CDC SVI Documentation 2022

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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.
  • 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 speak English well.
  • Identify communities that will need continued support to recover following an emergency or natural disaster.
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.
  • 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:

overall vulnerability categories with text description below

Text version of overall social vulnerability image:

  • Socioeconomic Status
    • Below 150% Poverty
    • Unemployed
    • Housing Cost Burden
    • No High School Diploma
    • No Health Insurance
  • Household Characteristics
    • Aged 65 & Older
    • Aged 17 & Younger
    • Civilian with a Disability
    • Single-Parent Households
    • English Language Proficiency
  • 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
  • Housing Type & Transportation
    • Multi-Unit Structures
    • Mobile Homes
    • Crowding
    • No Vehicle
    • Group Quarters

* 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:

  1. E Variables: Obtain estimates of the CDC/ATSDR SVI variables from the Census Bureau.
  2. EP Variables: Obtain or derive percentages for the 16 CDC SVI variables.
  3. EPL Variables: Rank the EP variables to get percentile rankings (or the CDC/ATSDR SVI rankings) for each of the 16 variables.
  4. SPL Variables: Sum the EPL variables by theme.
  5. RPL Variables: Rank the theme-specific SPL variable.
  6. Overall SPL Variable (SPL_THEMES): Sum the SPL variables from all four themes.
  7. 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 90th percentile, are given a flag value of 1 to indicate high social vulnerability. Tracts below the 90th 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.)

CDC SVI 2022 Data Dictionary

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

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