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.
