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. Author manuscript; available in PMC: 2022 Aug 1.
Published in final edited form as: Psychol Addict Behav. 2021 Jun 10;35(5):501–513. doi: 10.1037/adb0000733

Individual Differences in the Associations between Risk Factors for Alcohol Use Disorder and Alcohol Use-Related Outcomes

Angela M Haeny 1, Ralitza Gueorguieva 1,2, Asti Jackson 1, Meghan E Morean 1, Suchitra Krishnan-Sarin 1, Kelly S DeMartini 1, Godfrey D Pearlson 1, Alan Anticevic 1, John H Krystal 1, Stephanie S O’Malley 1
PMCID: PMC9211405  NIHMSID: NIHMS1703231  PMID: 34110841

Abstract

Background.

Family history of alcohol use disorder (FH+) and impulsivity-related traits are known risk factors for problem drinking that have been investigated in predominately White samples. This cross-sectional study examined whether these risk factors vary by sex in the overall, majority White sample and in a Black subsample.

Methods.

A model building regression procedure was used to investigate the combined effect of FH+ and impulsivity-related traits on alcohol quantity, frequency, and problems by sex (overall sample: N=757, 50% female, 73% White, agemean=33.74, SD=11.60; Black subsample: n=138, 47% female, agemean=33.60, SD=9.87).

Results.

Overall Sample.

No sex differences were found in the compounding effects of FH+ and impulsivity-related traits on alcohol outcomes. Males reported more physical, social, and overall alcohol-related problems than females. FH+ was positively associated with all alcohol-related consequences. Poor self-regulation was the only trait associated with all alcohol outcomes.

Black Subsample.

A three-way interaction suggested a negative association between inhibition and frequency of alcohol use among FH+ males only. A two-way interaction also suggested impulse control was associated with more interpersonal alcohol-related problems among males only. Main effects were also found in the expected direction such that higher impulsivity and FH+ were associated with poorer alcohol outcomes.

Conclusion.

These findings suggest no sex differences in the overall sample in the interactive effects of established risk factors for AUD on alcohol outcomes, and that poor self-regulation may be key for personality-targeted alcohol prevention and intervention programs. Preliminary findings of sex differences in the Black subsample should be replicated.

Keywords: Black/African American, sex, family history of alcohol use disorder, impulsivity-related traits, alcohol outcomes

1.1. Introduction

Alcohol use disorder (AUD) is a highly prevalent and disabling disorder. Almost one-third of adults in the United States of America (U.S.) may be affected at some point in their lifetime (Grant et al., 2015). The high rate of AUD suggests the need to understand factors associated with risk for alcohol use and problem drinking. Etiological research implicates a positive family history of AUD (FH+) as conferring risk for problem drinking (e.g., Goodwin et al., 1973; Mellentin et al., 2016). Impulsivity, a heterogenous construct with many measures used to assess it (Berg et al., 2015; Dick et al., 2010; Sharma et al., 2014), is another well-established risk factor for the development of problem drinking (e.g., Zucker et al., 2011). Broadly, impulsivity has been defined as the tendency to act before considering the consequences of one’s actions (Berg et al., 2015; Dick et al., 2010; Moeller et al., 2001; Nigg, 2016). Research linking impulsivity with family history of AUD suggests that FH+ individuals tend to be more impulsive (e.g., DeVito et al., 2013; Saunders and Schuckit, 1981; Sher et al., 1991). Given the heterogeneity of the impulsivity construct and the many measures used to assess it, there is a need to identify specifically which impulsivity-related traits interact with family history status to confer risk for alcohol use and problems. Recent research using the same datasets as the present study begins to fill this gap in the literature by providing evidence of a positive association between impulsivity and alcohol-related problems among FH+ individuals (Haeny et al., 2020). Specifically, positive associations were found between poor self-regulation (assessed by the Barratt Impulsiveness Scale) and interpersonal alcohol-related consequences among FH+ individuals only, and between poor self-regulation and impulse control alcohol-related problems regardless of family history status though the effect was stronger among FH+ individuals. However, it remains to be known whether this pattern of associations hold regardless of sex.

1.2. Sex Differences

An extensive literature exists related to sex differences in risk factors and patterns of alcohol use (Agabio et al., 2017). National epidemiologic data indicate males (36%) have a higher prevalence of lifetime AUD than females (22.7%;Grant et al., 2015). Despite having a lower lifetime prevalence, females who drink experience more negative health consequences of drinking than males (Agabio et al., 2016, 2017). This is due, in part, to greater physiological effects of alcohol on females (Agabio et al., 2017). For example, females reach a higher blood alcohol concentration after consuming the same amount of alcohol as males (e.g., Nolen-Hoeksema and Hilt, 2010), and experience greater cognitive impairment, sleepiness, and higher subjective response to the intoxicating effects of alcohol after consuming fewer drinks relative to males (e.g., Arnedt et al., 2011; Miller et al., 2009). Further, females have quicker progression from first use of alcohol to the development of AUD (also referred to as telescoping) (Agabio et al., 2017), suggesting potential differences in the risk pathway for developing AUD. Females also experience higher rates of social stressors (e.g., stigma related to their alcohol use; gender discrimination) that contribute to risk for alcohol use and problems (Keyes et al., 2010; Keyes et al., 2019; Otiniano Verissimo et al., 2014).

Regarding sex differences in FH+ and risk for alcohol problems, early research suggested a stronger effect in females of FH+ status on AUD diagnosis, negative alcohol consequences, and alcohol quantity and frequency (Sher et al., 1991). In addition, other researchers found a stronger association between maternal and paternal AUD and alcohol problems among female offspring compared to male offspring (Sørensen et al., 2011) and that females were more vulnerable to the impact of multiple parents with AUD (Yoon et al., 2013). In contrast, an earlier epidemiological study suggested FH+ males were at greater risk of alcohol problems than FH+ females (Mathew et al., 1993) and a more recent study suggested a stronger impact of paternal AUD on alcohol problems among male than female offspring with no sex differences in the impact of maternal AUD on offspring alcohol problems (Long et al., 2018). Thus, the literature remains mixed on whether risk for alcohol problems is greater among FH+ females or males.

In terms of impulsivity-related traits, males generally score higher than females (Nolen-Hoeksema & Hilt, 2010; Sher et al., 1991). Regarding the association between impulsivity-related traits and alcohol use, although some findings have been mixed (e.g., Cortés Tomás et al., 2014; Rutledge and Sher, 2001; Nederkoorn et al., 2009; Weafer and de Wit, 2014), the overwhelming evidence suggests a similar association between impulsivity-related traits and alcohol outcomes between males and females (Byrnes et al., 1999; Coskunpinar et al., 2013; Wilsnack et al., 2018). Data from a large twin study suggested the association between behavioral undercontrol, a broad domain reflecting impulsivity-related traits, and family history of AUD was similar between males and females but that behavioral undercontrol accounted for a larger proportion of genetic risk for AUD in females than males (Slutske et al., 2002). However, it remains unknown which specific impulsivity-related traits interact with FH+ to increase risk for alcohol use and problems and whether this varies by sex. Additionally, most of the literature examining sex differences in risk for alcohol use and problems is based on majority White samples. Given differences in the prevalence of alcohol use and problems across racialized groups (Grant et al., 2015), it is important to investigate these associations in other racial groups including Black adults.

1.3. Risk for Alcohol Use and Problems among Black Adults

Race is a social factor that serves as a proxy for the effects of racism on health (Goodman, 2000; Yearby, 2018). Racism explains the wealth gap across race in the U.S. and why Black people are disproportionately more likely to experience other social stressors such as living in low income neighborhoods, unemployment, and incarceration (Gee et al., 2019; Zambrana, 2017). Given the interactive effects of stress and impulsivity on problem drinking (Fox et al., 2010), the added stress of racism and its associated consequences may contribute to the differential effects of other risk factors for alcohol use outcomes among Black adults.

Although Black adults tend to have lower lifetime prevalence of AUD relative to other racial groups (Grant et al., 2015), Black people who consume alcohol tend to be at comparable and or even higher risk for negative alcohol-related consequences (e.g., physical injuries, accidents, alcohol-related illnesses, interpersonal problems). Further, there is substantial evidence that alcohol use trajectories vary across racialized groups (see Zapolski et al., 2014 for a review). Examples include, Black youth tend to have a later age of drinking onset compared to White youth (e.g., Bachman et al., 1991; Johnson et al., 2005; Johnston et al., 1994; Zapolski et al., 2014), and the rates of any and heavy use are much lower for Black compared to White young adults (e.g., Meilman et al., 1994; SAMHSA, 2011; Zapolski et al., 2014). Differences in alcohol use patterns across racialized groups suggest the possibility that factors associated with increased risk for problem drinking, such as family history of AUD and impulsivity-related traits, may function differently as well.

FH+ is a well-established risk factor for alcohol use and problems, but much of the existing research has been conducted using predominately White samples (Cotton, 1979; Sher et al., 1991; Stone et al., 2012). The limited research on the impact of FH+ on problem drinking among Black individuals is old. Early research found a stronger association between FH+ and heavy drinking among Black compared to White individuals even after adjusting for sociodemographic variables (Darrow et al., 1992). However, another older study found risk for problem drinking based on FH+ decreased to a greater extent with age among Black compared to White individuals (Russell et al., 1990). The lack of recent research highlights the need for additional work examining the relation between family history and problem drinking among Black people.

Like research on family history of AUD, most studies investigating impulsivity as a risk factor for problem drinking include predominately White samples and few conduct moderation or subgroup analyses by racialized group (e.g., Adams et al., 2012; Ayer et al., 2011; Clapper et al., 1994; Lopez-Vergara et al., 2016). Further, most research on the association of impulsivity and substance misuse among Black individuals is limited to adolescents or young adults. The available literature suggests the developmental course of impulsivity-related traits may differentially impact subsequent alcohol and drug use such that some impulsivity-related traits (e.g., sensation seeking, thrill and adventure seeking) may increase risk for early substance use for White but not Black adolescents (Cooper & Wood, 2003; Hittner & Swickert, 2006; Pedersen et al., 2012). However, other researchers have found similar trajectories in impulsivity-related traits (risk taking propensity and impulsivity assessed by the Eysenck Impulsivity Subscale, version 7; EI-7 subscale; Eysenck et al., 1985) between Black and White individuals (Collado et al., 2014). It is possible that the inconsistency in findings may be due, in part, to the use of various measures that tap different fundamental aspects of impulsivity across studies. Given the heterogeneity of impulsivity as a construct, it is important to identify specifically which facets of impulsivity are associated with increased alcohol use and problems among Black individuals.

Given that unpublished data from National Epidemiological Survey on Alcohol Related Conditions (Grant et al., 2015) suggests a wider gap in the lifetime prevalence of AUD between Black males (29%) and Black females (16%) and the underrepresentation of Black people in this literature, the present study also investigated sex differences in risk for alcohol use and problems among Black male and female adults.

1.4. Study Objectives

The aim of this study was to investigate whether there are sex differences in the association between common AUD risk factors (i.e., family history and impulsivity-related traits) for alcohol use and consequences. Specifically, we investigated the association of FH+, impulsivity-related traits (based on the most commonly used measurement approaches), and their interactions with quantity and frequency of alcohol use and alcohol-related problems and whether these associations were moderated by sex. These associations were also investigated in a Black subsample as preliminary data for future research. Given the inconsistent literature on sex differences in familial liability for alcohol problems and the limited inclusion of Black participants in research in this area, these analyses were exploratory. Before investigating the associations of family history and impulsivity on alcohol outcomes, we tested for measurement invariance of each of our measures across sex to increase our confidence that any sex differences found were not due to measurement bias.

2.1. Methods

2.2. Participants

The data were drawn from two center projects, the Transdisciplinary Tobacco Use Research Center (TTURC) and the Center for the Translational Neuroscience of Alcohol (CTNA). A detailed description of these data can be found in Haeny et al. (2020). Briefly, the objective of TTURC was to evaluate factors associated with failure to quit smoking to inform smoking cessation treatment efforts. TTURC participants were enrolled based on interest in smoking cessation. The objective of CTNA was to understand neurobiological risk for AUD to inform AUD treatment. CTNA participants consisted of current drinkers at low and high risk for AUD based on family history status. Secondary analysis of the center data has been approved by the Institutional Review Board at Yale University. The combined sample consisted of 757 participants (50% female, 73% White, mean age = 33.74, SD = 11.60). The subgroup analyses of Black participants (n = 138) were 47% female with a mean age = 33.60, SD = 9.87. Demographic characteristics of the sample are in Table 1.

Table 1.

Sample Characteristics

Black Subsample (n = 138) Overall Sample (N = 757)
Female (N = 64) Male (N = 74) Female (N = 375) Male (N = 382)
Demographics
 Age 33.19 (10.13) 33.96 (9.70) 33.43 (12.04) 32.29 (10.50)
 ≥12 years of education 88.89% 95.77% 93.75% 96.74%
 Black 100% 100% 17.49% 19.84%
 American Indian/Alaska Native 0.55% 0.80%
 Asian 2.73% 1.88%
 Multiracial 0.82% 1.07%
 Other 3.28% 4.56%
 White 75.14% 71.85%
 Latine 1.79% 7.84% 4.82% 11.19%
Substance use
 Alcohol use frequency
  1–11 annually 12.90% 16.44% 13.22 % 12.30%
  1–3 monthly 17.74% 13.70% 27.27% 17.76%
  1–2 weekly 29.03% 15.07% 27.27% 25.68%
  3–4 weekly 14.52% 19.18% 13.77% 16.67%
  5–6 weekly 12.90% 21.92% 10.19% 18.31%
  Daily 12.90% 13.70% 8.26% 9.29%
 Drinks per day
  1 14.75% 8.33% 15.08% 12.12%
  2 32.79% 19.44% 32.40% 24.24%
  3–4 21.31% 22.22% 26.54% 23.14%
  5–6 16.39% 22.22% 13.41% 20.66%
  7+ 14.75% 27.78% 12.57% 19.83%
 SIP alcohol problems
  Physical .84 (1.51) 1.22 (1.96) 0.65 (1.35) 1.23 (1.83)
  Interpersonal 0.48 (1.10) 1.08 (2.19) 0.38 (1.16) 0.77 (1.71)
  Intrapersonal 1.25 (1.83) 1.30 (2.04) 0.82 (1.53) 1.30 (1.93)
  Impulse control .95 (1.25) 1.61 (1.96) 0.80 (1.17) 1.40 (1.58)
  Social responsibility 1.02 (1.92) 1.56 (2.44) 0.65 (1.50) 1.33 (2.10)
  SIP total 4.55 (6.60) 6.77 (9.78) 3.30 (5.91) 6.02 (8.22)
 Current smoker 56.25% 67.57% 52.53% 53.66%
 Family history of AUD 56.25% 47.30% 50.13% 43.46%
Impulsivity
 BSCS: Impulse control 10.15 (4.66) 12.07 (4.19) 11.23 (4.48) 11.92 (3.62)
 BIS-11
  Poor self-regulation 8.57 (2.57) 8.21 (2.07) 8.18 (2.21) 8.72 (2.29)
  Impulsive behavior 7.25 (2.32) 7.08 (2.23) 7.21 (2.05) 7.64 (2.20)
 BIS-BAS
  Inhibition 11.17 (2.64) 10.37 (2.48) 11.69 (2.33) 10.71 (2.41)
  Drive 9.25 (2.07) 9.25 (1.71) 8.81 (1.87) 8.92 (1.94)
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Note. BSCS = Brief Self-Control Scale. BIS-11 = Barratt Impulsiveness Scale. BISBAS = Behavioral Inhibition System/Behavioral Activation System. SIP = Short Inventory of Problems. AUD = alcohol use disorder. CTNA = The Center for the Translational Neuroscience of Alcohol sample. TTURC = The Transdisciplinary Tobacco Use Research Center sample.

2.3. Measures

All measures included in this study were assessed in both the TTURC and CTNA. Demographic information assessed included race, sex, age, and socioeconomic status. Sex and race were defined based on self-report. For the subgroup analyses, participants who endorsed Black regardless of whether they endorsed another race (e.g., Black and White) were categorized as Black. Quantity and frequency of alcohol consumption were assessed based on the National Institute on Alcohol Abuse and Alcoholism, 2003 Council Task Force on Recommended Alcohol Questions (National Institute on Alcohol Abuse and Alcoholism, 2003). Alcohol quantity was a 5-level ordinal variable: 1 drink per day (1), 2 drinks per day (2), 3–4 drinks per day (3), 5–6 drinks per day (4), and 7 or more drinks per day (5). Alcohol use frequency was a 6-level ordinal variable: 1–11 times a year (1), 1–3 times monthly (2), 1–2 times weekly (3), 3–4 times weekly (4), 5–6 times weekly (5), and every day (6). Alcohol problems were assessed using the Short Inventory of Problems (Miller, Tonigan, & Longabaugh, 1995) was used to assess alcohol related problems. The Short Inventory of Problems consists of 15-items assessing five areas: physical (e.g., “Because of my drinking, I have not eaten properly”; α = .82), interpersonal (e.g., “My drinking has damaged my social life, popularity, or reputation as a person”; α = .86), intrapersonal (e.g., “My drinking has gotten in the way of my growth as a person”; α = .86), impulse control (e.g., “When drinking, I have done impulsive things that I regretted later”; α = .74), and social responsibility (e.g., “I failed to do what is expected of me because of my drinking”; α = .87). The Short Inventory of Problems also yields a total score (α = .95).

Family History of AUD.

Positive family history of AUD (FH+) was a dichotomous variable (1 = yes, 0 = no). FH+ was assessed by the Psychiatric Family History by interview (i.e., FHAM; Rice et al., 1995) in the CTNA. Participants were asked, “Has drinking ever caused any of your relatives to have problems with health, family, job or police?” Participants who endorsed this question also specified the relationship of their relative and information needed to assess whether the relative met DSM-IV AUD criteria. In CTNA, FH+ was determined if participants reported 1) their biological father and 2) at least one other first- or second-degree biological relative (excluding their biological mother) met criteria for AUD. CTNA participants were considered family history negative (FH-) if they could reliably report their family history for at least 3 first-degree biological relatives and none of their first- or second-degree relatives met criteria for AUD.

In the TTURC, the following question was used: “Has any of your blood relatives ever had what you would call a significant drinking problem? For example, have they had at least one of the following problems due to their drinking behavior: Legal problems (e.g. traffic violations, disorderly conduct, public intoxication), health problems (e.g., blackouts, DTs, cirrhosis of the liver), marital or family problems, work problems, received treatment for alcoholism (e.g., AA, Antabuse, detox), or social problems (e.g., fights, loss of friends)?” TTURC participants were FH+ if they reported at least 1 first- and 1 second- degree relative with a history of problem drinking, and FH- if they reported none of their blood relatives had a history of problem drinking.

Impulsivity.

Three brief measures of impulsivity were used in this study and are described in greater detail elsewhere (Haeny et al., 2020; Morean et al., 2014). The brief version of the Barratt Impulsiveness Scale (Patton et al., 1995) consisted of 8-items and two factors: poor self-regulation (e.g., “I am self-controlled” [reverse scored]; α = .74) and impulsive behavior (e.g., “I do things without thinking”; α = .72). The brief version of the Behavioral Inhibition and Activation Scales (Carver and White, 1994) consisted of 13 items and four factors: behavioral inhibition (“I worry about making mistakes.”; α = .73), drive (“When I want something, I usually go all out to get it.”; α = .76), fun seeking (“I often act on the spur of the moment.”; α = .69), and reward responsiveness (“When I’m doing well at something I love to keep at it.”; α = .58). It was decided a priori to exclude any subscales with coefficient alphas <.70, so the fun seeking and reward responsiveness were dropped from subsequent analyses. The brief version of the Brief Self-Control Scale (Tangney et al., 2004) consisted of 7 items and two subscales: impulse control (“I can’t stop myself from doing something, even if I know it’s wrong.”; α = .81) and self-discipline (“People would say I have iron self-discipline.”; α = .60). The self-discipline subscale was excluded due to coefficient alpha < .70.

2.4. Data Analysis

Multigroup confirmatory factor analyses in MPlus 8.1 (Muthén & Muthén, 2018) were used to investigate measurement invariance across female and male individuals on the problem drinking (i.e., Short Inventory of Problems) and impulsivity measures (i.e., Barratt Impulsiveness Scale, Behavioral Inhibition and Activation Scales, Brief Self-Control Scale) in the combined sample. The ordinal nature of the impulsivity measures was accounted for in the factor analyses.

We first tested configural invariance by constraining the factor structure to equality across sex. Configural invariance was established if the model fit indices were within range: CFI and TLI > .90; RMSEA < .07; and SRMR < .08. We then tested metric invariance by constraining the factor loadings to equality across sex. Metric invariance was determined if change in model fit from the configural model did not exceed the cutoffs: RMSEA ≥ .015, CFI ≥ −.01 or SRMR ≥ .030 (Chen, 2007). Finally, we tested scalar invariance by constraining the item intercepts to equality across sex. Scalar invariance was established if change in model fit from the metric model did not exceed the cutoffs: CFI ≥ −.010 in addition to change in SRMR ≥ .010 or RMSEA ≥ .015 (Chen, 2007).

To test the associations of family history of AUD, impulsivity, and their interactions with alcohol outcomes by sex, analysis of covariance was used for continuous outcomes (i.e., problem drinking assessed by the Short Inventory of Problems) and cumulative logistic regression was used for ordinal variables (i.e., alcohol quantity and frequency variables). The proportional odds assumption was tested and met for alcohol quantity but not alcohol frequency. However, when accounting for unequal slopes in the model, the odds ratios (ORs) for each variable did not vary at each level of the alcohol frequency variable. Given that the test of proportional odds tends to be liberal (Peterson & Harrell, 1990) and the findings do not change, we did not account for unequal slopes in the final model for alcohol frequency. These analyses were conducted in SAS 9.4 (SAS Institute Inc, Cary, North Carolina). The data were evaluated using descriptive statistics including examining the correlations between variables. Log-transformations were used to normalize the residuals for the continuous variables assessed by the Short Inventory of Problems. Notably, 76 participants were dropped from the final analyses because their family history status could not be determined. Results from logistic regression analyses indicated participants dropped from the final analyses were more likely to be lower on impulse control (OR: .94, 95% CI: .89, .99) and were less likely to drink 1–2 times weekly compared to daily (OR: .43, 95% CI: .21, .85). Otherwise, no differences were found between groups.

We used a model building procedure to develop our final models. First the association between each impulsivity factor independently and their interaction with family history of AUD were evaluated to identify which facets of impulsivity to include in the final models. Subfactors of impulsivity that predicted alcohol outcomes independently of other impulsivity factors were included in the final models. The final models also included three-way interaction terms between each facet of impulsivity, family history of AUD, and sex. Non-significant interaction terms were trimmed from the final models so that at each step the model was hierarchically well-formulated. This procedure was used for each alcohol outcome, and all models were adjusted for age, race, ethnicity, years of education, and data source (i.e., TTURC; CTNA). We used the Bonferroni-Holm (Holm, 1979) method to adjust for multiple post-hoc tests within the same model. Both adjusted and unadjusted results are presented in the tables and only effects that remained significant after adjusting for multiple tests are discussed in the results section. Sex differences were also investigated in the Black subsample as preliminary findings. Given that the Black subsample findings were considered preliminary due to the small sample size, we did not correct for multiple tests and bivariate associations were provided between sex, family history status, impulsivity-related traits and alcohol outcomes for the Black subsample only.

3.1. Results

3.2. Measurement Invariance

We found evidence of configural, metric, and scalar invariance by sex for the total Short Inventory of Problems, 5-factor Short Inventory of Problems, Behavioral Inhibition and Activation Scales, Barratt Impulsiveness Scale, and Brief Self-Control Scale including all subscales (Table 2). These findings indicate that the factor structure of each measure, the magnitude of the factor loadings, and the mean responses across items on each factor did not vary across females and males.

Table 2.

Multigroup Confirmatory Factor Analyses Testing Measurement Invariance by Sex for Each Measure in the Combined Sample

CFI RMSEA SRMR ΔCFI ΔRMSEA ΔSRMR
BIS/BAS
 Configural .972 .049 .049
 Metric .971 .047 .051 .001 .002 −.002
 Scalar .962 .049 .052 .009 −.002 −.001
BIS
 Configural .983 .068 .034
 Metric .988 .052 .036 −.005 .016 −.002
 Scalar .981 .053 .039 .007 −.001 −.003
BSCS
 Configural .993 .049 .026
 Metric .985 .065 .033 .008 −.016 −.007
 Scalar .979 .057 .035 .006 .008 −.002
SIP (1 factor)
 Configural .991 .059 .036
 Metric .994 .045 .040 −.003 .014 −.004
 Scalar .994 .041 .041 0 .004 −.001
SIP (5 factors)
 Configural .993 .056 .040
 Metric .995 .047 .041 −.002 .009 −.001
 Scalar .994 .043 .041 .001 .004 0
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Note. BIS/BAS = Behavioral Inhibition System/Behavioral Activation System. BIS = Barratt Impulsiveness Scale. BSCS = Brief Self-Control Scale. SIP = Short Inventory of Problems. Model fit indices: CFI and TLI > .90; RMSEA < .07; and SRMR < .08. Cutoffs for metric invariance: RMSEA ≥ .015, CFI ≥ −.01 or SRMR ≥ .030. Cutoffs for scalar invariance: CFI ≥ −.010 in addition to change in SRMR ≥ .010 or RMSEA ≥ .015.

3.3. Overall Sample: Sex Differences

No interactions were found between sex, FH+, and impulsivity-related traits. Main effects of sex were found such that males reported more overall alcohol-related problems (male Least Square Mean [LSM] = 1.05, SE = .13, female LSM = .79, SE = .13), physical alcohol-related problems (male LSM = .40, SE = .08, female LSM = .28, SE = .08), and impulse control alcohol-related problems (male LSM = .53, SE = .07, female LSM = .37, SE = .07) than females. Main effects of family history status were found for all outcomes except for quantity and frequency of alcohol use and were in the expected direction. Poor self-regulation was associated with all alcohol outcomes and impulsive behavior was only associated with overall alcohol problems and impulse control alcohol-related problems (Tables 3 and 4).

Table 3.

The Association of Family History of Alcohol Use Disorder and Impulsivity and Their Interaction on Alcohol Outcomes

Alcohol Frequency
OR (95% CI)		 Drinks per Day
OR (95% CI)		 SIP Total
β (SE)
Overall Sample
 Main Effects
  Sex (female) .93 (.68, 1.28) 1.00 (.54, 1.03) −.13 (.09) *
  Family History Positive for AUD 1.07 (.78, 1.49) 1.36 (.98, 1.88) .15 (.09) *
  BIS
   Poor Self-Regulation 1.11 (1.02, 1.20) * 1.09 (1.02, 1.17) .17 (.02) *
   Impulsive Behavior .15 (.02) *
  BIS/BAS
   Inhibition 1.05 (.96, 1.14)
   Drive 1.00 (.92, 1.09)
  BSCS
   Impulse Control .90 (.85, .94)
Black Subgroup
 Main Effects
  Sex (female) 1.03 (.53, 2.02) - −.04 (.23)
  Family History Positive for AUD 1.30 (.65, 2.59) - .24 (.24)
  BIS
   Poor Self-Regulation
   Impulsive Behavior
  BIS/BAS
   Inhibition
  BSCS
   Impulse Control .30 (.03)
 Interaction Effects
  Inhibition*male*FH+ .41 (.23, .74)
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Note. Each alcohol outcome was modeled separately. The table displays the results for the final models. Betas are regression coefficients from the final model for each outcome. SE = standard error of regression coefficient. BIS = Barratt Impulsiveness Scale.BIS/BAS = Behavioral Inhibition System/Behavioral Activation System. BSCS = Brief Self-Control Scale.

FH = family history of alcohol use disorder (FH+ = positive). AUD = alcohol use disorder. SIP = Short Inventory of Problems. All models adjusted for age, sex, and data source.

-

= not estimable because it was part of an interaction term.

Bold = p < .05.

*

= effect remains after adjusting for multiple tests.

Table 4.

Differences in the Association of Family History of Alcohol Use Disorder and Impulsivity and Their Interaction on Alcohol Problems

Short Inventory of Problems (SIP) Subscales
Physical
β (SE)		 Interpersonal
β (SE)		 Intrapersonal
β (SE)		 Impulse Control
β (SE)		 Social
β (SE)
Overall Sample
 Main Effects
  Sex (Female) −.11 (.05) * −.09 (.04) −.08 (.05) .14 (.05) * .10 (.05)
  Family History Positive for AUD .10 (.05) * .11 (.04) * .18 (.05) * .11 (.05) * .10 (.05)
  BIS
   Poor Self-Regulation .14 (.01) * .13 (.01) * .17 (.01) * .14 (.01) * .18 (.01) *
   Impulsive Behavior .05 (.01) .08 (.01) .09 (.01) .21 (.01) * .07 (.01)
  BIS/BAS
   Inhibition
   Drive −.02 (.01)
  BSCS
   Impulse Control .004 (.01)
Black Subgroup
 Main Effects
  Sex (Female) −.04 (.12) .52 (.34) −.16 (.12) −.03 (.14)
  Family History Positive for AUD .10 (.13) −.27 (.18) −.66 (.47) .23 (.14)
  BIS
   Poor Self-Regulation −.26 (.03)
   Impulsive Behavior .27 (.04)
  BIS/BAS
   Drive −.05 (.03) .03 (.03) .02 (.03)
  BSCS .55 (.02)
   Impulse Control .27 (.02) .22 (.02) .34 (.02)
 Interaction Effects
  BIS Poor Self-Regulation*FHP .91 (.05)
  BSCS*Sex (female) −.62 (.03)
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Note. Each alcohol outcome was modeled separately. The table displays the results for the final models. All models adjusted for age, sex, and data source. SE = standard error of regression coefficient. BSCS = Brief Self-Control Scale. BIS = Barratt Impulsiveness Scale. BIS/BAS = Behavioral Inhibition System/Behavioral Activation System. SIP = Short Inventory of Problems. FH = family history of alcohol use disorder (FHN = negative, FHP = positive). AUD = alcohol use disorder. BISpsr = poor self-regulation subscale of the Barratt Impulsiveness Scale. BISib = impulsive behavior subscale of the Barratt Impulsiveness Scale.

-

Indicates the effect was not included in the final model. Betas are regression coefficients from the final model for each outcome.

Bold = p < .05.

*

= effect remains after adjusting for multiple tests.

3.4. Black Subsample: Sex Differences

Spearman rank correlations suggested positive associations between impulsivity-related traits and alcohol outcomes (Table 5). Bivariate associations indicated a positive association between inhibition and intrapersonal alcohol-related problems among FH+ females only (Table 6). In addition, a positive association was found between impulsive behavior and impulse control alcohol-related problems, and inhibition and alcohol quantity among FH+ males only.

Table 5.

Spearman Rank Correlation Table for the Black Subgroup

1 2 3 4 5 6 7 8 9 10 11 12 13
1. BIS Poor Self-Regulation 1.00
2. BIS Impulsive Behavior 0.42 1.00
3. BIS/BAS Inhibition 0.10 0.15 1.00
4. BIS/BAS Drive −0.02 0.14 0.16 1.00
5. BSCS Impulse Control 0.14 0.25 −0.04 0.06 1.00
6. SIP: Physical 0.04 0.11 0.01 0.03 0.25 1.00
7. SIP: Interpersonal −0.03 0.13 0.11 0.10 0.13 0.73 1.00
8. SIP: Intrapersonal 0.11 0.25 0.14 −0.03 0.16 0.72 0.75 1.00
9. SIP: Impulse Control 0.16 0.39 0.06 0.05 0.32 0.67 0.70 0.74 1.00
10. SIP: Social Responsibility 0.08 0.26 0.15 0.12 0.20 0.74 0.79 0.81 0.72 1.00
11. SIP Total 0.12 0.29 0.07 0.06 0.32 0.83 0.79 0.89 0.89 0.87 1.00
12. Frequency of Alcohol Use 0.27 0.16 −0.11 0.00 0.22 0.26 0.19 0.21 0.28 0.27 0.29 1.00
13. Drinks Per Day 0.17 0.09 −0.22 0.01 0.11 0.29 0.28 0.25 0.30 0.31 0.32 0.48 1.00
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Note. BSCS = Brief Self-Control Scale. BIS = Barratt Impulsiveness Scale. BIS/BAS = Behavioral Inhibition System/Behavioral Activation System. SIP = Short Inventory of Problems.

Bold represents p < .05.

Table 6.

Bivariate Associations between Impulsivity-Related Traits and Alcohol-Related Consequences among the Black Subsample

Physical β Interpersonal β Intrapersonal β Impulse Control β Social β SIP Total β Alcohol Quantity χ2 Alcohol Frequency χ2
FH+ Females
 BISpsr −0.12 0.14 0.03 0.28 0.12 0.09 0.00 0.00
 BISib −0.15 −0.04 0.02 0.11 −0.05 0.01 0.07 0.21
 BBinh 0.19 0.20 0.40 0.36 0.35 0.32 1.33 0.05
 BBdrive −0.10 0.06 −0.18 −0.06 0.05 −0.08 0.10 0.34
 BSCSic −0.09 −0.15 −0.07 0.04 −0.16 −0.02 0.59 0.15
FH+ Males
 BISpsr 0.03 −0.08 0.09 0.35 0.01 0.14 2.12 3.84
 BISib −0.04 −0.03 0.05 0.39 0.08 0.13 0.15 0.95
 BBinh −0.01 −0.01 −0.01 −0.11 −0.08 −0.08 7.36 0.93
 BBdrive 0.09 0.18 0.08 0.19 0.15 0.14 0.00 0.75
 BSCSic 0.12 0.21 0.04 0.28 0.26 0.22 0.05 1.42
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Note. Standardized regression weights are reported. FH+ = family history positive for AUD. SIP = Short Inventory of Problems.

Bold indicates p < .05.

A three-way interaction was found between sex, FH+, and inhibition on drinks per day in the Black subsample (Figure 1). Specifically, a negative association was found between inhibition and frequency of alcohol use among FH+ males only (slope β = −.30, SE = .10, t28 = −2.93, p = .007). A two-way interaction was found such that impulse control was associated with more interpersonal alcohol-related problems among males only (slope β = .04, SE = .02, t68 = 2.07, p = .04; Figure 2). Main effects were also found and were in the expected direction (Tables 3 and 4).

Figure 1.

Figure 1

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Interaction Effect between Family History, Sex, and Inhibition on Frequency of Drinking in the Black Subsample

Note. BIS/BAS = Behavioral Inhibition and Activation Scales. FH+ = family history positive for alcohol use disorder. FH- = family history negative for alcohol use disorder.

Slope for FH+ females β = −.12, SE = .12, t27 = −.98, p = .34; slope for FH+ males β = −.30, SE .10, t28 = −2.93, p = .007; slope for FH- females β = −.13, SE = .08, t24 = −1.53, p = .14; slope for FH- males β = .002, SE = .08, t31 = .03, p = .98. Inhibition range: FH+ female: 4–15, FH+ male: 6–15, FH- female: 6–16, FH+ male: 5–16.

The shaded regions represent 95% confidence intervals.

Figure 2.

Figure 2

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Interaction between Sex and Impulse Control on the Interpersonal Alcohol Problems in the Black Subsample

Note. BSCS = Brief Self-Control Scale.

Slope for females β = −.001, SE = .01, t52 = −.09, p = .93; slope for males β = .04, SE = .02, t68 = 2.07, p = .04. Impulse contsrol range: female: 4–20, Male: 4–20.

The shaded regions represent 95% confidence intervals.

4. Discussion

This study suggests that in a predominately White sample there are no differences between female and male individuals in the compounding effects of impulsivity-related traits and family history of AUD on quantity and frequency of alcohol use and alcohol-related problems. Although the literature has been mixed on the association between family history of AUD and alcohol outcomes by sex (e.g., Long et al., 2018; Mathew et al., 1993; Sher et al., 1991; Yoon et al., 2013), our findings in a predominantly White sample are consistent with prior studies indicating no differences across sex in risk for alcohol problems among those who are impulsive (e.g., Byrnes et al., 1999; Coskunpinar et al., 2013; Wilsnack et al., 2018). We also replicated prior research (e.g., Sher et al., 1991) indicating males (regardless of family history status or impulsivity-related traits) and family history positive individuals tend to experience more alcohol-related consequences. The only impulsivity-related factor consistently associated with all alcohol outcomes was poor self-regulation. Although other impulsivity-related traits were independently associated with alcohol outcomes, poor self-regulation was the only impulsivity-related trait that remained significant in the context of other impulsivity-related variables. These findings suggest that poor self-regulation may be a better predictor of alcohol use and problems than the other impulsivity-related traits assessed in this study.

Given that racism can result in negative health consequences among Black people in this country, it is imperative that we ensure Black people are represented in research to increase equitable opportunity to reap the benefits from research. To our knowledge, this is the first study to examine associations between family history, impulsivity-related traits, and alcohol outcomes across sex among Black adults. Contrary to our prior findings in the overall sample suggesting associations between FH+, higher impulsiveness, and alcohol consequences (Haeny et al., 2020), our preliminary findings suggest that FH+ Black males who are less inhibited engage in greater frequency of alcohol use. Similarly, despite prior research indicating no sex differences in the association between impulsivity-related traits and alcohol outcomes (Byrnes et al., 1999; Coskunpinar et al., 2013; Wilsnack et al., 2018), our preliminary findings in the Black subsample suggest specifically that impulse control is associated with interpersonal alcohol consequences among males only. Consistent with prior research that family history of AUD is associated with poorer alcohol outcomes (Grigsby et al., 2016; Mellentin et al., 2016; Yoon et al., 2013), our preliminary findings suggest that poor self-regulation was associated with impulse control alcohol consequences among FH+ individuals only. However, none of these prior studies investigated the combination of family history, impulsivity-related traits, sex, and alcohol outcomes among Black adults. Prior research indicating that a greater portion of the variance in genetic risk for AUD is accounted for by impulsivity-related traits in females than males (Slutske et al., 2002) and that sensation seeking and lack of perseverance mediated the association between polygenic risk score and alcohol outcomes (Ksinan et al., 2019) suggests that certain impulsivity-related traits may be more heritable than others. Given that race is a social construct, we would not expect genetic differences in the effect of family history of AUD and impulsivity-related traits on alcohol outcomes across race nor would race be an appropriate moderator to include in genetic studies unless seeking to understand the potential impact of racism on outcomes. It is important to investigate these associations in a Black subsample to increase the representation of Black participants in research seeking to understand risk factors for AUD and because prior research suggests that the persistent stress from racism can have intergenerational effects and impact the expression of biological factors through epigenetics (Sullivan, 2013; Turecki & Meaney, 2016). Thus, it is imperative that we include racially diverse participants in our studies as we seek to further understand risk for AUD and broaden our scope of risk (e.g., racial discrimination, internalized racism) and protective factors (e.g., racial socialization, ethnic identity) assessed. The findings herein on the Black subsample are considered preliminary given the small sample size to investigate three-way interactions; thus, we encourage these findings to be replicated in a larger sample before definitive conclusions are drawn.

This study assumed the combination of risk factors for alcohol use and problems may be impacted by social factors related to anti-Black racism; however, race was the only indicator of racism included in this study. Future research should include other measures of racism (e.g., racial discrimination, racial segregation, racial attitudes) to more precisely identify how racism is impacting risk for AUD. Prior research found that experiences of racial discrimination led to increased anger and decreased self-control which in turn predicted increased substance use among Black adolescents (Gibbons et al., 2012). Another study found that racial discrimination moderated the relation between the impulsivity-related trait lack of premeditation and alcohol use among Black and Asian young adults (Latzman et al., 2013). Future research should examine how the impact of racism and other social stressors impact the combined effect of FH+ and impulsivity-related traits on alcohol outcomes among Black people. Using a multi-stage modeling approach (e.g., Sartor et al., 2007) could inform the point at which personality-targeted intervention programs would be most effective.

Increasing representation of Black, Indigenous, and People of Color in research can involve collecting diverse samples with representation of multiple racialized groups to increase the generalizability of the findings. Researchers interested in investigating differences across racialized groups could be intentional about collecting large enough samples of racially diverse participants to conduct moderator analyses. Given the patterns of alcohol use often vary within racialized groups (e.g., sex, acculturation, social class) researchers may recruit a specific racialized group and examine within group and cultural differences. Further suggestions are provided by Burlew et al. (2019).

Although it would be reasonable to expect each impulsivity measure to be associated with the impulse control alcohol-related problems subscale, this was not the case for inhibition or drive, which highlights the importance of identifying which facets of impulsivity are associated with alcohol-related problems given the heterogeneity of impulsivity as a construct. It seems measures of impulsivity thought to reflect avoidant behaviors (e.g., inhibition) or approach behaviors (e.g., drive) (Gray, 1987; McNaughton & Gray, 2000) are not associated with impulse control alcohol-related problems in this sample. However, measures broadly assessing impulsiveness related to poor self-regulation, impulsive behavior, and impulse control are associated with alcohol-related problems in the impulse control domain.

There are several factors that limit the generalizability of these findings. First, this was a secondary analysis of a geographically-limited convenience sample, and the purpose of the original studies was not to examine sex differences or race subgroup analyses. Although we combined two datasets for a larger sample size to test our hypotheses, the cell sizes decreased when investigating three-way interactions especially in the Black subgroup analyses, so we may have been underpowered to detect effects. In addition, we combined anyone who identified as Black with those who identified as Black and another race, increasing the heterogeneity of the Black subgroup. Further, gender identity was not assessed so it is possible that the findings may differ among those who identify as women, men, or anywhere else on the gender identity spectrum. The findings may not generalize to other facets of impulsivity that were not assessed in this study, and it remains to be investigated whether these findings would hold for behavioral tasks that purport to assess impulsivity. In addition, family history was assessed differently in the two datasets combined for these analyses. To identify specifically which facets of impulsivity interact with family history to confer risk for alcohol use and problems, multiple tests were conducted requiring the need to adjust for multiple comparisons. Although the objective of adjusting for multiple comparisons is to reduce the Type 1 error rate, these adjustments can also result in increasing the Type 2 error rate. Future research could examine these associations using a latent modeling approach to reduce the number of statistical tests. Finally, the directionality of the association between impulsivity and the alcohol outcomes could not be determined in this cross-sectional study.

Despite these limitations, major strengths of this study are that we combined two datasets to provide a larger dataset to investigate sex differences and race subgroup analyses in key risk factors for AUD using multiple alcohol outcomes. Additional research in this area should examine differences in these risk factors across other racial/ethnic groups and the social construct of gender identity using comprehensive assessments of the different facets of impulsivity. In summary, these findings highlight the importance of collecting sample sizes large enough to examine sex differences and race subgroup analyses in the association between risk factors and alcohol-related outcomes. These findings also suggest that personality-targeted alcohol prevention and intervention programs may benefit from targeting components of impulsiveness assessed by the Barratt Impulsiveness Scale, the poor self-regulation scale in particular.

Public Health Significance:

This study suggests the combination of risk factors (family history and impulsivity-related traits) for alcohol use and problems differs across sex in a Black subsample but not in a predominately White sample. Although these findings should be considered preliminary and replicated in future research, they highlight the importance of attending to race and sex when seeking to understand risk pathways for alcohol use disorder.

Acknowledgements:

Supported by P50AA15632, P50DA13334, P50AA012870, R25 DA035163, and T32DA019426 from the National Institutes of Health. Thank you to Drs. Carolyn Sartor and Evonnia Woods for providing feedback on portions of the manuscript.

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