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. Author manuscript; available in PMC: 2025 Apr 1.
Published in final edited form as: J Youth Adolesc. 2023 Oct 17;53(4):799–813. doi: 10.1007/s10964-023-01881-9

Individual and Community level Developmental Adversities: Associations with Marijuana and Alcohol Use in Late-Adolescents and Young Adults

Michael I Demidenko 1,2, Edward D Huntley 3, Licheng Du 3, Caitlin Estor 3, Yajuan Si 3, Christine Wagner 3, Philippa Clarke 3,4, Daniel P Keating 1,3
PMCID: PMC10923158  NIHMSID: NIHMS1966866  PMID: 37848746

Abstract

Exposure to community and individual level stressors during adolescence has been reported to be associated with increased substance use. However, it remains unclear what the relative contribution of different community- and individual-level factors play when alcohol and marijuana use become more prevalent during late adolescence. The present study uses a large longitudinal sample of adolescents (Wave 1: N = 2,017; 55% Female; 54.5% White, 22.3% Black, 8% Hispanic, 15% other) to evaluate the association and potential interactions between community- and individual-level factors and substance use from adolescence to young adulthood (Wave 1 to Wave 3 Age Mean [SD]: 16.7 [1.1], 18.3 [1.2], 19.3 [1.2]). Across three waves of data, multilevel modeling (MLM) is used to evaluate the association between community affluence and disadvantage, individual household socioeconomic status (SES, measured as parental level of education and self-reported public assistance) and self-reported childhood maltreatment with self-reported 12-month alcohol and 12-month marijuana use. Sample-selection weights and attrition-adjusted weights are accounted for in the models to evaluate the robustness of the estimated effects. Across the MLMs, there is only a significant positive association between community affluence and parental education with self-reported alcohol use but not self-reported marijuana use. In post hoc analyses, higher neighborhood affluence in older adolescents was associated with higher alcohol use and lower use in younger adolescents; the opposite association was found for neighborhood disadvantage. Consistent with past literature, there is a significant positive association between self-reported childhood maltreatment and self-reported 12-month alcohol and 12-month marijuana use. Results are largely consistent across weighted and unweighted analyses, however, in weighted analyses there is a significant negative association between community disadvantage and self-reported 12-month alcohol use. This study demonstrates a nuanced relationship between community- and individual-level factors and substance use during the transitional window of adolescence which should be considered when contextualizing and interpreting normative substance use during adolescence.

Keywords: Adolescence, Alcohol Use, Marijuana Use, Geographic Information Systems, Neighborhood Disadvantage, Childhood Maltreatment, Young Adult

Introduction

Contextual factors are crucial to understanding the increased rates in adolescent substance use behaviors (Volkow & Blanco, 2023; Willoughby et al., 2021). Developmental adversities at both the community level (e.g., neighborhood disadvantage, neighborhood affluence) and the individual level (e.g., childhood maltreatment, household socioeconomic status [SES]) are potential sources of stress within structural and social processes (Booth & Crouter, 2001) that give rise to negative health outcomes throughout development (Nelson et al., 2020) across highly divergent samples from multiple studies (Madigan et al., 2023). Exposure to developmental adversity during childhood and adolescence is reported to be associated with increased substance use (Afifi et al., 2012; Andersen, 2019). However, findings remain mixed regarding the relative contributions of community- and individual-level indicators of developmental adversity and the use of alcohol and marijuana (Jackson et al., 2014; Karriker-Jaffe, 2011). The purpose of this study is to evaluate the association between different community and individual level adversities with alcohol and marijuana use during the transitional window of adolescence when substance use becomes more prevalent.

Individual and Community Level Indicators of Developmental Adversity

The assessment of developmental adversity varies across studies (Smith & Pollak, 2020) and includes various self-report measures like Adverse Childhood Experiences (ACES) (Felitti et al., 1998) and expanded ACES (E-ACES) incorporating community factors (Zhen-Duan et al., 2023), as well as administrative (Johansson et al., 2015) and geolocation data to calculate neighborhood disadvantage. Developmental adversity encompasses cumulative and chronic exposures related to household and neighborhood disadvantage, along with exposure to potentially traumatic events (Bernstein et al., 2003; Danese, 2020). Community-level factors are comprised of broader neighborhood characteristics that relate to concentrated poverty or crime. Neighborhood disadvantage consists of a scarcity of community resources (alternatively, neighborhood affluence consists of increased community resources) and increased toxic environments that can negatively impact health (Clarke et al., 2013). Meanwhile, individual level adversities related to household-level factors are comprised of family-SES, such as parental employment, income, and education (Hyde et al., 2020). Individual level adversities can also arise through forms of maltreatment (e.g., abuse and neglect; McLaughlin et al., [2014]). Combined community- and individual-level factors can impact adolescent-limited and emergent psychosocial and behavioral changes (Casey, 2015).

The multilevel interaction of community and household-level disadvantages can alter development and downstream behaviors (Hyde et al., 2020). Specifically, the reciprocal interaction among the household environment and community-level indicators may uniquely contribute to risky behaviors, such as substance use, during adolescence (Oshri et al., 2020). As a function of differential susceptibility (Belsky, 2016; Ellis et al., 2022), adolescents experiencing different community (i.e., disadvantage) and individual (i.e., material insufficiency and/or maltreatment) indicators of developmental adversity may be more susceptible to negative effects of the environment and in turn may engage in greater substance use.

Developmental Adversity and Substance Use

Prevalence in alcohol and marijuana increases throughout adolescence and early adulthood and is an acknowledged societal concern that is reported to contribute to poor health-related outcomes (Kann et al., 2018). In 2019, 33% of 12th graders had reported being drunk and 36% had used marijuana in the last year (Johnston et al., 2020). While the trend in binge drinking has declined (Johnston et al., 2020), in general, the rise in self-reported alcohol and marijuana use continues until the developmental transition of young adulthood (18-to-25-year-olds; Johnston et al. [2020]; Substance Abuse and Mental Health Services Administration. [2020]). Understanding the role of contextual factors during the high-risk period when both substance use disorders and psychopathology emerge (Caspi et al., 2020) presents an opportunity for intervention and prevention that arises from enhanced modifiability while neural and behavioral plasticity are elevated (Andrews & Westling, 2016).

Over the last decade, high levels of neighborhood disadvantage (or low affluence), low household-SES and maltreatment have been reported to be associated with increased substance use during adolescence. (For more details, see a non-exhaustive systematic review in supplemental Section 1). Both neighborhood disadvantage (Barr, 2018; Handley et al., 2015) and neighborhood disorder (Furr-Holden et al., 2011; Jackson et al., 2016) have been associated with increased alcohol and marijuana use. Higher household-SES has been reported to be associated with decreased alcohol (Andrabi et al., 2017; Leventhal et al., 2015) and marijuana use (Andrabi et al., 2017; Green et al., 2017). Likewise, maltreatment, in the forms of neglect and abuse, has been shown to be associated with increased alcohol (Dubowitz et al., 2016; Mills et al., 2016; Shin et al., 2013) and marijuana use (Afifi et al., 2012; Duprey et al., 2017). However, the direction of the associations between community and individual level adversities and substance use has been mixed across studies.

While the above studies reported positive associations, several studies have reported negative or no association(s) between community and individual level adversities and substance use in adolescents. Specifically, studies have reported negative associations between community disadvantage and alcohol use (Deutsch, 2019), positive associations between community affluence and alcohol (Barr, 2018) and marijuana use (Coley et al., 2018), or no association between substance use and household-SES (Bosque-Prous et al., 2017; Gerra et al., 2020; Milliren et al., 2017), community disadvantage (Fagan et al., 2014; Fairman et al., 2020; Jensen et al., 2017). Since these studies often measure individual or community-level factors in isolation, the mixed findings with regards to adolescent substance use are often difficult to compare and reconciling differences becomes cumbersome.

Methodological Differences Among Studies

Current inconsistencies in findings may be explained by both sample characteristics and methodological decisions. For example, the investigation of age cohorts (e.g., 10–15-year-olds; (Deutsch, 2019; Handley et al., 2015) that precede the age groups in which substance use becomes more prevalent (Johnston et al., 2020) and mixed findings as a result of variations in the units of analysis (Jackson et al., 2014; Karriker-Jaffe, 2011). By studying cohorts that predate changes in marijuana use (Carliner et al., 2017) among adolescents (Johnston et al., 2020), nuanced differences in associations between adversities and alcohol and/or marijuana use may be missed. Furthermore, by using different approaches in characterizing disadvantage (or affluence), studies risk explaining different components of variance.

In a non-exhaustive search of the literature on substance use and developmental adversities during adolescence, key differences arise in cohort studies and outcome measures for substance use (For expanded details, see supplemental Section 1). For example, of the 28 studies reviewed, nine (32%) of studies utilized the National Longitudinal Study of Adolescent to Adult Health (Add Health) study (Barr, 2018; Coley et al., 2018; Deutsch, 2019; Milliren et al., 2017) or community level measures derived from U.S. Census data from 2000 or earlier (Fagan et al., 2015; Handley et al., 2015; Jensen et al., 2017). Of the 21 studies (75%) that focused on individual (e.g., parental SES, education) and community level measures (e.g., disadvantage, disorder, affluence), only seven (33%) examined associations with both alcohol and marijuana. These differences make it challenging to draw inferences about the association between developmental adversities and alcohol and marijuana use when the samples are largely the same (e.g., Add Health) and only self-reported alcohol or marijuana use are measured.

In addition, community-level factors are often composed of different items across studies and so comparing across studies is often challenging. Although time-invariant factors of community disadvantage using U.S. Census data (Miles et al., 2016) and nationally represented factors of disadvantage and affluence across 15-years of data in adults exist (Clarke et al., 2013), some studies use sample specific approaches in their operationalization of community-level factors. Sample specific approaches included multivariate methods, such as exploratory factor or principal components analyses, to derive measures of community disadvantage and/or advantage (Barr, 2018; Fagan et al., 2015; Shih et al., 2017), or standardized composite and summed scores (Deutsch, 2019; Fairman et al., 2020; Jensen et al., 2017). While these are reasonable decisions, unless the variables in those studies are present in future work the ability to derive factors and composite scores that represent similar constructs would be cumbersome (McNeish & Wolf, 2020). These nuanced differences may change the interpretation of the association between community-level factors (e.g., disadvantage and affluence) and substance use. Using factor derived scores that are based on robust measurement properties and national data may harmonize the variables across studies and increase the reproducibility of the underlying constructs and thus interpretations.

Current Study

The current study evaluates the associations among individual level and community level adversities and self-reported 12-month alcohol and marijuana use. Based on the variability in past work when operationalizing community disadvantage/affluence, such as use of community- and/or individual-level indicators, the current study uses community and nationally representative data to derive factors of community disadvantage and affluence. Aim 1 evaluates whether a) maltreatment and/or indicators of household material insufficiency or precarity (i.e., parent education, public assistance) and b) community level factor of disadvantage consistently relates to alcohol or marijuana use. Based on consistent literature that maltreatment results in increased substance use, it is hypothesized that maltreatment would be associated with increased alcohol and marijuana use (Hypothesis 1). Given the association between high SES households and alcohol use, it is hypothesized that alcohol use would be positively associated with household SES (individual) and negatively with the neighborhood disadvantage factor (Hypothesis 2a), but that marijuana use would be negatively associated with household SES and positively with neighborhood disadvantage (Hypothesis 2b). In Aim 2, findings for neighborhood disadvantage independent variable are compared to models using neighborhood affluence independent variable to evaluate if and how interpretations differ for alcohol or marijuana use. As this area has been understudied with variability among studies, no directional hypotheses are specified beyond those listed for Aim 1. In Aim 3, the differential-susceptibility hypothesis is evaluated to determine whether there is a meaningful moderation between different levels of experienced childhood maltreatment and neighborhood disadvantage (or affluence) and alcohol or marijuana use. It is hypothesized that greater disadvantage and higher maltreatment would be associated with greater substance use (Hypothesis 3).

Methods

Participants Data

Study characteristics are briefly summarized here with additional details described in supplemental Section 2. Participants are from the Adolescent Health Risk Behavior (AHRB) study, a longitudinal study designed to characterize behavioral and cognitive correlates of risk behavior trajectories from mid-adolescence to emerging adulthood. An accelerated cohort design using quota sampling was utilized to approximate the diversity of the statewide population and recruit a nonprobability sample of 10th and 12th grade students from nine public school districts from Southeastern Michigan. An expanded description of the sample is provided in the supplemental materials.

Survey procedures were designed to protect students’ privacy by allowing confidential and voluntary participation and were approved by the University of Michigan Institutional Review Board. Parents of eligible participants were initially contacted by mail and provided with a study brochure and an informed consent document, and both active parental consent and adolescent assent for participation were obtained.

In Wave 1 (W1), youth (N = 2,017; Table 1) completed computer-assisted self-interview questionnaires in their school (March 2015 – February 2016). Web-based computer assisted self-interview questionnaires were administered in two subsequent follow-up assessments, Wave 2 (W2; February 2017 – October 2017; N = 913) and Wave 3 (W3; February 2018 – October 2018; N = 913; see Table 1). Of the Wave 1 sample, 1130 (56%) participants completed at least one subsequent wave of data collection. Each individual follow-up wave had a 45% response rate, which is comparable to the response rate reported for the national Monitoring the Future (MTF) panel survey (Patrick et al., 2018). Robust analyses using sampling and attrition weights are discussed below, and implications of missing data will be discussed in the limitations section. To provide a longitudinal index of neighborhood-level social exposures of adversity, residential histories are obtained from participants at each wave of data collection. These data are then linked to the publicly available, National Neighborhood Data Archive (NaNDA; www.openicpsr.org/openicpsr/nanda) containing annual contextual measures for locations across the United States from 2000 onwards. NaNDA offers theoretically derived, spatially referenced, nationwide measures of the physical and social environment. The Neighborhood Change Database (Tatian, 2003) is utilized to reconcile any administrative boundary changes from one census year to the next in NaNDA.

Table 1.

Sample Characteristics for Wave 1 – Wave 3

Wave 1 Wave 2 Wave 3
(n = 2017) (n = 913) (n = 913)
M (SD)
Age (years) 16.7 (1.1) 18.3 (1.2) 19.3 (1.19)
Years Education 11.0 (1.0) 12.5 (1.2) 13.5 (1.3)
Childhood Maltreatment 5.5 (7.3) - -
Neighborhood Disadvantage 0.12 (0.09) 0.11 (0.09) 0.11 (0.09)
Neighborhood Affluence 0.35 (0.16) 0.38 (0.17) 0.38 (0.17)
12-month Alcohol use 2.08 (1.48) 2.79 (1.93) 3.28 (2.06)
12-month Marijuana use 1.76 (1.67) 2.44 (2.10) 2.73 (2.29)
n (%)
Sex
 Female 1114 (55.2%) 542 (59.4%) 560 (61.3%)
 Male 893 (44.3%) 358 (39.2%) 353 (38.7%)
Race
 White Non-Hispanic 1100 (54.5%) 556 (60.9%) 548 (60.0%)
 Black or African American 449 (22.3%) 162 (17.7%) 185 (20.3%)
 Hispanic All Races 159 (7.9%) 54 (5.9%) 59 (6.5%)
 Other 299 (14.8%) 128 (14.0%) 121 (13.3%)
Parental Education
 High School or Less 487 (24.1%) 183 (20.0%) 171 (18.7%)
 Some College 567 (28.1%) 227 (24.9%) 234 (25.6%)
 College 559 (27.7%) 272 (29.8%) 287 (31.4%)
 Beyond College 327 (16.2%) 194 (21.2%) 198 (21.7%)
Moved
 No Move 1440 (71.4%) 644 (70.5%) 661 (72.4%)
 Did Move 400 (19.8%) 183 (20.0%) 182 (19.9%)
Public Assistance (PA)
 No History PA 1464 (72.6%) 662 (72.5%) 681 (74.6%)
 History of PA 543 (26.9%) 238 (26.1%) 232 (25.4%)
Female head of household
 Yes 1352 (67.0%) 600 (65.7%) 586 (64.2%)
 No 652 (32.3%) 300 (32.9%) 327 (35.8%)
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Reminder: 12-month alcohol & Marijuana use is self-reported on an ordinal scale, 1 = “0 occasions” to 7 = “40 or more occasions”

Measures

Alcohol and marijuana use.

Substance use items for 12-month alcohol use and 12-month marijuana use are drawn from and are identical to those used in annual, national surveys (Monitoring the Future; Johnston et al. 2020). For Alcohol Use, participants responded to “On how many occasions (if any) have you had any alcoholic beverage to drink—more than just a few sips during the last 12 months?”, using a seven-point Likert scale, 1 = “0 occasions” to 7 = “40 or more occasions”. For Marijuana Use, participants responded to “On how many occasions (if any) have you used marijuana or hashish during the last 12 months?”, using a seven-point Likert scale, 1 = “0 occasions” to 7 = “40 or more occasions”

Sociodemographic covariates and socioeconomic status (SES).

Sociodemographic covariates, reported by each participant, included the participant’s age in years, sex at birth and race. Household (individual level) SES is assessed using self-reported levels of two indicators: parent education and having received public assistance. Participants reported the level of education completed by each of their parents. Response options included: grade school or less (1), some high school (2), completed high school (3), some college (4), completed college (5), graduate or professional school after college (6), and don’t know or does not apply (7). Scores of both parents, if available, are averaged together and participants with a single parent, that parent’s educational attainment is used with higher scores indicating more education (Bachman et al., 2011). Public assistance is measured at Wave 1 using a single dichotomous variable, gauging whether or not the participant’s family has received public assistance of any kind (Do your parents, or the most important person in raising you, receive public assistance [by public assistance, we mean: welfare, Bridge Card, EBT, disability benefits, etc.]?) (Cunningham et al., 2014).

Childhood maltreatment.

To assess participants’ level of exposure to potentially traumatic events, exposure to emotional, physical, and sexual abuse is assessed at Wave 1 using the three subscales of the Childhood Trauma Scale – Short Form (CTQ-SF; Bernstein et al., 1994, 2003), a self-report measure that utilizes 28 items from the original 70-item Childhood Trauma Questionnaire (CTQ; Bernstein et al., 1994). Participants responded to “Please indicate how true each of the following statements is for you. While I was growing up…” with 5 items for each of the three abuse subscales. Example items included: “People in my family said hurtful or insulting things to me” (emotional abuse), “People in my family hit me so hard that it left me with bruises or marks” (physical abuse), and “Someone tried to make me do sexual things or watch sexual things” (sexual abuse). All items are rated on a 5-point Likert scale ranging from 0 (never true) to 4 (very often true). Scores ranged from 0 – 60 with higher scores indicating more childhood maltreatment. Retrospective reports of childhood maltreatment have been shown to be comparable to prospective self-report items (Newbury et al., 2018). Recent reviews have indicated that, although retrospective, subjective accounts of childhood maltreatment may be more relevant to mental health outcomes than objective indicators obtained contemporaneously (Danese, 2020; Danese & Widom, 2020). Adequate reliability of the CTQ-SF has been documented in adolescent and adult samples with α = .66 – .92 (Bernstein et al., 2003; Scher et al., 2004) and in the present sample (α = .86). Furthermore, self-reported rates reflect the subjective experience of maltreatment in young adults and have not been shown to suffer from rates of underreporting that is often observed in agency records (Gilbert et al., 2009).

Neighborhood measures of disadvantage and affluence.

NaNDA measures of neighborhood disadvantage and affluence are based on a nation-wide factor analysis of census indicators (Clarke et al., 2013; Morenoff et al., 2007) utilizing data derived from 5-year estimates of the American Community Survey (ACS; years 2013–2017) using the census tract as a proxy for neighborhood. Data from the ACS year 2015 are used in this study to match the initial wave of data collection (Wave 1; March 2015-February 2016). Census tracts have on average about 4,000 people and are designed to capture homogenous areas that roughly map to neighborhoods (Clarke et al. 2013). Neighborhood disadvantage (primary) is an index based on the proportion of female headed families with children, proportion of households with public assistance income or food stamps; proportion of families with income below the federal poverty level; proportion of population age 16+ unemployed. Scores range from 0 – 1 with higher scores indicating higher levels of neighborhood disadvantage. The scale showed good reliability in a national sample of youth (α > .93) and in the present sample (α = .89). Neighborhood affluence (secondary) is an index based on concentrations of adults with a college education; with incomes >$75K; and employed in managerial and professional occupations. Distinct from simply being the absence of neighborhood disadvantage, neighborhood affluence is associated with higher levels of social control and leverage over local institutions that can foster social environments that facilitate health (Browning & Cagney, 2003). Scores range from 0 – 1 with higher scores indicating higher levels of neighborhood affluence. Based on the factor scores derived from previous work, the index showed good reliability in a national sample of youth (α > .94) and in the present sample (α = .93).

The NaNDA measures are selected based on prior factor analyses with strong reliability using national samples, and on the alignment with prior research on the impacts of neighborhood disadvantage and advantage on youth substance use (Barr, 2018). Other neighborhood measures, such as neighborhood disorder or population density, were not included because they were not part of this geolocation data collection method owing to the necessity of field observation and interviewing, or the unavailability of measures validated on a national sample. Neighborhood disadvantage and affluence were measured at each wave, thus, if a participant moved the census tract was recalculated using the address they provided.

Analyses

Descriptive statistics and correlations.

Analysis are performed using R version 4.2.1 (R Core Team, 2022). R Markdown code and .html output for the analyses below are available at the OSF link (https://osf.io/q9smk/). The descriptive statistics are reported to provide insights about the variables in these analyses at each of the three waves. Data are reported as mean (standard deviation) or n (%). Bivariate associations are reported among single and multi-wave (W1-W3) variables to describe the relationships in this sample.

Multi-level model (MLM).

Given that the longitudinal and community data contain statistical dependencies within subjects across time (W1 – W3) and neighborhood (census tract), multilevel modeling is used (MLM; or hierarchical linear models [HLM]) to account for this dependency in these data (Ram & Grimm, 2007; Woltman et al., 2012). The nested MLM leverages all the longitudinal data for substance use (three waves) to evaluate the between-subject association between the independent (e.g., neighborhood disadvantage/affluence, parental education, public assistance or maltreatment) and dependent variables dependent variables (i.e., self-reported 12-month alcohol or marijuana use). In these analyses, correlated errors account for clustering of time (i.e., within subject waves) and neighborhood (i.e., tracts) and covariates. The analyses are performed for complete cases in four stages as implemented in the lme4 package (Bates et al., 2020), with an a priori α < .05. Each MLM reports unstandardized beta estimates (b) and the associated standard errors (SE) for each estimate and the associated p-value. All non-nominal predictors are grand mean centered.

In the first stage of the model, Model 1, the contribution of the covariates for past 12-month alcohol and past 12-month marijuana use are reported. The model includes the fixed effects for age, sex (male = 1), and race/ethnicity (White Non-Hispanic, reference). Next, stage 2 Model 2, the individual level (household SES) fixed effects, parental education and history of public assistance are added to the Model 1 covariates. In this model, the estimated association between household SES and substance use behaviors in adolescents are evaluated after accounting for the covariates. Models are reported separately for self-reported alcohol and marijuana use.

In Model 3, fixed effects of the neighborhood disadvantage and childhood maltreatment are added to Model 2. This model estimates association between maltreatment, parent education, use of public assistance, and community level disadvantage with alcohol or marijuana use. This model provides insights about the meaningful association among individual and community level stressors for alcohol or marijuana use in a single model adjusting for the linear intercorrelations among those items. Conversely, for Aim 2, to examine how neighborhood-level disadvantage and advantage differ in their association with alcohol or marijuana use, the fixed effect of neighborhood affluence instead of disadvantage is used.

Finally, for Aim 3, the meaningful between-subject association at different levels of experienced childhood maltreatment and community disadvantage (or advantage), and alcohol or marijuana use are evaluated. The interaction is tested by adding the fixed effect interaction in Model 4 to disadvantage and affluence models (Model 3), separately.

Sensitivity analyses: Attrition and population weights.

Weights calibrated to the ACS are constructed to adjust for potential bias due to the non-probability sampling design and to possible attrition bias in the AHRB sample. Unlike model-based approaches that are tailored to missingness of a particular variable, weighting is commonly used as it is agnostic about the survey variables of interest (Little, 2019; Si et al., 2022). Details are provided in supplemental Section 3. The weighted ACS data for 15–19-year-olds are used to approximate the population distribution and post-stratification weights are calculated for the AHRB sample. In this procedure, ACS and AHRB are iteratively matched on four demographic characteristics: sex, race and ethnicity, household size, and parental employment status. The response indicators are predicted in the follow-up waves and apply inverse propensity score weighting.

Results

Descriptive Statistics

The descriptive statistics for each wave are reported in Table 1. The full W1 sample (N = 2,017) contained 380 unique census tracts. Due to the missing data, there were 302 tracts modeled in the full model (Model 3) for alcohol use and 296 tracts for marijuana use. At W1, the mean age is 16.7 (SD = 1.1) and 55% are female. In W2 and W3 (N = 913), the mean age is 18.3 (SD = 1.2) and 19.3 (SD = 1.2), and 59% and 61% are female, respectively (Table 1). Consistent with national trends in adolescent substance use (Johnston et al., 2020), self-reported alcohol use increased from W1 (M = 2.1, SD = 1.5) to W3 (M = 3.3, SD = 2.1), and marijuana use from W1 (M = 1.8, SD = 1.7) and W3 (M = 2.7, SD = 2.3). Bivariate associations among study’s variables are reported in supplemental Section 4, Table S2 and plotted in Figure 1. In the current sample, 20% of participants reported moving in W2 and 25% reported moving in W3 (Table 1), which may have contributed to changes in census tracts. The correlation between W1 and W3 neighborhood disadvantage, and W1 and W3 neighborhood affluence, were relatively stable, r = .77 and .84, respectively. This consistency is consistent with previous work that found neighborhood SES across development to be relatively stable (Wheaton & Clarke, 2003). As expected, neighborhood disadvantage and affluence were inversely correlated, at each wave, r = −.72, −.73, and −.72.

Figure 1.

Figure 1.

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Correlation for study variables, Wave 1 (W1) – Wave 3 (W3) Sex: Male = 1, Female = 0; ParEdu = Parental Education; Alc12 = 12-month alcohol use; Mar12 = 12-month marijuana use; CTQt = Childhood Trauma Questionnaire Total; PubAsst = History Public Assistance (1/0); Disadv Neighborhood Disadvantage; Affl = Neighborhood Affluence

Effect of Individual Level Maltreatment

Consistent with Hypothesis 1, the self-reported total score of CTQ was associated with 12-month substance use (Table 2 and Table 3). Specifically, there was a significant positive association between childhood maltreatment and self-reported 12-month alcohol use, b = .02, p = <.001, when adjusted for covariates, parental education, history of public assistance and neighborhood disadvantage. Likewise, there was a significant positive association between childhood maltreatment and 12-month self-reported marijuana use, b = .04, p <.001, when adjusted for covariates, individual level household SES and neighborhood disadvantage. Consistent with prior literature, this suggests that subjective experience in self-reported maltreatment is associated with both 12-month alcohol and marijuana use in late adolescents and young adults.

Table 2.

Multilevel Models: Association Between Disadvantage (IV) and 12-month Alcohol and Marijuana Use (DV)

Model 1 Model 2 Model 3 Model 4
12-month Self-Reported Alcohol Use
Predictors b SE p b SE p b SE p b SE p
Intercept 2.77 0.06 <.001 2.62 0.12 <.001 2.62 0.12 <.001 2.62 0.12 <.001
Age 0.48 0.02 <.001 0.49 0.02 <.001 0.49 0.02 <.001 0.49 0.02 <.001
Male −0.1 0.07 0.17 −0.09 0.07 0.18 −0.06 0.07 0.38 −0.06 0.07 0.38
White Non-Hispanic (Reference)
 Black −0.58 0.09 <.001 −0.58 0.09 <.001 −0.54 0.10 <.001 −0.54 0.10 <.001
 Hispanic −0.11 0.14 0.42 −0.05 0.14 0.727 −0.04 0.14 0.791 −0.04 0.14 0.80
 Other −0.34 0.10 0.001 −0.35 0.1 0.001 −0.36 0.10 0.00 −0.35 0.10 0.001
ParEdu 0.08 0.04 0.02 0.08 0.04 0.02 0.08 0.04 0.02
PubAsst 0.13 0.08 0.12 0.12 0.09 0.16 0.12 0.09 0.16
Disadv −0.68 0.45 0.13 −0.69 0.45 0.13
Childhood
Maltreatment 0.02 0.01 0.00 0.02 0.01 0.001
Disadv * Childhood
Maltreatment 0.01 0.05 0.81
12-month Self-Reported Marijuana Use
Predictors b SE p b SE p b SE p b SE p
Intercept 2.17 0.07 <.001 1.85 0.15 <.001 1.93 0.15 <.001 1.94 0.15 <.001
Age 0.35 0.02 <.001 0.35 0.02 <.001 0.35 0.02 <.001 0.35 0.02 <.001
Male 0.15 0.09 0.09 0.17 0.09 0.05 0.24 0.09 0.01 0.25 0.09 0.01
White Non-Hispanic (Reference)
 Black −0.04 0.12 0.75 −0.09 0.12 0.48 −0.15 0.12 0.24 −0.15 0.12 0.23
 Hispanic 0.11 0.17 0.54 0.09 0.18 0.61 0.10 0.18 0.59 0.09 0.18 0.61
 Other 0.04 0.13 0.74 0.04 0.13 0.74 −0.05 0.13 0.69 −0.06 0.13 0.66
ParEdu −0.09 0.05 0.04 −0.06 0.05 0.16 −0.06 0.05 0.17
PubAsst 0.26 0.11 0.01 0.19 0.11 0.08 0.18 0.11 0.08
Disadv 0.32 0.56 0.57 0.35 0.56 0.53
Childhood
Maltreatment 0.04 0.01 <.001 0.04 0.01 <.001
Disadv * Childhood
Maltreatment −0.05 0.06 0.39
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ParEdu: Parental Education; PubAsst: History of Public Assistance; Disadv: Neighborhood Disadvantage; Childhood Maltreatment from the Childhood Trauma Questionnaire

Table 3.

Multilevel Models: Association Between Affluence (IV) and 12-month Alcohol and Marijuana Use (DV)

Model 1 Model 2 Model 3 Model 4
12-month Self-Reported Alcohol Use
Predictors b SE p b SE p b SE p b SE p
Intercept 2.77 0.06 <.001 2.62 0.12 <.001 2.61 0.12 <.001 2.61 0.12 <.001
Age 0.48 0.02 <.001 0.49 0.02 <.001 0.49 0.02 <.001 0.49 0.02 <.001
Male −0.10 0.07 0.17 −0.09 0.07 0.18 −0.06 0.07 0.40 −0.06 0.07 0.40
White Non-Hispanic (Reference)
 Black −0.58 0.09 <.001 −0.58 0.09 <.001 −0.56 0.10 <.001 −0.56 0.10 <.001
 Hispanic −0.11 0.14 0.42 −0.05 0.14 0.73 −0.05 0.14 0.75 −0.04 0.14 0.75
 Other −0.34 0.10 0.00 −0.35 0.10 0.00 −0.37 0.10 <.001 −0.37 0.10 <.001
ParEdu 0.08 0.04 0.02 0.07 0.04 0.07 0.07 0.04 0.07
PubAsst 0.13 0.08 0.12 0.13 0.09 0.14 0.13 0.09 0.14
Affl 0.50 0.24 0.04 0.50 0.24 0.04
Childhood
Maltreatment 0.02 0.01 0.00 0.02 0.01 0.00
Affl * Childhood
Maltreatment −0.01 0.03 0.80
12-month Self-Reported Marijuana Use
Predictors b SE p b SE p b SE p b SE p
Intercept 2.17 0.07 <.001 1.85 0.15 <.001 1.93 0.15 <.001 1.93 0.15 <.001
Age 0.35 0.02 <.001 0.35 0.02 <.001 0.35 0.02 <.001 0.35 0.02 <.001
Male 0.15 0.09 0.09 0.17 0.09 0.05 0.24 0.09 0.01 0.24 0.09 0.01
White Non-Hispanic (Reference)
 Black −0.04 0.12 0.75 −0.09 0.12 0.48 −0.14 0.12 0.26 −0.14 0.12 0.25
 Hispanic 0.11 0.17 0.54 0.09 0.18 0.61 0.1 0.18 0.57 0.1 0.18 0.57
 Other 0.04 0.13 0.74 0.04 0.13 0.74 −0.05 0.13 0.72 −0.05 0.13 0.71
ParEdu −0.09 0.05 0.04 −0.06 0.05 0.21 −0.06 0.05 0.21
PubAsst 0.26 0.11 0.01 0.18 0.11 0.09 0.18 0.11 0.08
Affl −0.21 0.29 0.48 −0.21 0.29 0.48
Childhood
Maltreatment 0.04 0.01 <.001 0.04 0.01 <.001
Affl * Childhood
Maltreatment 0.01 0.03 0.72
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ParEdu: Parental Education; PubAsst: History of Public Assistance; Disadv: Neighborhood Disadvantage; Childhood Maltreatment from the Childhood Trauma Questionnaire

Effect of Individual Level Household SES

There was a significant association between parental education and substance use, but some effects changed in models when adjusted for self-reported maltreatment and neighborhood characteristics. With respect to 12-month alcohol, consistent with Hypothesis 2a, there is a significant positive association between higher parental education self-reported alcohol use, b =.08, p < .05 even after adjusting for covariates (Model 2, Table 2). This effect is small but remains significant even after adjusting for individual maltreatment and neighborhood disadvantage (b = .08, p <.05), in Model 3 (Table 2). However, history of public assistance is not associated with self-report alcohol use (b = .13, p > .05). Meanwhile, counter to Hypothesis 2b, while parental education and public assistance were associated with marijuana use, b = −.09, p < .05 and b = .26, p = .01, respectively, they were no longer significant after adjusting for maltreatment and disadvantage Model 3 (p > .05).

Differences Across Neighborhood Disadvantage & Affluence

Aim 2 evaluated whether the association between community-level factors and self-report 12-month alcohol or marijuana occasions meaningfully differ across neighborhood disadvantage and neighborhood affluence. Contrary to Hypothesis 2a, although there was a negative association between neighborhood disadvantage and 12-month alcohol use (Model 3, Table 2), this relationship was not significant (b= −.68, p = .14; Table 2). However, the negative bivariate association between neighborhood disadvantage and self-reported alcohol use increase across waves. Thus, in a post hoc exploratory comparison an interaction term between W1 age and neighborhood disadvantaged is added. Specifically, an interaction term between W1 age and neighborhood disadvantage is added in Model 3 for self-reported alcohol use. This resulted in a significant interaction, b = −.83, p < .001, suggesting that higher neighborhood disadvantage in older adolescents is associated with lower alcohol use and higher neighborhood disadvantage in younger adolescents is associated with higher use (see predicted marginal effects plot, supplemental Figure S3). There was no meaningful association between 12-month marijuana use and neighborhood disadvantage (Table 3, b = .32, p > .05), and the bivariate association remained stable and negligible across each wave.

When the association between community-level factors and substance use is evaluated using neighborhood affluence as the independent variable, the inverse relationship is found. Specifically, there is a significant positive association between neighborhood affluence and self-reported 12-month alcohol use, b = .50, p < .001 (Table 2). Like neighborhood disadvantage, the bivariate association between affluence and self-reported alcohol use increased across each wave. In a post hoc comparison an interaction term between W1 age and neighborhood affluence is evaluated. Like the disadvantage model, there is a significant interaction between W1 age and neighborhood affluence, b = .72, p < .001, suggesting that higher neighborhood affluence in older adolescents is associated with higher self-reported 12-month alcohol and higher neighborhood affluence in younger adolescents is associated with lower self-reported 12-month alcohol use (see predicted marginal effects plot, supplement Figure S4). Meanwhile, like neighborhood disadvantage, there is no association between neighborhood affluence and 12-month marijuana use, b = −.21, p > .05, and bivariate associations are stable and negligible at each wave.

Interaction of Maltreatment and neighborhood disadvantage.

In Aim 3, the moderating effect of self-reported maltreatment on the association between neighborhood disadvantage and self-reported alcohol or self-reported marijuana use was evaluated. In all models, there was no evidence to support Hypothesis 3 that there would be differential susceptibility to substance use in adolescents who experienced higher rates of maltreatment and higher neighborhood disadvantage. Specifically, the interaction between neighborhood disadvantage and maltreatment are not significant for alcohol use (b = .01, p > .05; Table 2) or marijuana use (b = −.05, p > .05; Table 3). The null finding is consistent with post hoc models examining this difference across neighborhood affluence (supplemental Table S3 and Table S4).

Sensitivity and Posthoc Analyses

Comparison between weighted and unweighted analyses.

All analyses are rerun using population and attrition weights. Except for the association between neighborhood disadvantage and 12-month alcohol use changing in magnitude and significant threshold from unweighted (b = −.68, p = .14) to weighted (b = −1.78, p = .01), the remaining interpretations were largely unchanged (see supplemental Section 4, Table S3 and S4). The change in magnitude and significance interpretation in the weighted analyses is consistent with Hypothesis 2a regarding the significant negative association between neighborhood disadvantage and self-reported 12-month alcohol use.

Posthoc analyses: Transformed Substance Use Measures.

As is common in normative adolescent samples, both self-reported 12-month alcohol use and 12-month marijuana use measures were skewed and zero inflated. A cubic root transformation (^[1/3]) is applied to dependent variables, alcohol and marijuana, and the MLM models are reran. The magnitude of the effects did not meaningfully differ between the transformed and untransformed data (see supplemental Table S5 & S6).

Discussion

Findings remain mixed on the relative contribution of different community- and individual-level factors play when alcohol and marijuana use become more prevalent during late adolescence. This study attempted to address this gap by evaluating how individual-level indicators of self-reported maltreatment and household SES (i.e., parental education and public assistance), community-level indicators (i.e., neighborhood disadvantage and affluence) and their interaction are associated with self-reported 12-month reported alcohol or 12-month marijuana use in a large late adolescent and young adult sample. This work also considered whether the interpretations meaningfully differed across community-level indicators of disadvantage and affluence for alcohol and marijuana use. Four key findings are observed in these analyses: There was no support for the differential-susceptibility hypothesis, the association between individual- and community-level indicators and substance use meaningfully differed across levels of measurement and post hoc analyses demonstrated that age moderated the association between neighborhood affluence (and disadvantage) and self-reported 12-month alcohol use. This demonstrates that the associations between developmental adversities and substance use are nuanced and dependent on units of analyses.

In the context of individual and community level disadvantage, there was some evidence to support the hypothesis that self-reported alcohol use is negatively associated with disadvantage during the transitional window of adolescence. Specifically, individual-level indicators of parental education had a positive significant association between 12-month reported alcohol use and parental levels of education but there was no significant association with reported history of public assistance. While the association between self-reported alcohol use and parental education is small, it is consistent with some prior work that reported positive associations between self-reported alcohol use and parental education (Humensky, 2010; Kendler et al., 2014; Patrick et al., 2012) but inconsistent with others (Andrabi et al., 2017; Leventhal et al., 2015; Pape et al., 2017). Differences between some findings may be attributed to sample ages (i.e., early versus late adolescents) and types of alcohol use measured (i.e., initiation, lifetime or 12-month). Second, for community-level indications of disadvantage, 12-month alcohol use was negatively associated (in the unweighted analysis) with neighborhood disadvantage but was not significant. This result is consistent with prior work that reported no association (Fagan et al., 2015; Fairman et al., 2020; Jensen et al., 2017). However, in the weighted population analysis the association between community disadvantage and self-reported alcohol use is significant. This difference in unweighted and weighted analyses demonstrates an importance of adjusting for population and attrition weights. Furthermore, in the unweighted population analysis post hoc analyses of age-by-disadvantage interaction demonstrated a robust negative association between neighborhood disadvantage and alcohol use. The latter finding suggests that young adults experiencing higher neighborhood disadvantage report lower alcohol use. This is consistent with previous work reporting lower alcohol use in disadvantaged schools (Coley et al., 2018).

Opposite of the association between neighborhood disadvantage and alcohol use, prior research has reported that community affluence is associated with increased alcohol use in college (Barr, 2018) and affluent school samples (Coley et al., 2018). Analyses from the current study revealed a consistent positive association between neighborhood affluence and self-reported alcohol use in both unweighted/weighted analyses. While speculative, based on prior work (Barr, 2018; Coley et al., 2018; Willoughby et al., 2021), this finding may represent increased use as a function of social circumstances (i.e., college, see Willoughby et al., 2013), being of legal age and greater variation in the data of individuals reporting alcohol use. Future work should evaluate how neighborhood affluence and disadvantage moderate the proportion of adolescents reporting first time use or increased initiation during college and legal drinking age.

Compared to alcohol use, the current analyses demonstrated inconsistent evidence for the hypothesized positive association between marijuana use and individual- and community-level indicators of disadvantage. First, there is a small significant association between 12-month self-reported marijuana use and parental levels of education and reported history of public assistance. This finding is consistent with others reporting a negative association with household SES and marijuana use (Andrabi et al., 2017; Gerra et al., 2020; Lee et al., 2016). However, this effect is no longer significant in the model that includes the association of childhood maltreatment and neighborhood disadvantage in the MLM. Furthermore, no significant association is observed between neighborhood disadvantage (and affluence) and self-reported marijuana use in both unweighted and weighted analyses. Unlike neighborhood disadvantage and alcohol use, the association between marijuana use and neighborhood disadvantage/affluence remained relatively stable across waves. This demonstrates the importance of considering indicators of community-level indicators to understand the covariation amongst parental and household variables for different substances.

While the findings here replicate the association between maltreatment and substance use, there is a more nuanced relationship between individual- and community-level indications of disadvantage. This is consistent with work from a large early adolescent sample showing that different operationalities of individual and community-level factors may result in different conclusions (Demidenko et al., 2022). Based on the results presented in the current study, variation in alcohol use can be explained, to some extent, by affluent individual- and community-level characteristics. For example, the results suggest parental education and neighborhood affluence have a consistent positive association with alcohol use. However, this increase in alcohol use may be a normative part of adolescents during this transitional period (Willoughby et al., 2013; 2021) and may not be representative of early onset (Pape et al., 2017), dependency (Kendler et al., 2014) or neighborhood deterioration (Furr-Holden et al., 2011). Future work should incorporate broader measures of individual- and community-level indicators to fully describe the nuanced relationship between adversities and substance use.

Study Limitations

While the current study uses a large, normative sample of adolescents, there is significant attrition across the waves of data as is common in longitudinal analyses. Wave 2 and Wave 3 samples included only 45% of the Wave 1 sample, respectively. While attrition weights are calculated to address the selective attrition bias, there may be residual uncounted biases which may meaningfully impact the results and interpretations. Estimated effects should be interpreted with this in mind.

In the current study, parental education and history of public assistance are proxy measures of household SES. In large early adolescent samples, individual level adversities such as income-to-needs ratios, parent reported education, and material and economic deprivation do not always fully overlap (DeJoseph et al., 2022). Thus, alternative measures of household SES, such as continuous measures of income and material/economic deprivation may impact the covariation and interpretation of results. For example, in the current study there was no evidence to support the differential-susceptibility hypothesis. This may be a function of the study design and/or the hypothesis may depend on continuous economic variables and their unique interaction with severe maltreatment.

This work used a self-reported measure of 12-month alcohol and 12-month marijuana use. There are several ways to capture use and severity of use. For example, studies may use lifetime, 12-month or 30-day use, binge occasions, time of initiation, family history of dependence, or other measures. These variables have empirical associations in the literature but provide qualitatively different information about the environment and type of behaviors. Furthermore, the linear MLM uses a seven-point ordinal substance use scale as the dependent variable. While simulated studies indicate that the bias in fitting linear models on ordinal outcomes is reduced at 7+ categories (Bauer & Sterba, 2011), this is an important limitation of how the effects are estimated here. Thus, the results from this work should be replicated using different substance use measures to contextualize how severity impacts the direction and magnitude of the effects during the transitional window of adolescence.

Conclusion

Findings remain mixed regarding the relative contributions of community- and individual-level indicators of developmental adversity and the use of alcohol and marijuana. The current study evaluated how different individual-level indicators and community-level indicators of developmental adversities and their interaction are associated with self-reported alcohol or marijuana use in a normative adolescent sample. The analyses demonstrated a nuanced association between individual level and community level adversities and substance use during the transitional window of adolescence. Specifically, there is a consistent positive association between self-reported maltreatment and self-reported alcohol and marijuana use during adolescence. In addition, self-reported alcohol use is positively associated with parental education (as a proxy for household SES) and negatively with neighborhood disadvantage. In post hoc analyses, age moderated the association between alcohol use and neighborhood disadvantage (and affluence). However, the association between individual- and community-level indicators were largely null for self-reported marijuana use. This work demonstrates the importance of including both individual- and community-level indicators of adversities to get a more nuanced understanding of the associations during late adolescence and early adulthood when substance use becomes more prevalent.

Supplementary Material

Supplementary Materials

Acknowledgments

This research was supported, in part, by a grant from the Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD; R01HD075806, D.P. Keating, Principal Investigator). The authors thank Peter Batra, Joshua Hatfield, Meredith House, Kyle Kwaiser, Kathleen LaDronka and the U-M Survey Research Operations staff for their support. Portions of these data were presented at the 2017 biennial meeting of the Society for Research in Child Development.

Funding

This work was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development, Grants R01HD075806 (PI: Keating), K01HD091416 (PI: Maslowsky), and R24HD042849 (to the Population Research Center at the University of Texas at Austin, of which Maslowsky is a faculty affiliate). MD was also supported by the NICHD Developmental Psychology Training Grant (5T32HD007109-34, V.C. McLoyd & C.S. Monk) and the Ruth L. Kirschstein Postdoctoral Individual Research Service Award through the National Institute on Drug Abuse (F32DA055334)

Footnotes

Compliance with Ethical Standards

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Ethical approval

The study has been granted ethical approval by the University of Michigan Institutional review Board.

Informed consent

Informed consent was obtained from parents of all minor participants included in the study. Minor participants also provided assent to participate. Participants aged 18 and older provided informed consent to participate.

Conflict of Interest

The authors declare that they have no conflict of interest

Data Sharing Declaration

The investigators are committed to sharing the data generated through this research, however, data collection is currently ongoing and is not currently publicly available. Under the terms of our grant, we intend to make data available to the wider research community. This includes all self-report, neurocognitive, and imaging parameters which will be included in the database, along with demographic information that does not risk confidentiality.

References

  1. Afifi TO, Henriksen CA, Asmundson GJG, & Sareen J (2012). Childhood Maltreatment and Substance Use Disorders among Men and Women in a Nationally Representative Sample. The Canadian Journal of Psychiatry, 57(11), 677–686. 10.1177/070674371205701105 [DOI] [PubMed] [Google Scholar]
  2. Andersen SL (2019). Stress, sensitive periods, and substance abuse. Neurobiology of Stress, 10, 100140. 10.1016/j.ynstr.2018.100140 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Andrabi N, Khoddam R, & Leventhal AM (2017). Socioeconomic disparities in adolescent substance use: Role of enjoyable alternative substance-free activities. Social Science & Medicine, 176, 175–182. 10.1016/j.socscimed.2016.12.032 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Andrews JA, & Westling E (2016). Substance use in emerging adulthood. In The Oxford handbook of emerging adulthood (pp. 521–542). Oxford University Press. [Google Scholar]
  5. Bachman JG, O’Malley PM, Johnston LD, Schulenberg JE, & Wallace JM (2011). Racial/Ethnic Differences in the Relationship Between Parental Education and Substance Use Among U.S. 8th-, 10th-, and 12th-Grade Students: Findings from the Monitoring the Future Project. Journal of Studies on Alcohol and Drugs, 72(2), 279–285. 10.15288/jsad.2011.72.279 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Barr PB (2018). Neighborhood conditions and trajectories of alcohol use and misuse across the early life course. Health & Place, 51, 36–44. 10.1016/j.healthplace.2018.02.007 [DOI] [PubMed] [Google Scholar]
  7. Bates D, Maechler M, Bolker B, cre, Walker S, Christensen RHB, Singmann H, Dai B, Scheipl F, Grothendieck G, Green P, Fox J, Bauer A, & Krivitsky PN (2020). lme4: Linear Mixed-Effects Models using “Eigen” and S4 (1.1–26) [Computer software]. https://CRAN.R-project.org/package=lme4
  8. Bauer DJ, & Sterba SK (2011). Fitting Multilevel Models with Ordinal Outcomes: Performance of Alternative Specifications and Methods of Estimation. Psychological Methods, 16(4), 373–390. 10.1037/a0025813 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Belsky J (2016). The Differential Susceptibility Hypothesis: Sensitivity to the Environment for Better and for Worse. JAMA Pediatrics, 170(4), 321–322. 10.1001/jamapediatrics.2015.4263 [DOI] [PubMed] [Google Scholar]
  10. Bernstein DP, Fink L, Handelsman L, & Foote J (1994). Childhood Trauma Questionnaire. 10.1037/t02080-000 [DOI] [Google Scholar]
  11. Bernstein DP, Stein JA, Newcomb MD, Walker E, Pogge D, Ahluvalia T, Stokes J, Handelsman L, Medrano M, Desmond D, & Zule W (2003). Development and validation of a brief screening version of the Childhood Trauma Questionnaire. Child Abuse & Neglect, 27(2), 169–190. 10.1016/s0145-2134(02)00541-0 [DOI] [PubMed] [Google Scholar]
  12. Booth A, & Crouter AC (Eds.). (2001). Does it take a village? Community effects on children, adolescents, and families (pp. xi, 263). Lawrence Erlbaum Associates Publishers. [Google Scholar]
  13. Bosque-Prous M, Kuipers MAG, Espelt A, Richter M, Rimpelä A, Perelman J, Federico B, Brugal MT, Lorant V, & Kunst AE (2017). Adolescent alcohol use and parental and adolescent socioeconomic position in six European cities. BMC Public Health, 17(1), 646. 10.1186/s12889-017-4635-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Browning CR, & Cagney KA (2003). Moving beyond Poverty: Neighborhood Structure, Social Processes, and Health. Journal of Health and Social Behavior, 44(4), 552–571. 10.2307/1519799 [DOI] [PubMed] [Google Scholar]
  15. Carliner H, Brown QL, Sarvet AL, & Hasin DS (2017). Cannabis use, attitudes, and legal status in the U.S.: A review. Preventive Medicine, 104, 13–23. 10.1016/j.ypmed.2017.07.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Casey BJ (2015). Beyond Simple Models of Self-Control to Circuit-Based Accounts of Adolescent Behavior. Annual Review of Psychology, 66(1), 295–319. 10.1146/annurev-psych-010814-015156 [DOI] [PubMed] [Google Scholar]
  17. Caspi A, Houts RM, Ambler A, Danese A, Elliott ML, Hariri A, Harrington H, Hogan S, Poulton R, Ramrakha S, Rasmussen LJH, Reuben A, Richmond-Rakerd L, Sugden K, Wertz J, Williams BS, & Moffitt TE (2020). Longitudinal Assessment of Mental Health Disorders and Comorbidities Across 4 Decades Among Participants in the Dunedin Birth Cohort Study. JAMA Network Open, 3(4). 10.1001/jamanetworkopen.2020.3221 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Clarke P, Morenoff J, Debbink M, Golberstein E, Elliott MR, & Lantz PM (2013). Cumulative Exposure to Neighborhood Context: Consequences for Health Transitions over the Adult Life Course. Research on Aging, 36(1), 115–142. 10.1177/0164027512470702 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Coley RL, Sims J, Dearing E, & Spielvogel B (2018). Locating Economic Risks for Adolescent Mental and Behavioral Health: Poverty and Affluence in Families, Neighborhoods, and Schools. Child Development, 89(2), 360–369. 10.1111/cdev.12771 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Cunningham RM, Ranney M, Newton M, Woodhull W, Zimmerman M, & Walton MA (2014). Characteristics of Youth Seeking Emergency Care for Assault Injuries. Pediatrics, 133(1), e96–e105. 10.1542/peds.2013-1864 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Danese A (2020). Annual Research Review: Rethinking childhood trauma-new research directions for measurement, study design and analytical strategies. Journal of Child Psychology and Psychiatry, 61(3), 236–250. 10.1111/jcpp.13160 [DOI] [PubMed] [Google Scholar]
  22. Danese A, & Widom CS (2020). Objective and subjective experiences of child maltreatment and their relationships with psychopathology. Nature Human Behaviour, 4(8), Article 8. 10.1038/s41562-020-0880-3 [DOI] [PubMed] [Google Scholar]
  23. DeJoseph ML, Herzberg MP, Sifre RD, Berry D, & Thomas KM (2022). Measurement matters: An individual differences examination of family socioeconomic factors, latent dimensions of children’s experiences, and resting state functional brain connectivity in the ABCD sample. Developmental Cognitive Neuroscience, 53, 101043. 10.1016/j.dcn.2021.101043 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Deutsch AR (2019). Selection and Socialization Influences on Adolescent Alcohol Use: The Individual and Joint Contexts of Neighborhood Disadvantage and Population Density. Substance Use & Misuse, 54(10), 1663–1678. 10.1080/10826084.2019.1608247 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Dubowitz H, Thompson R, Arria AM, English D, Metzger R, & Kotch JB (2016). Characteristics of Child Maltreatment and Adolescent Marijuana Use: A Prospective Study. Child Maltreatment, 21(1), 16–25. 10.1177/1077559515620853 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Duprey EB, Oshri A, & Caughy MO (2017). Childhood Neglect, Internalizing Symptoms and Adolescent Substance Use: Does the Neighborhood Context Matter? Journal of Youth and Adolescence, 46(7), 1582–1597. 10.1007/s10964-017-0672-x [DOI] [PubMed] [Google Scholar]
  27. Ellis BJ, Sheridan MA, Belsky J, & McLaughlin KA (2022). Why and how does early adversity influence development? Toward an integrated model of dimensions of environmental experience. Development and Psychopathology, 1–25. 10.1017/S0954579421001838 [DOI] [PubMed] [Google Scholar]
  28. Fagan AA, Wright EM, & Pinchevsky GM (2014). The protective effects of neighborhood collective efficacy on adolescent substance use and violence following exposure to violence. Journal of Youth and Adolescence, 43(9), 1498–1512. 10.1007/s10964-013-0049-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Fagan AA, Wright EM, & Pinchevsky GM (2015). Exposure to violence, substance use, and neighborhood context. Social Science Research, 49, 314–326. 10.1016/j.ssresearch.2014.08.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Fairman BJ, Goldstein RB, Simons-Morton BG, Haynie DL, Liu D, Hingson RW, & Gilman SE (2020). Neighbourhood context and binge drinking from adolescence into early adulthood in a US national cohort. International Journal of Epidemiology, 49(1), 103–112. 10.1093/ije/dyz133 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Felitti VJ, Anda RF, Nordenberg D, Williamson DF, Spitz AM, Edwards V, Koss MP, & Marks JS (1998). Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. The Adverse Childhood Experiences (ACE) Study. American Journal of Preventive Medicine, 14(4), 245–258. 10.1016/s0749-3797(98)00017-8 [DOI] [PubMed] [Google Scholar]
  32. Furr-Holden CDM, Lee MH, Milam AJ, Johnson RM, Lee K-S, & Ialongo NS (2011). The growth of neighborhood disorder and marijuana use among urban adolescents: A case for policy and environmental interventions. Journal of Studies on Alcohol and Drugs, 72(3), 371–379. 10.15288/jsad.2011.72.371 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Gerra G, Benedetti E, Resce G, Potente R, Cutilli A, & Molinaro S (2020). Socioeconomic Status, Parental Education, School Connectedness and Individual Socio-Cultural Resources in Vulnerability for Drug Use among Students. International Journal of Environmental Research and Public Health, 17(4), 1306. 10.3390/ijerph17041306 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Gilbert R, Widom CS, Browne K, Fergusson D, Webb E, & Janson S (2009). Burden and consequences of child maltreatment in high-income countries. The Lancet, 373(9657), 68–81. 10.1016/S0140-6736(08)61706-7 [DOI] [PubMed] [Google Scholar]
  35. Green KM, Doherty EE, & Ensminger ME (2017). Long-term consequences of adolescent cannabis use: Examining intermediary processes. The American Journal of Drug and Alcohol Abuse, 43(5), 567–575. 10.1080/00952990.2016.1258706 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Handley ED, Rogosch FA, Guild DJ, & Cicchetti D (2015). Neighborhood Disadvantage and Adolescent Substance Use Disorder: The Moderating Role of Maltreatment. Child Maltreatment, 20(3), 193–202. 10.1177/1077559515584159 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Humensky JL (2010). Are adolescents with high socioeconomic status more likely to engage in alcohol and illicit drug use in early adulthood? Substance Abuse Treatment, Prevention, and Policy, 5, 19. 10.1186/1747-597X-5-19 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Hyde LW, Gard AM, Tomlinson RC, Burt SA, Mitchell C, & Monk CS (2020). An ecological approach to understanding the developing brain: Examples linking poverty, parenting, neighborhoods, and the brain. The American Psychologist, 75(9), 1245–1259. 10.1037/amp0000741 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Jackson N, Denny S, & Ameratunga S (2014). Social and socio-demographic neighborhood effects on adolescent alcohol use: A systematic review of multi-level studies. Social Science & Medicine, 115, 10–20. 10.1016/j.socscimed.2014.06.004 [DOI] [PubMed] [Google Scholar]
  40. Jackson N, Denny S, Sheridan J, Zhao J, & Ameratunga S (2016). The role of neighborhood disadvantage, physical disorder, and collective efficacy in adolescent alcohol use: A multilevel path analysis. Health & Place, 41, 24–33. 10.1016/j.healthplace.2016.07.005 [DOI] [PubMed] [Google Scholar]
  41. Jensen M, Chassin L, & Gonzales NA (2017). Neighborhood Moderation of Sensation Seeking Effects on Adolescent Substance Use Initiation. Journal of Youth and Adolescence, 46(9), 1953–1967. 10.1007/s10964-017-0647-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Johnston LD, Miech RA, O’Malley PM, Bachman JE, & Patrick ME (2020). Monitoring the Future National Survey Results on Drug Use, 1975–2019: Overview, Key Findings on Adolescent Drug Use. In Institute for Social Research. Institute for Social Research. https://eric.ed.gov/?id=ED604018 [Google Scholar]
  43. Kann L, McManus T, Harris WA, Shanklin SL, Flint KH, Queen B, Lowry R, Chyen D, Whittle L, Thornton J, Lim C, Bradford D, Yamakawa Y, Leon M, Brener N, & Ethier KA (2018). Youth Risk Behavior Surveillance—United States, 2017. Morbidity and Mortality Weekly Report. Surveillance Summaries (Washington, D.C.: 2002), 67(8), 1–114. 10.15585/mmwr.ss6708a1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Karriker-Jaffe KJ (2011). Areas of disadvantage: A systematic review of effects of area-level socioeconomic status on substance use outcomes. Drug and Alcohol Review, 30(1), 84–95. 10.1111/j.1465-3362.2010.00191.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Kendler KS, Gardner CO, Hickman M, Heron J, Macleod J, Lewis G, & Dick DM (2014). Socioeconomic status and alcohol-related behaviors in mid- to late adolescence in the Avon Longitudinal Study of Parents and Children. Journal of Studies on Alcohol and Drugs, 75(4), 541–545. 10.15288/jsad.2014.75.541 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Lee JY, Brook JS, Finch SJ, & Brook DW (2016). An Adverse Family Environment During Adolescence Predicts Marijuana Use and Antisocial Personality Disorder in Adulthood. Journal of Child and Family Studies, 25(2), 661–668. 10.1007/s10826-015-0246-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Leventhal AM, Bello MS, Unger JB, Strong DR, Kirkpatrick MG, & Audrain-McGovern J (2015). Diminished Alternative Reinforcement as a Mechanism Underlying Socioeconomic Disparities in Adolescent Substance Use. Preventive Medicine, 80, 75–81. 10.1016/j.ypmed.2015.05.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Little R (2019). Statistical Analysis with Missing Data., 3rd Edition. Wiley. [Google Scholar]
  49. Madigan S, Deneault A-A, Racine N, Park J, Thiemann R, Zhu J, Dimitropoulos G, Williamson T, Fearon P, Cénat JM, McDonald S, Devereux C, & Neville RD (2023). Adverse childhood experiences: A meta-analysis of prevalence and moderators among half a million adults in 206 studies. World Psychiatry, 22(3), 463–471. 10.1002/wps.21122 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. McLaughlin KA, Sheridan MA, & Lambert HK (2014). Childhood Adversity and Neural Development: Deprivation and Threat as Distinct Dimensions of Early Experience. Neuroscience and Biobehavioral Reviews, 47, 578–591. 10.1016/j.neubiorev.2014.10.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. McNeish D, & Wolf MG (2020). Thinking twice about sum scores. Behavior Research Methods. 10.3758/s13428-020-01398-0 [DOI] [PubMed] [Google Scholar]
  52. Miles JN, Weden MM, Lavery D, Escarce JJ, Cagney KA, & Shih RA (2016). Constructing a Time-Invariant Measure of the Socio-economic Status of U.S. Census Tracts. Journal of Urban Health: Bulletin of the New York Academy of Medicine, 93(1), 213–232. 10.1007/s11524-015-9959-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Milliren CE, Richmond TK, Evans CR, Dunn EC, & Johnson RM (2017). Contextual Effects of Neighborhoods and Schools on Adolescent and Young Adult Marijuana Use in the United States. Substance Abuse: Research and Treatment, 11, 1178221817711417. 10.1177/1178221817711417 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Mills KL, Goddings A-L, Herting MM, Meuwese R, Blakemore S-J, Crone EA, Dahl RE, Güroğlu B, Raznahan A, Sowell ER, & Tamnes CK (2016). Structural brain development between childhood and adulthood: Convergence across four longitudinal samples. NeuroImage, 141, 273–281. 10.1016/j.neuroimage.2016.07.044 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Morenoff JD, House JS, Hansen BB, Williams DR, Kaplan GA, & Hunte HE (2007). Understanding social disparities in hypertension prevalence, awareness, treatment, and control: The role of neighborhood context. Social Science & Medicine (1982), 65(9), 1853–1866. 10.1016/j.socscimed.2007.05.038 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Nelson CA, Bhutta ZA, Harris NB, Danese A, & Samara M (2020). Adversity in childhood is linked to mental and physical health throughout life. BMJ, 371, m3048. 10.1136/bmj.m3048 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Newbury JB, Arseneault L, Moffitt TE, Caspi A, Danese A, Baldwin JR, & Fisher HL (2018). Measuring childhood maltreatment to predict early-adult psychopathology: Comparison of prospective informant-reports and retrospective self-reports. Journal of Psychiatric Research, 96, 57–64. 10.1016/j.jpsychires.2017.09.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Oshri A, Duprey EK, Liu S, & Gonzalez A (2020). Chapter 14 - ACEs and resilience: Methodological and conceptual issues. In Asmundson GJG & Afifi TO (Eds.), Adverse Childhood Experiences (pp. 287–306). Academic Press. 10.1016/B978-0-12-816065-7.00014-8 [DOI] [Google Scholar]
  59. Pape H, Norström T, & Rossow I (2017). Adolescent drinking—A touch of social class? Addiction, 112(5), 792–800. 10.1111/add.13721 [DOI] [PubMed] [Google Scholar]
  60. Patrick ME, Couper MP, Laetz VB, Schulenberg JE, O’Malley PM, Johnston LD, & Miech RA (2018). A Sequential Mixed-Mode Experiment in the U.S. National Monitoring the Future Study. Journal of Survey Statistics and Methodology, 6(1), 72–97. 10.1093/jssam/smx011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Patrick ME, Wightman P, Schoeni RF, & Schulenberg JE (2012). Socioeconomic status and substance use among young adults: A comparison across constructs and drugs. Journal of Studies on Alcohol and Drugs, 73(5), 772–782. 10.15288/jsad.2012.73.772 [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. R Core Team. (2022). R: A language and environment for statistical computing. (4.2.1) [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/ [Google Scholar]
  63. Ram N, & Grimm K (2007). Using simple and complex growth models to articulate developmental change: Matching theory to method. International Journal of Behavioral Development, 31(4), 303–316. 10.1177/0165025407077751 [DOI] [Google Scholar]
  64. Scher CD, Forde DR, McQuaid JR, & Stein MB (2004). Prevalence and demographic correlates of childhood maltreatment in an adult community sample. Child Abuse & Neglect, 28(2), 167–180. 10.1016/j.chiabu.2003.09.012 [DOI] [PubMed] [Google Scholar]
  65. Shih RA, Parast L, Pedersen ER, Troxel WM, Tucker JS, Miles JNV, Kraus L, & D’Amico EJ (2017). Individual, peer, and family factor modification of neighborhood-level effects on adolescent alcohol, cigarette, e-cigarette, and marijuana use. Drug and Alcohol Dependence, 180, 76–85. 10.1016/j.drugalcdep.2017.07.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Shin SH, Miller DP, & Teicher MH (2013). Exposure to childhood neglect and physical abuse and developmental trajectories of heavy episodic drinking from early adolescence into young adulthood. Drug and Alcohol Dependence, 127(1–3), 31–38. 10.1016/j.drugalcdep.2012.06.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Si Y, West BT, Veliz P, Patrick ME, Schulenberg JE, Kloska DD, Terry-McElrath YM, & McCabe SE (2022). An empirical evaluation of alternative approaches to adjusting for attrition when analyzing longitudinal survey data on young adults’ substance use trajectories. International Journal of Methods in Psychiatric Research, 31(3), e1916. 10.1002/mpr.1916 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Smith KE, & Pollak SD (2020). Rethinking Concepts and Categories for Understanding the Neurodevelopmental Effects of Childhood Adversity. Perspectives on Psychological Science, 1745691620920725. 10.1177/1745691620920725 [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Substance Abuse and Mental Health Services Administration. (2020). Key substance use and mental health indicators in the United States: Results from the 2019 National Survey on Drug Use and Health ((HHS Publication No. PEP20-07-01-001, NSDUH Series H-55)). Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. https://www.samhsa.gov/data/ [Google Scholar]
  70. Tatian PA (2003). CensusCD Neighborhood Change Database (NCDB): 1970 – 2000 Tract Data. The Urban Institute. https://geolytics.com/media/pdf/NCDB-LF-Data-Users-Guide.pdf [Google Scholar]
  71. Volkow ND, & Blanco C (2023). Substance use disorders: A comprehensive update of classification, epidemiology, neurobiology, clinical aspects, treatment and prevention. World Psychiatry, 22(2), 203–229. 10.1002/wps.21073 [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Wheaton B, & Clarke P (2003). Space Meets Time: Integrating Temporal and Contextual Influences on Mental Health in Early Adulthood. American Sociological Review, 68(5), 680–706. 10.2307/1519758 [DOI] [Google Scholar]
  73. Willoughby T, Heffer T, Good M, & Magnacca C (2021). Is adolescence a time of heightened risk taking? An overview of types of risk-taking behaviors across age groups. Developmental Review, 61, 100980. 10.1016/j.dr.2021.100980 [DOI] [Google Scholar]
  74. Woltman H, Feldstain A, MacKay JC, & Rocchi M (2012). An introduction to hierarchical linear modeling. Tutorials in Quantitative Methods for Psychology, 8(1), 52–69. 10.20982/tqmp.08.1.p052 [DOI] [Google Scholar]
  75. Zhen-Duan J, Colombo D, & Alvarez K (2023). Inclusion of Expanded Adverse Childhood Experiences in Research About Racial/Ethnic Substance Use Disparities. American Journal of Public Health, 113(S2), S129–S132. 10.2105/AJPH.2023.307220 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Materials
NIHMS1966866-supplement-Supplementary_Materials.pdf (1.3MB, pdf)

Data Availability Statement

The investigators are committed to sharing the data generated through this research, however, data collection is currently ongoing and is not currently publicly available. Under the terms of our grant, we intend to make data available to the wider research community. This includes all self-report, neurocognitive, and imaging parameters which will be included in the database, along with demographic information that does not risk confidentiality.

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