Socioeconomic Disparities Explain Increased Exacerbations Among Black Patients With Severe Asthma
Bibliographic record
Abstract
BACKGROUND: In the United States (US), Black patients with asthma experience higher exacerbation rates compared with non-Black patients. OBJECTIVE: To identify factors that might explain the exacerbation rate association with race in a cohort of patients with severe asthma (SA). METHODS: CHRONICLE was an observational study of US adults with SA treated by allergists-immunologists or pulmonologists. The analysis population was patients not receiving biologic treatment. We used propensity score (PS) methods to identify factors associated with Black race. Non-Black-non-Hispanic or Latino patients (non-Black) were the control group. A generalized linear model (GLM) assessed the association between Black race and exacerbation rate, adjusted for the PS. RESULTS: Between February 2018 and July 2022, 180 Black and 574 non-Black patients were eligible for PS analysis. Socioeconomic status was the strongest discriminator of race (C statistic of 0.75), followed by environment (0.65), demographics (0.64), smoking status (0.55), and comorbidities (0.55). Before adjusting for PS, the GLM showed a 1.28-fold higher exacerbation rate among Black patients compared with non-Black patients (rate ratio = 1.28; 95% CI, 1.01-1.62; P = .039). In the PS-adjusted GLM, Black race was no longer associated with the exacerbation rate (rate ratio = 0.87; 95% CI, 0.56-1.35; P = .522). Results were similar for asthma-related emergency department visit and hospitalization rates. CONCLUSIONS: Higher exacerbation rates in Black patients with SA may be explained by factors associated with Black race, such as socioeconomic status. Addressing socioeconomic disparities and social determinants of health may help reduce the exacerbation risk difference observed between Black and non-Black patients with SA.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".