A Bayesian Hierarchical Excess Mortality Model to Assess the Total Impact of COVID-19 on Opioid Users Pubblico

Abstract

COVID-19 has a large scale negative impact on the health of opioid users. As such, monitoring small-area opioid mortality trends has significant implications to informing preventative resource allocation. The total impact of COVID-19 on opioid users is unknown due to a lack of comprehensive data on COVID-19 cases, inaccurate diagnostic coding, and lack of data coverage. To assess the impact of COVID-19 on small area opioid mortality, we developed a Bayesian hierarchical excess opioid mortality modeling approach. We incorporate spatio-temporal autocorrelation structures to allow for sharing of information across small areas and time. Excess mortality is defined as the difference between the observed trends after a crisis and the expected trends based on observed historical trends, which captures the total increase in observed mortality rates compared to what was expected prior to the crisis. We illustrate the application of our approach to assess excess opioid mortality risk estimates for 159 counties in GA. Utilizing our proposed approach will help to inform interventions in opioid related public health responses, policies, and resource allocation. Additionally, we provide a general framework to improve in the estimation and mapping of health indicators during crisis periods.

Table of Contents

Introduction Methods Data Summary of model approach Data model for observed opioid-mortality counts Process model for unobserved latent opioid mortality log-relative-risks Derivation of Excess mortality and associated uncertainties Computation Results Global Parameter Estimates Excess Across Georgia Large County Population Cases Moderate County Population Cases Small County Population Cases Discussion Appendix Graphical Representation of the BHEOM model Notation Table

About this Master's Thesis

Rights statement

• Permission granted by the author to include this thesis or dissertation in this repository. All rights reserved by the author. Please contact the author for information regarding the reproduction and use of this thesis or dissertation.

School	Rollins School of Public Health
Department	Biostatistics
Subfield / Discipline	Biostatistics - MPH & MSPH
Degree	M.S.P.H.
Submission	Master's Thesis
Language	English
Research Field	Health Sciences, Epidemiology Health Sciences, Public Health Statistics
Parola chiave	Excess mortality estimation Bayesian hierarchical methods COVID-19 Opioid mortality
Committee Chair / Thesis Advisor	Emily N. Peterson, Emory University
Committee Members	Alex Edwards, Emory University Lance A. Waller, Emory University

Ultima modifica

Primary PDF

Thumbnail	Title	Date Uploaded	Actions
	A Bayesian Hierarchical Excess Mortality Model to Assess the Total Impact of COVID-19 on Opioid Users ()	2024-04-08 11:23:09 -0400	Press to Select an action Download

Supplemental Files

Thumbnail	Title	Date Uploaded	Actions