How Racism Affects American Neighborhoods

Measuring the Enduring Imprint Of Structural Racism on American Neighborhoods
Zachary Dyer, Matthew J. Alcusky, Sandro Galea, and Arlene Ash
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The social determinants of health, or the conditions in which people grow, live, learn, and work, are the primary drivers of health. These factors are deeply intertwined with a long history of systemic and reinforcing discriminatory policies, commonly referred to as structural racism, which have created disparities in neighborhood resources that promote healthy communities. These disparities continue to shape ethnic and racial health inequities today. Yet, measurements of structural racism and its effect on community health have remained limited in both scope and practical use, constraining their utility.

To address this gap, the authors of this study created the Structural Racism Effect Index (SREI), a multidimensional measure that uses census tract–level data to create a summary score comparing resources available to US communities with their corresponding major health indicators.

In constructing this index, researchers reviewed current, relevant literature to identify nine social determinant of health domains, which included built environment, criminal justice, education, employment, housing, income and poverty, social cohesion, transportation, and wealth. They then selected specific variables within each domain using nationally representative data from the US Census Bureau’s American Community Survey 2015–19 estimates, the National Center for Health Statistics, and the Centers for Disease Control and Prevention’s PLACES Project. Then they compared the domains with tract data on life expectancy, diabetes prevalence, and grouped proportion of the population identifying as Black, Latine, and Indigenous. Findings were categorized as metropolitan, micropolitan, small town, or rural, based on the Census Bureau’s commuting area codes.

They constructed the index so that the higher the index number, the greater the experience of structural racism. Putting the new index to the test, researchers calculated SREI scores for 97 percent of the US population, including all 50 states, Washington, DC, and Puerto Rico, to validate how closely their index scores predicted average health status in a community. Additionally, the researchers compared the SREI with other leading indices, including the Area Deprivation Index, Social Vulnerability Index, Social Deprivation Index, and Child Opportunity Index to test whether the SREI more closely correlates with measures of health, such as life expectancy.

Key findings
  • The SREI showed significant differences in health outcomes based on community resources. When comparing areas with the lowest and highest SREI scores, the least-resourced communities had a lower life expectancy by 9.6 years, 130 percent higher average diabetes prevalence, and over 307 percent more people identifying as Black, Latine, and Indigenous. In the most-resourced areas according to the SREI, the average life expectancy was 82.6 years, with an average diabetes prevalence of 7.6 percent. In contrast, the least-resourced areas had an average life expectancy of 73.0 years and an average diabetes prevalence of 17.5 percent.
  • The SREI measure had the strongest connection to life expectancy, poor mental and physical health, high blood pressure, and asthma.
  • Overall, the SREI was better at explaining differences in health status compared with the other measures studied. The R2 values for the SREI were above 0.7, while for all other indices, except the Child Opportunity Index, they were around 0.5 or lower.
Policy implications
  • Multiple centuries of policies that maintained segregation and limited access to resources drive current inequities in all SREI domains. Present day contributions to past and future disinvestment in neighborhoods and resulting health inequities are results of policy choices and are preventable.
  • Effective health equity policy approaches need to shift from health care investments to structurally embedded inequities such as access to health care, housing, education, and others. Though significant investments in outreach workers, housing vouchers, and afterschool programs have helped address health-related social needs, many policies reinforce or miss the structural forces that create these inequities.
  • Data driven decisionmaking is only as good as measurement and data used. Any neighborhood measure of the social determinants of health will capture some of the picture, but the SREI is the first to include all the domains central to perpetuating racial inequity. For researchers, the SREI may be the most accurate measure of exposure to structural racism.
  • The SREI can meaningfully guide racial justice work. This measure can be used as a tool to provide insights to help guide investments to equitably develop communities and inform medical benefit programs, educational scholarships, and more efforts that seek to counter structural racism’s effects.