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Recovery Ecosystem Index Map
BASE MAP
Causes of Death
STATES
| 21.8 |
Example Text
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| 26.6 | Recovery Ecosystem Index Score |
| 26.6 |
Hover over a variable in the data table, and its definition will appear below
| Component | Score | Sub-Component | Harrison County | Kentucky | United States |
| SUD Treatment | 5 | Substance Use Treatment Facilities per 100k | 1.9% | 2.9% | 3.6% |
| Buprenorphine Providers per 100k | 3.4 | 4.2 | 5.3 | ||
| Average Distance to Nearest MAT Provider (miles) | 18.1 | 22.9 | 28.7 | ||
| Mental Health Providers per 100k | $48,438 | $50,589 | $62,843 | ||
| Continuum of SUD Support | 5 | Recovery Residences per 100k | 3.0 | 2.2 | 1.8 |
| Average Distance to Nearest SSP (miles) | 38.8 | 35.3 | 24.1 | ||
| NA or SMART Meetings per 100k | 14.8% | 4.3% | 3.8% | ||
| Is there a Drug-Free Communities Coalition? | 976.2 | 984.4 | 816.5 | ||
| Is there a Drug Court? | 976.2 | 984.4 | 816.5 | ||
| State SUD Policy Environment Score (10=highest; 0=lowest) | 976.2 | 984.4 | 816.5 | ||
| Infrastructure and Social | 3 | One or More Vehicles | 17.6% | 17.3% | 13.4% |
| Broadband Access | 1.0 | 0.6 | 0.5 | ||
| Social Associations per 10k | 2.5 | 2.2 | 2.5 | ||
| Severe Housing Cost Burden | 79.0% | 77.6% | 82.1% |
Millions of individuals are estimated to have a substance use disorder, contributing to serious health, social, and economic consequences. Ensuring services and resources to support individuals in recovery from substance use disorders is good for our residents, our communities, and our economies.
Models that identify elements of strong recovery ecosystems have been developed.1, 2 Examples of key features include treatment services, recovery residences, harm reduction organizations, employment opportunities, and prevention organizations.
Source: Fletcher Group, Inc.
To better understand the strength of the substance use disorder recovery ecosystem in communities across the nation and inform efforts to support individuals in recovery, NORC at the University of Chicago, East Tennessee State University, and Fletcher Group, Inc. developed this tool to allow users to assess important elements of the recovery ecosystem in their communities.
1Ashford, Robert D., Austin M. Brown, Rachel Ryding, and Brenda Curtis. “Building Recovery Ready Communities: The Recovery Ready Ecosystem Model and Community Framework.” Addiction Research & Theory 28, no. 1 (January 2, 2020): 1–11. https://doi.org/10.1080/16066359.2019.1571191.
2Behringer, Bruce. “Responding to Appalachian Voices: Steps in Developing Substance-Use Recovery Ecosystems.” J Appalach Health 2, no. 3 (2020): 117–32.https://doi.org/10.13023/JAH.0203.10.
The interactive tool was created in JavaScript using the Leaflet library. Data were processed using SAS and converted from shapefile to TopoJSON using the sf library in R web client. Several data sources were accessed in the development of this tool.
The tables below describe each of the data sources and definitions for the base layer data and secondary overlays.
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 |
| Number of Providers Licensed to Administer Buprenorphine Per Capita |
SAMHSA (N-SSATS Data) (As of February 2022) |
Number of providers licensed to administer buprenorphine per 100,000 residents | |
| Average Distance to Nearest Medication-Assisted Treatment (MAT) Provider |
amfAR (Based on N-SSATS Data) (2017) |
Average number of miles between zip codes without a provider and the nearest zip code with a provider | |
| Number of Mental Health Providers Per Capita |
County Health Rankings and Roadmaps (data from CMS, National Provider Identification) |
Number of mental health providers per 100,000 residents. Mental health providers are defined as psychiatrists, psychologists, licensed clinical social workers, counselors, marriage and family therapists, mental health providers that treat alcohol and other drug abuse, and advanced practice nurses specializing in mental health care. | |
| Continuum of SUD Support | Number of Recovery Residences Per Capita |
SAMHSA (N-SSATS Data) (As of February 2022) |
Number of recovery residences per 100,000 residents |
| Average Distance to Nearest Syringe-Service Program (SSP) |
amfAR (Based on N-SSATS Data) (2018) |
Average number of miles between zip codes without a facility and the nearest zip code with a facility | |
| Number of Narcotics Anonymous (NA) or Self-Management and Recovery Training (SMART) Meetings per Capita |
NA Meeting Search SMART Meeting Search (As of May 2022) |
Number of NA or SMART meetings per 100,000 residents | |
| Drug Court Presence |
National Drug Court Resource Center (As of February 2022) |
The value is 1 if there is at least one drug court in the county and 0 if there are no drug courts | |
| Drug-Free Communities Coalition Presence |
ONDCP Lists of FY 2021 Drug Free Coalition Grant Recipients (As of February 2022) |
The value is 1 if there is at least one Drug-Free Communities coalition in the county and 0 if there are no Drug-Free Communities coalitions | |
| Policy Environment Score |
Prescription Drug Abuse Policy System; amfAR’s Opioid & Health Indicators Database;The Policy Surveillance Program: A LawAtlas Project |
The score is on a scale of 1 (weakest) to 10 (strongest) based on 9 policies (policies provided in the table below). | |
| Infrastructure and Social | Vehicle Availability |
U.S. Census Bureau American Community Survey (2016-2020) |
Severe Housing Cost Burden |
| Severe Housing Cost Burden |
County Health Rankings and Roadmaps (Using 2016-2020 American Community Survey data) |
Percentage of households spending 50% or more of their income on housing costs | |
| Broadband Access |
U.S. Census Bureau American Community Survey (2016-2020) |
Percentage of households with broadband | |
| Social Association Per Capita |
County Business Patterns (2019) |
Number of social associations per 10,000 residents |
The table below describes each of the data sources and definitions for the Policy Environment Score.
| Variable | Data Source | Definition | Response Options |
|---|---|---|---|
| Parole Violation Protection |
Prescription Drug Abuse Policy System (2021) |
The law provides protection from probation or parole violations. | 1 = Yes 0 = No 0 = No Data |
| Good Samaritan |
Prescription Drug Abuse Policy System (2021) |
The jurisdiction has a drug overdose Good Samaritan Law. | 1 = Yes 0 = No 0 = No Data |
| Overdose Reporting and Sentencing |
Prescription Drug Abuse Policy System (2021) |
Reporting an overdose is considered a mitigating factor in sentencing. | 1 = Yes 0 = No 0 = No Data |
| Commercial Required MOUD* Coverage |
Prescription Drug Abuse Policy System (2020) |
The state requires commercial insurers to provide coverage for MOUD. | 1 = Yes 0 = No 0 = No Data |
| Behavioral Health MOUD Coverage |
Prescription Drug Abuse Policy System (2020) |
The state Medicaid plan includes coverage for behavioral health supports for MOUD. | 1 = Yes 0 = No 0 = No Data |
| MOUD Provision |
Prescription Drug Abuse Policy System (2020) |
The state has an approved Medicaid State Plan Amendment to facilitate the provision of MOUD. | 1 = Yes 0 = No 0 = No Data |
| Licensed SUD Programs |
Prescription Drug Abuse Policy System (2020) |
Licensed SUD programs are required to facilitate access to MOUD programs. | 1 = Yes 0 = No 0 = No Data |
| SSP Legality |
amfAR’s Opioid & Health Indicators Database (2021) |
State law allows for the operation of syringe service programs (SSPs). | 2 = Legal 1 = Locally Permitted 0 = Not Legal |
| Syringe Posession |
The Policy Surveillance Program: A LawAtlas Project (2019) |
State law allows for the possession of syringes by SSP participants. | 1 = Yes 0 = No 0 = No Data |
*MOUD = Medications for opioid use disorder
The Recovery Ecosystem Index is calculated for each county in the United States using standardized values of 14 indicators belonging to one of three component classes associated with the recovery ecosystem. The three components represented are SUD Treatment, Continuum of SUD Support, and Infrastructure and Social. Each of these three components is comprised of several subcomponents reflecting aspects of that dimension that are aggregated to create the component score.
First, each indicator, also known as a subcomponent, was scaled to have a mean of 0 and a standard deviation of 1. This is referred to as the standardized subcomponent value and allows each subcomponent to affect the final index score equally.
At the component level, the subcomponent standardized values within the component are summed to create a component value. Then a clustering algorithm grouped all of the counties into five homogenous groups according to the sum of the standardized values thus creating a score for each county for each component.
Finally, all subcomponent values are summed for each county to create the recovery ecosystem value. The counties are then grouped into five classes with one representing the strongest recovery ecosystem and five representing the weakest ecosystem. This one-to-five score is the final rural Recovery Ecosystem Index score.
There are some limitations that should be considered when utilizing the Recovery Ecosystem Index. First, some indicators are intended to serve as proxies for aspects of a recovery ecosystem, but are not perfect indicators. It is often difficult to obtain valid data that is reported consistently at the county-level. While the index is designed to be as comprehensive as possible based on available data, the index may not assess all aspects of a strong recovery ecosystem. Additionally, the data represent a range of time periods. Therefore, the data provides a general sense of the overall recovery ecosystem, but will not capture more recent changes.
The Recovery Ecosystem Index is supported by the Health Resources and Services Administration (HRSA) of the US Department of Health and Human Services (HHS) as part of an award totaling $13.7M with 0% financed with non-governmental sources. The contents are those of the authors and do not necessarily represent the official views of, nor endorsed by HRSA, HHS, or the US Government.
To help protect rural families and communities from the ravages of opioid and substance use, the Fletcher Group, Inc.not-for-profit researches and provides best-practice technical assistance to expand the quality and capacity of recovery housing as well as the evidence-based services needed for long-term recovery. To learn more about the innovative “recovery ecosystem” model and the technical assistance that may be available to you free of charge, visit www.fletchergroup.org.
NORC at the University of Chicago conducts research and analysis that decision-makers trust. As a nonpartisan research organization and a pioneer in measuring and understanding the world, we have studied almost every aspect of the human experience and every major news event for more than eight decades. Today, we partner with government, corporate, and nonprofit clients around the world to provide the objectivity and expertise necessary to inform the critical decisions facing society.
East Tennessee State University (ETSU) is a public university located in the northeast Tennessee region bordered by Kentucky, North Carolina, and Virginia. With over 100 years of experience improving the lives and well-being of individuals and communities, ETSU has grown to be a national leader in addressing key health issues through education, research, and service. ETSU is home to a robust Academic Health Sciences Center—ETSU Health—and over 10 centers with diverse research and service interests, including the ETSU Center for Rural Health Research and ETSU Addiction Science Center.
The ETSU/NORC Rural Health Equity Research Center’s mission is to develop strategies and recommendations for policy makers, rural healthcare providers and rural communities to mitigate the individual and community-level impacts of substance use disorder (SUD), improve access to healthcare and social services, and improve population health. The ETSU/NORC Rural Health Equity Research Center builds on a history of successful collaboration between the ETSU Addiction Science Center, ETSU’s Center for Rural Health Research, and the NORC Walsh Center for Rural Health Analysis. This partnership bridges NORC’s long history of rural health research and evaluation and ETSU’s commitment to honor its rural heritage.
For more information please contact:
Megan Heffernan, MPH
Research Scientist, Public Health Research, NORC at the University of Chicago
heffernan-megan@norc.org | (301) 310-5089
Michael Meit, MA, MPH
Deputy Director, ETSU/NORC Rural Health Equity Research Center
Director of Research and Programs, ETSU Center for Rural Health Research
Senior Fellow, Public Health Research, NORC at the University of Chicago
meitmb@etsu.edu | (240) 273-2751
Dave Johnson
CEO, Fletcher Group, Inc.
djohnson@fletchergroup.org
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Embed table for Menifee County, KY in 2011 - 2015
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The term “Recovery Ecosystem” is used to describe the community-level factors that are in place to support individuals in recovery from substance use disorder (SUD). This tool allows community organizations, policymakers, researchers, and the general public to create county-level maps to understand these factors in their communities and where additional resources are most needed to provide support to individuals in recovery. Insights derived from this tool can be used to target resources and interventions to enhance recovery ecosystems.
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 the overall Recovery Ecosystem Index score, comprised of 14 indicators across three domains, and domain specific sub-scores including the Substance Use Disorder Treatment score, Continuum of SUD Support score, or the Infrastructure and Social Factors score. Each of these scores are described in more detail on the Recovery Ecosystem Index page. Additionally, the user can select either all drug overdose or opioid overdose mortality rates as the base layer indicator. You can use the List of Counties to link directly to data on a particular county, or click on specific counties on the map.
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. HRSA definitions of urban/rural are used for this tool.
Use the “Filter by state” drop down to highlight a specific state on the map.
Choose variables from the left-hand column to layer county-level socio-demographic and economic data on top of the base-layer data. Additionally, the Recovery Ecosystem Index score and subcomponent scores or the drug overdose mortality rates can be added as an overlay, in addition to the base map. By showing the variables as translucent circles of varying sizes, the tool allows users to clearly see how a given measure relates to the base layer data.
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.
There is also a drop down for “Map Overlays.” Available map overlays include: geolocations of Native American Reservations; outline of persistent poverty counties; location of major highways; and Federally Defined Regions (e.g., Appalachia, Delta, U.S.-Mexico border region).
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) Recovery Ecosystem Index scores and underlying data; 2) Socio-demographic data; 3) Economic data; 4) Drug overdose mortality data; and 5) Detailed information on policy indicators. For all data on the county fact sheets, county, state, and national data are provided, to provide benchmarks for counties.
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 |