Methodology
Risk Environment Framework
Organization and data selection for the OEPS is guided by a risk environment framework (Rhodes 2002). The “risk environment” as a framework for understanding and reducing drug-related harms emphasizes the broader contextual domains in which opioid use occurs.
Our approach is further rooted in the socioecological model of substance use, which includes multiple levels of the physical and social environments that interact and overlap to impact health. We apply this socio-ecological model of health and use it to further build on previous research on risk environments (Cooper et al 2016, Ciccarone 2017).
Data and research models must reflect this transdisciplinary and multi-level approach. The risk environment framework shifts the focus of drug-related harm research away from individuals, and toward environmental factors driving or enabling trends at the community level. We see this approach encouraging greater understanding of the spatial and community contexts in which opioid use harm occurs.
Multiple Spatial Scales
Most datasets are available at multiple spatial scales, including Census tract, ZIP Code or ZIP Code Tract Area (ZCTA), county, and state. You can filter and explore available datasets by spatial scale here.
Data Themes
Data included in the OEPS is grouped thematically for ease of exploration, indices development, and model integration. Data is stratfied across six themes:
*For complete case rate, mortality, and additional COVID-19 data, please visit the US COVID Atlas -- a free and open source pandemic data archive and visualization tool, also led by the Healthy Regions and Policies Lab.
The OEPS also includes geography boundary shapefiles from the US Census Bureau’s TIGER/Line (2018) for Census tracts, ZCTAs, counties, and states.
Data Standards
Please see the Data Standards page for more on our file naming conventions, geographic identifiers, data formatting, and guidelines for contributors.
Documentation
Please refer to the Metadata Docs or the complete Data Documentation for more information about individual datasets and variables.