S003329172000361Xjra 1838..1846

Background. Clinical intuition suggests that personality disorders hinder the treatment of depression, but research findings are mixed. One reason for this might be the way in which current assessment measures conflate general aspects of personality disorders, such as overall severity, with specific aspects, such as stylistic tendencies. The goal of this study was to clarify the unique contributions of the general and specific aspects of personality disorders to depression outcomes. Methods. Patients admitted to the Menninger Clinic, Houston, between 2012 and 2015 (N = 2352) were followed over a 6–8-week course of multimodal inpatient treatment. Personality disorder symptoms were assessed with the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 4th edition Axis II Personality Screening Questionnaire at admission, and depression severity was assessed using the Patient Health Questionnaire-9 every fortnight. General and specific personality disorder factors estimated with a confirmatory bifactor model were used to predict latent growth curves of depression scores in a structural equation model. Results. The general factor predicted higher initial depression scores but not different rates of change. By contrast, the specific borderline factor predicted slower rates of decline in depression scores, while the specific antisocial factor predicted a U shaped pattern of change. Conclusions. Personality disorder symptoms are best represented by a general factor that reflects overall personality disorder severity, and specific factors that reflect unique personality styles. The general factor predicts overall depression severity while specific factors predict poorer prognosis which may be masked in prior studies that do not separate the two. With clinical depression ranked as the world’s leading cause of disability (World Health Organization, 2017), there is a pressing need to understand predictors of prognosis. One important aspect of depression is that it frequently co-occurs with other disorders, including personality disorders (Friborg et al., 2014). Patients diagnosed with a comorbid personality disorder (PD) show a more severe and persistent course of depression when left untreated (Cyranowski et al., 2004; Grilo et al., 2010). Clinical intuition suggests that PDs hinder treatment for depression (Clarkin, Petrini, & Diamond, 2019). Yet, results from meta-analyses are mixed: some support the link between PDs and poorer depression outcomes (Newton-Howes et al., 2014; Newton-Howes, Tyrer, & Johnson, 2006; Reich, 2003) while others not (Kool et al., 2005; Mulder, 2002). Studies vary widely in their choice of treatments, outcome measures, and sample characteristics, making any task of aggregating findings challenging and inconclusive (French, Turner, Dawson, & Moran, 2017). However, a consistent finding is that controlled studies tend to report a weaker relationship between PDs and depression outcomes (Mulder, 2002). For instance, controlling for baseline depression severity often negates the adverse effect of PDs on depression outcomes (De Bolle et al., 2011; Erkens et al., 2018; van Bronswijk et al., 2018). This implies that PDs are associated with higher depression scores throughout treatment, but the pattern of change is no different to patients without a PD diagnosis (Fowler et al., 2018; Moradveisi, Huibers, Renner, Arasteh, & Arntz, 2013). In short, a PD diagnosis does not alter general responses to treatment; it just predicts a poorer start. Another issue concerns the measurement validity of PDs. PDs are assessed using categorical criteria that represent distinct entities, but comorbidity rates among PDs are too high to be considered truly distinct (Tyrer, Reed, & Crawford, 2015). Predicting depression outcomes from the presence of a specific personality disorder would conflate the unique aspects of that disorder with aspects shared with other disorders. As Mulder (2002) put it, ‘Classification problems mean that it remains unclear whether personality disorder categories are a general measure of personality pathology affecting outcome or whether individual categories, or clusters, predict different outcomes. ’ (p. 366). Unless the general and specific aspects of PDs are separated out, it is uncertain how each contributes to depression outcomes. A statistical method for separating out the general and specific aspects of a measure is the bifactor model (Markon, 2019). In this factor analytic model, the covariance among a set of items is attributed to a general latent variable or ‘factor’ that summarizes the common variance among items, as well as specific factors that summarize the covariance among specific clusters of items (Reise, 2012). Put differently, responses to each item are decomposed for the variance associated with a general underlying construct, as well as the variance associated with specific constructs. These sources of variance are considered orthogonal, that is, the specific factors reflect distinct constructs not explained by the general construct (Chen, West, & Sousa, 2006). A growing number of studies have shown that the positive correlations among PD symptom ratings or diagnoses are best explained by a general PD factor, as well as specific factors that reflect individual PDs or PD clusters (Conway, Hammen, & Brennan, 2016; Jahng et al., 2011; Sharp et al., 2015; Williams, Scalco, & Simms, 2018; Wright, Hopwood, Skodol, & Morey, 2016). The general PD factor is thought to reflect the severity of individuals’ personality dysfunction on a continuum (Sharp et al., 2015), and predicts social functioning, occupational functioning, treatment use, and suicidality (Conway et al., 2016). The meaning of the specific factors is less clear, but they are thought to reflect stylistic expressions of disturbance (Wright et al., 2016). While the bifactor model is primarily a statistical tool for estimating the general and specific variance within a measure, it also maps onto alternative nosologies of PD that separate out severity and style (Skodol et al., 2011b; Tyrer et al., 2011). The mixed predictive value of PDs for depression outcomes might result from current PD measures conflating two sources of variance – general and specific personality pathology – that differ in their direction of influence. For instance, if general PD reflects overall illness severity, then it may predict higher depression scores overall, giving the impression that PDs predict poorer outcomes. However, if general PD or its sequelae are controlled for (e.g. via baseline randomization or covarying baseline depression severity), then depression scores might normalize, giving the impression that PDs do not predict poorer outcomes. In either case, general PD would predict the overall severity of depression, not the rate of change. Specific PD factors might reflect stylistic expressions that predict differential rates of change, but these effects are masked by general PD severity. We tested this hypothesis by estimating the unique contribution of general and specific PD factors to changes in depression severity over an inpatient treatment using the bifactor model and latent growth models.