Click here to download a Microsoft Excel file containing the data used in the Overdose Mapping Tool.

The interactive tool was created in JavaScript using the Leaflet library. Data was processed using SAS and converted from shapefile to TopoJSON using the Mapshaper web client. Several data sources were accessed in the development of this tool (more information on the data sources used in the prosperity index can be found on the prosperity index tab):

1. CDC National Center for Health Statistics (NCHS) National Vital Statistics System (NVSS) Multiple Cause of Death File
2. U.S. Census Bureau, American Community Survey
3. Bureau of Labor Statistics Quarterly Census of Employment and Wages
4. Federal Communications Commission
5. Substance Abuse and Mental Health Services Administration (SAMHSA) Behavioral Health Treatment Locator
6. USDA Economic Research Service

The tool presents crude mortality rates for the population. The combined population estimates for the time period (either 2010-2013, 2014-2017 or 2018-2021) are the denominator for the mortality rates. Five-year average mortality rates were used for this tool in order to maximize the number of counties with a reliable crude mortality rate. For counties with fewer than 10 deaths over the five-year time period, the number of deaths is suppressed, and therefore a mortality rate is not provided. However, when possible, we have calculated the maximum crude mortality rate based on the population and the assumption of less than 10 deaths.

The table below describes each of the data sources and definitions for the variables included in the tool.

This tool allows researchers, policymakers, journalists, and the general public to create county-level maps illustrating the relationship between community and population demographics and fatal drug overdoses—including opioids—in the United States. Insights derived from this tool can be used to target resources and interventions, and inform media coverage related to overdose deaths in the U.S.

The user has the option to select the data to include as the base layer of the map. The options for the base layer include Drug Overdose or Opioid Overdose Mortality or the overall Prosperity Index or its sub-component scores. More detail on the Prosperity Index can be found on the “Prosperity Index” page. For the overdose mortality data, you can select the timeframe from the drop down on the top left of the map. The three options are: 2018-2021, 2014-2017, and 2010-2013. You can use the List of Counties to link directly to data on a particular county, or click on specific counties on the map.

Click on the ”timeframe” drop down on the upper-left section of the screen to change the years represented for the drug overdose and socio-demographic and economic data.

To view state-level data, click the "state/county" drop down in the upper-right section of the screen and select "State".

Use the “urban/rural” drop down to compare data from rural and urban counties.

Choose variables from the left-hand column to layer county-level economic and demographic data on top of the baseline drug overdose mortality data. By showing the variables as translucent circles of varying sizes, the tool allows users to clearly see how a given measure relates to the baseline drug overdose mortality rate. For example, choosing “Poverty Rate” will demonstrate the relationship between an individual county’s poverty rate and its overdose mortality rate. Additionally, behavioral health resources, such as mental health and substance use facilities per capita, can be added as an overlap to the map.

When second-layer data has been added onto a base map, users can select “Open Correlation Graph” to see a graph that shows the correlation between the two indicators, including the correlation coefficient. Correlation coefficients are typically used to evaluate the association between two variables. These coefficients range from -1 to +1 and represent the strength of the relationship between the two variables. Values of 0 indicate no meaningful associations between the two variables. As the correlation values approach either end (-1 or +1) of the range, the association becomes stronger, where values closer to 0 indicate weaker relationships. Negative values (those less than 0) indicate decreasing or inverse relationships (as one variable increases, the other decreases), and positive values (greater than 0) demonstrate increasing relationships (as one variable increases, the other also increases). The two types of correlation coefficients used for this tool include Pearson’s correlation coefficient and Spearman’s correlation coefficient. Pearson’s correlation coefficient is used here when examining two continuous variables (numerical in scale). Spearman’s correlation coefficient is used when at least one of the variables (or possibly both) are not continuous (i.e., categorical or ordinal in nature). Pearson’s correlation coefficient is specific to evaluating the linear association between the two continuous variables, while Spearman’s correlation coefficient does not require a linear relationship, merely an increasing (or decreasing) association between the variables.

• Pearson’s correlation coefficient – Ranges from -1 to +1 and describes the linear (straight line) relationship between the chosen variables. A value of 0 describes no linear relationship, while a -1 represents a perfectly inverse (decreasing) linear relationship and +1 represents a perfect (increasing) linear relationship.
• Spearman’s correlation coefficient – Ranges from -1 to +1 and describes the general relationship (not necessarily linear) between the chosen variables and is used here if at least one variable is not truly continuous. A value of 0 describes no relationship between the two variables, while a -1 represents an inverse (decreasing) relationship and +1 represents an increasing relationship.

On the left hand side of the screen, there is a drop down for “Add Map Overlay.” Available map overlays include: geolocations of Native American Reservations; outline of persistent poverty counties; location of major highways; and outlines of federally defined regions.

For each county, there are three fact sheets, which can be found by clicking on “View Details” when selecting a county. The fact sheets include: 1) Drug Overdose Mortality data; 2) Prosperity Index data; 3) Socio-Demographic data; 4) Economic data; and 5) Behavioral Health Resources data. For all data tables, county, state, and national data are provided, to provide benchmarks for counties. The name and location of substance use and mental health facilities are also provided in the drop down menus at the bottom of the fact sheet.

The Recovery Ecosystem Index (REI) was developed with support from a Technical Expert Panel (TEP) convened by the ETSU/NORC Rural Health Equity Research Center (RHERC) and Fletcher Group, Inc. TEP members represented a diverse group of stakeholders with a wide range of expertise. The TEP included: Dr. Robert Ashford (Unity Recovery), Matt Boggs (Ryker Douglas), Dr. Anita Chandra (RAND Corporation), Dr. Kimberly Dash (Education Development Center), John Gale (University of Southern Maine, Cutler Institute), Peter Gaumond (Office of National Drug Control Policy), Carolyn Hardin (National Association of Drug Court Professionals), Christopher Hart (Unity Recovery), Sierra Helfrich (Centers for Disease Control and Prevention), Patrick Hibbard (JEAP Training Institute at Oregon Social Learning Institute), Pam Johnson (FAHE), Dr. John Kelly (Harvard Medical School, Recovery Research Institute), Dr. Ron Manderscheid (National Association for Rural Mental Health, National Association of County Behavioral Health and Developmental Disability Directors), and Robin Phillips (National Rural Transit Assistance Program).

The Recovery Ecosystem Index provides a single numerical measure designed to reflect the strength of the recovery ecosystem of a county. For the overall Recovery Ecosystem Index score, 1 represents the strongest and 5 represents the weakest recovery ecosystem.

The index was designed to measure the strength of rural county-level recovery ecosystems, and provide data to support community planning, programming and technical assistance designed to strengthen recovery ecosystems throughout the rural United States. The index is broken down into three components that impact the strength of a recovery ecosystem: Substance Use Disorder (SUD) Treatment; Continuum of SUD Support; and Infrastructure and Social.

The SUD Treatment component includes the number of substance use treatment facilities per capita, number of providers licensed to administer buprenorphine per capita, average distance to nearest medication-assisted treatment (MAT) provider, and the number of mental health providers per capita.

The Continuum of SUD Support component includes the number of recovery residences per capita, average distance to nearest syringe-service program (SSP), number of Narcotics Anonymous (NA) or Self-Management and Recovery Training (SMART) meetings per Capita, drug court presence, Drug-Free Communities Coalition grant presence, and policy environment score.

The Infrastructure and Social component includes vehicle availability, severe housing cost burden, broadband access, and social associations per capita.

To select the indicators, we first conducted a literature review to determine the key components of a recovery ecosystem. We developed a list of all potential indicators based on this review and input from the TEP. We then prioritized a list of indicators based on TEP feedback, and ultimately included the following list of indicators. In order to be selected, the indicator had to have publicly available data for all counties in the U.S.

Component	Indicator	Data Source	Definition
SUD Treatment	Number of Substance Use Treatment Facilities Per Capita	SAMHSA (N-SSATS Data) (As of February 2022)	Number of substance use treatment facilities per 100,000 residents
	Substance Use Disorder (SUD) Treatment score	See Recovery Ecosystem Index page	Substance Use Disorder (SUD) Treatment score
	Continuum of SUD Support score	See Recovery Ecosystem Index page	Continuum of SUD Support score
	Infrastructure and Social Factors score	See Recovery Ecosystem Index page	Infrastrucutre and Social Factors score