Study Overview


An association between social and neighborhood characteristics and health outcomes has been reported but remains poorly understood owing to complex multidimensional factors that vary across geographic space.
To quantify social determinants of health (SDOH) as multiple dimensions across the continental United States (the 48 contiguous states and the District of Columbia) at a small-area resolution and to examine the association of SDOH with premature mortality within Chicago, Illinois.
In this cross-sectional study, census tracts from the US Census Bureau from 2014 were used to develop multidimensional SDOH indices and a regional typology of the continental United States at a small-area level (n = 71 901 census tracts with approximately 312 million persons) using dimension reduction and clustering machine learning techniques (unsupervised algorithms used to reduce dimensions of multivariate data). The SDOH indices were used to estimate age-adjusted mortality rates in Chicago (n = 789 census tracts with approximately 7.5 million persons) with a spatial regression for the same period, while controlling for violent crime.
Fifteen variables, measured as a 5-year mean,were selected to characterize SDOH as small-area variations for demographic characteristics of vulnerable groups, economic status, social and neighborhood characteristics, and housing and transportation availability at the census-tract level. This SDOH data matrix was reduced to 4 indices reflecting advantage, isolation, opportunity, and mixed immigrant cohesion and accessibility, which were then clustered into 7 distinct multidimensional neighborhood typologies. The association between SDOH indices and premature mortality (defined as death before age 75 years) in Chicago was measured by years of potential life lost and aggregated to a 5-year mean. Data analyses were conducted between July 1, 2018, and August 30, 2019.with a spatial regression for the same period, while controlling for violent crime.
Among the 71901 census tracts examined across the continental United States,amedian (interquartile range) of 27.2% (47.1%) of residents had minority status, 12.1% (7.5%) had disabilities, 22.9% (7.6%) were 18 years and younger, and 13.6% (8.1%) were 65 years and older. Among the 789 census tracts examined in Chicago, a median (interquartile range) of 80.4% (56.3%) of residents had minority status, 10.2% (8.2%) had disabilities, 23.2% (10.9%) were 18 years and younger, and 9.5% (7.1%) were 65 years and older. Four SDOH indices accounted for 71% of the variance across all census tracts in the continental United States in 2014. The SDOH neighborhood typology of extreme poverty, which is of greatest concern to health care practitioners and policy advocates, comprised only 9.6% of all census tracts across the continental United States but characterized small areas of known public health crises. An association was observed between all SDOH indices and age-adjusted premature mortality rates in Chicago (R2 = 0.63; P < .001), even after accounting for violent crime and spatial structures.
The modeling of SDOH as multivariate indices rather than as a singular deprivation index may better capture the complexity and spatial heterogeneity underlying SDOH. During a time of increased attention to SDOH, this analysis may provide actionable information for key stakeholders with respect to the focus of interventions.

Author Team

Marynia Kolak, PhD, MFA, MS

Assistant Inst. Professor of GIScience, University of Chicago

Jay Bhatt, DO

Chief Medical Officer, American Hospital Association

Yoon Hong Park, MPP

Data Fellow, Cook County Government

Norma A. PadroĢn, PhD, MPH, MA

Health Economics Director, Anthem Digital

Ayrin Molefe, PhD

Senior Statistican, AHA Center for Health Innovation

* Note: affiliations indicated are current as of January 2020. For affiliation during manuscript work, see original article.

Technical Details

This Site

This atlas website was scrapped together by lead author Marynia Kolak with Carto and Bootstrap, with user testing expertise & support by Dante Kolak. As such, we appreciate your patience as the site continues to mature and grow over time with additional features and content. If you're interested in contributing, check out

Scripts & Data

Scripts, raw data, and analytic results from the study will be made available mid-late 2020. GeoDa and R were used for all analysis. Please check back for details.


Questions? Email Marynia: mkolak at uchicago dot edu