Identifying pathways between psychiatric symptoms and psychosocial functioning in the general population

Abstract

The present study aims to identify pathways between psychiatric network symptoms and psychosocial functioning and their associated variables among functioning clusters in the general population. A cross-sectional web-based survey was administered in a total of 3,023 individuals in Brazil. The functioning clusters were derived by a previous study identifying three different groups based on the online Functioning Assessment Short Test. Networking analysis was fitted with all items of the Patient-Reported Outcomes Measurement Information System for depression and for anxiety (PROMIS) using the mixed graphical model. A decision tree model was used to identify the demographic and clinical characteristics of good and low functioning. A total of 926 (30.63%) subjects showed good functioning, 1,436 (47.50%) participants intermediate functioning, and 661 (21.86%) individuals low functioning. Anxiety and uneasy symptoms were the most important nodes for good and intermediate clusters but anxiety, feeling of failure, and depression were the most relevant symptoms for low functioning. The decision tree model was applied to identify variables capable to discriminate individuals with good and low functioning. The algorithm achieved balanced accuracy 0.75, sensitivity 0.87, specificity 0.63, positive predictive value 0.63 negative predictive value 0.87 (p<0.001), and an area under the curve of 0.83 (95%CI:0.79–0.86, p<0.01). Our results show that individuals who present psychological distress are more likely to experience poor functional status, suggesting that this subgroup should receive a more comprehensive psychiatric assessment and mental health care.