Assessment of care provision integration in a community-based mental health system: balanced care model implementation in Andalusia (Spain).

Background: Andalusia is the second largest region in Spain, and it has developed a comprehensive mental health (MH) plan that encourages the consolidation of the balanced care model. However, its geographical and socioeconomic disparity is a great challenge for a community-based MH system. Both the assessment of the implementation of the MH plan and the development of new tools to support decision-making can be considered critical.

Impact of the workforce allocation on the technical performance of mental health services: the collective case of Helsinki-Uusimaa (Finland).

Background: Long-term mental health (MH) policies in Finland aimed at investing in community care and promoting reforms have led to a reduction in the number of psychiatric hospital beds. However, most resources are still allocated to hospital and community residential services due to various social, economic and political factors. Despite previous research focussing on the number and cost of these services, no study has evaluated the emerging patterns of use, their technical performance and the relationship with the workforce structure.

Patterns of mental healthcare provision in rural areas: A demonstration study in Australia and Europe.

Introduction: Mental healthcare systems are primarily designed to urban populations. However, the specific characteristics of rural areas require specific strategies, resource allocation, and indicators which fit their local conditions. This planning process requires comparison with other rural areas.

A systematic review of the international evolution of online mental health strategies and recommendations during the COVID-19 pandemic.

Introduction: The global health crisis caused by the COVID-19 pandemic has had a negative impact on mental health (MH). As a response to the pandemic, international agencies and governmental institutions provided an initial response to the population's needs. As the pandemic evolved, the population circumstances changed, and some of these international agencies updated their strategies, recommendations, and guidelines for the populations.

Use of a decision support system for benchmarking analysis and organizational improvement of regional mental health care: Efficiency, stability and entropy assessment of the mental health ecosystem of Gipuzkoa (Basque Country, Spain).

Decision support systems are appropriate tools for guiding policymaking processes, especially in mental health (MH), where care provision should be delivered in a balanced and integrated way. This study aims to develop an analytical process for (i) assessing the performance of an MH ecosystem and (ii) identifying benchmark and target-for-improvement catchment areas. MH provision (inpatient, day and outpatient types of care) was analysed in the Mental Health Network of Gipuzkoa (Osakidetza, Basque Country, Spain) using a decision support system that integrated data envelopment analysis, Monte Carlo simulation and artificial intelligence.

The critical factor: The role of quality in the performance of supported accommodation services for complex mental illness in England.

Rehabilitation services have a key role in ensuring integrated and comprehensive mental health (MH) care in the community for people suffering from long-term and severe mental disorders. MH-supported accommodation services aim to promote service users' autonomy and independence. Given the complexity associated with MH-supported accommodation services in England, a comparative evaluation of critical performance indicators, including service provision and quality of care, seems to be necessary in designing evidence-informed policies.

Modelling the balance of care: Impact of an evidence-informed policy on a mental health ecosystem.

Major efforts worldwide have been made to provide balanced Mental Health (MH) care. Any integrated MH ecosystem includes hospital and community-based care, highlighting the role of outpatient care in reducing relapses and readmissions. This study aimed (i) to identify potential expert-based causal relationships between inpatient and outpatient care variables, (ii) to assess them by using statistical procedures, and finally (iii) to assess the potential impact of a specific policy enhancing the MH care balance on real ecosystem performance.

[Everything is on the map: Integrated Mental Health Atlases as support tools for service planning. SESPAS Report 2020].

Objective: This article reviews the usability of the Integrated Atlases of Mental Health as a decision support tool for service planning following a health ecosystem research approach.

Method: This study describes the types of atlases and the procedure for their development. Atlases carried out in Spain are presented and their impact in mental health service planning is assessed.

A decision support system for assessing management interventions in a mental health ecosystem: The case of Bizkaia (Basque Country, Spain).

Evidence-informed strategic planning is a top priority in Mental Health (MH) due to the burden associated with this group of disorders and its societal costs. However, MH systems are highly complex, and decision support tools should follow a systems thinking approach that incorporates expert knowledge. The aim of this paper is to introduce a new Decision Support System (DSS) to improve knowledge on the health ecosystem, resource allocation and management in regional MH planning.

Causal Modelling for Supporting Planning and Management of Mental Health Services and Systems: A Systematic Review.

Mental health services and systems (MHSS) are characterized by their complexity. Causal modelling is a tool for decision-making based on identifying critical variables and their causal relationships. In the last two decades, great efforts have been made to provide integrated and balanced mental health care, but there is no a clear systematization of causal links among MHSS variables.

Relative Technical Efficiency Assessment of Mental Health Services: A Systematic Review.

The current prevalence of mental disorders demands improved ways of the management and planning of mental health (MH) services. Relative technical efficiency (RTE) is an appropriate and robust indicator to support decision-making in health care, but it has not been applied significantly in MH. This article systematically reviews the empirical background of RTE in MH services following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Use of the self-organising map network (SOMNet) as a decision support system for regional mental health planning.

Background: Decision-making in mental health systems should be supported by the evidence-informed knowledge transfer of data. Since mental health systems are inherently complex, involving interactions between its structures, processes and outcomes, decision support systems (DSS) need to be developed using advanced computational methods and visual tools to allow full system analysis, whilst incorporating domain experts in the analysis process. In this study, we use a DSS model developed for interactive data mining and domain expert collaboration in the analysis of complex mental health systems to improve system knowledge and evidence-informed policy planning.

A multi-level analysis of the relationship between spatial clusters of outpatient-treated depression, risk factors and mental health service planning in Catalonia (Spain).

Background: Previous research identified high/low clusters of prevalence of outpatient-treated depression at municipal level in Catalonia (Spain). This study aims to analyse potential risk factors, both socioeconomic and related to the mental health service planning, which could influence the occurrence of hot/cold spots of depressed outpatients at two geographical levels: municipalities and service catchment areas.

Method: Hot/cold spots were examined in relation to socioeconomic indicators at municipal level, such as population density, unemployment, university education, personal income, and also those related to service planning at catchment area level, such as adequacy of healthcare, urbanicity, accessibility and the availability of mental health community centres.

Identification and location of hot and cold spots of treated prevalence of depression in Catalonia (Spain).

Background: Spatial analysis is a relevant set of tools for studying the geographical distribution of diseases, although its methods and techniques for analysis may yield very different results. A new hybrid approach has been applied to the spatial analysis of treated prevalence of depression in Catalonia (Spain) according to the following descriptive hypotheses: 1) spatial clusters of treated prevalence of depression (hot and cold spots) exist and, 2) these clusters are related to the administrative divisions of mental health care (catchment areas) in this region.

Methods: In this ecological study, morbidity data per municipality have been extracted from the regional outpatient mental health database (CMBD-SMA) for the year 2009.

Development of a new spatial analysis tool in mental health: identification of highly autocorrelated areas (hot-spots) of schizophrenia using a Multiobjective Evolutionary Algorithm model (MOEA/HS).

Aims: This study had two objectives: (1) to design and develop a computer-based tool, called Multi-Objective Evolutionary Algorithm/Hot-Spots (MOEA/HS), to identify and geographically locate highly autocorrelated zones or hot-spots and which merges different methods, and (2) to carry out a demonstration study in a geographical area where previous information about the distribution of schizophrenia prevalence is available and which can therefore be compared.

Methods: Local Indicators of Spatial Aggregation (LISA) models as well as the Bayesian Conditional Autoregressive Model (CAR) were used as objectives in a multicriteria framework when highly autocorrelated zones (hot-spots) need to be identified and geographically located. A Multi-Objective Evolutionary Algorithm (MOEA) model was designed and used to identify highly autocorrelated areas of the prevalence of schizophrenia in Andalusia.

Spatial analysis to identify hotspots of prevalence of schizophrenia.

Introduction: The geographical distribution of mental health disorders is useful information for epidemiological research and health services planning.

Objective: To determine the existence of geographical hotspots with a high prevalence of schizophrenia in a mental health area in Spain.

Method: The study included 774 patients with schizophrenia who were users of the community mental health care service in the area of South Granada.