Evaluation of suicide prevention activities

8.2 Challenges of measuring outcomes for suicide prevention

Page last updated: January 2014

Outcome measurement is a challenge for many public health initiatives. This is largely because of the multifaceted nature not only of the initiatives but also of their outcomes (Section 8.1).

Suicide prevention is not exempt from these challenges. Indeed, it could be argued that outcome measurement is particularly challenging given that a determination of death by suicide involves establishing the:

  • Mechanism, ie, how a person died
  • Intent, ie, whether the death was accidental, homicide or from intentional self-harm (suicide) 77
This, in essence, involves measuring motivation in an empirical way. This can be problematic, especially, for example, where death or injury from motor accidents78 and drug overdoses is concerned. Furthermore, while completed suicide is the ultimate, distal outcome measure, other outcome measurements are also pertinent.

As outlined earlier (Section 4.10.2), difficulties in outcome measurement in the suicide context are further compounded by the following:

  • Suicide rates may be influenced by many factors including a range of personal characteristics as well as socio-cultural factors such as economic conditions, stigma relating to mental illness and suicide, and access to means of suicide.
  • Completed suicide is a statistically rare event. This makes it difficult to achieve the statistical power that is necessary to identify patterns and causation, or to draw conclusions about reductions in the suicide rate. This is particularly true in the case of subgroup analysis.
  • There is limited suicide data on specific target groups, data on protective and risk factors, pathways to suicide and mental health statistics. This creates difficulties in understanding the impact of programs on target groups.
  • Ethical issues make it difficult to randomise people into those who receive help and those who do not. This results in having to use proxy measures in many suicide interventions. Again, problems with low prevalence problems in small subgroups apply.
  • Given that suicide prevention programs do not operate in isolation, attribution is difficult to determine.
Given the long timeframes between some interventions and outcomes (eg, interventions that involve building resilience), the long-term outcomes from programs cannot be measured without longitudinal studies.

Barriers exist in establishing longitudinal effects of programs on reductions in the suicide rate. Small program size and short program duration can diminish statistical power of studies and thus limit the ability to establish causation and assess the effects of the program.

The quality of suicide data is problematic, particularly in relation to timeliness, consistency of process across jurisdictions and improving the identification of Aboriginal and Torres Strait Islander peoples at the time of death.79 Some have argued that ABS figures underestimate the total figures.80

Without appropriate outcome measurement, funders and policy makers may rely on anecdotal and other information to determine whether a program should be continued, expanded upon, refined or eliminated. Such evidence may not be fully representative of outcomes being achieved as projects are likely to present best case examples that may be atypical of the broader cohort(s) served. This not only hinders the extrapolation of findings from individual cases to the broader population but also renders comparison of achievements across projects impractical. Best case examples have clear value, particularly in terms of illustrating what could potentially be achieved, however failure to include negative cases limits the learnings that can be drawn from project experiences. 81

77 Australian Bureau of Statistics, Suicides, Australia, 2010.
78 Australian Bureau of Statistics, Causes of Death, Australia, 2011.
79 Australian Bureau of Statistics, Causes of Death, Australia, 2011.
80 Williams et al, 'Accuracy of Official Suicide Mortality Data in Queensland'.
81 S Funnell & P Rogers, Purposeful program theory: effective use of theories of change and logic models, Jossey–Bass, San Francisco, 2011.