1. Phenotypic Differences as Moderators of the Association Between Social Context
in Early Adolescence and Escalations in Alcohol Use
Matthew D. Scalco & Craig R. Colder
Psychology Department, State University of New York at Buffalo U B
University at Buffalo
State University of New York U B
University at Buffalo
State University of New York
BACKGROUND
RESULTS
Effortful Control x AUPP
Trajectories of Alcohol Use (AU) and Alcohol Use Disorder (AUD) in Adolescence
Structure of the Two Part Latent Growth Curve Model
Probability of initiation of AU increases across adolescence
There is variability in levels of use and trajectories of AU after initiation occurs (e.g., Colder et al., 2002)
Only a portion of youth meet criteria for AUD (Anthony, Chen, & Storr, 2005; Sung et al., 2004)
AUD symptoms are unusual prior to age 13 and increase to a peak between (Sung et al., 2004)
Factors that can predict youth who will escalate in levels of AU and who are at increased risk for AUD after initiation are of interest to prevention and intervention efforts
Both latent growth curves were parameterized such that the first indicator was set to 0 and the last indicator was set to 1. Remaining indicators were freely estimated to model the non-linear change that was apparent form descriptive statistics in each portion, which will now be referred to as the Binary (initiation of AU) and Continuous (levels of AU) portions, respectively. Nested chi-square tests suggested that all intercepts and growth factors had significant variability around the mean trends. Subsequently, mean centered IVs and interaction terms were added as predictors of each Intercept and Growth factor from each portion.
B_Intercept
B_Growth
C_Intercept
C_Growth
AUPP
0.24
-0.15
0.28
0.01
CFAU
0.18
-0.16
0.08
0.06 EC
-0.05
0.03
0.00 NA
0.12
-0.14
-0.21
SUR
0.07
0.02
SUR x CFAU
-0.03
0.04
0.15
SUR x AUPP
-0.10
NA x CFAU
-0.04
0.05
0.10
NA x AUPP
-0.07
0.09
EC x CFAU
EC x AUPP
0.16
-0.20
AGE
0.19
0.27
0.21
INTERCEPTS
10.92
-1.47
2.83
R-Squared
0.20
0.32
Developmental Theory of AU and AUD
Deviations in temperament are believed to interact with the environment, especially the social context in early adolescence to impact the liability for early initiation, escalation of AU, and AUD (Dawes et al., 2000; Vanyukov et al., 2012)
Log Transformed Quantity by
Frequency of Alcohol Use
Temperament
Surgency (SUR)
High Intensity Pleasure, Shyness, and Affiliation (reward sensitivity, extraversion)
Effortful Control (EC)
Attention, Inhibitory Control, Activational Control (executive functioning)
Negative Affect (NA)
Fear, Frustration/Irritability (emotional distress)
SUR and EC have been linked to the initiation and escalation of AU
Note. Bolded values = p < 0.05 and italicized and bolded values are p > 0.05 and p < B = Binary or Initiation of AU and C = Continuous or Levels of AU. CFAU = close friend alcohol use; AUPP = alcohol use with parents permission; SUR = Surgency; EC = Effortful Control; and Negative Affect = NA.
Social Factors
Surgency x CFAU
Close friend AU (CFAU) and AU with parents permission (AUPP)
Each linked to initiation and CFAU to escalation of SU (Cruz et al., 2012; Scalco et al., 2015; Zehe et al., 2015)
Hypothesized to operate via socialization mechanisms
Different temperaments may increase or decrease risk for this process
Synthesis
Summary of Results
Superimposing developmental theories of AU and AUD on the empirical literature suggests:
Temperament x CFAU/temperament x AUPP may predict who will have escalations in AU
Testing this question requires separating initiation of AU from growth in levels of AU
Two part growth model (Olsen & Schafer, 2001)
As hypothesized the social factors had main effects on initiation at baseline and growth in the probability of initiating, but interacted with temperament to predict levels of AU
The one exception is EC x AUPP which predicted all growth outcomes
The combination of (1) CFAU with high SUR and (2) AUPP with either high SUR or low EC led to higher levels of AU at baseline and throughout adolescence
Low SUR in conjunction with CFAU and high SUR without CFAU = increases in AU
But did not surpass the sample level trend until later in adolescence (16 – 17)
Log Transformed Quantity by
Frequency of Alcohol Use
Study Hypotheses
H1: Surgency, Effortful Control, Negative Affect, Close Friend AU, and AU with parents permission will all predict early initiation of AU without parents permission
High Surgency, Low Effortful Control, Low Negative Affect, High CFAU, and
AUPP will all predict higher probability of AU initiation
H2: Interactions, Surgency x CFAU, Surgency x AUPP, Effortful Control x CFAU, Effortful Control x AUPP, Negative Affect x CFAU, and Negative Affect x AUPP will predict escalations in AU, specifically
Combinations of the predictions above (e.g., High Surgency and High CFAU) will lead to increases in levels of AU across adolescence (age: 11-18)
CONCLUSIONS
Developmental Theories and Trajectories of Alcohol Use (AU) in Adolescence
There were largely main effects for initiation of AU
Social environment impacts early initiation of AU regardless of temperament
Poor regulation + parental permission for AU further increased risk for initiation
However, levels of AU were predicted by several interactions
High SUR + CFAU and AUPP = highest risk
Low EC + AUPP = second highest risk
Supports developmental theories which suggest that the social environment and temperament interact to deflect trajectories toward and away from risk for AUD
Surgency x AUPP
METHOD
Social Exclusion Condition
Control Condition
Participants were drawn from two six wave longitudinal studies designed to test risk and protective factors for adolescent SU. Inclusion criteria were a 10 to 12 year-old child at the time of recruitment with no language or physical disabilities that would preclude participation. The 2 community samples included 387 and 378 families for a total of 765 families. The mean age for adolescents at the first assessment was 11.8 (SD = .79). Subsequent assessments occurred annually. At W1-W3 for both samples, target adolescents provided the names of four close friends and one was recruited into the study (close friend peer) to provide collateral reports. Peers were required to be within two years of age of the target adolescent and could not be a sibling. Attrition rate by W3 was 6.5% and completers vs. non-completers did not differ on any demographic or study variables.
Measures
Implications and Future Directions
Log Transformed Quantity by
Frequency of Alcohol Use
Adolescent and Peer SU. Items taken from The National Youth Survey (NYS) were used to assess self-reported peer and adolescent lifetime and past year quantity and frequency of alcohol use “without parents permission” at each wave of assessment (Elliott & Huizinga, 1983). Lifetime use of alcohol with parental permission was assessed with a dichotomous item at W1.
Temperament. The Early Adolescent Temperament Questionnaire-Revised (EATQ-R, Ellis & Rothbart, 2001), a parent report measure, was used to assess target adolescent temperament at W1. The EATQ-R includes 8 subscales, which combine to form three broad dimensions, including surgency, negative affect, and effortful control (Cronbach’s alpha were .83, .73, and .89 respectively). Evidence for the validity of this structure has been provided with high test retest correlations at the latent level (0.81 – 0.94; M = 0.88; Scalco, Lengua, & Colder, 2015).
AU with parent permission in the context of poor regulation or high reward sensitivity and extraversion led to increases in levels of AU without permission
Although more research is needed, this may suggest that “supervised drinking” in the context of certain traits in early adolescence (Mage = ) promotes problematic drinking
Close friend AU in the context of high reward sensitivity and extraversion also led to higher initial levels of AU (Mage = 12.8) and steady increases throughout adolescence
Future work may replicate, look at contextual variables (e.g., parties, dinner, etc.) related to early AU with parents permission, continuous measures of AU with parents permission, and whether temperament/social contexts transacts with AU
Control Condition
Data Analytic Plan
A Two-Part Latent Growth Curve Model was run on qxf indices across 6 waves of longitudinal data (W1-W6).
Binary portion = W1-W6 and Continuous portion = W2-W6 (allows for temporal precedence; moreover, levels of AU were very low at W1)
The Santora Bentler Chi-square test was used to test whether adding variances and covariances improved model fit.
Qxf indices were log transformed to meet distributional assumptions as is common in these models (Olsen & Schafer, 2001; Muthen, 2001).
Age served as a control variable for all models.
Surgency, Effortful Control, Negative Affect, CFAU and AUPP were then added as predictors of the growth factors.
All IV’s were mean centered.
Finally all 2-way interactions were added. For significant interactions:
Model implied trajectories were plotted at combinations of the moderator (-1 SD, +1 SD) and IVs (USE/NO USE).
This research was supported by two grants from NIDA (R01 DA and R01 DA019631) awarded to Dr. Craig Colder. The content of this poster is solely the responsibility of the authors and does not necessarily represent the official views of NIDA. Correspondence should be addressed to Matthew Scalco, B.A., Psychology Department, Park Hall, University at Buffalo, State University of New York, Buffalo, NY