Chronic Medication Use in Children Insured by Medicaid: A Multistate Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Little is known about the use of chronic medications (CMs) in children. We assessed the prevalence of CM use in children and the association of clinical characteristics and health care resource use with the number of CMs used. METHODS: This is a retrospective study of children ages 1 to 18 years using Medicaid from 10 states in 2014 grouped by the annual number of CMs (0, 1, 2-4, 5-9, and ≥10 medications), which are defined as a dispensed ≥30-day prescription with ≥2 dispensed refills. Trends in clinical characteristics and health care use by number of CMs were evaluated with the Cochran-Armitage trend test. RESULTS: Of 4 594 061 subjects, 18.8% used CMs. CM use was 44.4% in children with a complex chronic condition. Across all children, the most common CM therapeutic class was neurologic (28.9%). Among CM users, 48.8% used multiple CMs (40.3% used 2-4, 7.0% used 5-9, and 0.5% used ≥10). The diversity of medications increased with increasing number of CMs: for 1 CM, amphetamine stimulants were most common (29.0%), and for ≥10 CMs, antiepileptics were most common (7.1%). Of $2.3 billion total pharmacy spending, 59.3% was attributable to children dispensed multiple CMs. Increased CM use (0 to ≥10 medications) was associated with increased emergency department use (32.1% to 56.2%) and hospitalization (2.3% to 36.7%). CONCLUSIONS: Nearly 1 in 5 children with Medicaid used CMs. Use of multiple CMs was common and correlated with increased health care use. Understanding CM use in children should be fundamentally important to health care systems when strategizing how to provide safe, evidence-based, and cost-effective pharmaceutical care to children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".