Systems modelling and simulation to inform strategic decision making for suicide prevention in rural New South Wales (Australia)

Abstract

Background: The need to understand and respond to the unique characteristics and drivers of suicidal behaviour in rural areas has been enabled through the Australian Government’s 2015 mental health reforms facilitating a move to an evidence-based, regional approach to suicide prevention. However, a key challenge has been the complex decision-making environment and lack of appropriate tools to facilitate the use of evidence, data and expert knowledge in a way that can inform contextually appropriate strategies that will deliver the greatest impact. This paper reports the co-development of an advanced decision support tool that enables regional decision makers to explore the likely impacts of their decisions before implementing them in the real world. Methods: A system dynamics model for the rural and remote population catchment of Western New South Wales was developed. The model was based on defined pathways to mental health care and suicidal behaviour and reproduced historic trends in the incidence of attempted suicide (self-harm hospitalisations) and suicide deaths in the region. A series of intervention scenarios were investigated to forecast their impact on suicidal behaviour over a 10-year period. Results: Post-suicide attempt assertive aftercare was forecast to deliver the greatest impact, reducing the numbers of self-harm hospitalisations and suicide deaths by 5.65% (95% interval, 4.87−6.42%) and 5.45% (4.68−6.22%), respectively. Reductions were also projected for community support programs (self-harm hospitalisations: 2.83%, 95% interval 2.23−3.46%; suicide deaths: 4.38%, 95% interval 3.78−5.00%). Some scenarios produced unintuitive impacts or effect sizes that were significantly lower than what has been anticipated under the traditional evidence-based approach to suicide prevention and provide an opportunity for learning. Conclusion: Systems modelling and simulation offers significant potential for regional decision makers to better understand and respond to the unique characteristics and drivers of suicidal behaviour in their catchments and more effectively allocate limited health resources.

The state of psychiatric research in the Asia Pacific region

“…There is research on groups at higher risk (eg, men (Armstrong et al, 2020), first responders (Kyron et al, 2020), military (Syed Sheriff, Van Hooff, Malhi, Grace, & McFarlane, 2020), indigenous Australians (Leckning et al, 2020)), service evaluation (Watson & Spiteri, 2020), and appropriate portrayal of suicide in the media (Pirkis et al, 2020). Apart from nation‐wide initiatives, the National Mental Health Commission (2014) made recommendations to fund suicide prevention programs at a regional level ‐ as such, there is research into developing tools to guide regional funding of suicide prevention initiatives (Atkinson et al, 2020; Page et al, 2018).…”

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

“…Despite this momentum, growing evidence of effective suicide prevention interventions, and the release of successive action plans, current strategies are not delivering substantial impacts [11][12][13]. SB has a complex aetiology with a wide range of contributing factors, both individual and contextual, and is rarely a result of any single cause [14,15].…”

“…Systems modelling is a robust quantitative method of complex systems science, an interdisciplinary field that studies the nature and behaviour of complex systems underpinned by well-established mathematical theory of nonlinear dynamics [29][30][31][32][33]. It provides a robust method for mapping and quantifying the complex causal mechanism driving mental health and suicide outcomes [11,[34][35][36]. Systems modelling is uniquely able to capture population and demographic dynamics, changes over time in social and economic drivers of psychological distress, mental disorders and suicidal behaviours (including feedback loops), workforce dynamics and the changing relationship between service supply versus demand, and the potentially nonadditive (interdependencies and interacting) effects of intervention combinations, factors that bedevil traditional analytic approaches.…”

The Science of Complex Systems Is Needed to Ameliorate the Impacts of COVID-19 on Mental Health

“…Applications of systems modeling in mental health research and practice have already demonstrated value in providing improved decision support capability and a better understanding of the different ways even "evidence-based" interventions can play out in diverse systems and settings. For example, a system dynamics model of suicide prevention developed and validated for a rural and remote population catchment of New South Wales, Australia, demonstrated that some evidence-based interventions were likely to deliver little or no populationlevel impact [e.g., general practitioner (GP) training], and some combinations were projected to result in an unintended increase in suicidal behavior (e.g., GP training plus mental health education programs) (26). This unintended consequence arising from two "evidence-based" interventions applied in combination is explained by the imbalance they generate in the dynamics of service capacity vs. demand for services which is regionally specific.…”

“…Moreover, traditional approaches focus on looking backwards to generate understanding of the factors that have driven past events, rather than using the current best knowledge and a range of evidence and data sources to look forward and to anticipate and strategically act to mitigate future trajectories. Without adoption of a more advanced approach, the mental health needs of populations-even in times of genuine crisis-may be met with well-meaning acts of government that are reactive rather than strategic, formulated "on the run, " and based on the prioritization of initiatives that have been heavily lobbied or represent "seemingly" good evidence-based investments, but can be ineffective, or even counterproductive (26).…”