Abstract for: System Dynamics Modeling of Thailand’s Prevention and Control of Metabolic Syndrome

Metabolic syndrome, characterized by central obesity, dyslipidemia, hypertension, and insulin resistance, is a major global health challenge of non-communicable diseases (NCDs) in developing countries like Thailand. Systemic factors beyond individual lifestyle choices influence the progression of this cardio-metabolic-kidney (CMK) disease spectrum. Existing research on diabetes or hypertension in isolation provides limited insights. This study develops a national-level model to assess systemic policy interventions for managing metabolic syndrome in Thailand. System dynamics modeling was used to evaluate policy interventions to assess their long-term impacts on disease control, where late diagnoses, limited healthcare capacity, and a hospital-centric system worsen disease outcomes. This study hypothesized that systemic bottlenecks and structural interventions influence metabolic syndrome progression. Policy experimentation was conducted to evaluate options for healthcare system capacity improvements, financial incentives for prevention, and environmental policy changes. Restructuring the food environments, healthcare systems, and financing models is more effective than focusing on individual behaviors. Insights include 1) Targeting central obesity through food regulations and urban planning has a greater long-term impact than behavioral changes, 2) Healthcare system reforms such as early screening and integrated primary care reduce disease burden, and 3) Financial incentives for prevention and management optimize resources, encourage early intervention, and lower long-term healthcare costs. Effective policy interventions for metabolic syndrome go beyond individual behavioral modification. Systemic interventions addressing central obesity and early detection yield long-term benefits while shifting from a reactive, hospital-based system to proactive primary care strengthens prevention. Future work will validate model outcomes with national data, refine policy scenarios through Group Model Building (GMB), and engage stakeholders to implement multi-sectoral strategies to reduce metabolic syndrome prevalence sustainably. AI was for grammatical correction.