Alcohol likely has similar deleterious consequences on the brain. The present dose-dependent associations are consistent with our previous findings, as Squeglia et al., found decreases in cortical thickness estimates associated with heavy episodic alcohol use in males,and accelerated declining brain volume trajectories in a large prospective investigation examining individuals who transitioned to heavy drinking .Alcohol likely interferes with neural development of the cerebral cortex, and thinner cortices observed with more cumulative use reported may represent non-beneficial pruning and/or inhibition of cell generation or cell death.Limitations of the present study include self-report of substance use, which can introduce measurement error. Further, while this study was prospective, participants were not assessed prior to initiation of substance use. However, previous work in our laboratory finds marijuana-related associations with white matter integrity in a sample of individuals assessed pre- and post-initiation of substance use.Nevertheless, future work should determine the influence of pre-existing differences on cortical metrics. The current investigation included users of both marijuana and alcohol, and despite controlling for alcohol use, it remains unclear what is precisely the result of marijuana as compared to the combination of co-occurring marijuana and alcohol use. Our sample was predominately male,however gender should be evaluated and future studies will focus on differential gender effects on brain morphometry in adolescent marijuana users. Group did not statistically differ on days since last use of cannabis square pot and alcohol use,likely influenced by the monitored abstinenceb period,therefore acute effects may not have been captured in our reported findings.
Astatistically significant within-subjects effect was not widely observed,which may be attributed to the smaller sample size combined with a more restricted age range. We tried to reduce the number of correlational analysis that were conducted, however given that effects were modest, future work should replicate findings. Studies should continue to follow existing adolescent cohorts to understand neural and behavioral changes that occur into young adulthood. Understanding how co-occurring marijuana and alcohol use influences both macrostructural and microstructural brain development, along with structural and functional connectivity, will help clinical interventions target neural vulnerabilities to develop novel and effective interventions to reduce marijuana misuse as prevalence rates of marijuana continue to increase .To guide recruitment, the Adolescent Brain Cognitive Development Study required a method for identifying children at high risk for early-onset substance use that may be utilized during the recruitment process.In this context, childhood risk refers to characteristics identified at ages 9 or 10 years that predict adverse outcomes in adolescence, and “high risk” refers to a categorical classification of some children as having increased risk compared to others. The construction of a brief measure for childhood substance use risk involves the identification of characteristics that predict early-onset substance use in mid to late adolescence. The identification and evaluation of optimal items for a brief childhood measure to serve as a high-risk screener ideally involves data from several large prospective studies with assessments initiated prior to the typical age of onset of substance use. To inform ABCD Study recruitment, secondary analyses are needed with data-sets collected prior to ABCD Study initiation.
In this context, a set of analyses with available data focused on a specific substance use outcome was determined to be most likely to be informative and feasible. While other substance use outcomes are also important, early-onset marijuana use is a relevant target.Marijuana is the most commonly used illicit drug by adolescents, and regular marijuana use identifies youth likely to develop cannabis use disorder.In these secondary data analyses, the definition of early-onset marijuana use was defined by the initiation of regular use as indicated in the available data-sets.The studies contributing data-sets were the Center for Education and Drug Abuse Research,the Pittsburgh Youth Study,the Pittsburgh Girls Study,and the Michigan Longitudinal Study.In the studies contributing data to the secondary analyses described here, the definitions of regular marijuana use differed by sample due to measurement variations. The variations in the definitions of regular marijuana use were as follows:five or more use occasions in the past year and;six or more occasions in the past year.By efficiently identifying children at high risk for early-onset marijuana use,a brief and effective measure of childhood risk measure could be utilized as a screen to identify high risk children in prevention research, primary medical care, and mental health clinic settings. The present analyses were specifically undertaken to inform the development a childhood high risk screen for use in the ABCD Study.The ABCD Study is the National Institute of Healths’ large-scale prospective population study of the biological and environmental factors that influence young people’s ability to successfully navigate adolescence. The study has a special emphasis on the risk and protective factors that influence marijuana and other substance use, and subsequent health problems including substance use disorders. Utilizing data from previously conducted studies, the present study was thus undertaken to develop and establish the efficiency of a short measure to identify youth at high risk for early-onset marijuana use with optimal features for use in the ABCD Study.
To achieve this goal, the risk level of a potential participant needs to be determined at the time of recruitment and prior to their scheduling for the extensive ABCD Study assessment protocol. Consequently, the optimal ABCD Study high risk screen has several characteristics: extreme brevity, including less than ten items;lack of sensitive items that may raise confidentiality concerns at this early stage of considering participation; consistency with prior research. These characteristics were taken into consideration in the analyses that follow. Historically, studies focusing on mental disorders such as schizophrenia, alcohol use disorder, and major depressive disorder, have used positive family history as a risk marker.Family history has been demonstrated to identify children at high risk of later substance use disorders in many prospective studies.However, a detailed family history may involve the parent being asked to disclose their own socially undesirable, embarrassing or, in some cases, illegal behavior. There have been alternative strategies to acquire this information, such as the use of publicly available records of drunk driving or other drug offenses, or the use of hospital records to identify parental diagnosis.Obtaining such records would not be feasible in the initial recruitment phase of the ABCD Study. Regardless of the method for obtaining this information, requesting this information at the point of introducing the ABCD Study raises the real possibility that the parent will decline study involvement. Few longitudinal studies have formulated and tested measures for identifying high risk children likely to exhibit early-onset marijuana use. There have been several approaches developed for predicting substance use disorders, but relatively few have targeted the adolescent developmental period. One of the risk measures developed to identify high risk children is the SUD Transmissible Liability Index developed by Vanyukov, Tarter, Clark and colleagues,using longitudinal data from the CEDAR study. Although the TLI is sophisticated in its development, it is long,uses different portions of existing instruments, and is under copyright. In addition, the TLI did not focus on the age 15 outcome of marijuana use, and the publications did not use Receiver Operating Characteristic Area Under the Curve analyses to determine an optimal threshold score.
Another screening instrument, the DSM Guided Cannabis Screen has unknown predictive value because it was constructed using cross-sectional data from a small clinical sample aged 14–59. Therefore, the current study fills a significant gap in the empirical literature. This report describes the process and results of secondary data analyses to prospectively identify a brief screening measure applicable to age 9–10-year-old children that would predict early-onset marijuana use in the 5–7 years following the initial screening measurement. To acquire data useful for developing this screening measure, we needed to identify population-based prospective studies which began assessments in late childhood, had been continued at least through ages 14–17,included marijuana use variables at both age periods, measured domains previously identified in the literature as predictive of adolescent substance use disorder outcomes, and had a sufficient number of measures in these domains that were shared across these studies so that screening validation could be replicated across different demographic groups .The objectives of these secondary data analyses were as follows:To develop a brief screener for 9–10-year-old boys and girls to predict early-onset marijuana and other substance use in mid adolescence with demonstrated predictive utility across four longitudinal data sets; To dichotomize the outcome variable, which will reduce shrinkage,improve replicability and practical utility.; To replicate findings across construction and validation samples.The advantage of this dual analysis approach is that we could construct a screener that considers shrinkage that typically happens between construction of a screener and subsequent validation in another sample. In summary,trim tray the objective was to develop a brief and feasible approach to the identification of children at increased risk for early onset marijuana use that may inform the ABCD Study recruitment procedures.The potential items for analyses were identified by examining prior research,prior analyses with the available data-sets, particularly the extensive analyses with CEDAR data,identifying pertinent items available in the four longitudinal projects used in these secondary analyses, and deliberations on the acceptability of areas of inquiry for potential participants during the recruitment process. Based on these considerations, the constructs represented by the pool of items to be considered included child externalizing behaviors, child internalizing behaviors, and parent tobacco smoking. Child externalizing behaviors. In the case of the ABCD Study design, we are projecting from ages 9–10, when marijuana use typically is minimal and not a viable risk item for screening purposes. Therefore, for candidate items on child externalizing behaviors, we considered non-substance use characteristics that other studies have found to predict early-onset substance use in mid adolescence, particularly child externalizing behaviors.
Potential externalizing behaviors considered were vandalism, lying, and disobedience at school.Child internalizing behaviors. In addition, we examined whether selected internalizing behaviors augmented predictions. After examining potential internalizing items’ correlations with both the tentative screener and with the outcome variable, we initially focused on the following items : unhappy, sad or depressed; too fearful or anxious; secretive or keep things to oneself; self-conscious or easily embarrassed. After considering which internalizing items correlated with the externalizing screener at that point, we finally focused on: unhappy, sad or depressed; too fearful or anxious.Parent smoking. For candidate items on parent behaviors, parent smoking was also considered a viable candidate. This candidate item for the screener was available in the 4 study data sets.We searched for equivalent predictor items of interest in each data-set. This is very important because we needed construct convergence among the four longitudinal data-sets. We used prorating in cases where there were missing items so that we would maximize the numbers of participants. Note that sample sizes varied somewhat due to missing cases for each analysis. In the PYS data-set, we combined parent and child information on child predictor variables to obtain a best estimate of the child behavior. For example, a behavior was counted when either the parent or child reported the behavior. Item scores were recoded as “Yes” or “No” where necessary to make them uniform across studies. For example, the Child Behavior Checklist [CBCL] has response options of 0, 1 or 2 then item scores were recoded as Yes or No. We undertook separate analyses for each gender. We first determined which items were predictive of the outcome. We next summed significant items into an index, examined AUC, and computed sensitivity, specificity, and positive predictive power for the summary screening score. If the variance accounted for by these indicators proved too low, we repeated the procedure for “new items”. In the final analyses, three of the studies used CBCL items,and one study used data based on self-reported antisocial behavior,MFQ,and the Child Symptom Inventory.The items from the CBCL, the MFQ, and the CSI were highly comparable.The intercorrelation results of the predictor items showed that some items were significantly negatively correlated with the outcome variable, and other items correlated with the outcome non-significantly across all three data-sets. This reduced the number of viable items in the Pittsburgh data-sets to 14. The Michigan group derived their own scale of 9 items.In brief, a procedure very similar to that described here for the three Pittsburgh data-sets was used. We intercorrelated available predictor variables that overlapped with those originally identified across externalizing, hyperactivity/impulsivity, internalizing, and temperament items with the outcome variable. This method was used to reduce the item pool, based on predictive accuracy.