Childhood asthma diagnoses declined during the COVID-19 pandemic in the United States
Bibliographic record
Abstract
BACKGROUND: Prior studies have documented declines in pediatric asthma exacerbations and asthma-related health care utilization during the COVID-19 pandemic, but less is known about the incidence of asthma during the pandemic. METHODS: We conducted a retrospective cohort study of children under age 18 without a prior diagnosis of asthma within a large US commercial claims database. Incident asthma was defined using a combination of diagnosis codes, location of services, and medication dispensing. Crude quarterly rates of asthma diagnosis per 1000 children were calculated, and the incidence rate ratio and 95% confidence interval were estimated for newly diagnosed asthma during versus before the pandemic using negative binomial regression, adjusted for age, sex, region, and season. RESULTS: Compared with 3 years prior to the pandemic, crude incident diagnosis rates of asthma decreased by 52% across the first four quarters of the US pandemic. The covariate-adjusted pandemic-associated incidence rate ratio was 0.47 (95% confidence interval 0.43, 0.51). CONCLUSIONS: New diagnoses of childhood asthma in the US declined by half during the first year of the pandemic. These findings raise important questions whether pandemic-related changes in infectious or other triggers truly altered the incidence of childhood asthma beyond the well-described disruptions in healthcare access.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.018 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".