Age trends in direct medical costs of pediatric asthma: a population-based study
Bibliographic record
Abstract
Background: Quantifying age trends in healthcare costs of pediatric asthma leads to better understanding of the natural history of the disease and informed decision-making on the allocation of healthcare resources. Methods: We identified children with incident asthma from the health administrative data of British Columbia, Canada (Jan 1998 to Dec 2015), and followed them from their first diagnosis of asthma or wheezing until age 18. We estimated direct medical costs (in 2016 Canadian dollars [$]), including inpatient and outpatient encounters and pharmacy costs, attributed to asthma (primary outcome) and other respiratory diseases (secondary outcome). We assessed the impact of sex and socioeconomic status on age trends, adjusting for calendar effect. Results: The final analysis included 44,552 children with asthma (62% boys). From age 0 to 18, costs of asthma/wheezing and other respiratory conditions decreased from $1,036 to $29/child-year, and from $1,145 to $31/child-year, respectively. Children under 3 years of age incurred 4–fold higher costs for asthma/wheezing and other respiratory conditions. In particular, costs of asthma hospitalizations were 10 times higher in this age group compared to older children. Age trends were generally similar between sex groups and across socioeconomic status. However, medication costs for asthma/wheezing decreased in boys, whereas those in girls declined during childhood but increased during adolescence. Conclusions: The highest costs of pediatric asthma are concentrated in children younger than 3. Age trends were generally consistent between sex and across socioeconomic status.
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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 | 0.004 |
| 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".