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Activity Number: 513 - Bayesian Learning for the Health Care and Disparities in the 21st Century
Type: Topic Contributed
Date/Time: Thursday, August 11, 2022 : 8:30 AM to 10:20 AM
Sponsor: International Society for Bayesian Analysis (ISBA)
Abstract #322830
Title: Bayesian Learning for Disparities and Race Related Measures from Integrated Data Sources, Using Rare Event and Hierarchical Models
Author(s): Tanujit Dey* and Anjishnu Banerjee and sounak chakraborty
Companies: Harvard University and Medical College of Wisconsin and university of missouri
Keywords: Health disparities; rare events; risk adjustment; Bayesian Learning
Abstract:

Health disparities are a key area of interest at the regional, and national levels. While there is literature on the understanding of disparities at the population level, there is limited understanding of factors at the hospital level, which are building blocks for the population level trends. A dearth of statistical methods, which can distill out factors at the hospital levels are among the key contributors to this lack of understanding. In this proposal, innovative learning methods are proposed which can lead to better understanding of such factors. Novelties in the proposal include sophisticated adjustments for factors such as rare events; a wholistic and nuanced treatment of ethnicity and race data beyond naïve techniques; and, multilevel risk adjustment calculators, which can contribute to efficient models and improved inference.


Authors who are presenting talks have a * after their name.

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