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.
Highlights
At the outset of the COVID-19 pandemic, systems models were rapidly deployed in many countries to estimate the likely trajectories of transmission, mortality, and health system burden, to determine the most impactful mitigation strategies, and to most effectively allocate limited resources [1,2,3]
These results demonstrate that the top two best performing intervention combinations (i.e., (i) post-suicide attempt assertive aftercare, community support programs to increase community connectedness, and technology enabled care coordination; (ii) post-suicide attempt assertive aftercare, community support programs to increase community connectedness and family education and support) delivered impacts that were highly consistent across all four possible COVID-19 mental health trajectories, reducing suicide deaths by between
Results of the analysis of best performing interventions for different time horizons are presented in Figure 3 and Figure 5. These results demonstrate that for each COVID-19 mental health trajectory, the ranking of best performing intervention combinations changes depending on the time horizon under consideration
Summary
At the outset of the COVID-19 pandemic, systems models were rapidly deployed in many countries to estimate the likely trajectories of transmission, mortality, and health system burden, to determine the most impactful mitigation strategies, and to most effectively allocate limited resources [1,2,3]. The extent to which decision makers engaged with modelling and simulation to help inform proactive and timely actions to arrest virus transmission varied across nations. Countries, such as Australia and New Zealand, that engaged early and consistently with the modelling, avoided the significant adverse impacts on health system, economic, and social indicators seen elsewhere in the world. The pandemic has helped to highlight the significant value of systems models as decision support tools, providing the ability to test the likely impact of policy and planning scenarios (helping to understand what combination of strategies are needed, at what time, at what scale, and for how long), and informing proactive and effective action despite the complexity, uncertainties, and imperfect knowledge that characterise an evolving crisis [6]. Beyond its benefits for informing decision making, systems modelling has long been used to advance scientific understanding of the spread of human disease from the first compartmental model of smallpox described by Daniel Bernoulli in 1776, to the Nobel Prize winning dynamic transmission modelling of malaria developed by Ronald Ross in the early 20th
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