Impact of insurance status on MRI phenotypes in MS.

Health disparities Magnetic resonance imaging Multiple sclerosis Retrospective study

Journal

Multiple sclerosis and related disorders
ISSN: 2211-0356
Titre abrégé: Mult Scler Relat Disord
Pays: Netherlands
ID NLM: 101580247

Informations de publication

Date de publication:
09 Oct 2024
Historique:
received: 08 03 2024
revised: 25 07 2024
accepted: 05 10 2024
medline: 16 10 2024
pubmed: 16 10 2024
entrez: 15 10 2024
Statut: aheadofprint

Résumé

Health insurance in the United States varies in coverage of essential diagnostic tests, therapies, and specialists. Health disparities between privately and publicly insured patients with MS have not been comprehensively assessed. The objective of this study is to evaluate the impact of public versus private insurance on longitudinal brain outcomes in MS. Lesional, thalamic, and gray and white matter volumes were extracted from longitudinal MRI of 710 MS patients. Baseline volumes and atrophy rates of lesional, thalamic, and gray and white matter volumes were compared across insurance groups. After image quality assessment, 376 (284 private / 92 public), 638 (499 / 139), and 331 (250 / 81), patients were in MS lesion, thalamic, gray and white matter analyses respectively. Baseline lesion volume was higher for publicly insured patients but increased at a slightly higher rate in those privately insured (p = 0.01). Baseline gray matter measurements were lower for patients with public insurance, but thalamic (p < 0.01) and gray matter (p < 0.01) atrophy rates were slightly higher in the private insurance group. Insurance type was associated with lesion, thalamic, and gray matter volumes. The results suggest that patients with public insurance may present with more advanced disease.

Sections du résumé

BACKGROUND BACKGROUND
Health insurance in the United States varies in coverage of essential diagnostic tests, therapies, and specialists. Health disparities between privately and publicly insured patients with MS have not been comprehensively assessed. The objective of this study is to evaluate the impact of public versus private insurance on longitudinal brain outcomes in MS.
METHODS METHODS
Lesional, thalamic, and gray and white matter volumes were extracted from longitudinal MRI of 710 MS patients. Baseline volumes and atrophy rates of lesional, thalamic, and gray and white matter volumes were compared across insurance groups.
RESULTS RESULTS
After image quality assessment, 376 (284 private / 92 public), 638 (499 / 139), and 331 (250 / 81), patients were in MS lesion, thalamic, gray and white matter analyses respectively. Baseline lesion volume was higher for publicly insured patients but increased at a slightly higher rate in those privately insured (p = 0.01). Baseline gray matter measurements were lower for patients with public insurance, but thalamic (p < 0.01) and gray matter (p < 0.01) atrophy rates were slightly higher in the private insurance group.
CONCLUSION CONCLUSIONS
Insurance type was associated with lesion, thalamic, and gray matter volumes. The results suggest that patients with public insurance may present with more advanced disease.

Identifiants

DOI: 10.1016/j.msard.2024.105919 PMID: 39406154
pubmed: 39406154
pii: S2211-0348(24)00495-4
doi: 10.1016/j.msard.2024.105919
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105919

Informations de copyright

Copyright © 2024. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of competing interest Erica B. Baller received funding by grant K23 MH133118. The following authors have nothing to declare: Melissa Lynne Martin, Timothy Robert-Fitzgerald, Matthew K. Schindler, Christopher Perrone, Guy Schultz, Selah Lynch, Nebojsa Mirkovic, Sunil Thomas, Ameena Elahi, Donovan Reid, Tyler M. Moore, Erica Baller, Theodore D. Satterthwaite, Matthew Cieslak, Sydney Covitz, Azeez Adebimpe, Abigail Manning, Clyde E. Markowitz, John A. Detre, Amit Bar-Or, Mihir Kakara, and Russell T. Shinohara

Auteurs

Melissa Lynne Martin (ML)

Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: martin30@pennmedicine.upenn.edu.

Timothy Robert-Fitzgerald (T)

Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA.

Matthew K Schindler (MK)

Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA.

Christopher Perrone (C)

Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA.

Guy Schultz (G)

Data Analytics Center, University of Pennsylvania, Philadelphia, PA 19104, USA.

Selah Lynch (S)

Clinical Research Informatics Core, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA.

Nebojsa Mirkovic (N)

Clinical Research Informatics Core, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA.

Sunil Thomas (S)

Clinical Research Informatics Core, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA.

Ameena Elahi (A)

Department of Information Services, University of Pennsylvania, Philadelphia, PA 19104, USA.

Donovan Reid (D)

Department of Information Services, University of Pennsylvania, Philadelphia, PA 19104, USA.

Tyler M Moore (TM)

Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA.

Erica B Baller (EB)

Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA.

Theodore D Satterthwaite (TD)

Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA.

Matthew Cieslak (M)

Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA.

Sydney Covitz (S)

Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA.

Azeez Adebimpe (A)

Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA.

Abigail Manning (A)

Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA.

Clyde E Markowitz (CE)

Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA.

John A Detre (JA)

Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA.

Amit Bar-Or (A)

Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA.

Mihir Kakara (M)

Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA.

Russell T Shinohara (RT)

Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA.

Classifications MeSH