Living Within the Redlines: How Structural Racism and Redlining Shape Diabetes Disparities

Diabetes, the eighth leading cause of death in the U.S., affects 38.4 million people, or 11.6% of the U.S. population, and one in five of them are unaware that they have diabetes (1). There are substantial racial disparities (2). American Indian or Alaska Native, Black, Hispanic, and Asian adults bear disparate burdens of diabetes at 15%, 12%, 12%, and 9%, respectively, compared with a prevalence of 6.9% among White adults (1).

While individual lifestyle factors such as diet and physical activity contribute to diabetes risk, structural determinants of health inequities and social, economic, and political mechanisms that generate social inequities may play a substantial role in the disproportionate burden of diabetes (2). According to the Solar and Irwin, writing for the World Health Organization (WHO) in “A Conceptual Framework for Action on the Social Determinants of Health” (here termed the WHO Framework for Action on Social Determinants) (3), “Structural determinants are those that generate or reinforce social stratification in the society and that define individual socioeconomic position.” Structural racism is a structural determinant of health (4,5). For this discourse, we adopt the following definition of structural racism: historical and contemporary policies, practices, and norms that create and maintain White supremacy by segregating racial and ethnic communities from access to opportunity and upward mobility, making it more challenging to secure high-quality education, jobs, housing, health care, and equal treatment in the criminal justice system (6).

Structural racism is an important driver of diabetes inequities (7). Deeply rooted in our society, it denotes the complex web of institutions, policies, and practices that perpetuate and reinforce racial inequalities (8). Systemic racism manifests in various forms, from residential segregation and unequal access to health care and education to discrimination in employment and the criminal justice system (9). These inequalities have far-reaching consequences on the health and well-being of communities experiencing the impact of structural racism that directly contribute to disparate burden of chronic diseases and worse health outcomes (10,11).

A proxy measure for structural racism in the U.S. is residential segregation; the physical separation of the races in residential contexts, government regulatory schemes contributed to propagating redlining, which is enshrined in the housing policies of the federal government and supported by major economic institutions (12,13). These Home Owners Loan Corporation (HOLC) discriminatory mapping and appraisal practices entrenched racial segregation and wealth disparities by directing resources away from communities of color and concentrating them in White neighborhoods, creating lasting inequities in housing and economic opportunity by systematically denying mortgages and other financial services to residents in these redlined neighborhoods (14). Although the connections between structural racism, residential segregation (redlining), and poorer diabetes outcomes have been previously recognized (7,15), the precise underlying mechanisms driving these disparities can sometimes be elusive (4,10).

In this issue of Diabetes Care, Egede et al. (16) make an opportune attempt to unravel the link between structural racism and diabetes outcomes. Using historic redlining as a proxy for structural racism, this study was focused on providing some insight into direct and indirect relationships between structural racism and the prevalence of diabetes in a national sample. Redlining not only directly impacts diabetes prevalence but also indirectly affects it through its influence on various social and economic factors that are known to be associated with health outcomes. Thus, the approach to identifying mechanistic pathways involved measures such as incarceration, poverty, discrimination, substance use, housing, education, unemployment, and food access, many of which are structural and intermediate determinants of health, according to the WHO Framework for Action on Social Determinants (3). The authors use an innovative approach, combining geospatial data with health outcomes, to unravel this complex interplay. The overall findings suggest that redlining, a proxy for structural racism in this study, has significant direct and indirect relationships with diabetes prevalence in the studied census tracts.

For direct associations, redlining was directly associated with a higher crude prevalence of diabetes. Redlining was also indirectly associated with diabetes prevalence through social determinants of health factors of incarceration, poverty, discrimination, smoking, housing instability, education, unemployment, and food access. These findings further underscore the long-lasting impact of discriminatory housing practices on the health of communities experiencing social marginalization and also, importantly, provide evidence for critical intervention targets (11,17).

Although an important step, this study does not claim to provide all the answers. For instance, one of the study's striking findings is the negative correlation between binge drinking and redlining and its subsequent impact on diabetes, suggesting that the relationship between these factors is more complex than initially thought. The negative correlation indicates that areas with higher levels of redlining tend to have lower rates of binge drinking, which in turn is associated with lower diabetes prevalence. This unexpected result raises important questions about the complex interplay of social, economic, and behavioral factors that shape health disparities. The authors suggest that this finding may reflect the higher prevalence of drinking in higher socioeconomic populations, highlighting the need for further research to untangle these relationships. Another consideration is that younger populations tend to have higher rates of binge drinking but lower rates of diabetes. Conversely, older populations, who may be more likely to live in redlined areas, have lower rates of binge drinking but higher rates of diabetes. These results highlight the importance of considering age-related differences in health behaviors and outcomes when examining the impact of area- and space-level factors on health disparities, i.e., perhaps a population versus place issue. Undoubtedly, more work must be done to better understand these complex relationships and identify intervention approaches or targets (18,19), as there are several unanswered questions.

One of the study’s strengths is in its use of geospatial data to quantify the effects of structural racism. By focusing on redlining, the authors attempted to provide a mechanistic explanation of how historical policies shape health disparities decades later, which is important for present and future policy making. Although the study focused on structural racism, the importance of individual-level risk factors cannot be overlooked; factors like smoking, drinking, and diet at the individual level impact cardiometabolic and diabetes outcomes (20). While the authors acknowledge the need to address these factors directly in health care settings, a more balanced discussion of the relative contributions of structural and individual determinants is important. There is indeed an urgent need for policymakers and researchers to simultaneouly focus on individual-level interventions as well as focus on addressing the root causes of health disparities (21). By recognizing the role of structural racism in shaping health outcomes, we can develop more effective and equitable interventions and programs that promote health and well-being (22).

This study by Egede et al. (16) opens new directions for research, such as exploring the impact of structural racism on other cardiometabolic disease like prediabetes and hypertension. Future studies may also consider the intersectionality of race, class, gender, and other social identities and their associations with structural discrimination in impacting health outcomes (23) and implementing community-engaged research to address health inequities (24,25).