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. 2021 Mar 12;19(1):61.
doi: 10.1186/s12916-021-01935-4.

Reducing youth suicide: systems modelling and simulation to guide targeted investments across the determinants

Jo-An Occhipinti #  1   2   3   4 , Adam Skinner #  5   6 , Frank Iorfino  5 , Kenny Lawson  7   8 , Julie Sturgess  9 , Warren Burgess  9 , Tracey Davenport  5 , Danica Hudson  9 , Ian Hickie  5
Affiliations

Reducing youth suicide: systems modelling and simulation to guide targeted investments across the determinants

Jo-An Occhipinti et al. BMC Med. .

Abstract

Background: Reducing suicidal behaviour (SB) is a critical public health issue globally. The complex interplay of social determinants, service system factors, population demographics, and behavioural dynamics makes it extraordinarily difficult for decision makers to determine the nature and balance of investments required to have the greatest impacts on SB. Real-world experimentation to establish the optimal targeting, timing, scale, frequency, and intensity of investments required across the determinants is unfeasible. Therefore, this study harnesses systems modelling and simulation to guide population-level decision making that represent best strategic allocation of limited resources.

Methods: Using a participatory approach, and informed by a range of national, state, and local datasets, a system dynamics model was developed, tested, and validated for a regional population catchment. The model incorporated defined pathways from social determinants of mental health to psychological distress, mental health care, and SB. Intervention scenarios were investigated to forecast their impact on SB over a 20-year period.

Results: A combination of social connectedness programs, technology-enabled coordinated care, post-attempt assertive aftercare, reductions in childhood adversity, and increasing youth employment projected the greatest impacts on SB, particularly in a youth population, reducing self-harm hospitalisations (suicide attempts) by 28.5% (95% interval 26.3-30.8%) and suicide deaths by 29.3% (95% interval 27.1-31.5%). Introducing additional interventions beyond the best performing suite of interventions produced only marginal improvement in population level impacts, highlighting that 'more is not necessarily better.'

Conclusion: Results indicate that targeted investments in addressing the social determinants and in mental health services provides the best opportunity to reduce SB and suicide. Systems modelling and simulation offers a robust approach to leveraging best available research, data, and expert knowledge in a way that helps decision makers respond to the unique characteristics and drivers of SB in their catchments and more effectively focus limited health resources.

Keywords: Decision analysis; Simulation; Strategic planning; Suicide prevention; Systems modelling.

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Conflict of interest statement

Authors AS, FI, KL, JS, WB, TD, and DH declare they have no conflicts of interest relevant to this work.

A/Professor Jo-An Occhipinti (née Atkinson) is both Head of Systems Modelling, Simulation & Data Science at the University of Sydney’s Brain and Mind Centre and Managing Director of Computer Simulation & Advanced Research Technologies (CSART). Professor Ian Hickie was an inaugural Commissioner on Australia’s National Mental Health Commission (2012–2018). He is the Co-Director, Health and Policy at the Brain and Mind Centre (BMC) University of Sydney, Australia. The BMC operates an early-intervention youth services at Camperdown under contract to headspace. Professor Hickie has previously led community-based and pharmaceutical industry-supported (Wyeth, Eli Lily, Servier, Pfizer, AstraZeneca) projects focused on the identification and better management of anxiety and depression. He was a member of the Medical Advisory Panel for Medibank Private until October 2017, a Board Member of Psychosis Australia Trust and a member of Veterans Mental Health Clinical Reference group. He is the Chief Scientific Advisor to, and a 5% equity shareholder in, InnoWell Pty Ltd. InnoWell was formed by the University of Sydney (45% equity) and PwC (Australia; 45% equity) to deliver the $30 M Australian Government-funded Project Synergy (2017–2020; a 3-year program for the transformation of mental health services) and to lead transformation of mental health services internationally through the use of innovative technologies.

Figures

Fig. 1
Fig. 1
A high-level overview of the causal structure and pathways of the system dynamics model
Fig. 2
Fig. 2
Differences in projected total (cumulative) numbers of self-harm hospitalisations between baseline and intervention scenarios (2021–2041). Numbers of cases (i.e. hospitalisations) and cases prevented are rounded to the nearest integer and were obtained assuming the default parameter values. Mean percentage reductions and 95% intervals reported in the rightmost column and plotted on the right were derived from the distributions of projected outcomes calculated in the sensitivity analyses (note that the 95% intervals provide a measure of the impact of uncertainty in the assumed intervention effects but should not be interpreted as confidence intervals)
Fig. 3
Fig. 3
Differences in projected total (cumulative) numbers of suicides between the baseline and intervention scenarios (2021–2041). Numbers of cases (i.e. suicides) and cases prevented are rounded to the nearest integer and were obtained assuming the default parameter values. Mean percentage reductions and 95% intervals reported in the rightmost column and plotted on the right were derived from the distributions of projected outcomes calculated in the sensitivity analyses (note that the 95% intervals provide a measure of the impact of uncertainty in the assumed intervention effects but should not be interpreted as confidence intervals)
Fig. 4
Fig. 4
Self-harm hospitalisation and suicide rates (per 105 population per year). Projections are shown for the baseline scenario (i.e. business as usual) and for selected combinations of mental health and suicide prevention interventions (post-attempt care plus social connectedness programs plus technology-enabled coordinated care; scenario g in Figs. 2 and 3) and social determinants interventions (a 50% reduction in childhood adversity plus a 50% increase in youth employment initiation; scenarios in Figs. 2 and 3)
See this image and copyright information in PMC

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