Insurance-Based Disparities in Stroke Center Access in California: A Network Science Approach

Author:

Zachrison Kori S.1ORCID,Hsia Renee Y.2ORCID,Schwamm Lee H.3ORCID,Yan Zhiyu1,Samuels-Kalow Margaret E.1ORCID,Reeves Mathew J.4ORCID,Camargo Carlos A.1ORCID,Onnela Jukka-Pekka5ORCID

Affiliation:

1. Departments of Emergency Medicine (K.S.Z., Z.Y., M.E.S.-K., C.A.C.), Massachusetts General Hospital and Harvard Medical School, Boston.

2. Department of Emergency Medicine, University of California San Francisco, San Francisco (R.Y.H.).

3. Neurology (L.H.S.), Massachusetts General Hospital and Harvard Medical School, Boston.

4. Department of Epidemiology and Biostatistics, Michigan State University, East Lansing (M.J.R.).

5. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (J.-P.O.).

Abstract

BACKGROUND: Our objectives were to determine whether there is an association between ischemic stroke patient insurance and likelihood of transfer overall and to a stroke center and whether hospital cluster modified the association between insurance and likelihood of stroke center transfer. METHODS: This retrospective network analysis of California data included every nonfederal hospital ischemic stroke admission from 2010 to 2017. Transfers from an emergency department to another hospital were categorized based on whether the patient was discharged from a stroke center (primary or comprehensive). We used logistic regression models to examine the relationship between insurance (private, Medicare, Medicaid, uninsured) and odds of (1) any transfer among patients initially presenting to nonstroke center hospital emergency departments and (2) transfer to a stroke center among transferred patients. We used a network clustering method to identify clusters of hospitals closely connected through transfers. Within each cluster, we quantified the difference between insurance groups with the highest and lowest proportion of transfers discharged from a stroke center. RESULTS: Of 332 995 total ischemic stroke encounters, 51% were female, 70% were ≥65 years, and 3.5% were transferred from the initial emergency department. Of 52 316 presenting to a nonstroke center, 3466 (7.1%) were transferred. Relative to privately insured patients, there were lower odds of transfer and of transfer to a stroke center among all groups (Medicare odds ratio, 0.24 [95% CI, 0.22–0.26] and 0.59 [95% CI, 0.50–0.71], Medicaid odds ratio, 0.26 [95% CI, 0.23–0.29] and odds ratio, 0.49 [95% CI, 0.38–0.62], uninsured odds ratio, 0.75 [95% CI, 0.63–0.89], and 0.72 [95% CI, 0.6–0.8], respectively). Among the 14 identified hospital clusters, insurance-based disparities in transfer varied and the lowest performing cluster (also the largest; n=2364 transfers) fully explained the insurance-based disparity in odds of stroke center transfer. CONCLUSIONS: Uninsured patients had less stroke center access through transfer than patients with insurance. This difference was largely explained by patterns in 1 particular hospital cluster.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Cardiology and Cardiovascular Medicine

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