Unraveling the Optimum Latent Structure of Attention-Deficit/Hyperactivity Disorder: Evidence Supporting ICD and HiTOP Frameworks

Harmonizing bifactor models of psychopathology between distinct assessment instruments: Reliability, measurement invariance, and authenticity

OBJECTIVES

Model configuration is important for mental health data harmonization. We provide a method to investigate the performance of different bifactor model configurations to harmonize different instruments.

METHODS

We used data from six samples from the Reproducible Brain Charts initiative (N = 8,606, ages 5-22 years, 41.0% females). We harmonized items from two psychopathology instruments, Child Behavior Checklist (CBCL) and GOASSESS, based on semantic content. We estimated bifactor models using confirmatory factor analysis, and calculated their model fit, factor reliability, between-instrument invariance, and authenticity (i.e., the correlation and factor score difference between the harmonized and original models).

RESULTS

Five out of 12 model configurations presented acceptable fit and were instrument-invariant. Correlations between the harmonized factor scores and the original full-item models were high for the p-factor (>0.89) and small to moderate (0.12-0.81) for the specific factors. 6.3%-50.9% of participants presented factor score differences between harmonized and original models higher than 0.5 z-score.

CONCLUSIONS

The CBCL-GOASSESS harmonization indicates that few models provide reliable specific factors and are instrument-invariant. Moreover, authenticity was high for the p-factor and moderate for specific factors. Future studies can use this framework to examine the impact of harmonizing instruments in psychiatric research.

Differences between DSM-5-TR and ICD-11 revisions of attention deficit/hyperactivity disorder: A commentary on implications and opportunities

Current ICD-11 descriptions for attention deficit hyperactivity disorder (ADHD) were recently published online, in the same year as the DSM-5-TR (text revised edition) was released. In this commentary, we compare and contrast the DSM-5/DSM-5-TR and ICD-11 diagnostic criteria, summarize important differences, and underscore their clinical and research implications. Overall, three major differences emerge: (1) The number of diagnostic criteria for inattention (IA), hyperactivity (HY) and impulsivity (IM) symptoms (i.e., DSM-5-TR has nine IA and nine HY/IM symptoms, whereas ICD-11 has 11 IA and 11 HY/IM sym-ptoms); (2) the clarity and standardization of diagnostic thresholds (i.e., the diagnostic thresholds for symptom count in IA and HY/IM domains are explicitly specified in DSM-5-TR, whereas in ICD-11 they are not); and (3) the partitioning of HY and IM symptoms into sub-dimensions (i.e., difference in partitioning HY and IM symptom domains relates to the differences between the current and previous editions of DSM and ICD, and this has important research implications). Currently, no ICD-11 based ADHD rating scales exist and while this absence represents an obstacle for respective research and clinical practice, it also presents opportunities for research development. This article highlights these challenges, possible remedies and novel research opportunities.

The factor structure of attention-deficit/hyperactivity disorder in schoolchildren

BACKGROUND

Most studies support a bifactor model of childhood ADHD with two specific factors. However, several studies have not compared this model with a bifactor model with three specific factors, few have tested the actual strength of the factors, and none have examined whether "talks excessively" should be treated as a hyperactivity versus impulsivity symptom in children with ADHD.

AIMS

To examine the factor structure of ADHD symptoms and evaluate the relative strength of potential factors.

METHODS

Parent-reports on the ADHD-Rating Scale (ADHD-RS-IV) were collected for 2044 schoolchildren from the general population and 147 children with ADHD from a clinical sample. Single-, two- and three-(correlated and bi-)factor models were tested using confirmatory factor analysis.

RESULTS

Most models had a satisfactory fit. However, a correlated three-factor model where "talks excessively" was included as an indicator of impulsivity, and especially a bifactor model with one strong, well-defined general and two/three (ICD-10 defined) weak specific factors fit the data slightly better than the remaining models.

CONCLUSIONS

The factor structure is best characterized by a bifactor model with a strong general factor and two/three weaker specific factors. Therefore, we suggest emphasizing the ADHD-RS-IV total score rather than the subscale scores in clinical practice.

Network analysis of 18 attention-deficit/hyperactivity disorder symptoms suggests the importance of "Distracted" and "Fidget" as central symptoms: Invariance across age, gender, and subtype presentations

The network theory of mental disorders conceptualizes psychiatric symptoms as networks of symptoms that causally interact with each other. Our present study aimed to explore the symptomatic structure in children with attention-deficit/hyperactivity disorder (ADHD) using network analyses. Symptom network based on 18 items of ADHD Rating Scale-IV was evaluated in 4,033 children and adolescents with ADHD. The importance of nodes was evaluated quantitatively by examining centrality indices, including Strength, Betweenness and Closeness, as well as Predictability and Expected Influence (EI). In addition, we compared the network structure across different subgroups, as characterized by ADHD subtypes, gender and age groups to evaluate its invariance. A three-factor-community structure was identified including inattentive, hyperactive and impulsive clusters. For the centrality indices, the nodes of "Distracted" and "Fidget" showed high closeness and betweenness, and represented a bridge linking the inattentive and hyperactive/impulsive domains. "Details" and "Fidget" were the most common endorsed symptoms in inattentive and hyperactive/impulsive domains respectively. On the contrary, the "Listen" item formed a peripheral node showing weak links with all other items within the inattentive cluster, and the "Loss" item as the least central node by all measures of centrality and with low predictability value. The network structure was relatively invariant across gender, age and ADHD subtypes/presentations. The 18 items of ADHD core symptoms appear not equivalent and interchangeable. "Distracted" and "Fidget" should be considered as central, or core, symptoms for further evaluation and intervention. The network-informed differentiation of these symptoms has the potentials to refine the phenotype and reduce heterogeneity.