Regression tree applications to studying alcohol-related problems among college students.

Machine learning algorithms hold promise for developing precision medicine approaches to addiction treatment yet have been used sparingly to identify predictors of alcohol-related problems. Recursive partitioning, a machine learning algorithm, can identify salient predictors and clinical cut points that can guide treatment. This study aimed to identify predictors and cut points of alcohol-related problems and to examine result stability in two separate, large data sets of college student drinkers (n = 5,090 and 2,808). Four regression trees were grown using the "rpart" package in R. Seventy-one predictors were classified as demographics (e.g., age), alcohol use indicators (e.g., typical quantity/frequency), or psychosocial indicators (e.g., anxiety). Predictors and cut points were extracted and used to manually recreate the tree in the other data set to test result stability. Outcome variables were alcohol-related problems as measured by the Alcohol Use Disorder Identification Test and Brief Young Adult Alcohol Consequences Questionnaire. Coping with depression, conformity motives, binge drinking frequency, typical/heaviest quantity, drunk frequency, serious harm reduction protective behavioral strategies, substance use, and psychosis symptoms best predicted alcohol-related problems across the four trees; coping with depression (cut point range: 1.83-2.17) and binge drinking frequency (cut point range: 1.5-2.5) were the most common splitting variables. Model fit indices suggest relatively stable results accounting for 17%-30% of the variance. Results suggest the nine salient predictors, particularly coping with depression motives scores around 2 and binge drinking frequency around two to three times per month, are important targets to consider when treating alcohol-related problems for college students. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

High test-retest reliability of the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) questionnaire completed by primary care patients in routine care.

BACKGROUND: The Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) is a three-item screening measure of unhealthy alcohol use that is widely used in healthcare settings. Evidence shows high test-retest reliability of the AUDIT-C in research samples, but most studies had limited external validity and used small samples that could not be used to evaluate reliability across demographic subgroups and/or screening modalities. This study evaluates the test-retest reliability of the AUDIT-C completed in routine care in a large primary care sample, including across demographic subgroups defined by age, sex, race, ethnicity, and screening modality (i.e., completed in-clinic or online). METHODS: We used electronic health record (EHR) data from Kaiser Permanente Washington. The sample included 18,491 adult primary care patients who completed two AUDIT-C screens 1-21 days apart as part of routine care in 2021. Test-retest reliability was evaluated for AUDIT-C total scores (0-12) and for a binary measure indicating unhealthy alcohol use (scores ≥3 women, ≥4 men). Using previously established cutoffs, we interpreted reliability coefficients >0.75 as indicating "excellent" reliability. RESULTS: AUDIT-C screens completed in routine care and documented in EHRs had excellent test-retest reliability for total scores (ICC = 0.87, 95% CI: 0.87-0.87) and the binary indicator of unhealthy alcohol use (κ = 0.79, 95% CI: 0.78-0.80). Reliability coefficients were good to excellent across all demographic groups and for in-clinic and online modalities. Higher reliability was seen when both screens were completed through online patient portals (ICC = 0.93, 95% CI: 0.93-0.93) versus in-clinic (ICC = 0.81, 95% CI: 0.79-0.82) or when one screen was completed using each modality (ICC = 0.83, 95% CI: 0.82-0.83). Lower reliability was seen in American Indian/Alaska Native (ICC = 0.82, 95% CI: 0.75-0.87) and multiracial individuals (ICC = 0.82, 95% 0.80-0.84). CONCLUSIONS: In real-world routine care conditions, AUDIT-C screens have excellent test-retest reliability across demographic subgroups and modalities (online and in-clinic). Future research should examine why reliability varies slightly across modalities and demographic subgroups.

Supervisory Alliance as a Moderator of the Effects of Behavioral Rehearsal on TF-CBT Fidelity: Results from a Randomized Trial of Supervision Strategies.

Exposure is an important element of treatment for many evidence-based treatments but can be challenging to implement. Supervision strategies to support exposure delivery may be an important tool to facilitate the use of exposure techniques; however, they must be considered and used in the context of the supervisory alliance. The present study examined relations between supervisory alliance and fidelity to the trauma narrative (TN; i.e., imaginal exposure) component of Trauma-Focused Cognitive Behavioral Therapy. We also examined how supervisory alliance moderated the effect of behavioral rehearsal use in supervision on TN fidelity. We analyzed data from a randomized controlled trial, in which forty-two supervisors and their clinicians (N = 124) from 28 Washington State community-based mental health offices participated. Clinicians were randomized to receive one of two supervision conditions-symptom and fidelity monitoring (SFM) or SFM with behavioral rehearsal (SFM + BR). Supervisory alliance alone did not predict delivery (i.e., occurrence) or extensiveness of delivery of the trauma narrative. Client-focused supervisory alliance moderated the effectiveness of behavioral rehearsal-as client-focused alliance increased, the odds of delivering the TN also increased significantly. Future research should further investigate how to appropriately match supervision techniques with supervisory dyads and explore the interplay of alliance with supervision techniques a supervisor might employ.

Feasibility, Engagement, and Usability of a Remote, Smartphone-Based Contingency Management Program as a Treatment Add-On for Patients Who Use Methamphetamine: Single-Arm Pilot Study.

BACKGROUND: In the United States, methamphetamine-related overdoses have tripled from 2015 to 2020 and continue to rise. However, efficacious treatments such as contingency management (CM) are often unavailable in health systems. OBJECTIVE: We conducted a single-arm pilot study to evaluate the feasibility, engagement, and usability of a fully remotely delivered mobile health CM program offered to adult outpatients who used methamphetamine and were receiving health care within a large university health system. METHODS: Participants were referred by primary care or behavioral health clinicians between September 2021 and July 2022. Eligibility criteria screening was conducted by telephone and included self-reported methamphetamine use on ≥5 out of the past 30 days and a goal of reducing or abstaining from methamphetamine use. Eligible participants who agreed to take part then completed an initial welcome phase that included 2 videoconference calls to register for and learn about the CM program and 2 "practice" saliva-based substance tests prompted by a smartphone app. Participants who completed these welcome phase activities could then receive the remotely delivered CM intervention for 12 consecutive weeks. The intervention included approximately 24 randomly scheduled smartphone alerts requesting a video recording of themselves taking a saliva-based substance test to verify recent methamphetamine abstinence, 12 weekly calls with a CM guide, 35 self-paced cognitive behavioral therapy modules, and multiple surveys. Financial incentives were disbursed via reloadable debit cards. An intervention usability questionnaire was completed at the midpoint. RESULTS: Overall, 37 patients completed telephone screenings, with 28 (76%) meeting the eligibility criteria and consenting to participate. Most participants who completed a baseline questionnaire (21/24, 88%) self-reported symptoms consistent with severe methamphetamine use disorder, and most had other co-occurring non-methamphetamine substance use disorders (22/28, 79%) and co-occurring mental health disorders (25/28, 89%) according to existing electronic health records. Overall, 54% (15/28) of participants successfully completed the welcome phase and were able to receive the CM intervention. Among these participants, engagement with substance testing, calls with CM guides, and cognitive behavioral therapy modules varied. Rates of verified methamphetamine abstinence in substance testing were generally low but varied considerably across participants. Participants reported positive opinions about the intervention's ease of use and satisfaction with the intervention. CONCLUSIONS: Fully remote CM can be feasibly delivered within health care settings lacking existing CM programs. Although remote delivery may help reduce barriers to treatment access, many patients who use methamphetamine may struggle to engage with initial onboarding. High rates of co-occurring psychiatric conditions in the patient population may also contribute to uptake and engagement challenges. Future efforts could leverage greater human-to-human connection, more streamlined onboarding procedures, larger incentives, longer durations, and the incentivization of non-abstinence-based recovery goals to increase uptake and engagement with fully remote mobile health-based CM.

Increased coping motives during the COVID-19 pandemic widen cannabis disparities between sexual minoritized and nonminoritized young adults: A bimonthly assessment of data preceding and spanning the pandemic.

OBJECTIVE: Since the start of the coronavirus pandemic, some U.S. adults have increased alcohol and cannabis use frequency to cope with distress. Among sexual minoritized young adults (SM YAs), coping-related use may be greater due to disproportionate negative social and financial consequences of the pandemic. Nonetheless, it remains unclear whether pandemic substance use has increased among SM YAs compared to non-SM YAs relative to prepandemic levels and whether heightened coping motives mediate these potential differences. METHOD: A total of 563 YAs (18-24 years at baseline; 31.0% SM) provided survey data collected across 12 bimonthly assessments. Six assessments were measured in 2015 or 2016 and six across the coronavirus pandemic (2020-2021). Controlling for prepandemic assessments matched by calendar month, latent structural equation models examined group differences in alcohol and cannabis frequency and consequences across the COVID-19 period and tested coping motives as mediators of these differences. RESULTS: Substance use and consequences were similar during the pandemic relative to prepandemic levels across groups. Nonetheless, compared to non-SM individuals, SM participants reported greater cannabis frequency, consequences, and cannabis coping motives during the pandemic independent of prepandemic levels. Cannabis use and consequences were each explained largely by coping motives during the pandemic among SM compared to non-SM YAs. These patterns were not found for alcohol outcomes. CONCLUSIONS: The COVID-19 pandemic has widened cannabis disparities between SM and non-SM YAs, due in part to pandemic-related increases in coping motives. Responsive public policy is needed that may prevent and remit SM cannabis disparities during societal crises. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Clouding Up Cognition? Secondhand Cannabis and Tobacco Exposure Related to Cognitive Functioning in Youth.

Background: Increasing legalization of cannabis, in addition to longstanding rates of tobacco use, raises concerns for possible cognitive decrements from secondhand smoke or environmental exposure, although little research exists. We investigate the relation between cognition and secondhand and environmental cannabis and tobacco exposure in youth. Methods: The Adolescent Brain Cognitive Development (ABCD) Study year 2 follow-up (N = 5580; 48% female) cognitive performance and secondhand or environmental cannabis or tobacco exposure data were used. Principal components analysis identified a global cognition factor. Linear mixed-effects models assessed global cognition and individual cognitive task performance by cannabis and/or tobacco environmental exposure. Sociodemographics and other potential confounds were examined. p values were adjusted using the false discovery rate method. Results: Global cognition was not related to any exposure group after testing corrections and considering confounds. Beyond covariates and family- and site-level factors, secondhand tobacco was related to poorer visual memory (p = .02), and environmental tobacco was associated with poorer visuospatial (p = .02) and language (p = .008) skills. Secondhand cannabis was related to cognition, but not after controlling for potential confounders (p > .05). Environmental cannabis was related to better oral reading (p = .01). Including covariates attenuated effect sizes. Conclusions: Secondhand tobacco exposure was associated with poorer visual memory, while environmental tobacco exposure was related to poorer language and visuospatial skills. Secondhand cannabis was not related to cognition after controlling for sociodemographic factors, but environmental cannabis exposure was related to better reading. Because, to our knowledge, this is the first known study of its kind and thus preliminary, secondhand cannabis should continue to be investigated to confirm results.

Assessing cross-lagged associations between depression, anxiety, and binge drinking in the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study.

BACKGROUND: Between 20 % and 30 % of teens suffer from depression or anxiety before reaching adulthood, and up to half also use or misuse alcohol. Although theories suggest bidirectional links between harmful alcohol use (e.g., binge drinking) and internalizing symptoms (i.e., depression and anxiety), empirical evidence to-date has been mixed. Systematic reviews have attributed mixed findings to limitations in study design, such as the utilization of between-person analyses and the focus on unidirectional effects. The goal of this study was to address these limitations by assessing bidirectional within-person associations between internalizing symptoms and binge drinking over the course of 5 years in the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) sample, a large cohort recruited at ages 12-21 and followed annually on substance use and psychiatric functioning. METHODS: We used latent curve models with structured residuals to examine within-person lagged associations between depression, anxiety, and past month counts of binge drinking using NCANDA data (N = 831). Analyses were supplemented with post-hoc power simulations. RESULTS: We found marginal evidence linking binge drinking with subsequent depression symptoms one year later among females. We found no evidence that depression or anxiety predicted subsequent binge drinking despite sufficient power. CONCLUSIONS: Social and cognitive consequences of binge drinking may predict later depression symptoms in adolescence and young adulthood for young women, though there was little evidence favoring self-medication models for binge drinking. We note several moderating variables and common factor mechanisms that may better explain this link.

Location matters: Regional variation in association of community burden of COVID-19 with caregiver and youth worry.

Our study characterized associations between three indicators of COVID-19's community-level impact in 20 geographically diverse metropolitan regions and how worried youth and their caregivers in the Adolescent Brain Cognitive Development℠ Study have been about COVID-19. County-level COVID-19 case/death rates and monthly unemployment rates were geocoded to participants' addresses. Caregivers' (vs. youths') COVID-19-related worry was more strongly associated with COVID-19's community impact, independent of sociodemographics and pre-pandemic anxiety levels, with these associations varying by location. Public-health agencies and healthcare providers should avoid adopting uniform "one-size-fits-all" approaches to addressing COVID-19-related emotional distress and must consider specific communities' needs, challenges, and strengths.

The partial derivative framework for substantive regression effects.

egression models are ubiquitous in the psychological sciences. The standard practice in reporting and interpreting regression models are to present and interpret coefficient estimates and the associated standard errors, confidence intervals and p-values. However, coefficient estimates have limited inferential utility if the outcome is modeled nonlinearly with respect to the substantively interpreted predictors. This is problematic in common modeling strategies, such as nonlinear predictor designs and/or generalized linear models. In the former, coefficients may correspond to product, power, log, and/or exponentially transformed units. In the latter, the relationship between the predictors and outcome are modeled via a function of the outcome, rather than the outcome in its original units. In both cases, the interpretation of the coefficients alone do not provide straightforward summaries of the data, and in fact may be misleading. We address these issues by developing a framework of regression effects by integrating two critical features. First, we explicitly model substantive variables in the units that provide the desired interpretation. Second, we use partial derivatives to summarize the relations between the substantive predictors and outcome variables to account for nonlinearities arising from modeling strategies. We show how to derive estimates and standard errors for quantities of interest in the interpretive units, as well as techniques to present the relationships between variables in meaningful ways. Finally, we provide demonstrations in both simulated and real data over a wide variety of models and estimation procedures. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Resilience to COVID-19: Socioeconomic Disadvantage Associated With Positive Caregiver-Youth Communication and Youth Preventative Actions.

Socioeconomic disadvantage is associated with larger COVID-19 disease burdens and pandemic-related economic impacts. We utilized the longitudinal Adolescent Brain Cognitive Development Study to understand how family- and neighborhood-level socioeconomic disadvantage relate to disease burden, family communication, and preventative responses to the pandemic in over 6,000 youth-caregiver dyads. Data were collected at three timepoints (May-August 2020). Here, we show that both family- and neighborhood-level disadvantage were associated with caregivers' reports of greater family COVID-19 disease burden, less perceived exposure risk, more frequent caregiver-youth conversations about COVID-19 risk/prevention and reassurance, and greater youth preventative behaviors. Families with more socioeconomic disadvantage may be adaptively incorporating more protective strategies to reduce emotional distress and likelihood of COVID-19 infection. The results highlight the importance of caregiver-youth communication and disease-preventative practices for buffering the economic and disease burdens of COVID-19, along with policies and programs that reduce these burdens for families with socioeconomic disadvantage.

The Pandemic's Toll on Young Adolescents: Prevention and Intervention Targets to Preserve Their Mental Health.

PURPOSE: Adolescence is characterized by dramatic physical, social, and emotional changes, making teens particularly vulnerable to the mental health effects of the COVID-19 pandemic. This longitudinal study identifies young adolescents who are most vulnerable to the psychological toll of the pandemic and provides insights to inform strategies to help adolescents cope better in times of crisis. METHODS: A data-driven approach was applied to a longitudinal, demographically diverse cohort of more than 3,000 young adolescents (11-14 years) participating in the ongoing Adolescent Brain Cognitive Development Study in the United States, including multiple prepandemic visits and three assessments during the COVID-19 pandemic (May-August 2020). We fitted machine learning models and provided a comprehensive list of predictors of psychological distress in individuals. RESULTS: Positive affect, stress, anxiety, and depressive symptoms were accurately detected with our classifiers. Female sex and prepandemic internalizing symptoms and sleep problems were strong predictors of psychological distress. Parent- and youth-reported pandemic-related psychosocial factors, including poorer quality and functioning of family relationships, more screen time, and witnessing discrimination in relation to the pandemic further predicted youth distress. However, better social support, regular physical activities, coping strategies, and healthy behaviors predicted better emotional well-being. DISCUSSION: Findings highlight the importance of social connectedness and healthy behaviors, such as sleep and physical activity, as buffering factors against the deleterious effects of the pandemic on adolescents' mental health. They also point to the need for greater attention toward coping strategies that help the most vulnerable adolescents, particularly girls and those with prepandemic psychological problems.

Interpreting Interaction Effects in Generalized Linear Models of Nonlinear Probabilities and Counts.

Psychology research frequently involves the study of probabilities and counts. These are typically analyzed using generalized linear models (GLMs), which can produce these quantities via nonlinear transformation of model parameters. Interactions are central within many research applications of these models. To date, typical practice in evaluating interactions for probabilities or counts extends directly from linear approaches, in which evidence of an interaction effect is supported by using the product term coefficient between variables of interest. However, unlike linear models, interaction effects in GLMs describing probabilities and counts are not equal to product terms between predictor variables. Instead, interactions may be functions of the predictors of a model, requiring nontraditional approaches for interpreting these effects accurately. Here, we define interactions as change in a marginal effect of one variable as a function of change in another variable, and describe the use of partial derivatives and discrete differences for quantifying these effects. Using guidelines and simulated examples, we then use these approaches to describe how interaction effects should be estimated and interpreted for GLMs on probability and count scales. We conclude with an example using the Adolescent Brain Cognitive Development Study demonstrating how to correctly evaluate interaction effects in a logistic model.

Making sense of some odd ratios: A tutorial and improvements to present practices in reporting and visualizing quantities of interest for binary and count outcome models.

OBJECTIVE: Generalized linear models (GLMs) such as logistic and Poisson regression are among the most common statistical methods for modeling binary and count outcomes. Though single-coefficient tests (odds ratios, incidence rate ratios) are the most common way to test predictor-outcome relations in these models, they provide limited information on the magnitude and nature of relations with outcomes. We assert that this is largely because they do not describe direct relations with quantities of interest (QoIs) such as probabilities and counts. Shifting focus to QoIs makes several critical nuances of GLMs more apparent. METHOD: To bolster interpretability of these models, we provide a tutorial on logistic and Poisson regression and suggestions for enhancements to current reporting practices for predictor-outcome relations in GLMs. RESULTS: We first highlight differences in interpretation between traditional linear models and GLMs, and describe common misconceptions about GLMs. In particular, we highlight that link functions (a) introduce nonconstant relations between predictors and outcomes and (b) make predictor-QoI relations dependent on levels of other covariates. Each of these properties causes interpretation of GLM coefficients to diverge from interpretations of linear models. Next, we argue for a more central focus on QoIs (probabilities and counts). Finally, we propose and provide graphics and tables, with sample R code, for enhancing presentation and interpretation of QoIs. CONCLUSIONS: By improving present practices in the reporting of predictor-outcome relations in GLMs, we hope to maximize the amount of actionable information generated by statistical analyses and provide a tool for building a cumulative science of substance use disorders. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Longitudinal Impact of Childhood Adversity on Early Adolescent Mental Health During the COVID-19 Pandemic in the ABCD Study Cohort: Does Race or Ethnicity Moderate Findings?

BACKGROUND: During the COVID-19 pandemic in the United States, mental health among youth has been negatively affected. Youth with a history of adverse childhood experiences (ACEs), as well as youth from minoritized racial-ethnic backgrounds, may be especially vulnerable to experiencing COVID-19-related distress. The aims of this study are to examine whether exposure to pre-pandemic ACEs predicts mental health during the COVID-19 pandemic in youth and whether racial-ethnic background moderates these effects. METHODS: From May to August 2020, 7983 youths (mean age, 12.5 years; range, 10.6-14.6 years) in the Adolescent Brain Cognitive Development (ABCD) Study completed at least one of three online surveys measuring the impact of the pandemic on their mental health. Data were evaluated in relation to youths' pre-pandemic mental health and ACEs. RESULTS: Pre-pandemic ACE history significantly predicted poorer mental health across all outcomes and greater COVID-19-related stress and impact of fears on well-being. Youths reported improved mental health during the pandemic (from May to August 2020). While reporting similar levels of mental health, youths from minoritized racial-ethnic backgrounds had elevated COVID-19-related worry, stress, and impact on well-being. Race and ethnicity generally did not moderate ACE effects. Older youths, girls, and those with greater pre-pandemic internalizing symptoms also reported greater mental health symptoms. CONCLUSIONS: Youths who experienced greater childhood adversity reported greater negative affect and COVID-19-related distress during the pandemic. Although they reported generally better mood, Asian American, Black, and multiracial youths reported greater COVID-19-related distress and experienced COVID-19-related discrimination compared with non-Hispanic White youths, highlighting potential health disparities.

Early Adolescent Substance Use Before and During the COVID-19 Pandemic: A Longitudinal Survey in the ABCD Study Cohort.

PURPOSE: Evaluate changes in early adolescent substance use during the coronavirus disease 2019 (COVID-19) pandemic using a prospective, longitudinal, nationwide cohort. METHODS: Participants were enrolled in the Adolescent Brain Cognitive Development Study. A total of 7,842 youth (mean age = 12.4 years, range = 10.5-14.6) at 21 study sites across the U.S. completed a three-wave assessment of substance use between May and August 2020. Youth reported whether they had used alcohol, nicotine, cannabis, or other substances in the past 30 days. Data were linked to prepandemic surveys that the same youth had completed in the years 2018-2020, before the advent of the COVID-19 pandemic. RESULTS: Past-30-day substance use remained stable in the 6 months since stay-at-home orders were first issued in U.S. states/counties; was primarily episodic (1-2 days in the past month); and was typically limited to a single substance. Using pretest/posttest and age-period designs, we found that compared to before the pandemic, fewer youth were using alcohol and more youth were using nicotine or misusing prescription drugs. During the pandemic, youth were more likely to use substances when they were more stressed by pandemic-related uncertainty; their family experienced material hardship; their parents used alcohol or drugs; or they experienced greater depression or anxiety. Neither engagement in social distancing nor worry about COVID-19 infection was associated with substance use. Several risk factors were stronger among older (vs. younger) adolescents. CONCLUSIONS: Among youth in early adolescence, advent of the COVID-19 pandemic was associated with decreased use of alcohol and increased use of nicotine and misuse of prescription drugs.

Preliminary Evidence for Cannabis and Nicotine Urinary Metabolites as Predictors of Verbal Memory Performance and Learning Among Young Adults.

OBJECTIVE: Verbal memory deficits are linked to cannabis use. However, self-reported episodic use does not allow for assessment of variance from other factors (e.g., cannabis potency, route of consumption) that are important for assessing brain-behavior relationships. Further, co-occurring nicotine use may moderate the influence of cannabis on cognition. Here we utilized objective urinary measurements to assess the relationship between metabolites of cannabis, 11-nor-9-carboxy-∆9-tetrahydrocannabinol (THCCOOH), and nicotine (cotinine) on verbal memory in young adults. METHOD: Adolescents and young adults (n = 103) aged 16-22 completed urinary drug testing and verbal memory assessment (RAVLT). Linear regressions examined the influence of THCCOOH and cotinine quantitative concentrations, and their interaction, on RAVLT scores, controlling for demographics and alcohol. Cannabis intake frequency was also investigated. Secondary analyses examined whether past month or recency of use related to performance, while controlling for THCCOOH and cotinine concentrations. RESULTS: THCCOOH concentration related to both poorer total learning and long delay recall. Cotinine concentration related to poorer short delay recall. Higher frequency cannabis use status was associated with poorer initial learning and poorer short delay. When comparing to self-report, THCCOOH and cotinine concentrations were negatively related to learning and memory performance, while self-report was not. CONCLUSIONS: Results confirm the negative relationship between verbal memory and cannabis use, extending findings with objective urinary THCCOOH, and cotinine concentration measurements. No moderating relationship with nicotine was found, though cotinine concentration independently associated with negative short delay performance. Findings support the use of both urinary and self-report metrics as complementary methods in substance use research.

Associations of developmental imbalance between sensation seeking and premeditation in adolescence and heavy episodic drinking in emerging adulthood.

BACKGROUND: Dual systems theories suggest that greater imbalance between higher reward sensitivity and lower cognitive control across adolescence conveys risk for behaviors such as heavy episodic drinking (HED). Prior research demonstrated that psychological analogues of these systems, sensation seeking and premeditation, change from childhood through emerging adulthood, and each has been independently linked with HED. However, few studies have assessed whether change over time in these developing analogues is prospectively associated with HED. Moreover, we know of no research that has shown whether within-person differences between higher sensation seeking and relatively lower premeditation across the adolescent period predict HED in emerging adulthood. METHODS: Prospective data from the National Consortium on Alcohol and NeuroDevelopment in Adolescence study (n = 715) were used to examine the association of sensation seeking and premeditation with HED among adolescents ages 16 to 20 years. We used novel applications of latent difference score modeling and growth curve analysis to test whether increasing sensation seeking, premeditation, and their imbalance over time are associated with HED across the study period, and whether these associations differed by sex. RESULTS: Whereas premeditation increased linearly from adolescence through emerging adulthood across sexes, males reported growth and females reported decline in sensation seeking. Sensation seeking in adolescence (and not premeditation) was associated with higher levels of HED by emerging adulthood. Importantly, greater imbalance between sensation seeking and premeditation was associated with higher levels of HED by emerging adulthood though we note that variability capturing this imbalance correlated highly (r = 0.86) with baseline levels of sensation seeking. CONCLUSIONS: Developmental imbalance between higher sensation seeking and lower premeditation in late adolescence may be a risk factor for greater HED in emerging adulthood.

Distress Tolerance as a Mechanism Linking Violence Exposure to Problematic Alcohol use in Adolescence.

Adolescents exposed to violence are at elevated risk of developing most forms of psychopathology, including depression, anxiety, and alcohol abuse. Prior research has identified emotional reactivity and difficulties with emotion regulation as core mechanisms linking violence exposure with psychopathology. Scant research has examined behavioral responses to distress as a mechanism in this association. This study examined the association of violence exposure with distress tolerance-the ability to persist in the face of distress-and whether lower distress tolerance linked violence exposure with subsequent increases in depression, anxiety, and alcohol abuse problems during adolescence. Data were collected prospectively in a sample of 287 adolescents aged 16-17 (44.3% male; 40.8% White). At Time 1, participants provided self-report of demographics, violence exposure, and psychopathology, and completed a behavioral measure of distress tolerance, the Paced Auditory Serial Addition Task. Four months later, participants (n = 237) repeated the psychopathology assessments. Violence exposure was associated with lower distress tolerance (ß = -.21 p = .009), and elevated concurrent psychopathology (ß = .16-.45, p = .001-.004). Low distress tolerance was prospectively associated with greater likelihood of abusing alcohol over time (OR = .63, p = .021), and mediated the association between violence exposure and greater levels (ß = .02, 95% CI [.001, .063]) and likelihood (OR = .03, 95% CI [.006, .065]) of alcohol use over time. In contrast, low distress tolerance was not associated concurrently or prospectively with internalizing symptoms. Results persisted after controlling for socio-economic status. Findings suggest that distress tolerance is shaped by early experiences of threat and plays a role in the association between violence exposure and development of problematic alcohol use in adolescence.

Substance use and sexual minority status: examining the mediating roles of stress and emotion dysregulation in young adult women.

Sexual minority women (SMW) report higher rates of substance use and disorder across the lifespan, and greater levels of minority stress in adolescence and young adulthood. Minority stress mediation models propose that higher levels of social stressors may increase emotion dysregulation, which in turn increases the propensity toward substance misuse. Few studies, however, have prospectively examined the impact of stressors and emotion dysregulation among SMW on early and escalating substance use. This longitudinal study examined whether emotion dysregulation and social stress mediated the association between sexual minority status and developing substance use (ages 17 through 22 years) in a sample of 2,201 heterosexual and 246 SMW participants in the Pittsburgh Girls Study. Results supported serial mediation processes of marijuana use risk: SMW reported higher levels of social stress in late adolescence, which in turn predicted greater emotion dysregulation that was associated with greater marijuana use by young adulthood.

Beyond path diagrams: Enhancing applied structural equation modeling research through data visualization.

INTRODUCTION: Structural equation modeling (SEM) is a multivariate data analytic technique used in many domains of addictive behaviors research. SEM results are usually summarized and communicated through statistical tables and path diagrams, which emphasize path coefficients and global fit without showing specific quantitative values of data points that underlie the model results. Data visualization methods are often absent in SEM research, which may limit the quality and impact of SEM research by reducing data transparency, obscuring unexpected data anomalies and unmodeled heterogeneity, and inhibiting the communication of SEM research findings to research stakeholders who do not have advanced statistical training in SEM. METHODS AND RESULTS: In this report, we show how data visualization methods can address these limitations and improve the quality of SEM-based addictive behaviors research. We first introduce SEM and data visualization methodologies and differentiate data visualizations from model visualizations that are commonly used in SEM, such as path diagrams. We then discuss ways researchers may utilize data visualization in SEM research, including by obtaining estimates of latent variables and by visualizing multivariate relations in two-dimensional figures. R syntax is provided to help others generate data visualizations for several types of effects commonly modeled in SEM, including correlation, regression, moderation, and simple mediation. DISCUSSION: The techniques outlined here may help spur the use of data visualization in SEM-based addictive behaviors research. Using data visualization in SEM may enhance methodological transparency and improve communication of research findings.