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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 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".