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IMPORTANCE: Incidental findings (IFs) are unexpected abnormalities discovered during imaging and can range from normal anatomic variants to findings requiring urgent medical intervention. In the case of brain magnetic resonance imaging (MRI), reliable data about the prevalence and significance of IFs in the general population are limited, making it difficult to anticipate, communicate, and manage these findings. OBJECTIVES: To determine the overall prevalence of IFs in brain MRI in the nonclinical pediatric population as well as the rates of specific findings and findings for which clinical referral is recommended. DESIGN, SETTING, AND PARTICIPANTS: This cohort study was based on the April 2019 release of baseline data from 11 810 children aged 9 to 10 years who were enrolled and completed baseline neuroimaging in the Adolescent Brain Cognitive Development (ABCD) study, the largest US population-based longitudinal observational study of brain development and child health, between September 1, 2016, and November 15, 2018. Participants were enrolled at 21 sites across the US designed to mirror the demographic characteristics of the US population. Baseline structural MRIs were centrally reviewed for IFs by board-certified neuroradiologists and findings were described and categorized (category 1, no abnormal findings; 2, no referral recommended; 3; consider referral; and 4, consider immediate referral). Children were enrolled through a broad school-based recruitment process in which all children of eligible age at selected schools were invited to participate. Exclusion criteria were severe sensory, intellectual, medical, or neurologic disorders that would preclude or interfere with study participation. During the enrollment process, demographic data were monitored to ensure that the study met targets for sex, socioeconomic, ethnic, and racial diversity. Data were analyzed from March 15, 2018, to November 20, 2020. MAIN OUTCOMES AND MEASURES: Percentage of children with IFs in each category and prevalence of specific IFs. RESULTS: A total of 11 679 children (52.1% boys, mean [SD] age, 9.9 [0.62] years) had interpretable baseline structural MRI results. Of these, 2464 participants (21.1%) had IFs, including 2013 children (17.2%) assigned to category 2, 431 (3.7%) assigned to category 3, and 20 (0.2%) assigned to category 4. Overall rates of IFs did not differ significantly between singleton and twin gestations or between monozygotic and dizygotic twins, but heritability analysis showed heritability for the presence or absence of IFs (h2 = 0.260; 95% CI, 0.135-0.387). CONCLUSIONS AND RELEVANCE: Incidental findings in brain MRI and findings with potential clinical significance are both common in the general pediatric population. By assessing IFs and concurrent developmental and health measures and following these findings over the longitudinal study course, the ABCD study has the potential to determine the significance of many common IFs.

Substance use is a major cause of disability globally. This has been recognized in the recent United Nations Sustainable Development Goals (SDGs), in which treatment coverage for substance use disorders is identified as one of the indicators. There have been no estimates of this treatment coverage cross-nationally, making it difficult to know what is the baseline for that SDG target. Here we report data from the World Health Organization (WHO)'s World Mental Health Surveys (WMHS), based on representative community household surveys in 26 countries. We assessed the 12-month prevalence of substance use disorders (alcohol or drug abuse/dependence); the proportion of people with these disorders who were aware that they needed treatment and who wished to receive care; the proportion of those seeking care who received it; and the proportion of such treatment that met minimal standards for treatment quality (“minimally adequate treatment”). Among the 70,880 participants, 2.6% met 12-month criteria for substance use disorders; the prevalence was higher in upper-middle income (3.3%) than in high-income (2.6%) and low/lower-middle income (2.0%) countries. Overall, 39.1% of those with 12-month substance use disorders recognized a treatment need; this recognition was more common in high-income (43.1%) than in upper-middle (35.6%) and low/lower-middle income (31.5%) countries. Among those who recognized treatment need, 61.3% made at least one visit to a service provider, and 29.5% of the latter received minimally adequate treatment exposure (35.3% in high, 20.3% in upper-middle, and 8.6% in low/lower-middle income countries). Overall, only 7.1% of those with past-year substance use disorders received minimally adequate treatment: 10.3% in high income, 4.3% in upper-middle income and 1.0% in low/lower-middle income countries. These data suggest that only a small minority of people with substance use disorders receive even minimally adequate treatment. At least three barriers are involved: awareness/perceived treatment need, accessing treatment once a need is recognized, and compliance (on the part of both provider and client) to obtain adequate treatment. Various factors are likely to be involved in each of these three barriers, all of which need to be addressed to improve treatment coverage of substance use disorders. These data provide a baseline for the global monitoring of progress of treatment coverage for these disorders as an indicator within the SDGs.

Objective: Despite the frequent comorbidity of substance use disorders (SUDs) and psychiatric disorders, it remains unclear if screening for substance use in behavioral health clinics is a common practice. The aim of this review is to examine what is known about systematic screening for substance use in outpatient behavioral health clinics. Methods: We conducted a PRISMA-based systematic literature search assessing substance use screening in outpatient adult and pediatric behavioral health settings in PubMed, Embase, and PsycINFO. Quantitative studies published in English before May 22, 2020 that reported the percentage of patients who completed screening were included. Results: Only eight articles met our inclusion and exclusion criteria. Reported prevalence of screening ranged from 48 to 100%, with half of the studies successfully screening more than 75% of their patient population. There were limited data on patient demographics for individuals who were and were not screened (e.g., gender, race) and screening practices (e.g., electronic versus paper/pencil administration). Conclusions: The results of this systematic review suggest that successful screening for substance use in behavioral health settings is possible, yet it remains unclear how frequently screening occurs. Given the high rates of comorbid SUD and psychopathology, future research is necessary regarding patient and clinic-level variables that may impact the successful implementation of substance use screening.

The current study sought to examine the diagnostic overlap in DSM‐IVand DSM‐5 alcohol use disorder (AUD) and determine the clinical correlates of changing diagnostic status across the 2 classification systems. DSM‐IVand DSM‐5 definitions of AUDwere compared using cross‐national community survey data in 9 low‐, middle‐, and high‐income countries. Participants were 31,367 respondents to surveys in the World Health Organization's World Mental Health Survey Initiative. The Composite International Diagnostic Interview, version 3.0, was used to derive DSM‐IVand DSM‐5 lifetime diagnoses of AUD. Clinical characteristics, also assessed in the surveys, included lifetime DSM‐IVanxiety; mood and drug use disorders; lifetime suicidal ideation, plan, and attempt; general functional impairment; and psychological distress. Compared with DSM‐IV AUD(12.3%, SE= 0.3%), the DSM‐5 definition yielded slightly lower prevalence estimates (10.8%, SE= 0.2%). Almost one‐third (n= 802) of all DSM‐IVabuse cases switched to subthreshold according to DSM‐5 and one‐quarter (n= 467) of all DSM‐IVdiagnostic orphans switched to mild AUDaccording to DSM‐5. New cases of DSM‐5 AUDwere largely similar to those who maintained their AUDacross both classifications. Similarly, new DSM‐5 noncases were similar to those who were subthreshold across both classifications. The exception to this was with regard to the prevalence of any lifetime drug use disorder. In this large cross‐national community sample, the prevalence of DSM‐5 lifetime AUDwas only slightly lower than the prevalence of DSM‐IVlifetime AUD. Nonetheless, there was considerable diagnostic switching, with a large number of people inconsistently identified across the 2 DSMclassifications. Compared with DSM‐IV AUD(12.3%, SE= 0.3%), the DSM‐5 definition yielded slightly lower prevalence estimates (10.8%, SE= 0.2%).Diagnostic switching was common with almost one‐third of all DSM‐IVabuse cases switching to subthreshold in DSM‐5 and one‐quarter of all DSM‐IVdiagnostic orphans switching to mild DSM‐5 AUD.Despite this, new cases of DSM‐5 AUDwere largely similar to those who maintained their AUDacross both classifications. Similarly, new DSM‐5 non‐cases were similar to those who were subthreshold across both classifications.

Controversy exists about the role of mental disorders in the consistently documented association between smoking and suicidal behavior. This controversy is addressed here with data from the nationally representative National Comorbidity Survey-Replication (NCS-R). Assessments were made of 12-month smoking, suicidal behaviors (ideation, plans, attempts), and DSM-IV disorders (anxiety, mood, impulse-control, and substance use disorders). Statistically significant odds ratios (2.9–3.1) were found between 12-month smoking and 12-month suicidal behaviors. However, the associations of smoking with the outcomes became insignificant with controls for DSM-IV mental disorders. Although clear adjudication among contending hypotheses about causal mechanisms cannot be made from the cross-sectional NCS-R data, the results make it clear that future research on smoking and suicidal behaviors should focus more centrally than previous research on mental disorders either as common causes, markers, or mediators.

Research has shown that both prenatal alcohol and tobacco exposure are associated with increased risk of significant adverse medical, developmental, and behavioral outcomes including substance abuse. Research on the outcomes of prenatal exposure to illicit drugs (PNDE) has also found increased physical and behavioral problems for gestationally drug-exposed children. However, a clear picture has not emerged on whether the consequences of PNDE are independent from those associated with having a substance abusing parent and whether PNDE increases vulnerability to drug abuse. Because of its typical co-occurrence with factors inherent in having a drug-abusing parent, PNDE is at least a marker of significant increased risk for a range of negative outcomes including greater vulnerability to substance abuse. Although a review of the relevant research literatures indicates that the direct consequences of PNDE appear to be generally both subtle and nonglobal, PNDE does appear to have negative developmental and behavioral outcomes, and there is evidence that it is a modest direct contributor to increased substance abuse vulnerability.

If multiple etiologies of substance use are truly at work in the population, then further strides in the accurate prediction of smoking and the use of other substances will likely be built on diverse pattern-centered approaches that explore the presence of multiple population subgroups across various substance use stages. The present study aimed to identify population subgroups defined by individual risk factors or risk factor constellations that prospectively predict specific smoking stages. Using data from the National Longitudinal Study of Adolescent Health (Add Health), analyses were conducted on the sample that took part in the baseline and 1 year follow-up assessment between 1994 and 1996. Classification and regression tree procedures were used to investigate the structure of individual risk factors, or constellations of risk, that define population subgroups with high rates of both experimental and established smoking. For each level of smoking, a relatively simple model including two subgroups predicted over half of the smoking cases. Findings also indicated that the two group models identified higher rates of regular smokers compared to experimental smokers. Deviant behaviors and alcohol use without permission independently predicted movement to experimentation at follow-up. Progression to regular smoking from both a nonsmoking and experimental smoking status at baseline were each predicted by smoking friends. Additionally, baseline levels of experimental use predicted movement from experimental to regular smoking, while a relatively low grade point average predicted rapid progression from baseline nonuse to regular use at follow-up. By identifying first approximations of patterns, these analyses may lead to clues regarding the major multiple mechanisms at work for the progression of smoking among adolescents.