Abstract summary 

The phenotypic expression of mental health (MH) conditions among people with HIV (PWH) in Uganda and worldwide are heterogeneous. Accordingly, there has been a shift toward identifying MH phenotypes using data-driven methods capable of identifying novel insights into mechanisms of divergent MH phenotypes among PWH. We leverage the analytic strengths of machine learning combined with inferential methods to identify novel MH phenotypes among PWH and the underlying explanatory features.A total of 277 PWH (46% female, median age = 44; 93% virally suppressed [<50copies/mL]) were included in the analyses. Participants completed the Patient Health Questionnaire (PHQ-9), Beck Anxiety Inventory (BAI), and the PTSD Checklist-Civilian (PCL-C). A clustering pipeline consisting of dimension reduction with UMAP followed by HBDScan was used to identify MH subtypes using total symptom scores. Inferential statistics compared select demographic (age, sex, education), viral load, and early life adversity between clusters.We identified four MH phenotypes. Cluster 1 ( = 76; ) endorsed clinically significant PTSD symptoms (average PCL-C total score > 33). Clusters 2 ( = 32; ) and 3 ( = 130; ) reported minimal PTSD symptoms, with modest BAI (Cluster 2) and PHQ-9 (Cluster 3) elevations. Cluster 4 ( = 39; ) reported no clinical MH symptom elevations. Comparisons revealed higher rates of sexual abuse during childhood among the vs. the ( = 0.03).We identified unique MH phenotypes among PWH and confirmed the importance of early life adversity as an early risk determinant for unfavorable MH among PWH in adulthood.

Authors & Co-authors:  Rubin Leah H LH Cho Kyu K Bolzenius Jacob J Mannarino Julie J Easter Rebecca E RE Dastgheyb Raha M RM Anok Aggrey A Tomusange Stephen S Saylor Deanna D Wawer Maria J MJ Nakasujja Noeline N Nakigozi Gertrude G Paul Robert R

Study Outcome 

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