Cost-effectiveness of system-level mental health strategies for young people in the Australian Capital Territory: a dynamic simulation modelling study.

BACKGROUND: Regional mental health planning is a key challenge for decision makers because mental health care is a complex, dynamic system. Economic evaluation using a system dynamics modelling approach presents an opportunity for more sophisticated planning and important evidence on the value of alternative investments. We aimed to investigate the cost-effectiveness of eight systems-based interventions targeted at improving the mental health and wellbeing of children, adolescents, and young adults in the Australian Capital Territory (ACT). METHODS: We assessed eight interventions for children and young people (aged ≤25 years) with low, moderate, and high-to-very-high psychological distress: technology-enabled integrated care, emergency department-based suicide prevention, crisis response service, family education programme, online parenting programme, school-based suicide prevention programme, trauma service for youths, and multicultural-informed care. We developed a system dynamics model for the ACT through a participatory process and calibrated the model with historical data, including population demographics, the prevalence of psychological distress, and mental health services provision. We calculated incremental cost-effectiveness ratios compared with business as usual for cost (AU$) per: quality-adjusted life-year (QALY), suicide death avoided, self-harm related hospital admissions avoided, and mental health-related emergency department presentation, using a 10-year time horizon for health-care and societal perspectives. We investigated uncertainty through probabilistic sensitivity analysis and deterministic sensitivity analysis, including using a 30-year timeframe. FINDINGS: From a societal perspective, increased investment in technology-enabled integrated care, family education, an online parenting programme, and multicultural-informed care were expected to improve health outcomes (incremental QALYs 4517 [95% UI -3135 to 14 507] for technology-enabled integrated care; 339 [91 to 661] for family education; 724 [114 to 1149] for the online parenting programme; and 137 [88 to 194] for multicultural-informed care) and reduce costs ($-91·4 million [-382·7 to 100·7]; $-12·8 million [-21·0 to -6·6]; $-3·6 million  [-6·3 to 0·2]; and $-3·1 million [-4·5 to -1·8], respectively) compared with business as usual using a 10-year time horizon. The incremental net monetary benefit for the societal perspective for these four interventions was $452 million (-351 to 1555), $40 million (14 to 74), $61 million (9 to 98), and $14 million (9 to 20), respectively, compared with business as usual, when QALYs were monetised using a willingness to pay of $79 930 per QALY. Synergistic effects are anticipated if these interventions were to be implemented concurrently. The univariate and probabilistic sensitivity analyses indicated a high level of certainty in the results. Although emergency department-based suicide prevention and school-based suicide prevention were not cost effective in the base case (41 QALYs [0 to 48], incremental cost $4·1 million [1·2 to 8·2] for emergency department-based suicide prevention; -234 QALYs [-764 to 12], incremental cost $90·3 million [72·2 to 111·0] for school-based suicide prevention) compared with business as usual, there were scenarios for which these interventions could be considered cost effective. A dedicated trauma service for young people (9 QALYs gained [4 to 16], incremental cost $8·3 million [6·8 to 10·0]) and a crisis response service (-11 QALYs gained [-12 to -10], incremental cost $7·8 million [5·1 to 11·0]) were unlikely to be cost effective in terms of QALYs. INTERPRETATION: Synergistic effects were identified, supporting the combined implementation of technology-enabled integrated care, family education, an online parenting programme, and multicultural-informed care. Synergistic effects, emergent outcomes in the form of unintended consequences, the capability to account for service capacity constraints, and ease of use by stakeholders are unique attributes of a system dynamics modelling approach to economic evaluation. FUNDING: BHP Foundation.

A Dynamic Approach to Economic Priority Setting to Invest in Youth Mental Health and Guide Local Implementation: Economic Protocol for Eight System Dynamics Policy Models.

Background: Mental illness costs the world economy over US2.5 Bn each year, including premature mortality, morbidity, and productivity losses. Multisector approaches are required to address the systemic drivers of mental health and ensure adequate service provision. There is an important role for economics to support priority setting, identify best value investments and inform optimal implementation. Mental health can be defined as a complex dynamic system where decision makers are challenged to prospectively manage the system over time. This protocol describes the approach to equip eight system dynamics (SD) models across Australia to support priority setting and guide portfolio investment decisions, tailored to local implementation context. Methods: As part of a multidisciplinary team, three interlinked protocols are developed; (i) the participatory process to codesign the models with local stakeholders and identify interventions for implementation, (ii) the technical protocol to develop the SD models to simulate the dynamics of the local population, drivers of mental health, the service system and clinical outcomes, and (iii) the economic protocol to detail how the SD models will be equipped to undertake a suite of economic analysis, incorporating health and societal perspectives. Models will estimate the cost of mental illness, inclusive of service costs (health and other sectors, where necessary), quality-adjusted life years (QALYs) lost, productivity costs and carer costs. To assess the value of investing (disinvesting) in interventions, economic analysis will include return-on-investment, cost-utility, cost benefit, and budget impact to inform affordability. Economic metrics are expected to be dynamic, conditional upon changing population demographics, service system capacities and the mix of interventions when synergetic or antagonistic interactions. To support priority setting, a portfolio approach will identify best value combinations of interventions, relative to a defined budget(s). User friendly dashboards will guide decision makers to use the SD models to inform resource allocation and generate business cases for funding. Discussion: Equipping SD models to undertake economic analysis is intended to support local priority setting and help optimise implementation regarding the best value mix of investments, timing and scale. The objectives are to improve allocative efficiency, increase mental health and economic productivity.