Health Care Resource Utilization and Costs Associated with Childhood Hypertension: A Population-Based Cohort Study
Bibliographic record
Abstract
Background: Pediatric hypertension has risen significantly in recent decades and has been linked to long-term cardiovascular and kidney-related morbidity. However, healthcare resource utilization and costs associated with pediatric hypertension are unclear. This study aims to compare healthcare resource use and costs between children with and without hypertension. Methods: Population-based retrospective cohort study of children aged 3-18 years diagnosed with hypertension from 1996 to 2021 in Ontario, Canada, using validated case definitions in health administrative databases. Each case was propensity score-matched with five controls without hypertension. Children were followed until death, provincial emigration, or March 31, 2022. Our primary outcome was rates of healthcare system utilization, including hospitalizations, emergency department(ED), and outpatient physician visits, analyzed by negative binomial regression. Secondary outcomes were total healthcare system costs and specialist physician follow-up. Results: We matched 25,605 children diagnosed with hypertension to 128,025 non-hypertensive controls. Baseline covariates were balanced after propensity score matching. Median age was 15 years[IQR 11-17], 49% were female, and prior comorbidities were uncommon(1% congenital heart disease, 1.7% malignancy, 0.4% diabetes). During median 12.9-year[IQR 6.8-19.9] follow-up, hypertensive children were more likely to be hospitalized(rate ratio [RR] 2.13, 95%CI 2.03-2.22, incidence rate [IR] 105.5 vs 62.8 events per 1000 person-years). Hypertensive children were also more likely to have an ED visit(RR 1.08, 95%CI 1.05-1.11) and outpatient visit(RR 1.33, 95%CI 1.31-1.34). Within 1 year of hypertension diagnosis, 40% of children saw a pediatrician, 24% saw a cardiologist, and 5% saw a nephrologist. Hypertension was associated with substantially higher total healthcare costs throughout follow-up(median $1375 vs. $384 per person-year). Conclusion: Healthcare utilization and costs were significantly higher among children and adolescents diagnosed with hypertension than matched non-hypertensive children. These results provide a basis for future cost-effectiveness studies of strategies to prevent childhood hypertension occurrence and late complications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.000 | 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".