CDC SVI Social Vulnerability Index - onetomapanalytics/Meta_Data GitHub Wiki

CDC Social Vulnerability Index (SVI)

General description

  1. Database primary purpose - The Social Vulnerability Index uses United States census variables at the tract level to help public local health officials and emergency response planners to identify and map communities that may need support in preparing for hazards or recovering from disaster
  2. Overall data type - Demographics, SDoH
  3. Dataset type - Longitudinal
  4. Data source - Census
  5. Data level - Tract level, county level
  6. Geographic location of the data collection sites - United States
  7. Sponsor, manager, or home institution - Centers for Disease Control and Prevention (CDC), Agency for Toxic Substances and Disease Registry (ATSDR)'s Geospatial Research, Analysis & Services Program (GRASP)
  8. Date range - 2010, 2014, 2016
  9. Geolocation data - State-, county-, and tract-level FIPS code, state and county name and abbreviation
  10. Longitudinal tracking - Track state, county and tract estimates
  11. Other - SVI can be used to (1) allocate emergency preparedness funding by community need; (2) estimate the amount and type of needed supplies (e.g., food, water, medicine, and bedding); (3) decide how many emergency personnel are required to assist people; (4) identify areas in need of emergency shelters; (5) 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; (6) identify communities that will need continued support to recover following an emergency or natural disaster

Applicable methods

  1. Association methods, such as logistic regression (1, 2), generalized linear regression (3, 4)
  2. Univariate statistical methods (5, 6)
  3. Latent variable modeling, such as factor analysis (7)
  4. Sensitivity analysis (8, 9)

High-impact designs

  1. Evaluate community-Level vulnerability and racial disparities (10)

  2. Validation of social vulnerability metrics (11)

  3. Determine whether diseases outcomes are related to neighborhood-level social vulnerability (12)

  4. Assess the association and interaction of SVI with patient-level race/ethnicity compared with the probability of undergoing a procedure (13)

  5. Evaluate the association of ICU capacity with hospital patient racial and ethnic composition (14)

  6. Assess the heterogeneity in spatial inequities in COVID-19 vaccination (15, 16) and mortality (17)

  7. Describe and quantify social disparities in treatment patterns (18)

Data dictionary

To access the SVI data dictionary, click here

Variable categories

  1. Population estimates (e.g., age groups, poverty, unemployment, per capita income, no diploma, disability, minority, uninsured)
  2. Housing (e.g., housing units estimates, households, mobile homes, people per room, vehicles)

Linkage to other datasets

  • Linkages can be established for any dataset that might have geographic descriptors such as FIPS codes; e.g., the Economic Innovation Group's Distressed Community Index (DCI) (4) and the U.S. census (19)