Linked Opioid Mortality Surveillance


What we are studying

Drug overdoses have increased significantly over the last two decades resulting in a public health crisis with generational implications. Kentucky’s drug overdose fatality rates have been some of the highest in the nation. Addressing the elevated drug overdose fatalities requires targeted multi-pronged public health approaches (including improved opioid prescribing, prevention interventions and programs, organizational and legislative policies, and community best practices). One strategy to inform drug overdose prevention programs and practices is to establish a state-wide comprehensive multi-source drug overdose fatality surveillance system to identify new and emerging risk factors associated with drug overdose fatalities and inform policymaker and community practitioner decisions. When implemented in near real-time, this system can provide the earliest understanding how a new abuse deterrent formulation (ADF) may be impacting overdose mortality.

Why it matters

The establishment of a novel near real-time opioid mortality surveillance system based on multiple linked data sources can be conceptualized as a registry study for all opioid patients in the state. The system is automated, prospective, near real-time, and includes all-cause mortality. This is an unprecedented technological advance in pharmacoepidemiology. Monthly data analysis will provide observations and identify potential trends associated with overall opioid dispensing and mortality. This effort in Kentucky can serve as a model for other states to enhance national opioid mortality monitoring.

How we are studying it

We are establishing a timely ADF-opioid mortality surveillance system (AOMSS) based on multiple data sources: death certificate, prescription drug monitoring program, autopsy, Medicaid claims, toxicology report, and coroner investigation reports. In addition, we will create online implementation in REDCap, establish or maintain data sharing agreements, and update a literature review. On an ongoing basis, we will review of AOMSS data for patterns of abuse; pathological changes; and associated diagnoses and medical conditions, and use AOMSS data as a sentinel to detect adverse effects to develop new statistical approaches. These findings will be compiled in guides for replication in other states.

In addition to the development of an electronically accessible database described above, we will conduct a prospective cohort study using AOMSS data of longitudinal progression-to-death and the role of ADFs in overdose prevention. We will use a prevalent new-user cohort design. The base cohort will include all ADF patients (including those who directly initiated the ADF drug as well as those who switched from comparator drugs), as well as users of a comparator drug(s). The initial cohort enrollment will be determined from PMP records of patients who had > 1 prescription billed to Medicaid during each year of the study period. We will use both prescription-based and time-based exposure denominators, adjusting for opioid prescription history for both number of prescriptions and length of use. We expect to use time-conditional propensity scores to identify comparator drug users similar to patients who switched to the study drug. Conditional logistic regression can be performed to compute time-conditional propensity scores for all exposure sets. Medicaid-based time-varying covariates included in the conditional logistic regression will be identified based on a predefined conceptual framework (DAG). Using time-conditional propensity scores, we plan to match new ADF users with non-ADF users, and employ Cox proportional hazards regression to model hazards of several opioid abuse outcomes. For fatal overdose outcomes, we will consider extended Cox multi-state transition models accounting for competing risks or informative censoring (i.e., cancer deaths).

How to use the results

While the results will not be generalizable to other populations, the methodology can be applied to different populations when future data sources are available. Using directed acyclic graphs (DAG), our detailed and explicit model for potential confounding can serve as a basis for further observational epidemiologic studies of ADFs.

Who is conducting and supporting the study

This study is led by Svetla Slavova and Daniela Moga at the University of Kentucky. Research team members include: Candace Brancato, Emily Slade, Heather Bush, Kelsey Carter, John Brown, Nabarun Dasgupta, Maryalice Nocera, Toska Cooper. The KIPRC houses one of the most comprehensive state overdose mortality surveillance systems in the country established with substantial funding from the Department of Justice, and sustained by funding from the Centers for Disease Control and Prevention (CDC) Prevention for States grant program. The linked PMP-death certificate data computing infrastructure is funded by the Kentucky Cabinet for Health and Family Services (CHFS) KASPER program. The monthly linkages of all-cause mortality death certificates with decedent’s prescription history over the last 10 years was established with funding from the Bureau of Justice Assistance, and sustained by the CHFS. A joint team of independent researchers at the University of Kentucky and University of North Carolina at Chapel Hill will analyze the survey results. This study has been registered with the University of Kentucky Institutional Review Board. All studies at the Opioid Data Lab are conducted by independent researchers at the University of Kentucky and the University of North Carolina at Chapel Hill, and do not necessarily represent the views of funders or partners. We are grateful to generations of taxpayers in Kentucky and North Carolina for supporting public universities. We are also grateful to US taxpayers for safeguarding public health by supporting FDA and this research project.

Svetla Slavova

Nabarun Dasgupta
Epidemiologist, Factotum

Shabbar Ranapurwala
Epidemiologist, Physician