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ProfitWise News and Views, 2014
Exploring the Correlations between Health and Community Socioeconomic Status in Chicago

Much research demonstrates that where you live – and the socioeconomic conditions present in that place – determine individual-level health outcomes.[1] The premise that individual stressors tend to aggregate themselves into communities with poor socioeconomic status (SES) leads to the conclusion that “where you live determines how long you live.” As former Federal Reserve Chairman Bernanke stated, “Factors such as educational attainment, income, access to healthy food and the safety of a neighborhood tend to correlate with individual health outcomes in that neighborhood.”[2] These factors are referred to as the social determinants of health.[3]

Using community level data available through the City of Chicago Data Portal[4],as well as aggregated census tract level economic data compiled by the Federal Reserve Bank of Chicago[5], this article explores community-level SES conditions and corresponding health outcomes in Chicago’s 77 communities to derive a localized perspective on a commonly accepted hypothesis that the socioeconomic conditions of places contribute to the health outcomes of residents.

Our analysis includes health outcomes that are influenced – at least in part – by one’s environment, including rate of infant mortality, low birth weight, prenatal care, preterm birth, lead screening, lead poisoning, teen birth, firearm-related casualties, cancers, diabetes, stroke and tuberculosis (TB).

The socioeconomic variables included in the analysis relate to housing, income and education, workforce, racial and ethnic composition, and ‘climate.’ They are organized as follows:



  • Percent of the population living in crowded housing
  • Percent of vacant units
  • Percent of owner-occupied housing

    Income and Education

  • Percent of the over-25 population with/without a high school diploma
  • Percent of the over-25 population with some college or a bachelor’s degree
  • Percent of families in poverty
  • Per capita income


  • Labor force participation
  • Unemployment rates
  • Self-employment in nonincorporated business rates

    Racial and Ethnic Composition

  • Percent of the population that is black
  • Percent of the population that is Hispanic
  • Percent of the population that is foreign born


  • Crime rates
  • 311 service call intensity rates
  • Home mortgage and small business lending volumes
  • Presence of financial institutions
  • Business counts


The first level of analysis correlates the socioeconomic data with health outcomes (e.g., how strongly, positively or negatively, do unemployment levels correlate with the incidence of diabetes?). Without proving causality, the strengths and directions of the correlations indicate patterns of association between SES and health outcomes.

Turning from the correlations, we sort both the community-level SES and health outcomes into quartiles. Within this ranking, we explore the extent to which health outcomes improve or deteriorate with various isolated socioeconomic factors related to income, employment, race and ethnicity, housing and climate (as organized above).

Next, we index Chicago’s communities by SES quartile outcomes with the corresponding health quartile outcome to provide a simple illustration of whether community level health outcomes improve as SES improves, and vice versa.

Returning to the hypothesis that community SES determines individual health outcomes, finally, we look for communities that disrupt this hypothesis by outperforming their SES quartile by at least one health quartile. These results are supplemented by field interviews with community development and health practitioners in two neighboring, contiguous communities – one that disrupts the hypothesis and one that does not.

Finally, we arrive at findings, from which we can draw some policy implications.

Please download the entire publication to see any charts, tables, sources, or notes included in this article.
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