Introduction
The United States is witnessing unprecedented growth in its multiracial population, with 33.8 million people (10.2% of the population) identifying with two or more race/ethnicity in the 2020 Census. This demographic shift has important implications for understanding racial health inequities, as emerging research suggests complex patterns that challenge traditional frameworks of racial health disparities. Recent studies indicate that multiracial individuals often report poorer mental and physical health outcomes compared to monoracial groups, even when they possess a higher socioeconomic status (Subica et al., 2017; Bratter and Eschbach, 2005; Choi et al., 2006; Bennett, 2011; Bratter and Damaske, 2013). This pattern represents a paradox that complicates our understanding of how socioeconomic status shapes racial health disparities.
While research has documented these disparities, the social mechanisms producing worse health outcomes among multiracial populations remain poorly understood. Multiracial individuals face unique social experiences that may contribute to health risks, including complex identity development processes (Jackson and Erving, 2020; Pauker et al., 2018; Sanchez et al., 2020), experiences of monoracism and discrimination (Johnston and Nadal, 2010), and higher rates of adverse childhood experiences (Giano et al., 2020; Merrick et al., 2018). Additionally, interracial families often experience greater family instability and reduced social support compared to monoracial families (Bratter and Whitehead, 2018).
This study systematically investigates the social and behavioral mechanisms underlying health disparities between multiracial and monoracial populations using data from the Behavioral Risk Factor Surveillance System (BRFSS) data (N = 4,363,547) (Mokdad, 2009). We examine both mental and physical health through self-reported measures of poor mental and physical health days. Our initial task is to quantify the health gap between multiracial and monoracial populations across three racial minority groups, namely, Black, American Indian or Alaska Native, and Asian, leveraging detailed race category information from BRFSS data. Subsequently, we examine four potential explanatory pathways: 1) The socioeconomic gradient hypothesis, which suggests that socioeconomic resources shape health outcomes, 2) The early life adversity hypothesis, focusing on childhood experiences and family dynamics, 3) The race-related experiences hypothesis, examining the health impacts of discrimination and stigma, and 4) The health behavior hypothesis, investigating behavioral patterns and coping mechanisms.
Our approach makes several important contributions to understanding racial health disparities. First, we account for diversity within the multiracial population, examining whether health patterns differ across Black multiracial, American Indian or Alaska Native multiracial, Asian multiracial, and other multiracial groups (Alba et al., 2018; Bratter and Whitehead, 2018; Strmic-Pawl, 2016). Previous research suggests considerable heterogeneity in multiracial experiences based on racial composition, with some groups facing greater social and economic barriers than others (Parker et al., 2015; Choi and Goldberg, 2021).
Second, we examine both mental and physical health outcomes. While earlier work has focused primarily on mental health and psychological distress (Bratter and Eschbach, 2005), recent studies indicate that multiracial individuals may face elevated risks for various physical health conditions, including chronic diseases and risky health behaviors (Subica et al., 2017; Choi et al., 2006). Lastly, we leverage a uniquely comprehensive dataset to investigate multiple pathways simultaneously. The BRFSS data allows us to examine early life experiences, including adverse childhood experiences (ACEs), which have been shown to disproportionately affect multiracial populations (Gilbert et al., 2015; Maguire-Jack et al., 2020). These data also capture race-related experiences and health behaviors that may serve as coping mechanisms (Chavez and Sanchez, 2010).
Background
The complex role of socioeconomic status in racial health disparities
Traditional frameworks for understanding racial health disparities have emphasized socioeconomic status (SES) as the primary mechanism linking race/ethnicity to health outcomes (Williams and Collins, 1995; Phelan and Link, 2015; Do et al., 2012). The SES-health gradient describes how higher socioeconomic positions provide individuals with flexible resources—including financial means, knowledge, prestige, power, and beneficial social connections—that enhance their ability to maintain and improve health (Phelan et al., 2010; Boen, 2016). However, recent research has identified important “health paradoxes” that challenge this framework.
The most well-documented of these is the Hispanic health paradox, whereby Hispanic Americans often report better health outcomes than non-Hispanic whites despite lower average SES (Boen and Hummer, 2019; Lariscy et al., 2016). This paradox has led researchers to examine how cultural practices, social ties, and health behaviors may protect health independent of socioeconomic resources (Fenelon, 2013). Our study builds on this work by investigating another apparent paradox: the finding that many multiracial groups report worse health outcomes despite higher average SES compared to some monoracial minority groups (Bratter and Gorman, 2011; Subica et al., 2017; Parker et al., 2015).
Early life adversity and health disparities
Recent research has highlighted the critical role of early life experiences in shaping adult health outcomes, with particular attention to adverse childhood experiences (ACEs) - including exposure to abuse, neglect, and household dysfunction. Studies consistently show stark disparities in ACE exposure across racial groups, with multiracial individuals facing a disproportionate burden (Gilbert et al., 2015; Giano et al., 2020; Merrick et al., 2018). Analysis of national data from 23 states shows that multiracial adults experience significantly higher exposure to adverse childhood experiences, with an average ACE score of 2.52 - approximately twice that of non-Hispanic White adults (1.52) and substantially higher than non-Hispanic Black adults (1.69) (Merrick et al. 2018). These elevated rates of early life adversity among multiracial individuals persist across different types of ACEs and various study populations (Gilbert et al., 2015; Giano et al., 2020), suggesting systematic differences in childhood environments and family dynamics that may have lasting health implications.
The elevated exposure to early life adversity among multiracial individuals may stem from family dynamics unique to interracial households. Interracial marriages face higher dissolution rates compared to monoracial marriages (Zhang and Van Hook, 2009), leading to greater family instability. Recent work shows that mothers in interracial relationships often report feeling ‘cut off’ from traditional support networks, with reduced access to instrumental and emotional support from extended family (Bratter and Whitehead, 2018). These patterns of family instability and reduced social support may reflect broader societal animosity toward interracial relationships and multiracial identity.
Early life adversity can impact adult health through multiple pathways. Exposure to childhood trauma and family instability has been linked to impaired stress response systems, increased inflammation, and compromised immune function (Ferraro et al., 2016; Miller et al., 2020). Beyond biological mechanisms, early adversity can affect health by disrupting the development of social relationships and coping resources. Recent studies show that individuals who experience frequent childhood abuse report lower perceived social support, greater relationship strain, and reduced social integration in adulthood (Ferraro et al., 2016; Woods-Jaeger et al., 2018).
Race-related experiences and identity-based stressors
Multiracial individuals face unique forms of stigma and discrimination that may contribute to health disparities. Recent theoretical work has developed the concept of ‘monoracism’—prejudice and discrimination specifically targeting multiracial people and multiracial families (Johnston and Nadal, 2010; Franco and O’Brien, 2018). Monoracist experiences can include exclusion from racial/ethnic communities, questioning of racial authenticity, and exoticification (Tran et al., 2016).
The impact of race-related stressors on multiracial health may be particularly complex due to the contextual nature of racial categorization. Research shows that how multiracial individuals are perceived and treated varies significantly based on physical appearance, social context, and the race of the perceiver (Pauker et al., 2018; Sanchez et al., 2020). This “racial malleability” can create additional stress as multiracial people navigate shifting racial ascriptions and expectations (Sims et al., 2020; Chen, 2019).
Discrimination and stigma affect health through both direct and indirect pathways. Recent studies document how racial discrimination triggers physiological stress responses, including elevated blood pressure, increased inflammation, and dysregulated cortisol patterns (Goosby et al., 2018; Williams, 2018). Identity-based stressors can also affect health indirectly by eroding self-worth, reducing access to social support, and promoting maladaptive coping behaviors (Thames et al., 2019; Pauker et al., 2018).
Health behaviors and coping mechanisms
Research consistently shows higher rates of health risk behaviors among multiracial populations, particularly in adolescence and young adulthood. Recent studies document elevated rates of substance use, including tobacco, alcohol, and cannabis, among multiracial youth compared to monoracial peers (Chavez and Sanchez, 2010; Goings et al., 2018). Multiracial adults show higher rates of obesity and other chronic health conditions compared to monoracial groups, even after accounting for socioeconomic differences (Subica et al., 2017).
These behavioral patterns may reflect attempts to cope with other sources of stress, including early life adversity and race-related experiences. Studies show that individuals exposed to childhood trauma and discrimination often engage in substance use and other risk behaviors as emotion regulation strategies (Ferraro et al. 2016; Jackson and Erving, 2020). Recent work has also highlighted how racial stigma can create barriers to health-promoting behaviors by affecting healthcare experiences and access to health-supportive environments (Lewis et al., 2015; Ray, 2019). The health impact of these behavioral patterns is substantial. Recent estimates suggest that differences in health behaviors account for significant portions of racial/ethnic disparities in mortality and morbidity (Rogers et al., 2017). However, focusing solely on individual health behaviors may obscure how structural factors shape behavioral options and constraints (Williams, Lawrence, and Davis, 2019).
Intersecting pathways and research questions
The theoretical frameworks outlined above suggest multiple, potentially interacting pathways linking multiracial status to health outcomes. While each pathway offers important insights, examining them in isolation may miss critical intersections. For example, early life adversity may affect health both directly and indirectly by shaping health behaviors and responses to race-related stressors (Gee et al., 2019). Similarly, socioeconomic resources may buffer or exacerbate the health impacts of discrimination and early life experiences (Colen et al., 2018; Pearson, 2008).
Understanding these complex pathways is particularly important given recent demographic trends. As the multiracial population continues to grow, traditional frameworks for conceptualizing racial health disparities may need revision. The apparent paradox of worse health outcomes despite higher socioeconomic status among some multiracial groups challenges assumptions about how social advantage translates into health benefits (Williams, 2018; Boen, 2016).
This study addresses several key gaps in current knowledge. First, while research has documented health disparities affecting multiracial populations, few studies have systematically compared the relative contribution of different explanatory pathways. Second, most previous work has treated multiracial individuals as a monolithic group, potentially obscuring important variations across different multiracial combinations. Third, the role of early life experiences in shaping multiracial health outcomes remains understudied, despite growing evidence of elevated ACE exposure in this population.
Drawing on these theoretical frameworks and gaps in existing research, we investigate the following research questions: 1) How do health outcomes vary across different multiracial populations, and how do these patterns relate to socioeconomic status? 2) To what extent do early life adversity, race-related experiences, and health behaviors explain observed health disparities between multiracial and monoracial groups? 3) Do these explanatory pathways operate differently for different multiracial populations? By examining these questions using a large, nationally representative dataset with detailed measures of potential mediating pathways, this study aims to advance understanding of how social processes create and maintain health disparities in an increasingly multiracial society.
Results
Identifying health disparities between multiracial and monoracial groups
Using detailed race and ethnicity questions from BRFSS, we classify respondents into mutually exclusive categories, first identifying Hispanic individuals (of any race), then categorizing non-Hispanic respondents as monoracial White, monoracial Black, monoracial Asian, monoracial Native Hawaiian/Pacific Islander, monoracial American Indian or Alaska Native (AIAN), Other monoracial race, Black multiracial (BM), American Indian or Alaska Native multiracial (AIANM), Asian multiracial (AM), and Other multiracial.
Table 1 presents weighted descriptive statistics for four pathway domains across these racial categories. Consistent with previous research (Bratter and Gorman, 2011; Subica et al., 2017; Parker et al., 2015), multiracial individuals generally report higher socioeconomic status than their monoracial counterparts. For example, Black multiracial individuals report higher rates of college education (59.5%) compared to monoracial Black individuals (50.7%). Similarly, employment rates are slightly higher among multiracial groups compared to their monoracial counterparts, though Asian multiracial individuals show lower educational attainment (68.4%) than monoracial Asian individuals (81.4%).
Table 1. Racial/ethnic Differences in Socioeconomic Status, Early Life Adversity, Race-Related Experiences, and Health Behaviors.
White | Black | Asian | Hawaiian | AIAN | Other | Hispanic | Black Multiracials | AIAN Multiracials | Asian Multiracials | Other Multiracials | |
---|---|---|---|---|---|---|---|---|---|---|---|
Number of Records (N) | 3467653 | 344857 | 73442 | 8925 | 63725 | 27634 | 254253 | 15015 | 40514 | 15483 | 11402 |
Proportion of Sample (%) | 80.2% | 8.0% | 1.7% | 0.2% | 1.5% | 0.6% | 5.9% | 0.4% | 0.9% | 0.4% | 0.3% |
Demographics | |||||||||||
Age (Weighted Average) | 47.923 | 43.637 | 40.968 | 37.975 | 44.012 | 44.042 | 39.251 | 39.050 | 46.441 | 36.108 | 39.714 |
Male (Weighted %) | 48.1% | 44.9% | 53.8% | 55.4% | 53.5% | 56.8% | 50.4% | 47.0% | 53.1% | 51.3% | 50.2% |
Health | |||||||||||
Psychological Distress (Weighted Average) | 3.371 | 3.993 | 2.187 | 3.441 | 5.319 | 4.160 | 3.667 | 5.358 | 6.041 | 3.528 | 4.378 |
Physical Distress (Weighted Average) | 3.585 | 3.942 | 1.972 | 3.130 | 5.781 | 3.947 | 3.650 | 4.388 | 6.446 | 2.756 | 4.099 |
Socioeconomic Status | |||||||||||
Income (Weighted Average) | 49585 | 36309 | 52704 | 45439 | 36587 | 42851 | 32669 | 39909 | 39505 | 50795 | 41149 |
Education (Weighted % within Each Racial Group) | |||||||||||
Less Than High School | 7.8% | 15.0% | 4.3% | 8.5% | 19.1% | 11.9% | 35.5% | 12.0% | 16.5% | 5.7% | 17.8% |
High School/GED | 29.5% | 34.3% | 14.3% | 27.3% | 34.6% | 25.8% | 28.9% | 28.5% | 31.3% | 25.9% | 30.7% |
Any College | 62.7% | 50.7% | 81.4% | 64.2% | 46.3% | 62.3% | 35.6% | 59.5% | 52.2% | 68.4% | 51.6% |
Employment Status (Weighted % within Each Racial Group) | |||||||||||
Employed | 67.3% | 60.0% | 71.8% | 70.1% | 60.3% | 65.2% | 73.6% | 62.2% | 58.6% | 69.2% | 70.1% |
Unemployed | 28.5% | 34.0% | 17.1% | 21.1% | 35.0% | 27.9% | 21.0% | 28.3% | 37.2% | 17.4% | 22.9% |
Student | 4.1% | 6.0% | 11.1% | 8.8% | 4.7% | 6.9% | 5.4% | 9.6% | 4.2% | 13.4% | 7.0% |
Early Life Adversity | |||||||||||
Parents Marital Status (Weighted % within Each Racial Group) | |||||||||||
Parents Married | 75.6% | 53.6% | 89.1% | 73.9% | 61.0% | 68.0% | 70.5% | 43.6% | 54.8% | 65.1% | 54.3% |
Parents Divorced | 23.8% | 41.1% | 10.3% | 22.0% | 37.6% | 30.4% | 28.2% | 52.5% | 42.1% | 34.0% | 43.3% |
Parents Never Married | 0.6% | 5.3% | 0.6% | 4.1% | 1.4% | 1.6% | 1.3% | 3.9% | 3.1% | 0.9% | 2.4% |
Parents Physically Abusive (Weighted %) | 11.5% | 9.8% | 8.0% | 17.1% | 19.8% | 16.6% | 19.5% | 19.9% | 30.8% | 16.9% | 22.0% |
Parents Mentally Abusive (Weighted %) | 27.7% | 23.3% | 17.6% | 38.9% | 40.4% | 30.0% | 25.7% | 36.5% | 46.4% | 35.6% | 36.8% |
Drug Abuse in Household (Weighted %) | 9.9% | 15.0% | 3.2% | 26.9% | 16.6% | 12.2% | 11.7% | 25.6% | 19.8% | 16.9% | 16.0% |
Alcohol Abuse in Household (Weighted %) | 24.5% | 23.6% | 7.9% | 26.1% | 34.9% | 24.1% | 27.0% | 34.4% | 40.8% | 26.6% | 25.3% |
Depression in Household (Weighted %) | 17.8% | 11.6% | 8.8% | 19.3% | 19.4% | 19.9% | 13.3% | 25.7% | 33.6% | 16.5% | 24.3% |
Formerly Incarceration in Household (Weighted %) | 6.2% | 16.1% | 2.4% | 14.8% | 16.8% | 8.0% | 10.0% | 24.7% | 16.8% | 10.2% | 15.0% |
Race-Related Experiences | |||||||||||
Think About Race (Weighted % within Each Racial Group) | |||||||||||
Never | 64.3% | 40.6% | 32.2% | 32.2% | 30.3% | 49.9% | 31.2% | 34.3% | 45.1% | 16.6% | 39.6% |
Yearly/Monthly | 24.9% | 18.0% | 31.6% | 43.8% | 23.3% | 20.8% | 22.1% | 27.7% | 30.9% | 65.5% | 47.1% |
Weekly | 6.1% | 8.8% | 14.1% | 5.7% | 12.5% | 10.8% | 10.5% | 14.5% | 11.9% | 10.0% | 5.9% |
Daily | 3.2% | 12.4% | 10.4% | 4.9% | 14.0% | 6.6% | 9.4% | 12.7% | 8.1% | 1.3% | 4.2% |
Hourly/Constantly | 1.6% | 20.2% | 11.7% | 13.4% | 20.0% | 12.0% | 26.8% | 10.8% | 4.0% | 6.6% | 3.1% |
Perception of Racial Healthcare Equity (Weighted % within Each Racial Group) | |||||||||||
Worse Than Other Races | 1.9% | 8.7% | 2.0% | 1.1% | 7.2% | 4.2% | 5.0% | 8.6% | 5.2% | 0.0% | 2.0% |
Same as Other Races | 82.1% | 78.5% | 84.8% | 85.6% | 76.4% | 73.1% | 78.8% | 73.3% | 79.4% | 91.8% | 87.3% |
Better than Other Race | 12.8% | 7.5% | 10.9% | 8.7% | 11.5% | 13.1% | 10.7% | 11.5% | 10.6% | 6.3% | 6.8% |
Worse than some but not all races | 0.8% | 3.4% | 0.9% | 2.3% | 3.1% | 4.4% | 1.2% | 3.7% | 2.2% | 0.0% | 3.0% |
Did not seek healthcare in past year | 1.9% | 1.6% | 1.4% | 1.8% | 1.6% | 4.2% | 4.1% | 3.0% | 2.5% | 2.0% | 0.9% |
Emotionally Unwell because of Race (Weighted %) | 5.0% | 23.6% | 19.8% | 25.1% | 19.6% | 15.0% | 17.1% | 30.4% | 12.1% | 4.5% | 12.9% |
Physically Unwell because of Race (Weighted %) | 3.8% | 13.4% | 7.8% | 7.6% | 11.3% | 8.8% | 10.6% | 12.5% | 8.1% | 4.2% | 6.0% |
Health Behaviors | |||||||||||
Smoking Status (Weighted % within Each Racial Group) | |||||||||||
Heavy Smoker | 15.8% | 14.5% | 6.4% | 14.9% | 24.9% | 16.4% | 9.1% | 17.4% | 25.6% | 13.8% | 14.8% |
Current Smoker | 4.7% | 7.0% | 3.6% | 7.6% | 9.8% | 6.5% | 7.5% | 8.6% | 7.5% | 5.9% | 7.7% |
Former Smoker | 27.6% | 16.3% | 13.9% | 17.2% | 23.9% | 21.4% | 17.6% | 17.3% | 27.9% | 18.5% | 21.0% |
Never Smoker | 52.0% | 62.2% | 76.2% | 60.2% | 41.5% | 55.7% | 65.8% | 56.8% | 39.0% | 61.9% | 56.5% |
Heavy Drinkers (Weighted %) | 5.9% | 3.5% | 2.0% | 5.7% | 6.5% | 5.2% | 4.4% | 5.7% | 6.6% | 6.4% | 6.0% |
Has Healthcare Coverage (Weighted %) | 88.9% | 79.0% | 87.5% | 82.4% | 76.9% | 79.4% | 63.4% | 78.6% | 79.7% | 87.2% | 76.1% |
Participated in Exercise in last month (Weighted %) | 78.0% | 68.9% | 77.4% | 78.8% | 71.2% | 74.1% | 67.0% | 76.9% | 74.0% | 82.1% | 75.9% |
Hawaiian Native Hawaiian or Other Pacific Islander, AIAN American Indian or Alaska Native.
Early life adversity measures show consistently higher exposure among multiracial groups. For example, both Black multiracial and Asian multiracial individuals report higher rates of parental divorce, verbal abuse, and physical abuse compared to their monoracial counterparts. For race-related experiences, multiracial individuals generally report less frequent thoughts about race and more positive perceptions of healthcare equity than monoracial individuals. Regarding health behaviors, multiracial individuals show higher rates of smoking and heavy drinking, though differences in healthcare-seeking behaviors are minimal.
Figure 1 presents differences in rates of poor mental and physical health days per month between multiracial and monoracial populations. Panels (a) and (b) display weighted average days of poor mental health and poor physical health across racial groups. The descriptive statistics show that American Indian or Alaska Native multiracial individuals report the highest rates, with 6.04 poor mental health days and 6.45 poor physical health days per month. Black multiracial individuals report higher rates compared to monoracial Black individuals, with differences of 1.4 more poor mental health days (5.4 vs 4.0 days) and 0.5 more poor physical health days (4.4 vs 3.9 days) per month. While Asian multiracial individuals show better overall health than monoracial Black and AIAN respondents, they report worse health compared to monoracial Asian individuals, with rate differences of 1.34 more poor mental health days and 0.79 more poor physical health days per month.
[See PDF for image]
Fig. 1
Health disparities between multiracial and monoracial minorities.
a, b Average days poor mental health and days of poor physical health across racial/ethnic groups, measured by the number of days mentally or physically not feeling good, during the month before the survey. The descriptive statistics are survey weighted thus include no confidential intervals. c, d Age-specific health discrepancies between multiracial and monoracial groups for Black, American Indian or Alaska Native (AIAN), and Asian populations, displayed with a 95% confidence interval assuming unequal variances. Visualizations illustrate that multiracial people suffer from worse health across the life course compared with monoracial groups, with particular health risks during the early- and mid- life course. The estimates in the late life course may be understated due to the non-adjustment of mortality disparities.
Panels (c) and (d) examine these health disparities across age groups, displaying the average multiracial-monoracial gap in poor mental and physical health days, respectively. Each column corresponds to a specific multiracial-monoracial comparison. The findings reveal that these health disparities persist across age groups and are not exclusive to any particular racial categories. However, the trajectory of health disparities for Asian multiracial individuals shows a unique pattern - the rate difference grows larger in early life but decreases at older ages. These observed age patterns might reflect period or cohort effects rather than life course trajectories, particularly given that federal recognition of multiple-race reporting only began in 2000. Additionally, these patterns may understate true health disparities, particularly among older adults, as our survey data exclude individuals who have died prematurely due to poor health, and interracial marriages were less common in earlier cohorts.
Examining four hypothetical pathways linking multiracial status to health outcomes
To systematically examine how different social mechanisms contribute to rate differences in poor health days between multiracial and monoracial groups, we employ negative binomial regression models and test changes in rate differences before and after adjustment for pathway variables. Our base model controls for demographic factors (age, gender) and includes survey year and state fixed effects. We then sequentially incorporate variables representing each hypothesized pathway. All analyses incorporate BRFSS survey weights to ensure population representativeness. The health disparities between multiracial and monoracial groups, both before and after adjusting for these pathways, are presented in Table 2 and illustrated graphically in Fig. 2.
Table 2. Multiracial-Monoracial Health Disparities, Pre- and Post-Adjusted by Racial/Ethnic Groups and Each Hypothetical Pathway.
Pathway | DV: Days of Poor Mental Health | DV: Days of Poor Physical Health | |||||
---|---|---|---|---|---|---|---|
Pre-Adjustment | Post-Adjustment | Sample Size (N) | Pre-Adjustment | Post-Adjustment | Sample Size (N) | ||
Black | SES | 0.264*** | 0.293*** | 3538549 | 0.242*** | 0.283*** | 3528276 |
AIAN | SES | 0.115*** | 0.172*** | 3538549 | 0.068* | 0.108*** | 3528276 |
Asian | SES | 0.423*** | 0.361*** | 3538549 | 0.473*** | 0.368*** | 3528276 |
Black | Early Life Adversity | 0.375** | 0.2 | 108581 | 0.412* | 0.152 | 108143 |
AIAN | Early Life Adversity | 0.500*** | 0.265 | 108581 | 0.157 | −0.077 | 108143 |
Asian | Early Life Adversity | 0.373** | 0.195 | 108581 | 0.162 | 0.063 | 108143 |
Black | Race-Related Experiences | 0.142 | 0.093 | 126118 | 0.067 | 0.056 | 125783 |
AIAN | Race-Related Experiences | 0.265 | 0.357* | 126118 | 0.197 | 0.278 | 125783 |
Asian | Race-Related Experiences | 0.212 | 0.503 | 126118 | 1.241 | 1.438 | 125783 |
Black | Health Behavior | 0.243*** | 0.217*** | 3941494 | 0.209*** | 0.226*** | 3926908 |
AIAN | Health Behavior | 0.116*** | 0.109*** | 3941494 | 0.076** | 0.097*** | 3926908 |
Asian | Health Behavior | 0.460*** | 0.386*** | 3941494 | 0.469*** | 0.404*** | 3926908 |
AIAN American Indian or Alaska Native.
Significance levels “*” p < 0.05, “**” p < 0.01, “***” p < 0.001. All tests are two-tailed.
[See PDF for image]
Fig. 2
Evaluation of proposed pathways contributing to multiracial-monoracial health disparity.
The left panel shows the health gap between each multiracial group and its monoracial reference group, pre- and post-adjustment, with coefficients derived from negative binomial regressions for associations between race and mental and physical health. The model is survey weighted with full control. The right panel depicts the pathway mediation effect for each multiracial-monoracial pair, employing the Karlson–Holm–Breen (KHB) method. Effects are indicated by colored circles: blue for positive mediation effect and red for negative suppression effect. The size of each circle represents the magnitude of the effect. The displayed standard errors are clustered at the level of the sampling unit.
The left panel of Fig. 2 presents the rate differences in poor health days between racial groups before and after adjusting for each pathway. For socioeconomic factors, the models reveal an interesting pattern. Before adjusting for education, income, and employment status, Black multiracial individuals reported 0.264 more poor mental health days and 0.242 more poor physical health days than monoracial Black individuals (p < 0.001). After adjustment, these rate differences actually increased to 0.293 more poor mental health days and 0.283 more poor physical health days (p < 0.001). Similarly, AIAN multiracial individuals initially reported 0.115 more poor mental health days and 0.068 more poor physical health days than monoracial AIAN individuals (p < 0.001 and p = 0.017, respectively). These rate differences also increased after socioeconomic adjustment to 0.172 more poor mental health days and 0.108 more poor physical health days (p < 0.001 and p = 0.001). These increases in rate differences after socioeconomic adjustment suggest that higher socioeconomic status among multiracial individuals may actually be suppressing even larger differences in poor health days. The complete statistical results of these socioeconomic adjustments for all groups are presented in Table 2.
In contrast, socioeconomic factors partially explain the rate differences between Asian multiracial and monoracial Asian individuals. Before adjustment, Asian multiracial individuals reported 0.423 more poor mental health days and 0.473 more poor physical health days (p < 0.001). After accounting for socioeconomic factors, these rate differences decreased to 0.361 more poor mental health days and 0.368 more poor physical health days (p < 0.001).
Among the four pathways examined, early life adversity most consistently explains the rate differences in poor health days between multiracial and monoracial groups. For Black multiracial individuals, before adjusting for early life conditions, they reported 0.375 more poor mental health days and 0.412 more poor physical health days than monoracial Black individuals (p = 0.008 and p = 0.013, respectively). After adjustment, these rate differences substantially decreased to 0.200 more poor mental health days (p = 0.177) and 0.152 more poor physical health days (p = 0.309), becoming statistically non-significant.
We observed a particularly notable pattern in AIAN comparisons. Before adjusting for early life conditions, AIAN multiracial individuals reported 0.500 more poor mental health days than monoracial AIAN individuals (p < 0.001) and 0.157 more poor physical health days (p = 0.231). After adjustment, not only did these rate differences decrease, but the direction of the relationship shifted: the poor mental health days difference decreased to 0.265 more days (p = 0.073), while the poor physical health days difference reversed to 0.077 fewer days (p = 0.559), though these adjusted differences were not statistically significant. For Asian multiracial individuals, adjustment for early life conditions reduced the rate differences by 0.178 days for poor mental health and 0.099 days for poor physical health.
The other two hypothesized pathways—health behaviors and race-related experiences - show limited explanatory power for rate differences in poor health days between multiracial and monoracial groups, with one exception. For Asian multiracial individuals, health behaviors partially explain their higher rates of poor health days compared to monoracial Asian individuals. Before adjusting for health behaviors, Asian multiracial individuals reported 0.460 more poor mental health days and 0.469 more poor physical health days (p < 0.001). After adjustment, these rate differences decreased to 0.386 more poor mental health days and 0.404 more poor physical health days (p < 0.001). Detailed results from these regression models are reported in Supplementary Tables 3–10.
To formally quantify these mediating effects, we employ the Karlson-Holm-Breen (KHB) mediation analysis. This approach decomposes rate differences in poor health days into direct effects (the portion remaining after accounting for mediators) and indirect effects (the portion explained by mediators) (Karlson et al., 2012). The results, plotted in the right panel of Fig. 2, reveal both significant positive mediation effects (blue circles) where pathways explain rate differences in poor health days, and negative suppression effects (red circles) where accounting for pathways actually increases observed rate differences.
The KHB analysis confirms our regression findings with greater precision. Socioeconomic factors significantly mediate rate differences only for Asian multiracial individuals (poor mental health days: coefficient = 0.095, p < 0.001; poor physical health days: coefficient = 0.109, p < 0.001), while actually suppressing rate differences for Black multiracial and AIAN multiracial groups. Early life adversity emerges as the most powerful mediating pathway across all groups, with positive and significant indirect effects for all racial categories (Black multiracial-poor mental health: coefficient = 0.588, p < 0.05; AIAN multiracial-poor mental health: coefficient = 0.89, p < 0.05; Asian multiracial-poor mental health: coefficient = 1.168, p < 0.05). Full results from the KHB analyses are presented in Supplementary Table 11, 12.
Decomposing pathway into variable-level mediation effects
The final phase of our analysis employs the KHB method to examine how specific variables within each pathway contribute to rate differences in poor health days between multiracial and monoracial groups (Fig. 3). Within the early life adversity pathway, several factors emerge as significant mediators: domestic violence between parents, household substance use, depression, and incarceration show the strongest mediating effects on rate differences in poor health days. Parental divorce significantly mediates rate differences in poor mental health days, but only for Asian multiracial individuals compared to monoracial Asian individuals (Fig. 3, Panel 2). Among health behaviors, risky practices such as smoking and heavy drinking mediate rate differences in poor health days for both Black multiracial-monoracial and Asian multiracial-monoracial comparisons.
[See PDF for image]
Fig. 3
Decomposing the pathway mediation effect into specific variables by the Karlson–Holm–Breen (KHB) method.
Colors for circles indicate direction of mediation effects and size of circles indicate effect size. The displayed standard errors are clustered at the level of the sampling unit.
For race-related experiences, our results reveal an unexpected pattern. Experiences of racial discrimination appear to suppress rather than mediate rate differences in poor health days for Black multiracial and Asian multiracial individuals (Fig. 3, Panel 3). This means that accounting for discrimination actually increases the observed differences in poor health days between these groups and their monoracial counterparts. Among health behaviors, regular exercise consistently shows suppression effects across all racial categories (Fig. 3, Panel 4). This indicates that multiracial individuals’ higher rates of physical activity partially mask even larger underlying differences in poor health days between multiracial and monoracial groups.
Discussion
Our study advances the understanding of health disparities in an increasingly multiracial society by systematically examining the mechanisms producing health differences between multiracial and monoracial populations. We find that Black multiracial, American Indian or Alaska Native multiracial, and Other multiracial populations paradoxically report worse health despite higher socioeconomic status compared to their monoracial counterparts. This pattern challenges traditional frameworks that emphasize socioeconomic status as the primary driver of racial health disparities (Do et al., 2012; Phelan and Link, 2015).
Several key findings emerge from our analysis. First, we document substantial heterogeneity in rates of poor health days across multiracial subgroups. While Black multiracial and American Indian or Alaska Native multiracial individuals show particularly pronounced higher rates of poor health days, Asian multiracial individuals demonstrate different patterns, supporting recent calls to examine specific multiracial combinations rather than treating multiracial status as monolithic (Alba et al., 2018; Bratter, 2018). Second, early life adversity emerges as the strongest mediator of rate differences in poor health days across groups, aligning with research on elevated ACE exposure among multiracial populations (Gilbert et al., 2015; Merrick et al., 2018) and the enduring health impacts of childhood experiences (Ferraro et al., 2016). Third, contrary to expectations from previous work on racial discrimination (Williams, 2018; Goosby et al., 2018), race-related experiences show suppression rather than mediation effects, particularly for Black multiracial individuals. This unexpected finding suggests the need to better understand how multiracial individuals navigate racial identity and discrimination (Sanchez et al., 2020). Fourth, health behaviors show complex patterns: while risky behaviors like smoking and drinking partially explain rate differences in poor health days, higher rates of exercise among multiracial individuals actually mask even larger underlying differences. Finally, the suppression effects we observe for socioeconomic status indicate that traditional resource-based explanations may mask important non-economic pathways shaping rates of poor health days among multiracial individuals, similar to patterns observed in the Hispanic health paradox (Boen and Hummer, 2019).
These findings have important implications for understanding racial differences in health outcomes. Our results demonstrate that health inequalities are not always synonymous with socioeconomic disadvantage. While socioeconomic pathways explain some racial/ethnic differences in health (Williams and Collins, 1995), our analysis reveals that socioeconomic factors can simultaneously produce and suppress these differences. The case of multiracial health outcomes particularly highlights how traditional resource-based frameworks may obscure other critical pathways to health inequality, similar to patterns observed among other racial/ethnic groups (Pearson, 2008; Williams, 2018).
Early life experiences emerge as crucial mechanisms shaping multiracial health outcomes. The strong mediating effects of childhood adversity suggest that animosity toward interracial relationships and multiracial identity can create lasting health impacts through family stress and reduced social support (Bratter and Whitehead, 2018; Nadal et al., 2013). This finding aligns with life course perspectives emphasizing how early life conditions create cascading effects on adult health through both biological and psychosocial pathways (Ferraro et al., 2016; Miller et al., 2011). The particular vulnerability of multiracial individuals to early life adversity may reflect the ways that interracial families face unique stressors and reduced access to traditional support systems (Bratter and King, 2008; Jackson and Erving, 2020).
Several limitations of our study warrant discussion. First, our data’s cross-sectional nature means that mediators and health outcomes are measured simultaneously, limiting causal inference. This is particularly relevant for socioeconomic status and health behaviors, where poor health could affect these factors rather than vice versa. Second, the 2001–2012 timeframe of our data may not fully capture contemporary patterns of multiracial identity and experience, especially given that it was not until 2000 that multiracial people could self-identify as such in federal data collection. Some respondents in our sample, particularly older adults, may have been born before Loving v. Virginia (the 1967 Supreme Court decision that legalized interracial marriage across the United States) and, as a result, may be less likely to identify as multiracial, even if they were born to parents of different races. This could introduce measurement errors in racial identification among older individuals. Third, our racial classification approach, while allowing examination of specific multiracial combinations, may not fully capture the complexity and fluidity of multiracial identity documented in recent research (Pauker et al., 2018; Sanchez et al., 2020).
Our findings suggest several important directions for future research and policy. First, scholars examining racial health disparities must move beyond simple socioeconomic explanations to consider how various social mechanisms may operate differently across racial groups (Brown et al., 2016; Pearson, 2008). The paradoxical finding that some multiracial populations experience worse health despite higher socioeconomic status highlights the need to examine multiple, potentially interacting pathways to health inequality. This aligns with recent theoretical work emphasizing how racism and discrimination operate through multiple life course pathways beyond socioeconomic status (Gee and Ford, 2011; Williams and Mohammed, 2013).
Second, our results underscore the importance of disaggregating multiracial populations in health research. The distinct patterns we observe across Black multiracial, American Indian or Alaska Native multiracial, and Asian multiracial groups support recent calls to examine heterogeneity within the multiracial population (Alba et al., 2018; Strmic-Pawl, 2016). The variation we observe in both health outcomes and mediating pathways suggests that treating multiracial status as a uniform category may mask important differences in how racial identity shapes health risks and resources (Parker et al., 2015).
Third, the strong mediating effects of early life conditions point to the need for interventions targeting family support and childhood well-being in multiracial households. Recent research documents how multiracial families face unique stressors and barriers to support (Bratter and Whitehead, 2018; Jackson and Erving, 2020), suggesting that programs reducing family stress and enhancing social support networks could have lasting health benefits. Additionally, healthcare providers should consider screening for adverse childhood experiences among multiracial patients, given their elevated risk for early life adversity (Gilbert et al., 2015; Merrick et al., 2018).
More broadly, our study demonstrates how changing demographic patterns may require revising traditional frameworks for understanding health inequality. As the multiracial population continues to grow, examining health disparities through a binary racial lens becomes increasingly inadequate (Bonilla-Silva, 2004; Williams, 2018). Future research should continue to investigate how complex racial identities and experiences shape health outcomes in an increasingly diverse society, while considering how structural racism and discrimination may operate differently for multiracial populations (Ray, 2019; Sanchez et al., 2020).
Data and methods
Data
We use nationally representative cross-sectional survey data from the 2001–2012 Behavioral Risk Factor Surveillance System (BRFSS), the largest telephone health survey in the United States (Mokdad, 2009). The BRFSS employs a multistage, stratified sampling approach to collect uniform, state-specific data on health, health behaviors, and disease risk factors related to diseases, injuries, and infections in the adult population (CDC, 2022). The dataset includes annual samples from all 50 states, and the District of Columbia, with sample sizes varying between 200,000 and 400,000 for each state. BRFSS questionnaires consist of three parts: 1) the core component, 2) optional modules, and 3) state-added questions. We use data from the core component to examine associations between racial/ethnic status, SES, and health. We are limited to the 2001–2012 BRFSS data, as the detailed race data necessary to construct measures of multiracial status are not publicly available for more recent years. We use supplemental data from the optional modules and state-added questions to explore the contribution of three non-socioeconomic pathways (early life social conditions, race-related experiences, and health behaviors) to racial/ethnic differences in health. We provide a detailed breakdown of missing data for each variable in Supplementary Table 1. While demographic characteristics, SES measurements, and health behaviors belong to the core component of the questionnaires, early life social conditions and race-related experiences are all optional modules only provided during select years and by select states (see Supplementary Table 2 for more information).
Health outcomes
Our analysis examines two self-reported health measures from BRFSS. The first measures days of poor mental health. Respondents were asked “thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?”, with response categories ranging from 0 to 30 days. The second measures days of poor physical health, based on the question “thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good?”, with responses again ranging from 0 to 30 days. These measures are widely used in population health research and avoid possible bias from relying on diagnosed conditions that could reflect differential healthcare access (Zahran et al., 2005; Pierannunzi et al., 2013). We report the distribution of these two dependent variables, as well as major demographic controls, in Table 1.
Race and ethnicity classification
We use detailed race and ethnicity questions in BRFSS to construct our primary independent variables. Respondents were first asked “Are you Hispanic or Latino?” and then “Which one or more of the following would you say is your race?” with options to select multiple categories: (1) White, (2) Black, (3) Asian, (4) Native Hawaiian or Other Pacific Islander, (5) American Indian or Alaska Native, and (6) Other Races. To create mutually exclusive categories, we first classify all respondents of Hispanic origin as Hispanic regardless of racial identification, which means we cannot identify multiracial patterns among Hispanic respondents. Among non-Hispanic respondents, we then classify participants into 10 categories: monoracial White, monoracial Black, monoracial Asian, monoracial Native Hawaiian/Pacific Islander, monoracial American Indian or Alaska Native, Other monoracial race, Black multiracial, American Indian or Alaska Native multiracial, Asian multiracial, and Other multiracial. The non-Hispanic specification is assumed for all racial categories in our analyses.
For multiracial respondents (those selecting multiple races), we create categories using a hierarchical approach that reflects historical patterns of racial classification. Following previous research on multiracial health disparities (Roth, 2005; James Davis, 2010; Bratter and Gorman, 2011), respondents identifying as Black and any other race(s) are classified as Black multiracial, reflecting the historical legacy of hypodescent rules in U.S. racial classification. Among remaining multiracial respondents, those identifying as American Indian or Alaska Native and any other race(s) are classified as American Indian or Alaska Native multiracial, though we acknowledge this may not fully capture Indigenous identity complexity (Quint et al., 2023). Next, remaining respondents selecting Asian and any additional race(s) are classified as Asian multiracial. All other multiracial combinations are classified as Other multiracial.
Four hypothetical pathways
Socioeconomic status
We include three interrelated measures of SES: income, education, and employment status, which are widely used in health disparities research (Williams, 2012). Income is reported in eight categories ranging from less than $10,000 to $75,000 or more. To facilitate interpretation, we convert these to a continuous measure using the midpoint value of each category (e.g., $12,500 for the $10,000–$14,999 category) and scale income in $1000 units for regression analyses. Educational attainment is coded into three categories: less than high school (reference), high school graduate or GED equivalent, and any college education. Finally, employment status classifies respondents as employed (reference), not employed, or student.
Early life social conditions
A subsample (2009–2012) of BRFSS respondents completed questions about early life experiences. These questions assess childhood exposure to family instability, abuse, and household challenges. We include a series of measures that serve as a proxy of disadvantaged early life social conditions. Family structure is measured by parental marital status (parents married [reference], parents divorced, parents never married). Child abuse is assessed through questions about physical abuse from parents (never [reference], once, more than once) and verbal abuse from parents (never [reference], once, more than once). Respondents also reported witnessing violence between parents or adults in the household (never [reference], once, more than once). Additional measures capture childhood household experiences, including living with people who: used illegal street drugs or misused prescription medications, had problems with alcohol use, experienced depression or mental illness, or had served time in a correctional facility (each coded as never lived with [reference] or lived with).
Race-related experiences
Another subsample (2004–2012) completed questions about race-related experiences and attitudes. These measures include: frequency of thinking about race (never [reference], yearly, monthly, weekly, daily, hourly, constantly), perceived treatment when seeking healthcare (same as other races [reference], worse than other races, better than other races, worse than some races but better than others, only encountered same race, did not seek healthcare in past 12 months), experiences of race-related emotional distress (no [reference], yes), and experiences of race-related physical distress (no [reference], yes).
Health behaviors
From the core survey component, we include measures of health-related behaviors: smoking status (never smoked [reference], former smoker, current occasional smoker, current regular smoker), alcohol consumption (heavy drinking defined as 2+ drinks daily for men, 1+ drinks daily for women), health insurance coverage (uninsured [reference], insured), and physical activity (any exercise in past month: no [reference], yes).
Control variables
All models include demographic characteristics that could confound the relationship between racial/ethnic status and health outcomes. We control for gender (male [reference], female) as health reporting patterns and healthcare utilization differ systematically by gender (Read and Gorman, 2010). Age (in years, range 18–80) is included to account for life course patterns in health status and because age distributions vary across racial groups. Survey year indicators and state fixed effects adjust for temporal trends in health outcomes and unobserved state-level factors that might affect both racial composition and health (e.g., healthcare policies, environmental conditions). In sensitivity analyses, including marital status produced substantively similar results.
Estimation and statistical procedures
We employ a two-part analytical strategy designed to both identify and explain racial/ethnic health disparities. First, we estimate the association between racial/ethnic status and health outcomes using negative binomial regression models. We choose negative binomial models over Poisson regression because our dependent variables (days of poor mental/physical health, range 0–30) show significant overdispersion, with variance exceeding the mean. For each health outcome, we follow a step-wise modeling strategy. Our analytical strategy employs a systematic model-building approach to examine how different pathways contribute to racial/ethnic health disparities. We begin with base models that adjust for demographic characteristics (age, gender) and include both state and year fixed effects to account for geographic and temporal variation. These base models establish the foundational racial/ethnic differences in health outcomes. We then sequentially examine key pathways by adding sets of variables related to socioeconomic status (income, education, employment), early life conditions (family structure, abuse exposure, residential environment), race-related experiences (discrimination, healthcare perceptions), and health behaviors (smoking, drinking, physical activity) to our models. This sequential approach allows us to observe how the coefficients associated with racial/ethnic status change as we account for each set of factors. The detailed outcomes of these models are reported in Supplementary Table 3–10.
For our comprehensive mediation analyses (presented in Figs. 2, 3 and Supplementary Tables 11, 12), we estimate models that simultaneously include all relevant pathway variables to quantify their mediating effects while accounting for potential interdependence between pathways. Throughout all analyses, we incorporate BRFSS survey weights to ensure nationally representative estimates. The inclusion of state and year fixed effects helps control for unobserved state-level characteristics and temporal trends that may influence health outcomes.
In these models, the coefficient estimates represent the difference in the expected log count of poor health days comparing each racial group to the reference category. For ease of interpretation, we also present the results as incidence rate ratios (IRR), which can be interpreted as the relative change in expected days of poor health associated with each racial category compared to the reference group.
Second, we conduct formal mediation analysis using the KHB routine to further quantify how different pathways contribute to racial/ethnic differences in health. The KHB method decomposes the total effect of racial/ethnic status on health outcomes into direct effects and indirect effects operating through each set of mediators (Kohler et al., 2011; Karlson et al., 2012). This approach is particularly appropriate for our analysis because it: (1) allows comparison of coefficients across nested nonlinear models, (2) handles multiple mediators simultaneously, and (3) provides formal tests of mediation effects.
Acknowledgements
We thank Jeffrey Lockhart and Yulin Yu for suggestions, and the members of Knowledge Lab at the University of Chicago for helpful discussions.
Author contributions
YC: Conceptualization, methodology, writing - original draft, writing - review and editing. LC: Conceptualization, methodology, data curation and analyses, writing - original draft, writing - review and editing. JT: Conceptualization, methodology, data curation and analyses, writing - original draft. HZ: Conceptualization, methodology, supervision, writing - review and editing. ZQ: Conceptualization, supervision, writing - review and editing. These authors jointly supervised this work (HZ and ZQ).
Data availability
The Behavioral Risk Factor Surveillance System (BRFSS) data is publicly available through the Centers for Disease Control and Prevention (CDC) data portal at https://www.cdc.gov/brfss/index.html. The specific years analyzed (2001–2012) can be accessed at https://www.cdc.gov/brfss/annual_data/annual_data.htm. We estimate all models using Stata 18.0, and apply the svy command to account for the BRFSS sampling protocol. Analysis code is available at https://github.com/lkcao/multiracial_paper.
Competing interests
The authors declare no competing interests.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study utilized secondary data from the Behavioral Risk Factor Surveillance System (BRFSS), which is publicly available and exempt from institutional review board approval as it contains de-identified data collected by the Centers for Disease Control and Prevention.
Informed consent
This article utilized secondary analysis of publicly available, de-identified data from the Behavioral Risk Factor Surveillance System (BRFSS). The original data collection by the Centers for Disease Control and Prevention included appropriate informed consent procedures for all participants. No additional informed consent was required for this secondary analysis.
Supplementary information
The online version contains supplementary material available at https://doi.org/10.1057/s41599-025-05370-1.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Abstract
The United States is witnessing rapid growth in multiracial populations, yet the social mechanisms producing health disparities between multiracial and monoracial groups remain poorly understood. Using the nationally representative Behavioral Risk Factor Surveillance System (2001–2012, N = 4,363,547), we examine mental and physical health outcomes through self-reported measures of poor mental and physical health days, systematically investigating four pathways potentially explaining multiracial-monoracial health disparities: 1) socioeconomic status, 2) early life adversity, 3) race-related experiences, and 4) health behaviors. Results based on negative binomial regressions and Karlson-Holm-Breen mediation tests reveal that Black multiracial, American Indian or Alaska Native multiracial, and Other multiracial individuals report worse mental and physical health despite higher socioeconomic status compared to their monoracial counterparts. Among Asian multiracial individuals, worse health outcomes compared to monoracial peers are partially attributed to socioeconomic factors and health behaviors. Across all multiracial groups, health disadvantages are largely explained by differences in early life social conditions, particularly exposure to family instability and adverse childhood experiences. Unexpectedly, race-related experiences show suppression rather than mediation effects, suggesting that accounting for discrimination actually increases observed health gaps. Our findings demonstrate how non-socioeconomic pathways, particularly early life adversity, play crucial roles in producing health disparities in an increasingly diverse society.
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1 University of Arizona, School of Sociology, Tucson, USA (GRID:grid.134563.6) (ISNI:0000 0001 2168 186X)
2 Purdue University, Department of Sociology, West Lafayette, USA (GRID:grid.169077.e) (ISNI:0000 0004 1937 2197); The University of Chicago, Knowledge Lab, Chicago, USA (GRID:grid.170205.1) (ISNI:0000 0004 1936 7822)
3 Oregon Department of Human Services and Oregon Health Authority, Office of Forecasting, Research and Analysis, Salem, USA (GRID:grid.238692.4) (ISNI:0000 0004 0456 1067)
4 Ohio State University, Department of Sociology, Columbus, USA (GRID:grid.261331.4) (ISNI:0000 0001 2285 7943); University of Toronto, Department of Sociology, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938)
5 Brown University, Department of Sociology, Providence, USA (GRID:grid.40263.33) (ISNI:0000 0004 1936 9094)