Linking Adverse Childhood Experiences to Socioeconomic Outcomes: Racial and Ethnic Differences in a Representative U.S. Sample

Linking Adverse Childhood Experiences to Socioeconomic Outcomes: Racial and Ethnic Differences in a Representative U.S. Sample | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Linking Adverse Childhood Experiences to Socioeconomic Outcomes: Racial and Ethnic Differences in a Representative U.S. Sample Xiyao Liu, Joshua Mersky This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8734358/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background Adverse childhood experiences (ACEs) have been reliably linked to poor adult health outcomes; less is known about their impact on socioeconomic outcomes. This study examines relationships between ACEs and socioeconomic outcomes in mid-adulthood, and whether these associations are moderated by race/ethnicity. Methods Five waves of data were drawn from the National Longitudinal Study of Adolescent to Adult Health (Add Health). Multivariate regression analyses were applied to examine main-effect associations between ACEs and socioeconomic outcomes along with the moderating effects of race/ethnicity. Results Controlling for household economic status in childhood, higher ACE scores were significantly associated with lower levels of educational attainment and income, higher rates of unemployment, and greater financial instability. The association between ACEs and financial instability was stronger for White participants than Black participants, while associations between ACEs and educational attainment, income, and unemployment were stronger for White participants than their Asian American counterparts. Conclusions ACEs appear to undermine not only health but also educational and economic attainments, underscoring the need for ACE prevention and intervention strategies. Yet, the moderation results raise questions about the extent to which targeting these strategies along racial/ethnic lines will redress socioeconomic disparities in the U.S. Adverse childhood experiences (ACEs) socioeconomic outcomes race/ethnicity disparity Background Adverse childhood experiences, or ACEs, are potentially traumatic events and conditions that occur before age 18. 1 Cumulative exposure to ACEs, including various forms of child maltreatment and household dysfunction, have been reliably linked to physical, mental, and behavioral health problems in later life. 1 – 3 These health-related effects pass along significant costs to society, with one study estimating that in the U.S. alone ACEs have an annual economic burden of $ 14.1 trillion. 4 Less is known about the socioeconomic costs of ACEs at the individual level. While poor educational and economic outcomes have been linked to individual forms of child maltreatment 5 – 9 and household dysfunction, 7, 10–13 few studies have explored the socioeconomic impact of ACEs in aggregate. One study found that higher ACE scores were associated with lower educational and economic attainments, 14 reinforcing a study that showed that adults with four or more ACEs were about twice as likely as adults with no ACEs to drop out of high school, be unemployed, and live in a low-income household. 15 Socioeconomic effects associated with high ACE exposure explained roughly 15–20% of the variance in adult health risks, 16 and higher ACE scores were associated with poor health and economic outcomes—even after controlling for childhood poverty. 17 However, each of the preceding ACE studies used cross-sectional data, and thus their ability to draw causal inferences is limited. Racial and Ethnic Disparities Although ACEs are disproportionately prevalent among historically marginalized racial and ethnic groups in the U.S., 18 it remains unclear whether the effects of ACEs vary by race/ethnicity. Studies that have explored this question have produced mixed results, with some indicating that ACEs have a greater impact on the physical and mental health of racial/ethnic minoritized groups, 19,20 others finding the opposite, 21,22 and still others reporting null effects. 23 , 24 In a recent analysis of data from the National Longitudinal Study of Adolescent to Adult Health (Add Health), one study found that the association between ACE scores and an index of economic disadvantage did not differ between Black, White, and Hispanic participants. 25 No other known studies have explored whether the associated effects of ACEs on socioeconomic outcomes are moderated by race/ethnicity. There is a pressing need to expand this line of ACE research while also including underrepresented racial/ethnic groups that have widely disparate socioeconomic outcomes. To illustrate, recent data from the U.S. Census Bureau indicate that approximately 58% of Asian American adults and 40% of non-Hispanic White adults have completed four or more years of college, as compared to 26% of Black adults, 21% of Hispanic adults, and 17% of American Indian and Alaska Native (AI/AN) adults. 26 White and Asian American adults also occupy higher income strata and have greater household wealth than their Black, Hispanic, and AI/AN counterparts. 27 , 28 However, it is unclear to what degree ACEs contribute to these disparities. Study Aims Prospective research is needed to assess the extent to which ACEs alter socioeconomic outcomes and whether these effects are distributed equally among racial/ethnic groups. Extending previous work, 25 we revisit data from the Add Health Study and assess the degree to which cumulative ACE exposure is associated with four key indicators of adult socioeconomic status: (1) educational attainment; (2) income; (3) unemployment; (4) financial instability. Our second aim is to test whether the associated effects of ACEs vary between five racial/ethnic groups: (1) American Indian/Alaska Native; (2) Asian; (3) Black; (4) Hispanic; (5) White. By expanding beyond the health consequences of ACEs and considering how they affect human capital overall and among population subgroups, the study may help to inform public policy and intervention efforts. Methods Sample and Data Add Health is a longitudinal study of a nationally representative sample of adolescents in grades 7–12 in the United States in 1994–95. A total of 20,745 adolescents (grades 7–12) and 85% of their parents (n = 17,670) participated in a baseline (i.e., Wave I) survey (1994–1995). All adolescents were recruited for a follow-up survey one year later in Wave II (1996), yielding a sample of 14,738 adolescents (grades 8–12). The original cohort was also asked to complete an in-home interview in 2001–2002 (Wave III; n = 15,197, ages 18–26) and again in 2008–2009 (Wave IV; n = 15,701, ages 24–32). A wave V survey was conducted between 2016–2018 with a subsample of 12,300 cohort members who ranged from 32 to 42 years of age. 29 For the current study, data collected from waves I through IV supply information about participant ACE histories, and data collected at wave V are used to measure indicators of socioeconomic status in adulthood. Add Health records were obtained from the Carolina Population Center pursuant to a data sharing agreement. Following eligibility criteria established in previous work, 30 the sample was restricted to participants who were missing data for no more than two of the ten ACEs measured. Participants who were categorized as “other” race/ethnicity or who missed race/ethnicity were dropped from the sample (n = 119). The final analytic sample includes participants who had complete information on cluster, strata, and sample weights at Wave V (N = 11,938). Measures Adverse Childhood Experiences Ten types of ACEs were assessed using information collected from multiple raters across multiple waves of data. Emotional neglect, household mental health concerns, household substance availability, and violent crime victimization were reported by study participants at Waves I and II when participants averaged 16 years of age. Physical abuse, emotional abuse, sexual abuse, physical neglect, and household incarceration that occurred before age 18 were reported by study participants at Waves III and IV. Parental alcoholism and parent divorce were assessed using parent reports at Wave I. Household substance use was coded by combining indicators of household substance availability and parental alcoholism. Each ACE was measured dichotomously, with each item coded 1 if an item was endorsed. Consistent with prior research, a continuous ACE score (range 0–10) was calculated by summing these indicators. Detailed information about ACE language and cutoff points can be found in Appendix A. Socioeconomic Outcomes Educational Attainment. Participants reported their highest level of education at wave V ranging from less than high school to a master’s degree or higher. Responses were recoded into a categorical variable with four levels: 1 = less than high school, 2 = high school diploma or equivalent, (including GED, post-high school training, and some college credits), 3 = bachelor’s degree and some graduate school training, and 4 = master’s degree and above. Household Income. Total annual household income was recorded at Wave V, with categories ranging from less than $ 5,000 to greater than or equal to $ 150,000. Replicating a previous Add Health study, 5 13 income categories were recoded into a continuous variable; the midpoint of each unit was divided by the square root of household size and transformed into an adjusted measure of household income using the natural log to reduce positive skew. Unemployment . At wave V, participants were asked if they were currently working for pay. Participants were categorized as currently unemployed if they had either never worked for pay or if they were not currently working but had worked for pay in the past. Financial Instability . The Wave V survey asked three items about financial difficulties experienced since 2008: (1) falling behind paying bills, (2) experiencing a foreclosure procedure, eviction, or repossession of something, and (3) being in debt if assets liquefied. Participants who responded affirmatively to any item were categorized as having experienced financial instability. Race/Ethnicity Participants were categorized into five mutually exclusive racial/ethnic groups, including Hispanic ethnicity and four non-Hispanic groups: AI/AN, Asian, Black, and White. Participants who reported “other” race/ethnicity or who were missing data were removed from the sample. Covariates Participants’ gender was recoded into female and male; non-binary gender information was unavailable. Parental education was accessed from Wave I parent survey, which was recoded into four categories (1 = less than high school, 2 = high school diploma or equivalent, post-high school training, or some college credits, 3 = bachelor’s degree or some graduate school training, and 4 = master’s degree and above). Childhood poverty was measured by household income at baseline, which was dichotomized to poor and non-poor based on the 1994 federal poverty level (FPL) adjusted for household size. To supplement missing data, families were classified as poor if they received any types of means-tested public assistance at baseline, including Supplemental Security Income (SSI), Aid to Families with Dependent Children (AFDC), or the Supplemental Nutrition Assistance Program (SNAP). Analysis Plan Descriptive analyses of participant demographics, ACEs, and study outcomes were performed for the full sample and stratified by race/ethnicity. Racial/ethnic differences in ACE scores and outcome variables were analyzed using chi-square tests for categorical variables and one-way ANOVA with post-hoc tests for continuous variables. Estimated effects of ACEs, race/ethnicity, and covariates were tested using ordered logistic regression for educational attainment, general linear regression for household income, and logistic regression for financial instability and unemployment. Each analysis proceeded in three steps. First, unadjusted effects of ACEs and race/ethnicity on socioeconomic outcomes were assessed. Second, covariates were added to the models to control for potential confounders. Third, to explore potential moderating effects of race/ethnicity, interaction terms between ACEs and each racial/ethnic group were added to the models. To reduce multicollinearity with interaction terms and enhance the interpretability of coefficients, ACE scores were centered for main effects and interaction terms. All descriptive analyses and regression models were conducted using SAS 9.4. Results Descriptive Characteristics As shown in Table 1 , nearly half (49.7%) of participants identified as female. The racial/ethnic composition of the sample was as follows: 65.7% non-Hispanic White, 16.0% non-Hispanic Black, 12.3% Hispanic, 3.9% Asian American, and 2.1% AI/AN. The mean ACE score for the full sample was 1.47 ( SE = 0.03); 67.5% of participants had at least one ACE and 10.4% had four or more ACEs (not shown). The prevalence of individual ACEs ranged from a low of 5.7% for household mental health concern (i.e., family suicide attempt) to 32.2% for child physical abuse. AI/AN participants had a mean ACE score of 2.11 ( SE = 0.17), which was significantly higher than the means of all other racial/ethnic groups. Non-Hispanic Black ( M = 1.66, SE = 0.07) and Hispanic ( M = 1.63, SE = 0.07) participants also had significantly higher mean ACE scores than non-Hispanic White participants ( M = 1.38, SE = 0.03). Asian Americans had a mean ACE score of 1.49 ( SE = 0.10), which did not differ significantly from the non-Hispanic White group. Table 1 Descriptive characteristics Total sample AI/AN (n = 206) NH Black (n = 2,408) Hispanic (n = 1,788) Asian American (n = 784) NH White (n = 6,752) N % or M(SE) % or M(SE) % or M(SE) % or M(SE) % or M(SE) % or M (SE) Gender 11,938 Female 49.7% 45.6% 51.0% 49.5% 50.1% 49.6% Race 11,938 AI/AN 2.1% NH Black 16.0% Hispanic 12.3% Asian 3.9% NH White 65.7% Parental education (Range: 1–4) 10,294 2.15 (0.03) 1.89 (0.07) 2.06 (0.05) 1.72 (0.06) 2.29 (0.08) 2.24 (0.03) Childhood poverty 10,353 18.7% 22.4% 40.4% 28.5% 12.2% 12.3% Outcomes Educational attainment (Range: 1–4) 11,892 2.43 (0.03) 2.24 (0.08) 2.34 (0.05) 2.25 (0.03) 2.65 (0.06) 2.45 (0.03) Income 9,795 58,164 (1326.62) 43,775 (3289.29) 39,250 (2081.59) 53,046 (2146.26) 72,326 (3415.74) 62,701 (1446.73) Currently unemployed 11,898 17.9% 18.0% 22.7% 18.3% 13.5% 16.9% Financial instability 11,897 55.7% 66.5% 72.1% 56.2% 42.5% 52.1% ACE Indicators Physical abuse 11,363 32.2% 41.1% 32.8% 36.1% 41.9% 30.5% Emotional abuse 10,466 28.6% 34.8% 25.1% 27.9% 35.3% 28.9% Sexual abuse 11,373 7.1% 12.9% 9.6% 7.7% 6.0% 6.3% Physical neglect 9,552 11.2% 14.5% 14.3% 13.0% 14.2% 9.9% Emotional neglect 11,773 11.3% 10.4% 11.6% 14.9% 11.6% 10.5% Household substance use 11,930 17.0% 34.2% 17.3% 15.5% 7.5% 17.3% Household mental health concern 11,898 5.7% 13.7% 5.4% 6.3% 5.3% 5.5% Household incarceration 10,544 9.1% 10.6% 15.8% 11.1% 3.1% 7.5% Parent divorce 10,349 19.1% 18.9% 28.5% 19.5% 12.1% 17.3% Violent crime victimization 8,898 15.1% 27.3% 21.6% 21.7% 11.7% 12.1% ACE scores (range: 0–10) 10,817 1.47 (0.03) 2.11 (0.17) 1.66 (0.07) 1.63 (0.07) 1.49 (0.10) 1.38 (0.03) Notes. AI/AN = American Indian and Alaska Native. NH = non-Hispanic. An analysis of study outcomes showed that the mean educational attainment level was 2.43 (SE = 0.03) on a scale of 1 to 4; 94.7% had at least a high school diploma or equivalent degree, and 36.7% had at least a bachelor’s degree (not shown). Among all racial/ethnic groups, mean educational attainment levels were highest among Asian American participants (M = 2.65, SE = 0.06) and lowest among AI/AN participants (M = 2.24, SE = 0.08). Average annual household income adjusted for household size was $ 58,164 ( SE = $ 1,327). Overall, 17.9% of participants were currently unemployed at Wave V, and more than half (55.7%) reported financial instability. Asian American participants reported the highest average income ( M = $ 72,326, SE = $ 3,416) and the lowest rates of unemployment (13.5%) and financial instability (42.5%), whereas non-Hispanic Black participants reported the lowest average income ( M = $ 39,250, SE = $ 2,082) and highest rates of unemployment (22.7%) and financial instability (72.1%). Main Effects of ACEs on Socioeconomic Outcomes Ordered logistic regression results for educational attainment are presented in Table 2 . Results shown in model 1a indicate that higher ACE scores were associated with a decreased odds of achieving higher educational attainment ( OR = 0.80, p <. 001), and model 2a shows the associations remained significant after adding covariates ( OR = 0.84, p < .001). Table 2 Ordered logistic regression models for education attainment Educational Attainment Predictor Variables Model 1a OR (95% CI) Model 2a OR (95% CI) Model 3a OR (95% CI) ACE Scores a 0.80*** (0.77, 0.84) 0.84*** (0.80, 0.88) 0.81*** (0.78, 0.86) Race/Ethnicity AI/AN 0.53* (0.33, 0.86) 0.70 (0.42, 1.16) 0.73 (0.43, 1.25) NH Black 0.67* (0.49, 0.92) 0.92 (0.69, 1.22) 0.91 (0.68, 1.22) Hispanic 0.56*** (0.45, 0.69) 0.96 (0.78, 1.18) 1.10 (0.91, 1.34) Asian 1.50* (1.04, 2.17) 1.22 (0.81, 1.84) 1.41 (0.96, 2.07) Female 1.67*** (1.42, 1.88) 1.67*** (1.49, 1.88) Parental education 2.73*** (2.39, 3.12) 2.73*** (2.40, 3.12) Childhood poverty 0.48*** (0.40, 0.59) 0.48*** (0.40, 0.59) Interaction Terms ACEs * AI/AN 0.94 (0.73, 1.21) ACEs * Black 1.08 (0.97, 1.21) ACEs * Hispanic 1.05 (0.93, 1.18) ACEs * Asian 1.34** (1.10, 1.63) Note . a ACE scores were grand-mean centered. Reference group for race/ethnicity was non-Hispanic White. AI/AN = American Indian or Alaska Native. NH = non-Hispanic. * < .05, ** < .01, *** < .001 Linear regression results for income are presented in Table 3 . Model 1b shows that higher ACE scores were negatively associated with income ( \(\:b\) = -0.09, p < .001), and model 2b shows that the ACE-income association remained significant after adding covariates ( \(\:b\) = -0.06, p < .001). Table 3 Linear egression models for income Income Predictor Variables Model 1b \(\:b\) (SE) Model 2b \(\:b\) (SE) Model 3b \(\:b\) (SE) ACE Scores a -0.09*** (0.01) -0.06*** (0.01) -0.07*** (0.01) Race/Ethnicity AI/AN -0.39** (0.11) -0.31** (0.09) -0.32** (0.10) NH Black -0.66*** (0.07) -0.50*** (0.06) -0.49*** (0.06) Hispanic -0.21** (0.06) 0.07 (0.07) 0.06 (0.07) Asian 0.22** (0.07) 0.22* (0.10) 0.22* (0.09) Female -0.16*** (0.03) -0.16*** (0.03) Parental education 0.28*** (0.03) 0.28*** (0.03) Childhood poverty -0.44*** (0.05) -0.44*** (0.05) Interaction Terms ACEs * AI/AN 0.03 (0.09) ACEs * Black 0.01 (0.03) ACEs * Hispanic 0.04 (0.04) ACEs * Asian 0.10* (0.04) Note . a ACE scores were grand-mean centered. Reference group for race/ethnicity was non-Hispanic White. AI/AN = American Indian or Alaska Native. NH = non-Hispanic. * < .05, ** < .01, *** < .001 Logistic regression results for unemployment and financial instability are presented in Table 4 . Model 1c shows higher ACE scores ( OR = 1.21, p < .001) were associated with higher probability of being unemployed, and model 2c shows the association was significant after adding covariates ( OR = 1.09, p < .001). Models 1d and 2d indicate associations between ACE scores and financial instability were robust across unadjusted ( OR = 1.27, p < .001) and adjusted analyses ( OR = 1.25, p < .001). Table 4 Binary logistic regression models for unemployment and financial instability Unemployment Financial Instability Predictor Variables Model 1c OR (95% CI) Model 2c OR (95% CI) Model 3c OR (95% CI) Model 1d OR (95% CI) Model 2d OR (95% CI) Model 3d OR (95% CI) ACE scores 1.12*** (1.07, 1.18) 1.09** (1.04, 1.16) 1.13*** (1.06, 1.20) 1.27*** (1.21, 1.32) 1.25*** (1.19, 1.32) 1.26*** (1.19, 1.34) Race/Ethnicity AI/AN 0.98 (0.58, 1.67) 0.87 (0.52, 1.43) 0.69 (0.37, 1.29) 1.57* (1.10, 2.24) 1.40 (0.96, 2.02) 1.34 (0.91, 1.95) NH Black 1.37* (1.07, 1.74) 1.16 (0.89, 1.51) 1.19 (0.90, 1.56) 2.30*** (1.93, 2.75) 1.99*** (1.64, 2.41) 2.00*** (1.65, 2.43) Hispanic 1.02 (0.78, 1.14) 0.89 (0.62, 1.27) 0.90 (0.63, 1.30) 1.15 (0.94, 1.40) 0.91 (0.73, 1.14) 0.91 (0.72, 1.13) Asian 0.80 (0.48, 1.35) 0.84 (0.50, 1.42) 0.80 (0.50, 1.29) 0.60** (0.43, 0.84) 0.65* (0.46, 0.91) 0.65* (0.46, 0.91) Female 1.85*** (1.54, 2.24) 1.86*** (1.54, 2.25) 1.17* (1.04, 1.33) 1.17* (1.03, 1.33) Parental education 0.79*** (0.70, 0.90) 0.79*** (0.70, 0.90) 0.75*** (0.70, 0.81) 0.75*** (0.69, 0.81) Childhood poverty 1.51*** (1.19, 1.90) 1.49*** (1.19, 1.88) 1.42*** (1.20, 1.68) 1.41*** (1.20, 1.67) Interaction Terms ACEs * AI/AN 1.24 (0.87, 1.78) 1.13 (0.85, 1.51) ACEs * Black 0.91 (0.80, 1.05) 0.86* (0.76, 0.97) ACEs * Hispanic 0.91 (0.79, 1.06) 1.09 (0.96, 1.24) ACEs * Asian 0.64* (0.45, 0.90) 0.99 (0.81, 1.20) Note . a ACE scores were grand-mean centered. Reference group for race/ethnicity was non-Hispanic White. AI/AN = American Indian/Alaska Native. NH = non-Hispanic. * < .05, ** < .01, *** < .001 Moderating Effects of Race/Ethnicity Tables 2 – 4 present results from multivariate models with interactions between ACEs and race/ethnicity (model 3a-d). Model 3a (Table 2 ) indicates the association between ACEs and educational attainment differed significantly between Asian American and non-Hispanic White participants ( OR = 1.34, p = .004). This finding suggests that, when holding ACEs constant, Asian American participants were more likely than non-Hispanic White participants to attain higher levels of education. Results shown in model 3b (Table 3 ) also indicate the link between ACEs and income differed significantly between Asian American and non-Hispanic White participants ( \(\:b\) = 0.10, p = .02). This result suggests that, when compared to non-Hispanic White participants at the same level of ACEs, Asian American participants had higher incomes. Additionally, model 3c (Table 4 ) indicates that the association between ACEs and unemployment differed between Asian American and non-Hispanic White participants ( \(\:OR\:\) = 0.64, p = .01). In other words, at the same level of ACE exposure, Asian American participants were less likely than non-Hispanic White participants to be unemployed. Finally, in model 3d (Table 4 ), there was a significant interaction signifying that the association between ACEs and the odds of financial instability was significantly stronger among non-Hispanic White participants than Black participants ( \(\:OR\:\) = 0.86, p = .01). Secondary Analysis To aid in the interpretation of the observed moderation effects, we stratified our main-effect analyses by race/ethnicity. As expected, ACE scores were negatively associated with educational attainment among non-Hispanic White participants ( OR = 0.83, p < .001), whereas ACEs were not associated with educational attainment among Asian American participants ( OR = 1.03, p = .78). Similarly, ACE scores were negatively associated with income among non-Hispanic White participants ( b = − 0.07, p < .001), but there was not a significant relationship between ACE scores and income among Asian American participants ( b = 0.002, p = .97). Moreover, ACE scores were positively associated with unemployment among non-Hispanic White participants ( OR = 1.12, p < .001), whereas there was a marginally significant association in the opposite direction among Asian American participants ( OR = 0.73, p = .06). Finally, ACE scores were significantly associated with financial instability among non-Hispanic White participants ( OR = 1.25, p < .001); results trended in the same direction for non-Hispanic Black participants but did not reach statistical significance ( OR = 1.09, p = .12). Discussion Building on decades of research linking ACEs to poor health-related outcomes, we explored the socioeconomic consequences of ACEs and whether they differed among major racial/ethnic groups in the U.S. Using nationally representative data from the Add Health Study, we confirmed that American Indian/Alaska Native, Black, and Hispanic adults had significantly higher ACE scores and poorer educational and economic outcomes than did their White counterparts. The findings support evidence that historically marginalized groups in the U.S. have a higher ACE burden 30 , 31 and are disproportionately represented at lower socioeconomic levels. 27 , 32 We also demonstrated that higher ACE scores were associated with lower education and income levels and higher rates of unemployment and financial instability in adulthood. The associated effects of ACEs were robust after controlling for parent education and childhood poverty – key predictors of adult socioeconomic outcomes. 33 , 34 Our findings reinforce several cross-sectional studies that have linked ACEs to educational and economic attainments, 14,15,17 lending confidence to the conclusion that ACEs broadly compromise health and welfare over the life course. Contrary to expectations, moderation tests showed that ACEs were more strongly linked to financial instability in White adults than Black adults. This finding should be interpreted cautiously given that: (1) race/ethnicity did not moderate the effects of ACEs on income or unemployment, and (2) a previous Add Health analysis found the association between ACEs and an economic index did not differ between Black and White participants. 25 Nevertheless, the financial instability result bears an explanation. One possibility is that, when compared to White adults, financial instability among Black adults may be more strongly tied to structural and social determinants than household adversities such as family violence or substance use. This interpretation aligns with the differential assortment hypothesis, 35 which has been put forward to explain why ACEs are more prevalent among minoritized racial/ethnic adults overall but less prevalent among low-income Black and Hispanic adults than low-income White adults. 30 , 36 , 37 Further research is needed to systematically test this hypothesis and, more broadly, to tease out complex and potentially confounding associations between ACEs, race/ethnicity, and socioeconomic status. Toward that end, there is a need to include underrepresented racial/ethnic groups in ACE research. We discovered that, compared to White adults with similar levels of ACE exposure, Asian American adults had higher levels of educational attainment and income and lower rates of unemployment. A plausible interpretation is that Asian American parents have higher academic expectations and make more significant investments in their children’s education overall, 33 and that these differences also manifest in households where ACEs are present. Some evidence suggests that harsher and less affectionate parenting often blends with high educational expectations and investments in Asian American households; 38 these parenting attitudes and behaviors may be less likely to co-occur in White households. Offering indirect support for this supposition, Asian American participants were more likely than White participants to report being physically and emotionally abused and physically and emotionally neglected even though they were raised in more socioeconomically advantaged households on average. Relatedly, self-reported experiences of abuse and neglect may have been less detrimental to Asian Americans if they perceived these parenting practices to be normative. This so-called cultural normativeness hypothesis. 39 , 40 has been investigated extensively in relation to children’s social-emotional outcomes, with mixed results. 40 – 42 Further research is needed to elucidate whether the long-term effects of ACEs on health and socioeconomic attainments vary along with different cultural norms and perceptions. Limitations Despite the methodological strengths of our study, four key limitations should be noted. First, the Add Health Study did not assess ACEs with a validated tool, and certain key ACEs such as domestic violence were omitted, which may have affected both the estimated prevalence and impact of ACEs. Second, ACEs were captured via retrospective self-reports, which are subject to memory and response biases. Third, outcomes were also measured based on self-report, which may be less accurate or precise than administrative records of educational and economic attainments. Fourth, the Add Health cohort was born in the late 1970s and early 1980s, which is advantageous for estimating the long-term effects of ACEs but may have limited generalizability to more recent generations. Implications and Future Directions We discovered that ACEs were associated with robust effects on adult socioeconomic outcomes above and beyond the effects of household poverty in childhood. The findings extend a long line of research linking ACEs to poor health, and they underscore the need to understand the mechanisms through which ACEs impact many domains of functioning. For instance, ACEs are associated with early neurobiological, cognitive, and socioemotional impairments, 9, 42–45 which are likely to have mental and behavioral health consequences that undermine educational and economic attainments. Yet, research also has shown that low educational attainment, unemployment, and economic insecurity are prospectively associated with poor physical and mental health. 46 – 48 Taken together, the results point to the need to further investigate discrete and synergistic processes that may explain why ACEs have profound and diverse effects over the life course. Given that ACEs have been linked to negative outcomes worldwide, it is reasonable to conclude that the deleterious effects of ACEs are to some degree universal. Yet, our findings also suggest that associations between ACEs and socioeconomic outcomes may vary to some degree among racial/ethnic groups in the U.S. Most notably, unlike other racial/ethnic groups, ACE scores were not significantly associated with educational and economic attainments among Asian American participants. The underlying reasons for these results are uncertain, though we suspect that they may be partly due to two related factors. First, in Asian American households, ACEs such as physical and emotional abuse may correlate with other parenting behaviors and investments that promote educational achievement and economic attainment. Second, perceptions of events may differ between racial/ethnic groups, and it is possible that reported experiences of physical and emotional abuse do not carry the same subjective meaning. This second interpretation aligns with the cultural normativeness hypothesis, and it is backed by evidence that perceptions of stressful experiences influence objective stress responses to experience. 49 The observed racial/ethnic differences are especially intriguing because they do not indicate that ACEs disproportionately impact marginalized racial/ethnic groups. Research has also failed to show that ACEs disproportionately affect the health of minoritized groups. Thus, targeting prevention and intervention efforts strictly along racial/ethnic lines may not be the best strategy to redress health and socioeconomic disparities in the U.S. Given that ACEs are highly stratified by socioeconomic status across racial and ethnic groups, 30,50 allocating ACE prevention and intervention resources while considering the socioeconomic context of households and communities may be more appropriate. 51 At the same time, because ACEs are a major public health problem, there is a need for effective solutions that can be implemented at scale. Future research should explore the impact of educational, economic, and health programs and policies on ACEs, including universal strategies that are distributed either equally throughout the population or equitably in proportion to the needs of population subgroups. Conclusion Extending research linking ACEs to poor health, this analysis of a nationally representative U.S. sample uncovered significant associations between higher ACE scores and lower socioeconomic attainments. Although the expected dose-response effects were present in the full sample, there was some variation by race and ethnicity. Unexpectedly, compared to non-Hispanic White adults with similar ACE scores, Black adults were less likely to be financially unstable and Asian American adults were more likely to have better educational and economic outcomes. Further research on the socioeconomic consequences of ACEs is warranted, and there is a particular need for studies that explore universal mechanisms of effect along with mechanisms that contribute to differential effects in population subgroups. The resulting evidence may generate more precise implications for both universal and targeted prevention and intervention strategies. Declarations Ethics approval and consent to participate. This research uses data from Add Health, which is currently directed by Robert A. Hummer at the University of North Carolina at Chapel Hill. Add Health was designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill. Waves I-V of Add Health were funded by grant P01 HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. No direct support was received from grant P01 HD31921 for this analysis. Add Health procedures were approved by the Institutional Review Board at the University of North Carolina at Chapel Hill, and all participants provided written informed consent at each wave of data collection. The current study involved secondary analysis of de-identified data. We confirmed that all methods were carried out following relevant guidelines, and regulations and conducted per the Declaration of Helsinki. Consent for publication. Not applicable. Availability of data and materials. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth) Competing interest. The authors declare that they have no competing interests Funding. No funding sources for this project. Author’s contribution. XYL conceptualized and designed the study, conducted the analyses, interpreted the data, drafted the manuscript, and substantially revised the work. JM contributed to data interpretation and substantially revised the manuscript. All authors read and approved the final manuscript. Acknowledgement. None. References Felitti VJ, Anda RF, Nordenberg D, Williamson DF, Spitz AM, Edwards V, et al. Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. 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JAMA Pediatr. 2018;172(11):1038. doi:10.1001/jamapediatrics.2018.2537 Crissey SR. Educational attainment in the United States: 2007. US Department of Commerce ; 2009 Jan. Available from: https://eric.ed.gov/?id=ED505040 Kao G, Thompson JS. Racial and ethnic stratification in educational achievement and attainment. Annu Rev Sociol. 2003;29(1):417–442. doi:10.1146/annurev.soc.29.010202.100019 Shaefer HL, Lapidos A, Wilson R, Danziger S. Association of income and adversity in childhood with adult health and well-being. Soc Serv Rev. 2018;92(1):69–92. doi:10.1086/696891 Drake B, Lee SM, Jonson-Reid M. Race and child maltreatment reporting: Are Blacks overrepresented? Child Youth Serv Rev. 2009;31(3):309–316. doi:10.1016/j.childyouth.2008.08.004 Author. 2018; [blind for review] Slopen N, Shonkoff JP, Albert MA, Yoshikawa H, Jacobs A, Stoltz R, Williams DR. Racial disparities in child adversity in the US: Interactions with family immigration history and income. 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Cultural differences in the association of harsh parenting with internalizing and externalizing symptoms: A meta-analysis. J Child Fam Stud. 2021;30:1–14. doi:10.1007/s10826-021-02113-z Anda RF, Felitti VJ, Bremner JD, Walker JD, Whitfield C, Perry BD, et al. The enduring effects of abuse and related adverse experiences in childhood: A convergence of evidence from neurobiology and epidemiology. Eur Arch Psychiatry Clin Neurosci. 2006;256(3):174–186. doi:10.1007/s00406-005-0624-4 Bradley RH, Corwyn RF. Socioeconomic status and child development. Annu Rev Psychol. 2002;53:371–399. doi:10.1146/annurev.psych.53.100901.135233 Reiss F. Socioeconomic inequalities and mental health problems in children and adolescents: A systematic review. Soc Sci Med. 2013;90:24–31. doi:10.1016/j.socscimed.2013.04.026 Brunello G, Fort M, Schneeweis N, Winter-Ebmer R. The causal effect of education on health: What is the role of health behaviors? Health Econ. 2016;25(3):314–336. doi:10.1002/hec.3141 Janlert U, Winefield AH, Hammarström A. Length of unemployment and health-related outcomes: A life-course analysis. Eur J Public Health. 2015;25(4):662–667. doi:10.1093/eurpub/cku186 Kopasker D, Montagna C, Bender KA. Economic insecurity: A socioeconomic determinant of mental health. SSM Popul Health. 2018;6:184–194. doi:10.1016/j.ssmph.2018.09.006 Smith KE, Pollak SD. Rethinking concepts and categories for understanding the neurodevelopmental effects of childhood adversity. Perspect Psychol Sci. 2021;16(1):67–93. doi:10.1177/1745691620920725 Madigan S, Thiemann R, Deneault AA, Fearon RP, Racine N, Park J, Lunney CA, Dimitropoulos G, Jenkins S, Williamson T, Neville RD. Prevalence of adverse childhood experiences in child population samples: A systematic review and meta-analysis. JAMA Pediatr. 2025;179(1):19-33. doi:10.1001/jamapediatrics.2024.4385 Walsh D, McCartney G, Smith M, Armour G. Relationship between childhood socioeconomic position and adverse childhood experiences (ACEs): A systematic review. J Epidemiol Community Health. 2019;73(12):1087-93. Additional Declarations No competing interests reported. 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While poor educational and economic outcomes have been linked to individual forms of child maltreatment \u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and household dysfunction,\u003csup\u003e7, 10\u0026ndash;13\u003c/sup\u003e few studies have explored the socioeconomic impact of ACEs in aggregate. One study found that higher ACE scores were associated with lower educational and economic attainments,\u003csup\u003e14\u003c/sup\u003e reinforcing a study that showed that adults with four or more ACEs were about twice as likely as adults with no ACEs to drop out of high school, be unemployed, and live in a low-income household.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Socioeconomic effects associated with high ACE exposure explained roughly 15\u0026ndash;20% of the variance in adult health risks,\u003csup\u003e16\u003c/sup\u003e and higher ACE scores were associated with poor health and economic outcomes\u0026mdash;even after controlling for childhood poverty.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e However, each of the preceding ACE studies used cross-sectional data, and thus their ability to draw causal inferences is limited.\u003c/p\u003e\n\u003ch3\u003eRacial and Ethnic Disparities\u003c/h3\u003e\n\u003cp\u003eAlthough ACEs are disproportionately prevalent among historically marginalized racial and ethnic groups in the U.S.,\u003csup\u003e18\u003c/sup\u003e it remains unclear whether the effects of ACEs vary by race/ethnicity. Studies that have explored this question have produced mixed results, with some indicating that ACEs have a greater impact on the physical and mental health of racial/ethnic minoritized groups,\u003csup\u003e19,20\u003c/sup\u003e others finding the opposite,\u003csup\u003e21,22\u003c/sup\u003e and still others reporting null effects.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e In a recent analysis of data from the National Longitudinal Study of Adolescent to Adult Health (Add Health), one study found that the association between ACE scores and an index of economic disadvantage did not differ between Black, White, and Hispanic participants.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e No other known studies have explored whether the associated effects of ACEs on socioeconomic outcomes are moderated by race/ethnicity.\u003c/p\u003e \u003cp\u003eThere is a pressing need to expand this line of ACE research while also including underrepresented racial/ethnic groups that have widely disparate socioeconomic outcomes. To illustrate, recent data from the U.S. Census Bureau indicate that approximately 58% of Asian American adults and 40% of non-Hispanic White adults have completed four or more years of college, as compared to 26% of Black adults, 21% of Hispanic adults, and 17% of American Indian and Alaska Native (AI/AN) adults.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e White and Asian American adults also occupy higher income strata and have greater household wealth than their Black, Hispanic, and AI/AN counterparts.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e However, it is unclear to what degree ACEs contribute to these disparities.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Aims\u003c/h2\u003e \u003cp\u003eProspective research is needed to assess the extent to which ACEs alter socioeconomic outcomes and whether these effects are distributed equally among racial/ethnic groups. Extending previous work,\u003csup\u003e25\u003c/sup\u003e we revisit data from the Add Health Study and assess the degree to which cumulative ACE exposure is associated with four key indicators of adult socioeconomic status: (1) educational attainment; (2) income; (3) unemployment; (4) financial instability. Our second aim is to test whether the associated effects of ACEs vary between five racial/ethnic groups: (1) American Indian/Alaska Native; (2) Asian; (3) Black; (4) Hispanic; (5) White. By expanding beyond the health consequences of ACEs and considering how they affect human capital overall and among population subgroups, the study may help to inform public policy and intervention efforts.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSample and Data\u003c/h2\u003e \u003cp\u003eAdd Health is a longitudinal study of a nationally representative sample of adolescents in grades 7\u0026ndash;12 in the United States in 1994\u0026ndash;95. A total of 20,745 adolescents (grades 7\u0026ndash;12) and 85% of their parents (n\u0026thinsp;=\u0026thinsp;17,670) participated in a baseline (i.e., Wave I) survey (1994\u0026ndash;1995). All adolescents were recruited for a follow-up survey one year later in Wave II (1996), yielding a sample of 14,738 adolescents (grades 8\u0026ndash;12). The original cohort was also asked to complete an in-home interview in 2001\u0026ndash;2002 (Wave III; n\u0026thinsp;=\u0026thinsp;15,197, ages 18\u0026ndash;26) and again in 2008\u0026ndash;2009 (Wave IV; n\u0026thinsp;=\u0026thinsp;15,701, ages 24\u0026ndash;32). A wave V survey was conducted between 2016\u0026ndash;2018 with a subsample of 12,300 cohort members who ranged from 32 to 42 years of age.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e For the current study, data collected from waves I through IV supply information about participant ACE histories, and data collected at wave V are used to measure indicators of socioeconomic status in adulthood.\u003c/p\u003e \u003cp\u003eAdd Health records were obtained from the Carolina Population Center pursuant to a data sharing agreement. Following eligibility criteria established in previous work,\u003csup\u003e30\u003c/sup\u003e the sample was restricted to participants who were missing data for no more than two of the ten ACEs measured. Participants who were categorized as \u0026ldquo;other\u0026rdquo; race/ethnicity or who missed race/ethnicity were dropped from the sample (n\u0026thinsp;=\u0026thinsp;119). The final analytic sample includes participants who had complete information on cluster, strata, and sample weights at Wave V (N\u0026thinsp;=\u0026thinsp;11,938).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAdverse Childhood Experiences\u003c/h2\u003e \u003cp\u003eTen types of ACEs were assessed using information collected from multiple raters across multiple waves of data. Emotional neglect, household mental health concerns, household substance availability, and violent crime victimization were reported by study participants at Waves I and II when participants averaged 16 years of age. Physical abuse, emotional abuse, sexual abuse, physical neglect, and household incarceration that occurred before age 18 were reported by study participants at Waves III and IV. Parental alcoholism and parent divorce were assessed using parent reports at Wave I. Household substance use was coded by combining indicators of household substance availability and parental alcoholism. Each ACE was measured dichotomously, with each item coded 1 if an item was endorsed. Consistent with prior research, a continuous ACE score (range 0\u0026ndash;10) was calculated by summing these indicators. Detailed information about ACE language and cutoff points can be found in Appendix A.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSocioeconomic Outcomes\u003c/h2\u003e \u003cp\u003e \u003cem\u003eEducational Attainment.\u003c/em\u003e Participants reported their highest level of education at wave V ranging from less than high school to a master\u0026rsquo;s degree or higher. Responses were recoded into a categorical variable with four levels: 1\u0026thinsp;=\u0026thinsp;less than high school, 2\u0026thinsp;=\u0026thinsp;high school diploma or equivalent, (including GED, post-high school training, and some college credits), 3\u0026thinsp;=\u0026thinsp;bachelor\u0026rsquo;s degree and some graduate school training, and 4\u0026thinsp;=\u0026thinsp;master\u0026rsquo;s degree and above.\u003c/p\u003e \u003cp\u003e \u003cem\u003eHousehold Income.\u003c/em\u003e Total annual household income was recorded at Wave V, with categories ranging from less than \u003cspan\u003e$\u003c/span\u003e5,000 to greater than or equal to \u003cspan\u003e$\u003c/span\u003e150,000. Replicating a previous Add Health study,\u003csup\u003e5\u003c/sup\u003e 13 income categories were recoded into a continuous variable; the midpoint of each unit was divided by the square root of household size and transformed into an adjusted measure of household income using the natural log to reduce positive skew.\u003c/p\u003e \u003cp\u003e \u003cem\u003eUnemployment\u003c/em\u003e. At wave V, participants were asked if they were currently working for pay. Participants were categorized as currently unemployed if they had either never worked for pay or if they were not currently working but had worked for pay in the past.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFinancial Instability\u003c/em\u003e. The Wave V survey asked three items about financial difficulties experienced since 2008: (1) falling behind paying bills, (2) experiencing a foreclosure procedure, eviction, or repossession of something, and (3) being in debt if assets liquefied. Participants who responded affirmatively to any item were categorized as having experienced financial instability.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRace/Ethnicity\u003c/h3\u003e\n\u003cp\u003eParticipants were categorized into five mutually exclusive racial/ethnic groups, including Hispanic ethnicity and four non-Hispanic groups: AI/AN, Asian, Black, and White. Participants who reported \u0026ldquo;other\u0026rdquo; race/ethnicity or who were missing data were removed from the sample.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eParticipants\u0026rsquo; \u003cem\u003egender\u003c/em\u003e was recoded into female and male; non-binary gender information was unavailable. \u003cem\u003eParental education\u003c/em\u003e was accessed from Wave I parent survey, which was recoded into four categories (1\u0026thinsp;=\u0026thinsp;less than high school, 2\u0026thinsp;=\u0026thinsp;high school diploma or equivalent, post-high school training, or some college credits, 3\u0026thinsp;=\u0026thinsp;bachelor\u0026rsquo;s degree or some graduate school training, and 4\u0026thinsp;=\u0026thinsp;master\u0026rsquo;s degree and above). \u003cem\u003eChildhood poverty\u003c/em\u003e was measured by household income at baseline, which was dichotomized to poor and non-poor based on the 1994 federal poverty level (FPL) adjusted for household size. To supplement missing data, families were classified as poor if they received any types of means-tested public assistance at baseline, including Supplemental Security Income (SSI), Aid to Families with Dependent Children (AFDC), or the Supplemental Nutrition Assistance Program (SNAP).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis Plan\u003c/h2\u003e \u003cp\u003e Descriptive analyses of participant demographics, ACEs, and study outcomes were performed for the full sample and stratified by race/ethnicity. Racial/ethnic differences in ACE scores and outcome variables were analyzed using chi-square tests for categorical variables and one-way ANOVA with post-hoc tests for continuous variables. Estimated effects of ACEs, race/ethnicity, and covariates were tested using ordered logistic regression for educational attainment, general linear regression for household income, and logistic regression for financial instability and unemployment. Each analysis proceeded in three steps. First, unadjusted effects of ACEs and race/ethnicity on socioeconomic outcomes were assessed. Second, covariates were added to the models to control for potential confounders. Third, to explore potential moderating effects of race/ethnicity, interaction terms between ACEs and each racial/ethnic group were added to the models. To reduce multicollinearity with interaction terms and enhance the interpretability of coefficients, ACE scores were centered for main effects and interaction terms. All descriptive analyses and regression models were conducted using SAS 9.4.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Characteristics\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, nearly half (49.7%) of participants identified as female. The racial/ethnic composition of the sample was as follows: 65.7% non-Hispanic White, 16.0% non-Hispanic Black, 12.3% Hispanic, 3.9% Asian American, and 2.1% AI/AN. The mean ACE score for the full sample was 1.47 (\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03); 67.5% of participants had at least one ACE and 10.4% had four or more ACEs (not shown). The prevalence of individual ACEs ranged from a low of 5.7% for household mental health concern (i.e., family suicide attempt) to 32.2% for child physical abuse. AI/AN participants had a mean ACE score of 2.11 (\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.17), which was significantly higher than the means of all other racial/ethnic groups. Non-Hispanic Black (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.66, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07) and Hispanic (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.63, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07) participants also had significantly higher mean ACE scores than non-Hispanic White participants (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.38, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03). Asian Americans had a mean ACE score of 1.49 (\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.10), which did not differ significantly from the non-Hispanic White group.\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\u003eDescriptive characteristics\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal sample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAI/AN\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;206)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNH Black\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,408)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,788)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAsian American (n\u0026thinsp;=\u0026thinsp;784)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNH White\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;6,752)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% or\u003c/p\u003e \u003cp\u003eM(SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% or\u003c/p\u003e \u003cp\u003eM(SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% or\u003c/p\u003e \u003cp\u003eM(SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e% or\u003c/p\u003e \u003cp\u003eM(SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e% or\u003c/p\u003e \u003cp\u003eM(SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e% or\u003c/p\u003e \u003cp\u003eM (SE)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI/AN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParental education\u003c/p\u003e \u003cp\u003e(Range: 1\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003cp\u003e(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003cp\u003e(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003cp\u003e(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003cp\u003e(0.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildhood poverty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational attainment\u003c/p\u003e \u003cp\u003e(Range: 1\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003cp\u003e(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003cp\u003e(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003cp\u003e(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003cp\u003e(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003cp\u003e(0.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58,164\u003c/p\u003e \u003cp\u003e(1326.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43,775\u003c/p\u003e \u003cp\u003e(3289.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39,250\u003c/p\u003e \u003cp\u003e(2081.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53,046\u003c/p\u003e \u003cp\u003e(2146.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72,326\u003c/p\u003e \u003cp\u003e(3415.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62,701\u003c/p\u003e \u003cp\u003e(1446.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently unemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial instability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eACE Indicators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical abuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmotional abuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSexual abuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical neglect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmotional neglect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold substance use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold mental health concern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold incarceration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParent divorce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViolent crime victimization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE scores\u003c/p\u003e \u003cp\u003e(range: 0\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003cp\u003e(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003cp\u003e(0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003cp\u003e(0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003cp\u003e(0.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNotes.\u003c/em\u003e AI/AN\u0026thinsp;=\u0026thinsp;American Indian and Alaska Native. NH\u0026thinsp;=\u0026thinsp;non-Hispanic.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAn analysis of study outcomes showed that the mean educational attainment level was 2.43 (SE\u0026thinsp;=\u0026thinsp;0.03) on a scale of 1 to 4; 94.7% had at least a high school diploma or equivalent degree, and 36.7% had at least a bachelor\u0026rsquo;s degree (not shown). Among all racial/ethnic groups, mean educational attainment levels were highest among Asian American participants (M\u0026thinsp;=\u0026thinsp;2.65, SE\u0026thinsp;=\u0026thinsp;0.06) and lowest among AI/AN participants (M\u0026thinsp;=\u0026thinsp;2.24, SE\u0026thinsp;=\u0026thinsp;0.08). Average annual household income adjusted for household size was \u003cspan\u003e$\u003c/span\u003e58,164 (\u003cem\u003eSE\u003c/em\u003e = \u003cspan\u003e$\u003c/span\u003e1,327). Overall, 17.9% of participants were currently unemployed at Wave V, and more than half (55.7%) reported financial instability. Asian American participants reported the highest average income (\u003cem\u003eM\u003c/em\u003e = \u003cspan\u003e$\u003c/span\u003e72,326, \u003cem\u003eSE\u003c/em\u003e = \u003cspan\u003e$\u003c/span\u003e3,416) and the lowest rates of unemployment (13.5%) and financial instability (42.5%), whereas non-Hispanic Black participants reported the lowest average income (\u003cem\u003eM\u003c/em\u003e = \u003cspan\u003e$\u003c/span\u003e39,250, \u003cem\u003eSE\u003c/em\u003e = \u003cspan\u003e$\u003c/span\u003e2,082) and highest rates of unemployment (22.7%) and financial instability (72.1%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMain Effects of ACEs on Socioeconomic Outcomes\u003c/h2\u003e \u003cp\u003eOrdered logistic regression results for educational attainment are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Results shown in model 1a indicate that higher ACE scores were associated with a decreased odds of achieving higher educational attainment (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.80, \u003cem\u003ep\u003c/em\u003e \u0026lt;. 001), and model 2a shows the associations remained significant after adding covariates (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.84, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001).\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\u003eOrdered logistic regression models for education attainment\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eEducational Attainment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1a\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2a\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3a\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE Scores \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80***\u003c/p\u003e \u003cp\u003e(0.77, 0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84***\u003c/p\u003e \u003cp\u003e(0.80, 0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81***\u003c/p\u003e \u003cp\u003e(0.78, 0.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/Ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI/AN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.53*\u003c/p\u003e \u003cp\u003e(0.33, 0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003cp\u003e(0.42, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003cp\u003e(0.43, 1.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67*\u003c/p\u003e \u003cp\u003e(0.49, 0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003cp\u003e(0.69, 1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003cp\u003e(0.68, 1.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56***\u003c/p\u003e \u003cp\u003e(0.45, 0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003cp\u003e(0.78, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003cp\u003e(0.91, 1.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.50*\u003c/p\u003e \u003cp\u003e(1.04, 2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003cp\u003e(0.81, 1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003cp\u003e(0.96, 2.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.67***\u003c/p\u003e \u003cp\u003e(1.42, 1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.67***\u003c/p\u003e \u003cp\u003e(1.49, 1.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParental education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.73***\u003c/p\u003e \u003cp\u003e(2.39, 3.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.73***\u003c/p\u003e \u003cp\u003e(2.40, 3.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildhood poverty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48***\u003c/p\u003e \u003cp\u003e(0.40, 0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48***\u003c/p\u003e \u003cp\u003e(0.40, 0.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInteraction Terms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEs * AI/AN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003cp\u003e(0.73, 1.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEs * Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003cp\u003e(0.97, 1.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEs * Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003cp\u003e(0.93, 1.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEs * Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34**\u003c/p\u003e \u003cp\u003e(1.10, 1.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote\u003c/em\u003e. \u003csup\u003ea\u003c/sup\u003e ACE scores were grand-mean centered. Reference group for race/ethnicity was non-Hispanic White. AI/AN\u0026thinsp;=\u0026thinsp;American Indian or Alaska Native. NH\u0026thinsp;=\u0026thinsp;non-Hispanic.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* \u0026lt; .05, ** \u0026lt; .01, *** \u0026lt; .001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLinear regression results for income are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Model 1b shows that higher ACE scores were negatively associated with income (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:b\\)\u003c/span\u003e\u003c/span\u003e = -0.09, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and model 2b shows that the ACE-income association remained significant after adding covariates (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:b\\)\u003c/span\u003e\u003c/span\u003e = -0.06, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001).\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\u003eLinear egression models for income\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1b\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:b\\)\u003c/span\u003e\u003c/span\u003e (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2b\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:b\\)\u003c/span\u003e\u003c/span\u003e (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3b\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:b\\)\u003c/span\u003e\u003c/span\u003e (SE)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE Scores \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.09*** (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.06*** (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.07*** (0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/Ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI/AN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.39** (0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.31** (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.32** (0.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.66*** (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.50*** (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.49*** (0.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.21** (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06 (0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22** (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22* (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22* (0.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.16*** (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.16*** (0.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParental education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28*** (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28*** (0.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildhood poverty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.44*** (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.44*** (0.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction Terms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEs * AI/AN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03 (0.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEs * Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01 (0.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEs * Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04 (0.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEs * Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10* (0.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote\u003c/em\u003e. \u003csup\u003ea\u003c/sup\u003e ACE scores were grand-mean centered. Reference group for race/ethnicity was non-Hispanic White. AI/AN\u0026thinsp;=\u0026thinsp;American Indian or Alaska Native. NH\u0026thinsp;=\u0026thinsp;non-Hispanic.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* \u0026lt; .05, ** \u0026lt; .01, *** \u0026lt; .001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLogistic regression results for unemployment and financial instability are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Model 1c shows higher ACE scores (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.21, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) were associated with higher probability of being unemployed, and model 2c shows the association was significant after adding covariates (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.09, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Models 1d and 2d indicate associations between ACE scores and financial instability were robust across unadjusted (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.27, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and adjusted analyses (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.25, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eBinary logistic regression models for unemployment and financial instability\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnemployment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eFinancial Instability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1c\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2c\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3c\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 1d\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel 2d\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModel 3d\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12***\u003c/p\u003e \u003cp\u003e(1.07, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09**\u003c/p\u003e \u003cp\u003e(1.04, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13***\u003c/p\u003e \u003cp\u003e(1.06, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.27***\u003c/p\u003e \u003cp\u003e(1.21, 1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.25***\u003c/p\u003e \u003cp\u003e(1.19, 1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.26***\u003c/p\u003e \u003cp\u003e(1.19, 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/Ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI/AN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003cp\u003e(0.58, 1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003cp\u003e(0.52, 1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003cp\u003e(0.37, 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.57*\u003c/p\u003e \u003cp\u003e(1.10, 2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003cp\u003e(0.96, 2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003cp\u003e(0.91, 1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37*\u003c/p\u003e \u003cp\u003e(1.07, 1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003cp\u003e(0.89, 1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003cp\u003e(0.90, 1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.30***\u003c/p\u003e \u003cp\u003e(1.93, 2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.99***\u003c/p\u003e \u003cp\u003e(1.64, 2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00***\u003c/p\u003e \u003cp\u003e(1.65, 2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003cp\u003e(0.78, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003cp\u003e(0.62, 1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003cp\u003e(0.63, 1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003cp\u003e(0.94, 1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003cp\u003e(0.73, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003cp\u003e(0.72, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003cp\u003e(0.48, 1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003cp\u003e(0.50, 1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003cp\u003e(0.50, 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.60**\u003c/p\u003e \u003cp\u003e(0.43, 0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.65*\u003c/p\u003e \u003cp\u003e(0.46, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.65*\u003c/p\u003e \u003cp\u003e(0.46, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.85***\u003c/p\u003e \u003cp\u003e(1.54, 2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.86***\u003c/p\u003e \u003cp\u003e(1.54, 2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.17*\u003c/p\u003e \u003cp\u003e(1.04, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.17*\u003c/p\u003e \u003cp\u003e(1.03, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParental education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79***\u003c/p\u003e \u003cp\u003e(0.70, 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79***\u003c/p\u003e \u003cp\u003e(0.70, 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.75***\u003c/p\u003e \u003cp\u003e(0.70, 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.75***\u003c/p\u003e \u003cp\u003e(0.69, 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildhood poverty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.51***\u003c/p\u003e \u003cp\u003e(1.19, 1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.49***\u003c/p\u003e \u003cp\u003e(1.19, 1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.42***\u003c/p\u003e \u003cp\u003e(1.20, 1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.41***\u003c/p\u003e \u003cp\u003e(1.20, 1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInteraction Terms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEs * AI/AN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003cp\u003e(0.87, 1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003cp\u003e(0.85, 1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEs * Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003cp\u003e(0.80, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.86*\u003c/p\u003e \u003cp\u003e(0.76, 0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEs * Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003cp\u003e(0.79, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003cp\u003e(0.96, 1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEs * Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64*\u003c/p\u003e \u003cp\u003e(0.45, 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003cp\u003e(0.81, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cem\u003eNote\u003c/em\u003e. \u003csup\u003ea\u003c/sup\u003e ACE scores were grand-mean centered. Reference group for race/ethnicity was non-Hispanic White. AI/AN\u0026thinsp;=\u0026thinsp;American Indian/Alaska Native. NH\u0026thinsp;=\u0026thinsp;non-Hispanic.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e* \u0026lt; .05, ** \u0026lt; .01, *** \u0026lt; .001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eModerating Effects of Race/Ethnicity\u003c/h2\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e present results from multivariate models with interactions between ACEs and race/ethnicity (model 3a-d). Model 3a (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) indicates the association between ACEs and educational attainment differed significantly between Asian American and non-Hispanic White participants (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.34, \u003cem\u003ep\u003c/em\u003e = .004). This finding suggests that, when holding ACEs constant, Asian American participants were more likely than non-Hispanic White participants to attain higher levels of education. Results shown in model 3b (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) also indicate the link between ACEs and income differed significantly between Asian American and non-Hispanic White participants (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:b\\)\u003c/span\u003e\u003c/span\u003e = 0.10, \u003cem\u003ep\u003c/em\u003e = .02). This result suggests that, when compared to non-Hispanic White participants at the same level of ACEs, Asian American participants had higher incomes.\u003c/p\u003e \u003cp\u003eAdditionally, model 3c (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) indicates that the association between ACEs and unemployment differed between Asian American and non-Hispanic White participants (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:OR\\:\\)\u003c/span\u003e\u003c/span\u003e= 0.64, \u003cem\u003ep\u003c/em\u003e = .01). In other words, at the same level of ACE exposure, Asian American participants were less likely than non-Hispanic White participants to be unemployed. Finally, in model 3d (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), there was a significant interaction signifying that the association between ACEs and the odds of financial instability was significantly stronger among non-Hispanic White participants than Black participants (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:OR\\:\\)\u003c/span\u003e\u003c/span\u003e= 0.86, \u003cem\u003ep\u003c/em\u003e = .01).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSecondary Analysis\u003c/h2\u003e \u003cp\u003eTo aid in the interpretation of the observed moderation effects, we stratified our main-effect analyses by race/ethnicity. As expected, ACE scores were negatively associated with educational attainment among non-Hispanic White participants (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.83, p \u0026lt; .001), whereas ACEs were not associated with educational attainment among Asian American participants (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.03, \u003cem\u003ep\u003c/em\u003e = .78). Similarly, ACE scores were negatively associated with income among non-Hispanic White participants (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.07, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), but there was not a significant relationship between ACE scores and income among Asian American participants (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, \u003cem\u003ep\u003c/em\u003e = .97). Moreover, ACE scores were positively associated with unemployment among non-Hispanic White participants (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.12, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), whereas there was a marginally significant association in the opposite direction among Asian American participants (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.73, \u003cem\u003ep\u003c/em\u003e = .06). Finally, ACE scores were significantly associated with financial instability among non-Hispanic White participants (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.25, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001); results trended in the same direction for non-Hispanic Black participants but did not reach statistical significance (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.09, \u003cem\u003ep\u003c/em\u003e = .12).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBuilding on decades of research linking ACEs to poor health-related outcomes, we explored the socioeconomic consequences of ACEs and whether they differed among major racial/ethnic groups in the U.S. Using nationally representative data from the Add Health Study, we confirmed that American Indian/Alaska Native, Black, and Hispanic adults had significantly higher ACE scores and poorer educational and economic outcomes than did their White counterparts. The findings support evidence that historically marginalized groups in the U.S. have a higher ACE burden\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and are disproportionately represented at lower socioeconomic levels.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e We also demonstrated that higher ACE scores were associated with lower education and income levels and higher rates of unemployment and financial instability in adulthood. The associated effects of ACEs were robust after controlling for parent education and childhood poverty \u0026ndash; key predictors of adult socioeconomic outcomes.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Our findings reinforce several cross-sectional studies that have linked ACEs to educational and economic attainments,\u003csup\u003e14,15,17\u003c/sup\u003e lending confidence to the conclusion that ACEs broadly compromise health and welfare over the life course.\u003c/p\u003e \u003cp\u003eContrary to expectations, moderation tests showed that ACEs were more strongly linked to financial instability in White adults than Black adults. This finding should be interpreted cautiously given that: (1) race/ethnicity did not moderate the effects of ACEs on income or unemployment, and (2) a previous Add Health analysis found the association between ACEs and an economic index did not differ between Black and White participants.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Nevertheless, the financial instability result bears an explanation. One possibility is that, when compared to White adults, financial instability among Black adults may be more strongly tied to structural and social determinants than household adversities such as family violence or substance use. This interpretation aligns with the \u003cem\u003edifferential assortment\u003c/em\u003e hypothesis,\u003csup\u003e35\u003c/sup\u003e which has been put forward to explain why ACEs are more prevalent among minoritized racial/ethnic adults overall but less prevalent among low-income Black and Hispanic adults than low-income White adults.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Further research is needed to systematically test this hypothesis and, more broadly, to tease out complex and potentially confounding associations between ACEs, race/ethnicity, and socioeconomic status.\u003c/p\u003e \u003cp\u003eToward that end, there is a need to include underrepresented racial/ethnic groups in ACE research. We discovered that, compared to White adults with similar levels of ACE exposure, Asian American adults had higher levels of educational attainment and income and lower rates of unemployment. A plausible interpretation is that Asian American parents have higher academic expectations and make more significant investments in their children\u0026rsquo;s education overall,\u003csup\u003e33\u003c/sup\u003e and that these differences also manifest in households where ACEs are present. Some evidence suggests that harsher and less affectionate parenting often blends with high educational expectations and investments in Asian American households;\u003csup\u003e38\u003c/sup\u003e these parenting attitudes and behaviors may be less likely to co-occur in White households. Offering indirect support for this supposition, Asian American participants were more likely than White participants to report being physically and emotionally abused and physically and emotionally neglected even though they were raised in more socioeconomically advantaged households on average. Relatedly, self-reported experiences of abuse and neglect may have been less detrimental to Asian Americans if they perceived these parenting practices to be normative. This so-called \u003cem\u003ecultural normativeness\u003c/em\u003e hypothesis.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e has been investigated extensively in relation to children\u0026rsquo;s social-emotional outcomes, with mixed results.\u003csup\u003e\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Further research is needed to elucidate whether the long-term effects of ACEs on health and socioeconomic attainments vary along with different cultural norms and perceptions.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eDespite the methodological strengths of our study, four key limitations should be noted. First, the Add Health Study did not assess ACEs with a validated tool, and certain key ACEs such as domestic violence were omitted, which may have affected both the estimated prevalence and impact of ACEs. Second, ACEs were captured via retrospective self-reports, which are subject to memory and response biases. Third, outcomes were also measured based on self-report, which may be less accurate or precise than administrative records of educational and economic attainments. Fourth, the Add Health cohort was born in the late 1970s and early 1980s, which is advantageous for estimating the long-term effects of ACEs but may have limited generalizability to more recent generations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eImplications and Future Directions\u003c/h2\u003e \u003cp\u003eWe discovered that ACEs were associated with robust effects on adult socioeconomic outcomes above and beyond the effects of household poverty in childhood. The findings extend a long line of research linking ACEs to poor health, and they underscore the need to understand the mechanisms through which ACEs impact many domains of functioning. For instance, ACEs are associated with early neurobiological, cognitive, and socioemotional impairments,\u003csup\u003e9, 42\u0026ndash;45\u003c/sup\u003e which are likely to have mental and behavioral health consequences that undermine educational and economic attainments. Yet, research also has shown that low educational attainment, unemployment, and economic insecurity are prospectively associated with poor physical and mental health.\u003csup\u003e\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e Taken together, the results point to the need to further investigate discrete and synergistic processes that may explain why ACEs have profound and diverse effects over the life course.\u003c/p\u003e \u003cp\u003eGiven that ACEs have been linked to negative outcomes worldwide, it is reasonable to conclude that the deleterious effects of ACEs are to some degree universal. Yet, our findings also suggest that associations between ACEs and socioeconomic outcomes may vary to some degree among racial/ethnic groups in the U.S. Most notably, unlike other racial/ethnic groups, ACE scores were not significantly associated with educational and economic attainments among Asian American participants. The underlying reasons for these results are uncertain, though we suspect that they may be partly due to two related factors. First, in Asian American households, ACEs such as physical and emotional abuse may correlate with other parenting behaviors and investments that promote educational achievement and economic attainment. Second, perceptions of events may differ between racial/ethnic groups, and it is possible that reported experiences of physical and emotional abuse do not carry the same subjective meaning. This second interpretation aligns with the cultural normativeness hypothesis, and it is backed by evidence that perceptions of stressful experiences influence objective stress responses to experience.\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe observed racial/ethnic differences are especially intriguing because they do not indicate that ACEs disproportionately impact marginalized racial/ethnic groups. Research has also failed to show that ACEs disproportionately affect the health of minoritized groups. Thus, targeting prevention and intervention efforts strictly along racial/ethnic lines may not be the best strategy to redress health and socioeconomic disparities in the U.S. Given that ACEs are highly stratified by socioeconomic status across racial and ethnic groups,\u003csup\u003e30,50\u003c/sup\u003e allocating ACE prevention and intervention resources while considering the socioeconomic context of households and communities may be more appropriate.\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e At the same time, because ACEs are a major public health problem, there is a need for effective solutions that can be implemented at scale. Future research should explore the impact of educational, economic, and health programs and policies on ACEs, including universal strategies that are distributed either equally throughout the population or equitably in proportion to the needs of population subgroups.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eExtending research linking ACEs to poor health, this analysis of a nationally representative U.S. sample uncovered significant associations between higher ACE scores and lower socioeconomic attainments. Although the expected dose-response effects were present in the full sample, there was some variation by race and ethnicity. Unexpectedly, compared to non-Hispanic White adults with similar ACE scores, Black adults were less likely to be financially unstable and Asian American adults were more likely to have better educational and economic outcomes. Further research on the socioeconomic consequences of ACEs is warranted, and there is a particular need for studies that explore universal mechanisms of effect along with mechanisms that contribute to differential effects in population subgroups. The resulting evidence may generate more precise implications for both universal and targeted prevention and intervention strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate.\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eThis research uses data from Add Health, which is currently directed by Robert A. Hummer at the University of North Carolina at Chapel Hill. Add Health was designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill. Waves I-V of Add Health were funded by grant P01 HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. No direct support was received from grant P01 HD31921 for this analysis. Add Health procedures were approved by the Institutional Review Board at the University of North Carolina at Chapel Hill, and all participants provided written informed consent at each wave of data collection. The current study involved secondary analysis of de-identified data. We confirmed that all methods were carried out following relevant guidelines, and regulations and conducted per the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication.\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials.\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eInformation on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interest.\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/em\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding.\u003c/em\u003e\u003c/strong\u003e No funding sources for this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor\u0026rsquo;s contribution.\u003c/em\u003e\u003c/strong\u003e XYL conceptualized and designed the study, conducted the analyses, interpreted the data, drafted the manuscript, and substantially revised the work. JM contributed to data interpretation and substantially revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgement.\u003c/em\u003e\u003c/strong\u003e None.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFelitti VJ, Anda RF, Nordenberg D, Williamson DF, Spitz AM, Edwards V, et al. Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. Am J Prev Med. 1998;14(4):245\u0026ndash;258. doi:10.1016/S0749-3797(98)00017-8\u003c/li\u003e\n\u003cli\u003eHughes K, Bellis MA, Hardcastle KA, Sethi D, Butchart A, Mikton C, et al. The effect of multiple adverse childhood experiences on health: A systematic review and meta-analysis. \u003cstrong\u003eLancet Public Health\u003c/strong\u003e. 2017;2(8):e356\u0026ndash;e366. doi:10.1016/S2468-2667(17)30118-4\u003c/li\u003e\n\u003cli\u003ePetruccelli K, Davis J, Berman T. 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JAMA Pediatr. 2025;179(1):19-33. doi:10.1001/jamapediatrics.2024.4385\u003c/li\u003e\n\u003cli\u003eWalsh D, McCartney G, Smith M, Armour G. Relationship between childhood socioeconomic position and adverse childhood experiences (ACEs): A systematic review. J Epidemiol Community Health. 2019;73(12):1087-93.\u003c/li\u003e\n\u003c/ol\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Adverse childhood experiences (ACEs), socioeconomic outcomes, race/ethnicity, disparity","lastPublishedDoi":"10.21203/rs.3.rs-8734358/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8734358/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAdverse childhood experiences (ACEs) have been reliably linked to poor adult health outcomes; less is known about their impact on socioeconomic outcomes. This study examines relationships between ACEs and socioeconomic outcomes in mid-adulthood, and whether these associations are moderated by race/ethnicity.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFive waves of data were drawn from the National Longitudinal Study of Adolescent to Adult Health (Add Health). Multivariate regression analyses were applied to examine main-effect associations between ACEs and socioeconomic outcomes along with the moderating effects of race/ethnicity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eControlling for household economic status in childhood, higher ACE scores were significantly associated with lower levels of educational attainment and income, higher rates of unemployment, and greater financial instability. The association between ACEs and financial instability was stronger for White participants than Black participants, while associations between ACEs and educational attainment, income, and unemployment were stronger for White participants than their Asian American counterparts.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eACEs appear to undermine not only health but also educational and economic attainments, underscoring the need for ACE prevention and intervention strategies. Yet, the moderation results raise questions about the extent to which targeting these strategies along racial/ethnic lines will redress socioeconomic disparities in the U.S.\u003c/p\u003e","manuscriptTitle":"Linking Adverse Childhood Experiences to Socioeconomic Outcomes: Racial and Ethnic Differences in a Representative U.S. Sample","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-04 14:15:57","doi":"10.21203/rs.3.rs-8734358/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-19T17:52:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-15T23:11:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-08T09:24:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-07T17:09:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99255939621812719489643508307240274513","date":"2026-03-07T17:07:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65704412547308405170352463906007525591","date":"2026-03-05T16:28:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119494824235846573255565912245183769699","date":"2026-02-27T14:42:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251757659199840035049277527742436688258","date":"2026-02-27T00:51:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-26T18:14:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-23T13:48:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-04T11:27:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-03T17:48:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-02-03T17:38:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"73a15d99-65c2-4bf4-aed0-4ca22f46a29f","owner":[],"postedDate":"March 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T19:38:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-04 14:15:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8734358","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8734358","identity":"rs-8734358","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}