Psychometrics, interpretation and clinical implications of hierarchical models of psychopathology
Authors: Erik Pettersson, Henrik Larsson, Paul Lichtenstein
Journal: World Psychiatry (2021)

As described eloquently by Lahey et al1, recent research on hierarchical models of psychopathology, which include a general factor of psychopathology at the top and several specific factors at lower levels, is changing psychiatry and clinical psychology. We believe that a general psychopathology factor could provide clinicians with a reliable measure of patients' overall distress and impairment, and that the specific factors might better highlight ways in which patients differ from one another. Nevertheless, as Lahey et al mention, the three issues of psychometrics, interpretation and clinical implications are still unsettled, and the discussion should be continued. Regarding psychometrics, Lahey et al note that the field has not yet settled on a particular statistical model to measure the hierarchy of psychopathology. We agree that it might be unwise to select a mea­surement model based on statistical comparisons, given that second-order and bi­factor solutions both tend to fit reasonably well. Our view is that the two statistical models are differently preferential depending on the research question, and that both have their respective advantages and disadvantages. An advantage of second-order models is that the lower-order factors tend to be relatively easy to interpret, because they are identified before isolating the general factor. In contrast, since bifactor models identify the general factor at the same time as the lower-order factors, the latter can sometimes be difficult to interpret, because they do not necessarily conform to preconceived construct notions. An advantage of bifactor models is that they allow for estimating associations between all the factors and covariates simultaneously. In contrast, as Lahey et al point out, second-order models with covariates lack one degree of freedom, and can therefore not be estimated. A statistical solution to this problem is to impose one constraint on the associations (e.g., assigning one lower-order factor as a perfect indicator of the general factor, such that it has zero residual variance, and thereby not estimate its association with the covariate), but the estimated association parameters are likely to differ depending on where the constraint is imposed. A different way to circumvent this problem is to use a second-order factor rotation matrix within an exploratory structural equation modeling framework2. This approach is advantageous because it allows for estimating associations between all the factors in second-order models and covariates simultaneously. Regarding interpretation, Lahey et al note that it remains somewhat unclear what the general and specific factors measure. Interpretation problems become apparent when associating general factor models with covariates that are partly non-specific. For example, whereas past research has indicated that the general factor is correlated with negative emotionality3, it is unclear whether this association is attributable to the unique part of negative emotionality, or what it shares with all other emotion traits. To address this question, it might be advantageous to identify general and specific factors also of such covariates. After isolating a general factor from negative emotionality in a sample of children, it was shown that the negative emotionality fear subscale correlated more strongly with internalizing than general psychopathology. Furthermore, the fear subscale switched sign from correlating positively to negatively with externalizing psychopathology4. On a related note, although all forms of mental health problems tend to be positively associated, a consequence of bifactor models can be that the residual factors sometimes become negatively associated. For example, in two of the initial bifactor studies of psychopathology, the observed and genetic correlations between internalizing and externalizing problems switched sign from positive to negative after adjusting for general psychopathology3, 5. However, it is unclear whether this inverse association reflects behavioral tendencies or a psychometric artifact, because isolating a general factor is akin to subtracting the mean correlation from a correlation matrix, after which some residual correlations are bound to be above zero and some below zero6. Emerging findings indicate that this inverse association might reflect behavioral tendencies. For instance, the criteria for psychopathy include a higher degree of antisocial traits but also a lower degree of negative emotionality. This inverse association seems to replicate when based on biological measures. A meta-analysis of male offenders demonstrated that there is a negative association between clinician-rated callousness and bodily reactivity to threatening stimuli (e.g., startle blink reflex)7. Furthermore, in a study of over one million Swedish male military conscripts, higher resting heart rate (adjusted for physical fitness as measured by a bike test, height, weight, and parental education) at age 18 predicted an increased risk of anxiety disorders decades later. Conversely, lower resting heart rate predicted an increased risk of later criminal convictions8. Because resting heart rate is considered an indicator of the fear circuitry system, one speculation is that the fearful versus fearless continuum might represent one factor that partly contributes to the inverse association between the internalizing and externalizing dimensions in bifactor models. Regarding clinical implications, Lahey et al note that there is not yet adequate data on whether the general factor of psychopathology could assist mental health practitioners. It might therefore be beneficial to look at other domains where a general factor has been used. A general factor has been used for over a century in the intelligence domain to diagnose learning disability and predict academic performance. Because the general factors of intelligence and psychopathology appear to have relatively similar magnitude and predictive validity2, general psychopathology might be as useful in the psychiatric domain as general intelligence has been in the education domain. One speculation is that general psychopathology could provide clinicians with a reliable measure of overall distress and impairment9, which could, for example, help predict prognosis or how much treatment a patient might need. A general factor has also been used in the self-reported personality pathology domain. Tellegen et al10 developed a Minnesota Multiphasic Personality Inventory scale to measure non-specific variance associated with unpleasant mood states, which they labeled demoralization. They observed that "this general factor appears to inflate correlations between attributes that are considered relatively independent"10. Therefore, they aimed to "remove from each clinical scale items primarily marking demoralization" in order to improve the ability to differentiate among patients10. We share their sentiment about the im­portance of discriminant validity, and speculate that one potential clinical advantage of measuring a general factor is that the remaining scales might better highlight differences between patients. This, in turn, might help guide treatment modality. For example, without isolating a general factor, individuals with a broad symptom load often display elevated scores on a wide range of psychiatric scales. However, after isolating a general factor, such individuals might only display elevated scores on a smaller subset of scales, which might represent a suitable target for initial treatment.