On March 8, 2020, there was a 650% increase in Twitter retweets using the term “Chinese virus” and related terms. On March 9, there was an 800% increase in the use of these terms in conservative news media articles. Using data from non-Asian respondents of the Project Implicit “Asian Implicit Association Test” from 2007–2020 (n = 339,063), we sought to ascertain if this change in media tone increased bias against Asian Americans. Local polynomial regression and interrupted time-series analyses revealed that Implicit Americanness Bias—or the subconscious belief that European American individuals are more “American” than Asian American individuals—declined steadily from 2007 through early 2020 but reversed trend and began to increase on March 8, following the increase in stigmatizing language in conservative media outlets. The trend reversal in bias was more pronounced among conservative individuals. This research provides evidence that the use of stigmatizing language increased subconscious beliefs that Asian Americans are “perpetual foreigners.” Given research that perpetual foreigner bias can beget discriminatory behavior and that experiencing discrimination is associated with adverse mental and physical health outcomes, this research sounds an alarm about the effects of stigmatizing media on the health and welfare of Asian Americans.

Background: Studies suggest that racial discrimination impacts health via biological dysregulation due to continual adaptation to chronic psychosocial stress. Therefore, quantifying chronicity is critical for operationalising the relevant aetiological exposure and hence maximising internal validity. Using one of the most common discrimination scales in the epidemiological literature, we develop a novel approach for more accurately assessing chronicity and compare it with conventional approaches to determine whether coding influences differential exposure classification and associations with hypertension and depression among African American women. Methods: Data are from a socioeconomically diverse cross section of 208 mid-life African American women in Northern California (data collection: 2012–2013). Racial discrimination was assessed using the Everyday Discrimination Scale (α=0.95), and was coded using two conventional approaches: (1) situation-based coding: number of different situations ever experienced; (2) frequency-based coding: sum of Likert scale responses ranging from 'never' to 'almost everyday'; and (3) a new chronicity-based coding approach: sum of responses, weighted to capture annual chronicity (eg, ‘a few times a month’=3×12=36×/year). Outcomes are hypertension and depressive symptomatology (10-item Center for Epidemiologic Studies-Depression Scale). Findings: Exposure classification differed by coding approach, by up to 41%. There was a positive association between racial discrimination and hypertension prevalence for chronicity coding only (prevalence ratio=1.61, 95% CI 1.03 to 2.49). For depressive symptoms, a dose–response relationship of similar magnitude was observed for all three coding approaches. Conclusion: Scale coding is an important methodological consideration for valid exposure assessment in epidemiological research. Coding can impact exposure classification and associations with important indicators of African American women’s mental and physical health.

This study examined whether killings of George Floyd, Ahmaud Arbery, and Breonna Taylor by current or former law enforcement officers in 2020 were followed by shifts in public sentiment toward Black people. Methods: Google searches for the names “Ahmaud Arbery,” “Breonna Taylor,” and “George Floyd” were obtained from the Google Health Application Programming Interface (API). Using the Twitter API, we collected a 1% random sample of publicly available U.S. race-related tweets from November 2019–September 2020 (N = 3,380,616). Sentiment analysis was performed using Support Vector Machines, a supervised machine learning model. A qualitative content analysis was conducted on a random sample of 3,000 tweets to understand themes in discussions of race and racism and inform interpretation of the quantitative trends. Results: The highest rate of Google searches for any of the three names was for George Floyd during the week of May 31 to June 6, the week after his murder. The percent of tweets referencing Black people that were negative decreased by 32% (from 49.33% in November 4–9 to 33.66% in June 1–7) (p < 0.001), but this decline was temporary, lasting just a few weeks. Themes that emerged during the content analysis included discussion of race or racism in positive (14%) or negative (38%) tones, call for action related to racism (18%), and counter movement/arguments against racism-related changes (6%). Conclusion: Although there was a sharp decline in negative Black sentiment and increased public awareness of structural racism and desire for long-lasting social change, these shifts were transitory and returned to baseline after several weeks. Findings suggest that negative attitudes towards Black people remain deeply entrenched.

Policy Points: Cultural racism—or the widespread values that privilege and protect Whiteness and White social and economic power—permeates all levels of society, uplifts other dimensions of racism, and contributes to health inequities. Overt forms of racism, such as racial hate crimes, represent only the “tip of the iceberg,” whereas structural and institutional racism represent its base. This paper advances cultural racism as the “water surrounding the iceberg,” allowing it to float while obscuring its base. Considering the fundamental role of cultural racism is needed to advance health equity. Context: Cultural racism is a pervasive social toxin that surrounds all other dimensions of racism to produce and maintain racial health inequities. Yet, cultural racism has received relatively little attention in the public health literature. The purpose of this paper is to 1) provide public health researchers and policymakers with a clearer understanding of what cultural racism is, 2) provide an understanding of how it operates in conjunction with the other dimensions of racism to produce health inequities, and 3) offer directions for future research and interventions on cultural racism.Methods: We conducted a nonsystematic, multidisciplinary review of theory and empirical evidence that conceptualizes, measures, and documents the consequences of cultural racism for social and health inequities.Findings: Cultural racism can be defined as a culture of White supremacy, which values, protects, and normalizes Whiteness and White social and economic power. This ideological system operates at the level of our shared social consciousness and is expressed in the language, symbols, and media representations of dominant society. Cultural racism surrounds and bolsters structural, institutional, personally mediated, and internalized racism, undermining health through material, cognitive/affective, biologic, and behavioral mechanisms across the life course.Conclusions: More time, research, and funding is needed to advance measurement, elucidate mechanisms, and develop evidence‐based policy interventions to reduce cultural racism and promote health equity.

In recent years, there has been growing interest in “moving beyond the individual” to measure area-level racism as a social determinant of health. Much of this work has aggregated racial prejudice data collected at the individual-level to the area-level. As this is a rapidly emerging area of research, we conducted a systematic literature review to describe evidence of the relationship between area-level racial prejudice and health, whether associations differed by race/ethnicity, and to characterize key conceptual and methodological considerations to guide future research. We searched four interdisciplinary databases for US-based, peer-reviewed articles measuring area-level racial prejudice by aggregating individual-level indicators of racial prejudice and examining associations with mental or physical health outcome(s). Data extraction followed PRISMA guidelines and also included theory and conceptualization, pathways to health, and strengths and limitations. Fourteen of 14,632 identified articles met inclusion criteria and were included in the review. Health outcomes spanned all-cause (n=4) and cause-specific (n=4) mortality, birth outcomes (n=4), cardiovascular outcomes (n=2), mental health (n=1), and self-rated health (n=1). All studies found a positive association between area-level racial prejudice and adverse health outcomes among racial/ethnic minoritized groups, with four studies also showing a similar association among Whites. Engagement with formal theory was limited and exposure conceptualization was mixed. Methodological considerations included unmeasured confounding and trade-offs between generalizability, self-censorship, and specificity of measurement. We suggest directions for future research to further develop the conceptual and methodological rigor of this work and inform evidence-based interventions to advance population health and reduce racial health inequities.

The objective of the current study is to investigate whether an area-level measure of racial sentiment derived from Twitter data is associated with state-level hate crimes and existing measures of racial prejudice at the individual-level. We collected 30,977,757 tweets from June 2015–July 2018 containing at least one keyword pertaining to specific groups (Asians, Arabs, Blacks, Latinos, Whites). We characterized sentiment of each tweet (negative vs all other) and averaged at the state-level. These racial sentiment measures were merged with other measures based on: hate crime data from the FBI Uniform Crime Reporting Program; implicit and explicit racial bias indicators from Project Implicit; and racial attitudes questions from General Social Survey (GSS). Living in a state with 10% higher negative sentiment in tweets referencing Blacks was associated with 0.57 times the odds of endorsing a GSS question that Black-White disparities in jobs, income, and housing were due to discrimination (95% CI: 0.40, 0.83); 1.64 times the odds of endorsing the belief that disparities were due to lack to will (95% CI: 0.95, 2.84); higher explicit racial bias (β: 0.11; 95% CI: 0.04, 0.18); and higher implicit racial bias (β: 0.09; 95% CI: 0.04, 0.14). Twitter-expressed racial sentiment was not statistically-significantly associated with incidence of state-level hate crimes against Blacks (IRR: 0.99; 95% CI: 0.52, 1.90), but this analysis was likely underpowered due to rarity of reported hate crimes. Leveraging timely data sources for measuring area-level racial sentiment can provide new opportunities for investigating the impact of racial bias on society and health.