Childhood Violence,High School Academic Environment,and Adult Alcohol Use Among Latinas and Black Women: A Structural Equation Modeling Study

Abstract

Objective:

Young Latinas and Black women drink less than women of other racial/ethnic groups but experience more alcohol-related problems in midlife. This study aims to identify modifiable factors to prevent adult onset of alcohol use disorder (AUD) in this population.

Methods:

Data were collected at six time points as part of the Harlem Longitudinal Development Study from 365 Latinas (47%) and Black (53%) women (mean age at time 1 = 14, standard deviation 1.3). Structural equation modeling was used to test hypothesized pathways from childhood physical and sexual abuse to AUD via depressive mood, anxiety disorders, and somatic complaints in the 20s. We also tested the moderation effect of the high school academic environment by including in the structural equation model two latent variable interaction terms between the school environment and each of the abuse variables.

Results:

Childhood physical and sexual abuse was positively associated with depressive mood, anxiety disorders, and somatic complaints when participants were in the 20s. Depressive mood mediated childhood abuse and AUD when women were in the 30s. The high school academic environment attenuated the effect of physical, but not sexual abuse, on depressive mood (β = −0.59, B = −9.38, 95% CI [−14.00, −4.76]), anxiety symptoms (β = −0.61, B = −14.19, 95% CI [−21.76, −6.61]), appetite loss (β = −0.41, B = −10.52, 95% CI [−15.61, −5.42]), and sleeplessness (β = −0.50, B = −9.56, 95% CI [−13.95, −5.17]) in the early 20s.

Conclusions:

Our findings underscore the need to invest in early violence prevention interventions and in education to ensure equitable access to quality, academically oriented, and safe schools.

Keywords

alcohol use disorder child abuse sexual physical abuse longitudinal studies gender-based violence

Introduction

Young Latinas and Black women are at greater risk of experiencing different forms of physical (Sheats et al., 2018) and community violence (Sacks & Murphey, 2018) than women of other racial/ethnic groups. Exposure to violence in childhood constitutes an adverse childhood experience (ACE) with pervasive harmful mental and physical health consequences (Pahl et al., 2021), including unhealthy alcohol use (Hughes et al., 2017), depression, and anxiety (Capasso et al., 2021; Pahl et al., 2019; Walsh et al., 2015). Experiencing violence increases the risk of unhealthy alcohol use (odds ratio [OR] = 1.3; 95% CI [1.0, 1.6]) (Norman et al., 2012), particularly when the abuse occurs in childhood and adolescence (Walsh et al., 2015). Mental health disorders may mediate experiencing violence and substance use problems (Pahl et al., 2023). Research has found that depressive symptoms mediate exposure to stress and alcohol-related problems among Black college students (Desalu et al., 2019).

Despite the association of violence exposure and alcohol use and the need to identify preventive factors for unhealthy alcohol use (Yakubovich et al., 2018), essential gaps remain in the literature. This is of particular importance since, despite lower drinking levels, Latinas and Black women experience worse alcohol-related social and health consequences in adulthood than women of other racial/ethnic groups (Witbrodt et al., 2014; Zapolski et al., 2014). Further, whereas alcohol-related problems decrease with age in the general population, they increase among Latinx and Black drinkers (Grant et al., 2012; Zapolski et al., 2017).

Theoretical Framework: A Life-Course Approach to Alcohol Use

The current analysis is grounded in a life-course approach to alcohol use (Crosnoe & Riegle-Crumb, 2007; Kuhl & Burrington, 2020). Factors such as social class, sex, and race/ethnicity shape the dynamics of interpersonal interactions within families, schools, and communities and shape an individual’s life experiences and expectations, influencing alcohol use patterns at different life stages (Desalu et al., 2019; Gilbert & Zemore, 2016).

As adolescents develop autonomy and gain independence from the family, the school and school-based peers become preponderant in shaping behavior and well-being (Ogden & Hagen, 2019; Smetana, 2017). In both affluent and poor contexts, the school environment is more predictive of substance use among high school students than family- and neighborhood-level influences (Coley et al., 2018; Lund et al., 2017). Several researchers have used longitudinal data to explore the association of school-level factors and the risk of alcohol use disorder (AUD) in adulthood. Guo et al. (2001) found that a strong sense of attachment to school and high achievement during adolescence were protective factors for AUD at age 21. Hayatbakhsh et al. (2011) found an inverse association between achievement in adolescence and the likelihood of developing AUD in young adulthood.

An extensive body of research has documented the risk for hazardous alcohol use among high school students in high-performing schools with mainly affluent students (Luthar et al., 2020). However, academically oriented school settings, that is, schools that foster academic excellence and learning and that have a college-going culture, may be protective for Black and Latinx students, mainly as alcohol use is not the cultural norm (Crosnoe & Riegle-Crumb, 2007; Dudovitz et al., 2018). For example, Dudovitz et al. (2018) found lower odds of hazardous alcohol use among boys from socioeconomically deprived neighborhoods in Los Angeles assigned by lottery to high-performing high schools compared to controls. In a longitudinal study, Crosnoe and Riegle-Crumb (2007) found that high individual academic achievement was associated with lower levels of drinking in high school but higher levels in college; however, this pattern was attenuated for students in high-achieving high schools. Further, students coming from high schools with higher proportions of Black and Latinx students were less likely to be drinkers in college (Crosnoe & Riegle-Crumb, 2007). Whereas drinking among white students could be explained by racial privilege (Peralta, 2004), structural racism and discrimination and internalized racialized expectations (e.g., related to bias by adults in positions of authority) may serve as a deterrent to drinking among students of color (Wade & Peralta, 2017).

Violence exposure is inextricably associated with a person’s living environment, daily routine, and lifestyle (Cohen et al., 1981; Dong et al., 2020). Attending more academically oriented schools may also reduce exposure to neighborhood crime and peer violence by increasing the time spent in academic activities and being surrounded by peers who also spend more time in such activities.

Violence Exposure and Mental Health

Persons with depression experience a variety of symptoms ranging from mood to somatic complaints. Mood complaints involve anhedonia (i.e., loss of pleasure) and nonreactive mood, whereas somatic or physical symptoms include disturbances in sleeping and eating patterns (Lamers et al., 2012; Silverstein & Angst, 2015). Meta-analytic findings have shown that women who experience intimate partner violence have higher odds of developing depression (OR = 1.8; 95% CI [1.3, 2.4]; I² = 37.5%; p = 0.172) (Bacchus et al., 2018); this association has been documented among Latinas and Black women (Hankin et al., 2010; Lacey et al., 2015; Lipsky et al., 2016), and in adolescent girls (Exner-Cortens et al., 2013). Anxiety disorders are characterized by excessive worry, restlessness, and fear in response to perceived threats (Penninx et al., 2021). Odds of experiencing anxiety symptoms are higher among women exposed to violence, particularly in childhood and adolescence (Walsh et al., 2015). A meta-analysis found a 3.0 odds of a lifetime diagnosis of anxiety disorders among persons who experienced sexual violence (95% CI [2.4, 3.9]) (Chen et al., 2010).

The Current Study

The current study uses longitudinal data collected over 25 years among Latinas and Black women in an urban setting to examine pathways from early sexual and physical violence to AUD in their 30s. This paper extends previous work in several ways. First, relatively little research has explicitly focused on AUD among Latinas and Black women (Zemore et al., 2018). Second, many studies focus on violence and alcohol use during adolescence and even emerging adulthood, but fewer explore these into the 30s (Cano et al., 2015; Wright et al., 2013) despite worsening alcohol-related consequences with age (Grant et al., 2012; Zapolski et al., 2017). Third, manifestations of depression include multiple mental and physical symptoms. Understanding the association of different symptomatology to incident problem alcohol use may be useful to identify women at risk for AUD. Fourth, insufficient attention has been paid to how the high school academic environment may shape future alcohol use among minoritized racial and ethnic groups.

This is a longitudinal study that aims to identify pathways from childhood experiences of sexual and physical violence to AUD in adulthood among Latinas and Black women. We hypothesize that (1) early exposure to sexual and physical violence is positively associated with the likelihood of experiencing AUD in the 30s; (2) this association is mediated by different depressive symptomatology; and (3) the school academic environment moderates this association; specifically, we propose that a school environment that is conducive to learning will attenuate the effect of early violence exposure on incident AUD. The initial conceptual model tested is depicted in the Supplemental Material Figure A.

Methods

Data and Participants

Data are from the Harlem Longitudinal Development Study, an observational cohort study that recruited self-identified Black or Latinx participants attending public middle and high schools in East Harlem, New York City, in 1990 (T1) (Pahl et al, 2006). Students who did not meet these criteria or who did not assent to participate were excluded from the study. Seven follow-up surveys were administered between 1994 and 2016. Participant mean age ranged from 14 at T1 to 38 at T8, as detailed in Table S1. The current analysis includes 6 waves of data (T1–T5 and T8) from 365 participants who self-identified as women and participated in T1 and T8. Participants were included even if they were missing at other waves. Survey administration methodology varied by wave and included interviews and self-completion surveys conducted in person, by phone, and by mail distribution (Pahl et al, 2006).

The Mount Sinai School of Medicine and the New York University School of Medicine institutional review boards approved all study procedures. We obtained a Certificate of Confidentiality from the National Institute on Drug Abuse of the National Institutes of Health. Written informed assent was obtained from all minors and parents provided passive consent; informed consent was obtained for all participants aged 18 and older.

Outcome Variable

Alcohol Use Disorder

AUD was assessed with 11 Yes/No items based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) (American Psychiatric Association, 2013). A sample item included, “Have you tried to reduce or stop drinking alcohol but failed.” Following DSM-5 criteria, the variable was converted to a binary measure, with participants meeting any 2 of the 11 criteria coded as having an AUD. Data were collected at T8 when participants were in their late 30s. Table S2 presents the detailed study variables.

Independent Variables

Experiencing Physical Violence

Experiencing physical violence in adolescence was assessed with four items (Chavez et al., 1994). The root question, “How often have you experienced each of the following?” was followed by items such as, “Someone hit you with a weapon or shot you.” Answer options ranged from 0 = never to 4 = ≥5 times; Cronbach’s α = 0.55. Data were collected at T2; participants’ mean age was 19.

Childhood Sexual Abuse

Experiencing sexual abuse in childhood was assessed by five items from the Childhood Trauma Questionnaire (Bernstein et al., 2003). The root question, “Please indicate how true the following statements are about your childhood,” was followed by items such as, “I was hurt if I didn’t do something sexual.” Answer options ranged from 0 = never true to 4 = very often true. The scale’s Cronbach’s alpha was 0.91. The variable was coded as a binary measure. The measure was assessed retrospectively at T8.

Mediator Variables

Depressive Symptoms: Mood Complaints

Mood complaints were assessed at three time points when participants were in their early, mid-, and late 20s with the Hopkins Symptom Checklist (Derogatis et al., 1974). Two items were assessed with the root question “Does each of the following describe you?” followed by items such as, “You sometimes feel unhappy, sad, or depressed,” with possible answer choices from 0 = completely false to 3 = completely true. Three items were assessed with the root question, “Over the last few years, how much were you bothered by?” followed by questions such as, “Feeling angry toward people you have no good reason to feel that way toward.” Possible responses ranged from 0 = not at all to 4 = extremely. The scale’s Cronbach’s alphas were 0.76, 0.80, and 0.80 at each respective time point.

Depressive Symptoms: Appetite Loss

Loss of appetite was measured by three items from the National Institute of Mental Health Diagnostic Interview Schedule for Children (Costello et al., 1985). Participants were asked, for example, to what extent they had been bothered by eating less than usual in the last few years. Answer options ranged from 0 = not at all to 4 = extremely; Cronbach’s alphas were 0.85, 0.87, and 0.86 in the early, mid-, and late 20s, respectively.

Depressive Symptoms: Sleeplessness

Trouble sleeping was measured at three time points by two 5-point Likert-type items (0 = not at all to 4 = extremely) from the National Institute of Mental Health Diagnostic Interview Schedule for Children (Costello et al., 1985). Participants were asked questions such as to what extent they awoke tired but could not fall back to sleep. Cronbach’s alphas were 0.69 in the early 20s, 0.67 in the mid-20s, and 0.78 in the late 20s.

Anxiety Symptoms

Three 5-point Likert-type items from the Hopkins Symptom Checklist (Derogatis et al., 1974) were employed to assess anxiety symptoms, including being fearful, tense, and nervous (0 = not at all, 4 = extremely) at three waves when the participants were in their 20s. Cronbach’s alphas were 0.77, 0.80, and 0.75 at each wave, respectively.

Moderating Variable

High School Environment

Whether the school environment promoted learning was asked by a 4-point 4-item scale (0 = Not at all true, 3 = Definitely true) assessed retrospectively at Time 3 (Brook et al., 1997). The root question, “How true is each of the following about your school?” was followed by the items: “Students work hard to get good grades,” “Students and teachers are proud of the school,” “Most students are very interested in getting good grades,” and “Students try to learn as much as they can.” Cronbach’s alpha was 0.77.

Covariates

Model covariates included participants’ age at the initial interview, race/ethnicity, socioeconomic status (SES), and whether the participant had been inebriated in adolescence. Race/ethnicity was assessed by: “What race/ethnicity do you most identify with?” (0 = Black, 1 = Latina). SES was based on five Yes/No items, which were summed, to assess the types of government aid participants received, such as social security supplemental insurance, Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), or unemployment insurance at T8. Possible scores range from 0 to 5; higher scores represent lower SES. Inebriation in adolescence was asked at T2 with the question, “During the last 5 years, how often have you gotten drunk?” Answer options ranged from 0 = never to 5 = ≥5 times; and were recoded as a binary variable (0 = never; 1 = ≥1).

Data Analytic Plan

First, we examined the distribution of the key measures in the overall sample and stratified by AUD at T8. Second, before hypothesis testing, we assessed factor loadings for each item within the domains of interest, that is, physical violence, mood and somatic depressive symptoms, anxiety symptoms, and school environment (See Table S3 for standardized factor loadings). We used structural equation modeling (SEM) to evaluate the hypothesized pathways from the experience of childhood physical and sexual abuse to women’s AUD in their 30s. The mediator variables were calculated by summing individual items prior to entering them into the model. Experiencing physical violence and the school environment were entered into the model as latent constructs composed of four items each, which allowed us to simultaneously assess how well the items loaded onto each domain. A measurement model was tested following an iterative, theory-guided process that entailed modifying the model until the satisfactory fit was achieved. Then, we used a latent moderated structural equations approach (Asparouhov & Muthén, 2019; Klein & Moosbrugger, 2000) to test the hypothesized moderation effect of school environment on violence experience in predicting mental health outcomes and physical manifestations. In a model for AUD, the outcome of interest, we tested two latent variable interaction terms between school environment and experiencing physical violence and school environment and childhood sexual abuse (CSA). Parameters and 95% confidence intervals were estimated and tested using Huber-White robust maximum likelihood algorithms (option MLR in Mplus) to account for nonnormality. Global model fit was assessed based on the chi-square test of exact fit, the Comparative Fit Index (CFI), the standardized root mean squared residual (SRMR), and the Root Mean Square Error of Approximation (RMSEA). Localized tests of fit examined modification indices. Univariate and bivariate analyses were conducted in Stata 17.0 (Stata Corporation L.P., College Station, TX, USA), and SEM was performed in Mplus 8.3 (Muthén & Muthén, Los Angeles, CA, USA).

Missing Data

Of the 365 participants who had non-missing data for AUD, none had missing values for physical violence, age, race/ethnicity, and inebriation in adolescence; 7% had missing values on CSA, 36% had missing values in the mediating variables assessed in the early 20s and school environment, 52% had missing values in the mediating variables assessed in mid-20s, and 7% had missing values in the late 20s. We used Pearson’s Chi-squared and t-tests to compare differences in covariates between those with and without missing values. Non-participants were more affluent and more likely to be African American than participants. In addition, they had fewer anxiety symptoms and were less likely to have experienced CSA and intoxication in adolescence (See Table S4). We used full-information maximum likelihood to account for missing observations.

Results

Descriptive Results

Descriptive results are presented in Table 1. Participants were, on average, aged 14 (standard deviation [SD] = 1.3) at the initial interview. Black women made up over half (53% of the sample). In their 30s, 40% of the sample received at least one form of government aid, with 18% receiving between 2 and 4 different types of aid (mean SES = 0.6; SD = 0.9). Almost 1 in 3 women (30%) experienced CSA, and 42% experienced at least one form of physical violence in adolescence (mean = 0.9; SD = 1.4). Mean school environment scores were moderate at 7.0 (SD = 2.4). More than 2 in 5 (43%) adolescent women reported getting inebriated at least once. In their 30s, 14.5% of the women experienced an AUD.

Table 1.

Descriptive Statistics of Study Variables by AUD Status Among Black and Latina Women in Their 30s, New York City (1990–2016).

	Range	Total	Past 3-Year AUD		p-Value
		Total	No	Yes
		N = 365 (100.0)	N = 312 (85.5)	N = 53 (14.5)
Measures		M (SD) or n (%)	M (SD) or n (%)
Age	12–17	14.1 (1.3)	14.1 (1.3)	13.6 (1.4)	.010
Race/ethnicity
Black		192 (52.6)	163 (52.2)	29 (54.7)	.739
Latina		173 (47.4)	149 (47.8)	24 (45.3)	.739
Socioeconomic status^a	0–4	0.6 (0.9)	0.6 (0.9)	0.8 (1.0)	.163
Childhood sexual abuse
No		238 (69.8)	211 (73.0)	27 (51.9)	.002
Yes		103 (30.2)	78 (27.0)	25 (48.1)	.002
Experienced physical violence	0–9	0.9 (1.4)	0.8 (1.4)	1.1 (1.6)	.321
School environment	0–12	7.0 (2.4)	7.2 (2.4)	6.1 (2.5)	.006
Inebriation in adolescence
No		207 (56.7)	186 (59.6)	21 (39.6)	.007
Yes		158 (43.3)	126 (40.4)	32 (60.4)	.007
Dep. mood (early 20s)	0–17	6.1 (3.6)	5.7 (3.4)	8.1 (4.1)	<.001
Dep. mood (mid-20s)	0–17	5.7 (3.7)	5.5 (3.5)	6.5 (4.6)	.192
Dep. mood (late 20s)	0–18	5.6 (3.6)	5.2 (3.4)	7.2 (4.1)	<.001
Appetite loss (early 20s)	0–12	2.8 (3.1)	2.5 (3.1)	3.8 (2.7)	.014
Appetite loss (mid-20s)	0–12	2.7 (3.0)	2.6 (3.0)	3.3 (3.2)	.244
Appetite loss (late 20s)	0–12	2.4 (2.7)	2.3 (2.7)	2.8 (2.6)	.182
Sleeplessness (early 20s)	0–8	2.6 (2.3)	2.5 (2.4)	2.7 (2.2)	.657
Sleeplessness (mid-20s)	0–8	2.6 (2.4)	2.5 (2.4)	3.1 (2.3)	.201
Sleeplessness (late 20s)	0–8	2.7 (2.4)	2.6 (2.4)	3.3 (2.7)	.058
Anxiety symptoms (early 20s)	0–12	3.2 (2.8)	3.0 (2.8)	3.9 (2.8)	.081
Anxiety symptoms (mid-20s)	0–12	2.9 (2.7)	2.9 (2.7)	2.6 (2.8)	.590
Anxiety symptoms (late 20s)	0–12	2.8 (2.5)	2.6 (2.4)	3.8 (2.7)	.001

Notes: AUD = alcohol use disorder; M = mean; SD = standard deviation.

The higher the number, the lower the socioeconomic status, based on the government poverty alleviation aid received.

Being younger (13.6 [SD = 1.4] vs. 14.1 [SD = 1.4]; p = .010), having experienced CSA (48% vs. 27%, p = .002), being in schools less conducive to learning (6.1 [SD = 2.5] vs. 7.2 [SD = 2.4]; p = .006], and adolescent inebriation (60% vs. 40%; p = 0.007) were associated with AUD as adults. Overall, at different time points during their 20s, elevated symptoms of mood and somatic depression and anxiety were associated with AUD in the 30s, with patterns varying by disorder (See Table 1).

We also examined the sample by race/ethnicity. Except for age, we found no significant differences between Latinas and Black women for socio-demographics, school environment, violence exposure, or alcohol use. Latinas (mean = 13.8; SD = 1.3) were younger than Black women (mean = 14.3; SD = 1.3; p = <.001). Latinas experienced more symptoms of depressive mood in the late 20s and elevated somatic depression in the early and mid-20s than Black women (Table S5).

Structural Equation Models

A modified structural equation model achieved adequate fit: χ² (N = 339; DF = 194) = 359.84, p < .001; CFI = 0.91; SRMR = 0.07; and RMSEA = 0.05 (90% CI [0.04, 0.06]), p = .472. When holding the covariates constant, CSA was positively associated with AUD in the 30s (β = 0.133, B = 0.104, 95% CI[0.026, 0.181]), while attending an academically oriented high school was negatively associated with the disorder (β = −0.169, B = −0.150, 95% CI [−0.266, −0.035]) (Table S6).

The school environment had a significant moderation effect on the effect of physical violence on depressive mood (β = −0.59, B = −9.38, 95% CI [−14.00, −4.76], appetite loss (β = −0.41, B = −10.52, 95% CI [−15.61, −5.42]), sleeplessness (β = −0.50, B = −9.56, 95% CI [−13.95, −5.17]), and anxiety symptoms (β = −0.61, B = −14.19, 95% CI [−21.76, −6.61]) in the early 20s, such that higher school environment scores reduced the effect of physical violence on poor mental health and associated physical manifestations. In turn, all the mental health variables were associated with time. For example, higher levels of depressive mood symptomatology in the early 20s were associated with higher levels in the mid-20s (β = 0.70, B = 0.65, 95% CI [0.48, 0.83]); and higher levels in the mid-20s were associated with higher levels in the late 20s (β = 0.72, B = 0.64, 95% CI [0.52, 0.77]). However, only depressive mood symptoms in the late 20s were significantly associated with AUD in the 30s (β = 0.44, B = 0.09, 95% CI [0.03, 0.15].

On the other hand, the interaction between CSA and the school environment was not significant. Experiencing CSA was associated with higher levels of depressive mood symptoms in the early 20 (β = 0.22, B = 1.02, 95% CI [0.51, 1.53]), appetite loss (β = 0.23, B = 1.72, 95% CI [0.98, 2.46]), sleeplessness (β = 0.17, B = 0.95, 95% CI [0.37, 1.53]), and anxiety symptoms (β = 0.20, B = 1.38, 95% CI [0.62, 2.09]), in the early 20s.

Figure 1 presents the final structural equation model with standardized path coefficients (covariances and correlations are omitted for simplicity but are detailed in Table S7). Table 2 presents the final model’s standardized and unstandardized coefficients and 95% CIs.

Figure 1.

Final structural equation model with standardized path coefficients.

Table 2.

Final Model Paths: Standardized Coefficients and Unstandardized Coefficients with 95% Confidence Intervals.

Outcome	Explanatory variable	β	B	95% CI	p-Value
Past-3-year alcohol use disorder	Depressive mood (late 20s)	0.444	0.088	[0.031, 0.145]	.011
	Appetite loss (late 20s)	−0.050	−0.007	[−0.020, 0.006]	.379
	Sleeplessness (late 20s)	−0.123	−0.019	[−0.038, 0.000]	.100
	Anxiety symptoms (late 20s)	−0.168	−0.025	[−0.056, 0.007]	.201
	Race/ethnicity	−0.079	−0.057	[−0.122, 0.008]	.151
	Age	−0.172	−0.047	[−0.073, −0.021]	.003
	Inebriation in adolescence	0.108	0.078	[0.012, 0.144]	.050
	SES	0.028	0.011	[−0.023, 0.045]	.610
Depressive mood (late 20s)	Depressive mood (mid-20s)	0.719	0.644	[0.519, 0.769]	<.001
	Race/ethnicity	0.050	0.178	[−0.212, 0.568]	.452
	Age	−0.019	−0.026	[−0.159, 0.108]	.752
	Inebriation in adolescence	−0.027	−0.098	[−0.484, 0.288]	.676
	SES	0.042	0.080	[−0.148, 0.308]	.565
Appetite loss (late 20s)	Appetite loss (mid-20s)	0.998	0.887	[0.658, 1.115]	<.001
	Race/ethnicity	−0.061	−0.328	[−1.024, 0.369]	.439
	Age	−0.034	−0.070	[−0.348, 0.209]	.681
	Inebriation in adolescence	−0.096	−0.520	[−1.163, 0.123]	.183
	SES	−0.045	−0.128	[−0.496, 0.239]	.565
Sleeplessness (late 20s)	Sleeplessness (mid-20s)	0.790	0.809	[0.604, 1.014]	<.001
	Race/ethnicity	−0.099	−0.462	[−0.994, 0.071]	.154
	Age	−0.030	−0.053	[−0.215, 0.110]	.594
	Inebriation in adolescence	−0.125	−0.585	[−1.155, −0.014]	.092
	SES	0.015	0.036	[−0.270, 0.342]	.846
Anxiety symptoms (late 20s)	Anxiety symptoms (mid-20s)	0.644	0.575	[0.499, 0.651]	<.001
	Race/ethnicity	0.046	0.226	[−0.179, 0.631]	.359
	Age	−0.021	−0.039	[−0.185, 0.107]	.658
	Inebriation in adolescence	0.052	0.255	[−0.151, 0.662]	.302
	SES	0.091	0.236	[0.020, 0.452]	.073
Depressive mood (mid-20s)	Depressive mood (early 20s)	0.696	0.651	[0.476, 0.826]	<.001
	Race/ethnicity	0.057	0.230	[−0.270, 0.729]	.449
	Age	0.043	0.065	[−0.110, 0.241]	.539
	Inebriation in adolescence	0.119	0.481	[0.029, 0.932]	.080
	SES	0.050	0.106	[−0.175, 0.387]	.535
Appetite loss (mid-20s)	Appetite loss (early 20s)	0.486	0.422	[0.291, 0.553]	<.001
	Race/ethnicity	0.080	0.479	[−0.187, 1.145]	.237
	Age	0.030	0.068	[−0.215, 0.352]	.692
	Inebriation in adolescence	0.040	0.245	[−0.426, 0.915]	.548
	SES	0.091	0.290	[−0.023, 0.602]	.127
Sleeplessness (mid-20s)	Sleeplessness (early 20s)	0.492	0.436	[0.343, 0.530]	<.001
	Race/ethnicity	0.117	0.531	[0.056, 1.006]	.066
	Age	−0.026	−0.045	[−0.212, 0.122]	.656
	Inebriation in adolescence	0.154	0.703	[0.206, 1.199]	.020
	SES	0.124	0.297	[0.054, 0.540]	.044
Anxiety symptoms (mid-20s)	Anxiety symptoms (early 20s)	0.593	0.517	[0.409, 0.624]	<.001
	Race/ethnicity	0.004	0.022	[−0.500, 0.543]	.945
	Age	−0.042	−0.086	[−0.272, 0.100]	.446
	Inebriation in adolescence	−0.002	−0.010	[−0.554, 0.534]	.976
	SES	−0.006	−0.018	[−0.296, 0.261]	.917
Depressive mood (early 20s)	Physical violence	0.581	3.753	[1.495, 6.012]	.006
	Childhood sexual abuse	0.219	1.021	[0.514, 1.528]	.001
	School environment	−0.243	−1.281	[−2.167, −0.396]	.017
	School environment × physical violence	−0.589	−9.377	[−13.996, −4.758]	.001
	Race/ethnicity	0.050	0.213	[−0.294, 0.720]	.490
	Age	−0.035	−0.057	[−0.235, 0.120]	.595
	Inebriation in adolescence	−0.078	−0.335	[−0.823, 0.152]	.258
	SES	0.162	0.368	[0.132, 0.604]	.010
Eating disorders (early 20s)	Physical violence	0.556	5.814	[3.340, 8.288]	<.001
	Childhood sexual abuse	0.228	1.720	[0.977, 2.462]	<.001
	School environment	−0.216	−1.834	[−2.867, −0.802]	.003
	School environment × physical violence	−0.409	−10.519	[−15.614, −5.424]	.001
	Race/ethnicity	0.120	0.834	[0.173, 1.496]	.038
	Age	0.009	0.023	[−0.193, 0.239]	.862
	Inebriation in adolescence	−0.084	−0.587	[−1.233, 0.058]	.135
	SES	0.013	0.046	[−0.297, 0.389]	.824
Sleeplessness (early 20s)	Physical violence	0.505	3.894	[1.940, 5.847]	.001
	Childhood sexual abuse	0.171	0.951	[0.368, 1.533]	.007
	School environment	−0.151	−0.947	[−1.793, −0.102]	.065
	School environment × physical violence	−0.504	−9.561	[−13.951, −5.171]	<.001
	Race/ethnicity	0.114	0.583	[0.047, 1.119]	.074
	Age	0.005	0.01	[−0.172, 0.192]	.929
	Inebriation in adolescence	−0.047	−0.240	[−0.762, 0.281]	.448
	SES	0.056	0.152	[−0.126, 0.429]	.368
Anxiety symptoms (early 20s)	Physical violence	0.417	3.936	[1.395, 6.477]	.011
	Childhood sexual abuse	0.199	1.375	[0.621, 2.087]	.002
	School environment	−0.152	−1.169	[−2.273, −0.066]	.081
	School environment × physical violence	−0.611	−14.186	[−21.760, −6.612]	.002
	Race/ethnicity	0.078	0.485	[−0.154, 1.131]	.211
	Age	−0.048	−0.112	[−0.337, 0.110]	.404
	Inebriation in adolescence	−0.035	−0.225	[−0.841, 0.396]	.554
	SES	0.089	0.295	[−0.023, 0.615]	.127

Notes: Bold values denote statistical significant at the p < 0.05 level. β = standardized coefficients; B = unstandardized coefficients; CI = confidence intervals; SES = socioeconomic status.

Discussion

Our findings contribute to the evidence on the enduring harmful mental health consequences of early exposure to physical and sexual violence in the transition to adulthood and into the 30s among Latinas and Black women. We found evidence supporting a pathway from exposure to childhood physical and sexual abuse to adult AUD via depressive mood throughout the 20s. A novel contribution of this research is the protective effect of a positive school environment. Specifically, a school environment that promoted academic achievement buffered women who experienced physical violence from the worse mental health outcomes that would have eventually led to AUD in the 30s via depressive mood.

Our findings are consistent with the literature showing the long-term health consequences of experiencing violence in childhood (Felitti et al., 1998; Font & Maguire-Jack, 2016). Fenton et al. (2013) found that physical and sexual abuse in childhood was associated with a 2.3 odds of developing AUD in adulthood. A recent meta-analysis estimated that the relative risk of harmful alcohol use was 1.4 (95% CI [1.2, 1.7]) among those experiencing one ACE and 1.8 (95% CI [1.2, 2.7]) among those experiencing two and more ACEs (Bellis et al., 2019).

Our findings are also consistent with prior research implicating ACEs in the etiology of mood and somatic depressive symptomatology, as well as of anxiety disorders. Meta-analytic findings estimated that experiencing physical abuse in childhood was associated with increased odds of developing depressive mood (OR = 1.5; 95% CI [1.2, 2.0]), anxiety disorders (OR = 1.5; 95% CI [1.3, 1.8]), and eating disorders in adulthood (OR = 2.6; 95% CI [1.2, 5.7]) (Norman et al., 2012). Another meta-analysis found that about 30% of cases of anxiety and 40% of cases of depression in North America could be attributed to ACEs (Bellis et al., 2019). Further, anorexia nervosa and bulimia are two frequent eating disorder diagnoses among women who experienced ACEs (Guillaume et al., 2016; Hicks White et l., 2018). Sleep disturbances in adulthood are frequent among those experiencing traumatic experiences in childhood (Kajeepeta et al., 2015), are common among women exposed to violence, and are an overlapping symptom of depression, anxiety, and posttraumatic stress disorder (Matos & Gonçalves, 2019).

It is unclear why depressive mood, but not the somatic manifestations of depression or anxiety, were associated with AUD. Prior literature, including meta-analytic findings, has documented a stronger association between depressive mood and unhealthy alcohol use than anxiety (Capasso et al., 2021; Schleider et al., 2019). For example, Schleider et al. (2019) found consistent associations between depressive mood and harmful alcohol use among adolescent girls, a majority of them Black, but not with anxiety. We can speculate that this is due to the physiological effect of alcohol as a depressant; drinking alcohol increases depressive symptoms, such as hopelessness and anhedonia, which are internalized and precipitate increased alcohol use, which worsens such symptoms (Ehlert et al., 2001).

It is also unclear why the school environment buffered girls experiencing physical but not sexual abuse from poor mental health outcomes. One possibility is that different types of abuse occur in different contexts. Whereas perpetrators of sexual abuse in childhood are most often people in a girl’s close interpersonal environment, such as an adult family member, physical violence in adolescence may more often be perpetrated by peers (Finkelhor et al., 2013; Tjaden & Thoennes, 2000). Among Latinas, a study found that 71% of CSA cases were perpetrated by adults in the girl’s close circle, predominantly family members (Cuevas & Sabina, 2010). On the other hand, most incidents of child physical abuse, particularly in adolescence, occur at the hands of peers (Finkelhor et al., 2013; Simon et al., 2018; Tjaden & Thoennes, 2000). Importantly, research has shown that survivors of abuse by relatives experience more severe trauma than abuse perpetrated by others (Gutner et al., 2006; Ullman, 2007). In sum, we propose that girls experiencing CSA had more severe trauma symptomatology than those experiencing physical abuse, not because of the type of trauma, but because of the context in which the abuse took place, and that the protective effect of the school environment was not a strong enough mitigator of this trauma.

Another reason the school academic environment had a buffering effect on physical violence but not on CSA may have been that the physical violence was situated in the neighborhood. As discussed, CSA is often an intrafamilial phenomenon, and physical abuse is an extrafamilial phenomenon. From experiments such as Moving to Opportunity, a randomized controlled trial in which families in disadvantaged neighborhoods received vouchers to move into low-poverty areas, we know that place matters; living in higher-income neighborhoods is associated with improved mental and physical health and higher educational attainment (Chetty et al., 2016). Conversely, living in a community with high crime rates is a risk factor for experiencing violence (Stueve & O’Donnell, 2008), and residential segregation by income, as is the case of New York City, is associated with higher rates of assault and property crime in low-income neighborhoods (Krivo et al., 2015). As hypothesized, girls in more academically oriented high schools may have been less exposed to violence by spending more time in school-related activities and with non-violent peers. However, academic engagement would not have protected girls from sexual abuse happening at home.

Schools are important settings to promote positive outcomes among youth, including those exposed to violence (Tremblay et al., 2020). A high-quality educational environment—both in terms of academic quality (e.g., high-quality teachers and curricula that foster student engagement and educational achievement) and climate quality (e.g., a school climate characterized by healthy interpersonal relationships between adults and students and among students that upholds respect, tolerance, and equal opportunities), can have a significant impact on educational and health outcomes (Deming et al., 2014) above and beyond individual-level student characteristics (Hayes-Greene & Love, 2018; Johnson et al., 2001). Whereas there is a body of literature associating high-achieving schools with poor mental health (Luthar & Kumar, 2018; Lutharet al., 2020), this association is likely moderated by social class, race/ethnicity, and sex. The buffering effect of academically oriented schools among adolescent girls of color may be explained by future orientation and the ability to plan for and work toward achieving future goals (Johnson et al., 2014). Hopes and dreams are essential to healthy adolescent development (Johnson et al., 2014), and future orientation may buffer the association of family stress and poor mental health among Black students in low-income neighborhoods (Kim et al., 2019). In addition, stronger future orientation has been positively associated with school attachment and student–teacher bonding (So et al., 2016), linked to better mental health (Millings et al., 2012; Watts et al., 2019). Academically oriented schools may have improved mental health outcomes by stimulating students’ hopes for the future, including graduating from high school, receiving a college education, and better career prospects.

Limitations

This study is not without limitations. One limitation is that the measures were self-reported, which may have led to underreporting, in part due to stigma surrounding mental health disorders. However, we used widely validated scales aligned with DSM criteria. A second limitation is that CSA was measured retrospectively. Studies have estimated that about 1 in 3 previously documented CSA instances are underreported or reported inconsistently (Langeland et al., 2015; Williams et al., 2000). Despite these shortcomings, retrospective measure of CSA is common practice (Hulme, 2004), and the Childhood Trauma Questionnaire is a widely used instrument with extensively validated psychometric properties (Bernstein et al., 2003; Georgieva et al., 2021). A third limitation is that our survey did not inquire about the details in which the abuse took place; we do not know the perpetrator, the periodicity, nor the severity of the abuse, all factors that influence mental and physical health consequences (Abrahams et al., 2013; Khadr et al., 2018). A study’s strength is its longitudinal nature, with data collected over a quarter of a century. Further, it examines the importance of the educational environment as an early protective factor for incident AUD among Black women and Latinas, a population that has not received sufficient focus concerning unhealthy alcohol use.

Conclusions

Childhood violence exposure, one of the most significant threats to mental and physical health in the U.S., is avoidable. Our findings underscore the need to invest in evidence-based violence prevention interventions. Additional research is warranted to understand (1) the role of chronic exposure to race/ethnicity- and sex-based discrimination and associated limitation of opportunities on the incidence of AUD in adulthood; (2) the mechanisms by which an academically oriented environment was protective of mental health; and (3) test early interventions that invest in neighborhood and school improvements.

Our findings warrant an assessment of early violence experiences among women presenting with symptoms of mood and somatic depression and anxiety and those in care for AUD (World Health Organization & Liverpool JMU Centre for Public Health, 2010). Importantly, identification of these experiences is insufficient without the appropriate linkages to trauma-informed psychosocial care and social protection when needed (Feltner et al., 2018). Evidence supports the integration of trauma-informed care to treat the sequelae of violence alongside alcohol use treatment as a more effective approach than addressing one condition individually (Roberts et al., 2015). Further, trauma-informed psychosocial interventions for Latinas and Black women should be culturally informed and incorporate sources of strength and support that are culturally relevant to the women in care (Kulkarni, 2019; Zhang et al., 2013) as key coping and resiliency factors (Ligiéro et al., 2009).

The findings underscore the importance of investing in primary prevention via interventions that aim to effect institutional and social change, particularly tackling norms that disempower women of color and that create educational and economic opportunities should be prioritized (Salazar de Pablo et al., 2021; Sheats et al., 2018). In addition to federal legislation, such as the Violence Against Women Act, which had a dramatic impact on the reduction of domestic violence, the most promising primary prevention programs have been community-wide, included both girls and boys and have sought to challenge entrenched gender and racial norms and acceptance of violence against women (Ellsberg et al., 2015; Salazar de Pablo et al., 2021). A high-priority policy agenda should be affording all students of color access to quality education in academically oriented environments that promote high achievement. Funding public education is a long-term investment that will result in the highest population-level health gains.

Supplemental Material

sj-docx-1-jiv-10.1177_08862605241243372 – Supplemental material for Childhood Violence, High School Academic Environment, and Adult Alcohol Use Among Latinas and Black Women: A Structural Equation Modeling Study

Supplemental material, sj-docx-1-jiv-10.1177_08862605241243372 for Childhood Violence, High School Academic Environment, and Adult Alcohol Use Among Latinas and Black Women: A Structural Equation Modeling Study by Ariadna Capasso, Yesim Tozan, Ralph J. DiClemente and Kerstin Pahl in Journal of Interpersonal Violence

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interests with respect to the authorship and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research and/or authorship of this article: This research was supported by the following grants from the NIH, awarded to Drs. Judith Brook and Kerstin Pahl: research grant R01 DA035408 from the National Institute on Drug Abuse and research grant R01 CA084063 from the National Cancer Institute.

ORCID iD

Ariadna Capasso

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Ariadna Capasso, PhD, (she/her) is a behavioral health scientist with expertise in the design, planning, and implementation of complex studies, including NIH-funded clinical trials and costing analyses. As Director of Research and Evaluation at Health Resources in Action, Ariadna oversees numerous studies in women’s health and violence prevention. She holds a PhD in Public Health from New York University.

Yesim Tozan (she/her), PhD, (she/her) Assistant Professor of Global Health, New York University. Dr. Yesim Tozan’s research centers on health decision science and priority setting and explores the costs of health care interventions using decision analytic models and the issues of health care resource allocation in low- and middle-income countries. Her main focus has been infectious disease prevention and control.

Ralph J. DiClemente, PhD, (he/him) Associate Dean of Public Health Innovation and Professor of Social and Behavioral Sciences, New York University. Dr. DiClemente is the author of 10 CDC-defined, evidence-based interventions for adolescents and young African-American women and men. He is the author of more than 540 peer-reviewed publications, 150 book chapters, and 21 books.

Kerstin Pahl, PhD, (she/her) Research Scientist, Nathan Kline Institute for Psychiatric Research. Dr. Pahl received her PhD in developmental psychology from NYU’s Steinhardt School for Culture, Education, and Human Development. Much of her research has focused on the longitudinal pathways to and the developmental patterns of health behaviors and well-being, including substance use and sexual behaviors.

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