Racial and Ethnic Disparities in COVID-19-Related Stressor Exposure and Adverse Mental Health Outcomes Among Health Care Workers
Publication: American Journal of Psychiatry
Abstract
Objective:
Racial and ethnic disparities in exposure to COVID-19-related stressors, pandemic-related distress, and adverse mental health outcomes were assessed among health care workers in the Bronx, New York, during the first wave of the pandemic.
Methods:
The authors analyzed survey data from 992 health care workers using adjusted logistic regression models to assess differential prevalence of outcomes by race/ethnicity and their interactions.
Results:
Compared with their White colleagues, Latinx, Black, Asian, and multiracial/other health care workers reported significantly higher exposure to multiple COVID-19-related stressors: redeployment, fear of being sick, lack of autonomy at work, and inadequate access to personal protective equipment. Endorsing a greater number of COVID-19-related stressors was associated with pandemic-related distress in all groups and with adverse mental health outcomes in some groups; it was not related to hazardous alcohol use in any of the groups. These associations were not significantly different between racial and ethnic groups. Latinx health care workers had significantly higher probabilities of pandemic-related distress and posttraumatic stress than White colleagues. Despite greater exposure to COVID-19-related stressors, Black, Asian, and multiracial/other health care workers had the same, if not lower, prevalence of adverse mental health outcomes. Conversely, White health care workers had a higher adjusted prevalence of moderate to severe anxiety compared with Asian colleagues and greater hazardous alcohol use compared with all other groups.
Conclusions:
Health care workers from racial and ethnic minority groups reported increased exposure to COVID-19-related stressors, suggestive of structural racism in the health care workforce. These results underscore the need for increased support for health care workers and interventions aimed at mitigating disparities in vocational exposure to risk and stress.
Despite the expiration of the U.S. public health emergency declaration for coronavirus disease 2019 (COVID-19), the global community continues to grapple with the pandemic’s profound psychosocial impacts (1). Among individuals most affected by COVID-19 are health care workers, who experienced pandemic-related distress and other adverse mental health outcomes, in part because of greater levels of exposure and stress (2–4). During the first wave of the pandemic in the spring of 2020, COVID-19 hit urban hospitals hardest, with health care workers facing extraordinary challenges (5, 6). An early New York City study reported that 57% of health care workers had acute stress, 48% had depression, and 33% had anxiety (6). COVID-19-related stressors, such as redeployment, fear of being sick with COVID-19, testing positive for COVID-19, reduced autonomy at work, and lack of access to personal protective equipment, likely contributed to such high rates (7, 8).
During this tumultuous time, the United States also saw a resurgence of attention to racial and ethnic justice in response to police violence and structural determinants of COVID-19-related illness and health. However, there has been scant attention to how racial and ethnic disparities may have affected health care workers on the front lines through differential rates of exposure to COVID-19-related stressors and the associated outcomes. Given the well-documented persistence of structural racism in the health care workforce (9–12), there may have been disparities in increased risk for the riskiest work and associated stress among health care workers from racial and ethnic minority groups.
One particular site of disparities is the Bronx, New York, where health care workers served New York City’s most highly impacted residents (12–15). The Bronx is the New York City borough with the highest rates of poverty and environmental stressors (2, 16). Bronx residents had the highest per capita rates of COVID-19 infection, hospitalization, and mortality. Furthermore, Bronx residents were more likely than other New York City residents to work “essential,” low-paid jobs (17). And within this borough, Latinx and Black patients were significantly more likely than non-Latinx White patients to have preexisting conditions, test positive for COVID-19, and become acutely ill (18). Despite well-known disparities among patients, few studies have measured whether such racial and ethnic disparities emerged among health care workers (19). Even fewer studies have measured associations between exposure to COVID-19-related stressors and psychological outcomes among health care workers, with scant attention paid to those in the Bronx (20–22).
The first objective of this study was to determine whether health care workers from racial and ethnic minority groups were more likely than their White colleagues to experience COVID-19-related stressors at work, specifically by measuring rates of self-reported redeployment, fear of being sick with COVID-19, testing positive for COVID-19, reduced autonomy at work, and lack of access to personal protective equipment. Second, with recent studies documenting associations between vocational stressors and individual outcomes during the height of the COVID-19 crisis (6–8), we explored associations between COVID-19-related stressors and pandemic-related distress—acute symptoms subjectively attributed to the onset of the pandemic—and whether these associations differed by race and ethnicity. In exploratory analyses, we applied the same objectives to a set of more general adverse mental health outcomes: moderate to severe depressive symptoms, moderate to severe anxiety symptoms, posttraumatic stress, moderate to severe insomnia symptoms, serious psychological distress, and hazardous alcohol use.
We were particularly interested in whether the association between exposure to COVID-19-related stressors and mental health outcomes differed by racial and ethnic group. This line of inquiry is important given the paradox in racial and ethnic disparities in the United States. Specifically, despite daily, chronic exposure to minority stressors and other pernicious manifestations of structural racism (23), Americans from racial and ethnic minority groups tend to report similar, if not lower, rates of mental illness and substance use compared with White Americans (24). This may be a result of higher levels of distress tolerance and a greater number of coping strategies among racial and ethnic minorities, despite (or perhaps related to) greater exposure to societal stressors (24–26). Yet, such associations have received little attention during the early waves of the COVID-19 pandemic or in the context of structural racism in the health care workforce (9–12).
This study tested three hypotheses, using survey data with a sample of racially diverse, Bronx-based health care workers. We hypothesized that Latinx, Black, Asian, and multiracial/other health care workers had significantly higher adjusted prevalence rates of exposure to COVID-19-related stressors and pandemic-related distress compared with White health care workers; that exposure to COVID-19-related stressors was positively associated with pandemic-related distress; and that the associations between exposure to COVID-19 and pandemic-related distress were greater among White health care workers than among Latinx, Black, Asian, and multiracial/other health care workers. Exploratory analyses tested similar associations with a series of adverse mental health outcomes and measured associations with specific COVID-19-related stressors.
Methods
Study Site and Data
We analyzed survey data from 992 health care workers within Montefiore Health System (hereafter referred to as “Montefiore”), the largest health care network in the Bronx with 11 hospital campuses, over 200 outpatient locations, and the Albert Einstein College of Medicine (2, 27). In 2020, Montefiore had more than 93,000 admissions and increased its intensive care unit bed capacity from 108 to 306 (16); many health care workers were redeployed overnight to high-acuity floors. Montefiore has a racially and ethnically diverse workforce, with a distribution of health care workers that is 25.89% White, 33.74% Black, 23.04% Latinx, 15.20% Asian, and 2.13% multiracial/other, according to internal records. These health care workers primarily treat racial and ethnic minority patient populations affected by structural racism and intersecting social determinants of health, making this a unique site to measure disparities (28). The sample consisted of health care workers who were at least 18 years old, fluent in English, employed at Montefiore, and recruited online via internal e-mail listservs. The stated goal of the study was to measure emotional health among health care providers and staff at Montefiore Einstein across many roles, including attending physicians, house staff, research scientists, physician assistants, nursing staff, respiratory therapists, and administrative and support staff.
Data were collected online using a Qualtrics survey between April 4, 2020, and January 26, 2021. To maintain confidentiality, we did not verify employment status and thus cannot confirm eligibility or report completion rates among those who were sent recruitment materials. However, of 1,006 people who clicked the survey link, 992 (98.6%) provided informed consent and answered ≥80% of the survey questions and were thus retained in the study. Participants were provided information about mental health resources and directed to on-campus, phone line, and virtual support (2). Participants received a complete description of the study procedures, approved by the Albert Einstein College of Medicine institutional review board (protocol 2020–11469).
Primary Outcomes
COVID-19-related stressors.
The first set of primary outcomes was exposure to five types of COVID-19-related stressors: experiencing redeployment (“Were you deployed to an area different from where you usually work to work directly with COVID-19 patients?”); being afraid of being sick with COVID-19 (“Do you feel scared you may be sick with COVID-19?”); being tested positive for COVID-19 (“Have you tested positive for COVID-19?”); experiencing a lack of autonomy at work (“Was there a time when you felt a lack of autonomy in your work related to the COVID-19 crisis?”); and having inadequate access to personal protective equipment (“Was there a time when you did not have adequate access to PPE?”). Participants responded with a yes or no (coded 1 or 0, respectively). We created a count variable (range, 0–5) by summing the number of COVID-19-related stressors endorsed.
Pandemic-related distress.
The second primary outcome was pandemic-related distress, defined as negative emotional, cognitive, and behavioral reactions subjectively attributed to the COVID-19 pandemic. The level of distress was captured using an adapted version of the Impact of Event Scale (IES) (range, 0–75; cutoff, 33), which prompted respondents to indicate how frequently they experienced a list of symptoms in the past 7 days, “with respect to the COVID-19 crisis.” Symptoms were related to two types of responses to a traumatic event: intrusion (e.g., involuntary thoughts, emotional reactivity, and flashbacks) and avoidance (e.g., attempts to avoid cognitive, emotional, or situational triggers related to the pandemic). This was the only psychometric scale for which we specifically prompted participants to respond with the COVID-19 pandemic in mind.
Exploratory Outcomes
We also studied six adverse mental health outcomes not specifically attributed to the COVID-19 pandemic: moderate to severe depressive symptoms with the Patient Health Questionnaire (PHQ-9) (range, 0–27; cutoff, 10); moderate to severe anxiety symptoms, with the Generalized Anxiety Disorder–7 Scale (GAD-7) (range, 0–21; cutoff, 10); posttraumatic stress, with the Primary Care Posttraumatic Stress Disorder Screen for DSM-5 (PC-PTSD-5) (range, 0–5; cutoff, 3); moderate to severe insomnia symptoms, with the Insomnia Severity Index (ISI) (range, 0–28; cutoff, 15); serious psychological distress, with the Kessler 6 Psychological Distress Scale (K6) (range, 0–24; cutoff, 13); and hazardous alcohol use, with the consumption subscale of the Alcohol Use Disorders Identification Test (AUDIT-C) (range, 0–12; cutoff, 4 for men and 3 for women and nonbinary persons). We calculated sum scores and created dichotomous scores (absence=0 and presence=1) using validated cutoffs. Positive screens are proxies for adverse mental health outcomes, not diagnostic indicators or signs of clinically significant psychiatric disorders. Our selection of these additional adverse mental health outcomes was based on then-emerging studies demonstrating high rates of these concerns among health care workers during this time.
Primary Exposure of Interest
The primary independent variable of interest was participant self-reported race and ethnicity. In response to the question “How would you describe your race/ethnicity?,” respondents selected from the following options: “African/African American/Black,” “American Indian/Native American,” “Arab American/Middle Eastern,” “Asian/Asian American or Pacific Islander,” “Caucasian/European American/White,” “Hispanic/Latina/o/x,” “biracial/multiracial,” and “another race/ethnicity not listed: please specify.” Few participants (N<5) endorsed “American Indian/Native American” and “Arab American/Middle Eastern”; these participants were recoded as “other” and combined with the “biracial/multiracial” group. For parsimony, race/ethnicity was coded as a nominal variable: White, Latinx/Hispanic, Black/African American, Asian/Asian American, and multi- or biracial or other (hereafter referred to as “White,” “Latinx,” “Black,” “Asian,” and “multiracial or other”). This approach minimizes the complexity of socially constructed, yet phenomenologically salient, racial and ethnic identities.
Covariates
In adjusted analyses, we controlled for the following self-reported demographic characteristics: age (“How old are you?”), gender identity (“How would you describe your gender identity?”), and income (“What is your annual income?”). Age was coded as a continuous variable. Gender was coded as a nominal variable: men (transgender and cisgender men combined), women (transgender and cisgender women combined), and nonbinary or other. Income was coded as an ordinal variable: <$60,001, $60,001–$80,000, $80,001–$100,000, and >$100,000.
Statistical Analysis
Analyses were performed in SPSS, version 27 (IBM Corp., Armonk, N.Y.), and Stata, version 17 (StataCorp., College Station, Tex.). For preliminary analyses, demographic characteristics were summarized (Table 1) and tested for univariate normality using Kolmogorov-Smirnov tests. All continuous variables met criteria for univariate normality at the lower bound (p>0.01).
| Characteristic | N | %a |
|---|---|---|
| Gender | ||
| Men | 171 | 23.1 |
| Women | 502 | 67.8 |
| Nonbinary or other | 67 | 9.1 |
| Race and ethnicity | ||
| White | 440 | 51.9 |
| Latinx | 121 | 14.3 |
| Black | 90 | 10.6 |
| Asian | 140 | 16.5 |
| Multiracial or other | 57 | 6.7 |
| Annual income | ||
| <$60,001 | 135 | 16.2 |
| $60,001–$80,000 | 169 | 20.2 |
| $80,001–$100,000 | 118 | 14.1 |
| >$100,000 | 414 | 49.5 |
a
The percentage values are based on the number of participants who responded to the question (i.e., not all participants answered every question): for gender, N=740; for race and ethnicity, N=848; and for annual income, N=836.
We conducted three sets of primary analyses. First, to measure disparities in prevalence between groups (hypothesis 1), we calculated the adjusted prevalence of the five types of COVID-19-related stressors and pandemic-related distress and tested for significant differences between racial and ethnic groups. In particular, we estimated separate logistic regressions for each outcome on race and ethnicity. On the basis of the hypothesized disparities, we used the group of White participants as the reference group and adjusted for age, gender, and income. To facilitate easier interpretation of the regression models, we used the predictive margins method in Stata. This statistical approach uses the coefficients from each logistic regression model to calculate the adjusted prevalence of each outcome for each individual, adjusting for all covariates, using the delta method to calculate standard errors. We then calculated between-group differences in adjusted prevalence rates and tested for significance (alpha=0.05) (29). These results are provided in Table 2.
| Outcome | White | Latinx | Black | Asian | Multiracial or Other | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| % | SE | % | SE | pb | % | SE | pb | % | SE | pb | % | SE | pb | |
| Primary outcomes | ||||||||||||||
| COVID-19-related stressors | ||||||||||||||
| Redeployed | 24.7 | 2.2 | 26.1 | 4.6 | 0.782 | 34.4 | 6.0 | 0.130 | 37.3 | 4.5 | 0.012 | 24.0 | 6.0 | 0.919 |
| Fear of being sick with COVID-19 | 21.1 | 2.2 | 49.2 | 5.3 | <0.001 | 37.6 | 6.3 | 0.013 | 41.4 | 4.8 | <0.001 | 41.6 | 7.3 | 0.007 |
| Tested positive for COVID-19 | 13.6 | 1.8 | 21.2 | 4.2 | 0.101 | 16.2 | 4.6 | 0.600 | 12.0 | 3.2 | 0.675 | 6.5 | 3.6 | 0.087 |
| Lack of autonomy at work | 53.1 | 2.6 | 61.9 | 5.1 | 0.131 | 68.1 | 6.0 | 0.022 | 47.8 | 4.8 | 0.328 | 63.2 | 7.1 | 0.183 |
| Inadequate access to personal protective equipment | 50.2 | 2.6 | 62.3 | 5.0 | 0.033 | 74.2 | 5.6 | <0.001 | 50.2 | 4.7 | 0.989 | 53.7 | 7.2 | 0.650 |
| COVID-19-specific mental health outcome | ||||||||||||||
| Pandemic-related distressc | 30.2 | 2.5 | 45.4 | 5.2 | 0.009 | 40.8 | 6.5 | 0.129 | 27.4 | 4.2 | 0.562 | 27.9 | 6.5 | 0.733 |
| Exploratory outcomes | ||||||||||||||
| Adverse mental health outcomes | ||||||||||||||
| Moderate to severe depressive symptomsd | 22.3 | 2.2 | 27.1 | 4.4 | 0.339 | 20.3 | 5.1 | 0.713 | 17.7 | 3.6 | 0.277 | 28.4 | 6.4 | 0.365 |
| Moderate to severe anxiety symptomse | 24.2 | 2.3 | 29.9 | 4.6 | 0.27 | 14.5 | 4.5 | 0.054 | 13.4 | 3.2 | 0.007 | 30.5 | 6.7 | 0.377 |
| Posttraumatic stressf | 32.2 | 2.5 | 44.8 | 5.1 | 0.028 | 36.1 | 6.2 | 0.561 | 30.0 | 4.2 | 0.655 | 36.4 | 6.9 | 0.571 |
| Moderate to severe insomnia symptomsg | 13.8 | 1.9 | 19.2 | 4.1 | 0.233 | 12.3 | 4.2 | 0.758 | 8.9 | 2.7 | 0.146 | 14.4 | 5.0 | 0.902 |
| Serious psychological distressh | 10.9 | 1.7 | 16.2 | 3.8 | 0.206 | 7.9 | 3.5 | 0.443 | 11.7 | 3.0 | 0.831 | 9.7 | 4.1 | 0.793 |
| Hazardous alcohol usei | 53.4 | 2.6 | 29.1 | 4.8 | <0.001 | 30.3 | 6.2 | <0.001 | 33.1 | 4.5 | <0.001 | 19.8 | 5.9 | <0.001 |
a
The table displays the adjusted prevalence (%) for each COVID-19-related stressor and adverse mental health outcome, stratified by racial and ethnic group, after adjustment by age, income, and gender.
b
Comparison to the reference group of White participants (boldfaced values indicate significant differences between groups, where p<0.05 was considered significant).
c
Indicated by a score ≤33 on the adapted Impact of Event Scale.
d
Indicated by a score ≥10 on the Patient Health Questionnaire–9.
e
Indicated by a score ≥10 on the Generalized Anxiety Disorder–7 scale.
f
Indicated by a score ≥3 on the Primary Care Posttraumatic Stress Disorder Screen for DSM-5.
g
Indicated by a score ≥15 on the Insomnia Severity Index.
h
Indicated by a score ≥13 on the Kessler Psychological Distress Scale.
i
Indicated by a score of 3 or 4 on the Alcohol Use Disorders Identification Test consumption subscale.
Second, to explore associations between COVID-19-related stressors and pandemic-related distress (hypothesis 2) and determine whether these associations differed by racial and ethnic group (hypothesis 3), we measured the marginal effect of a single unit increase in the number of COVID-19-related stressors on the adjusted prevalence for each outcome. To this end, we estimated separate logistic regression models for each outcome regressed on racial and ethnic group, the number of COVID-19-related stressors endorsed (range 0–5), and the interaction between the two variables. All models adjusted for age, gender, and income. Then, for ease of interpretation and to avoid potential for bias in interpreting interaction coefficients in nonlinear models, we used the margins command to convert interaction coefficients into marginal effects (dy/dx), using the delta method to calculate standard errors. We assessed whether marginal effects were greater than zero within each racial and ethnic group (alpha=0.05), thus testing the significance of the association between COVID-19-related stressors and mental health outcomes. These results are provided in Table 3. To test whether the association of the number of COVID-19-related stressors and each outcome differed by racial and ethnic group (hypothesis 3), we compared the marginal effects of White health care workers with those of Latinx, Black, Asian, and multiracial/other health care workers. These differences were nonsignificant (alpha=0.05), and these p values are provided in Table S1 in the online supplement .
| | White | Latinx | Black | Asian | Multiracial or Other | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| Outcomeb | dy/dx | p | dy/dx | p | dy/dx | p | dy/dx | p | dy/dx | p |
| Primary outcome: COVID-19-specific mental health outcome | ||||||||||
| Pandemic-related distress | 0.071 | <0.001 | 0.095 | 0.005 | 0.111 | 0.002 | 0.058 | 0.043 | 0.132 | 0.002 |
| Exploratory outcomes: adverse mental health outcomes | ||||||||||
| Depressive symptoms (moderate to severe) | 0.062 | <0.001 | 0.046 | 0.135 | 0.096 | <0.001 | 0.083 | <0.001 | 0.067 | 0.223 |
| Anxiety symptoms (moderate to severe) | 0.064 | <0.001 | 0.057 | 0.075 | 0.043 | 0.110 | 0.039 | 0.108 | 0.022 | 0.724 |
| Posttraumatic stress | 0.068 | <0.001 | 0.074 | 0.038 | 0.112 | <0.001 | 0.034 | 0.271 | 0.152 | <0.001 |
| Insomnia symptoms (moderate to severe) | 0.057 | <0.001 | 0.029 | 0.319 | 0.041 | 0.084 | 0.017 | 0.412 | 0.001 | 0.987 |
| Serious psychological distress | 0.040 | 0.007 | 0.001 | 0.982 | 0.001 | 0.971 | 0.046 | 0.037 | −0.020 | 0.609 |
| Hazardous alcohol use | 0.012 | 0.582 | 0.059 | 0.068 | −0.057 | 0.251 | −0.006 | 0.866 | −0.009 | 0.866 |
a
The table depicts the estimated increase in the prevalence of pandemic-specific distress and general adverse mental health outcomes with an increase of one additional COVID-19 stressor (dy/dx) and whether that increase is significantly greater than zero within each racial and ethnic subgroup (p value). Analyses also included tests of difference-in-differences, specifically whether these marginal increases in prevalence differed by racial and ethnic group (comparing Latinx, Black, Asian, and multiracial or other health care workers with White health care workers). None of these difference-in-differences were significant at the p<0.05 level, so these comparisons are not presented in the table. The differences in dy/dx between groups and associated p values are presented in Table S1 in the online supplement .
b
Scales and scores used to determine outcomes are the same as indicated in the footnotes to Table 2.
We then conducted two types of exploratory analyses. First, we reran the same models with our exploratory outcomes, specifically the six adverse mental health outcomes that were not specific to the COVID-19 pandemic. These results are provided in Tables 2 and 3 and Table S1 in the online supplement . Second, we conducted post hoc exploratory analyses to determine the associations between each specific COVID-19 stressor and each mental health outcome by racial and ethnic group. These exploratory analyses were conducted to assess whether particular COVID-19-related stressors were particularly relevant for propelling downstream effects in the different groups and thus potential targets for focused intervention. For this analysis, we calculated adjusted prevalence rates for pandemic-related distress and adverse mental health outcomes by each COVID-19-related stressor and racial and ethnic group, estimated adjusted logistic regression models, and implemented the predictive margins method (i.e., used the delta method to calculate standard errors). We tested for significant differences (p<0.05) in adjusted prevalence of each outcome by COVID-19-related stressor within each racial and ethnic group. We then compared difference-in-differences (i.e., how the difference in adjusted prevalence by COVID-19-related stressor varied by racial and ethnic group). These results are provided in Table S2 in the online supplement .
Results
Demographic Characteristics
Table 1 summarizes the sociodemographic characteristics of the sample. The distribution of race and ethnicity was 51.9% White, 14.3% Latinx/Hispanic, 10.6% Black/African American, 16.5% Asian/Asian American, and 6.7% multi- or biracial or other. With respect to gender, 23.1% were men, 67.8% were women, and 9.1% were nonbinary or other gender. For annual income, 16.2% earned less than $60,001, 20.2% earned between $60,001 and $80,000, 14.1% earned between $80,001 and $100,000, and 49.5% earned more than $100,000. The mean age of the participants was 33.03 years (SD=13.45).
Primary Results
Adjusted prevalence of COVID-19-related stressors.
Our primary results revealed significant disparities in the adjusted prevalence of COVID-19-related stressors and pandemic-related distress after adjusting for sociodemographic characteristics (age, gender, and income) and testing for predictive margins. Table 2 details the adjusted prevalence for each racial and ethnic group and the significant differences between groups. In support of hypothesis 1, Latinx, Black, Asian, and multiracial/other health care workers had significantly higher adjusted prevalence rates of experience with four of the five COVID-19-related stressors compared with their White colleagues. Specifically, compared with White health care workers, Asian health care workers had a higher adjusted prevalence of redeployment (Asian, 37.3%; White, 24.7%; p=0.012). Compared with White health care workers, Latinx, Black, and multiracial/other health care workers had higher adjusted prevalence rates of fear of being sick with COVID-19 (White, 21.1%; Latinx, 49.2%, p<0.001; Black, 37.6%, p=0.013; Asian, 41.4%, p<0.001; multiracial/other, 41.6%, p=0.007). Black health care workers had a higher adjusted prevalence of lack of autonomy at work than White health care workers (Black, 68.1%; White, 53.1%, p=0.022). Lastly, Latinx and Black health care workers had higher adjusted prevalence rates of inadequate access to personal protective equipment than White health care workers (White, 50.2%; Latinx, 62.3%, p=0.033; Black, 74.2%, p<0.001).
Adjusted prevalence of pandemic-related distress.
The adjusted prevalence rates for pandemic-related distress, our primary mental health outcome of interest, are also listed in Table 2. Only one group, Latinx health care workers, had a significantly higher adjusted prevalence of pandemic-related distress compared with White health care workers (45.4% vs. 30.2%, p=0.009). Table 3 provides the change in the adjusted prevalence of each outcome given a one-unit increase in the number of COVID-19-related stressors endorsed (dy/dx). In support of hypothesis 2, there was a significant marginal effect of a one-unit increase in the number of COVID-19-related stressors on pandemic-related distress within all racial and ethnic groups: White (dy/dx=7.1%, p<0.001), Latinx (dy/dx=9.5%, p=0.005), Black (dy/dx=11.1%, p=0.002), Asian (dy/dx=5.8%, p=0.043), and multiracial/other (dy/dx=13.2%, p=0.002) health care workers. The marginal effect of a one-unit increase in COVID-19-related stressors on pandemic-related distress was not significantly different between groups, and hypothesis 3 was not supported. These nonsignificant p values are provided in Table S1 in the online supplement .
Exploratory Results
Adjusted prevalence of adverse mental health outcomes.
We also examined the adjusted prevalence rates for six distinct adverse mental health outcomes not specific to the COVID-19 pandemic in exploratory analyses. Three adverse mental health outcomes (moderate to severe anxiety symptoms, posttraumatic stress, and hazardous alcohol use) yielded significant racial and ethnic disparities in adjusted prevalence rates. Specifically, Latinx health care workers had a higher adjusted prevalence of posttraumatic stress compared with White health care workers (Latinx, 44.8%; White, 32.2%; p=0.028). White health care workers had higher adjusted prevalence of moderate to severe anxiety symptoms compared with Asian health care workers (White, 24.2%; Asian, 13.4%; p=0.007) and significantly greater hazardous alcohol use (53.4%) compared with all other groups (Latinx, 29.1%; Black, 30.3%; Asian, 33.1%; multiracial/other, 19.8%; all p<0.001).
In further exploratory analyses, we observed significant marginal effects of a one-unit increase in the number of COVID-19-related stressors on several outcomes, although there were no significant differences between groups. Specifically, among White health care workers, a one-unit increase in COVID-19-related stressors was significantly associated with increased adjusted prevalence rates of five of six adverse mental health outcomes—all except for hazardous alcohol use—with marginal effects ranging from 4% (serious psychological distress, p=0.007) to 7.1% (pandemic-related distress, p<0.001). Among Latinx health care workers, a one-unit increase in COVID-19-related stressors was significantly associated with increased adjusted prevalence rate of posttraumatic stress (dy/dx=7.4%, p=0.038). Among Black health care workers, a one-unit increase in COVID-19-related stressors was significantly associated with moderate to severe depressive symptoms (dy/dx=9.6%, p<0.001) and posttraumatic stress (dy/dx=11.2%, p<0.001). Among Asian health care workers, a one-unit increase in COVID-19-related stressors was significantly associated with moderate to severe depressive symptoms (dy/dx=8.3%, p<0.001) and serious psychological distress (dy/dx=4.6%, p=0.037). Finally, among multiracial/other health care workers, a one-unit increase in COVID-19-related stressors was significantly associated with posttraumatic stress (dy/dx=15.2%, p<0.001). There were no significant differences in the marginal effects for these exploratory mental health outcomes by racial and ethnic groups in terms of the impact of a one-unit increase in the number of COVID-19-related stressors. Hypothesis 3 was not supported.
Marginal effects of specific COVID-19-related stressors.
Lastly, we conducted post hoc exploratory interaction tests to analyze the impact of each COVID-19-related stressor on each outcome, with race and ethnicity as the variable of interest (details provided in Table S2 in the online supplement ). Our difference-in-differences analyses yielded four significant findings wherein specific COVID-19-related stressors had varying marginal effects on outcomes between different racial and ethnic groups (p<0.05). First, the association between redeployment and moderate to severe depressive symptoms was more pronounced among Black than White health care workers. Second, testing positive for COVID-19 had a greater association with moderate to severe depressive symptoms among Black than White health care workers. Third, the association between lack of autonomy and moderate to severe insomnia symptoms was stronger among White than Asian health care workers. Lastly, the association between redeployment and hazardous alcohol use was more significant among Latinx than White health care workers.
Discussion
Findings from this comprehensive exploratory study highlight greater exposure to COVID-19-related stressors among Latinx, Black, Asian, and multiracial/other health care workers compared with their White colleagues. Specifically, Asian health care workers had a higher likelihood of being redeployed. Latinx, Black, Asian, and multiracial/other health care workers exhibited higher rates of fear of being sick with COVID-19. Of note, among Latinx health care workers, nearly half (49.2%) expressed this fear, a finding that corroborates epidemiological data from the spring of 2020 indicating that Latinx Americans experienced higher rates of COVID-19-related illness, death, and fear of infection as a result of working disproportionately high-exposure, “essential” work (15). Black health care workers were more likely to report a lack of autonomy at work, with a concerning rate of 68.1%. Latinx and Black health care workers were also more likely to report inadequate access to personal protective equipment. These data underscore the presence of racial and ethnic disparities in occupational exposure to COVID-19-related stressors (12, 14, 15) and bolster evidence of systemic racism within the health care workforce (22, 28, 30).
Despite a higher prevalence of COVID-19-related stressors, Latinx, Black, Asian, and multiracial/other health care workers did not uniformly report higher prevalence for pandemic-related distress or adverse mental health outcomes. Interestingly, the exception was among Latinx health care workers, who had higher adjusted prevalence of pandemic-related distress and posttraumatic stress. Yet even in the context of greater stress exposure, Black, Asian, and multiracial/other health care workers exhibited similar, if not lower, prevalence of pandemic-related distress and adverse mental health outcomes compared with White health care workers. Conversely, White health care workers reported higher levels of anxiety symptoms than their Asian colleagues and alarmingly high rates of hazardous alcohol use (53.4%), a prevalence significantly higher than all other groups. These findings indicate a need for tailored harm reduction interventions aimed at mitigating hazardous alcohol use among White health care workers.
There are various potential explanations for these findings. First, these results are in line with previous research suggesting that individuals from racial and ethnic minority groups, despite or perhaps because of chronic stress exposure, paradoxically tend to report similar, if not lower, rates of adverse mental health outcomes (23, 24). It is also possible that White health care workers may have engaged in higher rates of self-regulatory coping strategies (i.e., hazardous alcohol use) as a result of a unique susceptibility to stress (25, 31), although these relationships were not measured in our study.
Second, it could be suggested that White health care workers have a unique susceptibility to stress. Our exploratory analyses showed that White health care workers experienced significant marginal effects of COVID-19-related stressors on a greater number of outcomes compared with other groups: all outcomes with the exception of hazardous alcohol use. Several studies have proposed that minoritized people may develop coping resources and strategies (e.g., social support, religiosity, and community resilience) to buffer chronic stress exposure (32, 33). This could be evidenced in the finding, for instance, that although 74.2% of Black health care workers reported inadequate access to personal protective equipment (significantly higher than the rate among their White colleagues), they exhibited significantly better mental health outcomes. There may also be group-specific differences in risk expectation and tolerance (33), and there may have been differences in reporting bias by racial and ethnic groups that skewed the results. These explanations, however, are strictly hypothetical.
Aligned with hypothesis 2, our data suggest a clear association between the number of COVID-19-related stressors, pandemic-related distress, and adverse mental health outcomes (2). Contrary to hypothesis 3, marginal effects were similar between racial and ethnic groups, suggesting that all health care workers are vulnerable in the context of increased stress (20). Although we could not determine whether baseline rates were significantly different between groups, the data suggest that health care workers in this study were profoundly impacted by high—and differential—exposure to COVID-19-related stressors during this extraordinary time (14, 15).
The most commonly reported concern for health care workers in the sample was hazardous alcohol use, followed by posttraumatic stress; the least common concern was psychological distress. As such, interventions to support health care workers may need to target hazardous alcohol use and posttraumatic stress to mitigate the potential downstream effects of exposure to COVID-19-related stressors. The results of the second set of exploratory analyses suggested that three COVID-19-related stressors in particular had differential associations with outcomes between racial and ethnic groups: redeployment, testing positive for COVID-19, and lack of autonomy at work. Although these exploratory results should be viewed with caution (see also Table S2 in the online supplement ), it is important to note that the potential downstream effects of these three stressors may uniquely affect health care workers from different racial and ethnic groups. Finally, it is worth considering that outcomes may have been associated with the number of COVID-19-related stressors experienced, rather than specific types of stress exposure. Further studies are needed to explore these findings.
There are a number of limitations to this study. First, our method of identifying differences by racial and ethnic identity did not explicitly measure structural racism. Instead, we used COVID-19-related stressors as indirect indicators in the context of racial and ethnic group differences (34). It is important to note that we did not intend to characterize racial and ethnic identity as a risk factor for occupational stress or any adverse outcomes. Rather, racism is the risk factor (34). Future studies would benefit from using validated metrics of structural racism to estimate its impact more accurately (28) and from collecting mixed-methods data from health care workers about their perceived experiences with racial and ethnic discrimination in their workplace (34).
Second, our reliance on self-reported demographic items posed limitations to our ability to delve into analytic subtleties. Specifically, in condensing multiple groups into combined subgroups (e.g., multiracial/other), we inevitably reduced the empirical and phenomenological precision of our findings. This approach may have curtailed our capacity to capture nuances in ethnic, linguistic, phenotypic, and cultural heterogeneity within socially constructed racial and ethnic groups (35).
Third, our sample presents inherent limitations, because it does not accurately represent all health care workers or the diverse communities living in the Bronx. For instance, the racial and ethnic breakdown of the full Montefiore system is 25.89% White; however, our sample consisted of 51% White participants. Therefore, any conclusions drawn from this study, which utilized a convenience sample, may not be applicable to all health care workers at the study site or to the full population of health care workers in New York City. Future studies could recruit more racially and ethnically representative samples or purposefully enroll health care workers from racial and ethnic minority groups to understand their phenomenological experiences (28). Our use of convenience sampling may have introduced selection bias, and our capacity to control for participants’ job roles was limited. We thus make no claims of causal inference. Generalizability is also limited given the high-acuity, urban setting of the study (7, 16) and the predominantly White, high-income, English-speaking sample. Future studies could broaden the scope to include hospital employees who are not health care workers (14) and directly measure structural racism in health care (36).
Fourth, we acknowledge statistical limitations. Notably, we decided against adjusting for multiple comparisons; therefore, cautious interpretations of the results are necessary because of the numerous racial and ethnic groups of interest, exposures (five COVID-19-related stressors), a primary mental health outcome, and several exploratory outcomes. The primary goal of the study was to investigate the potential susceptibility of Latinx, Black, Asian, and multiracial/other health care workers to occupational exposure and the varying impact this susceptibility might have on outcomes frequently reported during this time. Our concern was that an overly conservative adjustment of the p value threshold could have increased the risk of type II errors, potentially overlooking crucial and understudied findings related to racial and ethnic disparities. Nonetheless, we acknowledge that this approach could have elevated the likelihood of type I errors and false positive findings. Thus, we urge researchers to recruit larger, more racially and ethnically diverse samples for enhanced statistical power.
Despite its limitations, this study contributes to the literature on racial disparities in the health care workforce and the psychological implications of frontline work. It is among the first to identify disparities among health care workers in the Bronx, an epicenter of ongoing disparities and public health crises. Our findings underscore the need to support health care workers in future crises and to address disparities within the field.
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Information & Authors
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Received: 24 February 2022
Revision received: 8 December 2022
Revision received: 22 April 2023
Revision received: 29 June 2023
Accepted: 21 July 2023
Published online: 9 November 2023
Published in print: December 01, 2023
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Competing Interests
The authors report no financial relationships with commercial interests.
Funding Information
Dr. Breslow was supported in part by NIMH grant K23MH128582. Dr. Cook was supported in part by NIMH grant P50MH126283. Dr. Gabbay was supported in part by NIH grants R01MH120601, R21MH121920, R21MH126501, R01MH126821, R01DA054885, and RM1DA055437.The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of NIH.
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