Dynamic Model of Stunting Incidents and Policies in Padang City
Abstract
Stunting remains a significant public health challenge with complex, interconnected causative factors. Traditional static approaches fail to capture the dynamic relationships between environmental, health, and socioeconomic determinants. Dynamic modeling is essential for understanding these multifaceted interactions and developing effective prevention policies. Despite declining trends, Padang City's high prevalance of stunting necessitates urgent, targeted interventions to achieve national targets. This study aims to develop a dynamic model analyzing stunting factors in Padang City and formulate effective prevention policies. A quantitative descriptive method with a dynamic systems approach was employed. Primary data were obtained through stakeholder questionnaires, while secondary data were obtained from the Padang City Health Office and Statistical Reports (2018-2023). Analysis utilized PowerSim Studio 10 for dynamic systems modeling and Interpretative Structural Modeling (ISM) for policy strategy development. Seven main factors influencing stunting were identified: low birth weight, maternal health, immunization, diarrhea, living in slum areas, access to clean water access, and household sanitation. Simulation modeling demonstrated that improving environmental conditions, increasing immunization coverage, and administering iron tablets could reduce stunting rates by 50% over 25 years. ISM analysis revealed environmental factors (sanitation, waste management, and access clean water) as key elements with the highest driving force. Consistent immunization coverage could reduce stunting prevalence from 24.2% to 12% within the projection period, while improved sanitation programs may lower prevalence by 15%. The research provides an integrated approach prioritizing basic infrastructure improvement, environmental sanitation enhancement, cross-sector coordination, and community capacity building. This dynamic modeling framework offers valuable insights for comprehensive stunting prevention strategies. However, limitations include a modeling-only scope requiring field validation for implementation effectiveness.
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