ABSTRACT
Nonmedical opioid (NMO) use (misuse of either prescription opioids or heroin) and related overdose and
mortality are rapidly growing public health problems. In particular, there is quite a bit of evidence that the NMO
epidemic is growing particularly rapidly outside of major city centers and urban areas (i.e., in suburban and
rural areas). While there has been a great deal of empirical evidence suggesting that features of physical and
social environments, taken together (i.e., using a built environment framework) represent strong predictors of
drug use and mental health outcomes in urban settings, there is a dearth of research assessing the built
environmental features of non-urban settings in order to predict risk for NMO outcomes.
The proposed study will compile data from secondary data sources for 566 municipalities in New Jersey to
address this gap. New Jersey was chosen for its epidemiological relevance and its availability of NMO
overdose data. In recent years, the highest rates of NMO overdose emergency room admissions have
occurred in counties comprised of suburban and rural areas. The proposed study will be the first to
systematically measure physical and social environmental features, i.e., the built environments, of non-urban
areas which are theoretically and empirically related to NMO use, in the service of developing a built
environment framework that can estimate municipality-level risk of NMO use and overdose in non-urban
settings.
This study will address the following specific aims: Aim 1. 1a. To develop a measurement strategy that
extends use of the built environment framework to describe features of the physical and social environments of
non-urban areas which are theoretically relevant for NMO use and overdose. 1b.To construct a spatial data
infrastructure of built environment data to be utilized in a Geographic Information System (GIS) with which to
test the feasibility and validity of this new built environment measure among both urban and non-urban
communities. Aim 2. To assess the validity of the measure produced in Aim 1 by examining its relationship to
various social and environmental constructs. We will assess the predictive validity of the new measure in part
by examining its ability to predict areas at higher risk for overdose at the municipality level (among both urban
and non-urban areas). We will also assess correlations of the new measure with other municipality level
variables in order to establish concurrent, convergent, and discriminant validity.
The development of this new built environment measure and corresponding spatial data infrastructure can be
replicated, thereby allowing public health departments and other service organizations to identify specific areas
with greatest risk for NMO morbidity and mortality. This, in turn, will allow them to strategically allocate
resources to these areas and to design and/or modify their prevention and intervention efforts to address area
vulnerabilities and to more directly and efficiently target high-risk populations.
