• 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. Yet, it is still unclear which social determinants of health best direct clinical and community health interventions and guide adjustments to quality measures and payments. We developed 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 were initially developed by Butler et al. (2012) using 2005–2009 American Community Survey (ACS) 5-year estimates and calculated at the Primary Care Service Areas (PCSA).  The index and the associated measures are updated each year with more recent data as soon as the ACS data becomes available. The SDI and the measures are available for the following years 1) ACS data (5-year estimates from 2008-2012, 2011-2015, 2012-2016, 2013-2017, 2014-2018, and 2015-2019) and 2) using additional geographies: 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 and used to quantify the socio-economic variation in health outcomes.

    Methodology

    The final 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 the rented housing unit, percent living in the overcrowded housing unit, percent of households without a car, and percent nonemployed 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). 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. SDI scores are available for all counties, census tracts, ZCTAs, and PCSAs.

    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 restrict to only include 7 measures with factor loadings greater than 0.60 (percent population with <100% FPL, percent population with less than 12 years of education, percent non-employed, percent population living in renter-occupied housing units, percent population living in crowded housing units, percent single-parent households, and percent population with no car). We constructed a final SDI measure based on weighted factor loading scores for each measure. We also examined the difference between models that include either nonemployed (those not seeking work) or unemployed (those actively seeking work). The factor loading for the model using nonemployed was slightly higher than for the model using unemployed. These initial results were reproduced for each period and for each geography. However, the inclusion of the non-employed versus the unemployed measure is largely dependent on the context of the social and political milieu prevalent in that period.

     

    Table 1. Domain and Variable Description

    Domain

    Variable

    Income

    Percent population less than 100% FPL (population under 0.99 /total population)

    Education

    Percent population 25 years or more with less than 12 years of education (population with less than high school diploma or 12 years of education/total population)

    Employment

    Percent Non-employed (not in labor force + unemployed) / (civilian + not in the labor force) for the population 16-64 years
    Housing Percent population living in renter-occupied housing units (Renter occupied housing units/ (Owner-occupied housing units + Renter occupied housing units))

     

    Percent population living in crowded housing units (Tenure by Occupants Per Room – a population with ≥ 1.01 occupants per room in Owner-occupied housing units and Renter occupied housing units) / total population

    Household Characteristics

    Percent single-parent households with dependents < 18 years (total single-parent households (male and female) with dependents <18 years)/total population)

    Transportation

    Percent population with no car (population with no vehicle available/total population)
    Demographics Percent high needs population – (population under 5 years of age + women between the ages of 15-44 years + everyone 65 years and over)/total population


    Source: 2015-2019 American Community Survey 5-Year Summary File (Sequence Number Table ID Lookup Table)


    Table 2. Variable descriptions, Sequence Number and Table IDs

    Description

    Sequence No

    Table ID

    Population Estimate

    SF0002

    B01001

    Percent population below 100% FPL (income in the past 12 months <100% FPL)

    SF0049

    C17001

    Percent population with less than 12 years education

    SF0042

    B15003

    Percent non-employed population (civilian)

    SF0075

    B23001

    Percent unemployed population (civilian)

    SF0075

    B23001

    Percent population in renter-occupied housing units

    SF0111

    B25003

    Percent population in crowded housing units

    SF0111

    B25044

    Percent single-parent household population with children <18 years

    SF0035

    B11003

    Percent population with no vehicle available

    SF0113

    B25044

    Percent high-needs population (persons aged 65 years, women (15-44 years and children <5 years)

    SF0002

    B01001


    Source: 2015-2019 American Community Survey 5-Year Summary File (Sequence Number Table ID Lookup Table)


    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
    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
    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
       

    County-Level Social Deprivation Index