Trends in Pediatric Blood Pressure–Lowering Prescription Fills During 2017–2023
Bibliographic record
Abstract
Introduction There are no national estimates for blood pressure–lowering prescription trends among the U.S. pediatric population. This study describes trends in blood pressure–lowering prescription fills among individuals aged 3–17 years by sex and age group. Methods Data were obtained from IQVIA's Total Patient Tracker database covering 94% of all outpatient retail prescription fills in the U.S. The key outcome was blood pressure–lowering prescription fills during 2017–2023, utilizing a list of 113 generic medications from 21 drug classes. In addition, a subset of 20 medications recommended in the 2017 American Academy of Pediatrics guideline was examined. Annual population percentage and percentage change compared with 2017 were reported, and average annual percentage change was estimated using Joinpoint regression. Results From 2017 to 2023, blood pressure–lowering prescription fills among those aged 3–17 years increased slightly from 1.93% (95% CI=1.88%, 1.98%) to 2.09% (95% CI=2.04%, 2.14%). Among males, blood pressure–lowering prescription fills remained stable (between 2.32% and 2.38%; average annual percentage change=−0.3%; p =0.545), whereas fills among females increased by 23.9% (from 1.49% to 1.84%; average annual percentage change=4.16%; p <0.001). The sharpest increase occurred among females aged 13–17 years (from 2.26% to 3.17%; average annual percentage change=6.3%; p <0.001). Prescription fills for guideline-recommended medications either remained stable or declined, with some variation by sex and age group. Conclusions Results indicate growth in blood pressure–lowering prescription fills, especially among females aged 13–17 years. Increases were driven by medications not included in the 2017 American Academy of Pediatrics guideline, suggesting that blood pressure–lowering medications may be increasingly prescribed for conditions other than pediatric hypertension.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".