(95) Opioid Overdose Risk Prediction in a State Medicaid Program: How Does Predicted Risk Change Over Time?

Friday, August 26, 2022

8:00 AM – 6:00 PM CEST

Location: Hall C

Publication Number: 426

Presenting Author(s)

Serena Jingchuan Guo, MP PhD
– Assistant Professor, University of Florida Department of Pharmaceutical Outcomes and Policy, University of Florida , Gainesville, Florida, United States

Co-Author(s)

Walid Gellad, MD, MPH
– Associate Professor, Center for Pharmaceutical Policy and Prescribing, Health Policy Institute, University of Pittsburgh, United States
Qingnan Yang
– Center for Pharmaceutical Policy and Prescribing, University of Pittsburgh, United States
Jeremy C. Weiss
– Heinz College, Carnegie Mellon University, United States
Julie M. Donohue
– Department of Health Policy and Management, University of Pittsburgh, United States
Gerald Cochran
– Program for Addiction Research, Clinical Care, Knowledge, and Advocacy, Division of Epidemiology, Department of Internal Medicine, University of Utah, United States
Adam J. Gordon
– Program for Addiction Research, Clinical Care, Knowledge, and Advocacy, Division of Epidemiology, Department of Internal Medicine, University of Utah, United States
Daniel Malone, RPh, PhD, FAMCP
– Professor, University of Utah College of Pharmacy, Salt Lake City, UT, United States
Kent Kwoh
– Division of Rheumatology, Department of Medicine, and the University of Arizona Arthritis Center, University of Arizona, United States
Courtney C. Kuza, PhD, MPH
– Program Manager, Center for Pharmaceutical Policy and Prescribing, University of Pittsburgh, Pittsburgh, PA, United States
Debbie Wilson, PhD, RN
– Affiliate Clinical Assistant Professor, Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, United States
Weihsuan Jenny Lo-Ciganic, PhD
– Associate Professor, University of Florida Department of Pharmaceutical Outcomes and Policy, Gainesville, Florida, United States

Background: The time lag encountered when accessing health care data is one major barrier to implementing opioid overdose prediction measures in practice. Little is known regarding how one’s predicted opioid overdose risk changes over time.

Objectives: We aimed to identify longitudinal patterns of individual predicted overdose risks after opioid prescriptions were initiated in a state Medicaid program.

Methods: We developed a retrospective cohort of Pennsylvania Medicaid beneficiaries aged 18 to 64 years who initiated opioid prescriptions between July 2017 and September 2018. We first applied a previously developed and validated machine-learning algorithm to obtain risk scores for opioid overdose emergency department or hospital visits in 3-month intervals for each beneficiary who initiated opioid therapy, until disenrollment from Medicaid, death, or the end of observation (December 2018). We performed group-based trajectory modeling to identify trajectories of these predicted overdose risk scores change over time.

Results: Among 318,585 eligible beneficiaries (mean age=39±12, female=66%, White=62% and Black=25%), 0.61% had ≥1 occurrences of opioid overdose in a median follow-up of 15 months. We identified five unique opioid overdose risk trajectories: three trajectories (accounting for 92% of the cohort) had consistent overdose risk over time including consistent-low risk(63%), consistent-medium risk(25%), and consistent-high risk(4%) groups; another two trajectories (accounting for 8%) had overdose risks that substantially changed over time, including a group that transitioned from high to medium risk(3%) and another group that increased from medium to high risk over time(5%).

Conclusions: Over 90% of Medicaid beneficiaries with ≥1 opioid prescription had consistent, predicted opioid overdose risks over 15-months. Applying opioid prediction algorithms developed from historical data may not be a major barrier to implementation in practice for the large majority of individuals.