Factor structure of the positive and negative syndrome scale (PANSS) in people at ultra high risk (UHR) for psychosis
Introduction
Research studies conducted on people at ultra-high risk (UHR) for psychosis (Yung and McGorry, 1996) often utilize assessment instruments employed in schizophrenia. One such example is the Positive and Negative Syndrome Scale (PANSS) (Kay et al., 1987), a 30-item scale with three subscales including positive symptoms, negative symptoms, and general psychopathology, which comprehensively assesses symptoms in schizophrenia. The PANSS has been the most widely used symptom severity rating scale in psychosis (van van Veelen and Sommer, 2014) for the management of clinical outcomes and intervention efficacy (Citrome et al., 2011; Dragioti et al., 2016; Emsley et al., 2003; Lindenmayer et al., 1994; Marder et al., 1997).
Research on the UHR population utilizing PANSS mostly reported the scores of the three subscales (Fusar-Poli et al., 2010; Mossaheb et al., 2013; van Rijn et al., 2011). However, past research have found the three subscales to be over simplified and could not adequately represent the structure of PANSS in schizophrenia (Kay and Sevy, 1990; Wallwork et al., 2012). Subsequent factor analytic works reported a few diverse multidimensional solutions of PANSS structure in schizophrenia, ranging from four- to seven-factor models (Emsley et al., 2003; Kay and Sevy, 1990; Van den Oord et al., 2006; White et al., 1997). Majority of those studies provided support for a five factor PANSS model (Jiang et al., 2013; Lehoux et al., 2009; Lindenmayer et al., 1994; Wallwork et al., 2012), but the lack of a broad consensus on the items loaded in each domain remains a pertinent issue (Wallwork et al., 2012). These discrepancies may, in part, be attributed to the different statistical methods used, the removal of items with multiple loadings (van der Gaag et al., 2006a; van der Gaag et al., 2006b), course of illness progression (Dollfus and Petit, 1995; Dragioti et al., 2016; Drake et al., 2003), the lack of validation in an independent sample, and the influence of cultural differences on clinical presentation (Jiang et al., 2013).
To our knowledge, no study has elucidated the dimensional structure of PANSS in UHR, and its use in describing the UHR phenomenology. While individuals are categorized to be at UHR for psychosis mainly from intensity and frequency of attenuated psychotic symptoms, studies have also shown that UHR individuals manifest other psychopathology such as negative symptoms, depressive and anxiety features that are associated with functional deficits at baseline and follow-up (Fusar-Poli et al., 2014a; Lim et al., 2015). Moreover, most of these UHR individuals do not transit to full-blown psychosis (Fusar-Poli et al., 2012). In light of this, concerns have been raised to the specificity of UHR construct as a risk for psychosis only or a risk for any psychiatric disorder, calling the need to extend the conceptual operationalization of the UHR construct, from a single psychosis dimension to a general psychopathology model (Fusar-Poli et al., 2014b; McGorry and van Os, 2013; Yung et al., 2010). Instead of delineating UHR individuals for specific psychopathology, the general psychopathology model posits the co-occurrence of non-specific sub-threshold expression of psychotic and non-psychotic symptoms that deteriorate, eventually leading to a severe psychopathology (Fusar-Poli et al., 2014b). In this regard, a dimensional approach could serve useful in explaining the UHR phenomenology by serving as phenotypic markers of symptom dimension, severity, and illness progression which could aid in the management of prominent at-risk presentation of pre-nosographic conditions, as well as provide a better understanding of the biological underpinnings of UHR psychopathology.
The aim of the current study is to explore the factor structure of PANSS in people with UHR. To our knowledge, no study has sought to examine a valid PANSS structure model for an accurate representation of sub-threshold clinical manifestation in UHR. The concurrent validity of PANSS and its correlations with functional outcomes would also be examined.
Section snippets
Study samples
The study sample consisted of a group of participants from the Longitudinal Youth at Risk Study (LYRIKS). Details of the study sample (Lee et al., 2013) and recruitment strategies (Mitter et al., 2014) have been described in detail previously. Briefly, LYRIKS is a prospective, observational study assessing the risk factors of psychosis (Lee et al., 2013; Lim et al., 2015). Recruitment adopted a hybrid approach where both help-seeking and non-help-seeking individuals from the community were
Descriptive and descriptive data
The demographic and the clinical characteristics of the UHR sample are shown in Table 1.
Building of PANSS factor model
Based on the fit indices (Table 2), the 5 to 7 factor models for PANSS fit the UHR sample. However, the loadings on the 7-factor model and 6-factor model were small. In the 7-factor model, one factor contained only one item (G2 Anxiety) reaching a loading of >0.4. Similarly, in the 6-factor model, one factor consisted of only two items (G2 anxiety and G4 Tension). Therefore, we selected the 5-factor model.
Discussion
In this study we derived five factors - positive, negative, cognitive/disorganization, anxiety/depression and hostility. The five factor model included 27 PANSS items with one item being loaded on two factors. Four of the derived factors (i.e., positive, negative, cognitive/disorganization, and anxiety/depression) were concordant with prodromal symptom structures in UHR samples as assessed on the Scale of Prodromal Symptoms (SOPS; Klaassen et al., 2011). The item of “motor retardation” cross
Funding source
This research was supported by the National Research Foundation Singapore under the National Medical Research Council Translational and Clinical Research Flagship Programme (Grant No.: NMRC/TCR/003/2008), Singapore Ministry of Health's National Medical Research Council under the Centre Grant Programme (Grant No.: NMRC/CG/004/2013).
Contributors
Study concept and design: Zixu Yang, Keane Lim, Max Lam, Jimmy Lee.
Acquisition of data: Zixu Yang.
Drafting of the manuscript: Zixu Yang, Keane Lim, Jimmy Lee.
Statistical analysis: Zixu Yang, Keane Lim, Max Lam.
Critical revision of the manuscript for important content: Zixu Yang, Keane Lim, Max Lam, Richard Keefe, Jimmy Lee.
Final approval of the version to be published: Zixu Yang, Keane Lim, Max Lam, Richard Keefe, Jimmy Lee.
Conflict of interest
Dr. Keefe has received investigator-initiated research funding support from the Department of Veterans Affairs, the National Institute of Mental Health, and the Singapore National Medical Research Council; received honoraria and serve as a consultant, speaker, or advisory board member for Abbvie, Acadia, Akebia, Akili, Alkermes, Astellas, Asubio, Avanir, AviNeuro/ChemRar, Axovant, Biogen, Boehringer Ingelehim, Cerecor, CoMentis, FORUM, Global Medical Education, GW Pharmaceuticals, Intracellular
Acknowledgements
The authors would like to express their sincere gratitude to Dr. Gurpreet Rekhi and Dr. Lim Sheng Foong, Caroline who trained and supervised research psychologists in conducting clinical assessments and all of the participants who participated in this study. This research was supported by the National Research Foundation Singapore under the National Medical Research Council Translational and Clinical Research Flagship Programme (Grant No.: NMRC/TCR/003/2008), Singapore Ministry of Health's
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