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Objective: Successive suicide prevention frameworks and action plans in Australia and internationally have called for improvements to mental health services and enhancement of workforce capacity. However, there is debate regarding the priorities for resource allocation and the optimal combination of mental health services to best prevent suicidal behaviour. This study investigates the potential impacts of service capacity improvements on the incidence of suicidal behaviour in the Australian context., Methods: A system dynamics model was developed to investigate the optimal combination of (1) secondary (acute) mental health service capacity, (2) non-secondary (non-acute) mental health service capacity and (3) resources to re-engage those lost to services on the incidence of suicidal behaviour over the period 2018-2028 for the Greater Western Sydney (Australia) population catchment. The model captured population and behavioural dynamics and mental health service referral pathways and was validated using population survey and administrative data, evidence syntheses and an expert stakeholder group., Results: Findings suggest that 28% of attempted suicide and 29% of suicides could be averted over the forecast period based on a combination of increases in (1) hospital staffing (with training in trauma-informed care), (2) non-secondary health service capacity, (3) expansion of mental health assessment capacity and (4) re-engagement of at least 45% of individuals lost to services. Reduction in the number of available psychiatric beds by 15% had no substantial impact on the incidence of attempted suicide and suicide over the forecast period., Conclusion: This study suggests that more than one-quarter of suicides and attempted suicides in the Greater Western Sydney population catchment could potentially be averted with a combination of increases to hospital staffing and non-secondary (non-acute) mental health care. Reductions in tertiary care services (e.g. psychiatric hospital beds) in combination with these increases would not adversely affect subsequent incidence of suicidal behaviour.

Objectives: This study investigates two approaches to estimate the potential impact of a population-level intervention on Australian suicide, to highlight the importance of selecting appropriate analytic approaches for informing evidence-based strategies for suicide prevention., Methods: The potential impact of a psychosocial therapy intervention on the incidence of suicide in Australia over the next 10 years was used as a case study to compare the potential impact on suicides averted using: (1) a traditional epidemiological measure of population attributable risk and (2) a dynamic measure of population impact based on a systems science model of suicide that incorporates changes over time., Results: Based on the population preventive fraction, findings suggest that the psychosocial therapy intervention if implemented among all eligible individuals in the Australian population would prevent 5.4% of suicides (or 1936 suicides) over the next 10 years. In comparison, estimates from the dynamic simulation model which accounts for changes in the effect size of the intervention over time, the time taken for the intervention to have an impact in the population, and likely barriers to the uptake and availability of services suggest that the intervention would avert a lower proportion of suicides (between 0.4% and 0.5%) over the same follow-up period., Conclusion: Traditional epidemiological measures used to estimate population health burden have several limitations that are often understated and can lead to unrealistic expectations of the potential impact of evidence-based interventions in real-world settings. This study highlights these limitations and proposes an alternative analytic approach to guide policy and practice decisions to achieve reductions in Australian suicide.

Keywords: systems modeling; simulation; mental health; suicide prevention; COVID-19; recession EN systems modeling simulation mental health suicide prevention COVID-19 recession N.PAG N.PAG 5 11/27/20 20201125 NES 201125 Introduction An Exemplar of Scientific Evolution To assist with proactive and effective responses to the global COVID-19 crisis, the scientific community has been rapidly deploying our most advanced analytic tools to model the dynamics of disease transmission based on existing (albeit imperfect) knowledge, data, and available human and material resources. Most countries, and international agencies, have begun to recognize the unprecedented magnitude of the adverse impact COVID-19 will have on population-based mental health outcomes, mental health services, and suicide risk ([20], [21]). This would allow mental health researchers to develop more sophisticated tools for rapid deployment to respond to national and regional threats to mental health (and national mental wealth), by being able to work collaboratively with governments and local planners to answer the critical questions of: " I what combination of responses are required, at what time, in what sequence, targeted at whom, with what intensity, and for how long?. [Extracted from the article]

There has been ongoing debate regarding the impact of reductions in psychiatric beds on suicide rates, and the potential effect of reallocation of acute hospital funding to community-based mental health programs and services. Computer simulation offers significant value in advancing such debate by providing a robust platform for exploring strategic resource allocation scenarios before they are implemented in the real world. We report an application that demonstrates a threshold effect of cuts to psychiatric beds on suicide rates and the role of context specific variations in population, behavioral, and service use dynamics in determining where that threshold lies. Findings have important implications for regional decision-making regarding resource allocation for suicide prevention. [ABSTRACT FROM AUTHOR]