|
Activity Number:
|
59
- Invited E-Poster Session I
|
|
Type:
|
Invited
|
|
Date/Time:
|
Sunday, August 8, 2021 : 5:45 PM to 6:30 PM
|
|
Sponsor:
|
Biometrics Section
|
|
Abstract #317358
|
|
|
Title:
|
Multilevel Modeling of Spatially Nested Functional Data: Spatiotemporal Patterns of Hospitalization Rates in the US Dialysis Population
|
|
Author(s):
|
Damla Senturk* and Yihao Li and Danh Nguyen and Sudipto Banerjee and Connie Rhee and Kamyar Kalantar-Zadeh and Esra Kurum
|
|
Companies:
|
UCLA and UCLA and UCI and University of California Los Angeles and UCI and UCI and UCR
|
|
Keywords:
|
Conditional autoregressive model;
Dialysis;
End-stage renal disease;
Multi-level functional data;
United States Renal Data System
|
|
Abstract:
|
End-stage renal disease patients on dialysis experience frequent hospitalizations. In addition to known temporal patterns of hospitalizations over the life span on dialysis, where poor outcomes are typically exacerbated during the first year on dialysis, variations in hospitalizations among dialysis facilities across the U.S. contribute to spatial variation. Utilizing national data from the United States Renal Data System (USRDS), we propose a novel multilevel spatiotemporal functional model to study spatiotemporal patterns of hospitalization rates among dialysis facilities. Hospitalization rates of dialysis facilities are considered as spatially nested functional data with longitudinal hospitalizations nested in dialysis facilities and dialysis facilities nested in geographic regions. A new efficient algorithm based on functional principal component analysis and Markov Chain Monte Carlo is proposed for estimation and inference. We report a novel application using USRDS data to characterize spatiotemporal patterns of hospitalization rates for over 400 health service areas across the U.S. and over the post-transition time on dialysis.
|
Authors who are presenting talks have a * after their name.
