As evidence for adverse consequences of marijuana use during adolescence on brain functioning accumulates,such research has the potential to improve prevention and intervention efforts through better education,thus reducing marijuana use and associated negative consequences.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 datasets 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 datasets.The studies contributing datasets were the Center for Education and Drug Abuse Research,cannabis drying 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,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.To ascertain replication of results,we used four existing longitudinal data sets.These data sets were utilized to build construction and validation samples for each sex,resulting in nine independent analyses.The four longitudinal data sets were from the CEDAR,PYS,PGS,and MLS.Where possible,we used both parent and child as informants,which is particularly important for externalizing behavior that is concealing in nature,because parents usually have less knowledge of the behavior compared to the child.The overall sample consisted of 882 boys and 368 girls.At the initiation of the study,81.3% of the boys were White,and 18.7% Non-White,and 74.7% of the girls were White,25.3% Non-White.Sample selection: Families were ascertained through two methods.The first involved recruitment through all district courts of fathers living in the area convicted for drunk driving with a biological son between the ages of 3 and 5 years old.Fathers were also required to be living with the boy and his biological mother.The second group were required to have the same family composition,but were ascertained through the same neighborhoods as the court-recruited families.Door to door canvassing was carried out to recruit two subgroups: families where neither parent met a lifetime substance use disorder diagnosis ; families where father met criteria for an alcohol use disorder but were not involved with the court.In addition to the original 3-5-year-old son and his biological parents,a female sibling within the range of 3–11,when present,was also recruited.If other siblings in the 3-11-year age range were also present in the home,they were recruited as well.Assessment at T1 for this study : average ages: 10.55 for boys and 10.61 for girls.

Where possible,we used both parent and child as informants,which is particularly important for the externalizing behaviors that are concealing in nature,because parents often are not aware of this type of child behavior.The outcome of interest was child self-report of marijuana use at about age 14.Attrition was 10%.The potential items for analyses were identified by examining prior research,prior analyses with the available datasets,greenhouse benches 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.The predicted outcome was marijuana use by ages 14–15 with a frequency that indicated greater than experimental use.The available outcome categories varied across the studies,including monthly use in CEDAR,use at five times or more in the past year in the PYS and PGS,and 6 or more times during the past year in the MLS.The presence of marijuana use at or above these thresholds for the depicted ages defined “early-onset marijuana use” in these secondary analyses.The evaluations of individual items and their combinations in relations to early-onset marijuana use were undertaken with Receiver Operating Characteristic statistics.This approach is typically used in evaluating screening for diseases,with several examples in the prior literature focusing on substance use frequency in relation to adolescent substance use disorders.Using ROC statistics,the evaluation of the prediction power of a screen is usually based on a 2 by 2 table,as illustrated in Fig.1.The quality of a screen is indicated by four parameters: Sensitivity and specificity,which refer to True Positives/,and True Negatives/,respectively,and Positive predictive value and negative predictive value,which refer to True Positives/True Positives + False Positives and True Negatives/True Negatives + False Negatives,respectively.

Area Under the Curve analyses were used to establish whether the prediction is better than chance; and what the optimal cut-off is to minimize false negative and false positive errors.AUC can range from 0.5 to 1.0,when sensitivity and specificity are considered equally important.In practice,AUC tends to be lower than 1.0,meaning that one cannot correctly classify all future marijuana users or correctly classify all future non-marijuana users.The general rule is that the higher the sensitivity,the lower the specificity.Lowering the cut-off score can increase sensitivity,but with the consequence that there will be more false positives.Where sample sizes from study sites were sufficient,we created two subgroups,labeled “construction” and “validation” samples,using a randomization method,the SPSS random variable generation function.This partitioning of the samples was done to avoid idiosyncratic findings.Sufficient sample sizes were available to take this approach for CEDAR boys,PYS,and PGS,but not for CEDAR girls,MLS boys,or MLS girls.To support scale construction yet allow for validation in these limited samples,weightings were applied so that there were more subjects assigned to the construction sub-sample than to the validation sub-sample.We searched for equivalent predictor items of interest in each dataset.This is very important because we needed construct convergence among the four longitudinal datasets.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.