The results of the current analysis show quantitative differences in the AP syndrome of the CBCL in children aged 7–12. The FMM analyses of the CBCL data reveal similar results as our earlier findings when we used the same approach with SWAN data obtained in the Finnish adolescents.4
The analysis of CBCL AP items shows that the samples consist of three latent classes that are located along correlated continua (“severe”, “moderate”, and “low” scoring AP classes). The severe and moderate classes are small (6–15%, depending on the age group) whereas the ‘low’ scoring class is the largest class (consistent with over 50% of children having low or no attention problems). These findings, using the FMM approach, advance the argument that the AP syndrome exists on a severity continuum, with evidence of a similar class structure across the developmental period of ages 7–12. Especially the sample of 7-year old boys (N=8079) has sufficient power to detect subtypes if present. With a general prevalence of ADHD of 8–12%, approximately 800 of the 7-year olds would be diagnosed, and subtypes within such a large group would be detectable using FMMs.15, 25
The current analysis, however, shows that even in younger children AP is best described in terms of severity differences, which matches our conclusion drawn from the analysis of adolescents.4
When comparing the different age groups, it is interesting that the two items that most closely map on the DSM H/I subtype, ‘can’t sit still’ and ‘impulsive’ both diminish in intensity with age (e.g. from age 7 to 10 to 12). This finding is consistent with the literature that hyperactivity symptoms diminish with age yet attention problems persist. We observed this pattern in both the larger, overlapping age samples, and in the sample with identical subjects at the three time points, hence the diminished intensity is not due to changes in the composition of the samples.
The fact that the three CBCL classes are ordered quantitatively is also reflected in the relation between AP and DSM-IV subtype diagnoses in a subsample observed at age 12. All or almost all children with a DSM-IV ADHD CT and H/I are in the ‘severe’ AP class. Children with DSM-IV PI are divided over the severe and moderate AP classes. Perhaps most important, none of the children with a DSM-IV ADHD subtype are in the low scoring majority class.
As the DSM-V process moves forward it will be important to consider these findings in light of the consideration of including a quantitative axis of diagnostic description. We have argued that a quantitative approach that allows for differences across ages and genders makes sense for both research and clinical work in children who suffer from psychopathologic conditions such as ADHD.3
From this work a clinician will benefit from knowing that attention problems exist on a severity continuum, thus presenting a clear invitation to develop evidenced based interventions that aim towards diminishing the severity of the symptoms within the continuum. In this way the treatment of ADHD is no different than the treatment of hypertension, in which a reasonable evidenced based method can be developed to evaluate and measure the movement from a pathological level (e.g. severe class AP or a diastolic pressure of 100) to a non-pathological level (e.g. AP low class or a diastolic BP of 80) rather than to the absence of attention or the absence of blood pressure. Further, the contention that subtypes of DSM-IV ADHD are not different in their FMM class membership may allow a more general treatment approach toward children with ADHD, which is closer to what happens in most clinics today in any event. For example, most clinicians do not vary their pharmacologic or behavioral treatments based on whether or not the child has DSM-IV ADHD CT, H/I, or PI.
Taken together, these data argue for considering DSM-IV ADHD as existing on a severity continuum rather than as discrete diagnostic categories. Implicit in the continuum argument is the need to identify common mediators of risk. In the area of ADHD it is obvious that the current approach of applying the same criteria to individuals of both gender and all ages is unrealistic. The continuum argument allows for the creation of normative distributions by age, gender, informant and ethnicity. Such advances, which seem so simple to accept, should be considered as key modifications in the DSM-V or subsequent editions of our diagnostic manuals.
Our study has a number of limitations. First, we focus exclusively on AP in boys. Our rationale is that prevalence of attention problems is higher in boys, and that the statistical power to detect subtypes increases with prevalence rates. The increasing sample size in the NTR will permit an analysis of attention problems in girls in the future. A second limitation concerns the fact that we relied on a specific statistical approach to detect subtypes, FMM. Other approaches such as the taxometric procedures developed by Meehl and colleagues have been used for this purpose.33
However, it has been shown that taxometric procedures have less power to detect classes than FMM.34
Third, we treated twins as individuals, thereby neglecting the genetically informative structure of the sample.35
A twin mixture model has recently been proposed, however, the model decomposes variance within
class into genetic and environmental components rather than the more interesting decomposition of differences between
In addition, twin mixture models assume that correct estimation of within class variance is unproblematic. However, that this may not be the case, especially when class proportions differ substantially (e.g., small minority classes and large majority classes).29
Fourth, regarding the utility of factor mixture modeling to support selection of subjects for prevention or treatment, it should be noted that simulation studies have demonstrated high error rates in assigning subjects to classes.29
This clearly limits the potential of mixture analyses for selection purposes. The current study shows that factor mixture analyses may be used to exclude
subjects that are very unlikely to be affected (i.e., the low scoring majority class). Finally, the current study may be enhanced by including relevant gene candidates to predict class membership. Recent work demonstrates that that substantial sample sizes are needed to reliably detect small gene effects using FMMs.37