The Psychotic Symptom Rating Scales (PSYRATS) is an instrument designed to quantify the severity of delusions and hallucinations and is typically used in research studies and clinical settings focusing on people with psychosis and schizophrenia. It is comprised of the auditory hallucinations (AHS) and delusions subscales (DS), but these subscales do not necessarily reflect the psychological constructs causing intercorrelation between clusters of scale items. Identification of these constructs is important in some clinical and research contexts because item clustering may be caused by underlying etiological processes of interest. Previous attempts to identify these constructs have produced conflicting results. In this study, we compiled PSYRATS data from 12 sites in 7 countries, comprising 711 participants for AHS and 520 for DS. We compared previously proposed and novel models of underlying constructs using structural equation modeling. For the AHS, a novel 4-dimensional model provided the best fit, with latent variables labeled Distress (negative content, distress, and control), Frequency (frequency, duration, and disruption), Attribution (location and origin of voices), and Loudness (loudness item only). For the DS, a 2-dimensional solution was confirmed, with latent variables labeled Distress (amount/intensity) and Frequency (preoccupation, conviction, and disruption). The within-AHS and within-DS dimension intercorrelations were higher than those between subscales, with the exception of the AHS and DS Distress dimensions, which produced a correlation that approached the range of the within-scale correlations. Recommendations are provided for integrating these underlying constructs into research and clinical applications of the PSYRATS.

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BACKGROUND: Health care systems struggle to identify risk factors for suicide. Adverse social determinants of health (SDH) are strong predictors of suicide risk, but most electronic health records (EHR) do not include SDH data. OBJECTIVE: To determine the prevalence of SDH documentation in the EHR and how SDH are associated with suicide ideation and attempt. DESIGN: This cross-sectional analysis included EHR data spanning October 1, 2015-September 30, 2016, from the Veterans Integrated Service Network Region 4. PARTICIPANTS: The study included all patients with at least one inpatient or outpatient visit (n = 293,872). MAIN MEASUREMENTS: Adverse SDH, operationalized using Veterans Health Administration (VHA) coding for services and International Statistical Classification of Diseases and Related Health Problems (ICD)-10 codes, encompassed seven types (violence, housing instability, financial/employment problems, legal problems, f a m i l i a l / s o c i a l p r o b l e m s , l a c k o f a c c e s s t o care/transportation, and nonspecific psychosocial needs). We defined suicide morbidity by ICD-10 codes and data from the VHA's Suicide Prevention Applications Network. Logistic regression assessed associations of SDH with suicide morbidity, adjusting for sociodemographics and mental health diagnoses (e.g., major depression). Statistical significance was assessed with p < .01. KEY RESULTS: Overall, 16.4% of patients had at least one adverse SDH indicator. Adverse SDH exhibited dose-response-like associations with suicidal ideation and suicide attempt: each additional adverse SDH increased odds of suicidal ideation by 67% (AOR = 1.67, 99%CI = 1.60-1.75; p < .01) and suicide attempt by 49% (AOR = 1.49, 99%CI = 1.33-1.68; p < .01). Independently, each adverse SDH had strong effect sizes, ranging from 1.86 (99%CI = 1.58-2.19; p < .01) for legal issues to 3.10 (99%CI = 2.74-3.50; p < .01) for non-specific psychosocial needs in models assessing suicidal ideation and from 1.58 (99%CI = 1.10-2.27; p < .01) for employment/financial problems to 2.90 (99%CI = 2.30-4.16; p < .01) for violence in models assessing suicide attempt. CONCLUSIONS: SDH were strongly associated with suicidal ideation and suicide attempt even after adjusting for mental health diagnoses. Integration of SDH data in EHR could improve suicide prevention.

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