If SRET was removed from the equation, approximately equal contributions were seen from use of tobacco products and the number of SUD items.These analyses evaluated whether a long-term prospective study of the development of AUDs and/or SUDs in men corroborated several findings from prior cross-sectional and retrospective studies. The rates of alcohol-related conditions in the current sample reflect the fact that half of the men had an alcoholic parent. Over the 30 years, 41% of these probands developed an AUD, compared with an estimated 12 month or lifetime risk of 15 to 20% in the general population . However, considering the fact that these high-functioning probands had not been selected for an FH of SUDs, the 21% rate of drug-related conditions found in these analyses, compared with general population lifetime estimates of about 10% , might have been less expected.As predicted by Hypothesis 1 and several reports in the literature , the presence of either an AUD or an SUD was associated with an increased rate of the other diagnosis. This increase for SUDs among subjects with AUDs was in the range of individuals with alcohol abuse who developed an SUD as reported in an earlier study , although not as great an increase in SUDs evidenced in subjects with alcohol dependence in that investigation. The less robust increase in this study compared with some prior reports may reflect the exclusion from the protocol of subjects with severe antisocial problems, the few subjects with an FH of SUDs,greenhouse tables and the overall high functioning of these probands. The general similarity among the probands regarding baseline demography and psychiatric histories facilitated our ability to focus on the impact of several genetically influenced intermediate phenotypes that relate to the risk of SUDs and/or AUDs . Regarding Hypothesis 2 , in Tables 1 and 2, while the 4 outcome groups did not diffffer greatly on internalizing problems, higher scores on externalizing traits were related to SUDs, AUDs, and combined diagnoses.

The fact that the externalizing questionnaires had not been measured at T1, but were evaluated at T10 or T15, means that we can only describe associations of these scores with the 4 outcomes, although the trait nature of the measures implies a possible causal relationship. Regarding Hypothesis 3 , the first 2 tables also demonstrated a consistent relationship between a baseline low LR with the risk of AUDs, but LR was not related to the vulnerability toward SUDs. These findings underscore the heterogeneity among genetically influenced characteristics that impact on the risk of AUDs and/or SUDs. The results are consistent with the literature indicating that some intermediate phenotypes relate to both AUD and SUD development,while other characteristics, such as the low LR to alcohol and the skin flush associated with drinking, have been reported to be associated with the risk of AUDs alone . Regarding Hypothesis 4 , there were few unique earlier life correlates of combined diagnoses, and the predictors of Group 1 reflected items that related to AUDs alone and SUDs alone. One important caveat regarding this conclusion, however, relates to the differences across Groups 1, 2, and 3 regarding the performance of novelty seeking, sensation seeking, and impulsivity, as these externalizing characteristics are not homogenous . Additional study is needed to explore the possibility that the different scores on these questionnaires across Groups 1 to 3 may have clinically relevant implications. The data in Table 3 support Hypothesis 5 . Even in this prospective study of men at high risk of AUDs, the development of combined AUDs and SUDs was associated with heavier substance use and more alcohol and drug problems over the 30 years for men in Group 1 compared with Groups 2 or 3. This result was observed despite similar ages, racial distributions, education, religious involvement, as well as alcohol and drug histories at T1 for Groups 1 and 3. The course of combined diagnoses might reflect the impact of concomitant substance use itself, perhaps through greater impaired judgment, poor executive functioning, and problems in mood regulation associated with the exposure to multiple types of substances . Table 3 also offers information regarding the outcomes at T30 for SUDs and AUDs in these high-functioning men from a nonclinical population. As described in a recent article , 42% of these probands with AUDs no longer met criteria for alcohol abuse or dependence in the approximate 5 years prior to T30.

It is interesting to note that those results were seen for members of both Group 1 and Group 3. Remissions at T30 were even more striking for SUDs where >70% of Group 1 and 2 men no longer fulfilled criteria for these diagnoses, with similar rates for the combined diagnosis Group 1 and the SUD only Group 2. These impressive rates of recovery may reflect the combination of indicators of a good prognosis in these subjects regarding their nonclinical status at T1, the high proportion who were married during the follow-up, their high education level, and the general absence of premorbid conduct or antisocial problems, as well as the high rates of spontaneous recovery associated with substance-related conditions over time . Results might be different in less educated or more antisocial groups recruited from clinical populations . Several caveats must be kept in mind when viewing these findings. On the positive side, the SDPS is a large study with intensive follow-up interviews carried out about every 5 years over 30 years. The project was structured to allow for evaluation of the development of AUDs and related problems over time in individuals for whom the impact of several additional factors that affect the risk of alcoholism was partially controlled. Thus, the probands did not have severe conduct problems or prior histories of bipolar disorder or schizophrenia, each of which enhance the risk of all substance-related disorders . Those steps were taken to optimize the ability to evaluate the relationship between the low LR to alcohol and the future development of AUDs. These deliberate exclusions were also likely to have contributed to selecting probands with future high levels of functioning despite their elevated risk of AUDs. However, those steps and the relative homogeneity of the sample regarding sex, education, and race potentially limited the generalizability of the findings. An additional caveat is that, while the population was relatively large for a long-term intensive follow-up, the number of individuals developing SUDs was relatively small with resulting limited statistical power in some analyses. This contributed to the decision to combine abuse and dependence into a single category and to consider amphetamines and cocaine as stimulants.

It is also important to emphasize that while the results regarding externalizing personality test scores were consistent with the predicted direction of such values, only 3 questionnaires were used and those were measured at least 10 years after subjects entered the protocol. In addition, earlier life internalizing phenomena were evaluated by only baseline histories of depressions or mental health treatment. Finally, the approach to regressions used here may have optimized statistical power by minimizing standard errors and producing relatively narrow confidence intervals, although an alternate method, hierarchical logistic regression, produced similar results but has the limitation of not requiring that every item in the block add significantly to the prediction.Recreational marijuana commercialization is gaining momentum in the US. Among the 11 states and Washington DC that have legalized recreational marijuana since 2012, retail markets have been opened or anticipated in 10 states, where over a quarter of the US population live. Children are at a high risk of initiating marijuana use and developing adverse consequences related to marijuana. The rapidly evolving environment poses considerable concerns about children’s exposure to marijuana and related marketing and creates significant challenges for pediatricians preventing, treating,vertical farming and educating about marijuana related harms among children. As stated in its most recent policy statement about marijuana commercialization, the American Academy of Pediatrics “strongly recommends strict enforcement of rules and regulations that limit access and marketing and advertising to youth”. The presence of RMDs in neighborhoods and point-of-sale marketing such as advertising and promotional activities in RMDs might increase the visibility and awareness of marijuana products among children, whose perceptions and behaviors may be influenced. A study in Oregon found that dispensary storefront was the most common source of advertising seen after commercialization. Self-reported exposure to medical marijuana advertising was found to be related to higher levels of use and intentions of future use among children in California schools. Products, packages, and advertisements that are designed to be appealing to children are particularly concerning. Tobacco and alcohol literature repeatedly suggested that children are common targets of marketing. Despite the fact that all the states with marijuana commercialization have some form of prohibitions on child-appealing products and marketing, it remains undocumented as to what extent the marijuana industry is complying. This study is the first to comprehensively assess point-of-sale marketing practices in RMDs with a focus on those relevant to children. Unlike previous marijuana research relying on individual self-reported exposure measures, we adopted the direct and objective observation approach that has been commonly used in tobacco and alcohol studies on retail outlets. We audited RMDs near a representative and large sample of schools in California, the largest legal retail market in the US where over 10 million children can be potentially influenced. We identified product and packaging characteristics, advertising and promotional activities, and access restrictions in these dispensaries. This was a cross-sectional and observational study conducted in June-September in 2018.

We obtained a list of public schools in California that participated in the 2017-18 California Student Tobacco Survey . The CSTS schools were drawn using a two-stage stratified random sampling approach. California was first stratified into 22 regions. Schools within each region were then randomly selected, proportional to the number of students enrolled within the region. A total of 623 schools across California were sampled and invited, with 403 schools agreeing to participate. Among these 403 schools, 44 schools opted out before the survey was conducted, and 26 schools participated in the survey but were excluded from CSTS data due to low response rate. The final effective school sample size was 333, among which 256 were high schools and 77 were middle schools. The total number of students participating in the survey was 151,404, making it the largest school-based surveys in California. Our study focused on RMDs near these 333 schools. Six trained field workers audited retail environments in RMDs in closest proximity to the 333 schools . We first identified dispensaries using crowd sourced online websites, including Weedmaps, Wheres weed, Leafly, and Yelp. State licensing records were not used because they could not provide a complete list of dispensaries at the time of data collection. Specifically, 1) Marijuana commercialization in California took effect in January 2018. During the study period, California was in a transition stage when annual licenses were just issued, and most were not approved. 2) The licensing policy in California was not enforced, with a large portion of dispensaries operating without licenses. 3) For licensed dispensaries, the registered and actual business name and address often mismatched. Alternatively, we utilized crowd sourced databases, which were considered as reliable, up-todate, and comprehensive sources of dispensary directories. To identify the dispensary closest to a school, field workers entered school zip code in the online searchable databases. The street addresses of all the dispensaries with the school zip code were geocoded and mapped in ArcGIS to compute their distances to the school. Field workers then called the dispensary with the shortest distance to verify its address and operational status. These procedures were repeated if a dispensary was permanently closed or not verifiable via multiple calls until an active dispensary was identified. The primary focus was RMDs. Yet, medical marijuana dispensaries that require a doctors’ recommendation or state patient ID cards coexisted in California in 2018. During call verifications, if dispensary staff indicated that a doctors’ recommendation or a patient ID was required to enter the dispensary and make purchase, the dispensary was categorized as a MMD.i Fieldworkers also verified dispensary classification during the subsequent auditing. For those verified as MMDs, we repeated the aforementioned procedures until an active RMD was identified.