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

Abstract

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.

Decision makers’ experience of participatory dynamic simulation modelling: methods for public health policy

“…The finalised models in the case studies reported on here are being used as credible, communication tools to synthesise and facilitate use of evidence in policy conversations regarding prevention interventions. The models capture the complexity of real-world policy questions and provide a dynamic analytic tool that can overcome the limitations of traditional analysis methods [52, 53]. The ability to manipulate policy levers to determine impact of interventions on health outcomes (including the ability to test alternative reach, adoption, and effect scenarios) provided an important evidence base to support health policy and planning conversations and develop realistic insights into the impact of enhancing or expanding existing interventions and identify priority gaps and areas of need.…”

Bringing new tools, a regional focus, resource-sensitivity, local engagement and necessary discipline to mental health policy and planning

“…Traditional analytic tools for prioritising programs and services and their targets have important limitations when applied to complex problems such as determining how best to reduce mental disorder and suicidal behaviour in the population [43]. First, current methods determine the comparative burden each risk factor contributes in a given population, and the proportion of that condition that could be averted by targeting highburden risk factors [44].…”

“…First, current methods determine the comparative burden each risk factor contributes in a given population, and the proportion of that condition that could be averted by targeting highburden risk factors [44]. The assumptions underpinning these estimates are that risk factors are independent, and relationships between risk factors and outcomes are unidirectional, linear, and constant through time [43]. However, complex problems are characterised by interaction of risk factors, feedback loops (for example, unemployment contributes to depression and depression can prevent gainful employment), thresholds (or breaking points), and changing behaviour over time, all of which violate the assumptions of traditional analytic methods [45].…”

“…These limitations make them ill-suited for informing decision making to address complex public health problems. As a result the application of traditional methods can lead to unrealistic expectations of the potential impact of evidence-based interventions in real-world settings [43].…”

Systems modelling and simulation to inform strategic decision making for suicide prevention in rural New South Wales (Australia)

“…Applications of systems modelling and simulation to provide decision support capability in addressing complex and persistent public health problems have demonstrated their utility (Atkinson et al, 2018(Atkinson et al, , 2019a(Atkinson et al, , 2019bLoyo et al, 2013;Page et al, 2017Page et al, , 2018bRoberts et al, 2019). These tools combine local and expert knowledge with best available data and the body of research evidence, and have the unique feature of being able to capture population and demographic dynamics, changes over time in behavioural drivers, service interactions and workforce capacity and the potentially non-additive effects of intervention combinations.…”