The coronavirus disease 2019 (COVID- 19) pandemic threatens global health and national economies. Both the UN and the Centers for Disease Control and Prevention have called for increased attention to prevent stigma associated with COVID-19, 1,2 which could undermine disease control efforts, worsen mental health outcomes, and exacerbate disparities. [3] [4] [5] [6] This study examines perceived discrimination among U.S. residents and its relationship with mental distress.
Disease-associated stigma toward people, regardless of infection status, has been seen in previous outbreaks of novel viruses. [7] [8] [9] [10] For example, Mexican and Latinx individuals were shunned during the 2009 H1N1 pandemic owing to the virus' link to hog farms where migrants worked. 11 Family members of patients during the 2014 Ebola epidemic also faced stigmatization. 12 Such stigmatization could be attributable to implicit cognition associating disease avoidance with these individuals. 13, 14 Discrimination toward people who share social or behavioral characteristics with COVID-19 patients but may not carry the novel virus, termed COVID-19 associated discrimination (CAD), was first seen in heightened anti-Chinese rhetoric online 15, 16 and a rapid accumulation of reports on in-person racist acts against Asians. 17 Social media analyses showed a nearly 10-fold increase in the use of offensive language, 18, 19 which could be linkable to in-person hate incidents. 20 Soon after, CAD expanded to a broader subset of Americans regardless of race/ethnicity. For instance, people wearing face masks were perceived as more likely to carry the virus, 21, 22 although mask wearing was recommended for healthy individuals. 23 Essential workers in healthcare and service industries were considered high risk, 24, 25 when others were instructed to stay home. 26 CAD was also seen in other countries. 27 The current study uses national survey data to examine CAD among U.S. residents when the pandemic escalated across the country. It explores the contribution of risk factors including: (1) social and behavioral identifiers that might tie individuals to COVID-19 (e.g., race/ethnicity and mask wearing) and (2) in-person and social media encounters during which discriminatory acts might occur. Additionally, the study examines the association between perceived CAD and mental distress.
The survey cohort came from the Understanding America Study (UAS), a nationally representative panel maintained by the Center for Economic and Social Research at the University of Southern California. The panel consists of a random sample of residents aged ≥18 years across the U.S., recruited using address-based sampling since 2014. Respondents were invited by mail and provided a tablet and broadband Internet if they did not have Internet access. They then answered survey questions on a computer, tablet, or smartphone. The design of the UAS is documented extensively elsewhere, 28 and studies 29 Because prior discrimination experience could be a potential confounder in the current study, participants without such information were excluded from the analysis. Roughly 1 year before COVID-19 surveys, respondents were asked about discrimination experience in day-to-day life.
Panelists at the time (6,708) were invited to this survey, with 5,569 (83%) completing it. The analytic sample was then restricted to the 3,665 respondents who completed all three surveys.
The parent study protocol was approved by the IRB of University of Southern California.
This study assessed CAD using a four-item scale adapted from the Everyday Discrimination Scale Short Version, 34 abbreviated from a well-studied 9-item scale. [35] [36] [37] Respondents answered yes, no, or unsure to the question of whether they had the following experiences due to people thinking they might have the coronavirus: treated with less courtesy and respect than others; receiving poorer service at restaurants or stores; people act as if they were afraid of them; threatened or harassed. Perceived CAD was identified if one reported yes to any of the experiences. Sensitivity analysis showed similar results when unsure was coded as no or missing.
Mental distress was assessed using the Patient Health Questionnaire (PHQ-4) validated in the general population. 38, 39 Respondents answered how often in the past 14 days they felt bothered by: feeling anxious; not being able to stop or control worrying; feeling depressed; having little interest in doing things. Response options included: not at all (0), several days (1), more than half the days (2), and nearly every day (3) . Although the first two items assessed anxiety and the latter two assessed depression, the sum score measured the overall mental distress.
The study also included social and behavioral risk factors of CAD. Both COVID-19 surveys asked respondents whether they had worn a face mask or covering in the past 7 days. Social media exposure was only assessed in March, as number of minutes a respondent spent on social media in a day on average, which was then categorized as: none (0), 1-30 minutes (1), 31-60 minutes
(2), 61-120 minutes (3) , and more than 120 minutes (4) . Assuming respondents' social media usage was similar across months, responses were carried over to analyze the April data.
Working status was assessed in April as number of days in the past week a respondent worked at a job and days worked from home. The ratio between the two was then derived as: not working (0), working outside (1), working partially from home (2) , and working completely from home (3) . The April survey also asked about in-person interaction in the past 7 days (e.g., going to grocery store or pharmacy or visiting others).
February 2019 by asking respondents if they had been treated unfairly or harassed because of their parents' education or possessions. Prior mental distress was evaluated using the 8-item
Center for Epidemiologic Studies-Depression Scale 40,41 on a rolling basis, for this sample, 53% in 7 construct, controlling for these prior measures was useful to parse out the impact of the pandemic.
Additional covariates were used in this study. The COVID-19 surveys asked about respondents' symptoms experienced in the past 7 days (e.g., cough and fever). Although COVID-19 cannot be diagnosed based on symptoms alone, a combination of self-reported shortness of breath and fever, the two cardinal symptoms of the disease, 42 was considered indicative of a respondent potentially having COVID-19. Sociodemographic data were collected periodically in the UAS, for this sample, between December 2019 and March 2020.
Two contextual measures were constructed from publicly available data, quantifying the progression of and policy response to the pandemic in the state in which the respondent resided on the date they responded. Disease prevalence was constructed by dividing the total number of confirmed cases documented by the New York Times 43 over 1-year estimate of the state population based on the 2018 American Community Survey. 44 The number of days under states' Shelter-in-Place (SIP) orders was calculated using the effective dates archived by the New York Times. 26 
Analyses were performed using Stata, version 16 in May 2020, and restricted to respondents who completed both COVID-19 surveys and a prior survey on perceived discrimination (n=3,665). Differences in sample characteristics by whether one perceived CAD were tested using Pearson chi-square test for categorical variables and independent sample t-test for continuous variables.
Two distinct modeling strategies were used. The cross-sectional approach analyzed each month's data separately, because people's exposure to CAD could take different forms before (March) and during (April) the implementation of SIP orders requiring most people to stay home. The longitudinal approach then compared across months by predicting the April dependent variable while controlling for its March counterpart.
Multivariable logistic regression was used to predict the probability of a person perceiving CAD as a function of risk factors (e.g., race/ethnicity, wearing face masks) and prior discrimination experience, with AOR quantifying association and predicted probability assessing absolute risk.
Multivariable linear regression was then used to model mental distress (PHQ-4 score), using CAD as a predictor, rather than an outcome as in the logistic regression, while controlling for prior mental distress. All models adjusted for COVID-19 prevalence in each state, number of days under the SIP order, self-reported symptoms, and demographic characteristics. SEs were clustered at the state level using the Eicker-Huber-White estimator, 45 accounting for nonindependence of respondents who resided in the same state. Less than 5% of data were missing for each variable and listwise deletion was used in analyses.
The study sample consisted of 3,665 U.S. adults aged ≥18 years. Appendix Table 1 shows that three quarters (75%) were non-Hispanic white, more than half (55%) were female, three quarters (73%) were aged 18-64 years, and less than half (41%) reported having a bachelor's degree or higher. More than half (59%) had a household income <$75,000.
Compared with the excluded sample who completed COVID-19 surveys but not the prior discrimination survey, the analytic sample was more likely to be non-Hispanic white, male, older, and with higher income. Because the UAS oversampled Los Angeles County residents in the past year, 46 46% of the excluded sample came from Los Angeles, driving the differences.
Appendix Table 2 shows a similar composition of included and excluded non-Los Angeles samples.
The overall percentage perceiving CAD doubled, from 4% in March to 10% in April. Table 1 contrasts data by perceived CAD. In both months, relative to respondents who did not experience CAD, those who did were more likely to be racial/ethnic minorities, immigrants, younger, and to have disease-related symptoms, face mask usage, and prior discrimination experience. Additionally, those who perceived CAD in April tended to have a lower household income and reside in states with higher COVID-19 prevalence. Mental distress, during and prior to the pandemic, were higher for those perceiving CAD than for those who did not.
Cross-sectional analyses (Table 2) AOR=3.04, p<0.001 in Model 3), suggesting their potential to mediate the association between being Asian and perceiving CAD. Those who experienced discrimination before also experienced more CAD than those who did not (AOR=1.74, p<0.01). So did respondents who wore face masks in March relative to those who did not (AOR=3.51, p<0.001). Table 3 presents the AOR of perceiving CAD in April including risk factors presented in the March analysis (Model 4), while Appendix Table 3 includes the summary for Models 0-3. Because the April survey collected data on respondents' working status and social activities, these measures were added to Models 5 and 6. Compared with non-Hispanic whites, non-Hispanic blacks (AOR=1.80, p<0.001 in Model 6) and Asians (AOR=2.02, p<0.05) were more likely to experience CAD, but the racial/ethnic gaps were smaller than those observed in March. Prior discrimination experience remained a strong predictor of CAD (AOR=2.18, p<0.001).
In April, respondents who were second-(AOR=1.86, p<0.01) and third-(AOR=1.65, p<0.001) generation immigrants experienced more CAD relative to non-immigrants. Frequent social media users, who spent >2 hours in a day on average, were also at a higher risk than those who did not use social media (AOR=1.58, p<0.05). To understand the association with use of face masks, reports of mask wearing were interacted with respondents' working status, given most states implemented SIP orders in April requiring everyone except essential workers to stay home. Compared with people who did not work, only those who worked outside and wore face masks perceived more CAD (AOR=1.58, p<0.01). No difference was found for those who worked outside but did not wear face mask (AOR=1.26, p>0.05), nor those who worked partially or fully from home regardless of face mask usage. Engagement in social activities in which in-person discrimination might occur (e.g., going to grocery store or pharmacy) was not associated with more discrimination except for those with visitors at their own residence (AOR=1.32, p<0.01). Longitudinal analysis (Table 3 , last column) associated the increase in perceiving CAD over time with several aforementioned risk factors, including being non-Hispanic black, a second-or thirdgeneration immigrant, and a frequent social media user (i.e., spending >2 hours in a day on average), as well as having prior discrimination experiences, and a combination of working outside and using a face mask. Different from the cross-sectional analysis ( Table 3 , Model 6), attending a gathering with >10 people in April significantly predicted the increased CAD perception over time. Relative to non-Hispanic whites, Asians did not experience more CAD in April after accounting for their experience in March (AOR=1.35, p>0.05 ).
In addition to assessing factors associated with perceived CAD, subsequent analyses examined the relationship between CAD and mental health factors to determine potential impact (Appendix Table 4 ). Cross-sectional analysis revealed perceived CAD predicted a higher PHQ-4 score in March (b=0.77, p<0.001) and April (b=1.01, p<0.001) after adjusting for the prior score on 8-item Center for Epidemiologic Studies-Depression Scale and person-and state-level covariates (e.g., demographics, COVID-19 prevalence). The longitudinal approach associated perceived CAD with the PHQ-4 score in April after controlling for PHQ-4 score in March (b=0.50, p<0.001) and the same set of covariates.
Anecdotal discriminatory acts amid the COVID-19 pandemic have been widely documented in media reports. To the authors' knowledge, the current study provides the first systematic assessment on how perceived CAD is associated with potential risk factors (e.g., race/ethnicity, social media usage) and mental distress. Given the representativeness of this sample, the authors believe these results are generalizable to U.S. residents.
The findings suggest that perceptions of CAD are increasing though the overall prevalence remains relatively low. Although all racial/ethnic groups have experienced CAD, disparities are observed. Elevated risk among Asians in March confirms the media reports on hate crimes, 47 and it was in part mediated by their immigration status and use of face masks. Though their risk also appeared higher than non-Hispanic whites in April, it was mostly carried over from March.
Non-Hispanic blacks are another group who persistently experience more CAD, and their risk increased from March to April. This could reflect the longstanding stereotypes associating non-Hispanic blacks with the spread of infectious diseases, 48, 49 while the increased risk in April could be attributable to media coverage on their disproportionately higher mortality rate due to COVID-19. [50] [51] [52] Smaller racial/ethnic gaps in April than March could be due to the increase in CAD among non-Hispanic whites.
Besides race/ethnicity, wearing face masks is another risk factor consistently associated with CAD. This confirms media reports on the stigmatization of mask wearing during the pandemic, 21, 22 reflecting longstanding bias in the West 53 and contradicting public messaging across different disease control phases. 54, 55 In addition to mask-related cultural factors studied in previous outbreaks, 56 perceived CAD due to mask wearing could be attributable to the spotlight effect, in which individuals overestimate others' attention directed toward them when their appearances are different from people around. 57, 58 As many states transit to lifting SIP orders and reopen business and public activities, CAD associated with mask wearing, a highly recommended disease control metric, 59 calls for public awareness. This may ease as more authorities start to mandate face coverings in public 60 and wearing face masks become commonplace.
The results also point to CAD's potential association with other risk factors such as heavy social media usage (i.e., spending >2 hours on a day on average), being an immigrant, and engagement in some social activities. However, such relationships seem less robust and need further confirmation.
As for potential health outcomes of CAD, this study showcases substantial evidence linking CAD to increased mental distress, consistent with literature associating general discrimination with poorer mental health especially among racial/ethnic minorities. 61, 62 The relationship is particularly pertinent during this pandemic as it compounds mental health distress 63, 64 attributable to concerns of disease spread, austere social restrictions, and financial stress.
Findings from this study should be interpreted in light of several limitations. First, the analysis was based on observational data. The reported correlational relationships need further confirmation in stronger designs such as field experiment. Additionally, CAD was operationalized as people's self-reported perception instead of direct observation. Other data types detailing occasions or types of discrimination might provide an in-depth view. Moreover, risk factors examined here are limited. For example, coughing in public was not covered in the data although anecdotal evidence associates it with disease avoidance. Finally, mental health was assessed by perceived mental distress instead of mental illness as in other studies. 61 
The impact of the COVID-19 pandemic on public health, including mental health, will continue to reverberate for years to come. This study's initial findings of perceived CAD and its association with poorer mental health underscore the existing racial/ethnic inequalities experienced by many non-Hispanic blacks and Asians. The findings also suggested discrimination toward those who wear face masks, signaling the need to raise public awareness, especially as economies reopen and wearing a face mask is highly recommended to prevent transmission.
The project described in this paper relies on data from surveys administered by the Understanding America Study (UAS), which is maintained by the Center for Economic and Social Ms. Brenneke's work was supported by the National Institute on Drug Abuse (T32DA007292) and Dr. Le's work was supported by a training grant from the National Institute of Mental Health (T32MH103210). The research presented in this paper is that of the authors and does not reflect the official policy of NIH. No financial disclosures were reported by authors of this paper.
YL and BKF conceived the study and analyzed and interpreted the data; all authors contributed to drafting and revising the article. Contents of the article have not been presented or published elsewhere. 