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CDC/ATSDR Social Vulnerability Index

by Agency for Toxic Substances and Disease Registry, CDC

The SVI ranked communities by their capacity to withstand a disaster and was used by emergency planners well outside the federal government — state agencies, hospitals and researchers built on it. Captured 30 October 2024; the page now returns 404.

Period: 2024
Region: United States
Language: English
Rights: Public domain. A work of the United States Government is not subject to copyright under 17 U.S.C. § 105, so this is held in full with no exception claimed and none needed. Published by Agency for Toxic Substances and Disease Registry, CDC at https://www.atsdr.cdc.gov/placeandhealth/svi/index.html, which returned HTTP 404 when last checked on 2026-09-22.
Source: https://www.atsdr.cdc.gov/placeandhealth/svi/index.html

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CDC/ATSDR SOCIAL VULNERABILITY INDEX Agency for Toxic Substances and Disease Registry, CDC

Captured from the Internet Archive on 2026-09-22. This file holds 32 pages from atsdr.cdc.gov/placeandhealth/svi, the most substantial the Archive has beneath that address. It is a capture of what was published there, not a complete mirror: pages the Archive never visited are not here, and neither are images, datasets or downloads. Each page is headed with its title, its address and its capture date.

CDC SVI Documentation 2022 | Place and Health | ATSDR www.atsdr.cdc.gov/placeandhealth/svi/documentation/SVI_documentation_2022.html — captured 2024-07-12

CDC SVI DOCUMENTATION 2022

View print only PDF of CDC/ATSDR SVI 2022 Documentation [PDF – 531 KB]

CDC/ATSDR SVI 2022 Documentation – 5/21/2024

INTRODUCTION

WHAT IS SOCIAL VULNERABILITY?

Every community must prepare for and respond to hazardous events, whether a natural disaster like a tornado or a disease outbreak, or an anthropogenic event such as a harmful chemical spill. The degree to which a community exhibits certain social conditions, including high poverty, low percentage of vehicle access, or crowded households, among others, may affect that community’s ability to prevent human suffering and financial loss in the event of a disaster. These factors describe a community’s social vulnerability.

WHAT IS THE CDC/ATSDR SOCIAL VULNERABILITY INDEX?

ATSDR’s Geospatial Research, Analysis, & Services Program (GRASP) created the Centers for Disease Control and Prevention and Agency for Toxic Substances and Disease Registry Social Vulnerability Index (hereafter, CDC/ATSDR SVI or SVI) to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event.

SVI indicates the relative vulnerability of every U.S. census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts on 16 social factors, such as unemployment, racial and ethnic minority status, and disability status. Then, SVI further groups the factors into four related themes. Thus, each tract receives a ranking for each Census variable and for each of the four themes as well as an overall ranking.

Below, text that describes “tract” methods also refers to county methods.

HOW CAN THE SVI HELP COMMUNITIES BE BETTER PREPARED FOR HAZARDOUS EVENTS?

SVI provides specific socially and spatially relevant information to help public health officials and local planners better prepare communities to respond to emergency events such as severe weather, floods, disease outbreaks, or chemical exposure.

SVI CAN BE USED TO:

- Assess community need during emergency preparedness planning

- Estimate the type and quantity of needed supplies such as food, water, medicine, and bedding.

- Decide the number of emergency personnel required to assist people.

IMPORTANT NOTES ON SVI DATABASES

- All datasets are available for download in a CSV or Geodatabase format from https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html . SVI 2014, 2016, 2018, 2020, and 2022 are also available via ArcGIS Online. Search for “Social Vulnerability Index.”

- Any changes to American Community Survey (ACS) field names between SVI 2020 and 2022 are noted in the Data Dictionary below.

- When mapping or analyzing SVI data across multiple states or nationwide, use the U.S. database (i.e., select, “United States” in Geography menu), in which all tracts are ranked against one another. When mapping or analyzing SVI data within a single state, use the state-specific database, in which tracts are ranked only against other tracts in the specified state.

- Starting with SVI 2014, we’ve added a stand-alone, state-specific Commonwealth of Puerto Rico database. Puerto Rico is not included in the U.S.-wide ranking.

- Starting with SVI 2014, we’ve added a database of Tribal Census Tracts ( https://www.census.gov/newsroom/blogs/random-samplings/2012/07/decoding-state-county-census-tracts-versus-tribal-census-tracts.html ). Tribal tracts are defined independently of, and in addition to, standard county-based tracts. The tribal tract database contains only estimates, percentages, and their respective margins of error (MOEs), along with the adjunct variables described in the data dictionary below. Because of geographic separation and cultural diversity, tribal tracts are not ranked against each other nor against standard census tracts.

- Tracts with an estimated population of zero were not included in the ranking process (N = 857 for the U.S., N = 798 excluding Puerto Rico). Of these, 520 tracts (including those of Puerto Rico) were re-added to the SVI databases after the ranking procedure. 337 tracts did not have matching geometry in the 2022 cartographic boundary file and were excluded.

- A value of -999 in any field either means the value was unavailable from the original census data or we could not calculate a derived value because of unavailable Census data. Any cells with a value of -999 were not used for further calculations. For example, total flags do not include fields with a -999 value.

- Whenever available, we use Census-calculated MOEs. If Census MOEs are unavailable, for instance when aggregating variables within a table, we use approximation formulas provided by the Census. S. Census Bureau, Understanding and Using American Community Survey Data: What All Data Users Need to Know, U.S. Government Publishing Office, Washington, DC, 2020. pp. 59-67. https://www.census.gov/content/dam/Census/library/publications/2020/acs/acs_general_handbook_2020.pdf

- If more precise MOEs are required, see Census methods and data regarding Variance Replicate Tables here: https://www.census.gov/programs-surveys/acs/data/variance-tables.html .

- We use the variable “FIPS” as our geographic identification. Please note that a state FIPS code is two digits, a county FIPS code is three digits, and a census tract is 6 digits long. To identify a unique county, you must include the state FIPS along with the county FIPS (ex. 13089 is the state (13) + the county (089)). To identify a unique census tract, you must include the state and county FIPS along with the census tract (ex. 13089022404 is the state (13) + the county (089) + the census tract (022404)).

- Questions? Please visit the SVI website at http://svi.cdc.gov or email the SVI Coordinator at svi_coordinator@cdc.gov for additional information.

METHODS

VARIABLES USED

American Community Survey (ACS), 2018-2022 (5-year) data for the following estimates:

Text version of overall social vulnerability image:

- Racial & Ethnic Minority Status* Hispanic or Latino (of any race); Black and African American, Not Hispanic or Latino; American Indian and Alaska Native, Not Hispanic or Latino; Asian, Not Hispanic or Latino; Native Hawaiian and Other Pacific Islander, Not Hispanic or Latino; Two or More Races, Not Hispanic or Latino; Other Races, Not Hispanic or Latino

* Estimate total population – White, non-Hispanic population is equivalent to summing Estimate; Hispanic or Latino, Total Population + Estimate; Black and African American Not Hispanic or Latino + Estimate; American Indian and Alaska Native Not Hispanic or Latino + Estimate; Asian Not Hispanic or Latino + Estimate; Native Hawaiian and Other Pacific Islander Not Hispanic or Latino + Estimate; Two or More Races Not Hispanic or Latino + Estimate; Other Races Not Hispanic or Latino.

We used the Estimate total population – White, non-Hispanic – because this more direct calculation provides a smaller margin for error and a simpler calculation as recommended in the ACS guidance document (U.S. Census Bureau, Understanding and Using American Community Survey Data: What All Data Users Need to Know, U.S. Government Publishing Office, Washington, DC, 2020. p. 61.) https://www.census.gov/content/dam/Census/library/publications/2020/acs/acs_general_handbook_2020.pdf

The following adjunct variables were included in the SVI 2022 database:

- An estimate of daytime population derived from LandScan 2021 estimates**

- 2018-2022 ACS estimates for households without an internet subscription

- 2018-2022 ACS estimates for Hispanic/Latino persons, Not Hispanic or Latino Black/African American persons, Not Hispanic or Latino Asian persons, Not Hispanic or Latino American Indian and Alaska Native persons, Not Hispanic or Latino Native Hawaiian and Other Pacific Islander persons, Not Hispanic or Latino persons of two or more races, and Not Hispanic or Latino persons of some other race

** 2021 is the most recent year available at time of dataset release.

Adjunct variables are not used to calculate any SVI rankings; however, they may provide additional context and are included in the SVI database to make them readily accessible.

Estimated counts and percentages for each variable are included in the database. In addition, the MOE for each estimate, at the Census Bureau standard of 90% confidence, are also included. Confidence intervals can be calculated by subtracting the MOE from the estimate (lower limit) and adding the MOE to the estimate (upper limit). Tracts with relatively small sample sizes (i.e., populations) will have large MOEs. It is important to consider how sampling errors may impact conclusions in any analysis. https://www.census.gov/content/dam/Census/library/publications/2020/acs/acs_general_handbook_2020.pdf pp. 53-58.

RANKINGS

We ranked census tracts within each state, the District of Columbia, and Puerto Rico, to enable mapping and analysis of relative social vulnerability in individual states. We also ranked tracts for the entire United States against one another, for mapping and analysis of relative social vulnerability in multiple states, or across the U.S. SVI rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater social vulnerability.

For each tract, we generated its percentile rank among all tracts for 1) the 16 individual variables, 2) the four themes, and 3) its overall position.

Theme rankings: For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings.

The four theme ranking variables, detailed in the Data Dictionary below, are :

- Socioeconomic Status – RPL_THEME1

- Household Characteristics – RPL_THEME2

- Racial & Ethnic Minority Status – RPL_THEME3

- Housing Type & Transportation – RPL_THEME4

Overall tract rankings: We summed the sums for each theme, ordered the tracts, and then calculated overall percentile rankings. Please note taking the sum of the sums for each theme is the same as summing individual variable rankings. The overall summary ranking variable is RPL_THEMES .

The general steps to recreating SVI rankings are:

- E Variables: Obtain estimates of the CDC/ATSDR SVI variables from the Census Bureau .

- EP Variables: Obtain or derive percentages for the 16 CDC SVI variables.

- EPL Variables: Rank the EP variables to get percentile rankings (or the CDC/ATSDR SVI rankings) for each of the 16 variables.

- SPL Variables: Sum the EPL variables by theme.

- RPL Variables: Rank the theme-specific SPL variable.

- Overall SPL Variable (SPL_THEMES): Sum the SPL variables from all four themes.

- Overall RPL Variable (RPL_THEMES): Rank SPL_THEMES. This is the overall summary ranking variable.

Note: Areas with no data should not be included in the calculations.

FLAGS

Tracts in the top 10%, or the 90 th percentile, are given a flag value of 1 to indicate high social vulnerability. Tracts below the 90 th percentile are given a flag value of 0.

For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each tract as the number of all variable flags.

For a detailed description of SVI variable selection rationale and methods, see A Social Vulnerability Index for Disaster Management .

NOTE ON COMPARATIVE RANKINGS BETWEEN STATE AND NATIONAL DATABASES

The order of overall SVI rankings and SVI theme rankings of census tracts may differ between the U.S. and state SVI databases.

Overall and theme rankings are based on cumulative values that are relative to the number of census tracts being compared. Thus, differences between the order of rankings in the U.S. database and that of state databases may arise from the accumulation of differences in summing the percentile ranks for the individual SVI variables.

For example, using the 2018 Georgia SVI database, Fulton County has an overall SVI score of 0.2658 with a ranking of 117 out of 159 Georgia counties. However, using the 2018 U.S. SVI database, Fulton County has an overall SVI score of 0.5268, giving Fulton County a ranking of 125 out of the 159 Georgia counties. The ranking differences between the two databases are due to differences in summed percentile ranks.

In short, because a state has fewer census tracts than the U.S., relative differences are more pronounced at the state level than at the national level. These comparative differences, when summed, can result in a different rank order between the state and U.S. databases.

SVI 2022 UPDATES

For each SVI release, we review the American Community Survey (ACS) for any changes to the variables and to ensure we are using the most concise and accurate variables.

For the 2022 database, we remapped some of our EP variables directly to ACS percentage variables. This change largely meant, when possible, we favored percentage variables from the ACS Data Profile (DP) and Subject (S) tables rather than calculating from Detailed (B) table count estimates. During our analysis we found the new variable mappings improved SVI processing through simpler calculations, greater transparency, and better accuracy. Furthermore, some variable changes allowed us to use ACS-calculated margins of error rather than deriving our own. These updates follow ACS recommendations noted in their guidance document U.S. Census Bureau, Understanding and Using American Community Survey Data: What All Data Users Need to Know , U.S. Government Publishing Office, Washington, DC, 2020. See the data dictionary below for 2022 variable changes.

For percentage margins of error (MP) variables, we established a maximum value of 100 in accordance with ACS guidance on deriving margins of error. (2018-2022 ACS 5-year Accuracy of the Data (US). Pp 14-15.)

View print only PDF of CDC/ATSDR SVI 2022 Documentation [PDF – 531 KB]

CDC/ATSDR Social Vulnerability Index (SVI) www.atsdr.cdc.gov/placeandhealth/svi/index.html%5Cu003c/a%5Cu003e — captured 2023-05-19

The Minority Health Social Vulnerability Index (MH SVI) uses data from the CDC/ATSDR SVI. The MH SVI enhances existing resources to support the identification of racial and ethnic minority communities at greatest risk for disproportionate impact and adverse outcomes due to the COVID-19 pandemic. Explore information and data on the MH SVI .

Questions or Comments?

Contact the CDC/ATSDR SVI Coordinator

CDC SVI Publications & Materials | Place and Health | ATSDR www.atsdr.cdc.gov/placeandhealth/svi/publications/publications_materials.html — captured 2020-09-20

CDC SVI PUBLICATIONS & MATERIALS

The list below includes select SVI publications and materials authored by and recommended by GRASP for the scientists, researchers, emergency responders, and public health professionals in the SVI community.

CDC/ATSDR SVI MATERIALS

Introduction to CDC’s Social Vulnerability Index (SVI) external icon

Methods for CDC’s Social Vulnerability Index (SVI) external icon

Flanagan, B.E., Gregory, E.W., Hallisey, E.J., Heitgerd, J.L., & Lewis, B. (2011). A Social Vulnerability Index for Disaster Management. pdf icon [PDF – 2 MB] Journal of Homeland Security and Emergency Management, 8 (1).

Flanagan, B.E., Hallisey, E.J., Adams, E., & Lavery, A. (2018). Measuring Community Vulnerability to Natural and Anthropogenic Hazards: The Centers for Disease Control and Prevention’s Social Vulnerability Index. pdf icon external icon Journal of Environmental Health, 80 (10), 34-36.

Lehnert, E.A., Wilt, G., Flanagan, B., & Hallisey, E. Spatial exploration of the CDC’s Social Vulnerability Index and heat-related health outcomes in Georgia external icon . International Journal of Disaster Risk Reduction, 46.

Wolkin, A., Patterson, J.R., Harris, S., Soler, E., Burrer, S., McGeehin, M., & Greene, S. (2015). Reducing Public Health Risk During Disasters: Identifying Social Vulnerabilities. external icon Journal of Homeland Security and Emergency Management, 12 (4), 809–822.

Centers for Disease Control and Prevention (CDC). Planning for an Emergency: Strategies for Identifying and Engaging At-Risk Groups. pdf icon A guidance document for Emergency Managers: First edition. Atlanta (GA): CDC; 2015.

National Environmental Public Health Tracking Network Data Explorer (Populations and Vulnerabilities)

SVI COMMUNITY MATERIALS

Horney, J., Nguyen, M., Salvesen, D., Dwyer, C., Cooper, J., & Berke, P. (2017). Assessing the Quality of Rural Hazard Mitigation Plans in the Southeastern United States. external icon Journal of Planning Education and Research, 37 (1), 56–65.

Lue E. & Wilson, J.P. (2016). Mapping fires and American Red Cross aid using demographic indicators of vulnerability. external icon Disasters, 41 (2), 409-426.

Bakkensen, L.A., Fox-Lent, C., Read, L.K., & Linkov, I. (2016). Validating Resilience and Vulnerability Indices in the Context of Natural Disasters. external icon Risk Analysis, 37 (5).

Ikeda, M. & Ozanne, A. (2016). Human Security, Social Competence and Natural Disasters in Japan and New Zealand: A Case study of Filipino migrants. external icon Japan Social Innovation Journal, 6 (1).

Gay, J.L., Robb, S.W., Benson, K.M., & White, A. (2016). Can the Social Vulnerability Index Be Used for More than Emergency Preparedness? An Examination Using Youth Physical Fitness Data. external icon Journal of Physical Activity and Health, 13 (2), 121-130.

An, R. & Xiang, X. (2015). Social Vulnerability and Obesity among U.S. Adults. pdf icon external icon International Journal of Health Sciences, 3 (3), 7-12.

Berke, P., Newman, G., Lee, J., Combs, T., Kolosna, C., & Salvesen, D. (2015). Evaluation of Networks of Plans and Vulnerability to Hazards and Climate Change: A Resilience Scorecard. pdf icon external icon Journal of the American Planning Association, 81 (4), 287-302.

An, R. & Xiang, X. (2015). Social Vulnerability and Leisure-time Physical Inactivity among US Adults. external icon American Journal of Health Behavior, 39 (6), 751-760.

Horney, J., Simon, M., Grabich, S., & Berke, P. (2015). Measuring participation by socially vulnerable groups in hazard mitigation planning, Bertie County, North Carolina. external icon Journal of Environmental Planning and Management, 58 (5), 802-818.

Tate, E. (2013). Uncertainty Analysis for a Social Vulnerability Index. external icon Annals of the Association of American Geographers, 103 (3), 526-543.

Tarling, H.A. (2017). Comparative Analysis of Social Vulnerability Indices: CDC’s SVI and SoVI.® external icon Lund University Master’s Thesis.

Balbus, J., Crimmins, A., Gamble, J.L., Easterling, D.R., Kunkel, K.E., Saha, S, & Sarofim, M.C. (2016). The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment external icon (Ch. 1. Climate Change and Human Health & Ch. 9. Populations of Concern). U.S. Global Change Research Program , Washington, DC.

Nayanee G., Clavin, C.T., Petropoulos, Z.E., Mudd, A.B., Nek, R., & Tinkle, S.S. (2016). Case Studies of Community Resilience Policy. external icon U.S. Department of Commerce, National Institute of Standards and Technology.

Hurst, H. (2015). Disaster Recovery Centers: Catchment Analysis. pdf icon external icon Technical Report . Federal Emergency Management Agency Recovery Directorate.

Vick, J., Thomas-Trudo, S., Cole, M., & Samuels, A.D. (Eds.). (2015). Health equity in Nashville. pdf icon external icon Metro Nashville Public Health Department Division of Epidemiology and Research and RWJF Center for Health Policy at Meharry Medical College.

Mukhopadhyay, S. (2013). Environmental Public Health Tracking ASTHO Fellowship Report. external icon Tennessee Department of Health.

Esri – Living Atlas of the World – CDC Social Vulnerability Index external icon

Surgo Foundation – The COVID-19 Community Vulnerability Index external icon

STAT – COVID-19 Preparedness Scores Dashboard external icon

COVID-19 Healthcare Coalition – Vulnerable Population Dashboard external icon

Jvion – COVID Community Vulnerability Map external icon

Esri – Tutorial on Location Allocation for COVID-19 Response (video)

Esri – Understanding your Community, CDC Social Vulnerability Index external icon

Direct Relief – Hurricane Florence Social Vulnerability Dashboard external icon

Save the Children/Natural Hazards Center at U.C. Boulder – Gap Analysis of VOADs, Children, and Disaster Services external icon

Habitat for Humanity Social Vulnerability & Distressed Communities Indexes external icon

Catholic Charities USA Disaster Operations Map – Social Vulnerability external icon

North Carolina Social Vulnerability Index Tool external icon

Wisconsin Department of Health Services – Wisconsin Flood Risk Mapping Application external icon

BRACE – Illinois: Building Resilience Against Climate Effects external icon

New Hampshire Department of Health and Human Services Social Vulnerability Index external icon

Vermont Department of Health Social Vulnerability Index external icon

King County, WA – Social Vulnerability Index external icon

Harduvel, J. (2015). Public Housing under HOPE VI: A Spatial Analysis of Chicago. pdf icon external icon GIS at Tufts GIS Poster Expo Gallery.

Hurst, H. (2015). Allocating Disaster Recovery Centers. pdf icon external icon

CDC SVI Documentation 2018 | Place and Health | ATSDR www.atsdr.cdc.gov/placeandhealth/svi/documentation/SVI_documentation_2018.html — captured 2020-10-19

CDC SVI DOCUMENTATION 2018

View print only PDF of CDC SVI 2018 Documentation pdf icon [PDF – 423 KB]

CDC SVI 2018 DOCUMENTATION &NDASH; 1/31/2020

Please see data dictionary below.

Introduction

What is Social Vulnerability?

Every community must prepare for and respond to hazardous events, whether a natural disaster like a tornado or a disease outbreak, or an anthropogenic event such as a harmful chemical spill. The degree to which a community exhibits certain social conditions, including high poverty, low percentage of vehicle access, or crowded households, may affect that community’s ability to prevent human suffering and financial loss in the event of disaster. These factors describe a community’s social vulnerability.

What is CDC Social Vulnerability Index?

ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) created Centers for Disease Control and Prevention Social Vulnerability Index (CDC SVI or simply SVI, hereafter) to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event.

SVI indicates the relative vulnerability of every U.S. Census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts on 15 social factors, including unemployment, minority status, and disability, and further groups them into four related themes. Thus, each tract receives a ranking for each Census variable and for each of the four themes, as well as an overall ranking.

In addition to tract-level rankings, SVI 2010, 2014, 2016, and 2018 also have corresponding rankings at the county level. Notes below that describe “tract” methods also refer to county methods.

How can CDC SVI help communities be better prepared for hazardous events?

CDC SVI can be used to:

- Allocate emergency preparedness funding by community need.

- Estimate the type and amount of needed supplies such as food, water, medicine, and bedding.

- Decide how many emergency personnel are required to assist people.

Important Notes on CDC SVI Databases

- SVI 2014, 2016, and 2018 are available for download in shapefile format from https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html. SVI 2014 and 2016 are also available via ArcGIS Online. Search on “CDC’s Social Vulnerability Index.”

- For SVI 2000 and 2010, keep the data in geodatabase format when downloading from https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html. Converting to shapefile changes the field names.

- ACS field names have changed between SVI 2016 and 2018. Name changes are noted in the Data Dictionary below.

- For US-wide or multi-state mapping and analysis, use the US database, in which all tracts are ranked against one another. For individual state mapping and analysis, use the state-specific database, in which tracts are ranked only against other tracts in the specified state.

- Starting with SVI 2014, we’ve added a stand-alone, state-specific Commonwealth of Puerto Rico database. Puerto Rico is not included in the US-wide ranking.

- Starting with SVI 2014, we’ve added a database of Tribal Census Tracts external icon ( https://www.census.gov/newsroom/blogs/random-samplings/2012/07/decoding-state-county-census-tracts-versus-tribal-census-tracts.html external icon ). Tribal tracts are defined independently of, and in addition to, standard county-based tracts. The tribal tract database contains only estimates, percentages, and their respective margins of error (MOEs), along with the adjunct variables described in the data dictionary below. Because of geographic separation and cultural diversity, tribal tracts are not ranked against each other nor against standard census tracts.

- Tracts with zero estimates for total population (N = 645 for the U.S.) were removed during the ranking process. These tracts were added back to the SVI databases after ranking. The TOTPOP field value is 0, but the percentile ranking fields (RPL_THEME1, RPL_THEME2, RPL_THEME3, RPL_THEME4, and RPL_THEMES) were set to -999.

- For tracts with > 0 TOTPOP, a value of -999 in any field either means the value was unavailable from the original census data or we could not calculate a derived value because of unavailable census data.

- Any cells with a -999 were not used for further calculations. For example, total flags do not include fields with a -999 value.

- Whenever available, we use Census-calculated MOEs. If Census MOEs are unavailable, for instance when aggregating variables within a table, we use approximation formulas provided by the Census in Appendix A (pages A-14 through A-17) of A Compass for Understanding and Using American Community Survey Data here: https://www.census.gov/content/dam/Census/library/publications/2008/acs/ACSGeneralHandbook.pdf pdf icon external icon

If more precise MOEs are required, see Census methods and data regarding Variance Replicate Tables here: https://www.census.gov/programs-surveys/acs/technical-documentation/variance-tables.html external icon .

For selected ACS 5-year Detailed Tables, “Users can calculate margins of error for aggregated data by using the variance replicates. Unlike available approximation formulas, this method results in an exact margin of error by using the covariance term.”

- The U.S. Census Bureau reports that data collection errors prohibited the inclusion of income and poverty data from Rio Arriba County, New Mexico. Please see a more detailed explanation provided by the Census Bureau here: https://www.census.gov/programs-surveys/acs/technical-documentation/errata/125.html external icon .

- FIPS codes are generally defined as text to preserve leading zeros (0s). If you’re working with csv files, leading 0s are required to properly join or merge tables. ArcGIS maintains leading 0s in the FIPS code fields of csv files. To preserve leading 0s and create an Excel file in Excel for Office 365, follow these steps: Open a blank worksheet in Excel.

- Click Data in the menu bar and choose the icon From Text/CSV

- Navigate to the csv file and choose to Import

- In the dialog box that opens, choose to Transform Data

- In the Power Query Editor dialog box, for each of the FIPS columns (ST, STCNTY, FIPS for tracts and ST, FIPS for counties), right click the column name and choose to Change Type to Text.

- As prompted in the Change Column Type dialog box, choose to Replace current. Click Close and Load.

- Save As an Excel xlsx file.

- See the Methods section below for further details.

- Questions? Please visit the SVI website at http://svi.cdc.gov for additional information or email the SVI Coordinator at svi_coordinator@cdc.gov .

Methods

Variables Used

American Community Survey (ACS), 2014-2018 (5-year) data for the following estimates:

For SVI 2018, we included two adjunct variables, 1) 2014-2018 ACS estimates for persons without health insurance, and 2) an estimate of daytime population derived from LandScan 2018 estimates. These adjunct variables are excluded from SVI rankings.

Raw data estimates and percentages for each variable, for each tract, are included in the database. In addition, the margins of error (MOEs) for each estimate, at the Census Bureau standard of 90%, are also included. Confidence intervals can be calculated by subtracting the MOE from the estimate (lower limit) and adding the MOE to the estimate (upper limit). Because of relatively small sample sizes, some of the MOEs are high. It’s important to identify the amount of error acceptable in any analysis.

Rankings

We ranked Census tracts within each state and the District of Columbia, to enable mapping and analysis of relative vulnerability in individual states. We also ranked tracts for the entire United States against one another, for mapping and analysis of relative vulnerability in multiple states, or across the U.S. as a whole. Tract rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability.

For each tract, we generated its percentile rank among all tracts for 1) the fifteen individual variables, 2) the four themes, and 3) its overall position.

The four summary theme ranking variables, detailed in the Data Dictionary below, are :

- Socioeconomic – RPL_THEME1

- Household Composition & Disability – RPL_THEME2

- Minority Status & Language – RPL_THEME3

- Housing Type & Transportation – RPL_THEME4

For a detailed description of SVI variable selection rationale and methods, see A Social Vulnerability Index for Disaster Management pdf icon (https://www.atsdr.cdc.gov/placeandhealth/svi/img/pdf/Flanagan_2011_SVIforDisasterManagement-508.pdf).

Reproducibility Caveat

When replicating SVI using Microsoft Excel or similar software, results may differ slightly from databases on the SVI website or ArcGIS Online. This is due to variation in the number of decimal places used by the different software programs. For purposes of automation, we developed SVI using SQL programming language. Because the SQL programming language uses a different level of precision compared to Excel and similar software, reproducing SVI in Excel may marginally differ from the SVI databases downloaded from the SVI website. For future iterations of SVI, beginning with SVI 2018, we plan to modify the SQL automation process for constructing SVI to align with that of Microsoft Excel. If there are any questions, please email the SVI Coordinator at svi_coordinator@cdc.gov .

CDC SVI 2018 Data Dictionary – American Community Survey field names that changed between 2016 and 2018 are noted in RED and marked ‘Yes’ or ‘No’ in the ‘Field Name Changed Since 2016?’ column.

View print only PDF of CDC SVI 2018 Documentation pdf icon [PDF – 423 KB]

CDC SVI Documentation 2020 | Place and Health | ATSDR www.atsdr.cdc.gov/placeandhealth/svi/documentation/SVI_documentation_2020.html — captured 2022-11-20

CDC SVI DOCUMENTATION 2020

View print only PDF of CDC/ATSDR SVI 2020 Documentation [PDF – 671 KB]

CDC/ATSDR SVI 2020 Documentation – 8/5/2022

Introduction

What is Social Vulnerability?

Every community must prepare for and respond to hazardous events, whether a natural disaster like a tornado or a disease outbreak, or an anthropogenic event such as a harmful chemical spill. The degree to which a community exhibits certain social conditions, including high poverty, low percentage of vehicle access, or crowded households, may affect that community’s ability to prevent human suffering and financial loss in the event of disaster. These factors describe a community’s social vulnerability.

What is CDC/ATSDR Social Vulnerability Index?

ATSDR’s Geospatial Research, Analysis, & Services Program (GRASP) created the Centers for Disease Control and Prevention and Agency for Toxic Substances and Disease Registry Social Vulnerability Index (CDC/ATSDR SVI or simply SVI, hereafter) to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event.

SVI indicates the relative vulnerability of every U.S. Census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts on 16 social factors, including unemployment, racial and ethnic minority status, and disability, and further groups them into four related themes. Thus, each tract receives a ranking for each Census variable and for each of the four themes as well as an overall ranking.

In addition to tract-level rankings, SVI 2010, 2014, 2016, 2018, and 2020 also have corresponding rankings at the county level.

Notes below that describe “tract” methods also refer to county methods.

How can SVI help communities be better prepared for hazardous events?

SVI can be used to:

- Assess community need during emergency preparedness planning

- Estimate the type and amount of needed supplies such as food, water, medicine, and bedding.

- Decide how many emergency personnel are required to assist people.

Important Notes on SVI Databases

- SVI 2014, 2016, 2018, and 2020 are available for download in shapefile format from https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html . SVI 2014, 2016, 2018, and 2020 are also available via ArcGIS Online. Search for “CDC’s Social Vulnerability Index.”

- ACS field names changed between SVI 2018 and 2020. Name changes are noted in the Data Dictionary below.

- Starting with SVI 2014, we’ve added a stand-alone, state-specific Commonwealth of Puerto Rico database. Puerto Rico is not included in the US-wide ranking.

- Any cells with a -999 were not used for further calculations. For example, total flags do not include fields with a -999 value.

- Whenever available, we use Census-calculated MOEs. If Census MOEs are unavailable, for instance when aggregating variables within a table, we use approximation formulas provided by the Census in Appendix A (pages A-14 through A-17) of A Compass for Understanding and Using American Community Survey Data here: https://www.census.gov/content/dam/Census/library/publications/2008/acs/ACSGeneralHandbook.pdf

If more precise MOEs are required, see Census methods and data regarding Variance Replicate Tables here: https://www.census.gov/programs-surveys/acs/data/variance-tables.html . For selected ACS 5-year Detailed Tables, “Users can calculate margins of error for aggregated data by using the variance replicates. Unlike available approximation formulas, this method results in an exact margin of error by using the covariance term.”

- FIPS codes are generally defined as text to preserve leading zeros (0s). While working with csv files, leading 0s are required to properly join or merge tables. ArcGIS maintains leading 0s in the FIPS code fields of csv files. To preserve leading 0s and create an Excel file in Excel for Office 365, follow these steps: Open a blank worksheet in Excel.

- Click Data in the menu bar and choose the icon From Text/CSV

- Navigate to the csv file and choose to Import

- In the dialog box that opens, choose to Transform Data

- In the Power Query Editor dialog box, for each of the FIPS columns (ST, STCNTY, FIPS for tracts and ST, FIPS for counties), right click the column name and choose to Change Type to Text.

- As prompted in the Change Column Type dialog box, choose to Replace current. Click Close and Load.

- Save As an Excel xlsx file.

- See the Methods section below for further details.

- Questions? Please visit the SVI website for additional information or email the SVI Coordinator at svi_coordinator@cdc.gov .

Methods

Variables Used

American Community Survey (ACS), 2016-2020 (5-year) data for the following estimates:

Text version of overall vulnerability image:

- Racial & Ethnic Minority Status Hispanic or Latino (of any race); Black and African American, Not Hispanic or Latino; American Indian and Alaska Native, Not Hispanic or Latino; Asian, Not Hispanic or Latino; Native Hawaiian and Other Pacific Islander, Not Hispanic or Latino; Two or More Races, Not Hispanic or Latino; Other Races, Not Hispanic or Latino

For SVI 2020, adjunct variables were included:

- An estimate of daytime population derived from LandScan 2020 estimates

- 2016-2020 ACS estimates for households without a computer with a broadband Internet subscription

- 2016-2020 ACS estimates for Hispanic/Latino persons, Not Hispanic or Latino Black/African American persons, Not Hispanic or Latino Asian persons, Not Hispanic or Latino American Indian and Alaska Native persons, Not Hispanic or Latino Native Hawaiian and Other Pacific Islander persons, Not Hispanic or Latino persons of two or more races, and Not Hispanic or Latino persons of some other race

These adjunct variables are excluded from SVI rankings. We include these variables as adjunct variables because they can be helpful to explain more about the local areas in certain circumstances, and we want to make them easily accessible.

Raw data estimates and percentages for each variable, for each tract, are included in the database. In addition, the margins of error (MOEs) for each estimate, at the Census Bureau standard of 90%, are also included. Confidence intervals can be calculated by subtracting the MOE from the estimate (lower limit) and adding the MOE to the estimate (upper limit). Because of relatively small sample sizes, some of the MOEs are high. It is important to identify the amount of error acceptable in any analysis.

Rankings

For each tract, we generated its percentile rank among all tracts for 1) the 16 individual variables, 2) the four themes, and 3) its overall position.

The four summary theme ranking variables, detailed in the Data Dictionary below, are :

- Socioeconomic Status – RPL_THEME1

- Household Characteristics – RPL_THEME2

- Racial & Ethnic Minority Status – RPL_THEME3

- Housing Type & Transportation – RPL_THEME4

Flags

Tracts in the top 10%, i.e., at the 90 th percentile of values, are given a flag value of 1 to indicate high vulnerability. Tracts below the 90 th percentile are given a flag value of 0.

For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each tract as the number of all variable flags.

For a detailed description of SVI variable selection rationale and methods, see A Social Vulnerability Index for Disaster Management (https://www.atsdr.cdc.gov/placeandhealth/svi/img/pdf/Flanagan_2011_SVIforDisasterManagement-508.pdf).

Caveat for SVI State Databases

The order of overall SVI rankings and SVI theme rankings of census tracts and counties may differ between the U.S. and state SVI databases. A detailed explanation follows.

Overall and theme rankings are based on cumulative values that are relative to the number of census tracts or counties being compared. Thus, differences between the order of overall and theme rankings in the U.S. database and that of state databases may arise from the accumulation of differences in summing the percentile ranks for the individual SVI variables.

In short, because Georgia (or any state) has far fewer census tracts and counties than does the nation, differences in one or more variable percentages from one census tract or county to another are more pronounced at the state level than at the national level. Such differences, when summed across all variables, will in some cases result in a rank order change between the two databases.

If there are any questions, please contact the SVI Coordinator at svi_coordinator@cdc.gov .

SVI 2020 Updates

As our understanding of social vulnerability evolves over time, SVI must evolve as well. Beginning with SVI 2020, we made modifications to SVI theme names, individual SVI indicators, and adjunct data. We modified the name of Theme 2 from Household Composition & Disability to Household Characteristics, and we modified the name of Theme 3 from Minority Status & Language to Racial & Ethnic Minority Status. Within Theme 1 Socioeconomic Status, we modified the Below Poverty variable from the 100% federal poverty level to the 150% federal poverty level, considering the federal poverty line thresholds established for several federal health coverage policies. 1 Similarly, we included a No Health Insurance variable in Theme 1 Socioeconomic Status as a lack of health insurance coverage is increasingly considered a marker of lower socioeconomic status and a barrier to healthcare access. 2 Also, within Theme 1 Socioeconomic Status, we exchanged the Per Capita Income variable for Housing Cost Burden, which are households that spend 30% or more of annual income on housing costs. Recent studies have emphasized the importance of examining housing cost burden as opposed to per capita income as a better indicator of insufficient disposable income among households. 3,4 Further, we moved the English Language Proficiency variable from Theme 3 Racial & Ethnic Minority Status to Theme 2 Household Characteristics because the ACS variables are based on language spoken at home and are better suited in the Household Characteristics theme. Additionally, although people in racial and ethnic minority groups are overall more likely to have limited English language proficiency than non-Hispanic whites, most (90.9%) are English language proficient. 5 Thus, we moved the English Language Proficiency out of the Minority theme because it may have adversely affected the vulnerability ranking of communities in high minority areas of the country. Lastly, we included new adjunct variables: households without a computer with a broadband Internet subscription, and breakdowns of racial and ethnic minority populations. The coronavirus disease 2019 pandemic has underscored the importance of broadband Internet access as a social determinant of health, justifying the inclusion of data on the lack of broadband Internet access as an adjunct variable. 6 While we aggregate all racial and ethnic minority persons in Theme 3 Racial & Ethnic Minority Status, we recognize that SVI users may be interested in its component populations. A thorough literature review and internal validation were conducted to finalize the construction of SVI 2020.

- https://www.healthcare.gov/glossary/federal-poverty-level-fpl/

- McMaughan DJ, Oloruntoba O, Smith ML. Socioeconomic status and access to healthcare: Interrelated drivers for healthy aging. Front Public Health . 2020;8:231. doi:10.3389/fpubh.2020.00231

- Hernández D, Swope CB. Housing as a platform for health and equity: Evidence and future directions. Am J Public Health . 2019;109(10):1363-1366. doi:10.2105/AJPH.2019.305210

- Swope CB, Hernández D. Housing as a determinant of health equity: A conceptual model. Soc Sci Med . 2019;243:112571. doi:10.1016/j.socscimed.2019.112571

- U.S. Census Bureau; American Community Survey (ACS), Five-Year Public Use Microdata Sample (PUMS), 2016-2020; accessed via MDAT; ; (27 July 2022).

- Benda NC, Veinot TC, Sieck CJ, Ancker JS. Broadband Internet Access Is a Social Determinant of Health! Am J Public Health . 2020;110(8):1123-1125. doi:10.2105/AJPH.2020.305784

View print only PDF of CDC/ATSDR SVI 2020 Documentation [PDF – 671 KB]

CDC SVI Documentation 2016 | Place and Health | ATSDR www.atsdr.cdc.gov/placeandhealth/svi/documentation/SVI_documentation_2016.html — captured 2020-10-18

CDC SVI DOCUMENTATION 2016

View print only PDF of CDC SVI 2016 Documentation pdf icon [PDF – 405 KB]

CDC SVI 2016 DOCUMENTATION &NDASH; 2/13/2020

Please see the data dictionary as well as the CDC SVI 2016-2014 crosswalk below.

Introduction

What is Social Vulnerability?

Every community must prepare for and respond to hazardous events, whether a natural disaster like a tornado or a disease outbreak, or an anthropogenic event such as a harmful chemical spill. The degree to which a community exhibits certain social conditions, including high poverty, low percentage of vehicle access, or crowded households, may affect that community’s ability to prevent human suffering and financial loss in the event of disaster. These factors describe a community’s social vulnerability.

What is the Centers for Disease Control and Prevention Social Vulnerability Index?

ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) created CDC Social Vulnerability Index (SVI, hereafter) to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event.

In addition to tract-level rankings, SVI 2010, 2014, and 2016 also have corresponding rankings at the county level. Notes below that describe “tract” methods also refer to county methods.

How can SVI help communities be better prepared for hazardous events?

SVI can be used to:

- Allocate emergency preparedness funding by community need.

- Estimate the amount and type of needed supplies like food, water, medicine, and bedding.

- Decide how many emergency personnel are required to assist people.

- Identify areas in need of emergency shelters.

- Create a plan to evacuate people, accounting for those who have special needs, such as those without vehicles, the elderly, or people who do not understand English well.

- Identify communities that will need continued support to recover following an emergency or natural disaster.

Important Notes on the SVI Database

- SVI 2014 and 2016 are available for download in shapefile format from SVI Data and Documentation Download . SVI 2014 and 2016 are also available via ArcGIS Online. Search on “CDC’s Social Vulnerability Index.”

- For SVI 2000 and 2010, keep the data in geodatabase format when downloading from SVI Data and Documentation Download . Converting to shapefile changes the field names.

- A SVI 2016 to 2014 “crosswalk” is included in this documentation. See SVI 2014 documentation for the SVI 2014 to 2010 crosswalk.

- Starting with SVI 2014, we’ve added a stand-alone, state-specific Commonwealth of Puerto Rico database. Puerto Rico is not included in the US-wide ranking.

- Starting with SVI 2014, we’ve added a database of Tribal Census Tracts external icon ( https://www.census.gov/glossary/#term_TribalCensusTract external icon ).

- Tribal tracts are defined independently of, and in addition to, standard county-based tracts. The tribal tract database contains only estimates, percentages, and their respective MOEs, along with the adjunct variables described in the data dictionary below. Because of geographic separation and cultural diversity, tribal tracts are not ranked against each other nor against standard census tracts.

- Tracts with zero estimates for total population (N = 417 for the U.S.) were removed during the ranking process. These tracts were added back to the SVI databases after ranking. The TOTPOP field value is 0, but the percentile ranking fields (RPL_THEME1, RPL_THEME2, RPL_THEME3, RPL_THEME4, and RPL_THEMES) were set to -999.

- Any cells with a -999 were not used for further calculations. For example, total flags do not include fields with a -999 value.

- ArcGIS preserves leading 0s in the FIPS code fields of csv files. To preserve leading 0s in Excel, follow these steps: Open a blank worksheet in Excel.

- Click the DATA tab and choose to open a file from Text

- Navigate to the csv file and choose to Import

- In the Text Import Wizard, choose the Delimited data type, then Next

- Choose the Comma delimiter, then Next

- One by one, select fields based on FIPS codes (TRACTCE, ST, STCNTY, FIPS), set the Column data format to Text, then click Finish to open the csv with leading 0s preserved.

- See the Methods section below for further details.

- Questions? Please visit the SVI website at http://svi.cdc.gov for additional information or email the SVI Coordinator at svi_coordinator@cdc.gov .

Methods

Variables Used

American Community Survey (ACS), 2012-2016 (5-year) data for the following estimates:

For SVI 2016, we included two adjunct variables, 1) 2012-2016 ACS estimates for persons without health insurance, and 2) an estimate of daytime population derived from LandScan 2016 estimates. These adjunct variables are excluded from the SVI rankings.

Rankings

For each tract, we generated its percentile rank among all tracts for 1) the fifteen individual variables, 2) the four themes, and 3) its overall position.

- Socioeconomic – RPL_THEME1

- Household Composition & Disability – RPL_THEME2

- Minority Status & Language – RPL_THEME3

- Housing Type & Transportation – RPL_THEME4

For a detailed description of SVI variable selection rationale and methods, see A Social Vulnerability Index for Disaster Management pdf icon [PDF – 2 MB] .

Reproducibility Caveat

When replicating CDC SVI using Microsoft Excel or similar software, results may differ slightly from databases on the CDC SVI website or ArcGIS Online. This is due to variation in the number of decimal places used by the different software programs. For purposes of automation, we developed CDC SVI using SQL programming language. Because the SQL programming language uses a different level of precision compared to Excel and similar software, reproducing CDC SVI in Excel may marginally differ from CDC SVI databases downloaded from the CDC SVI website. For future iterations of CDC SVI, beginning with CDC SVI 2018; we plan to modify the SQL automation process for constructing CDC SVI to align with that of Microsoft Excel. If there are any questions, please email the CDC SVI Coordinator at svi_coordinator@cdc.gov .

SVI 2016 Data Dictionary – American Community Survey field names that changed between 2014 and 2016 are noted in RED and marked ‘Yes’ or ‘No’ in the ‘Field Name Changed Since 2014’ column.

SVI 2016 &NDASH; SVI 2014 CROSSWALK (ACS CHANGES)

Some of the American Community Survey (ACS) variable names changed from 2014 to 2016. This summary table lists only the SVI variables for which calculations are affected by these changes. The SVI 2016 Data Dictionary immediately above details all the calculations.

View print only PDF of CDC SVI 2016 Documentation pdf icon [PDF – 405 KB]

CDC SVI Documentation 2014 | Place and Health | ATSDR www.atsdr.cdc.gov/placeandhealth/svi/documentation/SVI_documentation_2014.html — captured 2020-10-19

CDC SVI DOCUMENTATION 2014

View print only PDF of CDC SVI 2014 Documentation pdf icon [PDF – 496 KB]

CDC SVI 2014 DOCUMENTATION &NDASH; 12/13/2017

Please see data dictionary and SVI 2014-2010 crosswalk below.

Introduction

What is Social Vulnerability?

Every community must prepare for and respond to hazardous events, whether a natural disaster like a tornado or a disease outbreak, or an anthropogenic event such as a harmful chemical spill. The degree to which a community exhibits certain social conditions, including high poverty, low percentage of vehicle access, or crowded households, may affect that community’s ability to prevent human suffering and financial loss in the event of disaster. These factors describe a community’s social vulnerability.

What is the Social Vulnerability Index?

ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event.

The Social Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. Census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. The SVI ranks the tracts on 15 social factors, including unemployment, minority status, and disability, and further groups them into four related themes. Thus each tract receives a ranking for each Census variable and for each of the four themes, as well as an overall ranking.

In addition to tract-level rankings, SVI 2010 and SVI 2014 also have corresponding rankings at the county level.

How can the SVI help communities be better prepared for hazardous events?

The SVI provides specific socially and spatially relevant information to help public health officials and local planners better prepare communities to respond to emergency events such as severe weather, floods, disease outbreaks, or chemical exposure.

The SVI can be used to:

- Allocate emergency preparedness funding by community need.

- Estimate the amount and type of needed supplies like food, water, medicine, and bedding.

- Decide how many emergency personnel are required to assist people.

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