Background: Attention-deficit/hyperactivity disorder (ADHD) is a risk factor for suicidal behavior, but the effect of ADHD medication on suicidal behavior remains unclear. This study aimed to examine the associations between medication treatment for ADHD and risk of suicide attempts. Methods:We identified a large cohort of patients with ADHD (N=3,874,728, 47.8% female) using data from commercial healthcare claims 2005-2014 in the US. We used population-level and within-individual analyses to compare risk of suicide attempts during months when individuals received prescribed stimulant or non-stimulant medication relative to months when they did not receive medication. Results:In both population-level and within-individual analyses, ADHD medication was associated with lower odds of suicide attempts (odds ratio [OR] =0.69, 95% CI: 0.66-0.73, and OR=0.61, 95% CI: 0.57-0.66, respectively). Similar reductions were found in children to middleaged adults and in clinically relevant subgroups, including ADHD patients with pre-existing depression or substance use disorder. The reduction was mainly seen for stimulant medication (OR=0.72, 95% CI: 0.66-0.77); non-stimulant medication was not associated with statistically significant changes in risk of suicide attempts (OR=0.94, 95% CI: 0.74-1.19). Sensitivity analyses assessing the influence of different exposure definitions, different outcome definitions, subsets of the cohort, and different analytic approaches provided comparable results.
Machine learning is on the rise. According to Scopus (www2. scopus.com), the number of publications in medicine with machine learning in the title, abstract, or as a keyword during 2016 to 2018 increased from 1658 to 3904. In psychiatry, applications of machine learning are proposed to improve the accuracy of diagnosis and prognosis and determine treatment choice. At the same time, much of this research has given insufficient attention to high-quality methods, clinical applications, and ethical aspects. This is compounded by poor reporting of performative measures and misleading claims about the high accuracy of such approaches. In this issue of JAMA Psychiatry, the article by Gradus and colleagues 1 raises important questions about the place of machine learning in research and practice.Machine learning is useful when analyzing several predictors, particularly if there are nonlinear observations and interaction terms that are difficult to theoretically conceptualize and practically model. A strength of the study by Gradus et al 1 is that it uses a robust data set of patient-level predictors from Danish national health care registers without imposing or relying on theoretical models. The authors tested 1365 parameters based on 334 individual predictors, which were mostly associated with somatic and psychiatric diagnoses and by temporal proximity with the suicide event by being categorized into 4 points of 6, 12, 24, and 48 months. Another strength is the outcome of death by suicide. As the study illuminates risk factors rather than prediction, this is important, as risk factors for suicide are different from self-harm and suicidal ideation. For example, male sex is more highly associated with suicide, whereas female sex is a risk factor for suicide ideation and attempts. 2 The use of high-quality Danish register data means that suicide is ascertained with negligible missing data; however, the authors did not consider injuries of undetermined intent as an outcome, which could lead to possible misclassification. Based on 10 outcomes per predictor recommended by methodologists, 3 the authors also had sufficient statistical power to investigate 14 463 suicides. A study of such large suicide numbers combined with hundreds of potentially modifiable predictors is an important advance for the field.The most informative aspects of the study by Gradus et al 1 are in highlighting the importance of psychiatric disorders as risk factors for suicide. The analyses simultaneously accounted for somatic and psychiatric disorders and a few sociodemographic factors (eg, income, civil status, age bands, and being an immigrant). It reported that diagnoses, such as schizophrenia and adjustment disorders, and markers of psy- Related article page 25Opinion EDITORIAL jamapsychiatry.com (Reprinted) JAMA
Few quantitative behavior genetic studies have examined why psychopathology is associated with suicide attempt (SA) and self-harm (SH) in adolescence. The present study analyzed data from the Child and Adolescent Twin Study in Sweden to examine the extent to which genetic and environmental factors explain SA/SH and its association with psychopathology in childhood, an often-cited risk factor of subsequent SA/SH. When children were 9 or 12 years old (n = 30,444), parents completed the Autism–Tics, AD/HD and other Comorbidities Inventory (Larson et al., 2010) regarding their children’s psychiatric problems as part of an ongoing, longitudinal study. At age 18 years (n = 10,269), adolescents completed self-report questionnaires, including SA/SH assessments. In a bifactor model of childhood psychopathology, a general factor of psychopathology was a statistically significant predictor of adolescent SA/SH at a higher magnitude (β, 0.25, 95% confidence interval [CI; 0.15, 0.34] for suicide attempt), as compared with specific factors of inattention, impulsivity, oppositional behavior, and anxiety/emotion symptoms. Quantitative genetic modeling indicated that the additive genetic influences on the general factor accounted for the association with each outcome (β, 0.24, 95% CI [0.13, 0.34] for suicide attempt). The results remained virtually identical when we fit a higher order factors model. Two additional outcomes demonstrated comparable results. The results extend current literature by revealing the shared genetic overlap between general psychopathology during childhood and adolescent SA/SH.
Research shows that childhood dysregulation is associated with later psychiatric disorders. It does not yet resolve discrepancies in the operationalization of dysregulation. It is also far from settled on the origins and implications of individual differences in dysregulation. This study tested several operational definitions of dysregulation using Achenbach attention, anxious/depressed, and aggression subscales. Individual growth curves of dysregulation were computed, and predictors of growth differences were considered. The study also compared the predictive utility of the dysregulation indexes to standard externalizing and internalizing indexes. Dysregulation was indexed annually for 24 years in a community sample (n ¼ 585). Hierarchical linear models considered changes in dysregulation in relation to possible influences from parenting, family stress, child temperament, language, and peer relations. In a test of the meaning of dysregulation, it was related to functional and psychiatric outcomes in adulthood. Dysregulation predictions were further compared to those of the more standard internalizing and externalizing indexes. Growth curve analyses showed strong stability of dysregulation. Initial levels of dysregulation were predicted by temperamental resistance to control, and change in dysregulation was predicted by poor language ability and peer relations. Dysregulation and externalizing problems were associated with negative adult outcomes to a similar extent.
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