E-013 Climate and stroke: using the climate vulnerability index to identify disparities in stroke burden and access to care
Bibliographic record
Abstract
The Lancet Countdown on health and climate change declared a ‘code red for a healthy future’ in 2021. Worsening air quality and temperature extremes are linked to increased incidence of stroke and disproportionately impact those who already experience a greater health burden, such as people with lower income or people who are underserved by health resources. We examined the association between stroke burden, distance to comprehensive stroke centers (CSC), and climate vulnerability as measured by the U.S. Climate Vulnerability Index (CVI). The CVI is a data-driven tool developed by the Environmental Defense Fund (EDF) that explores the intricate intersections of climate, environment, health, and drivers of neighborhood-level climate resilience. Neighborhood-level stroke prevalence data for the entire US was ascertained from the U.S. Centers for Disease Control and Prevention PLACES website and climate vulnerability rank scores were obtained from the Environmental Defense Fund’s (EDF) CVI dashboard. Analysis and the resulting maps were developed nationally and regionally using US census tract level data to evaluate: 1) the spatial differences in stroke prevalence between CVI score decile groups and 2) the differences in distance to a CSC between CVI score decile groups for 73,057 census tracts. Additionally, best fit linear regression models were developed using CVI as the independent variable to regress: 1) stroke prevalence and 2) distance to a CSC. Data were further stratified by redlining categories, and rural vs urban designations. Statistical analyses were performed in R. Stroke prevalence increased with CVI score throughout the US. Regionally, these relationships were strongest in the Southeast, (R2 = 0.34, p-value < 0.001) and the Midwest (R2 = 0.37, p-value < 0.001). For the entire US, stroke prevalence was 1.5 times higher in census tracts with highest climate vulnerability (i.e. 100th percentile CVI score), as compared to those census tracts in the 50th percentile CVI. The association between stroke burden and CVI was explained largely by a community’s baseline health, socioeconomic, and infrastructural disparities. There was a strong spatial overlap between rural counties and high CVI-stroke prevalence areas. In a more detailed visual analysis of the Southeastern US, in the metropolitan area of Atlanta, Georgia, we found historically red-lined neighborhoods clearly overlapped with high CVI-high stroke prevalence census tracts. Nationally, census tracts with the highest CVI scores (in the 100th percentile group) were 2.67 times farther from a CSC than neighborhoods ranking in the 50th percentile [44.6 vs 16.7 km, p<0.05]. In the Southeast, the distance difference between these groups was 1.8 times farther for the high CVI census tracts [60.8 vs 33.3 km, p<0.05]. Increasing climate vulnerability correlates with increased stroke burden and further distance to CSC throughout the USA and Southeast. The CVI - stroke burden association is explained by underlying baseline health, socioeconomic, and infrastructure disparities. These findings highlight the need for targeted public health interventions and resources to address the underlying drivers of the climate vulnerability stroke association as our worsening climate crisis threatens to exacerbate disparities in vulnerable populations. Disclosures R. Achey: None. A. Managan: None. Y. Fujii: None. M. Makhlouf: None. L. Schieb: None. B. Chekuri: None. E. Gillespie: None. T. O’Connor: None. M. Bain: None. N. Moore: None. M. Hussain: None. L. Walker: None. P. Tee Lewis: None.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".