Abstract
Depression is one of the leading causes of disease burden worldwide, with profound effects on quality of life, productivity, and life expectancy. In the United States, its prevalence is particularly high, placing substantial strain on both public health systems and economic stability. Despite advances in treatment and growing awareness, depression remains underdiagnosed and undertreated, especially among low-income and vulnerable populations. As the burden of mental illness continues to rise, quantifying its long-term health and economic impacts is essential for guiding healthcare policy and resource allocation. This study projects the future burden of depression in the United States by estimating healthcare expenditures and mortality for 2023-2032, drawing on nationally representative datasets including the Behavioral Risk Factor Surveillance System (BRFSS), the National Survey on Drug Use and Health (NSDUH), and the Healthcare Cost and Utilization Project (HCUP). Using linear regression modeling, the analysis examines trends in prevalence, healthcare utilization, treatment costs, and mortality, highlighting both direct healthcare costs and indirect costs from lost productivity and premature death. While linear modeling offers a straightforward approach to trend estimation, it may not fully capture nonlinear dynamics in depression prevalence and outcomes, and results should be interpreted with this limitation in mind. By 2030, the annual economic burden of major depressive disorder is projected to exceed $540 billion, with nearly 3,000 depression-related deaths annually. These findings underscore the urgent need for early intervention, expanded access to care, and targeted policies to address treatment disparities, thereby reducing both the economic and human toll of depression.