Modelling tool gives insights into suicide prevention

Greater GP training could prove one of the most effective ways to prevent suicide, according to researchers who used an innovative modelling tool to predict the impact of a range of interventions, new research shows.

The researchers, who outlined their work in a paper in Public Health Research & Practice, said that the impact of specific suicide prevention policies and programs introduced over the past 20 years remained unclear.

However they were able to test the likely impact of a range of interventions after developing a system dynamics (SD) model for suicide prevention. They combined research evidence and a range of other data to produce a kind of “what if” tool that could be used to test the impact of different combinations of policies and interventions in the virtual world, before they are implemented in the real world.

The model was used to hypothesise impacts over 10 years (2015–2025) of a combination of five current suicide prevention strategies proposed for population interventions in Australia:

  • GP training
  • Coordinated aftercare in those who have attempted suicide
  • School-based mental health literacy programs
  • Brief-contact interventions in hospital settings
  • Pscyhosocial treatment approaches

The modelling revealed that the largest reductions in suicide were associated with GP training (6%) and coordinated aftercare approaches (4%).

While the other interventions negligible impacts on suicide trends when simulated individually, the estimated proportion of prevented suicides for all interventions combined was 12%.

Suicide model demonstrates value of modelling

The researchers said the paper highlighted the value of system dynamic modelling methods for managing complexity and uncertainty.

“The model demonstrates a potential platform for integrating diverse evidence sources into an analytic tool that can allow policy makers to explore the likely impact of different policy and intervention scenarios over the short and longer term in a robust, risk-free and low-cost way,” they wrote. “That is, it can be used to conduct virtual experiments where real-world studies may not be feasible.”

Such modelling had the potential to be used as a decision-support tool for policy makers and program planners for community suicide prevention actions, they wrote.

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