Racial minorities bear a disproportionate burden of morbidity and mortality. These inequities might be explained by racism, given the fact that racism has restricted the lives of racial minorities and immigrants throughout history. Recent studies have documented that individuals who report experiencing racism have greater rates of illnesses. While this body of research has been invaluable in advancing knowledge on health inequities, it still locates the experiences of racism at the individual level. Yet, the health of social groups is likely most strongly affected by structural, rather than individual, phenomena. The structural forms of racism and their relationship to health inequities remain under-studied. This article reviews several ways of conceptualizing structural racism, with a focus on social segregation, immigration policy, and intergenerational effects. Studies of disparities should more seriously consider the multiple dimensions of structural racism as fundamental causes of health disparities.

Recent studies show that racism may influence health inequities. As individuals grow from infancy into old age, they encounter social institutions that may create new exposures to racial bias. Yet, few studies have considered this idea fully. We suggest a framework that shows how racism and health inequities may be viewed from a life course perspective. It applies the ideas of age-patterned exposures, sensitive periods, linked lives, latency period, stress proliferation, historic period, and cohorts. It suggests an overarching idea that racism can structure one’s time in asset-building contexts (e.g., education) or disadvantaged contexts (e.g., prison). This variation in time and exposure can contribute to racial inequities in life expectancy and other health outcomes across the life course and over generations.

Research shows that racial discrimination is related to illness among diverse racial and ethnic populations. Studies of racial discrimination and health among Asian Americans, however, remain underdeveloped. In this paper, the authors review evidence on racial discrimination and health among Asian Americans, identify gaps in the literature, and provide suggestions for future research. They identified 62 empirical articles assessing the relation between discrimination and health among Asian Americans. The majority of articles focused on mental health problems, followed by physical and behavioral problems. Most studies find that discrimination was associated with poorer health, although the most consistent findings were for mental health problems. This review suggests that future studies should continue to investigate the following: 1) the measurement of discrimination among Asian Americans, whose experiences may be qualitatively different from those of other racial minority groups; 2) the heterogeneity among Asian Americans, including those factors that are particularly salient in this population, such as ethnic ancestry and immigration history; and 3) the health implications of discrimination at multiple ecologic levels, ranging from the individual level to the structural level.

The adage “time is money” signifies that time itself is a major social resource, but the role of time as a determinant of health inequities remains underappreciated. Time is fundamental to health promotion and human agency, as in having time to exercise and maintain social relationships. Further, scarcity in time is related to stress and illness. Time is also racialized, such that racial/ethnic minorities often have less free time and suffer a time penalty in multiple facets of life. Such penalties manifest in problems such as greater time in prison or more time spent accessing services. We argue that time may be a social determinant of health that is shaped by racism across the life course. We focus on three aspects: time as age, time as exposure, and time as a resource and privilege. We distinguish between chronological age, biological age, and social age. We discuss issues of accelerated aging and potential interconnections with critical periods. We also examine racial inequities in time. By more deeply considering time, we may advance our understanding of racial inequities in health.

Why do racial inequalities endure despite numerous attempts to expand civil rights in certain sectors? A major reason for this endurance is due to lack of attention to structural racism. Although structural and institutional racism are often conflated, they are not the same. Herein, we provide an analogy of a “bucky ball” (Buckminster­fullerene) to distinguish the two concepts. Structural racism is a system of intercon­nected institutions that operate with a set of racialized rules that maintain White supremacy. These connections and rules al­low racism to reinvent itself into new forms and persist, despite civil rights interventions directed at specific institutions. To illustrate these ideas, we provide examples from the fields of environmental justice, criminal justice, and medicine. Racial inequities in power and health will persist until we redi­rect our gaze away from specific institutions (and specific individuals), and instead focus on the resilient connections among institu­tions and their racialized rules.Ethn Dis. 2021;31(Suppl 1):293-300; doi:10.18865/ed.31.S1.293

Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter’s Streaming Application Programming Interface (API) to collect 3,377,295 U.S. race-related tweets from November 2019–June 2020. Sentiment analysis was performed using support vector machine (SVM), a supervised machine learning model. Accuracy for identifying negative sentiments, comparing the machine learning model to manually labeled tweets was 91%. We investigated changes in racial sentiment before and following the emergence of COVID-19. Results: The proportion of negative tweets referencing Asians increased by 68.4% (from 9.79% in November to 16.49% in March). In contrast, the proportion of negative tweets referencing other racial/ethnic minorities (Blacks and Latinx) remained relatively stable during this time period, declining less than 1% for tweets referencing Blacks and increasing by 2% for tweets referencing Latinx. Common themes that emerged during the content analysis of a random subsample of 3300 tweets included: racism and blame (20%), anti-racism (20%), and daily life impact (27%). Conclusion: Social media data can be used to provide timely information to investigate shifts in area-level racial sentiment.