Unpacking Violent Behavior in Young Adulthood: The Relative Importance of Hazardous Alcohol Use

Abstract

Young adulthood is an important developmental period for investigating the nature of violent behavior. This study examines the unique contribution of alcohol use to violence perpetration among young adults in the Australian community, after accounting for the influence of sociodemographic, early life, trait, and well-being influences. Cross-sectional, self-report data was collected from 507 young adults aged 18-20 years in the Australian general community via an online survey. Sequential logistic regressions examined the relative and independent contribution of adverse childhood experiences (ACEs), impulsivity, psychological distress, and hazardous alcohol use to past-year violent behavior. Results show one in eight young adults aged 18-20 (13%) reported at least one act of violent behavior in the past year, primarily assault perpetrated against another person. Sequential logistic regression identified that after controlling for other risk factors, the number of ACEs reported and hazardous alcohol use were independently and positively associated with increased odds of reporting violent behavior in young adulthood. These findings demonstrate that ACEs and hazardous alcohol use are important, independent correlates of violent behavior in young adults. While preventing early adversity is key for reducing violence in the community, this evidence suggests that it is also important to target proximal causes such as hazardous alcohol use. Increasing early and widespread access to evidence-based, trauma-informed violence-prevention programs targeting risk factors across multiple settings is critical for reducing harm and supporting young people into healthy adulthood.

Keywords

violence young adult adverse childhood experiences hazardous alcohol use psychological distress impulsivity

Introduction

It is difficult to quantify the toll that violence takes on individuals, families, societies, and communities around the world, not just physically but psychologically, emotionally, relationally, intergenerationally and economically. The costs of violence are far higher than prevalence estimates would suggest. For example, a single homicide incident in Australia is estimated to cost $2.7 million which is not accounting for the impact on the dependents, family members and friends of victims and perpetrators (Smith et al., 2014). Experiencing and surviving violence is also associated with an increased risk of adverse outcomes including depression, anxiety, and post-traumatic stress disorder (Lagdon et al., 2014), violence perpetration and imprisonment (Barrett et al., 2011), suicide attempts, substance use problems, risky sexual behavior, hospitalizations and future violent victimization (Turanovic & Pratt, 2015).

Young adulthood is a distinct developmental period particularly susceptible to the impacts of violence perpetration. It is a period characterized by significant life changes including increased independence, maturity, responsibility and autonomy (Bonnie et al., 2014). While for some it can be a time of desistence from antisocial behavior (Stolzenberg & D’Alessio, 2008; Stouthamer-Loeber et al., 2004), for others it can be a stage of life where criminal trajectories are established, exacerbated, and maintained (Basto-Pereira & Farrington, 2019). Adolescents and young adults are at high risk of experiencing and engaging in violent behavior (Australian Institute of Health and Welfare, 2017; Farrington, 1986). Alongside illicit drug offences, acts intended to cause injury are the most common principal offence proceeded against by police in Australia (20%) with the largest proportion of offenders aged between 20 and 24 years (Australian Bureau of Statistics [ABS], 2020). Official crime statistics indicate that population prevalence in violent offending (e.g., homicide and acts intended to cause injury) peaks between the ages of 20 and 29 (ABS, 2020). However, administrative data are limited for only capturing crime that has been recorded, therefore can only provide part of the story. Past research has found that one in six (16%) 19-20-year-olds report being in a physical fight in the past year, with higher rates among males compared to females (25% vs. 9%, respectively) (Smart et al., 2003, p. 46). Self-reported data like this are important to complete this picture and illuminate the “dark figure of crime”—that is, crime that remains undetected, unreported and unrecorded (Hayes & Prenzler, 2014). Given the increased propensity for violence during this transitional life-stage it is critical to understand the key risk factors that contribute to violence perpetration during young adulthood (Fougere et al., 2013; Smart et al., 2003).

The socioecological model explains how individual and environmental influences interact and cluster in complex ways to increase susceptibility to perpetrating violent behavior (Centers for Disease Control and Prevention, 2015). Efforts to assess the relative impact of any specific influence on violence must situate such effects in context and in relation to other overlapping factors. Youth violence is predicted by individual (i.e., impulsivity), family (i.e., harsh parenting), and community (i.e., high crime neighborhoods) risk factors (Grunseit et al., 2008). Current evidence on the nature of violent behavior among young adults is lacking as much of the existing literature has examined intimate partner violence (IPV) and dating violence specifically (Farrington et al., 2017). While aggression is prevalent among young people, violent behavior is less common among young women and more frequently perpetrated by young men but it is equally experienced by men and women (Arseneault et al., 2000; ABS, 2018; Denson et al., 2018; Smart et al., 2003). Studies examining IPV prevalence among young adults typically find high rates of bidirectional IPV (i.e., being both a perpetrator and victim of violence; 37%-47%) and highlight the influence of childhood maltreatment (e.g., abuse and neglect) as a predictor of perpetrating and experiencing such violence (Renner & Whitney, 2012). Adverse childhood experiences (ACEs) are associated with negative health outcomes across physiological, cognitive, and affective domains that in turn increase vulnerability to antisocial behavior such as violence (Braga et al., 2017; Felitti et al., 1998; Hughes et al., 2017). These include reduced stress reactivity (cortisol, heart rate) and cognitive capacity, as well as difficulty with behavioral and emotional regulation, and impulsivity (Lovallo, 2013).

Alcohol is consistently and positively implicated in violent behavior among young people and adults (Miller et al., 2015; Zhang et al., 1997), with between 30% and 70% of violent crime estimated to be alcohol-related (Briscoe & Donnelly, 2001; Doherty & Roche, 2003; Poynton et al., 2005). Further, young people who binge drink are five times more likely to be violent compared to those who don’t (Williams et al., 2009). This is concerning as young people aged 18-24 are most likely to exceed the risk guidelines for single occasion alcohol use (five or more drinks, 41%) and one in seven (14.6%) report drinking more than 11 standard drinks on one occasion at least monthly (Australian Institute of Health and Welfare, 2019). Reviews of experimental studies confirm causal, dose dependent associations between alcohol, aggression, and violence but note the importance of individual and environmental influences (Exum, 2006; Tomlinson et al., 2016). The relationship between mental health symptoms such as psychological distress and violent behavior is less clear. Research suggests that rates of psychological distress (i.e., unpleasant feelings or emotions that interfere with day-to-day functioning) has been increasing in recent years, with young adults aged 18-24 most likely to report high or very high levels of psychological distress (Australian Bureau of Statistics, 2019). Some studies show people with depression have an increased risk of violent crime compared to the general population after controlling for previous offences and comorbid substance use (Fazel et al., 2015), whereas others find that severe mental illness (including depression) only predicts violence in the context of co-occurring substance use (Elbogen & Johnson, 2009).

Understanding the relative importance of alcohol use as a contributor to the perpetration of violent behavior proves challenging as mental ill-health and substance use frequently co-occur and risk factors tend to cluster and interact (Teesson et al., 2009). For example, risk markers for perpetrating violent behavior such as ACEs and externalizing and internalizing symptoms also predict problematic alcohol use (Farrington et al., 2017; Felitti et al., 1998; Fox et al., 2015). It is necessary to examine these influences in combination to isolate the more important explanatory factors that can be targeted for optimal intervention. Building the evidence around the nature of these relationships during young adulthood is critical, not only because of the increased risk of related harm at this time but also because this is a group for which developmentally appropriate support interventions are lacking (Williams et al., 2009).

The Current Study

Most of the research examining the nature of violent behavior among young adults has examined specific types of violence (e.g., IPV) or has been conducted in the United States (Derzon, 2010; Farrington et al., 2017). Further work is needed to improve understanding of the relative importance of individual-level, theory-driven risk factors for violence among young adults in Australia. Specifically, investigation of the role of alcohol in violence during young adulthood after accounting for other individual-level risk factors is required. Continuing to build the evidence-base will improve the capacity of prevention and early intervention efforts to reduce violence and the associated systemic and economic burden on individuals and societies around the world. The current study will progress this aim by delineating the relative strength of the relationship between alcohol use and the perpetration of violent behavior in young adulthood after accounting for the influence of demographic indicators, ACEs impulsivity and psychological distress.

Method

The data for this study were collected as part of an international longitudinal study examining prosocial and antisocial behavior in young adults, involving ten countries across five continents (Basto-Pereira et al., 2019). A total of 582 young adults from the Australian general community completed the baseline survey. The current study analyzed data for participants who completed the violence questionnaire (n = 507). Young adults aged 18-20 years were recruited between November 2018 and June 2019 through snowball sampling, advertisements on professional websites, and on social media (i.e., Facebook). A confidential online survey was administered to all participants, which took between 15 and 30 minutes to complete. The survey asked about sociodemographic characteristics, antisocial and prosocial behaviors, ACEs impulsivity, psychological distress and alcohol use. After completing the survey participants were entered into a draw to win a $500 gift voucher. Ethics approval was obtained from the University of Sydney Human Research Ethics Committee (2018/876).

Measures

Sample characteristics

Demographic information was collected including gender, age, years of schooling, current occupation and socioeconomic status (SES). Gender was coded as male, female, and transgender and gender diverse people (TGD). Years of schooling was coded as a binary variable (did they complete year 12: yes/no). Occupation was a categorical variable indicated as studying, employed, studying and working, or neither studying nor working. Participants’ SES was determined based on their occupation and education history if they were financially independent or their parent’s occupation and education history if they were not financially independent (Basto-Pereira et al., 2019; Pechorro et al., 2019).

Self-reported violence

Self-reports of perpetration of violent behavior committed during the past year were measured using a standardized interview tool developed for the National Youth Survey and National Institute of Justice multisite surveys and used in the Dunedin Multidisciplinary Health and Development Study (Arseneault et al., 2000; Elliott & Huizinga, 1989). The brief measure is comprised of seven items asking about common violent offences including assault (e.g., hit someone you lived with or didn’t live with), serious assault (e.g., hit someone you lived with or didn’t live with a weapon or with the idea of seriously hurting them), robbery (e.g., used a weapon or force to rob a person, shop, bank or other business), gang fighting, and sexual assault. The items were summed to create a composite score of total number of violent behaviors reported, however as the results were highly negatively skewed (skewness = 3.46 and kurtosis = 15.31), a binary indicator (yes/no) for any violent crime reported in the past year was used as the outcome variable in the analysis.

Adverse childhood experiences

Early life adversity was measured using the ACE scale (Dube et al., 2003; Felitti et al., 1998) which outlines 10 categories of traumatic experiences that are strongly linked with long-term negative outcomes such as mental health disorders and chronic disease (Hughes et al., 2017). Categories relate to child abuse, neglect, and household dysfunction including parent substance use problems, witnessing domestic violence, family member in jail, family member with mental illness and loss of a parent. The abuse and neglect categories are scored on a 5-point Likert scale with responses ranging from “never” to “very often” which were then coded as a binary variable (“yes” or “no”). The household dysfunction category was coded as binary (“yes” or “no”). The total number of ACEs were calculated by summing the number of dichotomous responses resulting in a total continuous score (range 0-10). The ACE scale demonstrated acceptable internal consistency in this sample (Cronbach’s α = 0.67). The use of the ACE scale is widespread and previous reviews support the validity of the ACE scale in research, with moderate agreement between retrospective and prospective measures of adversity (Hardt & Rutter, 2004; Reuben et al., 2016).

Impulsivity

Impulsivity was measured using the behavioral subscale of the Youth Psychopathic Inventory—Short Version (YPI-S) (van Baardewijk et al., 2010), which is an 18-item shortened version of the original 50 item scale. Higher scores indicate higher levels of trait impulsivity, thrill-seeking, and irresponsibility. (e.g., “It often happens that I do things without thinking ahead”). The subscale consists of six items that are scored across a 4-point Likert scale (strongly agree-strongly disagree). The tool has been validated with Australian young people and it correlates well with other measures of impulsivity such as the Child Behavior Checklist (Dolan & Rennie, 2007; Shepherd & Strand, 2016). Exploratory factor analysis conducted in the current sample has replicated the YPI-S factorial structure for young adults and the internal consistency for each dimension is good (α ≥ .70).

Psychological distress

Psychological distress was measured using the Depression, Anxiety, and Stress Scale (DASS-21) (Lovibond & Lovibond, 1995). The DASS-21 is a 21-item self-report tool that examines three interrelated indicators of emotional distress including depression, anxiety and stress. Each scale contains seven items to which respondents use a 4-point Likert scale to report how much they have experienced the symptom (0 = not at all, 4 = very much). In the current study respondents’ psychological distress scores were determined based on their average score across the three DASS-21 subscales (stress, anxiety, and depression). The DASS-21 was designed for application in research and clinical settings (Ng et al., 2007) and has been validated among young adults in Australia (Larcombe et al., 2016). Scores range from 0 to 42, with higher scores indicating greater psychological distress. The DASS total score provides a continuous measure which is suitable as an overall measure of distress and has good internal consistency, good construct validity, and correlates highly with other measures of anxiety, depression and general distress in this sample (Cronbach’s α = 0.87) and similar samples of young people (Henry & Crawford, 2005; Page et al., 2007).

Hazardous alcohol use

Hazardous alcohol use was measured through the Alcohol Use Disorders Identification Test (AUDIT) (Babor et al., 2001). The measure is a self-rating instrument with 10 multiple choice items scored on a 5-point Likert scale that measures consumption (amount and frequency), problems related to use, and dependence. Scores range from 0 to 40, with scores between 8 and 15 indicating a medium level of alcohol problems, scores over 16 indicating a high level of problems and scores over 20 indicating probable dependence (Babor et al., 2001). In this analysis, the total score was used as a continuous measure of hazardous alcohol use and problems. The AUDIT demonstrated good internal consistency in this sample (Cronbach’s α = 0.73). The AUDIT has been well validated, with results showing the measure has good psychometric properties to detect alcohol use problems and disorders in Australian populations and young adults (García Carretero et al., 2016; Kokotailo et al., 2004).

Statistical analyses

Descriptive analyses were run on each of the independent and dependent variables of interest. Sample characteristics were reported by gender and violence perpetration in the past year. Correlation analyses were initially run to examine bivariate associations between past year violence and predictor variables. Subsequently a sequential logistic regression was applied to examine the relative associations between past-year violent behavior and ACEs impulsivity, psychological distress, and hazardous alcohol use. Model-building steps were determined a priori, with sociodemographic covariates (gender and SES) added first, then ACEs followed by internalizing (psychological distress) and externalizing (impulsivity) symptoms together, and then hazardous alcohol use was added separately in the final stage (Step 4). This analysis sequence allowed for determining the incremental variance accounted for at each step and examination of the unique contribution of alcohol use to the model above all other factors. All statistical analyses were run in IBM SPSS Statistics Version 25 (IBM, 2017).

Results

Of the 582 participants who completed the baseline survey, 75 did not complete the questions about violent behavior and thus were excluded from this analysis. There were no significant differences between participants who completed the violent behavior self-report measure and those that did not on baseline covariates or for number of ACEs impulsivity, psychological distress, or hazardous alcohol use (ps > .05). All 507 participants included in the analysis had complete data on all other study measures. Multicollinearity was not detected (VIF < 1.4 for all variables).

Sample characteristics

Sample characteristics of participants are outlined in Table 1. The mean age of participants was 18.90 years (SD = .83) and 70% were female followed by male (26%) and TGD (4%). Participants were mostly identified as high socioeconomic background (SES) (49%), followed by medium (38%) then low (13%), and the majority had completed high school (94%). Close to half of the sample reported both working and studying (47%), followed by only studying (35%) or only working (14%) with a small number of people not working or studying at the time of the survey (4%).

Table 1.

Sample Characteristics for Those Who Reported Violence and Those Who Did Not Report Violence

	Reported Violence (n = 65)	Did Not Report Violence (n = 442)	Total (N = 507)
Age (M, SD)	18.83 (.82)	18.91 (.83)	18.90 (.83)
18	28/65 (43.1%)	174/442 (39.4%)	202/507 (39.8%)
19	20/65 (30.8%)	134/442 (30.3%)	154/507 (30.4%)
20	17/65 (26.2%)	134/442 (30.3%)	151/507 (29.8%)
Gender
Female	52/65 (80.0%)	302/442 (68.3%)	354/507 (69.8%)
Male	12/65 (18.5%)	118/442 (26.7%)	130/507 (25.6%)
TGD	1/65 (1.5%)	22/442 (5.0%)	23/507 (4.5%)
Did not complete year 12	6/65 (9.2%)	26/442 (5.9%)	28/507 (5.8%)
Socioeconomic status
Low	11/65 (16.9%)	57/442 (12.9%)	68/507 (13.4)
Med	23/65 (35.4%)	168/442 (38%)	191/507 (37.7%)
High	31/65 (47.7%)	217/442 (49.1%)	248/507 (48.9%)
Ethnic minority	15/65 (23.1%)	103/442 (23.3%)	118/507 (23.3%)
Occupation
Working	10/65 (15.4%)	59/442 (13.3%)	69/507 (13.6%)
Studying	18/65 (27.7%)	157/442 (35.5%)	175/507 (34.5%)
Working and studying	30/65 (46.2%)	210/442 (47.5%)	240/507 (47.3%)
Not working or studying	7/65 (10.8%)	16/442 (3.6%)	23/507 (4.5%)
Independent variables
Early life adversity (M, SD)	3.22 (2.81)	2.07 (1.84)	2.22 (1.94)
Impulsivity (M, SD)	2.36 (0.58)	2.07 (0.60)	2.10 (0.60)
Psychological distress (M, SD)	1.30 (0.61)	0.97 (0.67)	1.01 (0.67)
Hazardous alcohol use (M, SD)	10.86 (7.54)	7.59 (6.42)	8.01 (6.66)

Note. TGD = transgender and gender diverse people.

One in eight participants reported perpetrating at least one act of violence in the past year (Table 2). The most common violent behaviors reported by the sample were hitting someone they lived with or did not live with. Very few people reported they had used a weapon to attack someone they lived with or did not live with or been involved in a gang fight.

Table 2.

Types of Violent Behavior Reported in the Past Year for Male and Female Participants

Violent Behavior	Male (N = 130) Yes (%)	Female (N = 354) Yes (%)	TGD (N = 23) Yes (%)	Total (N = 507) Yes (%)
Hit someone you lived with	5/130 (3.8%)	33/354 (9.3%)	1/23 (4.3%)	39/507 (7.7%)
Hit someone you didn’t live with, with the idea of hurting them	4/130 (3.1%)	21/354 (5.9%)	1/23 (4.3%)	26/507 (5.1%)
Attacked someone you lived with, with a weapon or with the idea of seriously hurting them	2/130 (1.5%)	6/354 (1.7%)	1/23 (4.3%)	9/507 (1.8%)
Attacked someone you didn’t live with, with a weapon or with the idea of seriously hurting them or killing them	0/130 (0%)	2/354 (0.6%)	1/23 (4.3%)	3/507 (0.6%)
Used a weapon or force to rob a person, shop, bank, or other business	0/130 (0%)	0/354 (0%)	0/23 (0%)	0/507 (0%)
Been involved in a gang fight	2/130 (1.5%)	1/354 (0.3%)	0/23 (0%)	3/507 (0.6%)
Threatened or hurt someone to get them to have sex with you	0/130 (0%)	0/354 (0%)	0/23 (0%)	0/507 (0%)
Any violence in the past year	12/130 (9.2%)	52/354 (14.7%)	1/23 (4.3%)	65/507 (12.8%)

Most participants reported more than one ACE (M = 2.18, SD = 1.91) before the age of 18. Among people reporting any ACE, the most frequently reported ACEs related to household dysfunction, such as someone in the household having a mental illness (57%), divorce (34%), or substance abuse problems (28%). This was followed by physical abuse (21%) and emotional abuse (21%), emotional neglect (17%), sexual abuse (17%), physical neglect (14%), witnessing violence in the home (12%), and a family or household member having been incarcerated (3%).

The majority (65%) of young adults scored in the category of high risk for alcohol-related problems on the AUDIT (Babor et al., 2001). Overall, participants in this study reported a mean total score of 8.01 (SD = 6.66) indicating a medium level of alcohol problems. Over one in ten scored in the highest risk category stipulating that alcohol dependence is likely (13%). Almost one third of participants were categorized as high risk of alcohol-related harms (30%) which indicates a harmful pattern of alcohol use that may require brief intervention, further monitoring and diagnostic evaluation (Babor et al., 2001).

Bivariate correlations demonstrated past year violence was significantly correlated with ACEs (r = .20, p < .001), impulsivity (r = .17, p < .001), psychological distress (r = .16, p < .001), and hazardous alcohol use (r = .16, p < .001), but not gender (r = .03, p = .478) or SES (r = –.03, p = .563) *see Supplementary materials for more information.

Sequential logistic regression

Details about the sequential logistic regression findings are outlined in Table 3. In the baseline model, there was no evidence that gender or SES variables were associated with increased odds of violence. In the next stage of model building, the number of reported ACEs was significantly associated with increased odds of reporting past-year violence. In the following stage, the number of ACEs and impulsivity, but not psychological distress, were significantly associated violence. The final model was significant (χ2 = 40.23, df = 8, p < .001) with good fit to the data (Hosmer & Lemeshow: χ2 = 3.52, df = 8, p = .898). This means that the model significantly explained 14% (Nagelkerke R2) of the variance in violence and correctly classified 87% cases. Female gender was significantly associated with increased odds of violent behavior in the final model only, once accounting for other factors. ACEs and higher levels of alcohol use were significantly and independently associated with increased odds of violence among young adults. With every unit increase in AUDIT total score (range 0-29) the odds of reporting violence increased by 5% and with every additional ACE reported (range 0-10) the odds of reporting violent behavior increased by 22%.

Table 3.

Results of the Hierarchical Logistic Regression Models Predicting Past Year Violent Behavior

		OR	95% CI	Cox & Snell R Square	Nagelkerke R Square	p
Step 1				.011	.020	.233
	Male*
	Female	1.702	.876-3.304			.116
	TGD	.431	.053-3.494			.430
	Low SES*
	Medium SES	.678	.309-1.485			.331
	High SES	.715	.337-1.516			.382
Step 2				.047	.088	.000
	Male*
	Female	1.812	.919-3.571			.086
	TGD	.328	.039-2.732			.303
	Low SES*
	Medium SES	.883	.390-1.998			.766
	High SES	1.110	.496-2.485			.799
	Adverse childhood experiences	1.321	1.167-1.496			.000
Step 3				.065	.121	.008
	Male*
	Female	1.907	.952-3.820			.069
	TGD	.278	.033-2. 361			.241
	Low SES*
	Medium SES	1.030	.446-2.377			.945
	High SES	1.190	.525-2.697			.678
	Adverse childhood experiences	1.214	1.060-1.392			.005
	Impulsivity	1.762	1.090-2.848			.021
	Psychological distress	1.354	.872-2.101			.177
Step 4				.080	.150	.004
	Male*
	Female	2.094	1.035-4.239			.040
	TGD	.303	.035-2.651			.281
	Low SES*
	Medium SES	.941	.403-2.563			.889
	High SES	1.116	.486-2.563			.796
	Adverse childhood experiences	1.220	1.061-1.403			.005
	Impulsivity	1.475	.895-2.430			.128
	Psychological distress	1.289	.824-2.017			.266
	Hazardous alcohol use	1.054	1.011-1.098			.012

Note. * indicates the reference group.

Discussion

This study examined the role of hazardous alcohol use in relation to past year violent behavior after accounting for the impact of a number of known risk factors including ACEs, heightened impulsivity and psychological distress. The study found that hazardous alcohol use remains an important proximal predictor of violence perpetration even when accounting for other influences. This supports previous work showing that young adults who meet diagnostic criteria for alcohol use disorders account for a large proportion of violence in the community (Arseneault et al., 2000). While previous work has focused specifically on IPV during young adulthood less is known about rates of self-reported violence among young adults more broadly. This research provides up to date evidence about distinct violent behaviors while also allowing for cross-cultural comparison with previous work utilizing similar methodologies. Rates of violence among young adults in this Australian sample were relatively high with one in eight respondents (13%) reporting at least one act of violence in the past year, most commonly hitting someone with the intention of hurting them (i.e., assault). This is comparable to previous research showing one in six Australian young adults (aged 19-20; 16%) (Smart et al., 2003, p. 44) and one in nine young adults in the U.S. report past-year violent behavior (aged 18-19; 11%) (Loeber et al., 2017). Research from the Dunedin Multidisciplinary Health and Development Study found the prevalence of past year violence (defined as two or more different violent behaviors and court-recorded violence) among young adults aged 21 was approximately one in ten (10%) (Arseneault et al., 2000). Taken together, the findings here align with evidence that self-reports of violent behavior during young adulthood have remained relatively stable in recent decades across different studies conducted in Australia, New Zealand, and the United States.

These results demonstrated that for every additional ACE experienced, the odds of perpetrating violence increased by 22%. While the prevention of adversity first and foremost is optimal, the complexity of this challenge means that it is also important to provide intervention and support for those young people who have been exposed to trauma (Barrett et al., 2013; Fox et al., 2015). The most frequently reported ACEs related to someone in the household having mental illness or substance use problems, and parental separation. Experiences of living with someone who has been imprisoned was not frequently reported (1 in 33) but experiences of emotional and physical abuse were relatively common in this group of young adults (1 in 5). A study of ACEs experienced by 22,575 justice-involved young people found that each additional ACE increased the odds of being a serious chronic violent offender by 35% (Fox et al., 2015). Histories of maltreatment are characteristic of young adults engaging in violent crime (Dean et al., 2015; Derzon, 2010; Lawler et al., 2020a) however associations between early trauma and violence are not straightforward. The relationship is not direct, it is mediated by other factors such as how young people process and respond to their experiences of trauma (Faulkner et al., 2014). There is potentially an indirect association between ACEs and violence that is related to co-occurring alcohol use, as research suggests that people with post-traumatic-stress-disorder who use alcohol have more difficulties managing their anger (Barrett et al., 2013; Elbogen et al., 2010).

This study found there was a significant positive relationship between impulsivity, psychological distress and violence on the bivariate level, however, psychological distress was not significant in the regression accounting for early adversity. Reviews of personality risk factors for violence highlight both impulsivity and depressed mood as important predictors aggression and violence (Marcus, 2007). However, research examining the link between internalizing problems and violence remains mixed (Elbogen & Johnson, 2009; Fazel et al., 2015). The finding that there was not an independent link between psychological distress and violent behavior in the current study is consistent with previous work examining young adults with alcohol dependence. This research has shown that violent behavior can be best explained by alcohol use prior to the event rather than mental health problems or a history of conduct disorder (Arseneault et al., 2000). Similarly, our study found impulsivity was a robust predictor of violent behavior after controlling for psychological distress which supports previous research (Zhou et al., 2014). Yet, impulsivity did not predict violence after accounting for hazardous alcohol use. The cross-sectional nature of this study means it is not possible to comment on the direction of the alcohol-violence relationship here (Lawler et al., 2020b). Notwithstanding, it does suggest that the proximal role of alcohol is more important risk factor for violence during young adulthood than personality. As these results suggest, the role of impulsivity in violence may be better explained by hazardous alcohol use where impulsive personality increases the likelihood of hazardous alcohol use which in turn increases aggression. Moreover, it may be that a separate underlying mechanism common to hazardous alcohol use, heightened impulsivity and violence (such as problems with emotional regulation) drives all three behaviors (Garofalo & Wright, 2017).

The role of alcohol may also be more salient in predicting violent behavior in young adulthood because of increased prevalence in heavy drinking during this time (alcohol use becomes legal at age 18 in Australia) (Australian Institute of Health and Welfare, 2017). This study demonstrated the odds of reporting violence increases by 5% with each unit increase in total AUDIT score. To add to interpretability, every one standard deviation increase in AUDIT scores is associated with a 42% increase in risk for reporting past year violent behavior. This study contributes to previous research demonstrating that hazardous alcohol use is associated with related spikes in self-reported antisocial behavior and psychopathic features during young adulthood (Hammerton et al., 2017; Hawes et al., 2015). Drinking at hazardous levels may begin with enhancement motives but then the disinhibiting or direct (psychopharmacological) effects of the drug increase the risk of responding violently to a perceived threat (Barrett et al., 2011; Goldstein, 1985).

An alternate motivation for hazardous alcohol use among young people who are aggressive is to self-medicate distress resulting from the consequences of their behavior (e.g., rejection) (Fite et al., 2007; Kaplan et al., 2001). While research shows young people who are aggressive tend to drink more for enhancement rather than coping motivations (Kuntsche et al., 2006) there is evidence that both motivations (enhancement and coping) are associated with increased risk of alcohol-related aggression in university samples (Mihic et al., 2009; Øverup et al., 2015). Specific motivational influences aside, it is clear the context of hazardous alcohol use is an important contributor to violent behavior during young adulthood. Relevant prevention and treatment programs should be trauma-informed, address hazardous alcohol use and be implemented to adolescents and young adults early and widely across multiple settings.

Limitations and directions for future research

There are limitations to be acknowledged for this study. Due to the cross-sectional design it is not possible to determine the direction of the effect, it may be that people who are violent are more likely to drink alcohol. A longitudinal design would facilitate inferences about causality (Lawler et al., 2020b). Specifically, future research should investigate the explanatory relationships and potential underlying mechanisms between ACEs hazardous alcohol use, and violent behavior in young adulthood. A further limitation was the convenience sampling method via social media, which resulted in oversampling of female participants from high socioeconomic backgrounds. Interestingly, when the relationship between alcohol and violence is accounted for a significant relationship between female gender and violence emerged. However, the findings relating to gender effects should be interpreted with caution given the majority female respondents, and we note the association between gender and violence only emerged in the final model with wide confidence intervals. While young men are typically the targets of efforts to reduce aggression and violence, these findings highlight it is essential that young women are also the focus of preventive interventions. Future studies should investigate this further by replicating this work in a larger and more representative community sample. This study could also be extended by examining the role of community-level risk factors in the relationship between alcohol use and violence during young adulthood, such as neighborhood and peer influences.

These findings demonstrate high rates of violent behavior among young adults in Australia and highlight the importance of developing and delivering targeted violence prevention and early intervention programs for this group. Reviews of the literature confirm the potential for school-based prevention programs in reducing aggressive and impulsive behavior such as bullying, substance use and other conduct problems during adolescence (Cox et al., 2016; Kelly et al., 2020; Newton et al., 2020). Research demonstrates the intergenerational continuity of alcoholism and aggression which both influence children’s aggression and risk for later alcohol use-disorders (Fuller et al., 2003). Further, there is evidence for potential generational benefits on the offspring of young people who receive effective prevention interventions (Hill et al., 2020). Drug prevention initiatives that target executive functioning and improving socialization skills may be particularly beneficial (Giancola & Parker, 2001). Given relatively high rates of violent behavior during young adulthood, it is critical to shift the focus from response-based efforts (i.e., rehabilitation, punishment) to investing in violence prevention through trauma-informed interventions that focus on alcohol (Neville et al., 2014). Prevention interventions have significant potential to equip young people with the coping resources they need before they transition into young adulthood, a period of high-risk for both alcohol use and violent behavior.

Conclusion

This study has demonstrated the relative importance of hazardous alcohol in the perpetration of violent behavior during young adulthood. Violence is a significant challenge for communities, families, and individuals however it is possible to prevent violence with the provision of targeted support. Addressing structural determinants of violence such as exposure to early and cumulative adversity is key to reducing the burden of disease attributable to violence. In addition, individual-level factors such as hazardous alcohol consumption are relevant and important proximal influences on violent behavior during young adulthood. Evidence-based interventions delivered during adolescence that effectively prevent violence during young adulthood are critically needed to support people at risk of alcohol-related harm.

Footnotes

Author Note

Siobhan Lawler is supported by a Matilda Centre PhD Scholarship.

Declaration of Conflicting Interests

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Siobhan Lawler

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Siobhan Lawler, BA Psych/Crim (Hons) is a research assistant and PhD candidate at the Matilda Centre for Research in Mental Health and Substance Use at the University of Sydney. Her research examines the relationship between alcohol and other drug use, aggression and violence among young people. Siobhan coordinates of the Crime Research Portfolio at the Matilda Centre.

Lexine Stapinski, PhD, is a senior research fellow and lead in intervention and implementation at the Matilda Centre for Research in Mental Health and Substance Use, University of Sydney. She is a clinical psychologist and NHMRC Translating Research into practice fellow. She leads a program of research to innovate, evaluate, and translate interventions to reduce the burden of mental and substance use disorders.

Maree Teesson, AC, PhD, is a director of the Matilda Centre, a director of the NHMRC Centre of Research Excellence in Prevention and Early Intervention in Mental Illness and Substance Use (PREMISE), and an NHMRC principal research fellow at the University of Sydney. Maree is known nationally and internationally for her research on the comorbidity between mental health and substance use disorders.

Katrina Prior, PhD, is an Australian Rotary Health postdoctoral research fellow at the Matilda Centre for Research in Mental Health and Substance Use. Her research primarily focuses on reducing co-occurring anxiety and hazardous alcohol use among young people via online interventions.

Miguel Basto-Pereira, PhD, is a researcher and a lecturer at the ISPA—University Institute (Lisbon, Portugal). He is the codirector of the Master in Forensic Psychology program. He has published several works about the maltreatment-crime link, criminal careers, and the factors promoting desistance from crime. He is the coordinator of the International Study of Pro/Antisocial Behavior in Young Adults.

Nicola Newton, PhD, is an associate professor and director of Prevention at the Matilda Centre for Research in Mental Health and Substance Use at the University of Sydney. She leads a program of research in the prevention of substance use and mental health problems in adolescence with a particular focus on eHealth and translation.

Emma L. Barrett, PhD, is a senior research fellow and psychologist at the Matilda Centre for Research in Mental Health and Substance Use. She is also a program lead for Trauma and Crime Research at the Centre, where she leads a program of research examining the impacts of trauma across the lifespan.

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