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. OBJECTIVE: The purpose of this study was to observe the patterns of use and their technical performance (efficiency) of the main types of care of MH services in the Helsinki-Uusimaa region (Finland), and to analyse the potential relationship between technical performance and the corresponding workforce structure. METHODS: The sample included acute hospital residential care, non-hospital residential care and outpatient care services. The analysis was conducted using regression analysis, Monte Carlo simulation, fuzzy inference and data envelopment analysis. RESULTS: The analysis showed a statistically significant linear relationship between the number of service users and the length of stay, number of beds in non-hospital residential care and number of contacts in outpatient care services. The three service types displayed a similar pattern of technical performance, with high relative technical efficiency on average and a low probability of being efficient. The most efficient acute hospital and outpatient care services integrated multidisciplinary teams, while psychiatrists and nurses characterized non-hospital residential care. CONCLUSIONS: The results indicated that the number of resources and utilization variables were linearly related to the number of users and that the relative technical efficiency of the services was similar across all types. This suggests homogenous MH management with small variations based on workforce allocation. Therefore, the distribution of workforce capacity should be considered in the development of effective policies and interventions in the southern Finnish MH system.

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. This demonstration study aimed to describe and compare specialized rural adult mental health services in Australia, Norway, and Spain; and to demonstrate the readiness of the healthcare ecosystem approach and the DESDE-LTC mapping tool (Description and Evaluation of Services and Directories of Long Term Care) for comparing rural care between countries and across areas. Methods: The study described and classified the services using the DESDE-LTC. The analyses included context analysis, care availability, placement capacity, balance of care, and diversity of care. Additionally, readiness (Technology Readiness Levels - TRL) and impact analyses (Adoption Impact Ladder - AIL) were also assessed by two independent raters. Results: The findings demonstrated the usability of the healthcare ecosystem approach and the DESDE-LTC to map and identify differences and similarities in the pattern of care of highly divergent rural areas. Day care had a greater weight in the European pattern of care, while it was replaced by social outpatient care in Australian areas. In contrast, care coordination was more common in Australia, pointing to a more fragmented system that requires navigation services. The share between hospital and community residential care showed no differences between the two regions, but there were differences between catchment areas. The healthcare ecosystem approach showed a TRL 8 (the tool has been demonstrated in a real-world environment and it is ready for release and general use) and an AIL of 5 (the target public agencies provided resources for its completion). Two experts evaluated the readiness of the use of DESDE-LTC in their respective regional studies. All of them were classified using the TRL. Discussion: In conclusion, this study strongly supports gathering data on the provision of care in rural areas using standardized methods to inform rural service planning. It provides information on context and service availability, capacity and balance of care that may improve, directly or through subsequent analyses, the management and planning of services in 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. However, there is currently a lack of information on the attention given to response strategies by the different countries throughout the beginning of the pandemic. OBJECTIVES: 1) To evaluate the evolution of online MH strategies and recommendations of selected countries to cope with the MH impact of COVID-19 from the early stages of the pandemic (15 April 2020) to the vaccination period (9 June 2021) and 2) to review and analyse the current structures of these online MH strategies and recommendations. METHODOLOGY: An adaptation of the PRISMA guidelines to review online documents was developed with a questionnaire for MH strategies and recommendations assessment. The search was conducted on Google, including documents from April 2020 to June 2021. Basic statistics and Student's t test were used to assess the evolution of the documents, while a two-step cluster analysis was performed to assess the organisation and characteristics of the most recent documents. RESULTS: Statistically significant differences were found both in the number of symptoms and mental disorders and MH strategies and recommendations included in the initial documents and the updated versions generated after vaccines became available. The most recent versions are more complete in all cases. Regarding the forty-six total documents included in the review, the cluster analysis showed a broad distribution from wide-spectrum documents to documents focusing on a specific topic. CONCLUSIONS: Selected governments and related institutions have worked actively on updating their MH online documents, highlighting actions related to bereavement, telehealth and domestic violence. The study supports the use of the adaptation, including the tailor-made questionnaire, of the PRISMA protocol as a potential standard to conduct longitudinal assessments of online documents used to support MH strategies and recommendations.

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 unit of analysis was the 13 catchment areas defined by a reference MH centre. MH ecosystem performance was assessed by the following indicators: relative technical efficiency, stability and entropy to guide organizational interventions. Globally, the MH system of Gipuzkoa showed high efficiency scores in each main type of care (inpatient, day and outpatient), but it can be considered unstable (small changes can have relevant impacts on MH provision and performance). Both benchmark and target-for-improvement areas were identified and described. This article provides a guide for evidence-informed decision-making and policy design to improve the continuity of MH care after inpatient discharges. The findings show that it is crucial to design interventions and strategies (i) considering the characteristics of the area to be improved and (ii) assessing the potential impact on the performance of the global MH care ecosystem. For performance improvement, it is recommended to reduce admissions and readmissions for inpatient care, increase workforce capacity and utilization of day care services and increase the availability of outpatient care services.

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. Causal relationships (Bayesian network) between inpatient and outpatient care variables were defined by expert knowledge and confirmed by using multivariate linear regression (generalized least squares). Based on the Bayesian network and regression results, a decision support system that combines data envelopment analysis, Monte Carlo simulation and fuzzy inference was used to assess the potential impact of the designed policy. As expected, there were strong statistical relationships between outpatient and inpatient care variables, which preliminarily confirmed their potential and a priori causal nature. The global impact of the proposed policy on the ecosystem was positive in terms of efficiency assessment, stability and entropy. To the best of our knowledge, this is the first study that formalized expert-based causal relationships between inpatient and outpatient care variables. These relationships, structured by a Bayesian network, can be used for designing evidence-informed policies trying to balance MH care provision. By integrating causal models and statistical analysis, decision support systems are useful tools to support evidence-informed planning and decision making, as they allow us to predict the potential impact of specific policies on the ecosystem prior to its real application, reducing the risk and considering the population's needs and scientific findings.

[Everything is on the map: Integrated Mental Health Atlases as support tools for service planning. SESPAS Report 2020]. / Todo está en el mapa: Atlas Integrales de Salud Mental para la planificación de servicios. Informe SESPAS 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. Atlases comprise information on the local characteristics of the health care system, geographical availability of resources collected with the DESDE-LTC instrument and their use. Atlases use geographic information systems and other visualisation tools. Atlases follow a bottom-up collaborative approach involving decision-makers from planning agencies for their development and external validation. RESULTS: Since 2005, Integrated Atlases of Mental Health have been developed for nine regions in Spain comprising over 65% of the Spanish inhabitants. The impact on service planning has been unequal for the different regions. Catalonia, Biscay and Gipuzkoa, and Andalusia reach the highest impact. In these areas, health advisors have been actively involved in their co-design and implementation in service planning. CONCLUSIONS: Atlases allow detecting care gaps and duplications in care provision; monitoring changes of the system over time, and carrying out national and international comparisons, efficiency modelling and benchmarking. The knowledge provided by atlases could be incorporated to decision support systems in order to support an efficient mental health service planning based on evidence-informed policy.

Todo está en el mapa: Atlas Integrales de Salud Mental para la planificación de servicios. Informe SESPAS 2020 / Everything is on the map: Integrated Mental Health Atlases as support tools for service planning. SESPAS Report 2020

OBJETIVO: Este artículo revisa y evalúa el uso de los Atlas Integrales de Salud Mental como herramientas de apoyo a la planificación de servicios dentro del modelo de investigación de ecosistemas de atención de salud. MÉTODO: Se describen los tipos de atlas y el procedimiento para su elaboración. Se presentan los realizados en España y se evalúa su impacto en la planificación de servicios de salud mental. Los atlas agregan información sobre las características locales del sistema de atención, la disponibilidad geográfica de recursos recogida mediante el instrumento DESDE-LTC, y su uso. Utilizan un sistema de información geográfica y otras herramientas visuales. Siguen una metodología de abajo arriba con colaboración de personas decisoras de agencias de planificación para su elaboración y validación externa. RESULTADOS: Desde 2005 se han realizado Atlas Integrales de Salud Mental en nueve comunidades autónomas que comprenden alrededor del 65% de la población de España. Los atlas han tenido un impacto desigual en la planificación de servicios, con un mayor impacto en Cataluña, Vizcaya y Guipúzcoa, y Andalucía, donde responsables sociales han participado activamente en su codiseño y su aplicación a la planificación de servicios sociosanitarios. CONCLUSIONES: Los atlas permiten detectar carencias o duplicidades en la atención, monitorizar cambios a lo largo del tiempo, realizar comparaciones nacionales e internacionales, modelar la eficiencia y hacer análisis benchmark. Este conocimiento puede incorporarse a los sistemas de apoyo a la decisión para una más eficaz planificación de los servicios de salud mental basada en evidencia informada

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. Atlases comprise information on the local characteristics of the health care system, geographical availability of resources collected with the DESDE-LTC instrument and their use. Atlases use geographic information systems and other visualisation tools. Atlases follow a bottom-up collaborative approach involving decision-makers from planning agencies for their development and external validation. RESULTS: Since 2005, Integrated Atlases of Mental Health have been developed for nine regions in Spain comprising over 65% of the Spanish inhabitants. The impact on service planning has been unequal for the different regions. Catalonia, Biscay and Gipuzkoa, and Andalusia reach the highest impact. In these areas, health advisors have been actively involved in their co-design and implementation in service planning. CONCLUSIONS: Atlases allow detecting care gaps and duplications in care provision; monitoring changes of the system over time, and carrying out national and international comparisons, efficiency modelling and benchmarking. The knowledge provided by atlases could be incorporated to decision support systems in order to support an efficient mental health service planning based on evidence-informed policy

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. The Efficient Decision Support-Mental Health (EDeS-MH) is a DSS that integrates an operational model to assess the Relative Technical Efficiency (RTE) of small health areas, a Monte-Carlo simulation engine (that carries out the Monte-Carlo simulation technique), a fuzzy inference engine prototype and basic statistics as well as system stability and entropy indicators. The stability indicator assesses the sensitivity of the model results due to data variations (derived from structural changes). The entropy indicator assesses the inner uncertainty of the results. RTE is multidimensional, that is, it was evaluated by using 15 variable combinations called scenarios. Each scenario, designed by experts in MH planning, has its own meaning based on different types of care. Three management interventions on the MH system in Bizkaia were analysed using key performance indicators of the service availability, placement capacity in day care, health care workforce capacity, and resource utilisation data of hospital and community care. The potential impact of these interventions has been assessed at both local and system levels. The system reacts positively to the proposals by a slight increase in its efficiency and stability (and its corresponding decrease in the entropy). However, depending on the analysed scenario, RTE, stability and entropy statistics can have a positive, neutral or negative behaviour. Using this information, decision makers can design new specific interventions/policies. EDeS-MH has been tested and face-validated in a real management situation in the Bizkaia MH system.

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. This study aims to review the empirical background of causal modelling applications (Bayesian networks and structural equation modelling) for MHSS management. The study followed the PRISMA guidelines (PROSPERO: CRD42018102518). The quality of the studies was assessed by using a new checklist based on MHSS structure, target population, resources, outcomes, and methodology. Seven out of 1847 studies fulfilled the inclusion criteria. After the review, the selected papers showed very different objectives and subjects of study. This finding seems to indicate that causal modelling has potential to be relevant for decision-making. The main findings provided information about the complexity of the analyzed systems, distinguishing whether they analyzed a single MHSS or a group of MHSSs. The discriminative power of the checklist for quality assessment was evaluated, with positive results. This review identified relevant strategies for policy-making. Causal modelling can be used for better understanding the MHSS behavior, identifying service performance factors, and improving evidence-informed policy-making.

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. Finally, 13 studies were included, and the findings provide new standard classifications of RTE variables, efficiency determinants and strategies to improve MH management and planning.

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. METHODS: We combine an interactive visual data mining approach, the self-organising map network (SOMNet), with an operational expert knowledge approach, expert-based collaborative analysis (EbCA), to develop a DSS model. The SOMNet was applied to the analysis of healthcare patterns and indicators of three different regional mental health systems in Spain, comprising 106 small catchment areas and providing healthcare for over 9 million inhabitants. Based on the EbCA, the domain experts in the development team guided and evaluated the analytical processes and results. Another group of 13 domain experts in mental health systems planning and research evaluated the model based on the analytical information of the SOMNet approach for processing information and discovering knowledge in a real-world context. Through the evaluation, the domain experts assessed the feasibility and technology readiness level (TRL) of the DSS model. RESULTS: The SOMNet, combined with the EbCA, effectively processed evidence-based information when analysing system outliers, explaining global and local patterns, and refining key performance indicators with their analytical interpretations. The evaluation results showed that the DSS model was feasible by the domain experts and reached level 7 of the TRL (system prototype demonstration in operational environment). CONCLUSIONS: This study supports the benefits of combining health systems engineering (SOMNet) and expert knowledge (EbCA) to analyse the complexity of health systems research. The use of the SOMNet approach contributes to the demonstration of DSS for mental health planning in practice.

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. The analysis has been carried out through multilevel logistic regression models in order to consider the two different scales. RESULTS: Hot spots are related to high population density, unemployment, urbanicity, the adequacy of provision of mental health services, and accessibility to mental health community centres at both study levels. On the other hand, the multilevel model weakly explains cold spots, associating them with high personal incomes. LIMITATIONS: The dependent variables of the multi-level models are binary. This limits the interpretation of the results, since they cannot provide information about the variance of the dependent variables explained by the models. CONCLUSIONS: The results described diverse risk factors at two levels which are related to a high likelihood of hot and cold spots of depression. The findings show the relevance of health planning in the distribution of diseases and the utilisation of healthcare services.

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. The second level of analysis mapped small mental health catchment areas or groups of municipalities covered by a single mental health community centre. Spatial analysis has been performed using a Multi-Objective Evolutionary Algorithm (MOEA) which identified geographical clusters (hot spots and cold spots) of depression through the optimization of its treated prevalence. Catchment areas, where hot and cold spots are located, have been described by four domains: urbanicity, availability, accessibility and adequacy of provision of mental health care. RESULTS: MOEA has identified 6 hot spots and 4 cold spots of depression in Catalonia. Our results show a clear spatial pattern where one cold spot contributed to define the exact location, shape and borders of three hot spots. Analysing the corresponding domain values for the identified hot and cold spots no common pattern has been detected. CONCLUSIONS: MOEA has effectively identified hot/cold spots of depression in Catalonia. However these hot/cold spots comprised municipalities from different catchment areas and we could not relate them to the administrative distribution of mental care in the region. By combining the analysis of hot/cold spots, a better statistical and operational-based visual representation of the geographical distribution is obtained. This technology may be incorporated into Decision Support Systems to enhance local evidence-informed policy in health system research.

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. Hot-spots were statistically identified using exponential-based QQ-Plots (statistics of extremes). RESULTS: Efficient solutions (Pareto set) from MOEA/HS were analysed statistically and one main hot-spot was identified and spatially located. Our model can be used to identify and locate geographical hot-spots of schizophrenia prevalence in a large and complicated region. CONCLUSIONS: MOEA/HS enables a compromise to be achieved between different econometric methods by highlighting very special zones in complex areas where schizophrenia shows a high autocorrelation.

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. Spatial analysis (Kernel estimation) and Bayesian relative risks were used to locate potential hotspots. Availability and accessibility were both rated in each zone and spatial algebra was applied to identify hotspots in a particular zone. RESULTS: The age-corrected prevalence rate of schizophrenia was 2.86 per 1,000 population in the South Granada area. Bayesian analysis showed a relative risk varying from 0.43 to 2.33. The area analysed had a non-uniform spatial distribution of schizophrenia, with one main hotspot (zone S2). This zone had poor accessibility to and availability of mental health services. CONCLUSION: A municipality-based variation exists in the prevalence of schizophrenia and related disorders in the study area. Spatial analysis techniques are useful tools to analyse the heterogeneous distribution of a variable and to explain genetic/environmental factors in hotspots related with a lack of easy availability of and accessibility to adequate health care services.