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Prescription opioid epidemic in Pennsylvania: lessons from Medicare and Medicaid

Lobo, Carroline (2018) Prescription opioid epidemic in Pennsylvania: lessons from Medicare and Medicaid. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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This dissertation seeks to provide evidence for interventions that large health systems can utilize to help mitigate the prescription opioid epidemic in Pennsylvania.

Chapter one introduces the research problem.

Chapter two examines the potential for machine-learning approaches to better understand the heterogeneity of opioid use in Medicare. What constitutes potentially high-risk use of prescription opioids in Medicare is not clearly known. Using novel techniques of machine-learning, we identify five groups of Medicare beneficiaries with potentially high-risk opioid use patterns. We observe that these groups differ not only on measures of opioid use but also on important demographic characteristics, clinical characteristics and mortality.

Chapter three examines the associations between physician prescribing specialties and opioid-related outcomes of opioid-use disorder (OUD), misuse, and overdose. Little is known about the variations in risk of OUD, misuse, and overdose by type of opioid prescribing specialties. Using data from Pennsylvania Medicaid, we examine the associations between the index and dominant opioid prescribing specialty and OUD, misuse, and overdose. We observe that Medicaid enrollees who receive their index opioid prescription or a majority of their prescriptions from specialties that treat chronic pain -pain medicine and physical medicine and rehabilitation- are at higher risk for OUD and misuse compared to primary care.

Chapter four examines the associations between adherence to antidepressant medications among individuals with mood disorders and opioid use. Literature shows that antidepressants have anti-nociceptive effects in mitigating pain among individuals with mood disorders. Using Pennsylvania Medicaid data, we examine whether adherence to antidepressants among individuals with major depressive disorders (MDD) or anxiety disorders is associated with reduced opioid use. We observe that enrollees with MDD and no cancer, who achieve ≥ 20% adherence have significantly lower hazards ratios for opioid use than those who achieve <20% adherence.

This dissertation has important implications for public health. Our findings provide evidence for interventions that health-systems can use to: (i) identify high-risk beneficiaries who use opioids, (ii) support evidence-based prescribing in settings where patients are at an elevated risk for adverse outcomes of opioid use, and (iii) increase adherence to antidepressant medications among individuals with MDD.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Lobo, Carrolinecpl13@pitt.educpl130000-0002-9035-5184
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairDonohue,
Committee MemberChang,
Committee MemberCochran,
Committee MemberJalal,
Committee MemberKarp,
Committee MemberRoberts,
Date: 30 January 2018
Date Type: Publication
Defense Date: 27 November 2017
Approval Date: 30 January 2018
Submission Date: 26 November 2017
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 122
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Health Policy & Management
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Prescription Opioids, Medicare, Medicaid, Prescriber Specialty Machine Learning, Overdose, Abuse, Misuse
Date Deposited: 30 Jan 2018 22:45
Last Modified: 01 Jan 2019 06:15


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