Subtypes Versus Severity Differences in Attention-Deficit/Hyperactivity Disorder in the Northern Finnish Birth Cohort

University of Notre Dame, Notre Dame, IN, USA.
Journal of the American Academy of Child & Adolescent Psychiatry (Impact Factor: 7.26). 01/2008; 46(12):1584-93. DOI: 10.1097/chi.0b013e31815750dd
Source: PubMed


To investigate whether behaviors of inattention, hyperactivity, and impulsivity among adolescents in Northern Finland reflect qualitatively distinct subtypes of ADHD, variants along a single continuum of severity, or of severity differences within subtypes.
Latent class models, exploratory factor models, and factor mixture models were applied to questionnaire data of ADHD behaviors obtained from the Northern Finland Birth Cohort (NFBC). Latent class models correspond to qualitatively distinct subtypes, factor analysis corresponds to severity differences, and factor mixture analysis allows for both subtypes and severity differences within subtypes.
A comparison of the different models shows that models that distinguish between a low scoring majority class (unaffecteds) and a high scoring minority class (affecteds), and allow for two factors (inattentive, hyperactive-impulsive) with severity differences provide the best fit.
The analysis provides support that a high-scoring minority group (8.8% of males and 6.8% of females) likely reflects an ADHD group in the Northern Finland Birth Cohort, whereas the majority of the population falls into a low-scoring group of unaffecteds. Distinct factors composed of items of inattention and hyperactivity-impulsivity are evident for both sexes with considerable variability in severity within each class.

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Available from: Gitta H Lubke
    • "In the current study we will explore whether factor mixture modelling (FMM), a relatively new and promising technique, which combines aspects of LCA and FA (Lubke and Muthén, 2005; Clark et al., 2013), is more suitable to describe both differences in symptom categories and in depression severity in the general population. FMM has already been shown to be successful in describing the underlying psychopathology of other mental disorders, such as attention-deficit/hyperactivity disorder (Lubke et al., 2007), panic disorder (Roberson-Nay and Kendler, 2011) and alcohol use disorder (Jackson et al., 2014), and of major depression in a treatment seeking clinical population (Sunderland et al., 2013). Some studies described in Appendix A that tried to validate the distinct subtypes of depression by examining their demographic and clinical correlates found indications for differential, potential aetiological mechanisms. "
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    ABSTRACT: Background: In recent years, researchers have used various techniques to elucidate the heterogeneity in depressive symptoms. This study seeks to resolve the extent to which variations in depression reflect qualitative differences between symptom categories and/or quantitative differences in severity. Methods: Data were used from the Netherlands Mental Health Survey and Incidence Study-2, a nationally representative face-to-face survey of the adult general population. In a subsample of respondents with a lifetime key symptom of depression at baseline and who participated in the first two waves (n=1388), symptom profiles at baseline were based on symptoms reported during their worst lifetime depressive episode. Depressive symptoms and DSM-IV diagnoses were assessed with the Composite International Diagnostic Interview 3.0. Three latent variable techniques (latent class analysis, factor analysis, factor mixture modelling) were used to identify the best subtyping model. Results: A latent class analysis, adjusted for local dependence between weight change and appetite change, described the data best and resulted in four distinct depressive subtypes: severe depression with anxiety (28.0%), moderate depression with anxiety (29.3%), moderate depression without anxiety (23.6%) and mild depression (19.0%). These classes showed corresponding clinical correlates at baseline and corresponding course and outcome indicators at follow-up (i.e., class severity was linked to lifetime mental disorders at baseline, and service use for mental health problems and current disability at follow-up). Limitations: Although the sample was representative of the population on most parameters, the findings are not generalisable to the most severely affected depressed patients. Conclusions: Depression could best be described in terms of both qualitative differences between symptom categories and quantitative differences in severity. In particular anxiety was a distinguishing feature within moderate depression. This study stresses the central position anxiety occupies in the concept of depression.
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    • "One should also be clear whether the major goal of using FMM is to model nonnormality in an otherwise fairly conventional latent trait structure (i.e., factor mixture models that enforce strict invariance) or whether one is more interested in modeling latent subgroups that differ in qualitatively interesting ways, such as factor loadings (Masyn et al., 2010). Some of the most informative applications of FMMs to date have compared models that reflect plausible competing conceptions about the latent structure of psychopathology and that draw directly on prior theory (e.g., Lubke et al., 2007; Shevlin & Elklit, 2012). "
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    ABSTRACT: Over the past 75 years, the study of personality and personality disorders has been informed considerably by an impressive array of psychometric instruments. Many of these tests drawon the perspective that personality features can be conceptualized in terms of latent traits that vary dimensionally across the population. A purely trait-oriented approach to personality, however, might overlook heterogeneity that is related to similarities among subgroups of people. This article describes how factor mixture modeling (FMM), which incorporates both categories and dimensions, can be used to represent person-oriented and trait-oriented variability in the latent structure of personality. We provide an overview of different forms of FMM that vary in the degree to which they emphasize trait- versus person-oriented variability. We also provide practical guidelines for applying FMM to personality data, and we illustrate model fitting and interpretation using an empirical analysis of general personality dysfunction.
    Full-text · Article · Oct 2013 · Journal of Personality Assessment
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    • "Finally, we examined correlations between parent and child cognitive performance across three different levels of attention symptoms. Previous work has suggested nonlinear relationships between symptoms and behavior (Lubke et al. 2007). Children's scores on the attention survey were broken into three equal-sized groups. "
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    ABSTRACT: Understanding the relationship between brain and complex latent behavioral constructs like cognitive control will require an inordinate amount of data. Internet-based methods can rapidly and efficiently refine behavioral measures in very large samples that are needed for genetics and behavioral research. Cognitive control is a multifactorial latent construct that is considered to be an endophenotype in numerous neuropsychiatric disorders, including attention deficit/hyperactivity disorder (ADHD). While previous studies have demonstrated high correlations between Web- and lab-based scores, skepticism remains for its broad implementation. Here, we promote a different approach by characterizing a completely Web-recruited and tested community family sample on measures of cognitive control. We examine the prevalence of attention deficit symptoms in an online community sample of adolescents, demonstrate familial correlations in cognitive control measures, and use construct validation techniques to validate our high-throughput assessment approach. A total of 1214 participants performed Web-based tests of cognitive control with over 200 parent–child pairs analyzed as part of the primary study aims. The data show a wide range of “subclinical” symptomatology in a web community sample of adolescents that supports a dimensional view of attention and also provide preliminary narrow-sense heritability estimates for commonly used working memory and response inhibition tests. Finally, we show strong face and construct validity for these measures of cognitive control that generally exceeds the evidence required of new lab-based measures. We discuss these results and how broad implementation of this platform may allow us to uncover important brain–behavior relationships quickly and efficiently.
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