The Role of Social Cognition Skills (SCS) and Social Determinants of Health (SDH) in Predicting Symptoms of Mental Illness

Abstract

Background: Social factors, such as social cognition skills (SCS) and social determinants of health (SDH), may be vital for mental health, even when compared with classical psycho-physical predictors (demographic, physical, psychiatric, and cognitive factors). Although major risk factors for psychiatric disorders have been previously assessed, the relative weight of SCS and SDH in relation to classical psycho-physical predictors in predicting symptoms of mental disorders remains largely unknown. Methods: In this study, we implemented multiple structural equation models (SEM) from a randomized sample assessed in the Colombian National Mental Health Survey of 2015 (CNMHS, n=2,947) to evaluate the role of SCS, SDH and psycho-physical factors (totaling 17 variables) as predictors of mental illness symptoms (anxiety, depression and other psychiatric symptoms). Specifically, we assessed the structural equation modeling of (a) SCS (emotion recognition and empathy skills); (b) SDH (including the experience of social adversities and social protective factors); (c) and classical psycho-physical factors, including psychiatric antecedents, physical-somatic factors (chronic diseases), and cognitive factors (executive functioning). Findings: Results revealed that emotion recognition skills, social adverse factors, past medical history of psychiatric disorders, the presence of chronic diseases and cognitive functioning were the best predictors of symptoms of mental illness. Moreover, SCS, particularly emotion recognition skills, and SDH (experiences of social adversities, familial and social support networks) reached higher predictive values of symptoms of mental illness than classical psycho-physical factors. Interpretation: Our study provides unprecedented evidence on the impact of social factors in predicting symptoms of mental illness and highlights the relevance of these factors to track early states of disease. Funding Statement: AI is partially supported by CONICET, CONICYT/FONDECYT (Regular 1170010 and 1171200), FONDAP (15150012), the INECO Foundation, PICT (2017-1818 and 2017- 1820), the Inter-American Development Bank (IDB), the Global Brain Health Institute (GBHI), GBHI ALZ UK-20-639295, and NIH/NIA R01 AG057234. This study has been partially supported by Colciencias Colombia and the Ministerio de Salud Colombia, Grant 762-201. Declaration of Interests: The authors declare neither competing financial interests nor other conflicts of interest. Ethics Approval Statement: The study was approved by the institutional ethics committee.