CVD health Simulator - Advancing the Simulator to Improve Cardiovascular Disease (CVD) Health – CARDIO4Cities Simulator 3.0

Since 2022, Swiss TPH has been developing a system dynamics (SD) based decision support tool, the Simulator, for integration into the Novartis Foundation's Urban Population Health Initiative, which aims to reduce cardiovascular disease (CVD) mortality and morbidity in cities. The Initiative builds on the Foundation's CARDIO4Cities approach, one pillar of which concerns improving the availability, quality, integration and use of data and information for health. Within this approach, the Initiative seeks to strengthen data-informed, intersectoral decision-making that draws on the full range of data held across city sectors, with the long-term goal of improving CVD outcomes.

A key expected outcome of this multi-city initiative is the greater use of data in the planning and budgeting cycles of local health authorities. The Simulator responds to this need by using SD modelling to support population-level decision-making on CVD prevention and control. The model is co-created with local partners, including city health authorities, other municipal sectors and community representatives, and with thematic experts in CVD epidemiology and health economics. This participatory process draws on their knowledge of local causal mechanisms, data sources and decision-making contexts. Initially piloted in Brazil, 2022, the tool is now being adapted for use across 200 cities and countries.

The Simulator is designed to help decision-makers compare the projected effects of alternative CVD prevention and control strategies over time. It explicitly accounts for setting-specific resource and capacity constraints. Rather than prescribing a single optimal strategy, it allows users to explore trade-offs, delays and unintended consequences across policy options before committing resources. Its scalability rests on a generic model structure that can be parameterised and, where necessary, structurally adapted to local contextual and systemic factors. The development process has also generated, and continues to generate, learning on how SD modelling and systems thinking can be integrated into routine decision-making in low- and middle-income countries (LMICs).