Sumetsky, Natalie
(2017)
Spatiotemporal modeling of opioid abuse and dependence outcomes using Bayesian hierarchical methods.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
Opioid addiction is a major public health concern that presents a significant disease burden. In the past decade, drug overdose rates have soared. More research is necessary to inform policy and to ensure provision of proper care to individuals and communities in need. This thesis explores spatiotemporal models to assess ecological and demographic factors associated with opioid addiction risk on a ZIP-code level in Pennsylvania.
Bayesian hierarchical models are commonly used to explore complex spatiotemporal disease trends. Markov chain Monte Carlo (MCMC) simulations are a valuable albeit computationally costly tool in fitting models of this class. A newer method, integrated nested Laplace approximation (INLA), offers improved computational efficiency with comparable results for models with latent Gaussian fields. For example, a 2014 cross-sectional model discussed in this thesis took 5581 seconds to run using MCMC simulations, while INLA offered comparable results in seven seconds. Cross-sectional and longitudinal misalignment models with opioid abuse and dependence outcomes are compared using both methods.
Higher outcome risk is associated with areas with greater proportions of 45- to 64-year-olds, higher density, more retail clutter and manual labor establishments per square mile, higher unemployment, lower median income, and greater proportion of residents below the 150% poverty line. As regional needs differ, identifying high-risk community-level factors and locations carries great public health significance. Interventions and preventive efforts could then be tailored specifically to areas where the disease burden is greatest.
Share
| Citation/Export: |
|
| Social Networking: |
|
Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
25 September 2017 |
| Date Type: |
Publication |
| Defense Date: |
27 July 2017 |
| Approval Date: |
25 September 2017 |
| Submission Date: |
23 July 2017 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
47 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Public Health > Biostatistics |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Bayesian hierarchical models, spatiotemporal models, conditional autoregressive models, Markov chain Monte Carlo simulation, integrated nested Laplace approximation, opioid abuse, opioid dependence |
| Date Deposited: |
25 Sep 2017 14:23 |
| Last Modified: |
25 Sep 2017 14:23 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/33054 |
Metrics
Monthly Views for the past 3 years
Plum Analytics
Actions (login required)
 |
View Item |
![[feed]](/images/feed-icon-32x32.png){kind=icon}