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.

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.

Prospective, Randomised Two Centre Trial of Endovascular Repair of Abdominal Aortic Aneurysm With or Without Sac Embolisation.

OBJECTIVE: The benefit of aneurysm sac coil embolisation (ASCE) during endovascular aortic repair (EVAR) of abdominal aortic aneurysm (AAA) remains unclear. This prospective randomised two centre study (SCOPE 1: Sac COil embolisation for Prevention of Endoleak) compared the outcomes of standard EVAR in patients with AAA at high risk of type II endoleak (EL with EVAR with ASCE during the period 2014-2019. METHODS: Patients at high risk of type II EL were randomised to standard EVAR (group A) or EVAR with coil ASCE (group B). The primary endpoint was the rate of all types of EL during follow up. Secondary endpoints included freedom from type II EL related re-interventions, and aneurysm sac diameter and volume variation at two year follow up. Adverse events included type II EL and re-interventions. CTA and Duplex ultrasound scans were scheduled at 30 days, six months, one year, and two years after surgery. RESULTS: Ninety-four patients were enrolled, 47 in each group. There were no intra-operative complications. At M1, 16/47 early type II EL occurred (34%) in group A vs. 2/47 (4.3%) in group B (p < .001). At M6, 15/36 type II EL (41.7%) occurred in group A vs. 2/39 (4.26%) in group B (p < .001). At M12, 15/37 type II El (40.5%) occurred in group A vs. 5/35 (14.3%) in group B (p = .018). At 24 months, 8/32 type 2 El (25%) occurred in group A vs. 3/29 (6.5%) in group B (p = .19). Kaplan-Meier curves of survival free from EL and re-interventions were significantly in favour of group B (p < .001). Aneurysm sac volume decreased significantly in group B compared with group A at M6 (p = .081), at M12 (p = .004), and M24 (p = .001). CONCLUSION: For selected patients at risk of EL, ASCE seems effective in preventing EL at one, six, and at 12 months. However, the difference was not statistically significant at 24 months. ASCE decreases the re-intervention rate two years after EVAR. A significantly faster aneurysm volume shrinkage was observed at one and two years following surgery. (SCOPE 1 trial: NCT01878240).

Mental health planning at a very early stage of the COVID-19 crisis: a systematic review of online international strategies and recommendations.

BACKGROUND: Mental health care systems have been dramatically affected by COVID-19. Containment measures have been imposed, with negative consequences on population mental health. Therefore, an increase in both symptomatology and mental disorder incidence is expected. This research aims to identify, describe and assess the empirical background on online strategies and recommendations developed by international organizations and governments to cope with the psychological impact of COVID-19 at a very early stage of the pandemic. METHODS: The PRISMA guidelines were adapted to review online documents. A new questionnaire was developed to identify the existence of common patterns in the selected documents. Questions were classified into three domains: COVID-19 information, mental health strategies and mental health recommendations. A two-step cluster analysis was carried out to highlight underlying behaviours in the data (patterns). The results are shown as spider graphs (pattern profiles) and conceptual maps (multidimensional links between questions). RESULTS: Twenty-six documents were included in the review. The questionnaire analysed document complexity and identified their common key mental health characteristics (i.e., does the respondent have the tools for dealing with stress, depression and anxiety?). Cluster analysis highlighted patterns from the questionnaire domains. Strong relationships between questions were identified, such as psychological tips for maintaining good mental health and coping with COVID-19 (question n° 4), describing some psychological skills to help people cope with anxiety and worry about COVID-19 (question n° 6) and promoting social connection at home (question n° 8). CONCLUSIONS: When fast results are needed to develop health strategies and policies, rapid reviews associated with statistical and graphical methods are essential. The results obtained from the proposed analytical procedure can be relevant to a) classify documents according to their complexity in structuring the information provided on how to cope with the psychological impact of COVID-19, b) develop new documents according to specific objectives matching population needs, c) improve document design to face unforeseen events, and d) adapt new documents to local situations. In this framework, the relevance of adapting e-mental health procedures to community mental health care model principles was highlighted, although some problems related to the digital gap must be considered.

French experience of balloon pulmonary angioplasty for chronic thromboembolic pulmonary hypertension.

AIMS: To evaluate safety and efficacy of balloon pulmonary angioplasty (BPA) in a large cohort of patients with chronic thromboembolic pulmonary hypertension (CTEPH). METHODS: From 2014 to 2017, 184 inoperable CTEPH patients underwent 1006 BPA sessions. Safety and efficacy during the first 21 months (initial period) were compared with those of the last 21 months (recent period). A total of 154 patients had a full evaluation after a median duration of 6.1 months. RESULTS: Overall, there was a significant improvement in New York Heart Association functional class, 6-min walk distance (mean change +45 m), and a significant decrease in mean pulmonary artery pressure (PAP) and in pulmonary vascular resistance (PVR) by 26% and 43%, respectively. The percentage decreases of mean PAP and PVR were 22% and 37% in the initial period versus 30% and 49% in the recent period, respectively (p<0.05). The main complications included lung injury, which occurred in 9.1% of 1006 sessions (13.3% in the initial period versus 5.9% in the recent period; p<0.001). Per-patient multivariate analysis revealed that baseline mean PAP and the period during which BPA procedure was performed (recent versus initial period) were the strongest factors related to the occurrence of lung injury. 3-year survival was 95.1%. CONCLUSION: This study confirms that a refined BPA strategy improves short-term symptoms, exercise capacity and haemodynamics in inoperable CTEPH patients with an acceptable risk-benefit ratio. Safety and efficacy improve over time, underscoring the unavoidable learning curve for this procedure.

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.

An integrative cross-design synthesis approach to estimate the cost of illness: an applied case to the cost of depression in Catalonia.

BACKGROUND: Cost of illness (COI) studies are carried out under conditions of uncertainty and with incomplete information. There are concerns regarding their generalisability, accuracy and usability in evidence-informed care. AIMS: A hybrid methodology is used to estimate the regional costs of depression in Catalonia (Spain) following an integrative approach. METHODS: The cross-design synthesis included nominal groups and quantitative analysis of both top-down and bottom-up studies, and incorporated primary and secondary data from different sources of information in Catalonia. Sensitivity analysis used probabilistic Monte Carlo simulation modelling. A dissemination strategy was planned, including a standard form adapted from cost-effectiveness studies to summarise methods and results. RESULTS: The method used allows for a comprehensive estimate of the cost of depression in Catalonia. Health officers and decision-makers concluded that this methodology provided useful information and knowledge for evidence-informed planning in mental health. CONCLUSIONS: The mix of methods, combined with a simulation model, contributed to a reduction in data gaps and, in conditions of uncertainty, supplied more complete information on the costs of depression in Catalonia. This approach to COI should be differentiated from other COI designs to allow like-with-like comparisons. A consensus on COI typology, procedures and dissemination is needed.

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.

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.

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. This study aims to explore the influence of service quality indicators on the performance of MH-supported accommodation services in England. The analysed sample includes supported accommodation services from 14 nationally representative local authorities in England from the QuEST study grouped by three main types of care: residential care homes (divided into two subgroups: move-on and non-move-on oriented), supported housing and floating outreach. EDeS-MH (efficient decision support-mental health) was used to assess the performance indicators for the selected services by combining a Monte Carlo simulation engine, data envelopment analysis and a fuzzy inference engine for integrating expert knowledge. Depending on the type of care, six/seven quality domains were sequentially included after a baseline scenario (only technical) was analysed. Relative technical efficiency scores for the baseline scenarios revealed high performance in all the selected supported accommodation services, but the statistical variability was high. Quality domains significantly improved performance in every type of care. The inclusion of quality indicators has a positive impact on the global performance of each type of care. Remaining at the corresponding services more than expected for two years has a negative impact on performance. These findings can be considered from a planning perspective to facilitate the design of pathways of care with more realistic expectations about gaining autonomy in two years.

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.

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.

Integrating clinicians, knowledge and data: expert-based cooperative analysis in healthcare decision support.

BACKGROUND: Decision support in health systems is a highly difficult task, due to the inherent complexity of the process and structures involved. METHOD: This paper introduces a new hybrid methodology Expert-based Cooperative Analysis (EbCA), which incorporates explicit prior expert knowledge in data analysis methods, and elicits implicit or tacit expert knowledge (IK) to improve decision support in healthcare systems. EbCA has been applied to two different case studies, showing its usability and versatility: 1) Bench-marking of small mental health areas based on technical efficiency estimated by EbCA-Data Envelopment Analysis (EbCA-DEA), and 2) Case-mix of schizophrenia based on functional dependency using Clustering Based on Rules (ClBR). In both cases comparisons towards classical procedures using qualitative explicit prior knowledge were made. Bayesian predictive validity measures were used for comparison with expert panels results. Overall agreement was tested by Intraclass Correlation Coefficient in case "1" and kappa in both cases. RESULTS: EbCA is a new methodology composed by 6 steps:. 1) Data collection and data preparation; 2) acquisition of "Prior Expert Knowledge" (PEK) and design of the "Prior Knowledge Base" (PKB); 3) PKB-guided analysis; 4) support-interpretation tools to evaluate results and detect inconsistencies (here Implicit Knowledg -IK- might be elicited); 5) incorporation of elicited IK in PKB and repeat till a satisfactory solution; 6) post-processing results for decision support. EbCA has been useful for incorporating PEK in two different analysis methods (DEA and Clustering), applied respectively to assess technical efficiency of small mental health areas and for case-mix of schizophrenia based on functional dependency. Differences in results obtained with classical approaches were mainly related to the IK which could be elicited by using EbCA and had major implications for the decision making in both cases. DISCUSSION: This paper presents EbCA and shows the convenience of completing classical data analysis with PEK as a mean to extract relevant knowledge in complex health domains. One of the major benefits of EbCA is iterative elicitation of IK.. Both explicit and tacit or implicit expert knowledge are critical to guide the scientific analysis of very complex decisional problems as those found in health system research.

[On the roller coaster: An abridged history of mental health planning in Spain. SESPAS Report 2020]. / En la montaña rusa: breve historia del modelo de atención de salud mental en España. Informe SESPAS 2020.

Effective mental health change in Spain started in 1985 with the Report of the Ministerial Commission for the Psychiatric Reform that recommended integrating psychiatric care into the general health system, providing care in the patient's context and for specific diagnoses. The SESPAS 2002 Report carried out an analysis of this reform and recommended the creation of a permanent ministerial commission, the design of a national map of socio-sanitary mental health services, the creation of a coordination and promotion agency for and carrying out a financial analysis of resource provision and research. Since 2004, the Technical Committee for the Mental Health Strategy boosted the elaboration of a theoretical and normative framework that unfortunately did not lead to a road map for the improvement of the system. After 2011, during the financial crisis, the Ministry of Health lost the opportunity to lead a second phase of change of the mental health care, which was evidence-based: no key technical reports were published nor was an action plan based on data developed. Currently, the 1985 community mental health model is still the general framework of mental health care with the addition of aspects related to the recovery model and the balance of care model. Significant progress has been made in developing care systems assessment methods and data-based models that could advance mental health planning. The gap between general health attention and mental health care has increased and the expected reform of the mental health system will not take place in the near future.

[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.

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.

Is sex associated with adverse outcomes after percutaneous coronary intervention for CTO?

BACKGROUND: Conflicting results have been reported regarding sex differences in percutaneous coronary intervention (PCI), but their potential influence on clinical outcomes after chronic total coronary occlusion (CTO) PCI remains unknown. We aimed to identify sex-related differences in long-term clinical outcomes after CTO PCI. METHODS AND RESULTS: All consecutive patients undergoing CTO PCI between 2004 and 2012 were included in a prospective registry. Baseline, procedural characteristics and clinical outcomes were compared according to sex. Out of 1343 patients, 194 were female (14.4%). Women were older (68.5 ±â€¯9.9 vs 62.3 ±â€¯10.8 years, p < 0.001), more frequently diabetic (33.5% vs 26.4%, p = 0.026) and hypertensive (70.1% vs 57.4%, p < 0,001), whereas males were more frequently smokers (28.5% vs 15.5%, p < 0.001). J-CTO score was similar between both sexes (1.59 ±â€¯0.91 vs 1.51 ±â€¯0.88). The procedural success rate was also similar in men and women (74.0% vs 77.3%, respectively). At 8 years' follow-up, successful CTO PCI was associated with reduced mortality in women (14.8% vs 36.2%, p = 0.003) and men (18.5% vs 29.1%, p < 0.001). In successful CTO PCI cases, no sex-related differences were observed in terms of major adverse cardiac events. CONCLUSIONS: Our study suggests an equal benefit of CTO interventions with a marked reduction in mortality after successful CTO PCI in women and men alike.

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.