The Generalizability of Older Adult Self-Report (OASR) Syndromes of Psychopathology Across 20 Societies

OBJECTIVES: As the world population ages, psychiatrists will increasingly need instruments for measuring constructs of psychopathology that are generalizable to diverse elders. The study tested whether syndromes of co-occurring problems derived from self-ratings of psychopathology by US elders would fit self-ratings by elders in 19 other societies. METHODS/DESIGN: The Older Adult Self-Report (OASR) was completed by 12,826 60- to 102-year-olds in 19 societies from North and South America, Asia, and Eastern, Northern, Southern, and Western Europe, plus the US. Individual and multi-group confirmatory factor analyses (CFAs) tested the fit of the 7-syndrome OASR model, consisting of the Anxious/Depressed, Worries, Somatic Complaints, Functional Impairment, Memory/Cognition Problems, Thought Problems, and Irritable/Disinhibited syndromes. RESULTS: In individual CFAs, the primary model fit index showed good fit for all societies, while the secondary model fit indices showed acceptable to good fit. The items loaded strongly on their respective factors, with a median item loading of .63 across the 20 societies; and 98.7% of the loadings were statistically significant. In multi-group CFAs, 98% of items demonstrated approximate or full metric invariance. Fifteen percent of items demonstrated approximate or full scalar invariance and another 59% demonstrated scalar invariance across more than half of societies. CONCLUSIONS: The findings supported the generalizability of OASR syndromes across societies. The seven syndromes offer empirically-based clinical constructs that are relevant for elders of different backgrounds. They can be used to assess diverse elders, and as a taxonomic framework to facilitate communication, services, research and training in geriatric psychiatry. This article is protected by copyright. All rights reserved

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Demographic and Clinical Variables Associated With Transcranial Magnetic Stimulation Response in Depression: A Growth Mixture Modeling Study

Depression is a growing public health crisis impacting millions around the world. Transcranial Magnetic Stimulation (TMS) is a non-invasive treatment for depression which has been FDA approved but the factors related to how well patients respond are still under investigation. The current study aimed to identify different treatment response patterns based on Patient Health Questionnaire (PHQ-9) scores over the course of transcranial magnetic stimulation (TMS) treatment for depression and to identify the differences between these response classes on demographic and clinical variables. A total of 285 patients from a psychiatric clinic were included with a sizable number of Hispanics and Military Families. Growth mixture modeling (GMM) was used to classify participants according to their response during TMS treatment. Three classes were identified: Responsive (56.5%), Excellent Response (56.6%), and Non-Response (13.3%). Various demographic and clinical variables were compared across these classes using chi-square tests of independence and analysis of variance (ANOVA) revealing 12 significant differences/associates (p\u3c.01). Notably, higher depression severity at treatment initiation and comorbid chronic pain diagnosis was associated with poorer response. The results contribute to the literature confirming factors associated with TMS treatment response in a sample with underrepresented populations. Future research should include a follow-up at various timepoints to better understand the longevity of TMS treatment for depression. Likewise, brain biomarkers such as EEG could aid in better quantifying depression subtypes to further enhance treatment outcomes