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

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  • Jo-An Occhipinti

    (Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
    Computer Simulation & Advanced Research Technologies (CSART), Sydney, NSW 2021, Australia)

  • Danya Rose

    (Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia)

  • Adam Skinner

    (Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia)

  • Daniel Rock

    (Medical School, University of Western Australia, Perth, WA 6009, Australia
    WA Primary Health Alliance, Perth, WA 6008, Australia)

  • Yun Ju C. Song

    (Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia)

  • Ante Prodan

    (Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
    Computer Simulation & Advanced Research Technologies (CSART), Sydney, NSW 2021, Australia
    School of Computer, Data and Mathematical Sciences, Western Sydney University, Penrith, NSW 2751, Australia)

  • Sebastian Rosenberg

    (Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia)

  • Louise Freebairn

    (Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
    Computer Simulation & Advanced Research Technologies (CSART), Sydney, NSW 2021, Australia)

  • Catherine Vacher

    (Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
    St Vincent’s Clinical School, University of New South Wales, Sydney, NSW 2052, Australia)

  • Ian B. Hickie

    (Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia)

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.

Suggested Citation

  • Jo-An Occhipinti & Danya Rose & Adam Skinner & Daniel Rock & Yun Ju C. Song & Ante Prodan & Sebastian Rosenberg & Louise Freebairn & Catherine Vacher & Ian B. Hickie, 2022. "Sound Decision Making in Uncertain Times: Can Systems Modelling Be Useful for Informing Policy and Planning for Suicide Prevention?," IJERPH, MDPI, vol. 19(3), pages 1-15, January.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:3:p:1468-:d:736341
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    References listed on IDEAS

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    1. Richard Pitman & David Fisman & Gregory S. Zaric & Maarten Postma & Mirjam Kretzschmar & John Edmunds & Marc Brisson, 2012. "Dynamic Transmission Modeling," Medical Decision Making, , vol. 32(5), pages 712-721, September.
    2. Tom Kompas & R Quentin Grafton & Tuong Nhu Che & Long Chu & James Camac, 2021. "Health and economic costs of early and delayed suppression and the unmitigated spread of COVID-19: The case of Australia," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-18, June.
    3. Jo-An Occhipinti & Adam Skinner & P. Murali Doraiswamy & Cameron Fox & Helen Herrman & Shekhar Saxena & Elisha London & Yun Ju Christine Song & Ian B. Hickie, 2021. "Mental health: build predictive models to steer policy," Nature, Nature, vol. 597(7878), pages 633-636, September.
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