Static metrics of impact for a dynamic problem: The need for smarter tools to guide suicide prevention planning and investment

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.