Reducing youth suicide: systems modelling and simulation to guide targeted investments across the determinants

Abstract

Background Reducing suicidal behaviour (SB) is a critical public health issue globally. The complex interplay of social determinants, service system factors, population demographics, and behavioural dynamics makes it extraordinarily difficult for decision makers to determine the nature and balance of investments required to have the greatest impacts on SB. Real-world experimentation to establish the optimal targeting, timing, scale, frequency, and intensity of investments required across the determinants is unfeasible. Therefore, this study harnesses systems modelling and simulation to guide population-level decision making that represent best strategic allocation of limited resources. Methods Using a participatory approach, and informed by a range of national, state, and local datasets, a system dynamics model was developed, tested, and validated for a regional population catchment. The model incorporated defined pathways from social determinants of mental health to psychological distress, mental health care, and SB. Intervention scenarios were investigated to forecast their impact on SB over a 20-year period. Results A combination of social connectedness programs, technology-enabled coordinated care, post-attempt assertive aftercare, reductions in childhood adversity, and increasing youth employment projected the greatest impacts on SB, particularly in a youth population, reducing self-harm hospitalisations (suicide attempts) by 28.5% (95% interval 26.3–30.8%) and suicide deaths by 29.3% (95% interval 27.1–31.5%). Introducing additional interventions beyond the best performing suite of interventions produced only marginal improvement in population level impacts, highlighting that ‘more is not necessarily better.’ Conclusion Results indicate that targeted investments in addressing the social determinants and in mental health services provides the best opportunity to reduce SB and suicide. Systems modelling and simulation offers a robust approach to leveraging best available research, data, and expert knowledge in a way that helps decision makers respond to the unique characteristics and drivers of SB in their catchments and more effectively focus limited health resources.

The Impact of Technology-Enabled Care Coordination in a Complex Mental Health System: A Local System Dynamics Model

“…This process involved iteratively working on the model structure and assumptions; regular face validity checks by a diverse group of academic, clinical, policy, program planning, emergency services, and lived-experience stakeholders were undertaken to ensure accurate model representation, conceptualization, and outputs. A more detailed description of the model development process, structure, outputs, and calibration can be found in the primary paper [ 34 ].…”

“…All outputs were calculated every 0.4375 days (ie, one-sixteenth of a week) starting from January 1, 2011; these permitted comparisons of model outputs with historic data from 2011 to 2017 for validation. Forecasts of the impacts of intervention scenarios described below are simulated from the time of implementation in 2021 to the end of 2029 [ 34 ].…”

“…The fourth scenario is technology-enabled care coordination , which involves the use of online technology to facilitate delivery of multidisciplinary team-based care, in which medical and allied health professionals consider all relevant treatment options and collaboratively develop an individual treatment and care plan for each patient. Online technology enables enhanced coordination of care and facilitates communication between medical and allied health professionals, since each health professional involved in the care of a patient has access to the same information about that patient’s treatment history (see parameter details in Textbox 1 [ 17 , 25 , 34 , 36 , 37 ]).…”

Which Social, Economic, and Health Sector Strategies Will Deliver the Greatest Impacts for Youth Mental Health and Suicide Prevention? Protocol for an Advanced, Systems Modelling Approach

“…Eight sites, primarily defined by PHN boundaries, have been selected (two metropolitan, two outer urban, two regional, and two rural/remote sites) to capture variation in socioeconomic conditions, population density, demographic profile, mental health risk profile, and mental health service infrastructure and access. Our transparent, inclusive, and collaborative approach to the development of systems models (35,45,46) will be implemented at each of the eight sites.…”

“…At the outset of the pandemic several systems models were developed for the Australian context capturing the interacting social, economic, and health system drivers of mental health outcomes and suicidal behaviour at a regional, state, and national level (33)(34)(35). These models enable decision makers to better understand the likely trajectories of the prevalence of psychological distress, health service engagements and waiting times, mental health-related emergency department (ED) presentations, self-harm hospitalizations, and suicide deaths over the next 10 years.…”