27 August 2019.
A decision support tool developed by the Sax Institute shows how changes in the number of psychiatric beds might impact on suicide rates. And the answers could shed new light on suicide prevention policy.
Over the past decade, debate in the mental health community has included whether the number of available psychiatric beds might impact on suicide rates. The key question is at what point do bed cuts lead to harm?
Now, a new study undertaken by Associate Professor Jo-An Atkinson and Dr Adam Skinner of the Sax Institute’s Decision Analytics team is answering that question with sophisticated computer simulation. The model explores different bed cut scenarios in relation to community-based mental health service capacity, essentially providing researchers and policy makers with a flexible ‘what if’ tool for testing changes before they’re implemented in the real world.
“The unique ability of systems modelling and simulation to capture real world complexity and forecast the impact of alternative strategies in an interactive way, delivers insights that are not possible with traditional analytic methods,” says lead researcher Jo-An Atkinson.
The benefits of computer simulation
This new work comes at a critical moment in mental health. Figures show that on average six men take their lives in Australia every day, and recent research from Beyond Blue finds that suicidal behaviour could be up to three times higher than previous estimates.
This is no time for guesstimations in suicide prevention, especially since debate continues over whether further reallocation of resources away from acute hospitals has pushed them to a tipping point in their ability to provide appropriate services. Clearly, this is one area where we need to minimise the risk of harm in policy changes, which is what computer simulation offers. The Sax Institute’s model demonstrates that not all bed cuts are necessarily bad, but regional thresholds do exist, and there is a delicate balance between the number of beds in a given population catchment and the availability of community-based mental health services.
Making sense of the data
The research team originally developed the system dynamics model in partnership with Western Sydney Primary Health Network and their stakeholders to inform suicide prevention commissioning decisions. The model drew on a wide range of evidence and data sources, including population survey data, systematic reviews, administrative data and expert knowledge, covering vulnerability, psychological distress, mental disorders and suicidal behaviour, as well as pathways through mental health services. This model was used to simulate scenarios of cuts to psychiatric beds under different conditions related to community-based service capacity, forecasting the likely impact on suicide rates over the next ten years.
Findings suggest that while not all reductions to psychiatric beds result in increases in suicides, a “tipping point” does exist. But this threshold is influenced strongly by the availability of community-based mental health services.
A clearer vision for policy makers
Associate Professor Jo-An Atkinson says these sorts of dynamic modelling tools should be used to inform decision-making for complex problems such as suicide on a routine basis.
“We really want to drive more serious engagement with these tools and methods rather than continue with the current ‘invest and hope for the best’ approach. You’d never put someone on a rocket ship and blast them off to the moon without engaging in systems modelling, simulation and iterative refinement; we should be applying a similar, disciplined approach when investing in mental health and suicide prevention,” she says.
“People’s lives are on the line; we have more sophisticated tools for informing policy and planning decisions for suicide prevention, and we’ve shown how computer simulation can be used in helping to advance debate and make more effective use of available resources. Now all that is required is a serious commitment to making these tools more broadly available to regional decision makers to help them best serve their communities.”
Read the full paper here.
The Sax Institute’s Decision Analytics team harnesses the latest technologies and methods to develop adaptable decision support tools that forecast the impact of alternative decision options before they are implemented in the real world.
We work in partnership with government departments, policy agencies and program planners in health and social sectors, applying computer simulation and other technologies to provide decision makers with a low-risk way of understanding which combinations of interventions are likely to be the most effective over time. Find out more about the Sax Institute’s Decision Analytics here.