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We investigate how select identity characteristics moderate the role of several SDoH domains on major depressive disorder (MDD). Our study considers an analytical sample of 86,954 participants from the NIH-funded All of Us (AoU) Research Program in the USA. Our independent variables and moderators come from survey responses and our outcome is an EHR diagnostic code. We include race/ethnicity and gender/sexual identity to moderate the role of food insecurity, discrimination, neighborhood social cohesion, and loneliness in assessing risk for MDD diagnosis. We examine those moderating effects based on connections seen in the literature. Our findings illustrate the complexity of where and how people live their lives can have significant differential impact on MDD. Women (AOR = 1.60, 95% CI = [1.53, 1.68]) and LGBTQIA2+ individuals (AOR = 1.71, 95% CI = [1.60, 1.84]) exhibit a significantly higher likelihood of MDD diagnosis compared to cisgender heterosexual males. Our study also reveals a lower likelihood of MDD diagnosis among Asian/Asian American individuals (AOR = 0.41, 95% CI = [0.35, 0.49]) compared to White individuals. Our results align with previous research indicating that higher levels of food insecurity (AOR = 1.30, 95% CI = [1.17, 1.44]) and loneliness (AOR = 6.89, 95% CI = [6.04, 7.87]) are strongly associated with an increased likelihood of MDD. However, we also find that social cohesion (AOR = 0.92, 95% CI = [0.81, 1.05]) does not emerge as a significant predictor, contradicting some literature emphasizing the protective role of neighborhood cohesion. Similarly, our finding that transience (AOR = 0.95, 95% CI = [0.92, 0.98]) reduces the likelihood of MDD diagnosis contradicts conventional wisdom and warrants further exploration. Our study provides a reminder of the substantial challenges for research focused on marginalized community segments and that deliberate sampling plans are needed to examine those most marginalized and underserved. 

Social factors like family background, education level, financial status, and stress can impact public health outcomes, such as suicidal ideation. However, the analysis of social factors for suicide prevention has been limited by the lack of up-to-date suicide reporting data, variations in reporting practices, and small sample sizes. In this study, we analyzed 172,629 suicide incidents from 2014 to 2020 utilizing the National Violent Death Reporting System Restricted Access Database (NVDRS-RAD). Logistic regression models were developed to examine the relationships between demographics and suicide-related circumstances. Trends over time were assessed, and Latent Dirichlet Allocation (LDA) was used to identify common suiciderelated social factors. Mental health, interpersonal relationships, mental health treatment and disclosure, and school/work-related stressors were identified as the main themes of suicide-related social factors. This study also identified systemic disparities across various population groups, particularly concerning Black individuals, young people aged under 24, healthcare practitioners, and those with limited education backgrounds, which shed light on potential directions for demographic-specific suicidal interventions.