Sound Decision Making in Uncertain Times: Can Systems Modelling Be Useful for Informing Policy and Planning for Suicide Prevention?

Abstract

The COVID-19 pandemic demonstrated the significant value of systems modelling in supporting proactive and effective public health decision making despite the complexities and uncertainties that characterise an evolving crisis. The same approach is possible in the field of mental health. However, a commonly levelled (but misguided) criticism prevents systems modelling from being more routinely adopted, namely, that the presence of uncertainty around key model input parameters renders a model useless. This study explored whether radically different simulated trajectories of suicide would result in different advice to decision makers regarding the optimal strategy to mitigate the impacts of the pandemic on mental health. Using an existing system dynamics model developed in August 2020 for a regional catchment of Western Australia, four scenarios were simulated to model the possible effect of the COVID-19 pandemic on levels of psychological distress. The scenarios produced a range of projected impacts on suicide deaths, ranging from a relatively small to a dramatic increase. Discordance in the sets of best-performing intervention scenarios across the divergent COVID-mental health trajectories was assessed by comparing differences in projected numbers of suicides between the baseline scenario and each of 286 possible intervention scenarios calculated for two time horizons; 2026 and 2041. The best performing intervention combinations over the period 2021–2041 (i.e., post-suicide attempt assertive aftercare, community support programs to increase community connectedness, and technology enabled care coordination) were highly consistent across all four COVID-19 mental health trajectories, reducing suicide deaths by between 23.9–24.6% against the baseline. However, the ranking of best performing intervention combinations does alter depending on the time horizon under consideration due to non-linear intervention impacts. These findings suggest that systems models can retain value in informing robust decision making despite uncertainty in the trajectories of population mental health outcomes. It is recommended that the time horizon under consideration be sufficiently long to capture the full effects of interventions, and efforts should be made to achieve more timely tracking and access to key population mental health indicators to inform model refinements over time and reduce uncertainty in mental health policy and planning decisions.

The economic costs of alcohol‐related harms at the local level in New South Wales

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Systems modelling and simulation to guide targeted investments to reduce youth suicide and mental health problems in a low-middle-income country

“…However, despite this fragmentation and the di culty of identifying and collating available data, the quantity and quality of data available was su cient for model development. For some indicators which might not have been publicly available, data donations from participants served to ll gaps, showing the complementary nature and value of participatory processes for systems modelling (57) .…”

“…In addition, the collaborative nature of our approach enhances stakeholder familiarity with the software, ensuring user-friendliness and facilitating real-world impact exploration through feedback (57) .…”

Mental health systems modelling for evidence-informed service reform in Australia

“…Historically, SD models were commonly used in disease epidemiology, though they have been increasingly used in health system research in recent years (Darabi and Hosseinichimeh, 2020). System dynamics models in mental health have examined topics 12 such as patient flows (Long and Meadows, 2017), to understand population level impacts of evidence based interventions (Atkinson et al, 2020a), to proactively respond to crises (Occhipinti et al, 2021b;Occhipinti et al, 2022) and to inform the likely impacts of funding and/or resource reallocation (Occhipinti et al, 2022;Atkinson et al, 2019). Systems dynamic modelling has also been used to provide decision support tools to national and state policy makers and regional planners to forecast future population mental health trajectories and support better allocation of funding and resources.…”