Bi-stability and critical transitions in mental health care systems : a model-based analysis

Background: Delayed initiation and early discontinuation of treatment due to limited availability and accessibility of services may often result in people with mild or moderate mental disorders developing more severe disorders, leading to an increase in demand for specialised care that would be expected to further restrict service availability and accessibility (due to increased waiting times, higher out-of-pocket costs, etc.). Methods: We developed a simple system dynamics model of the interaction of specialised services capacity and disease progression to examine the impact of service availability and accessibility on the effectiveness and efficiency of mental health care systems. Results: Model analysis indicates that, under certain conditions, increasing services capacity can precipitate an abrupt, step-like transition from a state of persistently high unmet need for specialised services to an alternative, stable state in which people presenting for care receive immediate and effective treatment. This qualitative shift in services system functioning results from a ‘virtuous cycle’ in which increasing treatment-dependent recovery among patients with mild to moderate disorders reduces the number of severely ill patients requiring intensive and/or prolonged treatment, effectively ‘releasing’ services capacity that can be used to further reduce the disease progression rate. We present an empirical case study of tertiary-level child and adolescent mental health services in the Australian state of South Australia demonstrating that the conditions under which such critical transitions can occur apply in real-world services systems. Conclusions: Policy and planning decisions aimed at increasing specialised services capacity have the potential to dramatically increase the effectiveness and efficiency of mental health care systems, promoting long-term sustainability and resilience in the face of future threats to population mental health (e.g., economic crises, natural disasters, global pandemics)

The impact of reducing psychiatric beds on suicide rates

There has been ongoing debate regarding the impact of reductions in psychiatric beds on suicide rates, and the potential effect of reallocation of acute hospital funding to community-based mental health programs and services. Computer simulation offers significant value in advancing such debate by providing a robust platform for exploring strategic resource allocation scenarios before they are implemented in the real world. We report an application that demonstrates a threshold effect of cuts to psychiatric beds on suicide rates and the role of context specific variations in population, behavioral, and service use dynamics in determining where that threshold lies. Findings have important implications for regional decision-making regarding resource allocation for suicide prevention

"Stopping before you start" : reducing and preventing initiation of tobacco use in the ACT

Tobacco is the leading cause of preventable death in Australia and contributes to 5.4% of disease burden in the Australian Capital Territory. Initiation of tobacco use is most likely to occur during adolescence and young adulthood (at less than 20 years). Prevention of tobacco initiation involves a combination of regulatory, educational and health promotion interventions including restrictions on the sale of tobacco products. This paper reports on the development and use of an agent-based model to explore the impact of modifying three hypothetical regulatory and health promotion interventions: 1) increasing the minimum purchasing age for tobacco products, 2) reducing retail sales of tobacco products to persons under the minimum purchasing age and 3) reducing secondary sharing of tobacco products to persons under the minimum purchasing age using health promotion messaging. The model was built using a participatory approach that engaged policy officers, health promotion officers, epidemiologists, biostatisticians and computer scientists. The structure of the model included interacting state chart representations of smoking and level of concern about tobacco use (engagement status) and a pro-smoking score, which defined the hazard rate of initiation, cessation, and relapse. The pro-smoking score was a function of several risk factors including engagement, social effect of having more or fewer smoking peers, addiction and withdrawal levels and access to tobacco products. Parameterisation of the model drew on a range of data sources with local data being prioritised where it was available. A series of scenarios comparing the impact of the interventions on smoking prevalence rates and age of initiation are reported. Of the three interventions simulated, increasing the minimum purchasing age from 18 to 21 years had the greatest impact on smoking prevalence across the population, reducing the prevalence of smoking from 8.5% (95% CI 7.8, 9.2) to 6.9% (95% CI 6.4, 7.4) five years post-intervention and 4.1% (95% CI 3.8, 4.3) 20 years post intervention (Figure 1). The interventions aimed to reduce the sale of tobacco products to minors and reduce secondary sharing produced small reductions on their own. However, when implemented in combination with increasing the minimum purchasing age, they significantly increased the impact of this intervention from ten years post-implementation, ultimately resulting in a prevalence rate of 2.8% (95% CI 2.6, 3.0) 20 years post-implementation. Given the challenges associated with ceasing tobacco use, these in silico experiments demonstrate the importance of regulatory public health interventions to delay, and therefore potentially prevent initiation

Can the target set for reducing childhood overweight and obesity be met? : a system dynamics modelling study in New South Wales, Australia

The persistent prevalence of childhood overweight and obesity raises significant concerns about the impact on health, society and the economy. Responding to a target announced in September 2015 by the New South Wales (Australia) Premier to reduce childhood overweight and obesity by five percentage points by 2025, a system dynamics model was developed to support Government and stakeholders responsible for meeting the target. A participatory model building process, drawing cross-sectorial expertise, was undertaken to estimate the individual and combined impact of interventions on meeting the target. The model demonstrated that it is theoretically possible to meet the target by implementing a comprehensive combination of policies and programmes. When limited to existing and enhanced population health interventions, the modelled result did not reach the target. The project provides an example of how participatory simulation modelling can combine a broad range of interventions together into likely scenarios and usefully inform government decision-making

Clinical staging and the differential risks for clinical and functional outcomes in young people presenting for youth mental health care

Background: Clinical staging proposes that youth-onset mental disorders develop progressively, and that active treatment of earlier stages should prevent progression to more severe disorders. This retrospective cohort study examined the longitudinal relationships between clinical stages and multiple clinical and functional outcomes within the frst 12 months of care. Methods: Demographic and clinical information of 2901 young people who accessed mental health care at age 12–25 years was collected at predetermined timepoints (baseline, 3 months, 6 months, 12 months). Initial clinical stage was used to defne three fxed groups for analyses (stage 1a: ‘non-specifc anxious or depressive symptoms’, 1b: ‘attenuated mood or psychotic syndromes’, 2+: ‘full-threshold mood or psychotic syndromes’). Logistic regression models, which controlled for age and follow-up time, were used to compare clinical and functional outcomes (role and social function, suicidal ideation, alcohol and substance misuse, physical health comorbidity, circadian disturbances) between staging groups within the initial 12 months of care. Results: Of the entire cohort, 2093 young people aged 12–25 years were followed up at least once over the frst 12 months of care, with 60.4% female and a baseline mean age of 18.16 years. Longitudinally, young people at stage 2+ were more likely to develop circadian disturbances (odds ratio [OR]=2.58; CI 1.60–4.17), compared with individuals at stage 1b. Additionally, stage 1b individuals were more likely to become disengaged from education/employment (OR=2.11, CI 1.36–3.28), develop suicidal ideations (OR=1.92; CI 1.30–2.84) and circadian disturbances (OR=1.94, CI 1.31–2.86), compared to stage 1a. By contrast, we found no relationship between clinical stage and the emergence of alcohol or substance misuse and physical comorbidity. Conclusions: The diferential rates of emergence of poor clinical and functional outcomes between early versus late clinical stages support the clinical staging model’s assumptions about illness trajectories for mood and psychotic syndromes. The greater risk of progression to poor outcomes in those who present with more severe syndromes may be used to guide specifc intervention packages

'Turning the tide' on hyperglycemia in pregnancy : insights from multiscale dynamic simulation modeling

INTRODUCTION: Hyperglycemia in pregnancy (HIP, including gestational diabetes and pre-existing type 1 and type 2 diabetes) is increasing, with associated risks to the health of women and their babies. Strategies to manage and prevent this condition are contested. Dynamic simulation models (DSM) can test policy and program scenarios before implementation in the real world. This paper reports the development and use of an advanced DSM exploring the impact of maternal weight status interventions on incidence of HIP. METHODS: A consortium of experts collaboratively developed a hybrid DSM of HIP, comprising system dynamics, agent-based and discrete event model components. The structure and parameterization drew on a range of evidence and data sources. Scenarios comparing population-level and targeted prevention interventions were simulated from 2018 to identify the intervention combination that would deliver the greatest impact. RESULTS: Population interventions promoting weight loss in early adulthood were found to be effective, reducing the population incidence of HIP by 17.3% by 2030 (baseline ('business as usual' scenario)=16.1%, 95% CI 15.8 to 16.4; population intervention=13.3%, 95% CI 13.0 to 13.6), more than targeted prepregnancy (5.2% reduction; incidence=15.3%, 95% CI 15.0 to 15.6) and interpregnancy (4.2% reduction; incidence=15.5%, 95% CI 15.2 to 15.8) interventions. Combining targeted interventions for high-risk groups with population interventions promoting healthy weight was most effective in reducing HIP incidence (28.8% reduction by 2030; incidence=11.5, 95% CI 11.2 to 11.8). Scenarios exploring the effect of childhood weight status on entry to adulthood demonstrated significant impact in the selected outcome measure for glycemic regulation, insulin sensitivity in the short term and HIP in the long term. DISCUSSION: Population-level weight reduction interventions will be necessary to 'turn the tide' on HIP. Weight reduction interventions targeting high-risk individuals, while beneficial for those individuals, did not significantly impact forecasted HIP incidence rates. The importance of maintaining interventions promoting healthy weight in childhood was demonstrated

Using visual tools to improve clinical engagement and group understanding of complex IT concepts

This work describes how the use of a visual patient journey modeling tool significantly improved the engagement of clinical staff and their group understanding of the complex IT concepts associated with event-driven computer simulation. Such group understanding and engagement is acknowledged as critical to the successful implementation of a wide range of quality improvement initiatives [1, 2]. The specific example described in this paper relates to the improved utilisation of chemotherapy unit resources

A measurement protocol for channel assignment based topology control on multi-radio wireless mesh networks

Due to widespread use and limited radio spectrum of 802.11 based networks, interference between collocated networks is an important problem. In this paper we expand our prior work on the multiagent system for channel assignment based topology control on multi-radio wireless mesh networks. The aim of the system is to dynamically reduce the overall interference and increase the aggregate capacity of the network. To realise this goal agents require information about radio spectrum utilisation, thus we propose a distributed measurement protocol. To obtain good spectrum utilisation the measurements focus on empirical issues such as contention interference and the use of partially overlapping channels. The simulation results obtained for realistic scenarios of MR-WMN node densities and topologies show performance comparable with the current state of the art in addition to stability, flexibility and extensibility, features achieved by the multiagent system implementation

Investigating channel bonding and TXOP in 802.11n wireless networks

This paper provides a brief investigation of 802.11n based channel bonding protocol. The aim is to expose the protocol as a flow chart diagram and show how the increase in TXOP interval length affects overall channel utilization. In addition we investigate fairness of the protocol and demonstrate that the protocol is relatively unfair in respect to providing the same level of access to transmission opportunity to all neighboring access points. Furthermore we show that the fairness significantly decreases with the number of neighboring access points and only slightly decreases with an increase in TXOP length

Validation of two distributed, autonomous self-organisation algorithms for 802.11 mesh networks by simulation

Two algorithms in a “self-organisation of multi-radio mesh networks” project are described and validated by simulation. As they are to be deployed over large networks the two challenges have been the scalability and stability of the solution. The basic approach is that of a distributed, light-weight, cooperative multiagent system that guarantees scalability. As the solution is distributed it is unsuitable to achieve any global optimisation goal — in any case, we argue that global optimisation of mesh network performance in any significant sense is not feasible in real situations that are subjected to unanticipated perturbations and external intervention. Our overall goal is simply to reduce maintenance costs for such networks by removing the need for humans to tune the network settings. So stability of the algorithms is our main concern