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. Author manuscript; available in PMC: 2022 Sep 1.
Published in final edited form as: Psychol Violence. 2021 Sep;11(5):434–444. doi: 10.1037/vio0000380

Physical and Sexual Victimization Class Membership and Alcohol Misuse and Consequences among Sexual Minority and Heterosexual Female Youth

Jillian R Scheer 1, Nadav Antebi-Gruszka 2, Tami Sullivan 3
PMCID: PMC8932677  NIHMSID: NIHMS1738606  PMID: 35308039

Abstract

Objective:

Evidence demonstrates sexual orientation disparities in physical and sexual victimization and alcohol misuse and consequences among female youth; however, most extant research has used variable-centered approaches. The current study used latent class analysis (LCA), a person-centered approach, to: (1) model female youths’ physical and sexual victimization; (2) examine sexual orientation disparities in physical and sexual victimization latent class membership; (3) and use physical and sexual victimization latent class membership to predict female youths’ engagement in alcohol misuse and related consequences.

Method:

Participants were 7,185 youth assigned female sex at birth (77.0% heterosexual, 12.8% bisexual, 2.3% gay or lesbian; 46.8% racial minority) in grades 9 – 12 who completed the 2017 Youth Risk Behavior Survey – a school-based, cross-sectional survey.

Results:

LCA uncovered four classes: (1) “Poly-Victimization Class,” (2) “No Victimization Class,” (3) “Past-Year Sexual Victimization Class,” and (4) “Lifetime Rape Class.” Sexual orientation emerged as a significant predictor of class membership. Latent classes 3 and 4 were uniquely associated with alcohol misuse and consequences (i.e., binge drinking, riding with a drinking driver, and drinking and driving) among female youth.

Conclusions:

Our findings help to elucidate the patterns of physical and sexual victimization regarding timeline, tactic used, and context among female youth; sexual orientation disparities in latent class membership; and alcohol misuse correlates of class membership. Prevention efforts aimed at reducing physical and sexual victimization may ultimately reduce female youths’ risk of binge drinking, riding with drinking drivers, and drinking and driving.

Keywords: physical and sexual victimization, sexual orientation disparities, female youth, alcohol use, latent class analysis

Introduction

Physical victimization, such as being physically attacked, hit, hurt, or injured, and sexual victimization, including experiencing an attempted or completed nonconsensual sex act or noncontact sexual abuse, among youth represents an urgent public health concern. For instance, in nationally representative samples, up to 13% of adolescents report physical victimization, with female youth reporting more severe physical victimization compared to male youth (Merrick et al., 2018). Between 5.7% and 8.4% of female youth report experiencing sexual victimization, including rape (e.g., forced sexual intercourse; Merrick et al., 2018). In addition, at least 35% of female youth with histories of sexual victimization are likely to experience subsequent victimization (i.e., revictimization; Ports et al., 2016; Walker et al., 2019). Further, among nationally representative samples, 20% of young adults report victimization occurring in dating relationships (Wolitzky-Taylor et al., 2008), with female youth reporting more frequent and severe forms compared to male youth (Edwards et al., 2015; Exner-Cortens et al., 2013).

Of note are particularly high rates of physical and sexual victimization among sexual minority female youth (SMFY; e.g., female youth who identify as gay or lesbian, bisexual, or other non-heterosexual identities) compared to heterosexual female youth – disparities that may be driven by the societal stigmatization of sexual minority identities (Martin-Storey, 2015; Whitton et al., 2019). Studies suggest that between 50% and 85% of sexual minority women report lifetime sexual victimization experiences (Rothman et al., 2011) with research consistently demonstrating that bisexual women are particularly vulnerable to physical and sexual victimization and revictimization (Hequembourg et al., 2013). For instance, findings indicate that bisexual women are more likely to report experiences of rape than heterosexual or lesbian women (46.1% vs. 17.4% vs. 13.1%, respectively; Walters et al., 2013). Although these studies confirm the substantial burden of physical and sexual victimization among sexual minority women in general and among bisexual women in particular, consistent findings have not emerged among youth (Martin-Storey, 2015). As such, research is needed to extend previous findings by examining victimization rates between SMFY and heterosexual female youth and among heterogenous SMFY subgroups (e.g., female youth who identify as gay, lesbian, or bisexual) – findings which hold promise to inform targeted intervention efforts for at-risk youth.

Alcohol Misuse Correlates of Physical and Sexual Victimization

For both SMFY and heterosexual female youth, behavioral health outcomes, including alcohol misuse (e.g., binge drinking) and consequences (e.g., riding with a drinking driver, drinking and driving), have been linked to physical and sexual victimization (Edwards & Banyard, 2020; Newcomb et al., 2012). One longitudinal study found that female youth who experienced physical and sexual victimization in dating relationships were more likely to report binge drinking compared to female youth without such experiences (Exner-Cortens et al., 2013). Indeed, many female youth who experience physical or sexual victimization may misuse alcohol to avoid, reduce, or manage trauma-related outcomes, such as negative affect and interpersonal disconnections (Chilcoat & Breslau, 1998; Kushner et al., 1996). Further, negative reinforcement models suggest that alcohol use may reduce distress, which reinforces continued and potentially increased binge drinking and consequences (Sullivan et al., 2020). Advancing knowledge of types of victimization experiences that are more likely to be associated with alcohol misuse and consequences could a) inform targeted prevention efforts for female youth most at risk for alcohol use disorders and b) help providers to accurately attribute female youths’ trauma-related behavioral difficulties (Lowe et al., 2020). As such, the purpose of the current study is to elucidate patterns of physical and sexual victimization regarding timeline, tactic used, and context among female youth; examine sexual orientation disparities in latent class membership; and identify alcohol misuse correlates of class membership among female youth.

Sexual Orientation Disparities in Alcohol Misuse and Consequences among Female Youth

Alcohol misuse is especially pronounced among sexual minority youth compared to heterosexual youth. For instance, sexual minority youth are two-to-five times more likely to report underage drinking than heterosexual youth, with SMFY reporting particularly high rates of alcohol use (Dermody et al., 2019; Marshal et al., 2008). While the gender gap between male and female youth may be lessening in this regard (for a review, see Keyes et al., 2019), this finding may not extend to sexual minority youth (Marshal et al., 2008). Research suggests that risk and protective factors for drinking among heterosexual female youth (e.g., victimization, family support, respectively) may differ from SMFY, contributing to SMFY’s increasing rates of alcohol misuse relative to other groups (Newcomb et al., 2012). Moreover, sexual orientation disparities in binge drinking during adolescence continue into adulthood, with the largest rates of alcohol misuse among sexual minority women (Dermody et al., 2014).

Alcohol misuse may not be disproportionately elevated among all SMFY subgroups compared to heterosexual female youth (Hughes et al., 2016). Further, evidence is mixed regarding bisexual youths’ increased likelihood of engaging in alcohol-related risk behaviors compared with heterosexual youth (Marshal et al., 2009; Newcomb et al., 2012) and with their monosexual-identified counterparts (i.e., those who identify as gay or lesbian; Newcomb et al., 2012). Therefore, research is needed to clarify potential sexual-orientation-related disparities between gay, lesbian, or bisexual and heterosexual female youth and between gay, lesbian, or bisexual female youth in alcohol misuse (i.e., binge drinking) and consequences (i.e., riding with a drinking driver and drinking and driving).

Person- and Variable-Centered Approaches to Modeling Physical and Sexual Victimization

Research has historically conceptualized and measured victimization using variable-centered approaches. For instance, studies using SMFY samples have primarily relied on a total severity-frequency score of sexual victimization by type, a composite score of the number of victimization types, a mutually exclusive measure of lifetime victimization (e.g., no abuse, just childhood abuse, both childhood abuse and adult abuse, or just adult abuse), or the presence of each victimization type separately (Gilmore et al., 2014; Rhew et al., 2017). These studies typically demonstrate that behavioral health correlates are often a function of severity, frequency, and the number of victimization types. Further, epidemiologic studies of trauma-exposed women documented differences in health outcomes by victimization type. For example, one recent study demonstrated that forced sexual intercourse was associated with worse outcomes relative to other adverse experiences (Lowe et al., 2020). Nevertheless, variable-centered approaches limit information that might inform targeted interventions, such as whether some victimization combinations versus others are associated with alcohol misuse and consequences.

Mixture modeling approaches, such as latent class analysis (LCA), on the other hand, can assist in identifying latent subgroups of female youth based on distinct combinations of physical and sexual victimization (Lanza & Rhoades, 2013; Sessarego et al., 2019). Information gathered from using LCA might reveal underlying subgroups of female youth who have a high probability of endorsing distinct combinations of physical and sexual victimization versus others based on timeline (i.e., recent or lifetime), tactic used (i.e., forced sexual intercourse, sexual coercion, or physical violence), and context (i.e., occurring in a dating relationship versus not in a dating relationship; Choi et al., 2017). Applied to female youth, LCA might serve as a useful technique in a) identifying sexual orientation disparities in female youths’ likelihood of reporting distinct combinations of physical and sexual victimization and b) uncovering whether female youth reporting some combinations versus others might be more likely to report alcohol misuse and consequences. This information can be helpful in screening female youth most at risk for both physical and sexual victimization and alcohol misuse and consequences who might benefit from domestic violence or rape crisis services, differentiating alcohol-related treatment needs (e.g., focusing on preventing alcohol relapse versus reducing drinking and driving), and developing trauma-informed, integrated alcohol prevention and intervention efforts.

The Present Study

Using a population-based dataset of female youth across the US, we first examined sexual orientation disparities in victimization and alcohol misuse and consequences. Second, we aimed to expand upon existing youth-based victimization research (e.g., Baams, 2018; Sessarego et al., 2019), by utilizing person-centered statistical methods (Lanza & Rhoades, 2013). To do this, we utilized LCA to uncover classes of female youth reporting distinct combinations of victimization. Based on previous research, we hypothesized that the best fitting LCA model would consist of multiple classes based on timeline, tactic used, and context, including one class with no victimization. Third, we aimed to examine sexual orientation differences in victimization class membership. We hypothesized that SMFY would show greater representation in victimization exposure classes compared to heterosexual female youth. Fourth, we sought to determine whether alcohol misuse and consequences differed across victimization classes. We hypothesized that female youth in classes characterized by victimization exposure would be more likely to report alcohol misuse and consequences relative to female youth in a “no victimization” class. Associations were examined while controlling for age, race, and grade, and for school- and cyber-bullying, given the well-established connection between bullying and alcohol use (Goldbach et al., 2014). The binary logistic regression models of victimization classes predicting alcohol misuse and consequences also adjusted for sexual orientation.

Method

Participants

The current study utilized the national 2017 Youth Risk Behavior Survey (YRBS), a school-based, cross-sectional survey. The YRBS uses an independent three-stage cluster sample design to obtain a nationally representative sample of students in grades 9 through 12 who attend public and private schools in 50 states and the District of Columbia (Brener et al., 2013).

Procedures

Participation in the YRBS is anonymous and voluntary, and it adheres to local procedures to obtain parental consent. Students complete a self-administered questionnaire during a regular class period (Brener et al., 2013). The school-level response rate was 75%, the student-level response rate was 81%, and the overall response rate was 60% (Johns, 2018). A weighting factor was applied to account for nonresponse and oversampling. For details about the YRBS’ recruitment and participation procedures, see Brener et al. (2013). The YRBS was approved by the CDC’s institutional review board in Atlanta, Georgia.

Measures

Demographic Characteristics.

Participants indicated their age, sex assigned at birth (male or female), race, school grade, and sexual orientation. Racial groups were collapsed into white and racial minority. Sexual orientation groups were collapsed into heterosexual, gay or lesbian, and bisexual.

Bullying Exposure.

Participants were asked about the frequency of experiencing past-12-month bullying at school and online. School and cyberbullying were analyzed as two separate covariates. For both questions, response options included no (0) and yes (1).

Binge Drinking.

Binge drinking was assessed with the following question: “During the past 30 days, on how many days did you have ≥ 4 drinks of alcohol in a row (if you are female)?” Response options ranged from 0 (0 days) to 6 (≥ 20 days). Past-month binge drinking was dichotomized as 0 days = 0 and ≥ 1 day = 1.

Riding with a Drinking Driver.

Participants were asked the following question: “During the past 30 days, how many times did you ride in a car or other vehicle driven by someone who had been drinking alcohol?” Response options ranged from 0 (0 times) to 4 (≥ 6 times). Past-month riding with a drinking driver was dichotomized as 0 days = 0 and ≥ 1 day = 1.

Drinking and Driving.

Participants answered the question: “During the past 30 days, how many times did you drive a car or other vehicle when you had been drinking alcohol?” Response options ranged from 0 (I did not drive during the past 30 days/0 times) to 4 (≥ 6 times). Past-month drinking and driving was dichotomized as 0 days = 0 and ≥ 1 day = 1.

Lifetime Rape.

Lifetime rape was assessed with the question: “Have you ever been physically forced to have sexual intercourse when you did not want to?” Response options were no, coded 0, and yes, coded 1.

Past-Year Sexual Victimization.

Past-year sexual victimization was assessed with the following question: “During the past 12 months, how many times did anyone force you to do sexual things you did not want to do?” Response options ranged from 0 (0 times) to 4 (≥ 6 times). Past-year sexual victimization was dichotomized as 0 times = 0 and ≥ 1 time = 1.

Past-Year Sexual Victimization in Dating Relationships.

Participants responded to the question: “During the past 12 months, how many times did someone you were dating or going out with force you to do sexual things that you did not want to do?” Response options ranged from 0 (I did not go out with anyone/0 times) to 4 (≥ 6 times). Sexual victimization in dating relationships was dichotomized as 0 = I did not go out with anyone/0 times, and 1 = ≥ 1 time.

Past-Year Physical Victimization in Dating Relationships.

Past-year physical victimization in dating relationships was assessed with the question: “During the past 12 months, how many times did someone you were dating or going out with physically hurt you on purpose?” Response options ranged from 0 (I did not go out with anyone during the past 12 months/0 times) to 4 (≥ 6 times). Past-year physical victimization in dating relationships was dichotomized as 0 = I did not go out with anyone or 0 times, and ≥ 1 time. All victimization variables were treated as dichotomous given this study’s focus on advancing knowledge of the clustering of female youth with an accumulation of victimization risk, consistent with epidemiologic approaches that model adversity exposure (Anda et al., 2010).

Statistical Analysis Plan

Chi-square tests were used to assess for sexual orientation differences in our variables; significant differences were further analyzed using post-hoc tests of standard residuals (Beasley & Schumacker, 1995). Second, bivariate associations among study variables were tested using binary logistic regressions. Third, we used the three-step LCA approach, which prevents measurement bias related to class membership by correcting for classification error (Bakk et al., 2013), to carry out the remaining study aims: 1) uncover classes of female youth reporting distinct combinations of physical and sexual victimization; 2) examine associations between sexual orientation and class membership; and 3) test physical and sexual victimization class membership as a predictor of female youths’ alcohol misuse and consequences. We utilized the bias-adjusted maximum likelihood approach for all regression models (Bakk et al., 2013).

In the first step, we conducted LCA with four binary variables representing four forms of physical and sexual victimization: (1) lifetime rape; (2) past-year sexual victimization; (3) past-year sexual victimization in dating relationships; and (4) past-year physical victimization in dating relationships. While participants might answer affirmatively to both lifetime rape and past-year sexual victimization based on the same incident, LCA might help to identify whether these experiences are in fact distinct. That is, LCA is a useful tool to determine whether there are compositional differences distinguishing victimization timeline (i.e., recent or lifetime) and whether more than one victimization was experienced; Baams, 2018; Sengoelge et al., 2019).

We fit models with 1 to 6 classes and specified a priori the following criteria to identify the most optimally fitting LCA model: relative fit, including low Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and sample-size-adjusted BIC (SABIC); entropy above .70; a significant p-value for the Lo-Mendell-Rubin (LMR) test; class size (Lanza & Rhoades, 2013); and interpretability based on whether the classes were in line with prior research (Collins & Lanza, 2010). The average posterior probabilities of class membership were used to examine class homogeneity (Nylund et al., 2007). Posterior probabilities above 0.60 indicate that the classes are well separated and that the class assignment accuracy is adequate (Nagin, 2005).

In the second step, participants were assigned to their most-likely latent class based on the posterior membership probabilities derived from the first step (Vermunt & Magidson, 2016). In the third step, we employed two multinomial logistic regressions to model associations between sexual orientation with physical and sexual victimization classes identified by the LCA. To do this, we regressed latent class membership onto sexual orientation while accounting for classification error in class assignment (Bakk et al., 2013). Finally, three separate multinomial logistic regression models were used to examine latent class membership as a predictor of alcohol misuse and consequences. Binge drinking, riding with a drinking driver, and drinking and driving were each regressed onto class membership while accounting for classification error in class assignment. Regression models were adjusted for age, race, grade, and school- and cyber-bullying based on empirical evidence documenting the association between these predictors and both the independent and dependent variables among female youth (Atteberry-Ash et al., 2019). Post-stratification weights were used to adjust for nonresponse and oversampling in order to generate nationally representative prevalence estimates.

Assuming that the data are independent, normally distributed, and homogeneity of variance (Cohen, 2013), a power analysis, using G*Power (Erdfelder et al., 1996), determined that 7,185 female youth provided 99% power to detect a small-to-medium effect. While there currently exists no consensus regarding the minimum required sample sizes to correctly identify the number of latent classes, a simulation study by Nylund et al. (2007) showed that sample sizes of 500 are sufficient for detecting the correct number of latent classes using the sample-size-adjusted BIC statistics under most simulation conditions. Thus, it is assumed that our sample size yields sufficient power to detect a large effect size.

We present Wald test statistics, adjusted odds ratios (AOR), and 95% confidence intervals. Descriptive statistics were conducted in SPSS version 24 (IBM Corp., 2016), LCA was conducted in MPlus version 8.1 (Muthén & Muthén, 2017), and the bias-adjusted three-step latent class analytic approach (Bakk et al., 2013) was implemented in Latent GOLD software version 5.1 (Vermunt & Magidson, 2016).

Results

Sample Characteristics

Table 1 presents presence of victimization, alcohol misuse and consequences, and victimization class membership in the total sample and stratified by sexual orientation. Participants were 7,185 youth who were assigned female at birth and identified their sexual orientation as heterosexual (77.0%; n = 5,533), bisexual (12.8%; n = 921), and gay or lesbian (2.3%; n = 168). Most participants identified their race as White (53.2%; n = 3,822), followed by Multiracial (17.8%; n = 1,280); Black or African American (13.0%; n = 931), Hispanic/Latinx (9.2%; n = 662), Asian American (3.4%; n = 245), Native Hawaiian/other Pacific Islander (0.8%; n = 56); and American Indian/Alaska Native (0.4%; n = 25). Participants were in grades 9 (26.5%; n = 1,905); 10 (25.4%; n = 1,825), 11 (23.8; n = 1,712), and 12 (23.2%; n = 1,668). Missing data ranged from 1.0% for age to 11.1% for drinking and driving. We used complete case analysis, as recommend for studies using the YRBS (Brener et al., 2013). A total of 80.7% of female youth did not report victimization, 7.7% reported past-year sexual victimization, 7.4% reported lifetime rape, and 4.2% reported multiple forms of victimization.

Table 1.

Frequencies of study variables by sexual orientation among female youth (N = 7,185)*

Total sample n = 7,185 Heterosexual n = 5,533 (77.0%) Gay or lesbian n = 168 (2.3%) Bisexual n = 921 (12.8%) χ 2
N (%)a N (%)a N (%)a N (%)a
Presence of Victimization
Lifetime rape 753 (10.5%) 483 (8.8%) 34 (20.4%) 221 (24.3%) 202.75***
Past-year sexual victimization 978 (13.6%) 714 (13.4%) 23 (14.0%) 214 (24.5%) 73.32***
Past-year sexual victimization in dating relationships 474 (6.6%) 340 (6.5%) 15 (9.3%) 108 (12.5%) 39.99***
Past-year physical victimization in dating relationships 426 (5.9%) 270 (5.0%) 23 (14.0%) 113 (12.5%) 91.24***
Any victimization 1,305 (18.2%) 876 (16.3%) 46 (27.4%) 298 (32.4%) 140.58***
Alcohol Misuse and Consequences
Past-month binge drinking 953 (13.3%) 720 (13.9%) 28 (17.8%) 154 (18.1%) 11.71**
Past-month riding with a drinking driver 1,213 (16.9%) 941 (17.0%) 35 (21.1%) 174 (18.9%) 3.62
Past-month drinking and driving 166 (2.3%) 114 (2.2%) 4 (2.6%) 31 (3.6%) 6.47*
Physical and Sexual Victimization Latent Classes
Class 1 (“Poly-Victimization Class”) 315 (4.2%) 201 (3.5%) 15 (7.5%) 64 (6.8%) 28.46***
Class 2 (“No Victimization Class”) 6,064 (80.7%) 4,738 (83.3%) 149 (74.5%) 626 (67.0%) 168.25***
Class 3 (“Past-Year Sexual Victimization Class”) 575 (7.7%) 414 (7.3%) 10 (5.0%) 93 (9.9%) 10.14**
Class 4 (“Lifetime Rape Class”) 554 (7.4%) 338 (5.9%) 26 (13.0%) 152 (16.3%) 131.06***
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Note. Bolded cells represent statistically significant differences in victimization and alcohol misuse variables between SMFY and heterosexual female youth (post-hoc residual-level of ± 2.0). Gay or lesbian female youth did not differ in victimization exposure and alcohol misuse and consequences compared to bisexual female youth.

Percentages may not equal 100 due to missing data.

a

Weighted percentages.

☨

p < .10,

*

p < .05,

**

p < .01,

***

p < .001.

Overall, SMFY were more likely to report all forms of victimization and specifically, lifetime rape and past-year physical victimization in dating relationships compared to heterosexual female youth (see Table 1). Bisexual-identified participants were more likely to report victimization as well as past-month binge drinking and drinking and driving than heterosexual female youth. As reported in Table 1, SMFY subgroups did not differ in their reported victimization or alcohol misuse correlates. Table 2 demonstrates bivariate correlations between study variables, including victimization and alcohol misuse and consequences controlling for age, race, grade, school bullying, and cyberbullying. Study variables were associated in the expected direction and multicollinearity did not appear to be present.

Table 2.

Bivariate associations between physical and sexual victimization, alcohol misuse and consequences, and demographic variables

Lifetime rape AORa (95% CI) Past-year sexual victimization AORa (95% CI) Past-year sexual victimization in dating relationships AORa (95% CI) Past-year physical victimization in dating relationships AORa (95% CI) Binge drinking AORa (95% CI) Riding with a drinking driver AORa (95% CI) Drinking and driving AORa (95% CI)
1. Lifetime rape __ 8.61*** (6.62, 11.20) 1.14 (0.83, 1.57) 3.66*** (2.67, 5.02) 1.53** (1.17, 2.00) 1.05 (0.80, 1.37) 1.55 (0.92, 2.64)
2. Past-year sexual victimization __ 2.83*** (1.00, 2.90) 1.13 (.70, 1.83) 1.38* (1.01, 1.87) 1.52** (1.15, 2.01) 1.25 (0.65, 2.39)
3. Past-year sexual victimization in dating relationships __ 4.78*** (2.99, 7.65) 1.10 (0.75, 1.60) 0.96 (0.67, 1.37) 1.01 (0.48, 2.11)
4. Past-year physical victimization in dating relationships __ 1.32 (0.94, 1.86) 1.86*** (1.37, 2.54) 1.21 (0.63, 2.30)
5. Binge drinking __ 3.16*** (2.61, 3.84) 17.77*** (10.84, 29.13)
6. Riding with a drinking driver __ 3.52*** (2.34, 5.30)
7. Drinking and driving __
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Note. AOR = adjusted odds ratio; CI = confidence interval. AORs with 95% confidence intervals adjusted for age, race, grade, school bullying, and cyberbullying.

a

Weighted odds ratios.

*

p < .05,

**

p < .01,

***

p < .001.

Model Fit Assessment of Physical and Sexual Victimization Latent Classes

We estimated models of physical and sexual victimization latent classes ranging from one to six classes (see Table 3). Although the three-class solution had the lowest BIC and a significant p-value for the LMR test, the four-class solution had the lowest AIC and aBIC relative to other classes. In addition, the entropy remained relatively high with the four-class solution and decreased with the five- and six-class solutions, indicating that class separation is relatively high in the four-class solution, and suggesting better model fit than the five- and six-class solutions (Morgan, 2015). Further, the AIC, BIC, aBIC, and number of free parameters continue to increase in the five- and six-class solutions, indicating that class separation is relatively low in these classes, respectively. As such, the four-class LCA solution was deemed optimal based on the previously noted model-fit criteria (Lanza & Rhoades, 2013).

Table 3.

Model fit indices and model comparison statistics for mixture modeling of victimization experiences

Number of classes Akaike Information Criterion Bayesian Information Criterion Sample-Size Adjusted Bayesian Information Criterion Lo, Mendell, and Rubin Likelihood Ratio Test Bootstrapped Likelihood Ratio Test Entropy Number of Free Parameters
1 18286.56 18314.25 18301.54 N/A N/A N/A 4
2 14928.70 14991.01 14962.41 <.001 <.001 0.93 9
3 14859.07 14956.01 14911.51 <.001 <.001 0.96 14
4* 14835.96 14967.51 14907.14 0.51 <.001 0.89 19
5 14845.96 15012.13 14935.87 0.27 1.00 0.51 24
6 14855.96 15056.75 14964.60 0.74 1.00 0.69 29
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Note.

*

Model selected as providing the best fit, as demonstrated by the relatively small Akaike Information Criterion, Bayesian Information Criterion, statistically significant Bootstrapped Likelihood Ratio Test, relatively high entropy, and relatively few number of parameters.

Class 1, “Poly-Victimization Class,” was characterized by high probabilities of exposure to lifetime rape, past-year sexual victimization, and past-year physical and sexual victimization in dating relationships (n = 315; 4.2%). Class 2, “No Victimization Class,” represented low probabilities across all victimization forms (n = 6,064; 80.7%). Class 3, “Past-Year Sexual Victimization Class,” was characterized by a high probability of past-year sexual victimization, a moderate probability of past-year sexual victimization in dating relationships, and low probabilities of lifetime rape and past-year physical victimization in dating relationships (n = 575; 7.7%). Class 4, “Lifetime Rape Class,” represented a high probability of lifetime rape, a moderate probability of past-year sexual victimization, and low probabilities of past-year physical and sexual victimization in dating relationships (n = 554; 7.4%).

Sexual Orientation Disparities in Physical and Sexual Victimization Class Membership

Table 4 presents results from multinomial logistic regression models regressing class membership on sexual orientation. Gay or lesbian female youth compared to heterosexual female youth demonstrated lower odds of being in the “Past-Year Sexual Victimization Class” (AOR = 0.30, 95% CI: 0.15, 0.63) and greater odds of being in the “Lifetime Rape Class” (AOR = 1.71, 95% CI: 1.03, 2.83) than in the “No Victimization Class.” In addition, bisexual female youth compared to heterosexual female youth demonstrated greater odds of being in the “Past-Year Sexual Victimization Class” (AOR = 1.68, 95% CI: 1.29, 2.18) and the “Lifetime Rape Class” (AOR = 3.20, 95% CI: 2.56, 4.00) than in the “No Victimization Class.” There were no differences between gay or lesbian and bisexual female youth in participants’ likelihood of being in the “Poly-Victimization Class,” the “Past-Year Sexual Victimization Class,” or the “Lifetime Rape Class” compared to the “No Victimization Class.”

Table 4.

Multinomial logistic regression model of sexual orientation disparities in victimization exposure latent classes among female youth

Class 1 (“Poly-Victimization Class”; n = 315; 4.2%) Class 3 (“Past-Year Sexual Victimization Class”; n = 576; 7.7%) Class 4 (“Lifetime Rape Class”; n = 554; 7.4%) Wald statistic Omnibus p-value
AORa (95% CI) SE AORa (95% CI) SE AORa (95% CI) SE
Predictor Variables
Sexual orientation 133.05*** <.001
 Heterosexual ref ref ref
 Gay or lesbian 0.49 (0.37, 1.01) 0.37 0.30 (0.15, 0.63) 0.38 1.71 (1.03, 2.83) 0.26
 Bisexual 2.05 (0.80, 2.90) 0.18 1.68 (1.29, 2.18) 0.13 3.20 (2.56, 4.00) 0.11
Sexual minority sexual orientation 4.87 0.18
 Gay or lesbian ref ref ref
 Bisexual 0.60 (0.38, 1.29) 0.39 1.73 (0.79, 3.76) 0.40 1.26 (0.73, 2.17) 0.28
Covariate Variables
Age 0.98 (0.78, 1.24) 0.12 1.21 (1.02, 1.44) 0.09 0.74 (0.62, 0.87) 0.09 19.24*** <.001
Grade 1.20 (0.78, 1.46) 0.14 0.76 (0.63, 0.92) 0.10 1.59 (1.32, 1.92) 0.10 35.14*** <.001
Race 11.99** <.01
 Racial minority ref ref ref
 White 1.54 (0.81, 2.02) 0.14 1.15 (0.95, 1.40) 0.10 0.91 (0.74, 1.10) 0.10
School bullying exposure 1.34 (0.74, 1.90) 0.18 1.22 (0.94, 1.59) 0.13 1.25 (0.96, 1.62) 0.13 6.10 0.11
Cyberbullying exposure 2.85 (0.85, 4.02) 0.18 1.73 (1.33, 2.26) 0.14 1.78 (1.36, 2.30) 0.13 55.30*** <.001
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Note. AOR = adjusted odds ratio; CI = confidence interval; SE = standard error; ref = reference group. Boldface type indicates a significant AOR. Omitted (reference) category is Class 2 (“No Victimization Class”) for classes of physical and sexual victimization (n = 6,064; 80.7%). All models utilized the bias-adjusted maximum likelihood approach.

a

Weighted odds ratios.

*

p < .05,

**

p < .01,

***

p < .001.

Victimization Class Membership as a Predictor of Alcohol Misuse and Consequences

Table 5 presents results from three binary logistic regression models regressing alcohol misuse and consequences separately onto physical and sexual victimization class membership. A Wald test indicated that physical and sexual victimization class membership was a significant predictor of past-month binge drinking (Wald = 27.14, p < .001), past-month riding with a drinking driver (Wald = 24.63, p < .001), and past-month drinking and driving (Wald = 23.42, p < .001). Specially, female youth in the “Past-year Sexual Victimization Class” demonstrated greater odds of reporting past-month binge drinking (AOR = 1.36, 95% CI: 1.03, 1.78) and past-month riding with a drinking driver (AOR = 1.42, 95% CI: 1.12, 1.79) compared to female youth in the “No Victimization Class.” Female youth in the “Lifetime Rape Class” exhibited greater odds of past-month binge drinking (AOR = 1.72, 95% CI: 1.34, 2.23), past-month riding with a drinking driver (AOR = 1.57, 95% CI: 1.24, 1.98), and past-month drinking and driving (AOR = 2.39, 95% CI: 1.43, 4.01) compared to female youth in the “No Victimization Class.”

Table 5.

Binary logistic regressions of sexual and physical victimization classes on alcohol misuse and consequences among female youth

Past-month binge drinking Past-month riding with a drinking driver Past-month drinking and driving
Victimization Exposure Class AORa (95% CI) SE Wald statistic Omnibus p-value AORa (95% CI) SE Wald statistic Omnibus p-value AORa (95% CI) SE Wald statistic Omnibus p-value
Class 1 (“Poly-Victimization Class”) 1.74 (0.79, 2.41) 0.17 27.14*** <.001 1.54 (0.79, 2.10) 0.16 24.63*** <.001 3.20 (0.79, 5.77 0.30 23.42*** <.001
Class 2 (“No Victimization Class”) ref ref ref
Class 3 (“Past-Year Sexual Victimization Class”) 1.36 (1.03, 1.78) 0.14 1.42 (1.12, 1.79) 0.12 0.99 (0.87, 1.12) 0.36
Class 4 (“Lifetime Rape Class”) 1.72 (1.34, 2.23) 0.13 1.57 (1.24, 1.98) 0.12 2.39 (1.43, 4.01) 0.26
Covariate Variables
Sexual orientation 3.19 .20 2.35 0.31 3.05 .22
 Heterosexual ref ref ref
 Gay or lesbian 1.15 (0.67, 1.78) 0.22 1.32 (0.72, 1.92) 0.19 0.18 (0.02, 1.32) .10
 Bisexual 1.20 (0.82, 1.49) 0.11 0.96 (0.82, 1.16) 0.10 1.10 (0.64, 1.78) 0.24
Age 1.04 (0.88, 1.19) 0.07 .37 .54 1.22 (0.90, 1.37) 0.06 11.06*** <.001 1.36 (0.76, 1.87) 0.16 3.45 .06
Grade 1.35 (0.88, 1.57) 0.08 15.98*** <.001 0.80 (0.87, 0.91) 0.07 11.58*** <.001 1.70 (0.77, 2.43) 0.18 8.40** <.01
Race 72.30*** <.001 1.26 0.26 0.52 .47
 Racial minority ref ref ref
 White 1.94 (0.90, 2.26) 0.08 0.93 (0.87, 1.06) 0.07 1.71 (0.77, 2.45) 0.18
School bullying exposure 1.03 (0.81, 1.27) 0.11 .06 .80 1.06 (0.84, 1.28) 0.09 0.44 0.51 1.19 (0.65, 1.89) 0.23
Cyberbullying exposure 1.80 (0.87, 2.21) 0.11 31.01*** <.001 1.43 (0.86, 1.73) 0.10 13.84*** <.001 1.97 (0.74, 3.09) 0.23 8.67** <.01
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Note. AOR = adjusted odds ratio; CI = confidence interval; SE = standard error; ref = reference group Boldface type indicates a significant AOR. All models utilized the bias-adjusted maximum likelihood approach.

a

Weighted odds ratios.

*

p < .05,

**

p < .01,

***

p < .001.

Discussion

Using a nationally representative sample of female youth, the present study is among the first, to our knowledge, to provide a comprehensive description of distinct combinations of female youths’ physical and sexual victimization experiences; examine sexual orientation differences in physical and sexual victimization class membership; and identify associations between each of the four heterogeneous physical and sexual victimization classes with alcohol misuse and consequences. Although most female youth were included in the class characterized by low probabilities across all four victimization forms, a sizeable percentage of female youth was included in the “Poly-Victimization Class,” and even higher percentages in both the “Lifetime Rape Class” and the “Past-Year Sexual Victimization Class.” SMFY were more likely report physical and sexual victimization and alcohol misuse and consequences than heterosexual female youth, consistent with existing literature (Fish et al., 2019; Goldbach et al., 2014; Martin-Storey, 2015; Phillips et al., 2017; Whitton et al., 2019). Extending these findings, class membership did not differ between gay/lesbian and bisexual female youth. Our findings also demonstrated that victimization experiences have differential alcohol risk implications.

The current study’s findings highlight the importance of considering a person-centered approach in modeling physical and sexual victimization and alcohol misuse correlates. That is, female youth may report certain combinations of physical and sexual victimization that might not be directly observable when examining bivariate associations (Lanza & Rhoades, 2013). Further, the methodological approach of this study (i.e., LCA) allows for important contributions to the study of understanding the prevalence, clustering, and potential consequences of physical and sexual victimization in female youth, and SMFY in particular. Consistent with recent findings (Adams et al., 2016; Charak et al., 2015; Felix et al., 2019; Turner et al., 2016), this study confirms that distinct classes of physical and sexual victimization were observed among female youth and were differentially associated with binge drinking, riding with a drinking driver, and drinking and driving in this population.

In line with previous studies (Ford & Soto-Marquez, 2016; Phillips et al., 2017), findings from the current study present alarming rates of various forms of physical and sexual victimization as well as alcohol misuse and consequences among SMFY, and especially bisexual female youth, compared to their heterosexual counterparts. Gay or lesbian female youth and bisexual female youth did not differ in their victimization experiences or alcohol misuse correlates. Nevertheless, bisexual-identified female youths’ increased rates of victimization and alcohol misuse and consequences, relative to their heterosexual peers, may be partially explained by the constant stress related to bisexual stigma (e.g., negative attitudes toward bisexuality) that bisexual female youth face from both the heterosexual population and the sexual minority community (Doan Van et al., 2019; Feinstein & Dyar, 2017; Roberts et al., 2015). Indeed, anti-bisexual stereotypes, including that bisexual individuals are promiscuous, may leave bisexual female youth at heightened risk for physical or sexual victimization (Johnson & Grove, 2017). Further, our study’s findings provide novel and needed insights into factors related to sexual orientation disparities in alcohol misuse and consequences among female youth above and beyond bullying – a well-established predictor of alcohol-related outcomes (Phillips et al., 2017).

Female youth in all three classes characterized by relatively high probabilities of exposure to physical and sexual victimization – classes which were primarily made of SMFY – were at increased risk for alcohol misuse and consequences compared to female youth who reported no physical or sexual victimization. These findings build on prior research by underscoring that physical and sexual victimization, regardless of recency, type, and context, might exert a proximal influence on female youths’ vulnerability to alcohol misuse and related consequences. Indeed, trauma-exposed women may drink to cope with their feelings of psychological distress associated with victimization, potentially increasing alcohol use (Najdowski & Ullman, 2009; Sullivan et al., 2020) and risk for revictimization (Brooks-Russell et al., 2013). Notably, these patterns differed slightly for female youth who reported recent sexual victimization in general or lifetime rape but did not report poly-victimization.

Limitations

Our findings should be interpreted in light of several limitations. First, given our study’s cross-sectional design, causality cannot be inferred, warranting longitudinal research to establish directionality in these associations. Second, multiple variables were assessed using single items, which can impact the construct validity of measures. Third, participants might have answered affirmatively to both lifetime rape and past-year sexual victimization based on the same incident. However, LCA revealed that female youth who endorsed past-year sexual victimization had low probabilities of lifetime rape. Fourth, although this sample included a sizeable number of bisexual female youth, it included a limited number of gay- or lesbian-identified female youth. This could be due, in part, to youth increasingly adopting other non-monosexual identity labels, such as queer (Feinstein & Dyar, 2017). Similarly, this study did not assess for gender identity and so could not capture unique experiences of SMFY who identify as transgender or gender diverse. Finally, this study did not include questions about the perpetrator’s identity or relation to the survivor, context in which the abuse happened (e.g., school, family, online), or any additional tactic-related items (e.g., threats, alcohol-involved) – all of which represent critical nuances to further understanding victimization and its behavioral health correlates (Adams et al., 2016).

Research Implications

Overall, the present study helps us to better understand the patterns of physical and sexual victimization regarding timeline, tactic used, and context among female youth, sexual orientation disparities in latent class membership, and alcohol misuse correlates of class membership. Building on these current findings and negative reinforcement models, researchers should consider developing identity-affirmative, trauma-focused interventions in combination with motivational interviewing techniques to bolster SMFY’s capacity for emotion regulation, thus potentially reducing this populations’ alcohol misuse and related risk behaviors. Future longitudinal studies should probe the nature of, and risk and protective factors for, engagement in alcohol misuse and consequences among female youth who report multiple forms of recent and lifetime victimization. Future studies should also use micro-longitudinal approaches, such as ecological momentary assessment, to examine the proximal temporal influence of victimization on alcohol misuse and consequences. Finally, although this study demonstrated sexual orientation disparities in female youths’ alcohol misuse and consequences, future studies would benefit from including trauma-related and transdiagnostic constructs, such as trauma centrality, avoidance, emotion dysregulation, and thoughts of self-blame, to expand on the preliminary psychosocial trajectory identified in this study (Ford et al., 2010).

Clinical Implications

This study’s findings bear implications for multiple settings, including school and community centers specific for female youth, government organizations, and clinical practice. Given the endemic sexual orientation disparities in physical and sexual victimization and alcohol misuse and consequences, interventions designed to prevent and eliminate victimization exposure in youth would benefit from targeting SMFY. Such interventions could include: 1) comprehensive sexual and physical violence prevention efforts inclusive of SMFY’s experiences; 2) government-produced driving-safety promotional materials which highlight the increased risk for alcohol-related driving behaviors among SMFY compared to their heterosexual counterparts; 3) trauma-focused and identity-affirmative mental health interventions which focus on improving cognitive and affective coping strategies in response to victimization, such as trauma-focused cognitive behavioral therapy and cognitive processing therapy (Scheer & Poteat, 2018). Finally, trauma-focused behavioral health interventions developed specifically for female youth who experience lifetime or recent sexual victimization are especially warranted.

Conclusion

In sum, this is the first study, to our knowledge, to document heterogeneity in experiences of physical and sexual victimization among a large population-based sample of female youth, identify sexual orientation disparities in physical and sexual victimization latent class membership, and assess the extent to which various combinations of physical and sexual victimization experiences differentially relate to binge drinking and related risk behaviors, namely, riding with a drinking driver and drinking and driving, among female youth. Findings underscore the importance of identity-affirmative and trauma-focused prevention and intervention initiatives for female youth in general, and SMFY in particular, to reduce behavioral health outcomes of recent and lifetime physical and sexual victimization experiences. These initiatives may be particularly important for SMFY and for those who report engaging in recent binge drinking, riding with a drinking driver, and drinking and driving, as these youth were more likely to be in classes characterized by physical and sexual victimization.

Acknowledgements:

Jillian Scheer’s manuscript preparation time was supported by the Yale Center for Interdisciplinary Research on AIDS training program (T32MH020031; PI: Kershaw). The research presented herein is the authors’ own and does not represent the views of the funders, including the National Institutes of Health.

Footnotes

Author Disclosure Statement. No competing financial interests exist.

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