Balanced Optimization of the Disease Burden of Depression: Evidence from Chinese Cities

Depression places a heavy burden on both society and families. While much of the existing research focuses on quantifying the disease burden, there is a notable gap in studies addressing the optimization of this burden. In this paper, we propose an optimization model for the burden of depression (OMDB), which incorporates key real-world factors such as the clinical efficacy of antidepressants, health insurance reimbursement policies, and family financial strain. The model aims to identify the most effective antidepressant treatment regimen, offering scientific support for health insurance decision-making regarding the scope of reimbursement for antidepressant medications. To tackle the complex constraints of this model, we introduce a new LRPSO algorithm, an enhancement of the classical Particle Swarm Optimization (PSO) method. The innovative mapping rule improves both the computational efficiency and effectiveness of the algorithm in solving the OMDB model. Through validation on case studies of various scales, the results demonstrate that the proposed algorithm outperforms existing methods, with superior accuracy and faster convergence in optimizing the depression burden.