Rates of youth anxiety, depression, and self-harm have increased substantially in recent years. Expansion of clinical service capacity is constrained by workforce shortages and system fragmentation, and even substantial investment may not achieve the scale of growth required to address unmet need. Preventive strategies – such as strengthening social cohesion – are therefore essential to alleviate mounting pressures on the mental health system, yet their potential to compensate for these constraints remains unquantified.

This study employed a system dynamics model to explore the interplay between service capacity and social cohesion on youth mental health outcomes. The model was developed for a population catchment characterized by a mix of urban, suburban, and rural communities. Primary outcomes were prevalence of psychological distress and mental disorders, and incidence of mental health-related emergency department (ED) presentations among young people aged 15–24 years, projected over a 10-year time horizon. Two-way sensitivity analyses of services capacity and social cohesion were conducted.

Changes to specialized mental health services capacity growth had the greatest projected impact on youth mental health outcomes. Heatmaps revealed thresholds where improvements in social cohesion could offset negative impacts of constrained service capacity. For example, if services capacity growth was sustained at only 80% of baseline, improving social cohesion could still reduce years lived with symptomatic disorder by 6.3%. To achieve a similar scale of improvement without improvements in social cohesion, the current growth rate in services capacity would need to be more than double. Combining a doubling of service capacity growth with reversing the decline in social cohesion could reduce ED presentations by 25.6% and years with symptomatic mental disorder by 19.2%. A doubling of specialized, headspace, and GP services capacity growth could prevent 24,060 years lived with symptomatic mental disorder among youth aged 15–24.

This study provides a quantitative framework for understanding how social cohesion improvements can help mitigate workforce constraints in mental health systems, demonstrating the value of integrating service expansion with social cohesion enhancement strategies.

Accountable care models for Medicaid reimbursement aim to improve care quality and reduce costs by linking payments to performance. Oregon’s coordinated care organizations (CCOs) assume financial responsibility for their members and are incentivized to help clinics improve performance on specific quality metrics. This study explores how Oregon’s CCO model influences partnerships between payers and primary care clinics, focusing on strategies used to enhance screening and treatment for unhealthy alcohol use (UAU).

In this qualitative study, we conducted semi-structured interviews with informants from 12 of 13 Oregon CCOs active in 2019 and 2020. The interviews focused on payer–provider partnerships, specifically around UAU screening and treatment, which is a long-standing CCO metric. We used thematic analysis to identify key themes and causal-loop diagramming to uncover feedback dynamics and communicate key findings. Meadows’ leverage point framework was applied to categorize findings based on their potential to drive change.

CCO strategies to support clinics included building relationships, reporting on metric progress, providing EHR technical assistance, offering training, and implementing alternative payment methods. CCOs prioritized clinics with more members and those highly motivated. Our analysis showed that while the CCO model aligned goals between payers and clinics, it may perpetuate rural disparities by prioritizing larger, better-resourced clinics.

Oregon’s CCO model fosters partnerships centered on quality metrics but may unintentionally reinforce rural disparities by incentivizing support for larger clinics. Applying the Meadows framework highlighted leverage points within these partnerships.

DPV systems, typically small to medium-sized solar power installations on buildings, which primarily and directly supply electricity to industrial, commercial, or residential consumers in proximity. DPV is an advocated renewable substation for climate change and energy saving for merits of low installation costs, high energy efficiency, and the ability to provide decentralized power supply. Our research has theoretical significance in explaining and understanding the development and policy evolution of DPV in China and provide valuable suggestions for future industry policies during grid parity.

Since 2021, China has been phasing out its decade-long feed-in tariff policies, reducing the photovoltaic industry's dependency on subsidies. Despite the challenges posed by declining electricity prices and slowdown in economic growth, the authorities continue to prioritize the development of DPV due to its low investment costs, high energy efficiency, and decentralized power supply, and these technologies have already achieved demand-side parity. Driven by this phenomenon, this study examines the trajectory of DPV diffusion and the evolution of related policies over the last decade. It unravels the dynamic mechanism of DPV investment through theoretical analysis and develops a macro model to identify optimal installation strategies and renewable energy proportions. Our findings highlight the increasing role of green energy and suggest that green finance is crucial for stimulating DPV investment in the era of grid parity. The study concludes with practical recommendations for overcoming DPV challenges in China.

DPV has become a prominent renewable energy solution in other countries but not in China. We probe the system dynamics modeling to give explanation and solution during grid parity.

Models are increasingly used to inform the transformation of human–Earth systems towards a sustainable future, aligned with the sustainable development goals (SDGs). We argue that a greater diversity of models ought to be used for sustainability analysis to better address complexity and uncertainty. We articulate the steps to model global change socioeconomic and climatic scenarios with new models. Through these steps, we generate new scenario projections using a human–Earth system dynamics model. Our modelling brings new insights about the sensitivity of sustainability trends to future uncertainty and their alignment with or divergence from previous model-based scenario projections.

The future uncertainty and complexity of alternative socioeconomic and climatic scenarios challenge the model-based analysis of sustainable development. Obtaining robust insights requires a systematic processing of uncertainty and complexity not only in input assumptions, but also in the diversity of model structures that simulates the multisectoral dynamics of human and Earth system interactions. Here, we implement the global change scenarios, that is, the shared socioeconomic pathways and the representative concentration pathways, in a feedback-rich, integrated assessment model (IAM) of human–Earth system dynamics, called FeliX, to serve two aims: (1) to provide modellers with well-defined steps for the adoption of established scenarios in new IAMs and (2) to explore the impacts of model uncertainty and its structural complexity on the projection of these scenarios for sustainable development. Our modelling shows internally consistent scenario storylines across sectors, yet with quantitatively different realisations of these scenarios compared to other IAMs due to the new model's structural complexity. The results highlight the importance of enumerating global change scenarios and their uncertainty exploration with a diversity of models of different input assumptions and structures to capture a wider variety of future possibilities and sustainability indicators.

New study highlights the importance of global change scenario analysis with new, SDG-focused IAMs.

Regional planning may help to ensure that the specific measures implemented as part of a national suicide prevention strategy are aligned with the varying needs of local services and communities; however, there are concerns that the reliability of local programme development may be limited in practice.

The potential impacts of independent regional planning on the effectiveness of suicide prevention programmes in the Australian state of New South Wales were quantified using a system dynamics model of mental health services provision and suicidal behaviour in each of the state's ten Primary Health Network (PHN) catchments.

Reductions in projected suicide mortality over the period 2021–2031 were calculated for scenarios in which combinations of four and five suicide prevention and mental health services interventions (selected from 13 possible interventions) are implemented separately in each PHN catchment. State-level impacts were estimated by summing reductions in projected suicide mortality for each intervention combination across PHN catchments.

The most effective state-level combinations of four and five interventions prevent, respectively, 20.3% and 22.9% of 10 312 suicides projected under a business-as-usual scenario (i.e. no new policies or programmes, constant services capacity growth). Projected numbers of suicides under the optimal intervention scenarios for each PHN are up to 6% lower than corresponding numbers of suicides projected for the optimal state-level intervention combinations.

Regional suicide prevention planning may contribute to significant reductions in suicide mortality where local health authorities are provided with the necessary resources and tools to support reliable, evidence-based decision-making.

Policy-makers and practitioners have a need to assess community resilience in disasters. Prior efforts conflated resilience with community functioning, combined resistance and recovery (the components of resilience), and relied on a static model for what is inherently a dynamic process. We sought to develop linked conceptual and computational models of community functioning and resilience after a disaster.

We developed a system dynamics computational model that predicts community functioning after a disaster. The computational model outputted the time course of community functioning before, during, and after a disaster, which was used to calculate resistance, recovery, and resilience for all US counties.

The conceptual model explicitly separated resilience from community functioning and identified all key components for each, which were translated into a system dynamics computational model with connections and feedbacks. The components were represented by publicly available measures at the county level. Baseline community functioning, resistance, recovery, and resilience evidenced a range of values and geographic clustering, consistent with hypotheses based on the disaster literature.

The work is transparent, motivates ongoing refinements, and identifies areas for improved measurements. After validation, such a model can be used to identify effective investments to enhance community resilience. (Disaster Med Public Health Preparedness. 2018;12:127–137)

Depression is a complex public health problem with considerable variation in treatment response. The systemic complexity of depression, or the feedback processes among diverse drivers of the disorder, contribute to the persistence of depression. This paper extends prior attempts to understand the complex causal feedback mechanisms that underlie depression by presenting the first broad boundary causal loop diagram of depression dynamics.

We applied qualitative system dynamics methods to map the broad feedback mechanisms of depression. We used a structured approach to identify candidate causal mechanisms of depression in the literature. We assessed the strength of empirical support for each mechanism and prioritized those with support from validation studies. Through an iterative process, we synthesized the empirical literature and created a conceptual model of major depressive disorder.

The literature review and synthesis resulted in the development of the first causal loop diagram of reinforcing feedback processes of depression. It proposes candidate drivers of illness, or inertial factors, and their temporal functioning, as well as the interactions among drivers of depression. The final causal loop diagram defines 13 key reinforcing feedback loops that involve nine candidate drivers of depression.

Future research is needed to expand upon this initial model of depression dynamics. Quantitative extensions may result in a better understanding of the systemic syndrome of depression and contribute to personalized methods of evaluation, prevention and intervention.