Accumulating disadvantage: Mechanisms linking discrimination histories and adolescent health.

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Published before final editing as: Dev Psychol. 2026 Apr 13:10.1037/dev0002177. doi: 10.1037/dev0002177 Search in PMC Search in PubMed View in NLM Catalog Add to search Accumulating Disadvantage: Mechanisms Linking Discrimination Histories and Adolescent Health Aprile D Benner Aprile D Benner 1 Department of Human Development and Family Sciences, University of Texas at Austin Find articles by Aprile D Benner 1 , Crystal Li Crystal Li 2 Department of Human Development and Family Sciences, University of Texas at Austin Find articles by Crystal Li 2 , Jacob E Cheadle Jacob E Cheadle 3 Department of Sociology, University of Texas at Austin Find articles by Jacob E Cheadle 3 , Bridget J Goosby Bridget J Goosby 4 Department of Sociology, University of Texas at Austin Find articles by Bridget J Goosby 4 Author information Copyright and License information 1 Department of Human Development and Family Sciences, University of Texas at Austin 2 Department of Human Development and Family Sciences, University of Texas at Austin 3 Department of Sociology, University of Texas at Austin 4 Department of Sociology, University of Texas at Austin ✉ Email: [email protected] PMC Copyright notice PMCID: PMC13078640  NIHMSID: NIHMS2147512  PMID: 41973802 The publisher's version of this article is available at Dev Psychol Abstract Research has documented the pervasive impact on adolescents’ mental health, but its impact on physical health at this time in the life course is less well-understood. The current study fills this gap by investigating how accumulating experiences of peer-perpetrated discrimination across adolescence impact general health and whether unhealthy behaviors (i.e., substance use, poor sleep, lack of exercise) serve as an explanatory mediating pathway. In this study, we used longitudinal data from a racially/ethnically and socioeconomically diverse community sample in the Southern United States (45% White, 30% Latina/o/x, 8% Asian, 5% Black, and 13% biracial, multiracial, or another race/ethnicity; 58% female; 34% economically disadvantaged), following young people as they moved through adolescence and into young adulthood. We observed that experiences of peer-perpetrated discrimination tied to multiple social identities accumulated across time. Complementary person-centered analyses suggested that the majority of youth in our sample experienced low levels of peer-perpetrated discrimination with attenuated declines across time, with smaller proportions reporting either high and stable or moderate but increasing discrimination across time. Greater cumulative discrimination, as well as trajectory profiles characterized by high or increasing peer-perpetrated discrimination, were linked to poorer self-rated health, and sleep served as a central mechanism by which accumulating discrimination exacted its toll. Findings highlight that persistent or growing exposure to peer-perpetrated discrimination may set the stage for longer-term health consequences, and, as such, adolescence represents a critical window for intervention. Keywords: discrimination, self-rated health, alcohol use, sleep, exercise, adolescents Discrimination, or the differential treatment of marginalized groups perpetrated by individuals and societal actors ( Williams et al., 2019 ), is pervasive in the United States. Targets of discrimination are known to include a range of social identities and physical characteristics (e.g., race/ethnicity, sexual minority status, SES, weight; Benner et al., 2018 ; Lick et al., 2013 ; Puhl, 2025 ), with scholars increasingly highlighting the intersectionality of individuals’ social identities and the discrimination they experience ( Cole, 2009 ; Crenshaw, 2013). Children’s understanding of discrimination develops early in the life course. In-group preferences and out-group biases emerge during early to middle childhood ( Pauker et al., 2017 ), and by age 10, young people can identify overt and covert discriminatory treatment ( Brown & Bigler, 2005 ; Verkuyten et al., 1997 ). Moreover, reports of experiences of discrimination increase across adolescence and into young adulthood ( Benner & Graham, 2011 ; Pinedo et al., 2021 ). Indeed, discrimination is so ubiquitous in the early life course among youth with marginalized social identities that developmental models frequently stress the need to consider discrimination as a key context for child and adolescent development ( García Coll et al., 1996 ; Sanders-Phillips, et al., 2009 ; Spencer et al., 1997 ). The consequences of discrimination for adolescent well-being are well established, with meta-analyses consistently identifying links between discrimination (both broadly defined and tied specifically to race/ethnicity) and adolescents’ mental health, risky behaviors, academics, and physical health ( Benner et al., 2018 ; Cave et al., 2020 ), although physical health outcomes have received less attention in the extant literature to date ( Cave et al., 2020 ; Richman et al., 2018 ). Moreover, meta-analyses considering the impacts of discrimination across the lifespan have identified childhood and adolescence as particularly sensitive developmental periods ( Schmitt et al., 2014 ; de Frietas et al., 2018 ), and this coincides with health models that identify the etiology of many health inequities as taking root in the early life course ( Goosby et al., 2018a ; Jones et al., 2019 ; Shonkoff et al., 2012 ). Reflecting growing recognition in the public policy sphere, Healthy People 2030 identifies social determinants of health broadly and discrimination more specifically as key priority areas for promoting population health (U.S. Department of Health and Human Services, 2025). This is consistent with theoretical and empirical scholarship on structural racism, which posits that broader societal policies and practices disparately favor some groups over others and engender proximal environments where discrimination can thrive ( Williams & Mohammed, 2013 ), as well as the weathering hypothesis, which suggests that the adversity that individuals experience tied to chronic mistreatment due to marginalized social identities creates wear and tear on the body, leading to early deterioration of physical health ( Geronimus et al., 2006 ). The current study seeks to fill gaps in the current knowledge base on discrimination and health by investigating how the accumulating experiences of peer-perpetrated discrimination across adolescence impact unhealthy behaviors (i.e., substance use, poor sleep, lack of exercise) and subsequent ratings of general health. Theoretical Models Linking Discrimination and Health Numerous theoretical models identify discrimination as a threat to health and well-being across the life course. For example, the integrative model of minority child development suggests that discrimination, racism, prejudice, and oppression are key developmental contexts for racially and ethnically minoritized children; these factors drive young people’s interactions within the proximal contexts of their daily lives and significantly impact their development and well-being ( García Coll et al., 1996 ). Health-focused models, likewise, have posited that racial discrimination experienced at both the micro (interpersonal) and macro (institutional) levels impacts behavioral patterns and psychological and physiological responses, thereby contributing to individual health outcomes and group-level health inequities ( Sanders-Phillips et al., 2009 ; Williams & Mohammed, 2013 ). Consistent with these models centered on racism, broader frameworks, such as the minority stress model, assert that individuals with minoritized identities are more likely to experience both distal and proximal stress processes due to discrimination, which can compromise health ( Frost & Meyer, 2023 ). It must also be noted that stressors such as discriminatory treatment are not merely solitary events and can occur repeatedly over the life course and in different social domains ( Colen et al., 2018 ; Goosby et al., 2018b ). In line with life course theory ( Elder, 1998 ), accumulating disadvantages can compromise subsequent development; therefore, it is crucial to attend to how experiences of discrimination accumulate to influence health, as this remains an underexplored area of inquiry. In considering exactly how discrimination influences long-term health, health-related behavioral processes have been put forth as a central mediating mechanism ( Pascoe & Richman, 2009 ; Williams & Mohammed, 2013 ). Discrimination is a substantial stressor that can both undermine individuals’ self-regulation skills and activate coping mechanisms, with some coping strategies being more protective than others ( Richman et al., 2018 ). Consequently, although individuals encountering discrimination may engage in proactive coping stress management strategies (i.e., recognizing one’s strengths, working hard; Montoro et al., 2021 ), they may also enact more unhealthy behaviors such as substance use, exercise avoidance, or poor sleep health. Adverse coping behaviors may be enacted not only for perceived stress reduction but also because the stress associated with discriminatory experiences can undermine individuals’ ability to effectively regulate their decision-making, increasing the likelihood of engaging in unhealthy behaviors; engaging in more unhealthy behaviors, in turn, is linked to poorer long-term health ( Richman et al., 2018 ). The current study seeks to test this behavioral process model. Discrimination, General Health, and Health Behaviors In considering the links between discrimination and general health, self-rated health is one of the most common measures utilized in both population-based and community studies. Although research on this association is less common with individuals in the early life course, there is some evidence that discrimination is related to self-rated health for young adults ( Cano et al., 2023 ), and discrimination in adolescence has been linked to poorer self-rated health in middle adulthood ( Yang et al., 2019 ). Likewise, research with older adults suggests a link between discrimination and poorer self-rated health ( Chen & Yang, 2014 ; Hudson et al., 2013 ; Nicholson & Ahmmad, 2021 ). A limited body of scholarship also indicates that discrimination, when tied to multiple social identities, is more problematic during young adulthood and adulthood for self-rated health than when tied to only a single marginalized identity ( Denise, 2012 ; Ridgeway & Denney, 2023 ). Self-rated health, in turn, is significantly associated with morbidity and mortality ( Benyamini, 2011 ; Jylhä, 2009 ), highlighting its validity and importance as a marker of individuals’ physical health. This study aims to address the existing gap in the understanding of how cumulative experiences of discrimination undermine health in the early life course by examining such experiences in terms of multiple stigmatized identities. In considering the biobehavioral processes linking discrimination to general health, theory suggests that health behaviors are an important mediating mechanism ( Pascoe & Richman, 2009 ). Substance use is the health behavior most discussed in the adolescent discrimination literature. This attention likely stems from the rise in risky behaviors during adolescence ( Willoughby et al., 2021 ) and the fact that substance use can serve as a potential, though problematic, coping strategy for those experiencing stressful life events ( Gilbert & Zemore, 2016 ). Higher levels of discrimination tied to race, ethnicity, SES, and sexual minority status are linked to greater alcohol use, earlier initiation of alcohol and marijuana use, and an increased likelihood of subsequent progression into problematic drinking in both youth ( Ahuja et al., 2022 ; Haeny et al., 2019 ; Su et al., 2020 ) and adult populations ( Borrell et al., 2013 ; Kiekens et al., 2022 ; McCabe et al., 2019), with some nuances across groups identified (e.g., gender, sexual identity differences). Limited evidence also suggests that cumulative experiences of racial discrimination across adolescence is associated with greater substance use and alcohol-related problems in young adulthood ( Anderson et al., 2020 ; Brodish et al., 2011 ). Higher levels of substance use in the early life course, in turn, has been linked to poorer self-rated health and health outcomes ( Pascale et al., 2022 ; Patrick et al., 2020 ). Given biological and social changes in sleep patterns that emerge during adolescence ( Carskadon et al., 1998 ; El-Sheikh, 2011 ), sleep has also received prominent attention in the discrimination literature. Much of this work uses the daily diary methodology, finding that on days when youth experienced discrimination, they reported poorer sleep outcomes that evening, although there is some variation in the aspects of sleep affected (e.g., sleep duration, sleep quality, daytime sleepiness; Chen et al., 2022 , 2024 ; Yip et al., 2020 ) and in the overall versus daily links between discrimination and sleep ( Goosby et al., 2018b ). Findings with survey research and a study utilizing actigraphy highlighted the associations between higher levels of discrimination and lower sleep efficiency and shorter sleep duration ( Fuller-Rowell et al., 2023 ; Lorenzo et al., 2025 ). Poor sleep, in turn, is related to poorer self-rated health in adolescence and young adulthood ( Kuo et al., 2015 ; Yeo et al., 2019 ). Exercise is another important health behavior, although physical activity becomes less frequent during adolescence ( van Sluijs et al., 2021 ). Research linking discrimination to exercise is limited and predominantly conducted with adult samples. This work has generally documented a positive link between discrimination and exercise, such that higher levels of discrimination are related to greater physical activity and greater odds of engaging in vigorous exercise activities for both Black and White adults ( Borrell et al., 2013 ; Corral & Landrine, 2012 ), suggesting exercise as a potentially positive coping strategy. Two studies have identified gendered effects for Black youth, in particular, such that greater discrimination was been linked to higher levels of healthy behaviors (capturing both nutrition and exercise) for female adolescents ( Gibbons et al., 2021 ), and greater cumulative experiences of discrimination in adolescence were associated with more physical activity among female young adults ( Brodish et al., 2011 ). Physical activity has also been tied to many health outcomes, including better self-rated health ( Howie et al., 2020 ). The current study attends to substance use, sleep, and exercise as potential behavioral processes by which discrimination influences self-rated health, and we extend the current literature by considering peer-perpetrated discrimination in adolescence tied to multiple social identities that can place youth at risk for stigmatization (i.e., race/ethnicity, SES, sexual minority status, weight). While theory and the extant empirical base suggests that health behaviors likely serve a mechanistic role in linking discrimination to health, no study to our knowledge has tested the specific relations under study here. Discrimination as an Accumulating Disadvantage for Health One of the central critiques leveled in recent reviews of the literature linking discrimination and health is the lack of attention to how discrimination as a social stressor accumulates across time to influence health (see Cuevas et al., 2020 ; Lawrence et al., 2022 ; Miller et al., 2021 ). Studies examining the effects of chronic lifetime discrimination on health often rely on retrospective indicators asking individuals to quantify their experiences of discrimination across their lifetimes (e.g., Becares et al., 2024 ; Moody et al., 2019 ). A smaller number of studies have utilized prospective survey data to assess cumulative discrimination experiences. In some of this work, scholars have summed or averaged experiences of discrimination reported multiple times across adolescence ( Brody et al., 2014 , 2018 ) or created a latent variable to capture cumulative discrimination ( Brodish et al., 2011 ). Other longitudinal research has examined trajectories of discrimination across adolescence, identifying increases from early to middle adolescence ( Hughes et al., 2016 ) and middle to late adolescence and into young adulthood ( Benner & Graham, 2011 ; Greene et al., 2006 ; Pinedo et al., 2021 ; see Hughes et al., 2016 for an exception). A small body of scholarship has examined heterogeneity in trajectories of discriminatory experiences and identified subpopulations with different temporal exposure patterns. This research has utilized measures assessing both racial and ethnic discrimination specifically ( Brody et al., 2014 ; Niwa et al., 2014 ; Tynes et al., 2020 ) and discrimination more generally ( Baysu et al., 2024 ; Unger et al., 2016 ; Wei et al., 2023 ); the work spans early adolescence through young adulthood and includes Black, Asian, and Latina/o/x American youth as well as European ethnically minoritized youth. Across these studies, two consistent trajectories emerged—low-and-increasing and high-but-decreasing discrimination. Other common trajectories included stable trajectories characterized by low ( Baysu et al., 2024 ; Unger et al., 2016 ), moderate ( Baysu et al., 2024 ; Niwa et al., 2014 ; Wei et al., 2023 ), or high levels of discrimination ( Brody et al., 2014 ; Unger et al., 2016 ). Taken as a whole, this work highlights the merits of considering both cumulative levels and unique trajectory profiles of discrimination. In considering the implications of discrimination trajectories for health, the focus to date has centered on young people’s mental health, with scholars documenting that greater increases in peer-perpetrated discrimination were linked to poorer psychological adjustment and more behavioral problems ( Greene et al., 2006 ; Hughes et al., 2016 ). Likewise, studies identifying multiple discrimination trajectories have found that those capturing greater discrimination accumulation over time were linked to poorer mental health outcomes (e.g., self-esteem, depressive symptoms; Niwa et al., 2014 ; Tynes et al., 2020 ; Wei et al., 2023 ). How cumulative experiences of discrimination influence health behaviors and general health more broadly is generally an open question yet to be explored, despite existing documentation of the more temporally proximal links between these constructs ( Pascoe & Richman, 2009 ). As an exception, Unger and colleagues (2016) observed that those youth reporting low-stable experiences of discrimination from adolescence through early adulthood also tended to report lower alcohol use and marijuana use than those experiencing other discrimination trajectory patterns. At the physiological level, Brody and colleagues (2014) linked high and stable discrimination trajectories to greater allostatic load. The current study expands on this burgeoning literature by examining how heterogeneity in accumulating experiences of peer-perpetrated discrimination, modeled in multiple ways, predicts a range of health behaviors and self-rated health. Current Study The current study used longitudinal data from a community-based sample in the Southern U.S. to examine the links between peer-perpetrated discrimination and health across adolescence. First, we examined both young people’s cumulative histories of peer-perpetrated discrimination and whether adolescents’ experiences of discrimination clustered into meaningfully distinct trajectories. Here, peer-perpetrated discrimination broadly captured mistreatment tied to multiple social identities (i.e., race and ethnicity, sexual minority status, SES, weight). Based on prior studies using growth mixture modeling to capture differential trajectories ( Baysu et al., 2024 ; Tynes et al., 2020 ; Unger et al., 2016 ; Wei et al., 2023 ), we expected to observe at least two distinct trajectories: low-and-increasing and high-but-decreasing. Second, we examined the links between adolescents’ histories of peer-perpetrated discrimination and their self-rated health using two distinct approaches. In the first, we investigated how cumulative histories of peer-perpetrated discrimination were associated with adolescents’ self-rated health, with cumulative histories capturing the sum of discrimination across adolescence. In the second, we linked the distinct discrimination trajectories reflecting different exposure accumulation patterns to health. Given prior research identifying cross-sectional and short-term relations between discrimination and health ( Cave et al., 2020 ), we hypothesized that greater cumulative experiences of peer-perpetrated discrimination and trajectories of consistently high or increasing exposure would be related to poorer self-rated health when compared to those with declining or consistently low exposure. Finally, following the behavioral processes model ( Pascoe & Richman, 2009 ), we examined the extent to which health behaviors such as sleep problems, substance use, and exercise mediated the association between peer-perpetrated discrimination and health. Here, we hypothesized that young people who experienced more chronic discrimination across adolescence would exhibit more unhealthy behaviors, and these unhealthy behaviors, in turn, would be linked to poorer subsequent self-rated health. Sensitivity analyses determined the extent to which the observed results were generalizable across gender, race/ethnicity, and SES. The current study fills a recognized void in the extant literature by attending both to accumulating experiences of peer-perpetrated discrimination across adolescence and how these histories of discrimination are linked to health-related behavioral processes and subsequent self-rated health ( Lawrence et al., 2022 ; Miller et al., 2021 ). Moreover, recent reviews have highlighted a need for greater inclusion of marginalized populations as well as work that highlights the intersectionality of marginalized identities ( Cuevas et al., 2020 ; Lawrence et al., 2022 ), and the current study addresses this limitation by utilizing a racially/ethnically and socioeconomically diverse sample and including peer-perpetrated discrimination tied to multiple marginalized identities. Method Participants and Procedures Participants were drawn from a larger longitudinal study consisting of two phases (Project PISCES and Project LIBRA). Phase 1 used a cohort design, with 8 th grade students recruited in either the 2016–17 school year (cohort 1) or the 2017–18 school year (cohort 2). During Phase 1, data were collected annually across three waves. Recruitment for the Phase 2 follow-up began in the summer of 2020 during the initial phase of the COVID-19 pandemic (58% retention of eligible participants; see Benner et al., 2024 for more detailed information about recruitment). During Phase 2, data were collected every six months across three waves from Fall 2020 through Fall 2021. All phases of the study were approved by the University of Texas Institutional Review Board. All study materials (i.e., consent and assent forms, surveys) were available in English and Spanish. All materials were translated into Spanish and then back-translated into English by two bilingual, bicultural research assistants, with discrepancies resolved via consensus. At wave 1, most participants completed paper-and-pencil surveys, and for all subsequent waves, participants generally completed surveys online, with a small number completing surveys by mail or over the phone with research assistants. Participants received $20 to $25 for completing each survey. In total, the larger longitudinal study included 1,032 adolescents. The analytic sample for the current study was limited to 425 adolescents who participated in both Phase 1 and Phase 2. The analytic sample was ethnically and racially diverse, including 45% White, 30% Latina/o/x, 8% Asian, 5% Black, and 13% biracial, multiracial, or another race/ethnicity, and it was 58% female, 41% male, and 1% non-binary. Approximately one-third of the sample was economically disadvantaged (34%), such that they qualified for the federal Free or Reduced-Price Lunch (FRPL) program. At the final wave, participants averaged 18.08 years of age ( SD = 0.64). Measures Descriptive statistics and bivariate correlations for central constructs appear in Table 1 . Descriptive statistics for cumulative discrimination, self-rated health, and health behaviors by race/ethnicity are presented in Table S1 . Table 1. Bivariate correlations and descriptive statistics among modeled variables 1 2 3 4 5 6 7 8 9 10 11 Discrimination 1. Peer discrimination (W1) -- 2. Peer discrimination (W2) .59 *** -- 3. Peer discrimination (W3) .57 *** .69 *** -- 4. Peer discrimination (W4) .54 *** .65 *** .70 *** -- 5. Peer discrimination (W5) .50 *** .57 *** .62 *** .73 *** -- 6. Peer discrimination sum (W1-W5) .77 *** .84 *** .82 *** .86 *** .81 *** -- Health Behaviors 7. Self-rated health (W6) −.19 *** −.16 ** −.20 *** −.18 ** −.24 *** −.24 *** -- 8. Sleep problems (W5) .16 ** .13 * .21 *** .20 *** .21 *** .22 *** −.26 *** -- 9. Alcohol use (W5) .08 .06 .09 .06 .20 *** .12 * .03 −.02 -- 10. Marijuana use (W5) .12 * .15 ** .11 * .08 .18 *** .16 ** .00 .06 .44 *** -- 11. Exercise (W5) −.07 −.03 −.03 −.09 −.14 * −.08 .43 *** −.12 * .09 −.02 -- M 2.57 2.57 2.02 2.09 2.29 10.64 2.12 1.79 0.72 0.40 2.35 SD 2.77 2.82 2.53 2.53 2.79 10.41 1.10 1.14 1.35 1.18 1.34 Min 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Max 12.00 12.00 12.00 12.00 12.00 60.00 4.00 4.00 9.00 6.00 4.00 N 412 415 421 351 354 425 311 357 352 350 357 Open in a new tab Note . Total possible N = 425. W = Wave. * p < .05 ** p < .01 *** p < .001. Peer-perpetrated discrimination. Peer-perpetrated discrimination was assessed at the first five waves using a subscale of the Adolescent Discrimination Distress Index ( Fisher et al., 2000 ). Students rated three items related to the frequency of discrimination by peers (e.g., “Were you called insulting names by other kids?”), with separate stems querying whether this occurred due to the participant’s race/ethnicity, sexual orientation, weight (i.e., their body size), and/or SES (i.e., family wealth). Participants rated the frequency of each item over their lifetime on a scale of 0 ( never ) to 4 ( a whole lot ). A peer-perpetrated discrimination sum composite was created by first dichotomizing responses for each individual item (0 = never experienced , 1 = experienced once/twice or more ) and then summing these across all items and attributions within each wave (total possible range is 0 to 60). We also created a cumulative peer-perpetrated discrimination measure by summing all items across all attributions and waves. Self-rated health. At Wave 6, adolescents reported their general health using one item (i.e., “In general, how would you rate your health?” Harris et al., 2018). Responses ranged from 0 ( poor ) to 4 ( excellent ), with higher scores denoting better health. Sleep problems. Sleep problems were assessed at Wave 5 using two items assessing how often adolescents had trouble falling asleep and staying asleep through the night over the past four weeks (Harris et al., 2018). Ratings ranged from 0 ( never in the past 4 weeks ) to 4 ( five or more times a week ). A mean composite was created, with higher scores denoting more sleep problems ( r = .55, p < .001). Substance use. At Wave 5, alcohol use was assessed using the AUDIT-C ( Bush et al., 1998 ), which included three items. The first item (i.e., “How often do you have a drink containing alcohol?”) was rated from 0 ( never ) to 4 ( four or more times a week ). The second item (i.e., “How many drinks containing alcohol do you have on a typical day?”) was rated from 0 ( none ) to 5 ( ten or more ), and the third item (i.e., “How often do you have six or more drinks on one occasion?”) was rated from 0 ( never ) to 4 ( daily or almost daily ). A composite was created by calculating the sum of all items, ranging from 0–13, where higher scores denoted greater alcohol use. Also at Wave 5, marijuana use was assessed using one item (i.e., “On how many occasions (if any) in the past 30 days have you used marijuana (weed, pot) or hashish (hash, hash oil)?”; Johnston et al., 1987 ). Ratings ranged from 0 ( 0 times ) to 6 ( 40+ times ), with higher scores denoting greater marijuana use. Exercise. Adolescents reported exercise behaviors at Wave 5 using one item (i.e., “Over the past four weeks, how often did you exercise, such as running, walking, dancing, working out, or participating in a sport?”; Harris et al., 2018). Ratings ranged from 0 ( never in the past 4 weeks ) to 4 ( five or more times a week ), with higher scores denoting engaging in more exercise. Covariates. Analyses controlled for participant cohort (0 = cohort 2 , 1 = cohort 1 ), gender (0 = boy , 1 = girl ), race/ethnicity (0 = non-White , 1 = White ), economic disadvantage (0 = does not qualify for FRPL , 1 = qualifies for FRPL ), and age. Data Analytic Plan Analyses were conducted in Mplus 8.7 ( Muthén & Muthén, 1998–2017 ) using full information maximum likelihood to account for missing data. The study design and analysis plan were not preregistered. To address the first aim examining patterns of change in peer-perpetrated discrimination from 8 th to 12 th grades, we used growth mixture modeling ( Muthén, 2004 ), which is a person-centered approach that estimates growth trajectories while allowing for the possibility that subpopulations (i.e., multiple mean growth curves) exist in the data. Growth mixture models with between one and five classes were fit to the discrimination variables from Waves 1 to 5. Class enumeration relied on the Bayesian information criteria (BIC), sample size adjusted BIC (ABIC), and loglikelihood-based tests (Lo-Mendel-Rubin test) based on prior recommendations ( Nylund et al., 2007 ; Tofighi & Enders, 2008). The second aim examined links between adolescents’ histories of peer-perpetrated discrimination and self-rated health using two approaches. First, we conducted regression analyses in a path analysis framework to test the main effects of cumulative discrimination across Waves 1 to 5 (i.e., sum score) on self-rated health at Wave 6. Second, we used discrimination trajectory class membership as a predictor of self-rated health. After identifying which k- class model fit the data best, we saved out the final profile classifications, which were subsequently used in regression analyses as predictors of self-rated health, rotating the reference group to explore all possible effects. In both approaches, we controlled for the set of covariates. The third aim examined the extent to which unhealthy behaviors (i.e., sleep problems, substance use, lack of exercise) mediated the association between peer-perpetrated discrimination and self-rated health. Similar to the second aim, we used two approaches: first, using cumulative discrimination score as the exogenous predictor, and second using discrimination trajectory class membership as the exogenous predictor, including the set of covariates in each model. In both approaches, we tested direct and indirect effects, with standard errors of the indirect effects evaluated using the delta method. Lastly, we conducted sensitivity analyses using multiple group analyses to determine the extent to which the observed results were generalizable across gender, race/ethnicity, and SES. Separate sets of moderation were run for each background characteristic. We first estimated a base model in which all parameters were allowed to vary freely across groups, then constrained all individual model paths to be equivalent. We conducted Chi-square difference tests to determine whether the constraints led to significant declines in model fit, with significant declines suggesting differences across groups. Given small cell sizes across some of our trajectory classes for race/ethnicity, gender, and SES, sensitivity analyses were only conducted using the cumulative peer-perpetrated discrimination score. This work was not preregistered; study materials and study analysis code are available upon request to the first author. Results Adolescents’ Histories of Discrimination We used two methods to determine adolescents’ histories of peer-perpetrated discrimination tied to race/ethnicity, SES, sexual minority status, and weight. The first was a cumulative discrimination score that was the sum of discrimination across Waves 1 through 5, consistent with prior research (e.g., Simons et al., 2018 ). As evidenced in Figure 1 , adolescents’ reports reflected an accumulation of peer-perpetrated discrimination across waves and suggested that many youth experienced repeated discriminatory experiences over time. The second method was a growth mixture model, and using evidence from multiple class enumeration criteria, we concluded that the 3-class model fit the data best (see Table S2 ). Specifically, both the BIC and ABIC decreased substantially from the 2-class to the 3-class model. Although the BIC and ABIC continued to decline modestly from the 3-class to the 5-class models, the most substantial drop occurred between the two- and three-class solutions. Additionally, the LRT test indicated that the 3-class model fit the data better than the 2-class model. As shown in Figure 2 , the majority of adolescents exhibited low levels of peer-perpetrated discrimination that exhibited attenuated declines across study waves (84%); this class was labeled low-decreasing discrimination. A second class of adolescents exhibited high and stable levels of discrimination (10%), which was labeled high-stable . Lastly, 6% reported moderate levels of discrimination that substantially increased over time; this class was labeled moderate-accelerating . Figure 1. Accumulation of peer-perpetrated discrimination across study waves. Open in a new tab Note . Total possible N = 425. Figure 2. Growth mixture model results for adolescents’ histories of peer discrimination. Open in a new tab Note . Spring 2017 = W1 for C1; Spring 2018 = W1 for C2 & W2 for C1; Spring 2019 = W2 for C2 & W3 for C1; Spring 2020 = W3 for C2; Fall 2020 = W4 for C1 & C2; Spring 2021 = W5 for C1 & C2. Adolescents’ Histories of Discrimination and Self-rated Health We first tested associations between adolescents’ cumulative histories of peer-perpetrated discrimination (sum measure) and later self-rated health. As shown in Table 2 , greater exposure to discrimination across adolescence was linked to poorer self-rated health ( β = −0.19, p < .001). Next, we predicted health using the three discrimination trajectory classes, rotating the reference group. As shown in Table 3 , those in the high-stable discrimination class reported worse self-rated health than those in the low-decreasing discrimination class ( β = −0.16, p < .01). There were no other observed differences across discrimination classes. Table 2. Cumulative histories of peer-perpetrated discrimination predicting self-rated health β SE 95% CI p -value Cumulative peer discrimination −0.19 *** 0.05 [−0.29, −0.09] 0.000 Study cohort −0.18 * 0.09 [−0.35, −0.01] 0.034 Female −0.06 0.06 [−0.17, 0.06] 0.321 White −0.03 0.06 [−0.15, 0.08] 0.588 Economically disadvantaged −0.13 * 0.06 [−0.25, −0.01] 0.039 Age −0.01 0.09 [−0.18, 0.16] 0.927 Open in a new tab Note. N = 425. Standardized path parameters are presented, and significant effects are bolded. * p < .05 *** p < .001. Table 3. Peer-perpetrated discrimination trajectory class membership predicting self-rated health Reference group: Low-decreasing discrimination Reference group: High-stable discrimination β SE 95% CI p -value β SE 95% CI p -value Low-decreasing class -- -- -- -- 0.19 ** 0.07 [0.06, 0.33] 0.005 High-stable class −0.16 ** 0.06 [−0.27, −0.05] 0.005 -- -- -- -- Moderate-accelerating class −0.07 0.06 [−0.18, 0.04] 0.185 0.05 0.07 [−0.09, 0.18] 0.471 Study cohort −0.17 * 0.09 [−0.34, −0.003] 0.046 −0.17 * 0.09 [−0.34, −0.003] 0.046 Female −0.06 0.06 [−0.17, 0.05] 0.313 −0.06 0.06 [−0.17, 0.05] 0.313 White −0.03 0.06 [−0.14, 0.09] 0.680 −0.03 0.06 [−0.14, 0.09] 0.680 Economic disadvantage −0.14 * 0.06 [−0.26, −0.02] 0.022 −0.14 * 0.06 [−0.26, −0.02] 0.022 Age −0.03 0.09 [−0.19, 0.14] 0.753 −0.03 0.09 [−0.19, 0.14] 0.753 Open in a new tab Note. N = 425. Standardized path parameters are presented, and significant effects are bolded. * p < .05 ** p < .01. Linking Histories of Discrimination, Unhealthy Behaviors, and Self-Rated Health Next, we examined whether adolescents’ unhealthy behaviors (i.e., sleep problems, substance use, exercise) mediated the link between histories of peer-perpetrated discrimination and later self-rated health. Our first model, using the cumulative measure of discrimination, fit the data well (χ 2 (5) = 5.32, p = .38, RMSEA = 0.01 [CI: 0.00, 0.07], CFI = 0.99). As shown in Figure 3 , greater exposure to peer-perpetrated discrimination across adolescence was associated with greater sleep problems ( β = 0.20, p < .001), greater alcohol use ( β = 0.15, p < .01), and greater marijuana use ( β = 0.17, p < .01). In turn, greater sleep problems were linked with poorer self-rated health ( β = −0.17, p < .01), and more exercise was associated with better self-rated health ( β = 0.37, p < .001). Direct effects between cumulative discrimination histories and health persisted, such that greater cumulative exposure to discrimination was linked with poorer self-rated health ( β = −0.17, p < .01). Indirect effects are shown in Table 4 (Model 1), and we observed a significant indirect effect of cumulative discrimination on self-rated health through sleep problems ( β = −0.04, p < .05). Figure 3. Path model linking cumulative peer discrimination, unhealthy behaviors, and self-rated health. Open in a new tab Note. N = 425. Standardized path parameters are shown. Significant effects are represented with solid lines. W5 = Wave 5, W6 = Wave 6. Covariates omitted from the figure but tested in the model were cohort, gender, race/ethnicity, economic disadvantage, and age. ** p < .01, *** p < .001. Table 4. Path model indirect effects from cumulative histories of discrimination to self-rated health. Indirect Pathway β SE 95% CI p -value Model 1: Cumulative discrimination → health a Cumulative discrimination → sleep problems → health −0.04 * 0.01 [−0.06, −0.01] 0.011 Cumulative discrimination → alcohol use → health −0.003 0.01 [−0.02, 0.01] 0.703 Cumulative discrimination → marijuana use → health 0.01 0.01 [−0.01, 0.03] 0.233 Cumulative discrimination → exercise → health −0.004 0.02 [−0.04, 0.03] 0.814 Model 2: Discrimination trajectories → health b High-stable → sleep problems → health −0.02 0.01 [−0.04, 0.00] 0.114 High- stable → alcohol use → health −0.003 0.01 [−0.02, 0.01] 0.693 High- stable → marijuana use → health 0.01 0.01 [−0.01, 0.02] 0.318 High- stable → exercise → health −0.004 0.02 [−0.04, 0.03] 0.844 Moderate-accelerating → sleep problems → health −0.02 † 0.01 [−0.04, 0.00] 0.054 Moderate-accelerating → alcohol use → health −0.003 0.01 [−0.02, 0.01] 0.694 Moderate-accelerating → marijuana use → health −0.001 0.00 [−0.01, 0.01] 0.848 Moderate-accelerating → exercise → health −0.04 † 0.02 [−0.07, 0.00] 0.067 Open in a new tab Note . N = 425. Standardized path parameters are presented, and significant effects are bolded. † p < .10 * p < .05. a Model fit: X 2 (5) = 5.32, p = 0.38; CFI = 0.999; RMSEA = 0.01 [CI: 0.00, 0.07]. b Low-decreasing discrimination class is the omitted reference group. Standardized path parameters are presented, and significant effects are bolded. Model fit: X 2 (5) = 6.50, p = .0.26; CFI = 0.993; RMSEA = 0.03 [CI: 0.00, 0.08]. The second set of models, using the discrimination trajectory classes as predictors, fit the data well. Results for the model using the low-increasing discrimination class as the reference group (model fit: χ 2 (5) = 6.50, p = .26, RMSEA = 0.03 [CI: 0.00, 0.08], CFI = 0.99) are shown in Figure 4 . Results indicated that adolescents in the high-stable class reported greater alcohol use ( β = 0.12, p < .05) and greater marijuana use ( β = 0.14, p < .05) and marginally worse sleep ( β = 0.09, p = .08) compared to those in the low-decreasing class. In addition, adolescents in the moderate-accelerating class reported greater sleep problems ( β = 0.12, p < .05) and greater alcohol use ( β = 0.12, p < .05) and marginally less exercise ( β = −0.10, p = .06) compared to those in the low-decreasing class. Subsequently, greater sleep problems were linked with poorer self-rated health ( β = −0.19, p < .001), and more exercise was associated with better self-rated health ( β = 0.37, p < .001). Consistent with the main effects model, those in the high-stable discrimination class reported poorer self-rated health compared to those in the low-decreasing discrimination class ( β = −0.15, p < .01). As shown in Table 4 (Model 2), there were marginally significant indirect effects for the moderate-accelerating discrimination trajectory class (versus low-decreasing) on health via both sleep and exercise. Results for the model using the high-stable discrimination class as the reference group are presented in Figure S1 and Table S3 . One marginal difference emerged, wherein the moderate-accelerating class exhibited marginally lower marijuana use than the high-stable class ( β = −0.12, p = .07). Figure 4. Path model linking cumulative peer discrimination trajectory class membership, unhealthy behaviors, and self-rated health. Open in a new tab Note. N = 425. Low-decreasing discrimination class is the reference group. Standardized path parameters are shown. Significant effects are represented with solid lines. W5 = Wave 5, W6 = Wave 6. Covariates omitted from the figure but tested in the model were cohort, gender, race/ethnicity, economic disadvantage, and age. † p < .10, * p < .05, ** p < .01, *** p < .001. Sensitivity Analyses To examine the extent to which our findings using the summative score of peer-perpetrated discrimination were generalizable across gender, race and ethnicity, and SES, we used multiple group analyses. In the main effects model, results indicated that the effects of cumulative discrimination on later health were not significantly different between female versus male adolescents, White versus non-White adolescents, or economically disadvantaged versus non-economically disadvantaged adolescents (see Table S4 ). In the mediation model (see Table S5 ), we found that the pathway between cumulative discrimination and exercise differed by SES (χ 2 (1) = 8.55, p < .01). Specifically, for those who were economically disadvantaged, greater exposure to peer-perpetrated discrimination across adolescence was associated with more exercise ( β = 0.18, p < .05), whereas for those who were not economically disadvantaged, greater exposure to discrimination was linked with less exercise ( β = −0.14, p < .05). The path between marijuana use and self-rated health also differed by SES (χ 2 (11) = 4.74, p < .05). For those who were economically disadvantaged, marijuana use was not related to later health ( β = −0.17, p = .21); however, for those who were not economically disadvantaged, greater marijuana use was linked to better self-rated health ( β = 0.16, p < .01). No other group differences were observed. Discussion Because differential discriminatory treatment across social groups remains widespread in contemporary society, understanding how such adverse experiences shape developmental trajectories across the life course remains a critical area of research (e.g., Williams & Mohammed, 2009; Benner, 2017). These issues have taken on heightened political salience in recent years, and the growing partisan polarization around issues of race and other dimensions of group identity ( Jardina & Ollerenshaw, 2022 ; Hout & Maggio, 2021 ) has been shown to correlate with real-world discriminatory behaviors and negative outcomes for marginalized groups in the United States (Feinberg et al., 2019; Schaffner, 2020 ). While much of the discrimination and health research to date utilizes adult samples, moving the lens to adolescence can be particularly fruitful given that the etiology of health inequities emerges in the early life course ( Goosby et al., 2018a ). The present study aimed to advance the evidence base linking discrimination and health by examining how cumulative exposure to peer-perpetrated discriminatory experiences across adolescence shaped general health outcomes. Particular attention was placed on health-compromising behavioral pathways—including substance use, sleep difficulties, and reduced physical activity—as each has been identified as a stress-related mechanism linking social adversity to long-term health risk ( Pascoe & Richman, 2009 ; Lewis et al., 2015 ). Central to this aim was a flexible modeling strategy that leveraged both accumulation models, which indexed total discrimination exposure over time, and trajectory-based models, which identified distinct temporal patterns of discrimination exposure. This dual approach allowed for a more nuanced understanding of not only how much peer-perpetrated discrimination youth experienced, but when and in what patterns it occurred—a distinction increasingly recognized as critical for developmental and health research ( Ben-Shlomo & Kuh, 2002 ). Replicating prior research ( Benner & Graham, 2011 ; Pinedo et al., 2021 ), we observed that experiences of peer-perpetrated discrimination tied to multiple social identities accumulated across time. Our subsequent person-centered modeling approach provided more nuanced insights into this overall pattern, suggesting that the majority of youth in our sample experienced low levels of discrimination with attenuated declines across time. Smaller proportions of youth reported high and stable levels of discrimination or moderate levels of discrimination that increased substantially across time, with each of these trajectories having been documented in the burgeoning discrimination literature utilizing person-centered approaches ( Brody et al., 2014 ; Niwa et al., 2014 ; Tynes et al., 2020 ; Unger et al., 2016 ). Cumulative peer-perpetrated discrimination, in turn, was negatively associated with self-rated health, consistent with prior research conducted predominantly with adult populations (e.g., Cano et al., 2023 ). Interestingly, the average difference between the low-decreasing and high-stable trajectory classes was equal to approximately one standard deviation of cumulative peer-perpetrated discrimination, underscoring the significant burden chronic discrimination can exact on adolescent health. Neither the high-stable nor low-decreasing profiles distinguished statistically from the moderate-accelerating profile; the moderate-accelerating group was small, and it may be that more time is needed before health disparities manifest for a global measure of self-rated general health. Importantly, these associations were principally direct, although the pattern of findings when integrating the behavioral pathways suggested that peer-perpetrated discrimination contributes to setting the stage for future health consequences. More specifically, cumulative peer discrimination was associated with greater sleep problems and higher alcohol and marijuana use. Although only sleep provided a statistically significant indirect pathway, each of these behavioral pathways is associated with a range of health problems beyond general self-rated health in adulthood, including cardiovascular disease, cognitive decline, and mortality ( Clark et al., 2008 ; Gray et al., 2018 ; Medic et al., 2017 ). Thus, despite the limited evidence of significant indirect pathways, the findings are suggestive of a broader package of developmental consequences that can undermine health outcomes later in the life course if these trends continue. The discrimination trajectory profile results were generally consistent with the cumulative discrimination effects, wherein the links to self-rated health were principally direct. In terms of the health behaviors, the high-stable versus low-decreasing groups distinguished each other primarily through higher alcohol and marijuana use for the former. In contrast, the moderate-accelerating class, on average, had more sleep problems and higher alcohol use relative to the low-decreasing class, and through them a combined negative indirect effect on self-rated health. At the same time, the moderate-accelerating class generally did not differ from the high-stable group, with the exception of marginally lower marijuana use. Taken together, these results suggest that sleep is an important behavioral process linking peer-perpetrated discrimination and health in the early life course. Given these findings and a larger body of research documenting changes in sleep patterns and sleep problems that emerge during adolescence ( Owens et al., 2014 ), sleep may be an important target for interventions to promote health for those experiencing discriminatory treatment. Additionally, given that developmental patterns of substance use suggest an increase in both alcohol and marijuana use across late adolescence and into early adulthood ( Chen & Jacobson, 2012 ), future studies that extend the developmental lens further could shed additional light as to whether cumulative experiences of peer-perpetrated discrimination place individuals at greater risk for problematic alcohol and drug use that, in turn, relate to greater morbidity and mortality later in the life course. Finally, while exercise promoted general health, it did not emerge as a central explanatory pathway in the link between peer-perpetrated discrimination and health. Prior research with primarily adult populations suggests that discrimination may promote greater physical activity, potentially as a coping response ( Borrell et al., 2013 ; Corral & Landrine, 2012 ). As such, rather than serving a mediating mechanism, it may instead be that exercise as a proactive coping strategy could serve a buffering effect on the associations between discrimination and health, a relation that has been observed in adults for the link between discrimination and allostatic load ( Copeland et al., 2021 ). Unpacking how different forms of exercise (e.g., solitary, group-based) might promote coping for adolescents facing discrimination represents an area ripe for future inquiry. Strengths, Limitations, and Future Directions The current study highlighted the pile-up of discriminatory experiences that adolescents can face and the consequences of accumulating peer-perpetrated discrimination for adolescents’ health and health behaviors. The adolescent sample utilized was racially and ethnically and socioeconomically diverse, and we generally observed consistency in study findings across demographic characteristics. The peer-perpetrated discrimination measure we employed was broad in scope, capturing perceived exclusion and mistreatment based on race/ethnicity, sexual orientation, weight, and socioeconomic status. One advantage of this inclusive approach is that it reflects the multidimensional nature of adolescents’ lived experiences, where multiple marginalized identities often intersect and compound vulnerability to discrimination ( Cole, 2009 ; Crenshaw, 1991). By recognizing a wider range of stigmatized identities, the measure allows for a more ecologically valid assessment of how chronic social marginalization impacts adolescent health, particularly in a sociopolitical climate where discriminatory experiences may take many forms ( Pachter & Coll, 2009 ). Moreover, our dual analytic approach to examining discrimination allowed for a better understanding of total exposure to peer-perpetrated discrimination and temporal patterns of discrimination across adolescence as well as heterogeneity in experiences. While this study provides unique insights into peer-perpetrated discrimination and health, limitations must be acknowledged as well. Although analyses did not indicate overall differences between White and non-White adolescents, collapsing all non-White groups together for power considerations may mask important heterogeneity among Latinx, Asian, Black, biracial, and multiracial youth in the observed pattern of effects. In addition, the current study relied on adolescents’ self-reports of their perceived discrimination, health behaviors, and general health. Scholars have argued that discrimination is a subjective experience and that perceived discrimination has clear consequences for well-being ( Richman et al., 2018 ), and self-reported health is a strong predictor of mortality ( Jylhä, 2009 ), which suggests the utility of these measures. Future research, however, should utilize objective measures of health, as the current measure, while widely used, captures general health and does not specifically query physical health, and thus respondents could interpret the meaning of health rather broadly. Similarly, because both sleep and physical activity were self-reported, future research would benefit from incorporating objective measures of sleep and exercise (e.g., actigraphy). Sleep, in particular, is multidimensional and a more comprehensive measure (e.g., Pittsburgh sleep quality index; Buysse, 1989 ) may better capture the dimensions of sleep potentially variably impacted by discrimination, which could inform targeted interventions. The integration of other objective physical health measures (e.g., cardiovascular health, inflammatory response) could provide further insights into the link between discrimination and health. Additionally, the current study examined discrimination perpetrated by peers. While peers are particularly salient socializing agents in adolescence (Crosnoe, 2011), future research should examine how discrimination perpetrated by other sources and embedded in social institutions accumulates across time to impact health and health behaviors. Another area for future inquiry centers on extending the developmental lens to capture the accumulation of discriminatory experiences, and the impact on health and health behaviors, from adolescence into adulthood. Adolescence is among the healthiest times in the life course ( Kochanek et al., 2024 ), and it is likely that small differences in health tied to early experiences of discrimination will likely magnify across time to contribute to increasing heterogeneity in trajectories of health and well-being in mid-life and beyond. Conclusion Building on evidence of discrimination’s wide-ranging impacts on adolescent development, the present study highlighted how chronic and accumulating peer-perpetrated discrimination contributes to compromised health and health-related behaviors over time. Both cumulative and trajectory-based approaches demonstrated that discrimination experiences were linked to poorer self-rated health, largely through direct pathways. While behavioral mediators such as substance use and exercise did not explain associations between peer-perpetrated discrimination and health at this developmental stage, sleep problems emerged as a key mediating pathway, underscoring the importance of sleep as an early behavioral marker of risk. Notably, distinct discrimination trajectories revealed variation in how and when peer-perpetrated discrimination may shape health outcomes early in the life course. These findings suggest that persistent or growing exposure to discrimination may set the stage for longer-term health consequences. As such, adolescence represents a critical window for intervention. Future research should continue to track these developmental processes over time and across intersecting identities, with an emphasis on modifiable behaviors that may mitigate health risks across adolescence and into adulthood. Supplementary Material Supplemental Material NIHMS2147512-supplement-Supplemental_Material.docx (106.7KB, docx) Public Significance Statement. Theorical models suggest that behavioral processes are an important mechanism by which discrimination influences health, but this has not been well-examined in adolescence. The current study examined how discriminatory experiences perpetrated by peers accumulated across time, the consequences of peer-perpetrated discrimination for health, and whether health behaviors (substance use, sleep, and exercise) were an explanatory mechanism. Our findings documented the negative relation between peer-perpetrated discrimination and health and the importance of sleep, in particular, in this relation. Footnotes CRedit Benner: conceptualization, funding acquisition, methodology, project administration and supervision, writing—original draft, writing—review and editing Li: data curation and data analysis, methodology, visualization, writing—original draft, writing—review and editing Cheadle: conceptualization, methodology, writing—original draft, writing—review and editing Goosby: conceptualization, writing—review and editing References Ahuja M, Haeny AM, Sartor CE, & Bucholz KK (2022). Perceived racial and social class discrimination and cannabis involvement among Black youth and young adults. Drug and alcohol dependence, 232, 109304. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Anderson RE, Lee DB, Hope MO, Nisbeth K, Bess K, & Zimmerman MA (2020). Disrupting the Behavioral Health Consequences of Racial Discrimination: A Longitudinal Investigation of Racial Identity Profiles and Alcohol-Related Problems. 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