RESUMO
INTRODUCTION: Social determinants of health (SDH) influence and modify the risk for mental health disorders. To our knowledge, no study has explored SDH in the context of mental health in Saudi Arabia (SA) using population-based data. This study investigated the association between several SDH and anxiety and mood disorders in SA. METHODS: We utilized data from the nationally-representative Saudi National Mental Health Survey (SNMHS) conducted in 2014 to 2016. This study examined associations between personal-level, socioeconomic, physical health, and family environment characteristics and anxiety and mood disorders. Participants were classified as having anxiety-only disorders, mood-only disorders, or comorbidity of both disorders. Multinomial logistic regression models were employed to examine the associations between SDH and anxiety and/or mood disorders, comparing them to participants who had not experienced these disorders. RESULTS: A total of 4,004 participants were included in this analysis; the lifetime prevalence of disorders was: anxiety only (18%), mood only (3.8%), and comorbidity of both (5.3%). Regression models indicated that females, young adults (26-35 years), individuals with a higher level of education, and those who were separated or widowed had higher odds of experiencing anxiety and/or mood disorders. Furthermore, there was a significant and direct association between having physical chronic conditions and all three categories of anxiety and mood disorders. Experiencing Adverse Childhood Events (ACEs) was also associated with a significant risk of developing anxiety and/or mood disorders, with the highest risk associated with physical or sexual abuse, followed by violence and neglect. CONCLUSION: This study underscores the correlation between several personal-level, socioeconomic, and environmental SDH and anxiety and mood disorders in SA. These findings provide a foundation for future analyses examining the intricate interplay between upstream and downstream SDH in SA. Such research can enhance local scientific knowledge, aid in planning for social services, and inform policy decisions and treatment strategies.