (11%) of the variation, represents an up-and-down cyclical pattern. Highly correlated with PC1 are antiretrovirals, antiseizure agents and corticoid steroids, among other drug classes. CONCLUSION: Most drug therapeutic classes exhibited exponential-like upward expenditure (and utilisation) trends, clearly illustrating the overall rising expenditure burden for Medicaid.
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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 | 0.000 | 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".