National Versus State-Level Racial Disparities in Acute Stroke Interventions Using Get With The Guidelines-Stroke Data
Bibliographic record
Abstract
BACKGROUND: Racial disparities have been reported in stroke care, but understanding if there is regional variability is critical to focusing policies and resources. Here, we sought to study racial and ethnic inequity in the administration of thrombolysis and thrombectomy at the national and state levels. METHODS: We conducted a retrospective cohort study using Get With The Guidelines-Stroke Program registry data from 2003 to 2022 to evaluate racial disparities in the administration of acute stroke treatments in US patients. We used mixed-effects modeling to analyze national and state-level disparities, adjusting for relevant demographic, clinical, and hospital-level characteristics. RESULTS: A total of 660 369 patients were eligible for thrombolysis and 105 184 patients for thrombectomy. The mean age was 70.21±14.48 years, and 50.18% were female. The race/ethnic distribution was 69.06% of non-Hispanic White, 16.88% of non-Hispanic Black, 7.02% of Hispanic, 2.84% of Asian, and 4.20% of American Indian/Alaska Native/Hawaiian/Pacific Islander patients. Eligible non-Hispanic Black patients had statistically higher thrombolytic rates compared with non-Hispanic White patients (adjusted odds ratio [aOR], 1.04 [95% CI, 1.03–1.06]), indicating no racial disparities in thrombolytic treatment at the national level. Similarly, equal or higher rates of thrombolytic administration were noted in other race/ethnic groups at the national level (Asian: aOR, 1.12 [95% CI, 1.09–1.16]; Hispanic: aOR, 1.14 [95% CI, 1.12–1.17]; and other: aOR, 1.10 [95% CI, 1.07–1.13]; P <0.0001). However, when non-Hispanic Black patients were compared with non-Hispanic White patients at the individual state level, there were disparities in many of the stroke-belt states. Racial disparities remained significant at the national level between non-Hispanic Black and non-Hispanic White patients and eligible thrombectomy patients after adjusting for patient- and hospital-level covariates (aOR, 0.85 [95% CI, 0.82–0.89]; P <0.0001). CONCLUSIONS: These data suggest that racial/ethnic disparities in stroke care vary depending on the intervention and geographic location. Equitable utilization of thrombolysis nationally may underscore the benefits of quality improvement initiatives though state-level inequities persist. Endovascular thrombectomy utilization demonstrated race-based disparities in use, and further efforts are needed to ensure equitable care of patients with stroke in the United States.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".