Predict to Prevent: Dynamic Spatiotemporal Analyses of Opioid Overdose to Guide
Pre-Emptive Public Health Responses
PROJECT SUMMARY/ABSTRACT
Opioid overdose (OD) fatalities have reached crisis levels in all socioeconomic and geographic communities
in the US. By utilizing a first-of-its-kind statewide Public Health Data Warehouse (PHD) with multiple
linked administrative datasets and state-of-the-art Bayesian spatiotemporal models, we are in a unique
position to fill in the fundamental gaps in the field’s ability to rapidly identify current OD patterns,
predict future OD epidemics, and evaluate the effectiveness of public health and clinical interventions.
In Massachusetts (MA), the State Legislature enacted policy in 2015 that provided authorization to the MA
Department of Public Health (MDPH) to develop a massively linked administrative dataset to allow public
health officials and policymakers to better understand the extent of and contributors to the opioid OD epidemic.
The PHD Warehouse, representing 98% of the MA population, currently links data from 25+ distinct sources
(e.g., death records, all-payer claims, post-mortem toxicology, hospital discharges, and the prescription
monitoring program). Supported by strong preliminary studies demonstrating the power of the PHD and our
strong partnership with MDPH, we aim to develop a new population health analytic framework to support opioid
OD control in MA that can be generalizable to other parts of the country. Our Specific Aims are to: 1) Develop
a Bayesian multilevel spatiotemporal model to identify individual, interpersonal, community, and societal
factors that contribute to opioid OD; 2) develop an efficient Bayesian spatiotemporal model to identify time-
space OD clusters, and extend the model to construct a dynamic predictive model; and, 3) evaluate and
predict policy and intervention effects through model-based simulation studies to provide practical guidance
and decision-making support to public health officials. Aims 1, 2 and 3 can be easily adopted and reproduced
by users in other public health jurisdictions and sectors to foster cross-sector, cross-agency opioid OD control.
Our approach is innovative due to the use of PHD and sophisticated Bayesian spatiotemporal modeling
approaches. The proposed study is highly significant, because it is conceptualized to improve current and
future public health practice, facilitating data-driven and evidence-based implementation science interventions
in the locations at greatest risk and at the time when they are most needed. Our results can immediately and
significantly influence opioid OD prevention policies and practices, guiding pre-emptive public health and
clinical responses. We will develop our visualization tools, analytical approaches, and related code, in
collaboration with MDPH and our Community Advisory Board (CAB), to enhance PHD capabilities and improve
dissemination of findings. Our tools, approaches, and code will also be made available for national
dissemination, providing paradigm shifting approaches to address the opioid crisis. Our research directly
addresses NIDA’s goal to “Develop new and improved strategies to prevent drug use and its consequences.”
