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Benneyan Receives NSF EAGER Grant

June 14, 2017

MIE Professor James Benneyan was awarded a $257K NSF EAGER grant for "Development and Validation of Analytic Spatial-Temporal Models to Help Study and Mitigate the National Opioid-Heroin Co-Epidemic".

Abstract Source: NSF

The US is In the midst of a serious epidemic of opioid drug misuse and abuse. According to the Surgeon General's 2016 report, "Facing Addiction in America", in 2014, 28,647 people died from drug overdoses involving some type of opioid, including prescription pain relievers, heroin, and fentanyl. The report states that the "accumulated costs to the individual, the family, and the community are staggering and arise as a consequence of many direct and indirect effects, including compromised physical and mental health, increased spread of infectious disease, loss of productivity, reduced quality of life, increased crime and violence, increased motor vehicle crashes, abuse and neglect of children, and health care costs." The report also recognizes the potential for advances in clinical, operational, and informational technologies, coupled with health care and criminal justice reform efforts, to drive improvements in prevention and treatment services. This EArly-concept Grant for Exploratory Research (EAGER) project will address operational methods to understand and mitigate multiple interrelated opioid addiction epidemics. The methods will be developed and evaluated with ongoing feedback from an advisory group of policy makers, including state health departments serving multiple urban and rural populations. The potential impact of these new methods includes improved prevention strategies and support for effective allocation of resources between treatment and interdiction. The project will also educate several graduate and undergraduate engineering students who will be in a position to help provide solutions to current public sector issues that threaten the nation's health and security.

This award will support research to formulate high fidelity spatiotemporal analytical models of the interdependent national opioid and heroin disease epidemics. The model(s) developed will combine approaches based on differential equations, cellular automata, stochastic processes and will incorporate different geographic and social network topologies, account for regional and population heterogeneity, and capture behavioral characteristics that influence dynamics of disease spread. An important aspect of these models is the explicit consideration of the inter-relationships between multiple disease modalities, including such factors as substance availability, addiction progression, drug switching and substitutability, variable drug potency, and market (price/supply/demand) forces. The models will be developed and validated using data obtained from local and regional public health agencies. Models will be informed by qualitative ethnographic techniques that describe the dynamics of person-to-person spread and region-to-region spread. These models will serve as platforms for the assessment of current and future intervention strategies, and are expected to help inform policy makers that oversee resource allocation in state public health departments, treatment centers, social service agencies, and law enforcement agencies.