Methodological Advances in Evaluating Abuse Deterrent Opioid Analgesics

User Collection Public
UNC logo

This was a 4-year research and dissemination project which occurred from 2018 to 2022 that leveraged emerging advances in data connectivity and established new standards for methodological rigor in the evaluation of opioid analgesics. The overall goal was to develop and disseminate new open source epidemiologic tools to facilitate evaluation of abuse deterrent formulations [ADFs] and understanding of overdose mortality. The work was conducted at the University of Kentucky, University of North Carolina, University of Florida, with partnership with IBM Watson Health and NC Survivors Union. The suite of studies was primarily funded by the US Food and Drug Administration...

This was a 4-year research and dissemination project which occurred from 2018 to 2022 that leveraged emerging advances in data connectivity and established new standards for methodological rigor in the evaluation of opioid analgesics. The overall goal was to develop and disseminate new open source epidemiologic tools to facilitate evaluation of abuse deterrent formulations [ADFs] and understanding of overdose mortality. The work was conducted at the University of Kentucky, University of North Carolina, University of Florida, with partnership with IBM Watson Health and NC Survivors Union. The suite of studies was primarily funded by the US Food and Drug Administration (HHSF223201810183C); other federal agencies funded specific components of the work (e.g., data linkage). Views expressed are those of the authors, and do not necessarily reflect the views of the funder(s).

The first set of studies focused on the pharmacy setting, assessing accuracy of prescription records, and pharmacist (and physician) perceptions of certain opioid analgesics.

The second set of studies were epidemiology methods development for evaluating “abuse deterrent formulations” of opioid analgesics in the post-market setting, including measurement definitions, misclassification, and applied statistical innovation.

The third set of surveillance studies leveraged unique linked data to better understand substances involved in overdose deaths, and generated new insights into how medications for opioid use disorder can prevent death.

The fourth project was software development, which delivered a new machine learning algorithm for comparative drug safety science. Our public use tool allows researchers to more accurately identify deaths in a major insurance claims database used to support regulatory decision-making.

This collection includes published manuscripts, presentations and other project outcomes.

We are grateful to generations of taxpayers in Kentucky, North Carolina, and Florida for supporting public universities. We are also grateful to US taxpayers for safeguarding public health by supporting FDA and this research project.

Works (43)

Sort the listing of items  

1. Electronic medical records vs insurance claims: Comparing the magnitude of opioid use prior, during, and following surgery

2. Estimating the impact of prescribing limits on prolonged opioid use following surgery

3. Distinguishing Death from Disenrollment: Applying a Predictive Algorithm to Reduce Bias in Estimating the Risk of Rehospitalization

4. Patterns of Buprenorphine Initiation Treatment for Opioid Use Disorder and Association with Opioid-related Overdose Deaths

5. Pharmacist's Experience Dispensing Abuse Deterrent Formulation Opioids: A Multistate Survey of Dispensing Pharmacists

6. ACCURACY AND VALIDITY OF REPORTED OPIOID PRESCRIPTION DAYS’ SUPPLY

7. Intended and unintended consequences: Changes in opioid prescribing practices following two policies in North Carolina, 2012–2018 – A controlled interrupted time series analysis

8. External validation of a machine learning algorithm to distinguish death from disenrollment in claims data

9. Association of opioid dose reduction with opioid overdose and opioid use disorder among patients on high-dose long-term opioid therapy in North Carolina

10. Sociodemographic and Clinical Predictors of Prescription Opioid Use in a Longitudinal Community-Based Cohort Study of Middle-Aged and Older Adults