Associations Between Structural Stigma and Advanced Pubertal Development Persist for One Year Among Black Girls and Latinx Youth

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Research on determinants of this increased risk has focused almost exclusively on aspects of individuals (e.g., body-mass index) or their proximal environment (e.g., socioeconomic status), to the exclusion of broader macro-social factors. Using two years of Adolescent Brain Cognitive Development Study® data, we examined whether structural stigma (e.g., state-level policies, aggregated prejudicial attitudes) was associated with hormonal and perceived physical indicators of pubertal development. Baseline results documented more advanced pubertal development among Black girls (hormones) and Latinx youth (youth and/or caregiver report) in states characterized by higher (vs. lower) structural stigma. Observed associations were comparable in effect size to a well-established correlate of pubertal development, BMI, and remained or strengthened one year later among these stigmatized (vs. non-stigmatized) groups. Findings suggest the need to broaden the study of determinants of pubertal development to include macro-social factors. Biological sciences/Psychology/Human behaviour Health sciences/Biomarkers childhood and adolescence structural stigma puberty development social determinants of health Figures Figure 1 Figure 2 Figure 3 Introduction Puberty represents a critical developmental process that involves the complex interplay of hormonal and other neurobiological processes in order to prepare the body for physical maturation and sexual reproduction (Dorn et al., 2019). The timing of puberty has declined steadily among youth in the United States (US) and globally, with youth today beginning puberty up to two years earlier than youth several decades ago (Aksglaede et al., 2009; Eckert-Lind et al., 2020; Herman-Giddens et al., 2012; Hoyt et al., 2020; Ohlsson et al., 2019). Children and adolescents with stigmatized identities—including girls, Black youth, and Latinx youth—exhibit earlier pubertal timing and more advanced pubertal development relative to their same-aged, non-stigmatized peers (Chumlea et al., 2003; Herman-Giddens et al., 1997, 2001, 2012; Susman et al., 2010). For example, girls begin puberty up to 1.5 years earlier than boys in the US (Euling et al., 2008; Kaplowitz, 2004; Rosenfield et al., 2009; Sun et al., 2002). Black and Latinx girls experience earlier breast development (i.e., thelarche) and menstruation (i.e., menarche) compared to White girls (Biro et al., 2010, 2013; Chumlea et al., 2003; Herman-Giddens et al., 1997). Specifically, one study found that by age 8, only 18% of White girls had entered thelarche compared to 31% of Latinx girls and 43% of Black girls (Biro et al., 2010). Another study found that Black and Latinx girls, respectively, reached menarche four and six months earlier than White girls (Chumlea et al., 2003). Likewise, Black boys experience more advanced pubertal development than White boys, including earlier pubic hair (i.e., adrenarche) and genital (i.e., gonadarche) growth (Herman-Giddens et al., 2012). Furthermore, elevated levels of pubertal hormones have been observed among Black girls and Black boys relative to their same-aged, White peers (Herting et al., 2021). Advanced pubertal development has been linked to a number of adverse physical and mental health consequences (Mendle et al., 2007), such as diabetes (Janghorbani et al., 2014), depression and anxiety (Colich et al., 2020; Hamlat et al., 2019; Natsuaki et al., 2009), externalizing disorders (Platt et al., 2017), and substance use (Downing & Bellis, 2009). These consequences are especially prominent among girls (Barendse et al., 2022; Colich et al., 2020) and among racially and ethnically minoritized youth (Bleil et al., 2017; Deardorff et al., 2021; Ge et al., 2006; Hamlat et al., 2014). For example, girls who experience earlier (vs. later) pubertal development are at increased risk for breast cancer (Goldberg et al., 2020), internalizing and externalizing psychopathology (Barendse et al., 2022; Hamilton et al., 2014; Platt et al., 2017), and early sexual activity (Baams et al., 2016). Additionally, earlier (vs. later) breast development has been linked to more severe mental health symptoms among Latinx girls (Deardorff et al., 2021) and Black girls (Keenan et al., 2014). Similarly, earlier (vs. later) pubertal onset has been associated with depressive symptoms and externalizing symptoms among Black boys (Ge et al., 2016; Hamlat et al., 2014). These studies highlight the significant need to identify and intervene on determinants of advanced pubertal development, particularly among stigmatized youth. Research into correlates and predictors of advanced pubertal development among youth has overwhelmingly focused on aspects of individuals and their proximal environment, including body-mass index (BMI; Silventoinen et al., 2022); indicators of family socioeconomic status (SES) such as household income (Deardorff et al., 2014) and caregiver educational attainment (Colich, et al., 2020); and perceived discrimination (Argabright et al., 2022) as well as other forms of threat-related adversity (e.g., childhood emotional, physical, and sexual abuse; Colich, et al., 2020). This work identifies associations between many of these factors—particularly BMI and threat-related adversities—and the timing of puberty, demonstrating that they not only represent important determinants of puberty among youth generally, but also that they may partially explain well-established disparities in pubertal development between stigmatized and non-stigmatized youth (Argabright et al., 2022; Deardorff et al., 2014; Hamlat et al., 2022; Reagan et al., 2012; Seaton & Carter, 2019; Silventoinen et al., 2022). Although these studies have provided important insights into individual determinants of advanced pubertal development, the literature has largely ignored broader contextual factors that might also contribute to advanced pubertal development among youth, despite repeated calls for such scholarship (Carter & Seaton, 2024; Deardorff et al., 2019; Klopack et al., 2020; Neblett & Neal, 2022). One notable exception is a recent cohort study by Acker and colleagues (2023), which documented more advanced pubertal development among racially and ethnically diverse girls living in neighborhoods with more (vs. less) concentrated income. The present study provides a novel framework for widening the study of determinants of advanced pubertal development to the macro-social level by incorporating measures of structural stigma, defined as “societal-level conditions, cultural norms, and institutional policies and practices that constrain the opportunities, resources, and wellbeing of the stigmatized” (Hatzenbuehler & Link, 2014, p . 2). Exposure to structural stigma during childhood and adolescence is not only a fundamental driver of health disparities between stigmatized and non-stigmatized youth (Charlton et al., 2019; Hatzenbuehler, 2017; Hatzenbuehler et al., 2014, 2015; Martino et al., 2023; Raifman et al., 2017), but it is also an important source of within-group heterogeneity in adverse developmental and psychosocial outcomes among populations of stigmatized youth (Gordon et al., 2024; Hatzenbuehler et al., 2024a; Slopen et al., 2023). For example, Black and Latinx youth living in states characterized by higher (vs. lower) levels of structural racism and structural xenophobia, respectively, have reduced hippocampal volume (Hatzenbuehler et al., 2022) and higher levels of psychopathology (Martino et al., 2023; Slopen et al., 2023). Moreover, sexual minority (i.e., lesbian, gay, and bisexual) young adults raised in US states with higher (vs. lower) levels of structural homophobia demonstrate blunted cortisol reactivity to stress (Hatzenbuehler & McLaughlin, 2014). Life history theory (Hill & Kaplan, 1999; Ellis et al., 2009) and dimensional models of childhood adversity (Ellis et al., 2022; McLaughlin et al., 2021; McLaughlin & Sheridan, 2016) suggest that developmental processes such as puberty may be accelerated in environments characterized by greater threat in order to maximize the opportunity for reproduction before mortality. Because structural stigma has been conceptualized as a chronic form of threat-related adversity (Cardona et al., 2022; Christian et al., 2021; Diamond & Alley, 2022; Hollinsaid et al., 2023), we sought to interrogate structural stigma as a macro-social correlate of advanced pubertal development among stigmatized youth. Answering this question required a novel data structure atypical of most studies on pubertal development to date. First, we needed a dataset examining pubertal development among stigmatized youth exposed to macro-social contexts varying in structural stigma. Second, we needed a dataset assessing multiple indicators of pubertal development (e.g., hormone levels, physical markers). This data feature would permit us to examine whether the association between structural stigma and advanced pubertal development differs across indicators of potentially distinct pubertal processes, given evidence of relatively low correspondence between hormonal and external, physical indicators of pubertal development (Cheng et al., 2021; Herting et al., 2021; Mendle et al., 2019; Shirtcliff et al., 2009). Third, the dataset would need to include measures of individual correlates of pubertal development in order to ensure that structural stigma remains associated with advanced pubertal development even after accounting for aspects of individuals (e.g., BMI) and of their proximal environment (e.g., family SES) that might also be associated with pubertal development. Fourth, we needed a dataset that also included youth not holding the stigmatized identities of interest to serve as negative control groups to evaluate the consistency and specificity of findings among stigmatized (vs. non-stigmatized) youth (Lipsitch et al., 2010). Finally, we required a longitudinal dataset in order establish the consistency of results at separate points in development. Fortunately, a dataset meeting these requirements recently became available. Specially, the current study leveraged baseline and Year 1 (i.e., one-year follow-up) data from the Adolescent Brain Cognitive Development (ABCD) Study® to consider whether exposure to higher (vs. lower) levels of structural stigma was associated with advanced pubertal development among three groups of stigmatized youth. The ABCD Study® was uniquely situated to address this research question. It represents one of the largest multisite and longitudinal studies of child and adolescent development to date, enrolling 11,844 youth (primarily ages 9–10) at baseline from 21 sites located in 17 US states that vary substantially in sociopolitical context surrounding various stigmatized populations. We focused on three stigmatized groups—girls, Black youth, and Latinx youth—given ample evidence of advanced pubertal development among these populations (Chumlea et al., 2003; Herman-Giddens et al., 1997, 2001, 2012; Susman et al., 2010) and their considerable representation in the ABCD Study® (see “Methods” for sample sizes and demographics). In order to capture the distinct development processes implicated in puberty (Cheng et al., 2021; Dorn & Biro, 2011; Herting et al., 2021; Mendle et al., 2019; Shirtcliff et al., 2009), we examined salivary levels of three pubertal hormones (i.e., estradiol among girls; DHEA and testosterone among girls and boys) as well as youth and caregiver reports of external, physical markers of pubertal development as outcomes. To index advanced pubertal development in the form of pubertal age, these variables were residualized on chronological age (Colich et al., 2023; Sumner et al., 2019). In preregistered hypotheses informed by life history theory (Ellis et al., 2009; Hill & Kaplan, 1999) and dimensional models of childhood adversity (Ellis et al., 2022; McLaughlin et al., 2021; McLaughlin & Sheridan, 2016), as well as by prior research on structural stigma and other development and psychosocial outcomes (Hatzenbuehler et al., 2022, 2024a; Hatzenbuehler & McLaughlin, 2014; Martino et al., 2023; Slopen et al., 2023), we predicted that Black youth, Latinx youth, and girls living in ABCD Study® states characterized by higher (vs. lower) levels of structural stigma specific to these groups would evidence more advanced pubertal development at baseline and Year 1. Results In preregistered analyses, we used linear mixed-effects models to examine associations between structural racism, xenophobia, and sexism—quantified objectively using existing measures of state-level policies, aggregated prejudicial attitudes, and/or other societal conditions (Hatzenbuehler et al., 2022 )—and hormonal indicators as well as youth- and caregiver-reported external, physical markers of pubertal development among Latinx youth, Black youth, and girls, respectively. Outcome variables were first regressed on chronological age to index advanced pubertal development, and relevant analyses were stratified by birth-assigned sex. We tested these associations cross-sectionally at baseline and Year 1 of the ABCD Study®, enabling us to assess the consistency of results across two points in time during emerging adolescence. We controlled for BMI, family SES in the form of mean caregiver educational attainment, and state-level income inequality in order to evaluate whether structural stigma remained associated with advanced pubertal development over and above individual and proximal correlates of pubertal development as well as a broader contextual indicator of social inequality. Finally, as a form of negative control analysis (Lipsitch et al., 2010 ), we reran our primary models among non-stigmatized comparison groups (e.g., non-Latinx White girls for primary analyses conducted with Latinx girls), allowing us to evaluate whether and how consistently associations between structural stigma and advanced pubertal development were specific to stigmatized (vs. non-stigmatized) youth. Structural Stigma and Hormonal Indicators of Advanced Pubertal Development At baseline, Black girls living in ABCD Study® states characterized by higher (vs. lower) levels of structural racism experienced more advanced pubertal development on all three hormonal indicators (Fig. 1 A, Table 1 ): estradiol (β = 0.12, 95% CI [0.04, 0.20], p = 0.006); DHEA (β = 0.11, 95% CI [0.05, 0.17], p < 0.001); and testosterone (β = 0.13, 95% CI [0.06, 0.20], p < 0.001). At Year 1, we continued to observe significant associations between structural racism and these three hormonal indicators of advanced pubertal development among Black girls (Fig. 1 B, Table 1 ): estradiol (β = 0.10, 95% CI [0.01, 0.19], p = 0.033); DHEA (β = 0.14, 95% CI [0.06, 0.22], p < 0.001); and testosterone (β = 0.14, 95% CI [0.06, 0.23], p < 0.001). These associations remained significant when excluding Black girls who experienced menarche, suggesting that findings were not simply driven by post-menarche changes in pubertal hormones (Supplementary Table 1). We also found significant baseline associations between structural racism and two hormonal indicators of advanced pubertal development among White girls: DHEA (β = 0.07, 95% CI [0.01, 0.13], p = 0.032) and testosterone (β = 0.11, 95% CI [0.02, 0.19], p = 0.013). However, these associations were relatively smaller in magnitude among White (vs. Black) girls, and no such association was observed for the third hormonal indicator, estradiol, among White girls (Supplementary Table 2). Moreover, no significant associations between structural racism and hormonal indicators of advanced pubertal development were observed among White girls at Year 1 (Supplementary Table 2). Results from these negative control analyses provide suggestive evidence that structural racism was most consistently (i.e., for all three pubertal hormones) and persistently (i.e., at baseline and Year 1) associated with hormonal indicators of advanced pubertal development among Black (vs. White) girls. Table 1 Associations Between Structural Racism and Hormonal Indicators of Advanced Pubertal Development Among Black Girls Associations Between Structural Racism and Hormonal Indicators of Advanced Pubertal Development Among Black Girls: Estradiol Baseline Year 1 b SE z p β 95% CI b SE z p β 95% CI Intercept -1.303 0.607 -2.147 0.032* 0.002 (-0.073, 0.078) -1.186 0.715 -1.659 0.097 -0.014 (-0.111, 0.083) Structural racism 0.099 0.036 2.775 0.006** 0.118 (0.035, 0.202) 0.079 0.037 2.128 0.033* 0.098 (0.008, 0.188) BMI 0.014 0.004 3.675 < 0.001*** 0.112 (0.052, 0.171) 0.013 0.004 3.275 0.001** 0.112 (0.045, 0.180) Caregiver education 0.015 0.007 2.086 0.037* 0.066 (0.004, 0.128) -0.003 0.008 -0.380 0.704 -0.014 (-0.085, 0.058) State income inequality 1.226 1.227 0.999 0.318 0.037 (-0.035, 0.108) 1.440 1.446 0.995 0.320 0.045 (-0.043, 0.132) Collection time 0.020 0.006 3.304 < 0.001*** 0.105 (0.043, 0.167) 0.023 0.006 3.611 < 0.001*** 0.131 (0.060, 0.202) Associations Between Structural Racism and Hormonal Indicators of Advanced Pubertal Development Among Black Girls: DHEA Baseline Year 1 b SE z p β 95% CI b SE z p β 95% CI Intercept -0.038 0.602 -0.063 0.950 -0.003 (-0.062, 0.056) -0.900 0.789 -1.140 0.254 -0.006 (-0.087, 0.074) Structural racism 0.110 0.030 3.673 < 0.001*** 0.111 (0.052, 0.170) 0.142 0.040 3.545 < 0.001*** 0.140 (0.063, 0.217) BMI 0.034 0.004 7.959 < 0.001*** 0.231 (0.174, 0.287) 0.035 0.005 6.850 < 0.001*** 0.229 (0.164, 0.295) Caregiver education 0.002 0.008 0.245 0.807 0.007 (-0.052, 0.067) 0.001 0.010 0.102 0.919 0.004 (-0.065, 0.072) State income inequality -0.485 1.216 -0.399 0.690 -0.012 (-0.071, 0.047) 0.782 1.593 0.491 0.624 0.019 (-0.057, 0.096) Collection time -0.019 0.006 -3.014 0.003** -0.087 (-0.143, -0.030) -0.003 0.008 -0.349 0.727 -0.012 (-0.082, 0.057) Associations Between Structural Racism and Hormonal Indicators of Advanced Pubertal Development Among Black Girls: Testosterone Baseline Year 1 b SE z p β 95% CI b SE z p β 95% CI Intercept 0.350 0.521 0.672 0.502 -0.011 (-0.086, 0.064) -0.359 0.552 -0.651 0.515 -0.010 (-0.098, 0.078) Structural racism 0.092 0.026 3.589 < 0.001*** 0.130 (0.059, 0.200) 0.094 0.028 3.306 < 0.001*** 0.141 (0.057, 0.225) BMI 0.016 0.003 5.070 < 0.001*** 0.150 (0.092, 0.208) 0.014 0.003 4.005 < 0.001*** 0.137 (0.070, 0.203) Caregiver education -0.007 0.006 -1.045 0.296 -0.033 (-0.095, 0.029) -0.003 0.007 -0.520 0.603 -0.019 (-0.088, 0.051) State income inequality -0.468 1.036 -0.451 0.652 -0.016 (-0.088, 0.055) 0.619 1.115 0.555 0.579 0.023 (-0.059, 0.105) Collection time -0.016 0.005 -3.045 0.002** -0.098 (-0.161, -0.035) -0.004 0.006 -0.644 0.520 -0.024 (-0.097, 0.049) No significant associations between measures of structural stigma and hormonal indicators of advanced pubertal development were documented among the other stigmatized groups in our study, including for structural racism among Black boys, structural xenophobia among Latinx girls and boys, and structural sexism among girls (Supplementary Tables 3–6). Structural Stigma and External, Physical Markers of Advanced Pubertal Development At baseline, both Latinx girls (β = 0.19, 95% CI [0.12, 0.26, p < 0.001; Fig. 2 A) and boys (β = 0.10, 95% CI [0.03, 0.17], p = 0.005; Fig. 3 A) living in US states with higher (vs. lower) levels of structural xenophobia experienced more advanced pubertal development via caregiver-reported external, physical markers of puberty-related changes (Table 2 ). These associations remained significant for both Latinx girls (β = 0.21, 95% CI [0.14, 0.28], p < 0.001; Fig. 2 B) and boys (β = 0.10, 95% CI [0.02, 0.17], p = 0.009; Fig. 3 B) at Year 1 (Table 2 ). Likewise, Latinx girls living in states with higher (vs. lower) levels of structural xenophobia experienced more advanced pubertal development via youth-reported external, physical markers of puberty at baseline (β = 0.13, 95% CI [0.06, 0.20], p < 0.001; Fig. 2 A), and this association strengthened somewhat at Year 1 (β = 0.18, 95% CI [0.11, 0.25], p < 0.001; Fig. 2 B, Table 3 ). Although no such association was observed for Latinx boys at baseline, a significant association between structural xenophobia and more advanced pubertal development via youth-reported external, physical markers emerged among Latinx boys at Year 1 (β = 0.12, 95% CI [0.01, 0.24], p = 0.040; Fig. 3 A–B, Table 3 ). Although some significant associations between structural xenophobia and youth- and/or caregiver-reported external, physical markers of advanced pubertal development were observed among non-Latinx White girls and boys at baseline, no significant associations were found among non-Latinx White youth at Year 1 (Supplementary Tables 7–10). Taken together, these results suggest that structural xenophobia was more consistently associated with external indicators of advanced pubertal development among Latinx (vs. non-Latinx White youth), and most persistently so for Latinx girls. A relatively similar pattern of findings emerged when using alternate specifications (i.e., adrenarche-/gonadarche-related changes, pubertal development categories) of these external, physical markers (Supplementary Tables 11–26). Table 2 Associations Between Structural Xenophobia and Caregiver-Reported External, Physical Markers of Advanced Pubertal Development Among Latinx Girls and Boys Associations Between Structural Xenophobia and Caregiver-Reported External, Physical Markers of Advanced Pubertal Development Among Latinx Girls Baseline Year 1 b SE z p β 95% CI b SE z p β 95% CI Intercept -2.420 0.426 -5.679 < 0.001*** 0.004 (-0.051, 0.060) -2.645 0.526 -5.025 < 0.001*** 0.004 (-0.054, 0.062) Structural xenophobia 0.133 0.024 5.533 < 0.001*** 0.193 (0.124, 0.261) 0.170 0.029 5.785 < 0.001*** 0.211 (0.140, 0.283) BMI 0.034 0.004 9.385 < 0.001*** 0.270 (0.214, 0.326) 0.039 0.004 9.493 < 0.001*** 0.286 (0.227, 0.345) Caregiver education 0.000 0.004 0.033 0.974 0.001 (-0.057, 0.059) 0.002 0.005 0.352 0.725 0.011 (-0.049, 0.071) State income inequality 3.952 0.895 4.414 < 0.001*** 0.153 (0.085, 0.221) 4.207 1.106 3.804 < 0.001*** 0.138 (0.067, 0.209) Associations Between Structural Xenophobia and Caregiver-Reported External, Physical Markers of Advanced Pubertal Development Among Latinx Boys Baseline Year 1 b SE z p β 95% CI b SE z p β 95% CI Intercept -1.236 0.370 -3.345 < 0.001*** 0.010 (-0.047, 0.066) -1.674 0.425 -3.939 < 0.001*** 0.004 (-0.055, 0.062) Structural xenophobia 0.054 0.019 2.798 0.005** 0.097 (0.029, 0.165) 0.058 0.022 2.603 0.009** 0.095 (0.024, 0.167) BMI 0.014 0.003 5.209 < 0.001*** 0.146 (0.091, 0.200) 0.017 0.003 5.555 < 0.001*** 0.163 (0.106, 0.221) Caregiver education -0.009 0.004 -2.404 0.016* -0.071 (-0.129, -0.013) 0.005 0.004 1.175 0.24 0.036 (-0.024, 0.097) State income inequality 2.482 0.772 3.216 < 0.0001** 0.112 (0.044, 0.180) 2.843 0.889 3.198 < 0.001*** 0.117 (0.045, 0.189) Table 3 Associations Between Structural Xenophobia and Youth-Reported External, Physical Markers of Advanced Pubertal Development Among Latinx Girls and Boys Associations Between Structural Xenophobia and Youth-Reported External, Physical Markers of Advanced Pubertal Development Among Latinx Girls Baseline Year 1 b SE z p β 95% CI b SE z p β 95% CI Intercept -1.688 0.492 -3.432 < 0.001*** 0.001 (-0.056, 0.058) -2.206 0.541 -4.078 < 0.001*** 0.005 (-0.055, 0.064) Structural xenophobia 0.098 0.028 3.539 < 0.001*** 0.126 (0.056, 0.196) 0.146 0.030 4.814 < 0.001*** 0.180 (0.107, 0.253) BMI 0.025 0.004 6.049 < 0.001*** 0.181 (0.123, 0.240) 0.029 0.004 6.832 < 0.001*** 0.211 (0.151, 0.272) Caregiver education 0.004 0.005 0.882 0.378 0.027 (-0.033, 0.086) 0.004 0.005 0.701 0.483 0.022 (-0.040, 0.084) State income inequality 2.570 1.031 2.493 0.013* 0.088 (0.019, 0.158) 3.577 1.136 3.148 0.002** 0.117 (0.044, 0.190) Associations Between Structural Xenophobia and Youth-Reported External, Physical Markers of Advanced Pubertal Development Among Latinx Boys Baseline Year 1 b SE z p β 95% CI b SE z p β 95% CI Intercept -0.540 0.651 -0.830 0.406 -0.014 (-0.104, 0.076) -1.239 0.650 -1.906 0.057 -0.029 (-0.137, 0.079) Structural xenophobia 0.029 0.043 0.678 0.498 0.039 (-0.073, 0.151) 0.091 0.044 2.050 0.040* 0.122 (0.005, 0.238) BMI 0.004 0.004 1.008 0.314 0.029 (-0.027, 0.085) 0.010 0.004 2.760 0.006** 0.083 (0.024, 0.141) Caregiver education -0.013 0.005 -2.635 0.008** -0.078 (-0.136, -0.020) -0.004 0.005 -0.736 0.462 -0.023 (-0.083, 0.038) State income inequality 1.481 1.400 1.058 0.290 0.050 (-0.042, 0.142) 2.587 1.389 1.863 0.063 0.088 (-0.005, 0.180) * p < 0.05, ** p < 0.01, *** p < 0.001. No significant associations between measures of structural stigma and youth- or caregiver-reported external, physical markers of advanced pubertal development were present among other stigmatized groups, including for structural racism among Black girls and boys and for structural sexism among girls (Supplementary Tables 27–32). Discussion Youth with stigmatized identities, including racially (e.g., Black) and ethnically (e.g., Latinx) minoritized youth as well as girls, experience advanced pubertal development relative to their same-aged, non-stigmatized peers (Biro et al., 2010 ; Chumlea et al., 2003 ; Herman-Giddens et al., 2001 ; McDowell et al., 2007 ; Sun et al., 2002 ; Susman et al., 2010 ; Wu et al., 2002 ). Research into correlates of this increased risk has largely focused on aspects of individuals and their proximal environment (Argabright et al., 2022 ; Deardorff et al., 2014 ; Hamlat et al., 2022 ; Lee et al., 2010 ; Li et al., 2017 ; Seaton & Carter, 2019 ; Silventoinen et al., 2022 ), despite repeated calls to consider broader contextual factors operating at the macro-social level (Carter & Seaton, 2024 ; Deardorff et al., 2019 ; Neblett & Neal, 2022 ). Leveraging two years of ABCD Study® data, we provide novel evidence that one such factor—exposure to structural stigma at the state level (e.g., discriminatory laws/policies, aggregated prejudicial attitudes)—is associated with advanced pubertal development. Specifically, baseline results demonstrated that Black girls living in states characterized by higher (vs. lower) structural racism experienced more advanced pubertal development across three hormonal indicators. At baseline, we also documented evidence of more advanced pubertal development in the form of external, physical markers of puberty among Latinx girls (via youth and caregiver reports) and boys (via caregiver report) in states with higher (vs. lower) structural xenophobia. These significant cross-sectional associations between structural stigma and advanced pubertal development among Black girls and Latinx youth persisted and often strengthened in magnitude one year later. In addition, a significant association between structural xenophobia and advanced pubertal development via youth-reported external, physical markers of puberty emerged among Latinx boys at Year 1. Significant associations between structural stigma and advanced pubertal development were generally comparable in effect size to associations between BMI and advanced pubertal development in our models. This finding suggests that structural stigma, although operating at a more distal level, may have a comparable influence on pubertal development to BMI, one of the most widely studied and robust predictors of early and advanced puberty (Huang et al., 2020 ; Li et al., 2017 ; Rosenfield et al., 2009 ; Song et al., 2023 ). Although structural racism and xenophobia were also associated with some indicators of advanced pubertal development among non-stigmatized comparators at baseline (i.e., non-Latinx White girls and boys), no such associations were observed among these comparison groups at Year 1. By comparison, significant associations between structural stigma and advanced pubertal development were persistently observed among Black girls (for hormones) and Latinx youth (for youth and/or caregiver report), providing some evidence of the consistency and specificity of these findings among these stigmatized (vs. non-stigmatized) groups. In sum, we observed significant associations between structural stigma and one or more indicators of advanced pubertal development among two of the three groups of stigmatized youth included in our study, which either persisted from baseline to Year 1 or emerged at Year 1 only among stigmatized (vs. non-stigmatized) groups. These findings have at least three important implications. First, they suggest that youth from these stigmatized groups living in US states characterized by higher (vs. lower) levels of structural stigma may begin puberty earlier or experience puberty faster than their same-aged peers, which would provide a plausible explanation for our observations of more advanced pubertal development at baseline and Year 1 among Black girls (for hormones) and Latinx youth (for youth and/or caregiver report) in higher-stigma contexts. As significant proportions of Black girls and Latinx girls and boys reside in US states ranking above the national average on our study’s measures of structural racism and xenophobia (US Census Bureau, 2021 ), their considerable exposure to more (vs. less) stigmatizing macro-social environments may partially contribute to the earlier ages of pubertal onset consistently documented among these two stigmatized groups relative to their non-stigmatized peers in the research literature (Anderson & Must, 2005 ; Biro et al., 2010 ; Chumlea et al., 2003 ; Herman-Giddens et al., 1997 , 2001 , 2012 ; Sun et al., 2002 ; Wu et al., 2002 ). Second, because significant baseline associations between structural stigma and indicators of advanced pubertal development among Black girls and Latinx girls and boys, when observed, remained significant one year later, it is possible that these youth may experience more persistent risk for a range of chronic adverse mental and physical health outcomes linked to advanced pubertal development (Cheng et al., 2022 ; Colich et al., 2020 ; Day et al., 2015 ; Hamlat et al., 2019 ; Kaltiala-Heino et al., 2003 ; Mendle et al., 2007 ; Ullsperger & Nikolas, 2017 ). Third, evidence of significant associations between structural stigma and separate indicators of advanced pubertal development among these two stigmatized groups—namely, hormones among Black girls but external, physical markers among Latinx girls and boys—may suggest that the macro-social contexts surrounding these groups influence interrelated yet distinct aspects of pubertal development captured by these measures (Cheng et al., 2021 ; Herting et al., 2021 ; Shirtcliff et al., 2009 ). Future research is needed to clarify the neuroendocrine pathways underlying these oftentimes discordant indicators of pubertal development (Avendaño et al., 2017 ; Dorn & Biro, 2011 ; Farello et al., 2019 ; Herting et al., 2021 ; Huang et al., 2012 ; Parent et al., 2015 ; Shirtcliff et al., 2009 ) and to determine why exposure to distinct manifestations of structural stigma might activate different pubertal processes, as may also be the case for other forms of social adversity (e.g., Hamlat et al., 2022 ; Zhang et al., 2021). Contrary to our expectations, we did not observe significant associations between structural sexism and advanced pubertal development among girls generally. Whereas our study’s measures of structural racism and xenophobia primarily included state-level policies and/or aggregated prejudicial attitudes, the structural sexism measure incorporated indicators of other societal conditions, including women’s access social, economic, educational, and political resources at the state level. The inclusion of these indicators might explain the lack of observed associations between structural sexism and advanced pubertal development among girls in a few ways. First, these indicators may better reflect macro-social contexts characterized by material deprivation, wherein diminished access to resources may be less conducive to reproduction and thus delay the onset of puberty (Colich et al., 2020 , 2023 ). Second, although these and similar indicators have been associated with adverse health outcomes among adult women (Homan, 2019 ; McLaughlin et al., 2011 ), they may be less relevant to emerging adolescent girls in the ABCD Study®. Indeed, prior studies have not reliably documented associations between our study’s measure of structural sexism and adverse developmental or psychosocial outcomes among girls during emerging adolescence (Hatzenbuehler et al., 2022 ; Martino et al., 2023 ). Future research is needed to evaluate these possibilities and to test the generalizability of our findings across other operationalizations of structural sexism. Whereas significant associations between structural stigma and indicators of advanced pubertal development were found among Black and Latinx girls at both baseline and Year 1, these associations were not observed among Black boys and were somewhat less consistently observed among Latinx boys. This pattern of findings may be attributable to documented gender differences in pubertal timing between girls and boys, with puberty typically beginning later among boys (Mendle et al., 2019 ; Rosenfield et al., 2009 ; Sun et al., 2002 ). Accordingly, associations between structural stigma and advanced pubertal development among racially and ethnically minoritized boys may emerge more reliably later in adolescence, which can be examined as additional waves of ABCD Study® data become available. Study findings provide converging support for a small but growing number of studies documenting associations between exposure to structural stigma and altered developmental and adverse psychosocial outcomes—including elevated psychopathology (Gordon et al., 2024 ; Martino et al., 2023 ; Slopen et al., 2023 ), reduced hippocampal volume (Hatzenbuehler et al., 2022 ), and dysregulated cortisol reactivity to stress (Hatzenbuehler & McLaughlin, 2014 )—among stigmatized youth. Further, one recent cohort study found evidence of more advanced pubertal development among racially and ethnically diverse girls living in US neighborhoods with more (vs. less) concentrated income among White (vs. racially/ethnically minoritized) residents (Acker et al., 2023 ). Taken together with results from our study, these findings provide suggestive evidence that exposure to structural stigma and other macro-social factors during childhood and adolescence may interfere with puberty and a range of other developmental and psychosocial processes, particularly among stigmatized youth. Future research is needed to identify specific mechanisms through which structural stigma may be associated with advanced pubertal development. Elevated corticotropin-releasing hormone, which has been linked to altered pubertal and hippocampal development in animals (e.g., Brunson et al., 2001 ; Kinsey-Jones et al., 2010 ; Li et al., 2014 ), may represent a pluripotent mechanism through which the chronic stress and/or lack of social safety associated with structural stigma exposure alters pubertal development and perhaps other developmental processes (Diamond & Alley, 2021; Hatzenbuehler et al., 2022 ). Our study has several notable methodological strengths that can guide future research into macro-social correlates of advanced pubertal development. First, we linked objective indicators of structural stigma to individual-level data from ABCD Study® youth from 17 US states varying in societal-level conditions (e.g., state-level policies, aggregated prejudicial attitudes) surrounding race, ethnicity/immigration status, and gender, offering unprecedented tests of associations between structural stigma and advanced pubertal development among Black youth, Latinx youth, and girls. Second, we tested associations between structural stigma and multiple indicators of advanced pubertal development—including levels of three pubertal hormones as well as youth and caregiver reports of external, physical markers of puberty at baseline and Year 1—enabling us to evaluate findings across measures, informants, and time. Third, we controlled for BMI, family SES in the form of mean caregiver educational attainment, and state-level income inequality, demonstrating that our results were robust to two established individual correlates of advanced pubertal development and to a broader feature of youth’s macro-social environment, respectively. Fourth, we ran negative control analyses with non-stigmatized comparators, providing evidence that significant associations between structural stigma and advanced pubertal development observed at baseline persisted at Year 1 only among stigmatized (vs. non-stigmatized) youth, and most consistently so for Black and Latinx girls. These strengths notwithstanding, there are several study limitations that might also inform future research on structural stigma and advanced pubertal development. First, although the ABCD Study® represents one of the largest investigations into child and adolescent development to date, findings might not generalize to youth from US states not included in the study. To the extent that the exclusion of some US states limited variability in ABCD Study® youth’s exposure to structural stigma, however, our findings are likely conservative. Future studies with even greater variability in exposure to structural stigma are needed to examine this possibility. Our focus on structural stigma at the state level was warranted given ABCD Study® youth’s differential exposure to US states that vary systematically in levels of structural racism, xenophobia, and sexism. However, future studies would benefit from incorporating measures of structural stigma at more proximal geographic levels (e.g., cities, counties), which would not only account for within-state variability in these study outcomes but might also exert a stronger influence on pubertal development, as has been shown for psychosocial outcomes (e.g., identity concealment; Lattanner et al., 2021 ). If that is the case, then our study once again provides a relatively conservative test of the association between structural stigma and advanced pubertal development. Second, all three structural stigma measures included indicators of individual prejudicial attitudes aggregated to the state level and pooled across available years. This approach offers several benefits, including that it reduces measurement error by ensuring that there are sufficient observations for each US state and that it comprises a range of years overlapping with the lifespan of ABCD Study® youth, thereby approximating the macro-social contexts surrounding these youth across development (Hatzenbuehler et al., 2022 ). At the same time, this aggregation method may not account for temporal variability in implicit and explicit social attitudes, which have become less biased towards women and racially and ethnically minoritized groups in the US in recent years (Charlesworth & Banaji, 2022 ). However, there is evidence that relative rankings of aggregated prejudicial attitudes towards these groups have remained generally stable between US states over this period of time (Chae et al., 2015 ; Charles et al., 2018 ; McKetta et al., 2017 ), supporting the validity of a time-invariant approach. Nevertheless, future studies should take advantage of emerging methods, such as natural language processing of media and language corpora (Charlesworth et al., 2022 ), which may enable scholars to develop time-variant structural stigma measures and thus pinpoint when during the life course structural stigma is most consequential to developmental outcomes such as puberty. Third, although we examined associations between structural stigma and multiple indicators of pubertal development at baseline and Year 1 of the ABCD Study®, these analyses were cross-sectional. We selected a repeated cross-sectional approach because we were interested in whether structural stigma was associated with advanced pubertal development at two different time points in emerging adolescence. Future studies might employ longitudinal designs to examine whether structural stigma is also associated with the tempo of pubertal development by modeling change in hormonal or external, physical indicators of puberty over time. Although we cannot infer causality from repeated cross-sectional analyses, we can be confident in ruling out reverse causation as an explanation for our findings because pubertal development would not be expected to affect societal-level conditions. Fourth, many ABCD Study® youth hold multiple stigmatized identities and are thus exposed to structural stigma at the intersection of race, ethnicity, and/or gender. To our knowledge, measures of intersectional stigma at the structural level do not yet exist. Scholars have sought to surmount this measurement shortcoming by modeling interactions between two or more structural stigma measures (Homan et al., 2021 ; Pachankis et al., 2017 ), but we were underpowered to do so. The consistent associations between structural stigma and advanced pubertal development observed among Black girls in our study—but neither among girls generally nor among Black boys—may indicate that structural racism overlaps and intersects with structural sexism to have a particularly profound impact on pubertal development among Black girls. Measurement advances in quantifying intersectional forms of structural stigma are needed to evaluate this possibility (Hatzenbuehler et al., 2024a ). Conclusion Research into correlates and predictors of advanced pubertal development has focused almost exclusively on aspects of individuals and their proximal environment (Argabright et al., 2022 ; Deardorff et al., 2014 ; Hamlat et al., 2022 ; Lee et al., 2010 ; Li et al., 2017 ; Seaton & Carter, 2019 ; Silventoinen et al., 2022 ). We provide some of the first empirical evidence that macro-social factors—measured here in the form of structural stigma at the state level—are also associated with advanced pubertal development. Although significant associations between structural stigma and advanced pubertal development were observed among stigmatized and non-stigmatized youth at baseline, they persisted one year later only among the stigmatized, most consistently for Black and Latinx girls. Our study underscores the need for future scholarship to evaluate whether advanced pubertal development is implicated in the link between structural stigma and adverse psychosocial outcomes among these stigmatized groups (Martino et al., 2023 ; Slopen et al., 2023 ). Moreover, it provides a novel framework for broadening the lens of development research to consider features of macro-social contexts, including structural stigma, which may provide new insights into determinants of puberty and perhaps other developmental processes (Hatzenbuehler et al., 2024b ). Methods Participants and Procedures Participant data from the ABCD Study® were acquired from the NIMH Data Archive (ABCD Data Release 3.0; https://abcdstudy.org ). The present study drew data from baseline and Year 1 assessments conducted with ABCD Study® participants enrolled at one of 21 study sites located in 17 US states. ABCD Study® youth enrolled at a now defunct site were excluded (Dick et al., 2021 ). At baseline, study participants included 11,844 youth (predominantly ages 9–10; M = 9.9, SD = 0.62) and their caregivers. At Year 1, 11,225 participating youth (predominantly ages 10–11; M = 10.9, SD = 0.64) and their caregivers were retained. Our primary analytic samples included 5,662 girls at baseline and 5,352 girls at Year 1; 2,507 Black youth at baseline and 2,274 Black youth at Year 1; and 2,406 Latinx youth at baseline and 2,222 Latinx youth at Year 1 (see Supplementary Table 33 for demographics and other study variables by stigmatized group at baseline and Year 1). Girls were identified via caregiver-reported birth-assigned sex, and Black and Latinx youth were identified via caregiver-reported race and ethnicity. As caregivers could report multiple racial and ethnic identities, we included all youth with a racial identity of Black in primary analyses related to structural racism and all youth with an ethnic identity of Latinx in primary analyses related to structural xenophobia. ABCD Study® families with lower (vs. higher) mean caregiver educational attainment were less likely to be retained at Year 1; attrition did not vary as a function of youth’s age, birth-assigned sex, race, ethnicity, or structural stigma context. ABCD Study® recruitment methods and procedures are detailed elsewhere and were approved by Institutional Review Boards at each of the 21 study sites (Barch et al., 2018 ; Garavan et al., 2018 ; Karcher & Barch, 2021 ; Uban et al., 2018 ). Measures Structural Stigma . Structural stigma specific to gender (i.e., structural sexism), Black race (i.e., structural racism), and Latinx ethnicity/immigration status (i.e., structural xenophobia) was quantified at the state level using established measures of these constructs (Hatzenbuehler et al., 2022 ). Consistent with prior research (Hatzenbuehler et al., 2022 , 2024a ; Lattanner et al., 2021 ) and theory (Hatzenbuehler, 2016 ; Hatzenbuehler & Link, 2014 ) on structural stigma, these measures incorporated publicly available indicators of laws/policies, aggregated (implicit and explicit) prejudicial attitudes, and/or other societal-level conditions as proxies for states’ macro-social climates surrounding the three stigmatized groups included in our primary analyses: girls, Black youth, and Latinx youth. The exploratory factor analytic (EFA) methods for selecting and combining indicators for each measure are extensively described elsewhere (Hatzenbuehler et al., 2022 ). Briefly, candidate indicators for each structural stigma measure with a factor loading ≥ 0.40 were retained and used to create model-generated factor scores for structural sexism, racism, and xenophobia in each US state (see Supplementary Table 34 for structural stigma factor scores). These structural stigma measures have been previously associated with adverse neurodevelopmental (e.g., reduced hippocampal volume; Hatzenbuehler et al., 2022 ) and psychosocial (e.g., elevated psychopathology, decreased psychological intervention efficacy; Martino et al., 2023 ; Price et al., 2021 , 2022 ; Slopen et al., 2023 ) outcomes among stigmatized youth, including Black and Latinx youth as well as girls, providing evidence of their construct validity. The use of EFA further supports these measures’ validity (i.e., because indicators loaded onto single latent constructs of structural sexism, racism, and xenophobia) and increases their reliability (i.e., because measurement error is reduced by tapping into shared variance among indicators). Indicators and data sources for these structural stigma measures are summarized below and provided in Supplementary Table 35. Structural Sexism . The structural sexism measure included 18 single-item or composite indicators. Six indicators reflected women’s social (e.g., percent of women living in counties without an abortion provider), economic (e.g., women’s labor force participation), and political (e.g., women’s voter registration) autonomy at the state level (Hatzenbuehler et al., 2022 ). These indicators have been utilized in previous studies to quantify structural sexism (Homan, 2019 ; McLaughlin et al., 2011 ) and were acquired from public sources such as the Bureau of Labor Statistics, Current Population Survey, Institute for Women’s Policy Research, and Guttmacher Institute. Twelve additional indicators captured aggregated implicit (e.g., automatic associations of gender with science and careers) and explicit attitudes towards gender and women’s social status. Attitudinal indicators were obtained from Project Implicit (2003–2018) and the General Social Survey (1974–2014), pooled across available years, and aggregated to the state level. Structural Racism . The structural racism measure comprised 31 indicators broadly capturing aggregated explicit attitudes towards Black people. To create these indicators, individual responses to attitudinal items from Project Implicit (2002–2017), the General Social Survey (1973–2014), and the American National Election Survey (1992–2016) were pooled across time and aggregated to the state level. Items encompassed various dimensions of anti-Black prejudice, including support for policies limiting the rights and welfare of Black people and the endorsement of racial stereotypes. While other indicators of structural racism were considered in the EFA, only attitudinal indicators loaded onto this single factor, which may reflect the fact that anti-Black racial prejudice is often highest in US states where the population of Black residents is too small to reliably quantify other societal conditions (e.g., voter disenfranchisement, residential segregation) that have also been used to measure structural racism at the state level (Homan et al., 2021 ; Lukachko et al., 2014 ). Structural Xenophobia . The measure of structural xenophobia consisted of two single-item indicators and one composite indicator. The single-item indicators represented aggregated explicit attitudes towards Latinx people and towards immigrants acquired from the American National Election Survey. Individual responses to these two attitudinal items were pooled across available years (1996–2016 and 2004–2016, respectively) and aggregated to the state level. The third item included in this measure was a composite index summing the presence of 10 state laws/policies protective or prohibitive of immigrants’ rights (e.g., permitting application for a driver’s license irrespective of immigration status; Rhodes et al., 2020 ). Although not necessarily specific to Latinx people, attitudinal and policy indicators of the social climate surrounding immigrants were included in this measure because of the high salience of anti-immigration policies to Latinx people, the frequent conflation of Latinx ethnicity and immigration status in the US, and the concealability of immigration status, all of which put Latinx people at disproportionate risk of experiencing xenophobia and its consequences (Almeida et al., 2016 ; Morey, 2018 ; Vargas et al., 2017 ; Viruell-Fuentes et al., 2012 ). Indeed, exposure to higher (vs. lower) levels of structural xenophobia has been consistently associated with negative health outcomes among Latinx people irrespective of nativity status (Hatzenbuehler et al., 2017 ), including adverse neurodevelopmental (Hatzenbuehler et al., 2022 ) and psychological outcomes (Martino et al., 2023 ; Slopen et al., 2023 ) among Latinx youth. Hormonal Indicators of Advanced Pubertal Development . Salivary levels of three hormones were used to assess pubertal development at baseline and Year 1: DHEA and testosterone among girls and boys and estradiol among girls only. Salivary biomarkers were acquired from whole saliva collected via passive drool by trained research assistants. Additional information on the methods for collecting and assaying these salivary samples is provided elsewhere (Herting et al., 2021 ; Uban et al., 2018 ). Hormone levels were residualized on youth’s chronological age to index pubertal age for analysis (Colich et al., 2023 ; Sumner et al., 2019 ); higher (i.e., more positive) residuals represented more advanced pubertal development. Youth- and Caregiver-Reported External, Physical Markers of Advanced Pubertal Development . We also assessed youth and caregiver reports of external, physical markers of pubertal development at baseline and Year 1 using the Pubertal Development Scale (PDS; Petersen et al., 1998). We did so because pubertal hormone levels capture interrelated yet distinct aspects of pubertal development from these external, physical indicators, with which they are only modestly correlated (Cheng et al., 2021 ; Herting et al., 2021 ; Mendle et al., 2019 ; Shirtcliff et al., 2009 ). Five PDS items assessed puberty-related changes in height, body hair, facial hair, skin, and voice among boys. Four PDS items captured puberty-related changes in height, body hair, skin, and breast development among girls, and the fifth queried menarche status. Except for the latter item, which was rated 1 ( no ) or 4 ( yes ) for girls, PDS items were rated on a Likert-scale from 1 ( has not yet begun ) to 4 ( seems completed ). Internal consistency was adequate for youth report (girls: α = 0.61–0.70; boys: α = 0.51–0.61) and caregiver report (girls: α = 0.71–0.77; boys: α = 0.58–0.68). Items were averaged to compute mean youth- and caregiver-reported PDS scores, which were residualized on youth’s chronological age to quantify pubertal age for study analyses; higher (i.e., more positive) residuals represented more advanced pubertal development. For supplemental analyses of external, physical markers of pubertal development, we derived youth- and caregiver-reported PDS scores specific to adrenarche and gonadarche. Following prior studies (Herting et al., 2021 ; Shirtcliff et al., 2009 ), gonadal PDS scores were computed for girls by averaging growth, breast development, and menarche items and for boys by averaging voice and facial hair items boys. Adrenal PDS scores were calculated by averaging body hair and skin items for both girls and boys. We also created categorical youth- and caregiver-reported PDS scores (Acebo & Carskadon, 1993 ). For boys, the sum of body hair, facial hair, and voice items were first categorized as 3 ( pre-puberty ), 4–5 ( early-puberty ), 6–8 ( mid-puberty ), 9–11 ( late-puberty ), and 12 ( post-puberty ). For girls, menarche status and the sum of body hair and breast development items were first categorized as 2 without menarche ( pre-puberty ), 3 without menarche ( early-puberty ), ≥ 3 without menarche ( mid-puberty ), ≤ 7 with menarche ( late-puberty ), and 8 with menarche ( post-puberty ). Because prior analyses of ABCD Study® data have documented low endorsement of late-puberty and post-puberty PDS categories at baseline and Year 1 (Thijssen et al., 2020 ), we then combined mid-, late-, and post-puberty status into a single category. Gonadal/adrenal and categorical PDS scores were residualized on youth’s chronological age prior to supplemental analysis. Covariates . Drawing on prior research on both structural stigma (e.g., Hatzenbuehler et al., 2022 ) and pubertal development (e.g., Herting et al., 2021 ), we accounted for two individual covariates in all analyses. First, we controlled for family SES in the form of mean caregiver educational attainment, as reported on a range from 0 ( never attended school or only attended kindergarten ) to 21 ( doctoral degree ). Mean caregiver educational attainment was used as a proxy for family SES given substantial missingness in data on mean household income. Second, we also controlled for BMI—calculated as the ratio of youth’s weight to height (measured up to three times each to ensure consistency and then averaged)—given its robust association with pubertal development (Herting et al., 2021 ; Huang et al., 2020 ; Li et al., 2017 ; Rosenfield et al., 2009 ; Song et al., 2023 ; Thijssen et al., 2020 ). Outliers for BMI were winsorized at ± 3-SD from the mean. We additionally controlled for race/ethnicity (i.e., Latinx, non-Latinx White, non-Latinx Black, non-Latinx Asian, other racial/ethnic identities) in primary analyses with girls. We did not control for birth-assigned sex or age because analyses were stratified by birth-assigned sex and outcomes were residualized on chronological age. Analyses with hormonal indicators of pubertal development included an additional control for the time of salivary hormone sampling in hours since midnight. Finally, all analyses controlled for state-level income inequality using the Gini coefficient, which quantifies income maldistribution on a scale from 0 ( perfect equality ) to 1 ( perfect inequality ) and was acquired for included US states from the American Community Survey (US Census Bureau, 2018 ). Controlling for state-level inequality enabled us to examine whether observed associations between structural stigma and advanced pubertal development were robust to a broader feature of stigmatized youth’s macro-social context that might be expected to influence pubertal development among youth generally. Statistical Analysis Preregistration . Study analyses were preregistered on OSF ( https://osf.io/zm62s/ ). We note a few necessary deviations from our preregistration below. Power Analysis . Preregistered power analyses indicated that we were adequately powered (> 90%) to detect small effect sizes for all pubertal outcomes among stigmatized and non-stigmatized groups at baseline. We were similarly powered (> 90%) to detect small effect sizes for hormonal indicators of advanced pubertal development among these groups at Year 1. Although well powered (> 90%) to detect small effect sizes for youth- and caregiver-reported external, physical markers of advanced pubertal development among girls at Year 1, we only had adequate power (> 90%) to detect medium effect sizes for these outcomes among Black and Latinx boys and girls at Year 1. This somewhat lower available power was attributable to smaller samples of Black and Latinx youth and caregivers completing these assessments at Year 1; although this attrition was not associated with levels of structural stigma, findings from these analyses should be interpreted cautiously given this reduced statistical power. Data Transformation . We performed several preregistered data transformations prior to analysis. First, consistent with prior work and research recommendations (Chafkin et al., 2022 ; Ho et al., 2020 ; King et al., 2020 ; Shirtcliff et al., 2009 ; Sollberger & Ehlert, 2016 ), salivary levels of pubertal hormones were log-transformed, and outliers were winsorized at ± 3-SD from the mean. Transformations were performed separately for girls and boys at baseline and Year 1 given documented gender differences in pubertal timing and thus the need to stratify relevant analyses by birth-assigned sex (Euling et al., 2008 ; Hoyt et al., 2020 ; Rosenfield et al., 2009 ; Sun et al., 2002 ). Second, to model outcomes of advanced pubertal development, we regressed chronological age from log-transformed hormonal indicators of pubertal development and from youth- and caregiver-reported PDS scores separately for girls and boys at baseline and Year 1 (Colich et al., 2023 , Hamlat et al., 2014 ; Sumner et al., 2019 ). Positive residuals indexed more advanced pubertal development relative to chronological age, whereas negative residuals indexed less advanced pubertal development. Primary Analysis . We fit linear mixed-effects models for each advanced pubertal development outcome variable among each stigmatized group (i.e., girls, Black girls and boys, and Latinx girls and boys) separately at baseline and at Year 1. Analysis for Black and Latinx youth were stratified by birth-assigned sex. For each model, the relevant structural stigma measure (i.e., structural sexism for girls, structural racism for Black girls and boys, and structural xenophobia for Latinx girls and boys) and study covariates were specified as fixed effects. To account for the nested nature of the data, we included random intercepts for family and for the US state affiliated with each ABCD Study® site. We originally planned to include a random intercept for ABCD Study® site; however, we ultimately included a random intercept for US state because of the high overlap between sites and US states and because the latter provided an even more rigorous control for unmeasured confounding at the state level. Linear mixed-effects models were fit using the “glmmTMB” package in R given its ability to estimate model parameters reliably when random-effects variance is small (Brooks et al., 2017 ). Negative Control Analysis . As a form of negative control analysis (Lipsitch et al., 2010 ), we reran our primary models among non-stigmatized comparison groups consisting of youth who did not hold the stigmatized identity corresponding to each structural stigma predictor. Specifically, we performed negative control analyses at baseline and Year 1 for structural sexism among boys, structural racism among non-Latinx White girls and boys, and structural xenophobia among non-Latinx White girls and non-Latinx White boys. This approach enabled us to assess whether any associations between structural stigma and advanced pubertal development observed among stigmatized groups in our study were also observed among their non-stigmatized comparators. Because several studies have documented significant associations between structural stigma and adverse health outcomes among both stigmatized and non-stigmatized populations, especially when attitudinal measures of structural stigma are used (Lee et al., 2015 ; McKetta et al., 2017 ; Michaels et al., 2022 ; Nguyen et al., 2020 ), these negative control analyses elucidated when and how consistently significant associations between structural stigma and advanced pubertal development emerged only among stigmatized (vs. non-stigmatized) youth. For example, documenting significant associations between structural stigma at both baseline and Year 1 among at least some stigmatized (vs. non-stigmatized) youth might suggest that these groups face more persistent risk for chronic adverse health outcomes linked to advanced pubertal development (Cheng et al., 2022 ; Colich et al., 2020 ; Day et al., 2015 ; Hamlat et al., 2019 ; Mendle et al., 2007 ; Ullsperger & Nikolas, 2017 ). Supplemental Analysis . We preregistered several supplemental analyses to assess the robustness of our findings. First, we reran our primary analysis of external, physical markers of pubertal development using youth- and caregiver-reported PDS items specific to adrenarche and gonadarche (residualized on chronological age) as outcomes because they represent largely distinct pubertal processes with respect to time (i.e., adrenarche typically precedes gonadarche) and their hormonal correlates (i.e., androgens vs. gonadotropins; Mendle et al., 2019 ). Second, given the relatively low endorsement of latter pubertal stages previously documented among ABCD Study® youth at baseline and Year 1 (Thijssen et al., 2022), we repeated our primary analysis using three categories of youth- and caregiver-reported PDS scores (residualized on chronological age): pre-puberty, early-puberty, and mid-to-post-puberty. Third, in order to establish that any observed associations between structural stigma and hormonal indicators of advanced pubertal development were not simply driven by post-menarche changes in hormone levels among girls, we reran relevant models excluding girls who had experienced menarche. Declarations Data Transparency and Openness . Analyses were conducted in R (version 4.4.0; R Core Team, 2024). Analytic code is available on OSF (https://osf.io/zm62s/). Thresholds for statistical significance were set at p <0.05. As missingness for study covariates was minimal and at random, we conducted complete case analysis. We preregistered supplemental analyses using multiple imputation to handle potential missingness in study covariates. However, we did not conduct these analyses because there was minimal missing data for study covariates and because cluster sizes for ABCD Study® families were too small for multilevel imputation to produce reliable estimates (Audigier et al., 2018). Reporting Summary . Additional information on our study design is available in the Nature Portfolio Reporting Summary linked to this article. Author Contribution R.M. and N.H. developed the study, analyzed the data, wrote the manuscript text, and prepared the figures. M.H., K.M., and N.C. assisted with study design, analysis, and writing the manuscript text. All authors reviewed the manuscript. Data Availability The present study is a secondary analysis of data from the Adolescent Brain Cognitive Development study, a publicly available dataset. Full information on the data collection can be found at https://abcdstudy.org/. ABCD Study® data can be accessed via a data use agreement with the NIMH Data Archive. References Acebo, C. & Carskadon, M. A. A self-administered rating scale for pubertal development. J. Adolesc. Health . 14 (3), 190–195. https://doi.org/10.1016/1054-139x(93)90004-9 (1993). Acker, J. et al. 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Adverse childhood experiences and early pubertal timing among girls: A meta-analysis. Int. J. Environ. Res. Public Health . 16 (16). Article 2887. https://doi.org/10.3390/ijerph16162887 (2019). Zhou, X. et al. Overweight/obesity in childhood and the risk of early puberty: A systematic review and meta-analysis. Front. Pead. 10 , 795596. https://doi.org/10.3389%2Ffped.2022.795596 (2022). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5356422","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":388601556,"identity":"47365e1e-0ff4-4bd4-bfc9-1e4c5b97246e","order_by":0,"name":"Rachel Martino","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYNACAwYGfuYDDAyMDRC+BAMDM34dB4BaJNsSSNICsugYsVrMpx1+9vlDwR0542PMjz8w7rDLNzh+9uENhgrrxAYcWmRupxnPOGDwzNjsGJuZBOOZZMsNZ9KNLRjOpOPUIiGdYAz0y+HEbfcbzBgY25gNJBvS2CQY2w7j0ZL+Gaxlcxv75w+MbfUGkv3PgFr+4dOSA7FlAxuPAchwA34JkC0NeLUUM5wxOGwscYynTCKx7ThQyzNmi4Rj6cZ4HLaZoeLPYTn+NvbNHz62VRuw8acx3vhQYy2LSwsqSMBgjIJRMApGwSggCwAAW9FVGP5eF1IAAAAASUVORK5CYII=","orcid":"","institution":"Harvard University","correspondingAuthor":true,"prefix":"","firstName":"Rachel","middleName":"","lastName":"Martino","suffix":""},{"id":388601557,"identity":"e8cafc9d-c905-4e67-a6d1-0e0329b22231","order_by":1,"name":"Nathan Hollinsaid","email":"","orcid":"","institution":"Harvard University","correspondingAuthor":false,"prefix":"","firstName":"Nathan","middleName":"","lastName":"Hollinsaid","suffix":""},{"id":388601558,"identity":"90919675-e887-40d5-9526-6da501b0df4c","order_by":2,"name":"Natalie Colich","email":"","orcid":"","institution":"Harvard University","correspondingAuthor":false,"prefix":"","firstName":"Natalie","middleName":"","lastName":"Colich","suffix":""},{"id":388601559,"identity":"df25d27f-e16d-4a32-a8ac-2f41ae9a5739","order_by":3,"name":"Katie McLaughlin","email":"","orcid":"","institution":"Harvard University","correspondingAuthor":false,"prefix":"","firstName":"Katie","middleName":"","lastName":"McLaughlin","suffix":""},{"id":388601560,"identity":"5ac146c4-3564-437e-908c-03fac72f064f","order_by":4,"name":"Mark Hatzenbuehler","email":"","orcid":"","institution":"Harvard University","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Hatzenbuehler","suffix":""}],"badges":[],"createdAt":"2024-10-29 18:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5356422/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5356422/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-00378-8","type":"published","date":"2025-05-21T15:58:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71163624,"identity":"1dbfbbf2-06ea-4370-8676-093874ebad75","added_by":"auto","created_at":"2024-12-11 16:57:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":246872,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAssociations Between Structural Racism and Hormonal Indicators of Advanced Pubertal Development Among Black Girls\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote:\u003c/strong\u003e\u003c/em\u003e Panel A shows significant associations between structural racism and three hormonal indicators of advanced pubertal development among Black girls at baseline: estradiol (\u003cem\u003ep\u003c/em\u003e=0.006**), DHEA (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001***), and testosterone (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001***). Panel B shows that the associations between structural racism and all three hormonal indicators of advanced pubertal development remained significant among Black girls at Year 1: estradiol (\u003cem\u003ep\u003c/em\u003e=0.033*), DHEA (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001***) and testosterone (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001***).\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5356422/v1/dc10f0ba6df403caea3e610b.png"},{"id":71163610,"identity":"db000cd6-a27f-4e00-8d7e-611ab6ae32c4","added_by":"auto","created_at":"2024-12-11 16:57:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":170158,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAssociations Between Structural Xenophobia and Caregiver- and Youth-Reported External, Physical Markers of Advanced Pubertal Development Among Latinx Girls\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote:\u003c/strong\u003e\u003c/em\u003e Panel A shows significant associations between structural xenophobia and both caregiver-reported external, physical markers (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001***) and youth-reported external, physical markers (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001***) of advanced pubertal development among Latinx girls at baseline. Panel B shows that these associations between structural xenophobia and caregiver-reported external, physical markers (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001***) and youth-reported external, physical markers (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001***) remained significant among Latinx girls at Year 1.\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5356422/v1/42548b82cafb6979894d96e3.png"},{"id":71163612,"identity":"01eaac83-164d-4e54-9b0e-8855506a4043","added_by":"auto","created_at":"2024-12-11 16:57:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":170780,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAssociations Between Structural Xenophobia and Caregiver- and Youth-Reported External, Physical Markers of Advanced Pubertal Development Among Latinx Boys\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote:\u003c/strong\u003e\u003c/em\u003e Panel A shows a significant association between structural xenophobia and caregiver-reported external, physical markers (\u003cem\u003ep\u003c/em\u003e=.005**) of advanced pubertal development among Latinx boys at baseline. Although structural xenophobia was not significantly associated with youth-reported external, physical markers of advanced pubertal development among Latinx boys at baseline, Panel B shows that structural xenophobia was significantly associated with both caregiver-reported external, physical markers (\u003cem\u003ep\u003c/em\u003e=0.009**) and youth-reported external, physical markers (\u003cem\u003ep\u003c/em\u003e=0.040*) of advanced pubertal development among Latinx boys at Year 1.\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5356422/v1/4cb465accbb473b579243d53.png"},{"id":83460681,"identity":"38a1501f-9fba-454b-a2a6-5eb885492575","added_by":"auto","created_at":"2025-05-26 16:13:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2655071,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5356422/v1/6b36b571-a2f3-4e35-8591-a9329fd8c254.pdf"},{"id":71163970,"identity":"a018f8a9-d746-49b6-8114-533fe1fb358e","added_by":"auto","created_at":"2024-12-11 17:05:50","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":206866,"visible":true,"origin":"","legend":"","description":"","filename":"ABCDPubertySupplement91824.docx","url":"https://assets-eu.researchsquare.com/files/rs-5356422/v1/b8f518a9d7c52ab3a77dfa6c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations Between Structural Stigma and Advanced Pubertal Development Persist for One Year Among Black Girls and Latinx Youth","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePuberty represents a critical developmental process that involves the complex interplay of hormonal and other neurobiological processes in order to prepare the body for physical maturation and sexual reproduction (Dorn et al., 2019). The timing of puberty has declined steadily among youth in the United States (US) and globally, with youth today beginning puberty up to two years earlier than youth several decades ago (Aksglaede et al., 2009; Eckert-Lind et al., 2020; Herman-Giddens et al., 2012; Hoyt et al., 2020; Ohlsson et al., 2019). Children and adolescents with stigmatized identities\u0026mdash;including girls, Black youth, and Latinx youth\u0026mdash;exhibit earlier pubertal timing and more advanced pubertal development relative to their same-aged, non-stigmatized peers (Chumlea et al., 2003; Herman-Giddens et al., 1997, 2001, 2012; Susman et al., 2010). For example, girls begin puberty up to 1.5 years earlier than boys in the US (Euling et al., 2008; Kaplowitz, 2004; Rosenfield et al., 2009; Sun et al., 2002). Black and Latinx girls experience earlier breast development (i.e., thelarche) and menstruation (i.e., menarche) compared to White girls (Biro et al., 2010, 2013; Chumlea et al., 2003; Herman-Giddens et al., 1997). Specifically, one study found that by age 8, only 18% of White girls had entered thelarche compared to 31% of Latinx girls and 43% of Black girls (Biro et al., 2010). Another study found that Black and Latinx girls, respectively, reached menarche four and six months earlier than White girls (Chumlea et al., 2003). Likewise, Black boys experience more advanced pubertal development than White boys, including earlier pubic hair (i.e., adrenarche) and genital (i.e., gonadarche) growth (Herman-Giddens et al., 2012). Furthermore, elevated levels of pubertal hormones have been observed among Black girls and Black boys relative to their same-aged, White peers (Herting et al., 2021).\u003c/p\u003e\n\u003cp\u003eAdvanced pubertal development has been linked to a number of adverse physical and mental health consequences (Mendle et al., 2007), such as diabetes (Janghorbani et al., 2014), depression and anxiety (Colich et al., 2020; Hamlat et al., 2019; Natsuaki et al., 2009), externalizing disorders (Platt et al., 2017), and substance use (Downing \u0026amp; Bellis, 2009). These consequences are especially prominent among girls (Barendse et al., 2022; Colich et al., 2020) and among racially and ethnically minoritized youth (Bleil et al., 2017; Deardorff et al., 2021; Ge et al., 2006; Hamlat et al., 2014). For example, girls who experience earlier (vs. later) pubertal development are at increased risk for breast cancer (Goldberg et al., 2020), internalizing and externalizing psychopathology (Barendse et al., 2022; Hamilton et al., 2014; Platt et al., 2017), and early sexual activity (Baams et al., 2016). Additionally, earlier (vs. later) breast development has been linked to more severe mental health symptoms among Latinx girls (Deardorff et al., 2021) and Black girls (Keenan et al., 2014). Similarly, earlier (vs. later) pubertal onset has been associated with depressive symptoms and externalizing symptoms among Black boys (Ge et al., 2016; Hamlat et al., 2014). These studies highlight the significant need to identify and intervene on determinants of advanced pubertal development, particularly among stigmatized youth.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch into correlates and predictors of advanced pubertal development among youth has overwhelmingly focused on aspects of individuals and their proximal environment, including body-mass index (BMI; Silventoinen et al., 2022); indicators of family socioeconomic status (SES) such as household income (Deardorff et al., 2014) and caregiver educational attainment (Colich, et al., 2020); and perceived discrimination (Argabright et al., 2022) as well as other forms of threat-related adversity (e.g., childhood emotional, physical, and sexual abuse; Colich, et al., 2020). This work identifies associations between many of these factors\u0026mdash;particularly BMI and threat-related adversities\u0026mdash;and the timing of puberty, demonstrating that they not only represent important determinants of puberty among youth generally, but also that they may partially explain well-established disparities in pubertal development between stigmatized and non-stigmatized youth (Argabright et al., 2022; Deardorff et al., 2014; Hamlat et al., 2022; Reagan et al., 2012; Seaton \u0026amp; Carter, 2019; Silventoinen et al., 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough these studies have provided important insights into individual determinants of advanced pubertal development, the literature has largely ignored broader contextual factors that might also contribute to advanced pubertal development among youth, despite repeated calls for such scholarship (Carter \u0026amp; Seaton, 2024; Deardorff et al., 2019; Klopack et al., 2020; Neblett \u0026amp; Neal, 2022). One notable exception is a recent cohort study by Acker and colleagues (2023), which documented more advanced pubertal development among racially and ethnically diverse girls living in neighborhoods with more (vs. less) concentrated income.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe present study provides a novel framework for widening the study of determinants of advanced pubertal development to the macro-social level by incorporating measures of structural stigma, defined as \u0026ldquo;societal-level conditions, cultural norms, and institutional policies and practices that constrain the opportunities, resources, and wellbeing of the stigmatized\u0026rdquo; (Hatzenbuehler \u0026amp; Link, 2014, \u003cem\u003ep\u003c/em\u003e. 2). Exposure to structural stigma during childhood and adolescence is not only a fundamental driver of health disparities between\u0026nbsp;stigmatized and non-stigmatized youth (Charlton et al., 2019; Hatzenbuehler, 2017; Hatzenbuehler et al., 2014, 2015; Martino et al., 2023; Raifman et al., 2017), but it is also an important source of within-group heterogeneity in adverse developmental and psychosocial outcomes among\u0026nbsp;populations of stigmatized youth (Gordon et al., 2024; Hatzenbuehler et al., 2024a; Slopen et al., 2023). For example, Black and Latinx youth living in states characterized by higher (vs. lower) levels of structural racism and structural xenophobia, respectively, have reduced hippocampal volume (Hatzenbuehler et al., 2022) and higher levels of psychopathology (Martino et al., 2023; Slopen et al., 2023). Moreover, sexual minority (i.e., lesbian, gay, and bisexual) young adults raised in US states with higher (vs. lower) levels of structural homophobia demonstrate blunted cortisol reactivity to stress (Hatzenbuehler \u0026amp; McLaughlin, 2014). Life history theory (Hill \u0026amp; Kaplan, 1999; Ellis et al., 2009) and dimensional models of childhood adversity (Ellis et al., 2022; McLaughlin et al., 2021; McLaughlin \u0026amp; Sheridan, 2016) suggest that developmental processes such as puberty may be accelerated in environments characterized by greater threat in order to maximize the opportunity for reproduction before mortality. Because structural stigma has been conceptualized as a chronic form of threat-related adversity (Cardona et al., 2022; Christian et al., 2021; Diamond \u0026amp; Alley, 2022; Hollinsaid et al., 2023), we sought to interrogate structural stigma as a macro-social correlate of advanced pubertal development among stigmatized youth.\u003c/p\u003e\n\u003cp\u003eAnswering this question required a novel data structure atypical of most studies on pubertal development to date. First, we needed a dataset examining pubertal development among stigmatized youth exposed to macro-social contexts varying in structural stigma. Second, we needed a dataset assessing multiple indicators of pubertal development (e.g., hormone levels, physical markers). This data feature would permit us to examine whether the association between structural stigma and advanced pubertal development differs across indicators of potentially distinct pubertal processes, given evidence of relatively low correspondence between hormonal and external, physical indicators of pubertal development (Cheng et al., 2021; Herting et al., 2021; Mendle et al., 2019; Shirtcliff et al., 2009). Third, the dataset would need to include measures of individual correlates of pubertal development in order to ensure that structural stigma remains associated with advanced pubertal development even after accounting for aspects of individuals (e.g., BMI) and of their proximal environment (e.g., family SES) that might also be associated with pubertal development. Fourth, we needed a dataset that also included youth not holding the stigmatized identities of interest to serve as negative control groups to evaluate the consistency and specificity of findings among stigmatized (vs. non-stigmatized) youth (Lipsitch et al., 2010). Finally, we required a longitudinal dataset in order establish the consistency of results at separate points in development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFortunately, a dataset meeting these requirements recently became available. Specially, the current study leveraged baseline and Year 1 (i.e., one-year follow-up) data from the Adolescent Brain Cognitive Development (ABCD) Study\u0026reg; to consider whether exposure to higher (vs. lower) levels of structural stigma was associated with advanced pubertal development among three groups of stigmatized youth. The ABCD Study\u0026reg; was uniquely situated to address this research question. It represents one of the largest multisite and longitudinal studies of child and adolescent development to date, enrolling 11,844 youth (primarily ages 9\u0026ndash;10) at baseline from 21 sites located in 17 US states that vary substantially in sociopolitical context surrounding various stigmatized populations. We focused on three stigmatized groups\u0026mdash;girls, Black youth, and Latinx youth\u0026mdash;given ample evidence of advanced pubertal development among these populations (Chumlea et al., 2003; Herman-Giddens et al., 1997, 2001, 2012; Susman et al., 2010) and their considerable representation in the ABCD Study\u0026reg; (see \u0026ldquo;Methods\u0026rdquo; for sample sizes and demographics). In order to capture the distinct development processes implicated in puberty (Cheng et al., 2021; Dorn \u0026amp; Biro, 2011; Herting et al., 2021; Mendle et al., 2019; Shirtcliff et al., 2009), we examined salivary levels of three pubertal hormones (i.e., estradiol among girls; DHEA and testosterone among girls and boys) as well as youth and caregiver reports of external, physical markers of pubertal development as outcomes. To index advanced pubertal development in the form of pubertal age, these variables were residualized on chronological age (Colich et al., 2023; Sumner et al., 2019). In preregistered hypotheses informed by life history theory (Ellis et al., 2009; Hill \u0026amp; Kaplan, 1999) and dimensional models of childhood adversity (Ellis et al., 2022; McLaughlin et al., 2021; McLaughlin \u0026amp; Sheridan, 2016), as well as by prior research on structural stigma and other development and psychosocial outcomes (Hatzenbuehler et al., 2022, 2024a; Hatzenbuehler \u0026amp; McLaughlin, 2014; Martino et al., 2023; Slopen et al., 2023), we predicted that Black youth, Latinx youth, and girls living in ABCD Study\u0026reg; states characterized by higher (vs. lower) levels of structural stigma specific to these groups would evidence more advanced pubertal development at baseline and Year 1.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn preregistered analyses, we used linear mixed-effects models to examine associations between structural racism, xenophobia, and sexism\u0026mdash;quantified objectively using existing measures of state-level policies, aggregated prejudicial attitudes, and/or other societal conditions (Hatzenbuehler et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u0026mdash;and hormonal indicators as well as youth- and caregiver-reported external, physical markers of pubertal development among Latinx youth, Black youth, and girls, respectively. Outcome variables were first regressed on chronological age to index advanced pubertal development, and relevant analyses were stratified by birth-assigned sex. We tested these associations cross-sectionally at baseline and Year 1 of the ABCD Study\u0026reg;, enabling us to assess the consistency of results across two points in time during emerging adolescence. We controlled for BMI, family SES in the form of mean caregiver educational attainment, and state-level income inequality in order to evaluate whether structural stigma remained associated with advanced pubertal development over and above individual and proximal correlates of pubertal development as well as a broader contextual indicator of social inequality. Finally, as a form of negative control analysis (Lipsitch et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), we reran our primary models among non-stigmatized comparison groups (e.g., non-Latinx White girls for primary analyses conducted with Latinx girls), allowing us to evaluate whether and how consistently associations between structural stigma and advanced pubertal development were specific to stigmatized (vs. non-stigmatized) youth.\u003c/p\u003e\n\u003ch3\u003eStructural Stigma and Hormonal Indicators of Advanced Pubertal Development\u003c/h3\u003e\n\u003cp\u003eAt baseline, Black girls living in ABCD Study\u0026reg; states characterized by higher (vs. lower) levels of structural racism experienced more advanced pubertal development on all three hormonal indicators (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): estradiol (β\u0026thinsp;=\u0026thinsp;0.12, 95% CI [0.04, 0.20], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006); DHEA (β\u0026thinsp;=\u0026thinsp;0.11, 95% CI [0.05, 0.17], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); and testosterone (β\u0026thinsp;=\u0026thinsp;0.13, 95% CI [0.06, 0.20], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). At Year 1, we continued to observe significant associations between structural racism and these three hormonal indicators of advanced pubertal development among Black girls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): estradiol (β\u0026thinsp;=\u0026thinsp;0.10, 95% CI [0.01, 0.19], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033); DHEA (β\u0026thinsp;=\u0026thinsp;0.14, 95% CI [0.06, 0.22], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); and testosterone (β\u0026thinsp;=\u0026thinsp;0.14, 95% CI [0.06, 0.23], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These associations remained significant when excluding Black girls who experienced menarche, suggesting that findings were not simply driven by post-menarche changes in pubertal hormones (Supplementary Table\u0026nbsp;1). We also found significant baseline associations between structural racism and two hormonal indicators of advanced pubertal development among White girls: DHEA (β\u0026thinsp;=\u0026thinsp;0.07, 95% CI [0.01, 0.13], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032) and testosterone (β\u0026thinsp;=\u0026thinsp;0.11, 95% CI [0.02, 0.19], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013). However, these associations were relatively smaller in magnitude among White (vs. Black) girls, and no such association was observed for the third hormonal indicator, estradiol, among White girls (Supplementary Table\u0026nbsp;2). Moreover, no significant associations between structural racism and hormonal indicators of advanced pubertal development were observed among White girls at Year 1 (Supplementary Table\u0026nbsp;2). Results from these negative control analyses provide suggestive evidence that structural racism was most consistently (i.e., for all three pubertal hormones) and persistently (i.e., at baseline and Year 1) associated with hormonal indicators of advanced pubertal development among Black (vs. White) girls.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eAssociations Between Structural Racism and Hormonal Indicators of Advanced Pubertal Development Among Black Girls\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003eAssociations Between Structural Racism and Hormonal Indicators of \u003c/p\u003e \u003cp\u003eAdvanced Pubertal Development Among Black Girls: Estradiol\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003eYear 1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e 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0.180)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaregiver education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.037*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.004, 0.128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(-0.085, 0.058)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eState income inequality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.035, 0.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(-0.043, 0.132)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollection time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.043, 0.167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.060, 0.202)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAssociations Between Structural Racism and Hormonal Indicators of \u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAdvanced Pubertal Development Among Black Girls: DHEA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eYear 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ez\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003ez\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.062, 0.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(-0.087, 0.074)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStructural racism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.052, 0.170)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.063, 0.217)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.174, 0.287)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.164, 0.295)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaregiver education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.052, 0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(-0.065, 0.072)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eState income inequality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.071, 0.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(-0.057, 0.096)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollection time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.143, -0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(-0.082, 0.057)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAssociations Between Structural Racism and Hormonal Indicators of \u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAdvanced Pubertal Development Among Black Girls: Testosterone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eYear 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ez\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003ez\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.086, 0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(-0.098, 0.078)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStructural racism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.059, 0.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.057, 0.225)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.092, 0.208)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.070, 0.203)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaregiver education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.095, 0.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(-0.088, 0.051)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eState income inequality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.088, 0.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(-0.059, 0.105)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollection time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.161, -0.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(-0.097, 0.049)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNo significant associations between measures of structural stigma and hormonal indicators of advanced pubertal development were documented among the other stigmatized groups in our study, including for structural racism among Black boys, structural xenophobia among Latinx girls and boys, and structural sexism among girls (Supplementary Tables\u0026nbsp;3\u0026ndash;6). \u003cb\u003eStructural Stigma and External, Physical Markers of Advanced Pubertal Development\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAt baseline, both Latinx girls (β\u0026thinsp;=\u0026thinsp;0.19, 95% CI [0.12, 0.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) and boys (β\u0026thinsp;=\u0026thinsp;0.10, 95% CI [0.03, 0.17], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) living in US states with higher (vs. lower) levels of structural xenophobia experienced more advanced pubertal development via caregiver-reported external, physical markers of puberty-related changes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These associations remained significant for both Latinx girls (β\u0026thinsp;=\u0026thinsp;0.21, 95% CI [0.14, 0.28], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) and boys (β\u0026thinsp;=\u0026thinsp;0.10, 95% CI [0.02, 0.17], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) at Year 1 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Likewise, Latinx girls living in states with higher (vs. lower) levels of structural xenophobia experienced more advanced pubertal development via youth-reported external, physical markers of puberty at baseline (β\u0026thinsp;=\u0026thinsp;0.13, 95% CI [0.06, 0.20], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), and this association strengthened somewhat at Year 1 (β\u0026thinsp;=\u0026thinsp;0.18, 95% CI [0.11, 0.25], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Although no such association was observed for Latinx boys at baseline, a significant association between structural xenophobia and more advanced pubertal development via youth-reported external, physical markers emerged among Latinx boys at Year 1 (β\u0026thinsp;=\u0026thinsp;0.12, 95% CI [0.01, 0.24], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026ndash;B, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Although some significant associations between structural xenophobia and youth- and/or caregiver-reported external, physical markers of advanced pubertal development were observed among non-Latinx White girls and boys at baseline, no significant associations were found among non-Latinx White youth at Year 1 (Supplementary Tables\u0026nbsp;7\u0026ndash;10). Taken together, these results suggest that structural xenophobia was more consistently associated with external indicators of advanced pubertal development among Latinx (vs. non-Latinx White youth), and most persistently so for Latinx girls. A relatively similar pattern of findings emerged when using alternate specifications (i.e., adrenarche-/gonadarche-related changes, pubertal development categories) of these external, physical markers (Supplementary Tables\u0026nbsp;11\u0026ndash;26).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eAssociations Between Structural Xenophobia and Caregiver-Reported External, Physical Markers of Advanced Pubertal Development Among Latinx Girls and Boys\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003eAssociations Between Structural Xenophobia and Caregiver-Reported External, Physical Markers of \u003c/p\u003e \u003cp\u003eAdvanced Pubertal Development Among Latinx Girls\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003eYear 1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.051, 0.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-5.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(-0.054, 0.062)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStructural xenophobia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.124, 0.261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.140, 0.283)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.214, 0.326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.227, 0.345)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaregiver education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.057, 0.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(-0.049, 0.071)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eState income inequality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.085, 0.221)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.067, 0.209)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAssociations Between Structural Xenophobia and Caregiver-Reported External, Physical Markers of \u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAdvanced Pubertal Development Among Latinx Boys\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eYear 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ez\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003ez\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.047, 0.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-3.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(-0.055, 0.062)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStructural xenophobia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.029, 0.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.009**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.024, 0.167)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.091, 0.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.106, 0.221)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaregiver education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.016*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.129, -0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(-0.024, 0.097)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eState income inequality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.044, 0.180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.045, 0.189)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eAssociations Between Structural Xenophobia and Youth-Reported External, Physical Markers of Advanced Pubertal Development Among Latinx Girls and Boys\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003eAssociations Between Structural Xenophobia and Youth-Reported External, Physical Markers of \u003c/p\u003e \u003cp\u003eAdvanced Pubertal Development Among Latinx Girls\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003eYear 1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.056, 0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-4.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(-0.055, 0.064)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStructural xenophobia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.056, 0.196)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.107, 0.253)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.123, 0.240)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.151, 0.272)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaregiver education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.033, 0.086)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(-0.040, 0.084)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eState income inequality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.019, 0.158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.002**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.044, 0.190)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAssociations Between Structural Xenophobia and Youth-Reported External, Physical Markers of \u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAdvanced Pubertal Development Among Latinx Boys\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eYear 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ez\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003ez\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.104, 0.076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(-0.137, 0.079)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStructural xenophobia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.073, 0.151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.040*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.005, 0.238)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.027, 0.085)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.006**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(0.024, 0.141)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaregiver education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.136, -0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(-0.083, 0.038)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eState income inequality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.042, 0.142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(-0.005, 0.180)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNo significant associations between measures of structural stigma and youth- or caregiver-reported external, physical markers of advanced pubertal development were present among other stigmatized groups, including for structural racism among Black girls and boys and for structural sexism among girls (Supplementary Tables\u0026nbsp;27\u0026ndash;32).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eYouth with stigmatized identities, including racially (e.g., Black) and ethnically (e.g., Latinx) minoritized youth as well as girls, experience advanced pubertal development relative to their same-aged, non-stigmatized peers (Biro et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Chumlea et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Herman-Giddens et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; McDowell et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Susman et al., \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Research into correlates of this increased risk has largely focused on aspects of individuals and their proximal environment (Argabright et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Deardorff et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hamlat et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Seaton \u0026amp; Carter, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Silventoinen et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), despite repeated calls to consider broader contextual factors operating at the macro-social level (Carter \u0026amp; Seaton, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Deardorff et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Neblett \u0026amp; Neal, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Leveraging two years of ABCD Study\u0026reg; data, we provide novel evidence that one such factor\u0026mdash;exposure to structural stigma at the state level (e.g., discriminatory laws/policies, aggregated prejudicial attitudes)\u0026mdash;is associated with advanced pubertal development.\u003c/p\u003e \u003cp\u003eSpecifically, baseline results demonstrated that Black girls living in states characterized by higher (vs. lower) structural racism experienced more advanced pubertal development across three hormonal indicators. At baseline, we also documented evidence of more advanced pubertal development in the form of external, physical markers of puberty among Latinx girls (via youth and caregiver reports) and boys (via caregiver report) in states with higher (vs. lower) structural xenophobia. These significant cross-sectional associations between structural stigma and advanced pubertal development among Black girls and Latinx youth persisted and often strengthened in magnitude one year later. In addition, a significant association between structural xenophobia and advanced pubertal development via youth-reported external, physical markers of puberty emerged among Latinx boys at Year 1. Significant associations between structural stigma and advanced pubertal development were generally comparable in effect size to associations between BMI and advanced pubertal development in our models. This finding suggests that structural stigma, although operating at a more distal level, may have a comparable influence on pubertal development to BMI, one of the most widely studied and robust predictors of early and advanced puberty (Huang et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rosenfield et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although structural racism and xenophobia were also associated with some indicators of advanced pubertal development among non-stigmatized comparators at baseline (i.e., non-Latinx White girls and boys), no such associations were observed among these comparison groups at Year 1. By comparison, significant associations between structural stigma and advanced pubertal development were persistently observed among Black girls (for hormones) and Latinx youth (for youth and/or caregiver report), providing some evidence of the consistency and specificity of these findings among these stigmatized (vs. non-stigmatized) groups.\u003c/p\u003e \u003cp\u003eIn sum, we observed significant associations between structural stigma and one or more indicators of advanced pubertal development among two of the three groups of stigmatized youth included in our study, which either persisted from baseline to Year 1 or emerged at Year 1 only among stigmatized (vs. non-stigmatized) groups. These findings have at least three important implications. First, they suggest that youth from these stigmatized groups living in US states characterized by higher (vs. lower) levels of structural stigma may begin puberty earlier or experience puberty faster than their same-aged peers, which would provide a plausible explanation for our observations of more advanced pubertal development at baseline and Year 1 among Black girls (for hormones) and Latinx youth (for youth and/or caregiver report) in higher-stigma contexts. As significant proportions of Black girls and Latinx girls and boys reside in US states ranking above the national average on our study\u0026rsquo;s measures of structural racism and xenophobia (US Census Bureau, \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), their considerable exposure to more (vs. less) stigmatizing macro-social environments may partially contribute to the earlier ages of pubertal onset consistently documented among these two stigmatized groups relative to their non-stigmatized peers in the research literature (Anderson \u0026amp; Must, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Biro et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Chumlea et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Herman-Giddens et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1997\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Second, because significant baseline associations between structural stigma and indicators of advanced pubertal development among Black girls and Latinx girls and boys, when observed, remained significant one year later, it is possible that these youth may experience more persistent risk for a range of chronic adverse mental and physical health outcomes linked to advanced pubertal development (Cheng et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Colich et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Day et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hamlat et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kaltiala-Heino et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Mendle et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ullsperger \u0026amp; Nikolas, \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Third, evidence of significant associations between structural stigma and separate indicators of advanced pubertal development among these two stigmatized groups\u0026mdash;namely, hormones among Black girls but external, physical markers among Latinx girls and boys\u0026mdash;may suggest that the macro-social contexts surrounding these groups influence interrelated yet distinct aspects of pubertal development captured by these measures (Cheng et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Herting et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Shirtcliff et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Future research is needed to clarify the neuroendocrine pathways underlying these oftentimes discordant indicators of pubertal development (Avenda\u0026ntilde;o et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dorn \u0026amp; Biro, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Farello et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Herting et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Parent et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shirtcliff et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and to determine why exposure to distinct manifestations of structural stigma might activate different pubertal processes, as may also be the case for other forms of social adversity (e.g., Hamlat et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., 2021).\u003c/p\u003e \u003cp\u003eContrary to our expectations, we did not observe significant associations between structural sexism and advanced pubertal development among girls generally. Whereas our study\u0026rsquo;s measures of structural racism and xenophobia primarily included state-level policies and/or aggregated prejudicial attitudes, the structural sexism measure incorporated indicators of other societal conditions, including women\u0026rsquo;s access social, economic, educational, and political resources at the state level. The inclusion of these indicators might explain the lack of observed associations between structural sexism and advanced pubertal development among girls in a few ways. First, these indicators may better reflect macro-social contexts characterized by material deprivation, wherein diminished access to resources may be less conducive to reproduction and thus delay the onset of puberty (Colich et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Second, although these and similar indicators have been associated with adverse health outcomes among adult women (Homan, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; McLaughlin et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), they may be less relevant to emerging adolescent girls in the ABCD Study\u0026reg;. Indeed, prior studies have not reliably documented associations between our study\u0026rsquo;s measure of structural sexism and adverse developmental or psychosocial outcomes among girls during emerging adolescence (Hatzenbuehler et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Martino et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Future research is needed to evaluate these possibilities and to test the generalizability of our findings across other operationalizations of structural sexism.\u003c/p\u003e \u003cp\u003eWhereas significant associations between structural stigma and indicators of advanced pubertal development were found among Black and Latinx girls at both baseline and Year 1, these associations were not observed among Black boys and were somewhat less consistently observed among Latinx boys. This pattern of findings may be attributable to documented gender differences in pubertal timing between girls and boys, with puberty typically beginning later among boys (Mendle et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rosenfield et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Accordingly, associations between structural stigma and advanced pubertal development among racially and ethnically minoritized boys may emerge more reliably later in adolescence, which can be examined as additional waves of ABCD Study\u0026reg; data become available.\u003c/p\u003e \u003cp\u003eStudy findings provide converging support for a small but growing number of studies documenting associations between exposure to structural stigma and altered developmental and adverse psychosocial outcomes\u0026mdash;including elevated psychopathology (Gordon et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Martino et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Slopen et al., \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), reduced hippocampal volume (Hatzenbuehler et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and dysregulated cortisol reactivity to stress (Hatzenbuehler \u0026amp; McLaughlin, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u0026mdash;among stigmatized youth. Further, one recent cohort study found evidence of more advanced pubertal development among racially and ethnically diverse girls living in US neighborhoods with more (vs. less) concentrated income among White (vs. racially/ethnically minoritized) residents (Acker et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Taken together with results from our study, these findings provide suggestive evidence that exposure to structural stigma and other macro-social factors during childhood and adolescence may interfere with puberty and a range of other developmental and psychosocial processes, particularly among stigmatized youth. Future research is needed to identify specific mechanisms through which structural stigma may be associated with advanced pubertal development. Elevated corticotropin-releasing hormone, which has been linked to altered pubertal and hippocampal development in animals (e.g., Brunson et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Kinsey-Jones et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), may represent a pluripotent mechanism through which the chronic stress and/or lack of social safety associated with structural stigma exposure alters pubertal development and perhaps other developmental processes (Diamond \u0026amp; Alley, 2021; Hatzenbuehler et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study has several notable methodological strengths that can guide future research into macro-social correlates of advanced pubertal development. First, we linked objective indicators of structural stigma to individual-level data from ABCD Study\u0026reg; youth from 17 US states varying in societal-level conditions (e.g., state-level policies, aggregated prejudicial attitudes) surrounding race, ethnicity/immigration status, and gender, offering unprecedented tests of associations between structural stigma and advanced pubertal development among Black youth, Latinx youth, and girls. Second, we tested associations between structural stigma and multiple indicators of advanced pubertal development\u0026mdash;including levels of three pubertal hormones as well as youth and caregiver reports of external, physical markers of puberty at baseline and Year 1\u0026mdash;enabling us to evaluate findings across measures, informants, and time. Third, we controlled for BMI, family SES in the form of mean caregiver educational attainment, and state-level income inequality, demonstrating that our results were robust to two established individual correlates of advanced pubertal development and to a broader feature of youth\u0026rsquo;s macro-social environment, respectively. Fourth, we ran negative control analyses with non-stigmatized comparators, providing evidence that significant associations between structural stigma and advanced pubertal development observed at baseline persisted at Year 1 only among stigmatized (vs. non-stigmatized) youth, and most consistently so for Black and Latinx girls.\u003c/p\u003e \u003cp\u003eThese strengths notwithstanding, there are several study limitations that might also inform future research on structural stigma and advanced pubertal development. First, although the ABCD Study\u0026reg; represents one of the largest investigations into child and adolescent development to date, findings might not generalize to youth from US states not included in the study. To the extent that the exclusion of some US states limited variability in ABCD Study\u0026reg; youth\u0026rsquo;s exposure to structural stigma, however, our findings are likely conservative. Future studies with even greater variability in exposure to structural stigma are needed to examine this possibility. Our focus on structural stigma at the state level was warranted given ABCD Study\u0026reg; youth\u0026rsquo;s differential exposure to US states that vary systematically in levels of structural racism, xenophobia, and sexism. However, future studies would benefit from incorporating measures of structural stigma at more proximal geographic levels (e.g., cities, counties), which would not only account for within-state variability in these study outcomes but might also exert a stronger influence on pubertal development, as has been shown for psychosocial outcomes (e.g., identity concealment; Lattanner et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). If that is the case, then our study once again provides a relatively conservative test of the association between structural stigma and advanced pubertal development.\u003c/p\u003e \u003cp\u003eSecond, all three structural stigma measures included indicators of individual prejudicial attitudes aggregated to the state level and pooled across available years. This approach offers several benefits, including that it reduces measurement error by ensuring that there are sufficient observations for each US state and that it comprises a range of years overlapping with the lifespan of ABCD Study\u0026reg; youth, thereby approximating the macro-social contexts surrounding these youth across development (Hatzenbuehler et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). At the same time, this aggregation method may not account for temporal variability in implicit and explicit social attitudes, which have become less biased towards women and racially and ethnically minoritized groups in the US in recent years (Charlesworth \u0026amp; Banaji, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, there is evidence that relative rankings of aggregated prejudicial attitudes towards these groups have remained generally stable between US states over this period of time (Chae et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Charles et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; McKetta et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), supporting the validity of a time-invariant approach. Nevertheless, future studies should take advantage of emerging methods, such as natural language processing of media and language corpora (Charlesworth et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which may enable scholars to develop time-variant structural stigma measures and thus pinpoint when during the life course structural stigma is most consequential to developmental outcomes such as puberty.\u003c/p\u003e \u003cp\u003eThird, although we examined associations between structural stigma and multiple indicators of pubertal development at baseline and Year 1 of the ABCD Study\u0026reg;, these analyses were cross-sectional. We selected a repeated cross-sectional approach because we were interested in whether structural stigma was associated with advanced pubertal development at two different time points in emerging adolescence. Future studies might employ longitudinal designs to examine whether structural stigma is also associated with the tempo of pubertal development by modeling change in hormonal or external, physical indicators of puberty over time. Although we cannot infer causality from repeated cross-sectional analyses, we can be confident in ruling out reverse causation as an explanation for our findings because pubertal development would not be expected to affect societal-level conditions.\u003c/p\u003e \u003cp\u003eFourth, many ABCD Study\u0026reg; youth hold multiple stigmatized identities and are thus exposed to structural stigma at the intersection of race, ethnicity, and/or gender. To our knowledge, measures of intersectional stigma at the structural level do not yet exist. Scholars have sought to surmount this measurement shortcoming by modeling interactions between two or more structural stigma measures (Homan et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pachankis et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), but we were underpowered to do so. The consistent associations between structural stigma and advanced pubertal development observed among Black girls in our study\u0026mdash;but neither among girls generally nor among Black boys\u0026mdash;may indicate that structural racism overlaps and intersects with structural sexism to have a particularly profound impact on pubertal development among Black girls. Measurement advances in quantifying intersectional forms of structural stigma are needed to evaluate this possibility (Hatzenbuehler et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eResearch into correlates and predictors of advanced pubertal development has focused almost exclusively on aspects of individuals and their proximal environment (Argabright et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Deardorff et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hamlat et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Seaton \u0026amp; Carter, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Silventoinen et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We provide some of the first empirical evidence that macro-social factors\u0026mdash;measured here in the form of structural stigma at the state level\u0026mdash;are also associated with advanced pubertal development. Although significant associations between structural stigma and advanced pubertal development were observed among stigmatized and non-stigmatized youth at baseline, they persisted one year later only among the stigmatized, most consistently for Black and Latinx girls. Our study underscores the need for future scholarship to evaluate whether advanced pubertal development is implicated in the link between structural stigma and adverse psychosocial outcomes among these stigmatized groups (Martino et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Slopen et al., \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, it provides a novel framework for broadening the lens of development research to consider features of macro-social contexts, including structural stigma, which may provide new insights into determinants of puberty and perhaps other developmental processes (Hatzenbuehler et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and Procedures\u003c/h2\u003e \u003cp\u003eParticipant data from the ABCD Study\u0026reg; were acquired from the NIMH Data Archive (ABCD Data Release 3.0; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://abcdstudy.org\u003c/span\u003e\u003cspan address=\"https://abcdstudy.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The present study drew data from baseline and Year 1 assessments conducted with ABCD Study\u0026reg; participants enrolled at one of 21 study sites located in 17 US states. ABCD Study\u0026reg; youth enrolled at a now defunct site were excluded (Dick et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). At baseline, study participants included 11,844 youth (predominantly ages 9\u0026ndash;10; \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.9, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.62) and their caregivers. At Year 1, 11,225 participating youth (predominantly ages 10\u0026ndash;11; \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10.9, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.64) and their caregivers were retained. Our primary analytic samples included 5,662 girls at baseline and 5,352 girls at Year 1; 2,507 Black youth at baseline and 2,274 Black youth at Year 1; and 2,406 Latinx youth at baseline and 2,222 Latinx youth at Year 1 (see Supplementary Table\u0026nbsp;33 for demographics and other study variables by stigmatized group at baseline and Year 1). Girls were identified via caregiver-reported birth-assigned sex, and Black and Latinx youth were identified via caregiver-reported race and ethnicity. As caregivers could report multiple racial and ethnic identities, we included all youth with a racial identity of Black in primary analyses related to structural racism and all youth with an ethnic identity of Latinx in primary analyses related to structural xenophobia. ABCD Study\u0026reg; families with lower (vs. higher) mean caregiver educational attainment were less likely to be retained at Year 1; attrition did not vary as a function of youth\u0026rsquo;s age, birth-assigned sex, race, ethnicity, or structural stigma context. ABCD Study\u0026reg; recruitment methods and procedures are detailed elsewhere and were approved by Institutional Review Boards at each of the 21 study sites (Barch et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Garavan et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Karcher \u0026amp; Barch, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Uban et al., \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eStructural Stigma\u003c/b\u003e. Structural stigma specific to gender (i.e., structural sexism), Black race (i.e., structural racism), and Latinx ethnicity/immigration status (i.e., structural xenophobia) was quantified at the state level using established measures of these constructs (Hatzenbuehler et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consistent with prior research (Hatzenbuehler et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Lattanner et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and theory (Hatzenbuehler, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hatzenbuehler \u0026amp; Link, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) on structural stigma, these measures incorporated publicly available indicators of laws/policies, aggregated (implicit and explicit) prejudicial attitudes, and/or other societal-level conditions as proxies for states\u0026rsquo; macro-social climates surrounding the three stigmatized groups included in our primary analyses: girls, Black youth, and Latinx youth. The exploratory factor analytic (EFA) methods for selecting and combining indicators for each measure are extensively described elsewhere (Hatzenbuehler et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Briefly, candidate indicators for each structural stigma measure with a factor loading\u0026thinsp;\u0026ge;\u0026thinsp;0.40 were retained and used to create model-generated factor scores for structural sexism, racism, and xenophobia in each US state (see Supplementary Table\u0026nbsp;34 for structural stigma factor scores). These structural stigma measures have been previously associated with adverse neurodevelopmental (e.g., reduced hippocampal volume; Hatzenbuehler et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and psychosocial (e.g., elevated psychopathology, decreased psychological intervention efficacy; Martino et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Price et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Slopen et al., \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) outcomes among stigmatized youth, including Black and Latinx youth as well as girls, providing evidence of their construct validity. The use of EFA further supports these measures\u0026rsquo; validity (i.e., because indicators loaded onto single latent constructs of structural sexism, racism, and xenophobia) and increases their reliability (i.e., because measurement error is reduced by tapping into shared variance among indicators). Indicators and data sources for these structural stigma measures are summarized below and provided in Supplementary Table\u0026nbsp;35.\u003c/p\u003e \u003cp\u003e \u003cem\u003eStructural Sexism\u003c/em\u003e. The structural sexism measure included 18 single-item or composite indicators. Six indicators reflected women\u0026rsquo;s social (e.g., percent of women living in counties without an abortion provider), economic (e.g., women\u0026rsquo;s labor force participation), and political (e.g., women\u0026rsquo;s voter registration) autonomy at the state level (Hatzenbuehler et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These indicators have been utilized in previous studies to quantify structural sexism (Homan, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; McLaughlin et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and were acquired from public sources such as the Bureau of Labor Statistics, Current Population Survey, Institute for Women\u0026rsquo;s Policy Research, and Guttmacher Institute. Twelve additional indicators captured aggregated implicit (e.g., automatic associations of gender with science and careers) and explicit attitudes towards gender and women\u0026rsquo;s social status. Attitudinal indicators were obtained from Project Implicit (2003\u0026ndash;2018) and the General Social Survey (1974\u0026ndash;2014), pooled across available years, and aggregated to the state level.\u003c/p\u003e \u003cp\u003e \u003cem\u003eStructural Racism\u003c/em\u003e. The structural racism measure comprised 31 indicators broadly capturing aggregated explicit attitudes towards Black people. To create these indicators, individual responses to attitudinal items from Project Implicit (2002\u0026ndash;2017), the General Social Survey (1973\u0026ndash;2014), and the American National Election Survey (1992\u0026ndash;2016) were pooled across time and aggregated to the state level. Items encompassed various dimensions of anti-Black prejudice, including support for policies limiting the rights and welfare of Black people and the endorsement of racial stereotypes. While other indicators of structural racism were considered in the EFA, only attitudinal indicators loaded onto this single factor, which may reflect the fact that anti-Black racial prejudice is often highest in US states where the population of Black residents is too small to reliably quantify other societal conditions (e.g., voter disenfranchisement, residential segregation) that have also been used to measure structural racism at the state level (Homan et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lukachko et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eStructural Xenophobia\u003c/em\u003e. The measure of structural xenophobia consisted of two single-item indicators and one composite indicator. The single-item indicators represented aggregated explicit attitudes towards Latinx people and towards immigrants acquired from the American National Election Survey. Individual responses to these two attitudinal items were pooled across available years (1996\u0026ndash;2016 and 2004\u0026ndash;2016, respectively) and aggregated to the state level. The third item included in this measure was a composite index summing the presence of 10 state laws/policies protective or prohibitive of immigrants\u0026rsquo; rights (e.g., permitting application for a driver\u0026rsquo;s license irrespective of immigration status; Rhodes et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although not necessarily specific to Latinx people, attitudinal and policy indicators of the social climate surrounding immigrants were included in this measure because of the high salience of anti-immigration policies to Latinx people, the frequent conflation of Latinx ethnicity and immigration status in the US, and the concealability of immigration status, all of which put Latinx people at disproportionate risk of experiencing xenophobia and its consequences (Almeida et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Morey, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vargas et al., \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Viruell-Fuentes et al., \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Indeed, exposure to higher (vs. lower) levels of structural xenophobia has been consistently associated with negative health outcomes among Latinx people irrespective of nativity status (Hatzenbuehler et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), including adverse neurodevelopmental (Hatzenbuehler et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and psychological outcomes (Martino et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Slopen et al., \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) among Latinx youth.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHormonal Indicators of Advanced Pubertal Development\u003c/b\u003e. Salivary levels of three hormones were used to assess pubertal development at baseline and Year 1: DHEA and testosterone among girls and boys and estradiol among girls only. Salivary biomarkers were acquired from whole saliva collected via passive drool by trained research assistants. Additional information on the methods for collecting and assaying these salivary samples is provided elsewhere (Herting et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Uban et al., \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Hormone levels were residualized on youth\u0026rsquo;s chronological age to index pubertal age for analysis (Colich et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sumner et al., \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); higher (i.e., more positive) residuals represented more advanced pubertal development.\u003c/p\u003e \u003cp\u003e \u003cb\u003eYouth- and Caregiver-Reported External, Physical Markers of Advanced Pubertal Development\u003c/b\u003e. We also assessed youth and caregiver reports of external, physical markers of pubertal development at baseline and Year 1 using the Pubertal Development Scale (PDS; Petersen et al., 1998). We did so because pubertal hormone levels capture interrelated yet distinct aspects of pubertal development from these external, physical indicators, with which they are only modestly correlated (Cheng et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Herting et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mendle et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shirtcliff et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Five PDS items assessed puberty-related changes in height, body hair, facial hair, skin, and voice among boys. Four PDS items captured puberty-related changes in height, body hair, skin, and breast development among girls, and the fifth queried menarche status. Except for the latter item, which was rated 1 (\u003cem\u003eno\u003c/em\u003e) or 4 (\u003cem\u003eyes\u003c/em\u003e) for girls, PDS items were rated on a Likert-scale from 1 (\u003cem\u003ehas not yet begun\u003c/em\u003e) to 4 (\u003cem\u003eseems completed\u003c/em\u003e). Internal consistency was adequate for youth report (girls: α\u0026thinsp;=\u0026thinsp;0.61\u0026ndash;0.70; boys: α\u0026thinsp;=\u0026thinsp;0.51\u0026ndash;0.61) and caregiver report (girls: α\u0026thinsp;=\u0026thinsp;0.71\u0026ndash;0.77; boys: α\u0026thinsp;=\u0026thinsp;0.58\u0026ndash;0.68). Items were averaged to compute mean youth- and caregiver-reported PDS scores, which were residualized on youth\u0026rsquo;s chronological age to quantify pubertal age for study analyses; higher (i.e., more positive) residuals represented more advanced pubertal development.\u003c/p\u003e \u003cp\u003eFor supplemental analyses of external, physical markers of pubertal development, we derived youth- and caregiver-reported PDS scores specific to adrenarche and gonadarche. Following prior studies (Herting et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Shirtcliff et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), gonadal PDS scores were computed for girls by averaging growth, breast development, and menarche items and for boys by averaging voice and facial hair items boys. Adrenal PDS scores were calculated by averaging body hair and skin items for both girls and boys. We also created categorical youth- and caregiver-reported PDS scores (Acebo \u0026amp; Carskadon, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). For boys, the sum of body hair, facial hair, and voice items were first categorized as 3 (\u003cem\u003epre-puberty\u003c/em\u003e), 4\u0026ndash;5 (\u003cem\u003eearly-puberty\u003c/em\u003e), 6\u0026ndash;8 (\u003cem\u003emid-puberty\u003c/em\u003e), 9\u0026ndash;11 (\u003cem\u003elate-puberty\u003c/em\u003e), and 12 (\u003cem\u003epost-puberty\u003c/em\u003e). For girls, menarche status and the sum of body hair and breast development items were first categorized as 2 without menarche (\u003cem\u003epre-puberty\u003c/em\u003e), 3 without menarche (\u003cem\u003eearly-puberty\u003c/em\u003e), \u0026ge;\u0026thinsp;3 without menarche (\u003cem\u003emid-puberty\u003c/em\u003e), \u0026le;\u0026thinsp;7 with menarche (\u003cem\u003elate-puberty\u003c/em\u003e), and 8 with menarche (\u003cem\u003epost-puberty\u003c/em\u003e). Because prior analyses of ABCD Study\u0026reg; data have documented low endorsement of late-puberty and post-puberty PDS categories at baseline and Year 1 (Thijssen et al., \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), we then combined mid-, late-, and post-puberty status into a single category. Gonadal/adrenal and categorical PDS scores were residualized on youth\u0026rsquo;s chronological age prior to supplemental analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCovariates\u003c/b\u003e. Drawing on prior research on both structural stigma (e.g., Hatzenbuehler et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and pubertal development (e.g., Herting et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we accounted for two individual covariates in all analyses. First, we controlled for family SES in the form of mean caregiver educational attainment, as reported on a range from 0 (\u003cem\u003enever attended school or only attended kindergarten\u003c/em\u003e) to 21 (\u003cem\u003edoctoral degree\u003c/em\u003e). Mean caregiver educational attainment was used as a proxy for family SES given substantial missingness in data on mean household income. Second, we also controlled for BMI\u0026mdash;calculated as the ratio of youth\u0026rsquo;s weight to height (measured up to three times each to ensure consistency and then averaged)\u0026mdash;given its robust association with pubertal development (Herting et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rosenfield et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Thijssen et al., \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Outliers for BMI were winsorized at \u0026plusmn;\u0026thinsp;3-SD from the mean. We additionally controlled for race/ethnicity (i.e., Latinx, non-Latinx White, non-Latinx Black, non-Latinx Asian, other racial/ethnic identities) in primary analyses with girls. We did not control for birth-assigned sex or age because analyses were stratified by birth-assigned sex and outcomes were residualized on chronological age. Analyses with hormonal indicators of pubertal development included an additional control for the time of salivary hormone sampling in hours since midnight. Finally, all analyses controlled for state-level income inequality using the Gini coefficient, which quantifies income maldistribution on a scale from 0 (\u003cem\u003eperfect equality\u003c/em\u003e) to 1 (\u003cem\u003eperfect inequality\u003c/em\u003e) and was acquired for included US states from the American Community Survey (US Census Bureau, \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Controlling for state-level inequality enabled us to examine whether observed associations between structural stigma and advanced pubertal development were robust to a broader feature of stigmatized youth\u0026rsquo;s macro-social context that might be expected to influence pubertal development among youth generally.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003ePreregistration\u003c/b\u003e. Study analyses were preregistered on OSF (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/zm62s/\u003c/span\u003e\u003cspan address=\"https://osf.io/zm62s/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e We note a few necessary deviations from our preregistration below.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePower Analysis\u003c/b\u003e. Preregistered power analyses indicated that we were adequately powered (\u0026gt;\u0026thinsp;90%) to detect small effect sizes for all pubertal outcomes among stigmatized and non-stigmatized groups at baseline. We were similarly powered (\u0026gt;\u0026thinsp;90%) to detect small effect sizes for hormonal indicators of advanced pubertal development among these groups at Year 1. Although well powered (\u0026gt;\u0026thinsp;90%) to detect small effect sizes for youth- and caregiver-reported external, physical markers of advanced pubertal development among girls at Year 1, we only had adequate power (\u0026gt;\u0026thinsp;90%) to detect medium effect sizes for these outcomes among Black and Latinx boys and girls at Year 1. This somewhat lower available power was attributable to smaller samples of Black and Latinx youth and caregivers completing these assessments at Year 1; although this attrition was not associated with levels of structural stigma, findings from these analyses should be interpreted cautiously given this reduced statistical power.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData Transformation\u003c/b\u003e. We performed several preregistered data transformations prior to analysis. First, consistent with prior work and research recommendations (Chafkin et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ho et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; King et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shirtcliff et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Sollberger \u0026amp; Ehlert, \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), salivary levels of pubertal hormones were log-transformed, and outliers were winsorized at \u0026plusmn;\u0026thinsp;3-SD from the mean. Transformations were performed separately for girls and boys at baseline and Year 1 given documented gender differences in pubertal timing and thus the need to stratify relevant analyses by birth-assigned sex (Euling et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Hoyt et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rosenfield et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Second, to model outcomes of advanced pubertal development, we regressed chronological age from log-transformed hormonal indicators of pubertal development and from youth- and caregiver-reported PDS scores separately for girls and boys at baseline and Year 1 (Colich et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Hamlat et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sumner et al., \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Positive residuals indexed more advanced pubertal development relative to chronological age, whereas negative residuals indexed less advanced pubertal development.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePrimary Analysis\u003c/b\u003e. We fit linear mixed-effects models for each advanced pubertal development outcome variable among each stigmatized group (i.e., girls, Black girls and boys, and Latinx girls and boys) separately at baseline and at Year 1. Analysis for Black and Latinx youth were stratified by birth-assigned sex. For each model, the relevant structural stigma measure (i.e., structural sexism for girls, structural racism for Black girls and boys, and structural xenophobia for Latinx girls and boys) and study covariates were specified as fixed effects. To account for the nested nature of the data, we included random intercepts for family and for the US state affiliated with each ABCD Study\u0026reg; site. We originally planned to include a random intercept for ABCD Study\u0026reg; site; however, we ultimately included a random intercept for US state because of the high overlap between sites and US states and because the latter provided an even more rigorous control for unmeasured confounding at the state level. Linear mixed-effects models were fit using the \u0026ldquo;glmmTMB\u0026rdquo; package in R given its ability to estimate model parameters reliably when random-effects variance is small (Brooks et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eNegative Control Analysis\u003c/b\u003e. As a form of negative control analysis (Lipsitch et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), we reran our primary models among non-stigmatized comparison groups consisting of youth who did not hold the stigmatized identity corresponding to each structural stigma predictor. Specifically, we performed negative control analyses at baseline and Year 1 for structural sexism among boys, structural racism among non-Latinx White girls and boys, and structural xenophobia among non-Latinx White girls and non-Latinx White boys. This approach enabled us to assess whether any associations between structural stigma and advanced pubertal development observed among stigmatized groups in our study were also observed among their non-stigmatized comparators. Because several studies have documented significant associations between structural stigma and adverse health outcomes among both stigmatized and non-stigmatized populations, especially when attitudinal measures of structural stigma are used (Lee et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; McKetta et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Michaels et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nguyen et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), these negative control analyses elucidated when and how consistently significant associations between structural stigma and advanced pubertal development emerged only among stigmatized (vs. non-stigmatized) youth. For example, documenting significant associations between structural stigma at both baseline and Year 1 among at least some stigmatized (vs. non-stigmatized) youth might suggest that these groups face more persistent risk for chronic adverse health outcomes linked to advanced pubertal development (Cheng et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Colich et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Day et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hamlat et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mendle et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ullsperger \u0026amp; Nikolas, \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSupplemental Analysis\u003c/b\u003e. We preregistered several supplemental analyses to assess the robustness of our findings. First, we reran our primary analysis of external, physical markers of pubertal development using youth- and caregiver-reported PDS items specific to adrenarche and gonadarche (residualized on chronological age) as outcomes because they represent largely distinct pubertal processes with respect to time (i.e., adrenarche typically precedes gonadarche) and their hormonal correlates (i.e., androgens vs. gonadotropins; Mendle et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Second, given the relatively low endorsement of latter pubertal stages previously documented among ABCD Study\u0026reg; youth at baseline and Year 1 (Thijssen et al., 2022), we repeated our primary analysis using three categories of youth- and caregiver-reported PDS scores (residualized on chronological age): pre-puberty, early-puberty, and mid-to-post-puberty. Third, in order to establish that any observed associations between structural stigma and hormonal indicators of advanced pubertal development were not simply driven by post-menarche changes in hormone levels among girls, we reran relevant models excluding girls who had experienced menarche.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Transparency and Openness\u003c/strong\u003e. Analyses were conducted in R (version 4.4.0; R Core Team, 2024). Analytic code is available on OSF (https://osf.io/zm62s/). Thresholds for statistical significance were set at \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05. As missingness for study covariates was minimal and at random, we conducted complete case analysis. We preregistered supplemental analyses using multiple imputation to handle potential missingness in study covariates. However, we did not conduct these analyses because there was minimal missing data for study covariates and because cluster sizes for ABCD Study\u0026reg; families were too small for multilevel imputation to produce reliable estimates (Audigier et al., 2018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReporting Summary\u003c/strong\u003e. Additional information on our study design is available in the Nature Portfolio Reporting Summary linked to this article.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.M. and N.H. developed the study, analyzed the data, wrote the manuscript text, and prepared the figures. M.H., K.M., and N.C. assisted with study design, analysis, and writing the manuscript text. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe present study is a secondary analysis of data from the Adolescent Brain Cognitive Development study, a publicly available dataset. Full information on the data collection can be found at https://abcdstudy.org/. ABCD Study\u0026reg; data can be accessed via a data use agreement with the NIMH Data Archive.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcebo, C. \u0026amp; Carskadon, M. A. A self-administered rating scale for pubertal development. \u003cem\u003eJ. Adolesc. Health\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e (3), 190\u0026ndash;195. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/1054-139x(93)90004-9\u003c/span\u003e\u003cspan address=\"10.1016/1054-139x(93)90004-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1993).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcker, J. et al. Neighborhood racial and economic privilege and timing of pubertal onset in girls. \u003cem\u003eJ. Adolesc. 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Pead.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 795596. https://doi.org/10.3389%2Ffped.2022.795596 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"childhood and adolescence, structural stigma, puberty, development, social determinants of health","lastPublishedDoi":"10.21203/rs.3.rs-5356422/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5356422/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBlack and Latinx youth experience advanced pubertal development relative to their same-aged, non-stigmatized peers. Research on determinants of this increased risk has focused almost exclusively on aspects of individuals (e.g., body-mass index) or their proximal environment (e.g., socioeconomic status), to the exclusion of broader macro-social factors. Using two years of Adolescent Brain Cognitive Development Study\u0026reg; data, we examined whether structural stigma (e.g., state-level policies, aggregated prejudicial attitudes) was associated with hormonal and perceived physical indicators of pubertal development. Baseline results documented more advanced pubertal development among Black girls (hormones) and Latinx youth (youth and/or caregiver report) in states characterized by higher (vs. lower) structural stigma. Observed associations were comparable in effect size to a well-established correlate of pubertal development, BMI, and remained or strengthened one year later among these stigmatized (vs. non-stigmatized) groups. Findings suggest the need to broaden the study of determinants of pubertal development to include macro-social factors.\u003c/p\u003e","manuscriptTitle":"Associations Between Structural Stigma and Advanced Pubertal Development Persist for One Year Among Black Girls and Latinx Youth","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-11 16:57:45","doi":"10.21203/rs.3.rs-5356422/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-17T05:26:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-15T21:35:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-13T20:42:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-10T14:01:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-27T21:29:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28281807954853062135631432173021137735","date":"2024-11-25T12:53:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-22T18:04:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232388182325643019674038168583255865942","date":"2024-11-20T22:38:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131841115438490206939568284953386967117","date":"2024-11-20T19:56:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46708147761146404016931056685596298085","date":"2024-11-20T19:42:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333542968543527549805816068282296066350","date":"2024-11-18T21:59:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104730371145594945443793960956945884729","date":"2024-11-18T19:46:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-18T19:41:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-14T16:51:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-14T08:56:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-13T11:14:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-29T18:00:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0d380234-3ba6-45f3-967d-e02bfc3ad04d","owner":[],"postedDate":"December 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":41414398,"name":"Biological sciences/Psychology/Human behaviour"},{"id":41414399,"name":"Health sciences/Biomarkers"}],"tags":[],"updatedAt":"2025-05-26T16:10:30+00:00","versionOfRecord":{"articleIdentity":"rs-5356422","link":"https://doi.org/10.1038/s41598-025-00378-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-21 15:58:19","publishedOnDateReadable":"May 21st, 2025"},"versionCreatedAt":"2024-12-11 16:57:45","video":"","vorDoi":"10.1038/s41598-025-00378-8","vorDoiUrl":"https://doi.org/10.1038/s41598-025-00378-8","workflowStages":[]},"version":"v1","identity":"rs-5356422","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5356422","identity":"rs-5356422","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}