Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2024 May 1.
Published in final edited form as: Psychol Trauma. 2023 May;15(Suppl 1):S55–S64. doi: 10.1037/tra0001458

Adverse childhood experiences and alcohol use and misuse: Testing the impact of traditional and expanded adverse childhood experiences among racially/ethnically diverse youth transitioning into adulthood

Jenny Zhen-Duan 1,2, Daniella Colombo 3, Mario A Cruz-Gonzalez 1,4, Mercedes Hoyos 5, Kiara Alvarez 1,4,6
PMCID: PMC10356013  NIHMSID: NIHMS1897807  PMID: 37476532

Abstract

Objective:

Traditional Adverse Childhood Experiences (T-ACEs), such as abuse and neglect, have been associated with an increased risk of youth alcohol use and misuse. This study aims to compare associations of T-ACEs and Expanded ACEs (E-ACEs), an expanded set of ACEs that encompass community-level adversities, with alcohol use and misuse by race/ethnicity.

Method:

Data came from a three-wave (1998–1999; 1999–2000; 2004–2005) community-based study in Houston, including youth transitioning into adulthood. We compared associations between ACEs at Wave 1 and past-year alcohol use, abuse, and dependence at Wave 3.

Results:

Participants (n = 2,391) included White (n =908), Black (n = 898) and Latinx (n = 585) youth (M (SD) = 14.00 (2.04)) transitioning into young adulthood (M (SD) = 19.77 (2.34)). T-ACEs were associated with higher odds of alcohol use, abuse, and dependence (OR = 1.15, OR = 1.18, OR = 1.24, respectively) while E-ACEs increased the odds of alcohol dependence (OR = 1.23) in the total sample. No significant differences by race/ethnicity were found. Racial/ethnic differences in increased alcohol risk were observed for some ACE items, such as bullying and use for Latinx youth (OR = 2.13) and poverty and dependence for White youth (OR = 2.01).

Conclusions:

T-ACES and E-ACEs increase the risk of alcohol use and misuse. Results highlight the importance of preventing ACEs exposure as a risk factor for youth alcohol use and misuse. Public policies must also focus on preventing ACEs through multi-level interventions aimed at reducing violence, bullying, and financial instability.

Keywords: adverse childhood experiences, expanded adverse childhood experiences, alcohol, race, ethnicity

Adverse childhood experiences (ACEs), which are stressful life events that occur during childhood and adolescence, are associated with an increased risk of adolescent alcohol use and misuse (Dube et al., 2006; Rothman et al., 2008). The long-term impact of ACEs has been clearly documented, with consequences ranging from neurodevelopmental impairments, poor health persisting into adulthood, and even early death (Brown et al., 2009; Monnat & Chandler, 2015). While some researchers show that racial/ethnic minorities report greater ACEs than their White peers, few studies have examined ways ACEs impact alcohol use and misuse differentially. Of those available, some studies have found Black and Latinx individuals to be at greater risk for alcohol use and misuse compared to White individuals (Fagan & Novak, 2018; Lee & Chen, 2017) while others did not find associations between ACEs and alcohol use in any group (Garrido et al., 2018; Jones et al., 2022). However, most ACEs studies fail to capture community-level adversities that predominantly occur among racial/ethnic minorities (SmithBattle et al., 2022) and that are also linked to poor outcomes and substance use disorders (Hall et al., 2021; Wade et al., 2016). These Expanded ACEs (E-ACEs) include experiencing racism, violence, poverty, bullying, and history of living in foster care (Wade et al., 2014). More attention needs to be placed on understanding the ways ACEs, including E-ACEs, differentially impact alcohol use among racially/ethnically diverse groups.

ACEs and Alcohol Use and Misuse

Individuals who consume alcohol prior to the age of 15 are seven times more likely to develop alcohol use disorder than those who start consuming alcohol at the age of 21 (National Center on Addiction and Substance Abuse, 2011). Preventing alcohol use and misuse during adolescence remains a major public health priority, as early use is associated with learning and cognitive problems, alcohol use disorder in adulthood, and early death (Hu et al., 2017; Spear, 2018). Therefore, preventing factors that increase the risk of alcohol use during adolescence and beyond is critical (Lisdahl et al., 2013). Several mechanisms linking early childhood adversity to alcohol misuse have been posited, including that ACEs impact neurobiological functioning (e.g., stress response system, epigenetic and neurotransmitter systems; Brady & Back, 2012). These biological disruptions can lead to impairment in socioemotional and cognitive development, which makes individuals vulnerable to consuming alcohol to cope with the stressors (McLaughlin & Lambert, 2017). Contextual and cultural-related ACEs, such as the E-ACEs, may constitute additional adversities for racial/ethnic minority youth (Espinola et al., 2019) and make them particularly vulnerable for alcohol misuse.

Traditional ACEs

The 10 ACEs traditionally assessed in most published studies (herein referred to as Traditional ACEs, or T-ACEs), include experiencing physical, verbal, and sexual abuse; physical and emotional neglect; witnessing violence between household members; living with someone with a mental illness, substance use problem, or who has been incarcerated; and parental divorce/separation (Felitti et al., 1998). T-ACEs have been consistently associated with tobacco, marijuana, illicit drug use, and alcohol use among adolescents (Afifi et al., 2020; Benjet et al., 2013; S. M. Brown & Shillington, 2017; Gonçalves et al., 2016). Data on which groups are disproportionately exposed to T-ACEs remain mixed. For example, nationally representative studies show that racial/ethnic minorities report greater T-ACEs exposure (Giano et al., 2020; Gilbert et al., 2015), but studies with youth at risk for maltreatment (Fagan & Novak, 2018) and of low-income women (Mersky & Janczewski, 2018) report higher T-ACEs for White individuals compared to their Black and Latinx peers. Nonetheless, the question remains whether T-ACEs differentially impact alcohol use and misuse in racially/ethnically diverse youth.

T-ACEs predicted alcohol and drug misuse in studies conducted with Native American, Latinx, and Black youth samples (Brockie et al., 2015; Hicks et al., 2020; Ramos-Olazagasti et al., 2017). In comparison studies, the researchers found that compared to White adults, Black and Latinx adults exposed to T-ACEs were more likely to drink heavily (Lee & Chen, 2017), illustrating that the T-ACEs and alcohol misuse association may be stronger for racial/ethnic minorities. In a study with adolescents at risk for maltreatment, the authors showed that despite White youth reporting more T-ACEs than Black youth, T-ACEs were only associated with past-year alcohol or marijuana use for Black youth (Fagan & Novak, 2018). The scant but growing literature examining ways T-ACEs differentially impact outcomes among subgroups is shedding light on the nuanced differences between groups and highlighting that racial/ethnic minorities exposed to T-ACEs may be particularly vulnerable to alcohol use and misuse.

Expanded ACEs

The landmark T-ACE study highlighted ways common childhood adversities are causally associated with poor health across the lifespan (Felitti et al., 1998). However, the original study sample was comprised of mostly White, middle-income, educated people with healthcare access (Gilbert et al., 2015). In response, the Philadelphia ACEs study was launched to oversample racial/ethnic minority and low income individuals residing in urban areas and to explore which adversities were particularly salient for them (Wade et al., 2014). Certain adversities significantly impacted mental health and health outcomes among these vulnerable groups (Cronholm et al., 2015) and were not included in the T-ACEs studies. These E-ACEs, which are ACES often rooted in histories of systemic oppression and marginalization, included witnessing community violence, living in an unsafe neighborhoods, experiencing bullying, living in foster care, economic hardship, and discrimination (Wade et al., 2014).

Studies examining E-ACEs and their association with alcohol and drug use remain scant. In a study with a diverse sample of urban adults, researchers found that those with low socioeconomic status and those endorsing three or more E-ACEs had a 7-fold increased risk for substance use problems, compared to those reporting no E-ACEs (Wade et al., 2016). Similarly, both T-ACEs and E-ACEs were found to increase the likelihood of smoking, vaping, alcohol use, binge drinking, cannabis use, and intoxication among adolescents (Afifi et al., 2020). Racial/ethnic minorities are more likely to endorse E-ACEs than White people (Cronholm et al., 2015). In sum, E-ACEs are impactful adversities for racial/ethnic minorities, more so than some T-ACEs (e.g., parental divorce/separation) (Wade et al., 2014) and these have been linked to increased alcohol and drug use. However, no study to our knowledge, has examined how E-ACEs differentially impact alcohol use and misuse by race/ethnicity.

The purpose of our study was to investigate: 1) whether T-ACEs and E-ACEs were associated with higher odds of alcohol use and misuse over time in a longitudinal sample and 2) whether the odds differed by racial/ethnic group. We analyzed data from a large community-based prospective study of adolescents that collected three waves of comprehensive data on psychiatric diagnoses and life stressors, including childhood adversities, and that oversampled Black and Latinx youth to allow for racial/ethnic group comparisons. We hypothesized that T-ACEs and E-ACEs would be associated with greater odds of alcohol use and misuse and that the odds of alcohol use and misuse conditional on ACEs exposure would be higher among Black and Latinx youth, given that previous research has found that they are disproportionately impacted by both T-ACEs and E-ACEs (Cronholm et al., 2015; Gilbert et al., 2015).

Method

Participants and Procedure

Data were from the Teen Health 2000 (TH2K) study, a large, community-based prospective study that sampled households in the Houston metropolitan area enrolled in the two largest health maintenance organizations, with the goal of assessing DSM-IV psychiatric diagnoses, functional impairment, and a range of related stressors and resources among a large community sample of adolescents (Duong & Roberts, 2014; Roberts et al., 2006). One youth 11 to 17 years old was randomly sampled from each eligible household and Black and Latinx (~78% Mexican American) households were oversampled with the goal of conducting subgroup comparisons. Sample weights were developed and adjusted by poststratification to reflect the age, ethnicity, and gender distribution of the Houston metropolitan area using 2000 Census data (Duong & Roberts, 2014; Roberts et al., 2008, 2010).

Data were collected from each youth and one adult caregiver using audio computer-assisted personal interviews (questions were read to the respondent via headphones) and self-administered questionnaires. The interviews contained a structured interview, the DISC-IV, to generate DSM-IV diagnoses, including substance use disorders, (American Psychiatric Association, 2000), demographic data on the youth and the household; and questions about psychological functioning, stressors, and social context. The Wave 1 sample consisted of 4,175 youth 11–17 years old in 66% of the eligible households. Youth and caregivers were followed up about 12 months later (Wave 2, youth 12–18 years old), with a follow-up rate of 75% (3,134 youth-caregiver dyads), and 60 months later (Wave 3, youth 17–23 years old), with a follow-up rate of 80% from Wave 2 (2,503 youth). All youth and caregivers gave written informed consent prior to participation, and all procedures were approved by the University of Texas Health Sciences Center Committee for Protection of Human Subjects.

Measures

Traditional- and Expanded-Adverse Childhood Experiences (T-ACEs and E-ACEs) at Wave 1

This study did not use a specific ACEs questionnaire; rather, the authors mapped items from youth and parent questionnaires onto Section 1 of The Center for Youth Wellness ACE-Questionnaire (CYW ACE-Q), which includes both T-ACEs and E-ACEs (Harris & Renschler, 2015). Supplemental Table S1A lists the wording of all 10 T-ACEs from the CYW ACE-Q, the number of items used to approximate each ACE, and the item references. Many items had yes/no response options. When a range of responses was provided, these were dichotomized to reflect whether the ACE had occurred or not. When more than one item was included as an ACE indicator, an affirmative response to any item was sufficient to indicate exposure to an ACE. Similarly, youth and parent reports were mapped onto Section 2 of the CYW ACE-Q (see Supplemental Table S1A), which consists of nine items assessing exposure to community-level ACEs (Harris & Renschler, 2015). We mapped responses to eight statements corresponding to exposure to E-ACEs (the ninth, You have been separated from your primary caregiver through deportation or immigration, was not assessed in this survey). We included a ninth item assessing exposure to poverty, as evidence has supported including this stressor among expanded ACEs (SmithBattle et al., 2022). Cumulative ACEs were measured separately using a count variable for total number of T-ACEs (range 0–10) and E-ACEs (range 0–9).

Alcohol Use and Misuse at Wave 3

Three outcome variables were included using the alcohol module of the Diagnostic Interview Schedule for Children, Version 4 (DISC-IV), a highly structured diagnostic interview designed to be administered by lay interviewers to assess psychiatric disorders among children and adolescents (Shaffer et al., 2000). These were past year (1) alcohol use; and (2) alcohol abuse and (3) alcohol dependence using DSM-IV diagnostic criteria (Supplemental Table S1B).

Statistical Analyses

We described racial/ethnic differences in outcome variables at Wave 3, sociodemographic characteristics at Wave 1, and T-ACES and E-ACES at Wave 1 using χ2 tests that accounted for the complex survey design. Our descriptive analyses indicated that two of the outcomes (alcohol abuse and dependence) and some T-ACEs and E-ACEs had very low prevalence. When this is the case, classical standard techniques such as logistic regression often fail to provide meaningful results (Doerken et al., 2019). Thus, to test the association between T-ACEs and E-ACES at Wave 1 and outcome variables at Wave 3, and whether there were racial/ethnic differences in these associations, we fitted binary logistic regression models using the Firth’s bias reduction method. The Firth method reduces small-sample bias and allows to compute reliable, finite estimates in the case of perfect prediction (Heinze & Schemper, 2002). Alcohol use, alcohol abuse, and alcohol dependence were separately modeled as the dependent variables. We first tested the association of ACEs at Wave 1 and outcomes at Wave 3 in the overall sample. To estimate the independent effect of a particular set of ACEs, we estimated two sets of models, one including all T-ACEs simultaneously, and one including all E-ACEs simultaneously. Models in the overall sample adjusted for youth race/ethnicity (Black, Latinx, White), youth age (11–17 years old), youth sex (male, female), parental education (0–12 years, 13–14 years, 15+ years), family income (<$35,000, $35,000-$64,999, $65,000+), and parental marital status (married, other), all measured at Wave 1. To test whether there were racial/ethnic differences in the association between ACEs at Wave 1 and outcomes at Wave 3, we added two-way interactions between race/ethnicity and ACEs to these models. Finally, in all sets of models (i.e., models in the overall sample and models that included race/ethnicity and ACEs two-way interactions) we tested the association of cumulative ACEs at Wave 1 and outcomes at Wave 3.

To handle missing data (described in Supplementary Materials 2 and Table S2A), original survey weights at Wave 1 were adjusted for non-response and attrition at Wave 3 using inverse probability weighting. We estimated the predicted probability of response at Wave 3, π, using youth sociodemographic characteristics, alcohol use and misuse, and ACEs, all measured at Wave 1. We then adjusted the original sampling weights by the factor 1/π. Descriptive statistics were estimated using survey methods in the Stata software version 17 and logistic models using Firth’s bias correction were estimated in the RStudio software version 2022.07.2+576. Results from logistic models are presented in the form of Odds Ratios (ORs) and their 95% Confidence Intervals (95% CIs). Given the large number of ACEs that were tested (20 in the overall sample and 60 in models with interactions [20 ACEs in each racial/ethnic group]), the Bonferroni correction was applied to evaluate statistical significance (p < 0.05/20 in the overall sample and p < 0.05/60 in models with interactions).

Results

Descriptive statistics by race/ethnicity are presented in Table 1. There were racial/ethnic differences in all outcome variables at Wave 3. Compared to White youth, Black and Latinx youth showed a lower prevalence of alcohol use, alcohol abuse, and alcohol dependence. Across all racial/ethnic groups, about half of youth were between 13 and 15 years old and were females. Black and Latinx youth had lower levels of family income and parental education. There was also racial/ethnic variation in most T-ACEs and E-ACEs, and specific patterns varied across ACEs (see Supplementary Table S2B). Overall, Black and Latinx youth appeared to have lower exposure to T-ACEs, but higher E-ACEs, than White youth.

Table 1:

Weighted prevalence of outcome variables at Wave 3 and distribution of demographic characteristics at Wave 1

Total Sample Black Latinx White
Variable (N = 2,391)a (N = 898) (N = 585) (N = 908) P-Value
Outcome variable at Wave 3, N (%)
 Past-year alcohol use 1710 (74.1%) 587 (67.4%) 420 (73.1%) 703 (77.6%) < 0.001
 Past-year alcohol abuse 292 (13.5%) 65 (7.8%) 78 (12.7%) 149 (16.4%) < 0.001
 Past-year alcohol dependence 102 (5.2%) 14 (1.4%) 28 (4.8%) 60 (7.1%) < 0.001
Age, N (%)
 11–12 years old 683 (28.0%) 277 (29.4%) 184 (29.2%) 222 (26.5%) 0.70
 13–15 years old 1179 (43.0%) 432 (42.4%) 283 (41.9%) 464 (44.1%)
 16–17 years old 529 (29.0%) 189 (28.2%) 118 (28.8%) 222 (29.4%)
Gender, N (%)
 Male 1184 (51.4%) 418 (50.0%) 284 (50.1%) 482 (53.0%) 0.41
 Female 1207 (48.6%) 480 (50.0%) 301 (49.9%) 426 (47.0%)
Family income, N (%)
 Less than $35,000 494 (21.2%) 248 (31.3%) 174 (32.8%) 72 (8.7%) < 0.001
 $35,000-$64,999 875 (39.5%) 348 (41.9%) 240 (44.5%) 287 (34.8%)
 $65,000+ 853 (39.3%) 229 (26.8%) 125 (22.7%) 499 (56.5%)
Parental education, N (%)
 0–12 years 670 (35.3%) 171 (21.8%) 315 (58.3%) 184 (23.9%) < 0.001
 13–14 years 699 (28.0%) 304 (34.8%) 159 (26.3%) 236 (26.4%)
 15 + years 1022 (36.7%) 423 (43.4%) 111 (15.5%) 488 (49.7%)
Parental marital status, N (%)
 Married 1818 (79.6%) 569 (63.6%) 481 (82.1%) 768 (84.5%) < 0.001
 Other 573 (20.4%) 329 (36.4%) 104 (17.9%) 140 (15.5%)
Open in a new tab

Notes:

a

In total, N = 2,503 (out of 4,175) youth were followed-up at Wave 3, 112 of whom self-identified as “Other Race” and were not included in the current analyses.

Logistic regression models for the association between ACEs at Wave 1 and alcohol use, abuse, and dependence at Wave 3 are presented in Table 2, Table 3, and Table 4, respectively. (For ease of exposition, we present ORs and 95% CIs for those T-ACEs and E-ACEs that were statistically significant, but all logistic model coefficients are included in Supplementary Material 2, Tables S2C–S2E). As shown in Table 2, living with someone with a drinking or drug problem; feeling unsupported, unloved, and/or unprotected; and experiencing harassment or bullying at school were associated with an increased risk of alcohol use in the overall sample (1.81, 1.21, and 1.26 higher risk than youth not reporting these adversities, respectively), while being in foster care was associated with lower odds of alcohol use (0.27 times lower risk). In addition, we found a dose-response relation in the cumulative T-ACEs scores, such that with each additional T-ACE reported by the youth, the risk of alcohol use increased by 15%. Significant interactions with race/ethnicity indicated that living with someone with a drinking or drug problem increased the risk of alcohol use more among Latinx youth compared to White youth (two-way interaction OR [95% CI] = 2.10 [1.39, 3.20]), and experiencing harassment or bullying at school increased the risk of alcohol use more among Latinx youth compared to both Black and White youth (two-way interaction OR [95% CI] = 2.19 [1.70, 2.82] and 2.38 [1.77, 3.20], respectively). Further, compared to Black youth, being in foster care was significantly associated with lower odds of alcohol use among Latinx youth only (OR [95% CI] = 0.09 [0.03, 0.26]). Although the main effects of living with a parent or guardian who died and exposure to poverty were not significant, there was also a significant interaction between race/ethnicity and these adversities. Compared to both Black and Latinx youth, living with a parent or guardian who died was associated with lower odds of alcohol use among White youth only (OR [95% CI] = 0.26 [0.16, 0.42]), while exposure to poverty significantly increased this risk among White youth only (OR [95% CI] = 1.73 [1.10, 2.69]).

Table 2:

Weighted penalized (Firth correction) logistic regression models of ACEs at Wave 1 predicting past-year alcohol use at Wave 3.

Main Effects Total Samplea Main Effects Whiteb Main Effects Blackb Main Effects Latinxb
ACEs at Wave 1 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] Diff.
Traditional
 Lived with someone with a drinking or drug problem 1.81 [1.51, 2.18] * 1.22 [0.96, 1.56] 3.02 [1.78, 5.34] * 2.68 [1.93, 3.79] * LW
 Felt unsupported, unloved and/or unprotected 1.21 [1.09, 1.34] * 1.28 [1.10, 1.49]c 1.10 [0.87, 1.38] 1.20 [1.02, 1.42]c
  Cumulative traditional ACEs 1.15 [1.10, 1.20] * 1.15 [1.08, 1.22] * 1.13 [1.01, 1.26]c 1.17 [1.08, 1.27] *
Expanded
 Foster care 0.27 [0.15, 0.50] * 0.50 [0.22, 1.13] 1.69 [0.38, 10.53] 0.09 [0.03, 0.26] * BL
 Bullying 1.26 [1.13, 1.40] * 1.00 [0.85, 1.17] 0.97 [0.76, 1.23] 2.13 [1.76, 2.58] * LW, BL
 Parent or guardian died 0.68 [0.50, 0.93]c 0.26 [0.16, 0.42] * 1.42 [0.79, 2.62] 1.63 [0.84, 3.51] BW, LW
 Life-threatening illness 0.77 [0.64, 0.93]c 0.59 [0.45, 0.78] * 1.02 [0.69, 1.53] 0.86 [0.62, 1.21]
 Poverty 1.15 [1.03, 1.28]c 1.85 [1.54, 2.24] * 0.86 [0.67, 1.10] 0.78 [0.65, 0.94]c BW, LW
  Cumulative expanded ACEs 1.05 [1.01, 1.09]c 1.06 [1.00, 1.12] 0.93 [0.85, 1.01] 1.15 [1.08, 1.23] *
Open in a new tab

Notes: Diff.: Difference tests across racial/ethnic groups.

*

Odds ratio statistically significant after adjusting for multiple comparisons (p < 0.05/20 in the total sample; p < 0.05/60 in models with two-way interactions); BW significant difference between Black and White youth; LW significant difference between Latinx and White youth; BL significant difference between Black and Latinx youth. Four different models were estimated: One where all traditional ACEs were included simultaneously, one where traditional ACEs were included as a cumulative score, one where all expanded ACEs were included simultaneously, and one where expanded ACEs were included as a cumulative score. All models adjusted for youth age and sex, family income, parental education, and parental marital status, all measured at Wave 1.

a

Analyses in the total sample also adjusted for race/ethnicity.

b

Main effects among White, Black, and Latinx youth were derived from models that included two-way interactions between race/ethnicity and ACEs (rather than estimating separate models for each racial/ethnic group).

c

Odds ratio statistically significant at conventional levels (p < 0.05) but not after adjusting for multiple comparisons.

Table 3:

Weighted penalized (Firth correction) logistic regression models of ACEs at Wave 1 predicting past-year alcohol abuse at Wave 3.

Main Effects Total Samplea Main Effects Whiteb Main Effects Blackb Main Effects Latinxb
ACEs at Wave 1 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] Diff.
Traditional
 Pushed, grabbed, slapped, or hit hard 1.23 [0.97, 1.54] 1.88 [1.38, 2.53] * 2.04 [0.95, 4.07] 0.71 [0.44, 1.12] LW
 Touched in unwanted sexual way 1.74 [1.25, 2.39] * 1.50 [0.95, 2.31] 1.16 [0.32, 3.39] 2.04 [1.12, 3.56]c
 Household members hurt or threaten to hurt each other 1.02 [0.80, 1.29] 0.45 [0.28, 0.71] * 1.15 [0.58, 2.12] 1.71 [1.23, 2.36]c LW
 Parents or guardians were ever separated or divorced 1.51 [1.29, 1.77] * 1.58 [1.28, 1.96] * 1.10 [0.71, 1.68] 1.50 [1.11, 2.03]c
 Felt unsupported, unloved and/or unprotected 1.08 [0.95, 1.22] 0.81 [0.68, 0.97]c 1.17 [0.80, 1.72] 1.52 [1.21, 1.91] * LW
  Cumulative traditional ACEs 1.18 [1.12, 1.24] * 1.20 [1.12, 1.28] * 1.20 [1.01, 1.42]c 1.13 [1.03, 1.23]c
Expanded
 Foster care 1.02 [0.43, 2.13] 4.44 [1.76, 10.30]c 0.60 [0.00, 5.85] 0.05 [0.00, 0.37] * LW
 Arrested or incarcerated 0.80 [0.66, 0.96]c 1.13 [0.88, 1.43] 0.91 [0.50, 1.58] 0.31 [0.20, 0.46] * LW
 Poverty 0.91 [0.79, 1.05] 0.92 [0.75, 1.12] 1.65 [1.12, 2.42]c 0.64 [0.50, 0.82] *
Open in a new tab

Notes: Diff.: Difference tests across racial/ethnic groups.

*

Odds ratio statistically significant after adjusting for multiple comparisons (p < 0.05/20 in the total sample; p < 0.05/60 in models with two-way interactions); BW significant difference between Black and White youth; LW significant difference between Latinx and White youth; BL significant difference between Black and Latinx youth. Four different models were estimated: One where all traditional ACEs were included simultaneously, one where traditional ACEs were included as a cumulative score, one where all expanded ACEs were included simultaneously, and one where expanded ACEs were included as a cumulative score. All models adjusted for youth age and sex, family income, parental education, and parental marital status, all measured at Wave 1.

a

Analyses in the total sample also adjusted for race/ethnicity.

b

Main effects among White, Black, and Latinx youth were derived from models that included two-way interactions between race/ethnicity and ACEs (rather than estimating separate models for each racial/ethnic group).

c

Odds ratio statistically significant at conventional levels (p < 0.05) but not after adjusting for multiple comparisons.

Table 4:

Weighted penalized (Firth correction) logistic regression models of ACEs at Wave 1 predicting past-year alcohol dependence at Wave 3.

Main Effects Total Samplea Main Effects Whiteb Main Effects Blackb Main Effects Latinxb
ACEs at Wave 1 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] Diff.
Traditional
 Touched in unwanted sexual way 3.32 [2.04, 5.23] * 4.72 [2.36, 9.07] * 2.62 [0.02, 32.50] 3.32 [2.04, 5.23] *
 Lived with someone with drinking or drug problem 4.44 [3.50, 5.62] * 4.88 [3.59, 6.61] * 1.77 [0.40, 5.62] 4.44 [3.50, 5.62] *
 Parents or guardians were ever separated or divorced 0.60 [0.45, 0.79] * 0.60 [0.42, 0.84]c 1.43 [0.60, 3.27] 0.60 [0.45, 0.79] *
  Cumulative traditional ACEs 1.24 [1.15, 1.34] * 1.18 [1.08, 1.30] * 0.86 [0.56, 1.27] 1.24 [1.15, 1.34] *
Expanded
 Bullying 2.32 [1.88, 2.86] * 2.53 [1.94, 3.30] * 0.92 [0.41, 2.06] 2.32 [1.88, 2.86] *
 Life-threatening illness 0.15 [0.07, 0.30] * 0.29 [0.12, 0.56] * 0.24 [0.00, 1.84] 0.15 [0.07, 0.30] *
 Neighborhood violence 0.66 [0.53, 0.83] * 0.56 [0.41, 0.74] * 0.92 [0.38, 2.13] 0.66 [0.53, 0.83] *
 Arrested or incarcerated 1.86 [1.45, 2.38] * 1.45 [1.04, 2.00]c 1.92 [0.72, 4.69] 1.86 [1.45, 2.38] *
 Poverty 1.31 [1.05, 1.62]c 2.01 [1.54, 2.63] * 0.14 [0.02, 0.46] * 1.31 [1.05, 1.62]c BW, LW
  Cumulative expanded ACEs 1.23 [1.15, 1.32] * 1.21 [1.11, 1.32] * 1.08 [0.81, 1.44] 1.23 [1.15, 1.32] *
Open in a new tab

Notes: Diff.: Difference tests across racial/ethnic groups.

*

Odds ratio statistically significant after adjusting for multiple comparisons (p < 0.05/20 in the total sample; p < 0.05/60 in models with two-way interactions); BW significant difference between Black and White youth; LW significant difference between Latinx and White youth; BL significant difference between Black and Latinx youth. Four different models were estimated: One where all traditional ACEs were included simultaneously, one where traditional ACEs were included as a cumulative score, one where all expanded ACEs were included simultaneously, and one where expanded ACEs were included as a cumulative score. All models adjusted for youth age and sex, family income, parental education, and parental marital status, all measured at Wave 1.

a

Analyses in the total sample also adjusted for race/ethnicity.

b

Main effects among White, Black, and Latinx youth were derived from models that included two-way interactions between race/ethnicity and ACEs (rather than estimating separate models for each racial/ethnic group).

c

Odds ratio statistically significant at conventional levels (p < 0.05) but not after adjusting for multiple comparisons.

Results for the association between ACEs at Wave 1 and alcohol abuse at Wave 3 are presented in Table 3. Overall, being touched in a sexual way that was unwanted and having parents or guardians who were ever separated or divorced were associated with an increased risk of alcohol abuse, and so was each additional T-ACE experienced. Significant interactions with race/ethnicity indicated that compared to Latinx youth, being pushed, grabbed, slapped, or hit hard significantly increased the risk of alcohol abuse among White youth only (OR [95% CI] = 1.88 [1.38, 2.53]), while seeing or hearing household members hurt or threaten to hurt each other decreased this risk among White youth only (OR [95% CI] = 0.45 [0.28, 0.71]). In contrast, compared to White youth, feeling unsupported, unloved, and/or unprotected significantly increased the risk of alcohol abuse among Latinx youth only (OR [95% CI] = 1.52 [1.21, 1.91]), while being in foster care and youth’s report of ever being detained, arrested, or incarcerated was associated with a lower risk of alcohol abuse among Latinx youth only (OR [95% CI] = 0.05 [0.00, 0.37] and 0.31 [0.20, 0.46], respectively).

Regarding alcohol dependence (Table 4), being touched in a sexual way that was unwanted, living with someone with a drinking or drug problem, experiencing harassment or bullying at school, and youth’s report of ever being detained, arrested, or incarcerated were associated with increased risk. With each additional T-ACE and E-ACE reported by the youth, the risk of alcohol dependence increased by 24% and 23%, respectively. In contrast, having parents or guardians who were ever separated or divorced, having a serious medical procedure or life-threatening illness, and often seeing or hearing neighborhood violence were associated with lower odds of alcohol dependence. Significant interaction between race/ethnicity and poverty were found, as risk of alcohol dependence increased among White youth (OR [95% CI] = 2.01 [1.54, 2.63]), but the risk was lower among Black youth (OR [95% CI] = 0.14 [0.02, 0.46]).

Discussion

The purpose of this study was to examine whether experiencing T-ACEs and E-ACEs was associated with higher odds of alcohol use and misuse in a diverse longitudinal sample. Descriptive analyses indicated that White youth were more likely to be exposed to T-ACEs whereas Black and Latinx youth reported higher E-ACEs. In addition, compared to White youth, Black and Latinx youth showed a lower prevalence of alcohol use and misuse. Three main findings emerged. First, cumulative T-ACEs were associated with higher odds of alcohol use, alcohol abuse, and alcohol dependence in the total sample, and no significant differences by race/ethnicity were found. Second, cumulative E-ACEs were associated with higher alcohol dependence (but not alcohol use or alcohol abuse) in the total sample, and no significant differences by race/ethnicity were found. In fact, for each additional T-ACE and E-ACE reported by the youth, the risk of alcohol dependence increased by 24% and 23%, respectively. Third, certain subgroup variations were significant, including that experiencing bullying increased risks for alcohol use in Latinx youth compared to White and Black youth, and experiencing poverty increased the risk for alcohol dependence in White youth compared to their Latinx and Black peers.

Consistent with previous studies, exposure to T-ACEs, such abuse or neglect, increased the risk of alcohol use and misuse (Fagan & Novak, 2018; Mersky & Janczewski, 2018; Ramos-Olazagasti et al., 2017). In nationally representative studies, researchers have found that T-ACEs experienced during childhood were associated with binge drinking (Jung et al., 2020) and hazardous drinking during adulthood (Leung et al., 2016). Early initiators of alcohol use are particularly at risk for developing alcohol use disorder, and that in the average developmental trend alcohol use peaks in late adolescence into the mid-twenties (National Center on Addiction and Substance Abuse, 2011), or around the ages our participants were at Wave 3. It has been theorized that youth experiencing significant ACEs are particularly vulnerable to drinking to cope with stress (McLaughlin & Lambert, 2017) Thus, ACE-exposed youth should be considered to be a unique subgroup vulnerable to heavier drinking trajectories (Hingson et al., 2006; Maggs & Schulenberg, 2004). Prevention efforts are particularly important given that alcohol misuse is associated with heightened morbidity, mortality, psychosocial impairment, and health issues present during adolescence into adulthood (Hu et al., 2017; Spear, 2018).

E-ACEs, which include community-level stressors that extend beyond the immediate familial context such as discrimination, neighborhood violence, and experiencing poverty (Cronholm et al., 2015), were associated with higher odds of alcohol dependence in this study. The findings are consistent with previous studies showing that cumulative E-ACEs can increase the risk of substance misuse in adulthood (Wade et al., 2016) and poorer physical and mental health outcomes in adolescence (Afifi et al., 2020). Past research with youth has found that E-ACEs (i.e., foster care involvement, poverty, and living in unsafe communities) were related to increased odds of alcohol use, binge drinking, and alcohol intoxication (Afifi et al., 2020). However, we found that T-ACEs were related to alcohol use, abuse and dependence, while E-ACEs were only related to alcohol dependence. A prior study examining lifetime stressors and alcohol misuse found that stress levels and coping factors differentiated between criteria for alcohol abuse and dependence, such that those reporting higher levels of distress from their stressors were more likely to meet the criteria for alcohol dependence and not abuse (Johnson & Pandina, 2000). It is plausible that individuals in our study that experienced E-ACEs are highly distressed by them and therefore more likely to meet the diagnostic criteria for alcohol dependence, representing greater severity of alcohol use disorder. Future studies studying ACEs and alcohol use should evaluate the mediating role of stress and coping in impacting alcohol use disorder outcomes. Nonetheless, both cumulative T-ACEs and E-ACEs were associated with alcohol dependence, the more severe form of alcohol use disorder in our study, and remain important to include in future studies.

Our hypothesis that racial/ethnic minorities would be disproportionately affected by T-ACEs and E-ACEs was only partially supported. Overall, no significant differences in the cumulative ACEs-alcohol association by race/ethnicity were found. Certain subgroup differences were significant, and some were in unexpected directions. For example, exposure to bullying increased risks for alcohol use in Latinx youth compared to White and Black youth (but bullying was associated with lower odds of alcohol abuse in Latinx youth than White youth), exposure to poverty increased the risk of alcohol dependence among White youth more than their Latinx and Black peers, and no specific ACE increased the odds for Black youth. Findings that specific ACEs were not related to increased alcohol use among Black youth are consistent with national data showing that Black adults are less likely to have alcohol use disorders (Pacek et al., 2012) but contrary to findings that Black youth are more likely to drink alcohol than White youth (Fagan & Novak, 2018). Zapolski et al. (2014) argued that Black individuals may drink less due to cultural norms against excessive drinking, greater risk for legal problems related to drinking due to disproportionate criminalization of this group, or because other substances may be preferred over alcohol (Pacek et al., 2012). Our findings may reflect protective factors mitigating the effects of ACEs among Black youth, or they may reflect a different pathway in which exposure to ACEs results in vulnerability to other adverse health outcomes, rather than alcohol use.

Our findings suggest that Latinx youth may be particularly vulnerable to drinking when exposed to bullying, despite reporting less bullying victimization compared to White and Black youth. Afifi and colleagues (2020) found that youth exposed to both ACEs and bullying were more likely to use and misuse alcohol and drugs than those exposed to ACEs alone. Researchers have shown that Latinx youth experiencing racial/ethnic bullying were more likely to consume alcohol, whereas those who simultaneously perpetrated and experienced racial/ethnic bullying were more likely to use tobacco (Hong et al., 2021). Reasons for why Latinx youth may be more vulnerable to consuming alcohol when exposed to bullying compared to other racial/ethnic groups, however, remain unclear. Given that racial/ethnic bullying increases substance use and misuse among this group (Cardoso et al., 2018; Stone & Carlisle, 2017), it may be that racial/ethnic discrimination is compounding the adverse effects of bullying for Latinx. More research is needed to understand why this pattern did not hold for Black youth in the study, who are also racial/ethnic minorities. It is possible that certain cultural factors that were not included in our study, such as ethnic identity and adherence to cultural values, served as protective factors in the face of bullying for Black youth (Xu et al., 2020).

Previous studies have shown that individuals living in areas of higher concentrated poverty are more likely to consume alcohol and experience more complications related to their use (Rhew et al., 2020). In a study comparing the effects of social disadvantage (e.g., poverty) among racially/ethnically diverse adults, the authors found that Black and Latinx adults experienced more social disadvantage than their White peers (Mulia et al., 2008) and that social disadvantage was linked to higher alcohol misuse across three groups. Our finding that poverty was more strongly associated with risk for alcohol use among White youth may seem inconsistent with these findings; however, it is consistent with previous research finding that White youth and youth with high socioeconomic status reported poorer outcomes when exposed to ACEs, compared to their peers (Goldstein et al. 2021). Conceptualized within resilience frameworks, the authors posit that Black, Latinx and low-income individuals’ exposure to significant stressors result in developing more coping and resilience skills to deal with ACEs (Goldstein et al., 2021; Mc Gee et al., 2018). To this end, Black and Latinx youth exposed to the E-ACE of poverty may less vulnerable to alcohol use as a form of coping, given their significant and disproportionate exposure to other E-ACEs which may result in developing coping skills that protect them from alcohol use. To note, this potential explanation presents substantial ethical considerations for conceptualization of the impact of ACEs on racial/ethnic minority youth, as development of so-called individual resilience in the face of adverse and unjust conditions should not be expected of youth and may result in other long-term impacts by the expectation to withstand adversity despite “wear and tear” on physical and mental health.

An alternative explanation is that more multi-dimensional measures of social disadvantage, such as subjective social status, are necessary to understand the association of poverty with alcohol use among racially/ethnically diverse adolescents. Past research has demonstrated that subjective social status, or one’s perception of social standing relative to others, is a more consistent predictor of mental health disorders than objective measures such as family income (McLaughlin et al., 2012). Nonetheless, our findings suggest that E-ACEs could encompass a broader array of adverse experiences capable of impacting adolescents’ outcomes, more than just T-ACEs alone, and should be included in future studies (Cronholm et al., 2015).

This study has several limitations. The data was collected during the early 2000s and does not represent current sociopolitical and historical events. The ACEs framework was applied to this data retrospectively by mapping T-ACEs and E-ACEs constructs onto available data, as other studies have done (e.g., Caballero et al., 2017; Ramos-Olazagasti et al., 2017), meaning some ACEs were not measured (e.g., stress related to immigration status) or were measured differently than in the current standardized measures (e.g., family-level stressors may have included only parents while the ACEs measure refers to all household members). We relied on report of ACEs at Wave 1 to predict outcomes at Wave 3; however, this does not account for ACEs exposure that may have occurred in the intervening years between Waves 1 and 3. Self-report of both ACEs data and alcohol use outcomes are subject to recall and social desirability bias and may be underreported. However, the study used computer-assisted interviews in which questions were read to adolescents privately via headphones to facilitate reporting of sensitive information. Despite the limitations, the availability of a longitudinal community-based sample with oversampling of Black and Latinx youth makes this an important study to establish early evidence on the associations between ACEs and alcohol use among diverse adolescents.

Conclusion

Overall, the potential role of both T-ACEs and E-ACEs in alcohol use and misuse highlights the importance of multi-level interventions designed to reduce risk and increase promotive factors affecting a range of health outcomes. For example, prevention of T-ACEs can begin prenatally and shortly after birth through evidence-based strategies such as nurse home visiting programs to prevent maltreatment (Chaiyachati et al., 2018) and other population-based maltreatment prevention programs aimed at increasing parenting and social supports (Prinz et al., 2009). In addition, public health interventions may target prevention of community violence (Gavine et al., 2017), reduction of bullying in the schools (Kärnä et al., 2011), and disparity-reducing policies and interventions designed to support financial and household stability (Thornton et al., 2016). Finally, interventions to prevent and treat alcohol misuse among adolescents should incorporate a trauma-informed lens that takes into account exposure to adverse experiences across a range of ecological contexts.

Supplementary Material

Supplemental Material 1
Supplemental Material 2

Clinical Impact Statement:

Consistent with previous studies, we found that the family-level ACEs traditionally studied in the literature are associated with youth alcohol use and misuse. Additionally, our findings suggest that an expanded set of ACEs encompassing adversities particularly relevant to socioeconomically and racially/ethnically diverse populations are also associated with alcohol misuse. Racial and ethnic differences in alcohol use risk were observed for specific E-ACEs. It is imperative we expand our understanding and measurement of ACEs to include those occurring outside of the family, such as neighborhood violence and discrimination, which can detrimentally impact youth’s mental health.

Footnotes

We have no known conflicts of interest to disclose.

References

  1. Afifi TO, Taillieu T, Salmon S, Davila IG, Stewart-Tufescu A, Fortier J, Struck S, Asmundson GJG, Sareen J, & MacMillan HL (2020). Adverse childhood experiences (ACEs), peer victimization, and substance use among adolescents. Child Abuse & Neglect, 106, 104504. 10.1016/j.chiabu.2020.104504 [DOI] [PubMed] [Google Scholar]
  2. American Psychiatric Association. (2000). Quick reference to the diagnostic criteria from DSM-IV-TR. APA; Washington, DC. [Google Scholar]
  3. Benjet C, Borges G, Medina-Mora ME, & Méndez E (2013). Chronic childhood adversity and stages of substance use involvement in adolescents. Drug and Alcohol Dependence, 131(1–2), 85–91. [DOI] [PubMed] [Google Scholar]
  4. Brady KT, & Back SE (2012). Childhood Trauma, Posttraumatic Stress Disorder, and Alcohol Dependence. Alcohol Research : Current Reviews, 34(4), 408–413. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Brockie TN, Dana-Sacco G, Wallen GR, Wilcox HC, & Campbell JC (2015). The Relationship of Adverse Childhood Experiences to PTSD, Depression, Poly-Drug Use and Suicide Attempt in Reservation-Based Native American Adolescents and Young Adults. American Journal of Community Psychology, 55(3), 411–421. 10.1007/s10464-015-9721-3 [DOI] [PubMed] [Google Scholar]
  6. Brown DW, Anda RF, Tiemeier H, Felitti VJ, Edwards VJ, Croft JB, & Giles WH (2009). Adverse Childhood Experiences and the Risk of Premature Mortality. American Journal of Preventive Medicine, 37(5), 389–396. 10.1016/j.amepre.2009.06.021 [DOI] [PubMed] [Google Scholar]
  7. Brown SM, & Shillington AM (2017). Childhood adversity and the risk of substance use and delinquency: The role of protective adult relationships. Child Abuse & Neglect, 63, 211–221. [DOI] [PubMed] [Google Scholar]
  8. Caballero TM, Johnson SB, Buchanan CRM, & DeCamp LR (2017). Adverse Childhood Experiences Among Hispanic Children in Immigrant Families Versus US-Native Families. Pediatrics, 140(5), e20170297. 10.1542/peds.2017-0297 [DOI] [PubMed] [Google Scholar]
  9. Cardoso JB, Szlyk HS, Goldbach J, Swank P, & Zvolensky MJ (2018). General and Ethnic-Biased Bullying Among Latino Students: Exploring Risks of Depression, Suicidal Ideation, and Substance Use. Journal of Immigrant and Minority Health, 20(4), 816–822. 10.1007/s10903-017-0593-5 [DOI] [PubMed] [Google Scholar]
  10. Chaiyachati BH, Gaither JR, Hughes M, Foley-Schain K, & Leventhal JM (2018). Preventing child maltreatment: Examination of an established statewide home-visiting program. Child Abuse & Neglect, 79, 476–484. 10.1016/j.chiabu.2018.02.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Cronholm PF, Forke CM, Wade R, Bair-Merritt MH, Davis M, Harkins-Schwarz M, Pachter LM, & Fein JA (2015). Adverse Childhood Experiences: Expanding the Concept of Adversity. American Journal of Preventive Medicine, 49(3), 354–361. 10.1016/j.amepre.2015.02.001 [DOI] [PubMed] [Google Scholar]
  12. Doerken S, Avalos M, Lagarde E, & Schumacher M (2019). Penalized logistic regression with low prevalence exposures beyond high dimensional settings. PLOS ONE, 14(5), e0217057. 10.1371/journal.pone.0217057 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Dube SR, Miller JW, Brown DW, Giles WH, Felitti VJ, Dong M, & Anda RF (2006). Adverse childhood experiences and the association with ever using alcohol and initiating alcohol use during adolescence. Journal of Adolescent Health, 38(4), 444.e1–444.e10. 10.1016/j.jadohealth.2005.06.006 [DOI] [PubMed] [Google Scholar]
  14. Duong HT, & Roberts RE (2014). Perceived weight in youths and risk of overweight or obesity six years later. Journal of Psychosomatic Research, 76(1), 23–27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Espinola M, Zhen-Duan J, Suarez-Cano G, Mowry-Mora I, & Shultz JM (2019). The Impact of US Sociopolitical Issues on the Prejudicial Treatment of Latino Children and Youth. In Fitzgerald HE, Johnson DJ, Qin DB, Villarruel FA, & Norder J (Eds.), Handbook of Children and Prejudice: Integrating Research, Practice, and Policy (pp. 161–180). Springer International Publishing. 10.1007/978-3-030-12228-7_9 [DOI] [Google Scholar]
  16. Fagan AA, & Novak A (2018). Adverse childhood experiences and adolescent delinquency in a high-risk sample: A comparison of white and black youth. Youth Violence and Juvenile Justice, 16(4), 395–417. [Google Scholar]
  17. Felitti VJ, Anda RF, Nordenberg D, Williamson DF, Spitz AM, Edwards V, Koss MP, & Marks JS (1998). Relationship of Childhood Abuse and Household Dysfunction to Many of the Leading Causes of Death in Adults: The Adverse Childhood Experiences (ACE) Study. American Journal of Preventive Medicine, 14(4), 245–258. 10.1016/S0749-3797(98)00017-8 [DOI] [PubMed] [Google Scholar]
  18. Garrido EF, Weiler LM, & Taussig HN (2018). Adverse Childhood Experiences and Health-Risk Behaviors in Vulnerable Early Adolescents. The Journal of Early Adolescence, 38(5), 661–680. 10.1177/0272431616687671 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Gavine A, MacGillivray S, & Williams DJ (2017). Universal community-based social development interventions for preventing community violence by young people 12 to 18 years of age. The Cochrane Database of Systematic Reviews, 2017(1), CD011258. 10.1002/14651858.CD011258.pub2 [DOI] [Google Scholar]
  20. Giano Z, Wheeler DL, & Hubach RD (2020). The frequencies and disparities of adverse childhood experiences in the U.S. BMC Public Health, 20(1), 1327. 10.1186/s12889-020-09411-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Gilbert LK, Breiding MJ, Merrick MT, Thompson WW, Ford DC, Dhingra SS, & Parks SE (2015). Childhood Adversity and Adult Chronic Disease: An Update from Ten States and the District of Columbia, 2010. American Journal of Preventive Medicine, 48(3), 345–349. 10.1016/j.amepre.2014.09.006 [DOI] [PubMed] [Google Scholar]
  22. Goldstein E, Topitzes J, Miller-Cribbs J, & Brown RL (2021). Influence of race/ethnicity and income on the link between adverse childhood experiences and child flourishing. Pediatric Research, 89(7), Art. 7. 10.1038/s41390-020-01188-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Gonçalves H, Soares ALG, Santos A. P. G. dos, Ribeiro CG, Bierhals IO, Vieira LS, Hellwig NL, Wehrmeister FC, & Menezes A (2016). Adverse childhood experiences and consumption of alcohol, tobacco and illicit drugs among adolescents of a Brazilian birth cohort. Cadernos de Saude Publica, 32. [DOI] [PubMed] [Google Scholar]
  24. Hall A, Perez A, West X, Brown M, Kim E, Salih Z, & Aronoff S (2021). The Association of Adverse Childhood Experiences and Resilience With Health Outcomes in Adolescents: An Observational Study. Global Pediatric Health, 8, 2333794X20982433. 10.1177/2333794X20982433 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Harris NB, & Renschler T (2015). Center for Youth Wellness ACE-Questionnaire (CYW ACE-Q Child, Teen, Teen SR). Version 7.
  26. Heinze G, & Schemper M (2002). A solution to the problem of separation in logistic regression. Statistics in Medicine, 21(16), 2409–2419. 10.1002/sim.1047 [DOI] [PubMed] [Google Scholar]
  27. Hicks MR, Kernsmith P, & Smith-Darden J (2020). The Effects of Adverse Childhood Experiences on Internalizing and Externalizing Behaviors among Black Children and Youth. Journal of Child & Adolescent Trauma, 14(1), 115–122. 10.1007/s40653-020-00316-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hingson RW, Heeren T, & Winter MR (2006). Age at Drinking Onset and Alcohol Dependence: Age at Onset, Duration, and Severity. Archives of Pediatrics & Adolescent Medicine, 160(7), 739–746. 10.1001/archpedi.160.7.739 [DOI] [PubMed] [Google Scholar]
  29. Hong JS, Kim DH, Hunter SC, Cleeland LR, Lee CA, Lee JJ, & Kim J (2021). Racial/ethnic bullying subtypes and alcohol, tobacco, and marijuana use among US adolescents. Journal of Racial and Ethnic Health Disparities, 1–11. [DOI] [PubMed] [Google Scholar]
  30. Hu H, Eaton WW, Anthony JC, Wu L-T, & Cottler LB (2017). Age of first drunkenness and risks for all-cause mortality: A 27-year follow-up from the epidemiologic catchment area study. Drug and Alcohol Dependence, 176, 148–153. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Johnson V, & Pandina RJ (2000). Alcohol problems among a community sample: Longitudinal influences of stress, coping, and gender. Substance Use & Misuse, 35(5), 669–686. [DOI] [PubMed] [Google Scholar]
  32. Jones MS, Shurtliff TM, & Gerlinger J (2022). Racial differences in the effects of early adverse childhood experiences on adolescent delinquency: The role of middle childhood mechanisms. Journal of Criminal Justice, 82, 101977. 10.1016/j.jcrimjus.2022.101977 [DOI] [Google Scholar]
  33. Jung J, Rosoff DB, Muench C, Luo A, Longley M, Lee J, Charlet K, & Lohoff FW (2020). Adverse Childhood Experiences are Associated with High-Intensity Binge Drinking Behavior in Adulthood and Mediated by Psychiatric Disorders. Alcohol and Alcoholism, 55(2), 204–214. 10.1093/alcalc/agz098 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Kärnä A, Voeten M, Little TD, Poskiparta E, Kaljonen A, & Salmivalli C (2011). A Large-Scale Evaluation of the KiVa Antibullying Program: Grades 4–6. Child Development, 82(1), 311–330. 10.1111/j.1467-8624.2010.01557.x [DOI] [PubMed] [Google Scholar]
  35. Lee RD, & Chen J (2017). Adverse childhood experiences, mental health, and excessive alcohol use: Examination of race/ethnicity and sex differences. Child Abuse & Neglect, 69, 40–48. 10.1016/j.chiabu.2017.04.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Leung JPK, Britton A, & Bell S (2016). Adverse Childhood Experiences and Alcohol Consumption in Midlife and Early Old-Age. Alcohol and Alcoholism, 51(3), 331–338. 10.1093/alcalc/agv125 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Lisdahl KM, Gilbart ER, Wright NE, & Shollenbarger S (2013). Dare to Delay? The Impacts of Adolescent Alcohol and Marijuana Use Onset on Cognition, Brain Structure, and Function. Frontiers in Psychiatry, 4, 53. 10.3389/fpsyt.2013.00053 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Maggs JL, & Schulenberg JE (2004). Trajectories of Alcohol Use During the Transition to Adulthood. Alcohol Research & Health, 28(4), 195–201. [Google Scholar]
  39. Mc Gee SL, Höltge J, Maercker A, & Thoma MV (2018). Sense of Coherence and Stress-Related Resilience: Investigating the Mediating and Moderating Mechanisms in the Development of Resilience Following Stress or Adversity. Frontiers in Psychiatry, 9. https://www.frontiersin.org/articles/10.3389/fpsyt.2018.00378 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. McLaughlin KA, Costello EJ, Leblanc W, Sampson NA, & Kessler RC (2012). Socioeconomic Status and Adolescent Mental Disorders. American Journal of Public Health, 102(9), 1742–1750. 10.2105/AJPH.2011.300477 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. McLaughlin KA, & Lambert HK (2017). Child trauma exposure and psychopathology: Mechanisms of risk and resilience. Current Opinion in Psychology, 14, 29–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Mersky JP, & Janczewski CE (2018). Racial and ethnic differences in the prevalence of adverse childhood experiences: Findings from a low-income sample of U.S. women. Child Abuse & Neglect, 76, 480–487. 10.1016/j.chiabu.2017.12.012 [DOI] [PubMed] [Google Scholar]
  43. Monnat SM, & Chandler RF (2015). Long Term Physical Health Consequences of Adverse Childhood Experiences. The Sociological Quarterly, 56(4), 723–752. 10.1111/tsq.12107 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Mulia N, Ye Y, Zemore SE, & Greenfield TK (2008). Social Disadvantage, Stress, and Alcohol Use Among Black, Hispanic, and White Americans: Findings From the 2005 U.S. National Alcohol Survey. Journal of Studies on Alcohol and Drugs, 69(6), 824–833. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. National Center on Addiction and Substance Abuse. (2011). Adolescent Substance Use: America’s #1 Public Health Problem.
  46. Pacek LR, Malcolm RJ, & Martins SS (2012). Race/ethnicity differences between alcohol, marijuana, and co-occurring alcohol and marijuana use disorders and their association with public health and social problems using a national sample. The American Journal on Addictions, 21(5), 435–444. 10.1111/j.1521-0391.2012.00249.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Prinz RJ, Sanders MR, Shapiro CJ, Whitaker DJ, & Lutzker JR (2009). Population-Based Prevention of Child Maltreatment: The U.S. Triple P System Population Trial. Prevention Science, 10(1), 1–12. 10.1007/s11121-009-0123-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Ramos-Olazagasti MA, Bird HR, Canino GJ, & Duarte CS (2017). Childhood Adversity and Early Initiation of Alcohol Use in Two Representative Samples of Puerto Rican Youth. Journal of Youth and Adolescence, 46(1), 28–44. 10.1007/s10964-016-0575-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Rhew IC, Duckworth JC, Hurvitz PM, & Lee CM (2020). Within- and between-person associations of neighborhood poverty with alcohol use and consequences: A monthly study of young adults. Drug and Alcohol Dependence, 212, 108068. 10.1016/j.drugalcdep.2020.108068 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Roberts RE, Roberts CR, & Duong HT (2008). Chronic insomnia and its negative consequences for health and functioning of adolescents: A 12-month prospective study. Journal of Adolescent Health, 42(3), 294–302. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Roberts RE, Roberts CR, & Xing Y (2006). Prevalence of youth-reported DSM-IV psychiatric disorders among African, European, and Mexican American adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 45(11), 1329–1337. 10.1097/01.chi.0000235076.25038.81 [DOI] [PubMed] [Google Scholar]
  52. Roberts RE, Roberts CR, & Xing Y (2010). One-year incidence of suicide attempts and associated risk and protective factors among adolescents. Archives of Suicide Research, 14(1), 66–78. [DOI] [PubMed] [Google Scholar]
  53. Rothman EF, Edwards EM, Heeren T, & Hingson RW (2008). Adverse childhood experiences predict earlier age of drinking onset: Results from a representative US sample of current or former drinkers. Pediatrics, 122(2), e298–304. 10.1542/peds.2007-3412 [DOI] [PubMed] [Google Scholar]
  54. Shaffer D, Fisher P, Lucas CP, Dulcan MK, & Schwab-Stone ME (2000). NIMH Diagnostic Interview Schedule for Children Version IV (NIMH DISC-IV): Description, differences from previous versions, and reliability of some common diagnoses. Journal of the American Academy of Child & Adolescent Psychiatry, 39(1), 28–38. [DOI] [PubMed] [Google Scholar]
  55. SmithBattle L, Loman DG, Yoo JH, Cibulka N, & Rariden C (2022). Evidence for Revising the Adverse Childhood Experiences Screening Tool: A Scoping Review. Journal of Child & Adolescent Trauma, 15(1), 89–103. 10.1007/s40653-021-00358-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Spear LP (2018). Effects of adolescent alcohol consumption on the brain and behaviour. Nature Reviews Neuroscience, 19(4), 197–214. [DOI] [PubMed] [Google Scholar]
  57. Stone AL, & Carlisle SK (2017). Racial bullying and adolescent substance use: An examination of school-attending young adolescents in the United States. Journal of Ethnicity in Substance Abuse, 16(1), 23–42. 10.1080/15332640.2015.1095666 [DOI] [PubMed] [Google Scholar]
  58. Thornton RLJ, Glover CM, Cené CW, Glik DC, Henderson JA, & Williams DR (2016). Evaluating Strategies For Reducing Health Disparities By Addressing The Social Determinants Of Health. Health Affairs, 35(8), 1416–1423. 10.1377/hlthaff.2015.1357 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Wade R, Cronholm PF, Fein JA, Forke CM, Davis MB, Harkins-Schwarz M, Pachter LM, & Bair-Merritt MH (2016). Household and community-level Adverse Childhood Experiences and adult health outcomes in a diverse urban population. Child Abuse & Neglect, 52, 135–145. 10.1016/j.chiabu.2015.11.021 [DOI] [PubMed] [Google Scholar]
  60. Wade R, Shea JA, Rubin D, & Wood J (2014). Adverse childhood experiences of low-income urban youth. Pediatrics, 134(1), e13–20. 10.1542/peds.2013-2475 [DOI] [PubMed] [Google Scholar]
  61. Xu M, Macrynikola N, Waseem M, & Miranda R (2020). Racial and ethnic differences in bullying: Review and implications for intervention. Aggression and Violent Behavior, 50, 101340. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Zapolski TCB, Pedersen SL, McCarthy DM, & Smith GT (2014). Less drinking, yet more problems: Understanding African American drinking and related problems. Psychological Bulletin, 140, 188–223. 10.1037/a0032113 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Material 1
NIHMS1897807-supplement-Supplemental_Material_1.pdf (183.9KB, pdf)
Supplemental Material 2
NIHMS1897807-supplement-Supplemental_Material_2.pdf (207.5KB, pdf)

RESOURCES