Cost-effectiveness of Installing Barriers at Bridge and Cliff Sites for Suicide Prevention in Australia
The installation of barriers at bridge and cliff sites to reduce suicides is often resisted due to cost. This study shows barriers to be cost-effective at bridge sites, but further research is required for cliff sites.
Context
Installation of barriers at bridge and cliff sites has been shown to reduce suicides at these sites. But, there is often considerable resistance to installing barriers, with one key argument being cost.
Research and findings
The study examined the cost-effectiveness of installing barriers at bridge and cliff sites throughout Australia. The authors used an economic model to examine the costs, costs saved, and reductions in suicides if barriers were installed across identified bridge and cliff sites over five and 10 years.
Specific and accessible bridge and cliff sites across Australia that reported two or more suicides over a five year period were identified for analysis. A total of seven bridges and 19 cliff sites were included in the model. Inputs into the model included:
Relative risk estimates of effectiveness of barriers (in terms of reductions in suicide compared with no barriers) at bridge and cliff sites
- Relative risk estimates of substitution to nearby sites following barrier installation
- Monetary value associated with preventing suicide deaths
- Costs of implementing and maintaining the intervention over five and 10 years.
- The primary outcome was return on investment (ROI) comparing cost savings with intervention costs.
The model indicated that if barriers were installed at bridge sites, an estimated $145 million USD could be saved in preventing suicides over five years and $270 million USD over 10 years. The estimated return on investment ratio for building barriers over 10 years at bridges was 2.4, but the results for cliff sites were not significant. The authors note that only three cliff studies were included, and while these all showed evidence for effectiveness, the statistical power in the pooled analysis was limited.
The authors also point out that their estimates are likely to be an underestimate as they were unable to factor in cost savings associated with averted nonfatal suicide attempts.
Implications
Machine learning as a clinical decision support tool has a potential role in suicide prevention, but the evidence does not support the idea that machine learning algorithms are currently able to predict suicide. With further research and refinement, machine learning might develop into a tool that can complement and improve existing suicide prevention efforts by supporting clinicians and decision-making when assessing risk. But, its effectiveness relies on the availability of treatments and interventions to meet patient needs.