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Social epidemiology of early adolescent alcohol expectancies

BMC Public Health volume 23, Article number: 2502 (2023) Cite this article

Abstract

Purpose

To determine the sociodemographic correlates of alcohol expectancies (i.e., beliefs regarding positive or negative effects of alcohol) in a national (U.S.) cohort of early adolescents 10–14 years old. A second aim was to determine associations between alcohol sipping and alcohol expectancies.

Methods

We analyzed cross-sectional data from the Adolescent Brain Cognitive Development (ABCD) Study (N = 11,868; Year 2). Linear regression analyses were conducted to estimate associations between sociodemographic factors (sex, race/ethnicity, sexual orientation, household income, parental education, parent marital status, religiosity) and positive (e.g., stress reduction) and negative (e.g., loss of motor coordination) alcohol expectancies. Additional linear regression analyses determined associations between alcohol sipping and alcohol expectancies, adjusting for sociodemographic factors.

Results

Overall, 48.8% of the participants were female and 47.6% racial/ethnic minorities, with a mean age of 12.02 (SD 0.67) years. Older age among the early adolescent sample, male sex, and sexual minority identification were associated with more positive and negative alcohol expectancies. Black and Latino/Hispanic adolescents reported less positive and negative alcohol expectancies compared to White non-Latino/Hispanic adolescents. Having parents with a college education or greater and a household income of $200,000 and greater were associated with higher positive and negative alcohol expectancies. Alcohol sipping was associated with higher positive alcohol expectancies.

Conclusions

Older age, White non-Latino/Hispanic race, male sex, sexual minority status, higher parental education, and higher household income were associated with higher positive and negative alcohol expectancies. Future research should examine the mechanisms linking these specific sociodemographic factors to alcohol expectancies to inform future prevention and intervention efforts.

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Background

Previous literature has defined alcohol expectancies as beliefs regarding the positive or negative effects of alcohol [1]. Expectancy theory centers around these beliefs that drinking alcohol can have positive (e.g., stress relief) and negative (e.g., loss of motor coordination) effects [2]. Positive alcohol expectancies in adolescents have consistently been found to be predictive of future alcohol use [3, 4]. Negative alcohol expectancies were previously associated with less drinking [5]; however, more recent studies have shown negative alcohol expectancies to be associated with problematic drinking [3, 6]. Individuals with negative alcohol expectancies may influence their behavior in a manner that self-fulfills the negative effects they expect. The psychological concept of self-fulfilling prophecy was demonstrated in previous studies showing college students with more positive expectancies were more likely to report experiencing those same consequences as a result of their alcohol use that day [7]. This held true for students with more negative expectancies, such as becoming aggressive or embarrassing themselves [7]. Adolescent alcohol use has been associated with altered attention and memory, increased anxiety, and risk-taking behaviors [8]. Risky health behaviors like alcohol use are often initiated during adolescence [9]. Understanding the early correlates of alcohol expectancies in early adolescence is valuable given the role expectancies play in the development of later problematic drinking behaviors. Identifying sociodemographic factors such as sex, race/ethnicity, household income, parent education, and parent marital status will allow for the development of preventive interventions and public health programs.

There is currently limited literature examining the sociodemographic factors associated with alcohol expectancies in adolescence. Previous literature often focused on one sociodemographic factor and its associations with alcohol expectancies but did not focus on early adolescents. For example, previous research has found that men have greater positive and negative alcohol expectancies than women [10, 11]. Race and ethnicity and their associations with alcohol expectancies have been analyzed in 12–18-year-old participants. The studies found that while African-American youth initially had higher positive alcohol expectancies relative to White youth, more positive alcohol expectancies were only predictive of alcohol use in White youth [12, 13]. However, another study in the United Kingdom found that White British children had higher positive alcohol expectancies than Black and Asian British children [14]. Several studies have found that older age is associated with higher positive expectancies [14,15,16]. The current literature has also established positive associations between socioeconomic status and adolescent alcohol use [17]. As with socioeconomic status, higher parental education has been associated with more positive expectancies [14]. Other parental factors such as parental divorce have been associated with increased risk for adolescent drinking onset [18]. Religiosity has been associated with higher negative expectancies [19]. While previous literature has identified alcohol expectancies as a predictive risk factor for alcohol abuse in adult sexual minorities, there is currently scant literature analyzing the associations between sexual orientation and alcohol expectancies in early adolescents [20]. However, previous studies have found that sexual minority adolescents engage in alcohol use earlier and with steeper drinking trajectories into young adulthood relative to heterosexual youth [21].

One analysis of the Adolescent Brain Cognitive Development Study found that older age (within the 10-14-year-old age range) was associated with greater positive and negative alcohol expectancies in linear mixed-effects models [22]. Compared to non-Hispanic Black or Hispanic ethnicity, non-Hispanic white ethnicity was associated with higher positive and negative alcohol expectancies [22]. Male sex was associated with higher negative alcohol expectancies [22]. However, the study did not examine household income, parent education, parent marital status, religiosity, or sexual orientation associations with alcohol expectancies. Furthermore, the study speculated that previous alcohol experience (e.g., alcohol sipping) may impact alcohol expectancies, which was identified as an area of future research [22].

The breadth of literature has been useful in identifying possible sociodemographic factors for exploration in their associations with adolescent alcohol expectancies. However, the current literature has not targeted early adolescents nor focused on a large array of sociodemographic factors. The current study aims to bridge this gap in the literature by first examining the relationship between several sociodemographic characteristics and their associations with alcohol expectancies in a demographically diverse national cohort of early adolescents. Male sex, White non-Latino/Hispanic race/ethnicity, sexual minority status, higher parent education, and non-religiosity were hypothesized to be associated with greater positive and negative alcohol expectancies. Secondly, the study aimed to determine the relationship between alcohol sipping and alcohol expectancies. Alcohol sipping was hypothesized to be associated with greater alcohol expectancies.

Methods

Cross-sectional data from 2018 to 2020 (Year 2, 4.0 data release) of the Adolescent Brain Cognitive Development (ABCD) Study were analyzed. The ABCD Study® is the largest longitudinal study of health and brain development in American youth. At baseline (2016–2018), the study began following a cohort of 11,878 9-10-year-olds from 21 racially/ethnically diverse sites within the U.S. (Year 0). Further details about the ABCD Study recruitment, participants, protocol, and measures have been previously described [23]. Data analyzed in this study were collected by adolescents’ and parents’ reports at Year 2 (mean adolescent age 12.02 [SD 0.67] years, range 10–14 years) of the ABCD assessment. There was missing data for sociodemographic factors and alcohol expectancies among 2,863 participants. Comparisons of those missing versus not missing data are shown in Table S1; adolescents without missing data were more likely to be White non-Latino/Hispanic, have a household income of $75,000 and greater, have parents with higher education, and have parents who were married/partnered. The University of California, San Diego (UCSD) provided centralized institutional review board (IRB) approval while study sites obtained approval from their local IRBs. Caregivers provided written informed consent, and each adolescent provided written assent.

Measures

Dependent variables: alcohol expectancies

Alcohol expectancies data were collected by adolescents’ self-reported agreement/disagreement with statements about the effects of alcohol on a 5-point Likert scale ranging from 1 to 5, through the Alcohol Expectancy Questionnaire- Adolescent, Brief (AEQ-AB) [24]. The AEQ-AB questionnaire is a 7-item measure comprised of 2 component mean scores: positive alcohol expectancies (4 items) and negative alcohol expectancies (3 items). Having positive alcohol expectancies often includes beliefs that alcohol increases sociability, reduces tension, and increases sexual enjoyment. One could also have negative alcohol expectancies, or a belief that alcohol causes cognitive impairment, risky behavior, and aggression. Both positive and negative alcohol expectancies are associated with alcohol use [25]. Examples include, “Alcohol can help how well a person gets along with others (makes people want to have fun together)” (positive) and “Alcohol can hurt how well a person gets along with others (makes people mean to others)” (negative). See Table S2 for a full list of the questions and responses. Average scores on a particular component (positive alcohol expectancies; negative alcohol expectancies) were calculated as suggested in the literature and analyzed based on principal components (positive and negative alcohol expectancies) [24]. Cronbach alphas were 0.69 for positive alcohol expectancies and 0.69 for negative alcohol expectancies in the current analysis. The correlation between the two subscales was − 0.01.

Independent variables: sociodemographic characteristics

One parent/guardian per adolescent (85.3% mothers, 10.0% fathers, 2.3% adoptive parent, 1.0% custodial parent, 1.4% other) completed a demographic survey about the adolescent. Sex, race/ethnicity, and child religiosity were assessed at the baseline parent survey. Household income, highest parent education, and parent marital status were assessed at the Year 2 parent survey. More detailed descriptions of the questions, response options, and coding are shown in Table S3.

Sex

Parents reported the sex assigned at birth of the child (female or male).

Race/ethnicity

A single race/ethnicity variable was constructed based on parent report at baseline, following guidance from the National Longitudinal Study of Adolescent to Adult Health: White non-Latino/Hispanic, Latino/Hispanic, Black non-Latino/Hispanic, Asian non-Latino/Hispanic, Native American non-Latino/Hispanic, and other non-Latino/Hispanic [26].

Household income

Income at Year 2 was categorized into six categories based on parent report: less than $25,000, $25,000 through $49,999, $50,000 through $74,999, $75,000 through $99,999, $100,000 through $199,999, or $200,000 and greater.

Highest parent education

Parents reported in Year 2 the highest level of education of the parent or partner (highest of the two). Response categories were combined into high school or less and college education or more.

Parent marital status

Parents were asked in Year 2 their marital status. Married and living with a partner were combined. Widowed, divorced, separated, and never married were categorized as not married/not living with a partner.

Sexual orientation

Participants were asked in Year 2, “Are you gay or bisexual?” Response options included: yes, maybe, no, and don’t understand the question.

Child religiosity

Parents were asked at baseline, “What is the child’s religious preference?” Responses were combined into religious (Mainline Protestant, Evangelical Protestant, Historically Black Church, Roman Catholic, Jewish, Mormon, Jehovah’s Witness, Muslim, Buddhist, Hindu, Orthodox Christian, Unitarian, Other Christian) and not religious (Atheist, Agnostic, Something else, Nothing in Particular).

Alcohol sipping

Adolescents in the ABCD study completed an adapted version of the Timeline Follow-Back (TLFB) instrument, which is a retrospective, calendar-based self-report instrument for measuring the use of alcohol and other addictive substances [27, 28]. This instrument exhibits high test-retest reliability as well as discriminant and convergent validity with other measures [29]. The instrument has been validated in adolescents [30, 31] and prior studies have demonstrated the reliability and validity of self-reported data on alcohol use in children [32, 33]. The current analysis used the question of whether children had tried a sip of alcohol such as beer, wine, or liquor (rum, vodka, gin, whiskey) at Year 2.

Statistical analyses

Statistical analyses were performed using Stata 18 (StataCorp) and R Studio. For the first aim, multiple linear regression analyses were conducted to estimate associations between sociodemographic factors (age, sex, race/ethnicity, sexual orientation, household income, parental education, parent marital status, religiosity) and alcohol expectancies (separate regressions for positive and negative expectancies). For the second aim, multiple linear regression analyses were conducted to estimate associations between alcohol sipping and alcohol expectancies (separate regressions for positive and negative expectancies), adjusting for age, sex, race/ethnicity, sexual orientation, household income, parental education, parent marital status, and religiosity. Linear regression coefficients were standardized to β coefficients. Propensity weights were applied to match key sociodemographic variables in the ABCD Study to the American Community Survey from the U.S. [34]. The two-sided alpha was set at 0.05. To address missing data, we applied multiple imputation with chained equations using the mice package in R, with 10 imputed datasets formed with a final sample size of 11,868 [35].

In supplemental analyses, the prior linear regression analyses estimating the associations between sociodemographic factors and alcohol expectancies were stratified by never sipped alcohol versus sipped alcohol. Effect modification of sociodemographic factors by alcohol sipping on alcohol expectancies was tested.

Results

Table 1 describes sociodemographic characteristics among the adolescents of the ABCD Study who met the inclusion criteria (N = 11,868). 48.8% of the participants were female and 47.6% were racial/ethnic minorities. Mean positive and negative alcohol expectancies were 1.97 (SD = 0.79) and 4.11 (SD = 0.93), respectively.

Table 1 Sociodemographic and alcohol expectancies characteristics of Adolescent Brain Cognitive Development (ABCD) Study participants (N = 11,868)
Full size table

Table 2 shows multiple linear regression models examining sociodemographic correlations with positive and negative alcohol expectancies in the total sample. Each additional year of life among the early adolescent sample was associated with more positive (β = 0.12 [95% 0.10, 0.14]) and negative (β = 0.03 [95% 0.01, 0.05]) alcohol expectancies. Black non-Latino/Hispanic adolescents reported less positive (β = -0.12 [95% -0.19, -0.05]) and negative (β = -0.10 [95% -0.16, -0.04]) alcohol expectancies relative to White non-Latino/Hispanic adolescents. Similarly, Latino/Hispanic adolescents showed less positive (β = -0.10 [95% -0.16, -0.03]) and negative (β = -0.09 [95% -0.16, -0.02]) alcohol expectancies compared to White non-Latino/Hispanic adolescents. Sexual minority adolescents reported higher positive (β = 0.31 [95% 0.21, 0.41]) and negative (β = 0.14 [95% 0.04, 0.24]) alcohol expectancies compared to heterosexual peers. Adolescents who said “maybe” to the sexual minority questions reported higher positive (β = 0.27 [95% 0.17, 0.37]) alcohol expectancies compared to heterosexual peers. In general, a household income of $200,000 and greater was associated with higher positive and negative alcohol expectancies compared to lower income groups. Having parents with a high school education or less was associated with less positive (β = -0.12 [95% -0.18, -0.06]) and negative (β = -0.21 [95% -0.28, -0.15]) alcohol expectancies compared to having parents with a college education or greater. Reporting no religiosity was associated with higher positive (β = 0.14 [95% 0.09, 0.18]) but not negative alcohol expectancies compared to reporting religiosity.

Table 2 Sociodemographic associations with positive and negative alcohol expectancies in the Adolescent Brain Cognitive Development (ABCD) Study (N = 11,868)
Full size table

Sipping alcohol was associated with positive (β = 0.35 [95% CI 0.28, 0.42]) but not negative alcohol expectancies, in linear regression models adjusting for sociodemographic variables (Table S4).

Supplemental analyses show multiple linear regression models examining sociodemographic correlations with positive (Table S5) and negative (Table S6) alcohol expectancies, stratified by never sipped alcohol compared to sipped alcohol. In general, most of the sociodemographic associations held within the subsample who had never sipped alcohol. Overall, there were no significant interactions with alcohol sipping and sociodemographic factors for the association with positive alcohol expectancies (Table S7).

Discussion

The current study contributes new insights and extends previous literature by identifying several sociodemographic factors associated with positive and negative alcohol expectancies in a diverse nationwide sample of early adolescents ages 10–14 years old. Specifically, this study found older age, male sex, White non-Latino/Hispanic race, sexual minority status, higher household income, and higher parent education to be associated with higher positive and negative alcohol expectancies among early adolescents. It is notable that most of the sociodemographic associations with positive and negative alcohol expectancies held within the subsample who had never sipped alcohol. In addition, previous alcohol sipping is associated with higher positive alcohol expectancies. These results are consistent with previous literature [13, 15, 19, 20]; however, the associations between sexual minority status and positive and negative alcohol expectancies as well as lack of religiosity with positive alcohol expectancies are novel in their applicability to early adolescence. Stressors due to sexual minority status have previously been predictive of greater alcohol use [36]. These same stressors related to sexual minority status may also explain the associations with positive and negative alcohol expectancies in this group [36]. Past studies have posited that individuals dealing with minority stressors may use alcohol as a coping mechanism to deal with the discrimination they experience [37]. Thus, adolescents facing minority stressors may develop expectancies that drinking can help them temporarily alleviate stress or escape from their daily struggles. Previous literature has demonstrated associations between sexual identity uncertainty and a higher risk for alcohol use disorder [38]. As for religiosity, church attendance has been shown to serve as a positive influence that reduces the odds of youth’s alcohol use [39]. Lack of traditions around the morality of alcohol without religious affiliation may contribute to the higher positive alcohol expectancies seen in this group. Compared to Latino/Hispanic and Black adolescents, White non-Latino/Hispanic adolescents had higher positive and negative alcohol expectancies. This is consistent with previous literature that found White children to have higher positive alcohol expectancies [12, 13], but the current study also demonstrates White adolescents to have higher negative alcohol expectancies relative to other racial groups.

Regarding parental factors, having parents with a high school education or less was associated with less positive and negative alcohol expectancies. This is again consistent with previous literature as it pertains to positive expectancies [14]. but is novel in terms of its association with lower negative expectancies. Past literature determined that children with more educated parents had higher internalizing problems such as feeling sad or worried, which in turn, predicted higher negative alcohol expectancies [14]. While perceivably counterintuitive that significant associations were found in the same direction for positive and negative expectancies, this finding highlights the complexity of the processes that influence adolescent perceptions related to alcohol. Factors such as peer pressure and social norms may contribute to mixed positive and negative expectancies. It is also plausible that while an adolescent may intellectually understand the negative consequences of alcohol, they may still be influenced by the immediate positive feelings related to alcohol use. As other studies have found higher health literacy to be associated with higher positive expectancies, higher parent education may be explained by similar mechanisms [3]. The present study found significant associations between higher household income and higher positive and negative alcohol expectancies, similar to previous literature outside the U.S. [17]. However, associations between higher income and higher positive expectancies were stronger and more consistent than associations between higher income and higher negative expectancies.

The current study has several limitations. The alcohol expectancy measures were self-reported, which increases the risk for social-desirability bias. Analyses are cross-sectional. Adolescents with more than one race/ethnicity were categorized as only one race/ethnic group in the analysis (e.g., Black non-Latino/Hispanic), which may not capture the nuances of their multiracial identities. However, the current study has several strengths including the large, diverse, nationwide sample that targeted early adolescents and examined a variety of relevant sociodemographic factors.

Conclusions

The current study identified specific groups with demographic factors that may be protective against the development of risky alcohol attitudes. For example, Black non-Latino/Hispanic and Hispanic/Latino adolescents had less positive and negative alcohol expectancies. We identified specific groups that may be prone to developing risky alcohol attitudes. This includes older adolescents, sexual minority adolescents, or White non-Latino/Hispanic adolescents. The current study’s results indicate specific characteristics of early adolescents that imply susceptibility to future alcohol use based on their attitudes regarding alcohol. Our results offer useful implications within the realm of public health. Examples include school and community intervention programs that can change adolescent alcohol expectancies to reduce risk for and delay initiation of alcohol use [40, 41]. The current study also found novel associations between negative alcohol expectancies and several sociodemographic factors. As the interpretation of negative alcohol expectancies has differed between studies [42], future studies should further explore the role of negative alcohol expectancies in predicting future alcohol use. Future research should also examine the mechanisms between these sociodemographic factors and alcohol expectancy development.

Data availability

Data used in the preparation of this article were obtained from the ABCD Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA).

Abbreviations

ABCD:

Adolescent Brain Cognitive Development Study

AEQ-AB:

Alcohol Expectancy Questionnaire- Adolescent, Brief

IRB:

Institutional Review Board

UCSD:

University of California, San Diego

References

  1. Goldman MS, Del Boca FK, Darkes J. Alcohol expectancy theory: the application of cognitive neuroscience. Psychological theories of drinking and Alcoholism. 2nd ed. New York, NY, US: The Guilford Press; 1999. pp. 203–46.

    Google Scholar 

  2. Goldman MS. The alcohol expectancy concept: applications to assessment, prevention, and treatment of alcohol abuse. Appl Prev Psychol. 1994;3:131–44.

    Article  Google Scholar 

  3. Chisolm DJ, Manganello JA, Kelleher KJ, Marshal MP. Health literacy, alcohol expectancies, and alcohol use behaviors in teens. Patient Educ Couns. 2014;97:291–6.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Christiansen BA, Smith GT, Roehling PV, Goldman MS. Using alcohol expectancies to predict adolescent drinking behavior after one year. J Consult Clin Psychol. 1989;57:93–9.

    Article  CAS  PubMed  Google Scholar 

  5. Goldman M, Brown S, Christiansen B. Expectancy theory - thinking about drinking. Psychol Theor Drink Alcohol. 1987;:181–226.

  6. Zamboanga BL, Schwartz SJ, Ham LS, Borsari B, Van Tyne K. Alcohol expectancies, pregaming, drinking games, and Hazardous Alcohol Use in a multiethnic sample of College Students. Cogn Ther Res. 2010;34:124–33.

    Article  Google Scholar 

  7. Lee CM, Fairlie AM, Ramirez JJ, Patrick ME, Luk JW, Lewis MA. Self-fulfilling prophecies: documentation of real-world daily alcohol expectancy effects on the experience of specific positive and negative alcohol-related consequences. Psychol Addict Behav J Soc Psychol Addict Behav. 2020;34:327–34.

    Article  Google Scholar 

  8. Spear LP. Effects of adolescent alcohol consumption on the brain and behaviour. Nat Rev Neurosci. 2018;19:197–214.

    Article  CAS  PubMed  Google Scholar 

  9. Jackson KM, Sher KJ, Cooper ML, Wood PK. Adolescent alcohol and Tobacco use: onset, persistence and trajectories of use across two samples. Addiction. 2002;97:517–31.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Rauch SAM, Bryant JB. Gender and context differences in Alcohol expectancies. J Soc Psychol. 2000;140:240–53.

    Article  CAS  PubMed  Google Scholar 

  11. Lundahl LH, Davis TM, Adesso VJ, Lukas SE. Alcohol expectancies: effects of gender, age, and family history of Alcoholism. Addict Behav. 1997;22:115–25.

    Article  CAS  PubMed  Google Scholar 

  12. Banks DE, Zapolski TCB. Racial differences in the link between alcohol expectancies and adolescent drinking. Addict Behav. 2017;67:34–7.

    Article  PubMed  Google Scholar 

  13. Banks DE, Faidley MT, Smith GT, Zapolski TCB. Racial/ethnic differences in the time-varying association between alcohol expectancies and drinking during the transition from childhood to adolescence. J Ethn Subst Abuse. 2020;19:371–87.

    Article  PubMed  Google Scholar 

  14. Patrick ME, Wray-Lake L, Maggs JL. Early life predictors of alcohol-related attitudes among 11-year-old never drinkers. Addict Behav. 2017;66:26–32.

    Article  PubMed  Google Scholar 

  15. Miller PM, Smith GT, Goldman MS. Emergence of alcohol expectancies in childhood: a possible critical period. J Stud Alcohol. 1990;51:343–9.

    Article  CAS  PubMed  Google Scholar 

  16. Windle M, Davies PT. Depression and heavy alcohol use among adolescents: concurrent and prospective relations. Dev Psychopathol. 1999;11:823–44.

    Article  CAS  PubMed  Google Scholar 

  17. Huang R, Ho SY, Wang MP, Lo WS, Lam TH. Sociodemographic risk factors of alcohol drinking in Hong Kong adolescents. J Epidemiol Community Health. 2016;70:374–9.

    Article  PubMed  Google Scholar 

  18. Jackson KM, Rogers ML, Sartor CE. Parental divorce and initiation of alcohol use in early adolescence. Psychol Addict Behav. 2016;30:450–61.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Galen LW, Rogers WM. Religiosity, alcohol expectancies, drinking motives and their interaction in the prediction of drinking among college students. J Stud Alcohol. 2004;65:469–76.

    Article  PubMed  Google Scholar 

  20. Fish JN, Hughes TL, Alcohol, Expectancies. Heavy drinking, and indicators of Alcohol Use Disorders in a community-based sample of lesbian and bisexual women. LGBT Health. 2018;5:105–11.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Marshal MP, Friedman MS, Stall R, Thompson AL. Individual trajectories of substance use in lesbian, gay and bisexual youth and heterosexual youth. Addict Abingdon Engl. 2009;104:974–81.

    Article  Google Scholar 

  22. Sanchez M, Gonzalez MR, Fernandez A, Barton A, Diaz V, Wang W. Sociocultural influences on alcohol expectancies in early adolescence: findings from the ABCD study. Health Psychol off J Div Health Psychol Am Psychol Assoc. 2023. https://doi.org/10.1037/hea0001290.

    Article  Google Scholar 

  23. Barch DM, Albaugh MD, Avenevoli S, Chang L, Clark DB, Glantz MD, et al. Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: rationale and description. Dev Cogn Neurosci. 2018;32:55–66.

    Article  PubMed  Google Scholar 

  24. Stein LAR, Katz B, Colby SM, Barnett NP, Golembeske C, Lebeau-Craven R, et al. Validity and reliability of the Alcohol Expectancy Questionnaire-Adolescent, brief. J Child Adolesc Subst Abuse. 2007;16:115–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Lac A, Luk JW. Pathways from positive, negative, and specific Alcohol expectancies to Weekday and Weekend drinking to alcohol problems. Prev Sci. 2019;20:800–9.

    Article  PubMed  PubMed Central  Google Scholar 

  26. National Longitudinal Study of Adolescent to Adult Health. Frequently Asked Questions – Add Health. https://addhealth.cpc.unc.edu/documentation/frequently-asked-questions/. Accessed 29 Aug 2023.

  27. Lisdahl KM, Sher KJ, Conway KP, Gonzalez R, Ewing SWF, Nixon SJ, et al. Adolescent brain cognitive development (ABCD) study: overview of substance use assessment methods. Dev Cogn Neurosci. 2018;32:80–96.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Sobell L, Sobell M. Timeline follow-back: a technique for assessing self-reported alcohol consumption. In: Litten R, Allen J, editors. Measuring Alcohol Consumption. The Humana Press Inc; 1992. pp. 41–72.

  29. Fals-Stewart W, O’Farrell TJ, Freitas TT, McFarlin SK, Rutigliano P. The Timeline Followback reports of psychoactive substance use by drug-abusing patients: psychometric properties. J Consult Clin Psychol. 2000;68:134–44.

    Article  CAS  PubMed  Google Scholar 

  30. Harris SK, Knight JR, Van Hook S, Sherritt L, Brooks TL, Kulig JW, et al. Adolescent substance use screening in primary care: validity of computer self-administered versus clinician-administered screening. Subst Abuse. 2016;37:197–203.

    Article  Google Scholar 

  31. Levy S, Sherritt L, Harris SK, Gates EC, Holder DW, Kulig JW, et al. Test-retest reliability of adolescents’ self-report of substance use. Alcohol Clin Exp Res. 2004;28:1236–41.

    Article  PubMed  Google Scholar 

  32. Donovan JE, Leech SL, Zucker RA, Loveland-Cherry CJ, Jester JM, Fitzgerald HE, et al. Really underage drinkers: alcohol use among elementary students. Alcohol Clin Exp Res. 2004;28:341–9.

    Article  PubMed  Google Scholar 

  33. Smith GT, McCarthy DM, Goldman MS. Self-reported drinking and alcohol-related problems among early adolescents: dimensionality and validity over 24 months. J Stud Alcohol. 1995;56:383–94.

    Article  CAS  PubMed  Google Scholar 

  34. Heeringa S, Berglund P. A guide for population-based analysis of the adolescent brain Cognitive Development (ABCD) study baseline data. bioRxiv. 2020;:2020.02.10.942011.

  35. mice. : Multivariate Imputation by Chained Equations in R | Journal of Statistical Software. https://www.jstatsoft.org/article/view/v045i03. Accessed 31 Aug 2023.

  36. Wilson SM, Gilmore AK, Rhew IC, Hodge KA, Kaysen DL. Minority stress is longitudinally associated with alcohol-related problems among sexual minority women. Addict Behav. 2016;61:80–3.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Hatzenbuehler ML. How does sexual minority Stigma get under the skin? A psychological mediation Framework. Psychol Bull. 2009;135:707–30.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Hancock DW, Talley AE, Bohanek J, Iserman MD, Ireland M. Sexual Orientation Self-Concept Ambiguity and Alcohol Use Disorder Symptomology: the roles of motivated psychological distancing and drinking to cope. J Stud Alcohol Drugs. 2018;79:96–101.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Scott LD Jr, Munson MR, McMillen JC, Ollie MT. Religious involvement and its association to risk behaviors among older Youth in Foster Care. Am J Community Psychol. 2006;38:237–49.

    Article  Google Scholar 

  40. Spoth R, Trudeau L, Guyll M, Shin C, Redmond C. Universal intervention effects on substance use among young adults mediated by delayed adolescent substance initiation. J Consult Clin Psychol. 2009;77:620–32.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Foxcroft DR, Tsertsvadze A. Universal alcohol misuse prevention programmes for children and adolescents: Cochrane systematic reviews. Perspect Public Health. 2012;132:128–34.

    Article  PubMed  Google Scholar 

  42. Grazioli VS, Lewis MA, Garberson LA, Fossos-Wong N, Lee CM, Larimer ME. Alcohol expectancies and Alcohol outcomes: effects of the use of protective behavioral strategies. J Stud Alcohol Drugs. 2015;76:452–8.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank Anthony Kung and Levi Cervantez for editorial assistance.

Funding

J.M.N. was funded by the National Institutes of Health (K08HL1549350 and R01MH135492) and the Doris Duke Charitable Foundation (2022056). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The ABCD Study was supported by the National Institutes of Health and additional federal partners under award numbers U01DA041022, U01DA041025, U01DA041028, U01DA041048, U01DA041089, U01DA041093, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, and U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners/. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/principal-investigators.html. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report.

Author information

Authors and Affiliations

  1. Department of Pediatrics, University of California, San Francisco, 550 16th Street, 4th Floor, Box 0530, San Francisco, CA, 94158, USA

    Jason M. Nagata, Gabriel Zamora, Natalia Smith & Joan Shim

  2. Geisel School of Medicine, Dartmouth College, 1 Rope Ferry Rd, Hanover, NH, 03755, USA

    Omar M. Sajjad

  3. Factor-Inwentash Faculty of Social Work, University of Toronto, 246 Bloor St W, Toronto, ON, M5S 1V4, Canada

    Kyle T. Ganson

  4. Department of Management, Policy and Community Health, University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, TX, 77030, USA

    Alexander Testa

  5. Department of Population, Family, and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD, 21205, USA

    Dylan B. Jackson

Authors
  1. Jason M. Nagata
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  2. Gabriel Zamora
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  3. Natalia Smith
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  4. Omar M. Sajjad
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  5. Joan Shim
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  6. Kyle T. Ganson
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  7. Alexander Testa
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  8. Dylan B. Jackson
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Contributions

Jason Nagata – conceptualization, analysis, writing- original draft and revisions, supervision. Natalia Smith – conceptualization, data analysis, writing- original draft and revisions. Gabriel Zamora – conceptualization, writing- original draft and revisions. Omar Sajjad – conceptualization, writing-critical revisions. Joan Shim –data analysis, writing- revisions. Kyle Ganson, Alexander Testa, Dylan Jackson – writing-critical revisions. All authors approve of the final submitted version.

Corresponding author

Correspondence to Jason M. Nagata.

Ethics declarations

Ethics approval and consent to participate

The University of California, San Diego (UCSD) provided centralized institutional review board (IRB) approval and each participating site received local IRB approval:

Children’s Hospital Los Angeles, Los Angeles, California.

Florida International University, Miami, Florida.

Laureate Institute for Brain Research, Tulsa, Oklahoma.

Medical University of South Carolina, Charleston, South Carolina.

Oregon Health and Science University, Portland, Oregon.

SRI International, Menlo Park, California.

University of California San Diego, San Diego, California.

University of California Los Angeles, Los Angeles, California.

University of Colorado Boulder, Boulder, Colorado.

University of Florida, Gainesville, Florida.

University of Maryland at Baltimore, Baltimore, Maryland.

University of Michigan, Ann Arbor, Michigan.

University of Minnesota, Minneapolis, Minnesota.

University of Pittsburgh, Pittsburgh, Pennsylvania.

University of Rochester, Rochester, New York.

University of Utah, Salt Lake City, Utah.

University of Vermont, Burlington, Vermont.

University of Wisconsin—Milwaukee, Milwaukee, Wisconsin.

Virginia Commonwealth University, Richmond, Virginia.

Washington University in St. Louis, St. Louis, Missouri.

Yale University, New Haven, Connecticut.

Written informed consent was obtained from the parents/caregivers of adolescents, and written assent was obtained from adolescents. Given that adolescent participants were minors (10–14 years old), they were not able to give legal consent. All the methods were carried out in accordance with relevant guidelines and regulations.

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Not applicable.

Competing interests

The authors declare that they have no conflict of interest.

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Nagata, J.M., Zamora, G., Smith, N. et al. Social epidemiology of early adolescent alcohol expectancies. BMC Public Health 23, 2502 (2023). https://doi.org/10.1186/s12889-023-17434-5

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Keywords

BMC Public Health

ISSN: 1471-2458

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