DOI

10.17077/etd.zcd1c42b

Document Type

Dissertation

Date of Degree

Spring 2018

Access Restrictions

Access restricted until 07/03/2019

Degree Name

PhD (Doctor of Philosophy)

Degree In

Biostatistics

First Advisor

Brown, Grant D.

First Committee Member

Oleson, Jacob J.

Second Committee Member

Smith, Brian J.

Third Committee Member

Cavanaugh, Joseph E.

Fourth Committee Member

Carnahan, Ryan M.

Abstract

The abuse of and dependence on opioids are major public health problems, and have been the focus of intense media coverage and scholarly inquiry. This research explores the problem in Iowa through the lens of infectious disease modeling. We wanted to identify the current state of the crisis, factors affecting the progression of the addiction process, and evaluate interventions as data becomes available. We introduced a novel sequential Approximate Bayesian Computation technique to address shortcomings of existing methods in this complex problem space, after surveying the literature for available Bayesian computation techniques.

A spatial compartmental model was used which allowed forward and backward progression through susceptible, exposed, addicted, and removed disease states. Data for this model were compiled over the years 2006-2016 for Iowa counties, from a variety of sources. Prescription overdose deaths and treatment data were obtained from the Iowa Department of Public Health, possession and distribution arrest data were acquired from the Iowa Department of Public Safety, a measure of total available pain reliever prescriptions was derived from private health insurance claims data, and population totals were obtained from the US Census Bureau.

Inference was conducted in a Bayesian framework. A measure called the empirically adjusted reproductive number which estimates the expected number of new users generated from a single user was used to examine the growth of the crisis. Results expose the trend in recruitment of new users, and peak recruitment times. While we identify an overall decrease in the rate of spread during the study period, the scope of the problem remains severe, and interesting outlying trends require further investigation. In addition, an examination of the reproductive numbers estimated for contact within and between counties indicates that medical exposure, rather than spread through social networks, may be the key driver of this crisis.

Keywords

ABC, Approximate Bayesian Computation, Bayesian computation, Compartmental Model, Prescription Opioid

Pages

x, 137 pages

Bibliography

Includes bibliographical references (pages ).

Copyright

Copyright © 2018 Natalie Rose Langenfeld

Included in

Biostatistics Commons

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