E-013 Climate and stroke: using the climate vulnerability index to identify disparities in stroke burden and access to care
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,031 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».