High-Expenditure Pharmaceutical Use Among Children in Medicaid
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Medication use may be a target for quality improvement, cost containment, and research. We aimed to identify medication classes associated with the highest expenditures among pediatric Medicaid enrollees and to characterize the demographic, clinical, and health service use of children prescribed these medications. METHODS: Retrospective, cross-sectional study of 3 271 081 Medicaid-enrolled children. Outpatient medication spending among high-expenditure medication classes, defined as the 10 most expensive among 261 mutually exclusive medication classes, was determined by using transaction prices paid to pharmacies by Medicaid agencies and managed care plans among prescriptions filled and dispensed in 2013. RESULTS: Outpatient medications accounted for 16.6% of all Medicaid expenditures. The 10 most expensive medication classes accounted for 63.9% of all medication expenditures. Stimulants (amphetamine-type) accounted for both the highest proportion of expenditures (20.6%) and days of medication use (14.0%) among medication classes. Users of medications in the 10 highest-expenditure classes were more likely to have a chronic condition of any complexity (77.9% vs 41.6%), a mental health condition (35.7% vs 11.9%), or a complex chronic condition (9.8% vs 4.3%) than other Medicaid enrollees (all P < .001). The 4 medications with the highest spending were all psychotropic medications. Polypharmacy was common across all high-expenditure classes. CONCLUSIONS: Medicaid expenditure on pediatric medicines is concentrated among a relatively small number of medication classes most commonly used in children with chronic conditions. Interventions to improve medication safety and effectiveness and contain costs may benefit from better delineation of the appropriate prescription of these medications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".