System dynamics modelling of fall prevention in community and aged care

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MB Pinheiro1, D Currie2, S Khalatbari Soltani1, A Milat3, A Bauman1, K Howard1, C Sherrington1
1 University of Sydney, Sydney, NSW; 2 Sax Institute, Sydney, NSW; 3 NSW Agency for Clinical Innovation, Chatswood, NSW
Scientific Research
SP - Falls
Falls
View DOI

Abstract

Introduction

Economic evidence is essential for guiding decisions on the implementation and scaling of fall prevention strategies. This project aimed to develop and test a system dynamics model to project the health and economic impact of fall prevention initiatives for Australians aged 50 and over in community and residential aged care settings, over the period 2024–2035.

Method

We updated and expanded an existing system dynamics model of osteoporosis and related health and economic impact in Australia which was originally developed through a participatory approach involving a 32-member multidisciplinary modelling consortium. It enables users to simulate population-level fall prevention interventions by adjusting five key parameters: (i) intervention effectiveness (reduction in fall incidence), (ii) rollout duration, (iii) uptake percentage, (iv) intervention cost, and (v) target population age. Model validity was assessed through stakeholder workshops (face validity), historical data comparison, and benchmarking against a high-quality Markov model of public investment in fall prevention. The Markov model reported results for 22 scenarios. We modelled these same scenarios using the system dynamics model and calculated the agreement between the models in classifying the intervention as cost-effective using an arbitrary willingness to pay threshold of $50,000 per QALY or DALY gained.

Results

Stakeholder consultation confirmed high face validity, and the model successfully reproduced observed historical trends. Agreement between our model and the Markov model was 91% (20/22 interventions) in cost-effectiveness classification. The model interface was applied to fall prevention exercise programs as a case study, demonstrating the impact of varying parameter inputs. Best-practice approaches to parameter selection and model access are described.

Conclusion

The falls prevention system dynamics model has shown promising early validity and has the potential to support policy and investment decision-making. We recommend that parameter selection and model interpretation be conducted with cautious through participatory processes involving key stakeholders.

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