<p> The forces driving the prescription opioid epidemic currently raging across the United States include aggressive marketing, weak regulation, addiction, freely prescribing doctors, a glut of pills available for sharing, and easy access to illicit drugs like heroin. This thesis aims to quantitatively analyze the interactions between these drivers through construction of a System Dynamics model, in order to determine the efficacy of policy intervention through Prescription Drug Monitoring Programs. The System Dynamics model simulates the flow of doctors’ prescriptions to the two very different classes of prescription opioid patients. One class is the long-term pain patients whose tolerance and appetite for opioids grows over time, leading them to higher doses, often dangerously high, and yet also frequently to feeling under-medicated; the other is those patients prescribed opioids for short-term pain, who typically find that they have been given more pills than they need.</p><p> These “extra” pills find their way into the hands of friends and family who, in common with the patients who received prescriptions, are in jeopardy of addiction to the opioids. Those addicted repeatedly visit doctors, shopping for more. Sensitivity analysis results reveal that drug diversion is a major contributor to the opioid death rate; that mandatory PDMP use will slow but not stop opioid proliferation, and will cause long term pain patients to be under-treated in larger numbers; that a significant number of people addicted to prescription opioids will transition to heroin use for reasons of price and availability; and that the rate of opioid overdose deaths will remain high until and unless society is better educated about the risks of addiction. Overall, the study helps conclude that the efforts of state governments and the FDA will be insufficient to stem the flow of opioids, and that there is no simple intervention to thwart drug diversion and sharing of pills.</p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:10261352 |
| Date | 12 April 2017 |
| Creators | Gatley, Samuel |
| Publisher | Rochester Institute of Technology |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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