Routine logistic regression

<b>Background:</b> Vulnerable populations experience exacerbated mental health issues due to their social circumstances, further exacerbated by the COVID-19 pandemic. Therefore, the aim of this study was to investigate self-perceived mental health and social inequality among individuals residing in Brazilian slum and urban communities during the COVID-19 pandemic. <b>Methods:</b> This was a cross-sectional, descriptive-analytical study. A validated instrument was used, administered by equally trained field interviewers. Descriptive analyses and binary logistic regression were conducted to identify clinical and sociodemographic variables associated with self-perceived mental health. <b>Results:</b> A total of 1,319 individuals residing in Brazilian slum and urban communities participated in the study. Through binary logistic regression, it was observed that individuals with informal employment, earning less than a minimum wage, reported poorer mental health status. They were also more likely to feel sadder, more discouraged, and more overwhelmed. Additionally, there was an increase in cigarette consumption and antidepressant use. In conclusion, the results revealed an association between informal employment, low income, and adverse mental health outcomes. <b>Conclusions:</b> Addressing mental health disparities in this demographic group is crucial for implementing and planning specific interventions for this population, such as improving job security and providing mental health support services<br>