Social Deprivation Index (SDI)

Background

Addressing social determinants of health (SDOH) is critical to achieving the Triple Aim of lower costs, improved care, and better population health. It is unclear which social determinants of health best direct clinical and community health interventions and guide adjustments to quality measures and payments. We developed a Social Deprivation Index (SDI) to quantify levels of disadvantage across small areas, evaluate their associations with health outcomes, and address health inequities. This measure of social deprivation, in combination with other indicators, has potential application in identifying areas that need additional health care resources.

The Social Deprivation Index measures was initially developed by Butler et al. (2012) using 2005–2009 American Community Survey (ACS) 5-year estimates (http://www.census.gov/acs/www/) and calculated at the Primary Care Service Areas (PCSA).  This measure was updated 1) with more recent ACS data (5-year estimates from 2008-2012 and from 2011-2015) and 2) using additional areas: counties, census tract, Zip Code Tabulation Areas (ZCTA), and PCSAs.

SDI Defined

SDI is a composite measure of area level deprivation based on seven demographic characteristics collected in the American Community Survey (https://www.census.gov/programs-surveys/acs/) and used to quantify the socio-economic variation in health outcomes.

Methodology

The SDI is a composite measure of seven demographic characteristics collected in the American Community Survey (ACS): percent living in poverty, percent with less than 12 years of education, percent single parent household, percent living in rented housing unit, percent living in overcrowded housing unit, percent of households without a car, and percent non-employed adults under 65 years of age.

The SDI measure was calculated at four geographic areas: county, census tract, aggregated Zip Code Tabulation Area (ZCTA), and Primary Care Service Area (PSCA, v 3.1).

  • Counties are an administrative division of a state offering certain local governmental services.
  • Census tracts are semi-permanent county subdivisions.
  • ZCTAs are generalized U.S. Postal Service Zip Codes.
  • PCSAs are small areas of aggregated census tracts and were created by Dartmouth University for the Health Resources and Service Administration. PCSAs represent travel patterns of Medicare patients to primary care services. To construct the PCSA-level SDI, all the ACS population measures were first extracted at the census tract level and then, summarized at the PCSA level.

The SDI measures for the county, census tract, and ZCTA levels were developed directly from ACS population measures.

Based on similar international and national indices, we started with a larger list of 14 candidate measures available in the ACS.  We converted each of these measures into centiles to create a common scale for easy interpretation of results.

We used factor analysis methods to create the Social Deprivation Index. Factor analysis is a statistical technique used to investigate the relationship between a group of observed variables and an unobserved (or “latent”) variable underlying a concept. It allows exploration of the association among measures through reducing a large number of related variables into the underlying latent factors.

The output from the factor analysis model is a factor loading for each variable. The factor loading represents the strength of correlation between the variables that comprise the factor and the factor itself. The factor loadings can be interpreted as though they are regression coefficients; the higher the factor loading score, the greater the variation that is explained by that variable.

To simplify the model, we restricted it to only include measures with factor loadings greater than 0.60, thus excluding percent black and percent in high-needs age groups.

We constructed a final SDI measure based on weighted factor loading scores for each measure. We examined the difference between models that included either non-employed (those not seeking work) or unemployed (those actively seeking work). The factor loading for the model using non-employed was slightly higher than for the model using unemployed. These initial results were reproduced for each period and for each geography.

Domain

Variable

 

Domain

:

Income

Variable

:

Percent population less than 100% FPL

 

Domain

:

Education

Variable

:

Percent population 25 years or more with less than 12 years of education

 

Domain

:

Employment

Variable

:

Percent Non-employed

 

Domain

:
 

Variable

:

Percent unemployed

 

Domain

:

Housing

Variable

:

Percent population living in renter occupied and crowded housing units

 

Domain

:

Household Characteristics

Variable

:

Percent single-parent households with dependents < 18 years

 

Domain

:

Transportation

Variable

:

Percent population with no car

 

Domain

:

Demographics

Variable

:

Percent population black

 

Domain

:
 

Variable

:

Percent high needs population - under 5 years and, 65 years and over

 

Available Files

References

  1. Butler DC, Petterson S, Phillips RL, Bazemore AW. Measures of Social Deprivation That Predict Health Care Access and Need within a Rational Area of Primary Care Service Delivery. Health Services Research. 2013;48(2 Pt 1):539-559. doi:10.1111/j.1475-6773.2012.01449. https://www.ncbi.nlm.nih.gov/pubmed/22816561(www.ncbi.nlm.nih.gov)
  2. Philips RL, Liaw W, Crampton P. How Other Countries are Using Deprivation Indices. Health Affairs, 2016: 35 (11):1991-1998 http://content.healthaffairs.org/content/35/11/1991.abstract(content.healthaffairs.org)
  3. Liaw W. Krist AH, Tong ST, Sabo R. et al. Living in “Cold Spot” Communities Is Associated with Poor Health and Health Quality. J Am Board Fam Med. 2018; 31:342-350