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.
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.
The guide for monitoring and treatment of congenital hepatic hemangiomas (CHH) will depend on the subtype and the postnatal clinical behavior. Our aim is to present a series of CHH and characterize ...its clinical, histologic and genetic correlation, compared to cutaneous congenital hemangiomas (CCH).
A retrospective review of CHH patients diagnosed between 1991 and 2018 was performed. Clinical, morphological and histological data were analyzed and deep high-throughput sequencing was performed.
Sixteen patients with CHH were included. Five patients were followed up with serial ultrasounds while pharmacological treatment (corticosteroids and propranolol) was decided in five. Surgical resection was performed in five owing to hemorrhage and suspicion of malignancy, and the last patient underwent embolization. Histologic analysis was available in 7 patients and confirmed CHH, showing two different histological patterns that could be associated with the presence of somatic pathogenic variants in GNAQ and/or PIK3CA detected in the genetic testing. Review of 7 samples of CCH revealed some histologic differences compared to CHH.
CHH resemble its cutaneous homonym with similar clinical behavior. Histologic analysis can differentiate two subgroups while genetic testing can confirm mutations in GNAQ and in PIK3CA in a subset of CHH.
Treatment study.
IV
•Congenital hepatic hemangiomas (CHHs) resemble cutaneous hepatic hemangiomas (CCHs) with similar clinical behavior.•Histologic evaluation finds some differences between CHH and CCH.•In our series we differentiate two patterns in histologic analysis that can be associated with the presence of somatic pathogenic variants in GNAQ and/or PIK3CA.
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.
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.
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).
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.
Use of decision support systems may improve policy-making for management of mental health services and systems. System performance can be analyzed by using Relative Technical Efficiency (RTE), ...stability and entropy indicators. These indicators summarize resource availability, utilization and results as a balance between inputs (resources) and outputs (outcomes). Nevertheless, performance and stability assessment of mental health systems is complex because of difficulty related to data collection, results interpretation and translation of information into practice. The present mental health context requires better ways of planning for allocating resources and improving outcomes. The objective of this study is to assess the RTE, stability and entropy 2012-2015 variations as a consequence of a policy developed by the Mental Health Network of Gipuzkoa (Basque Country, Spain). The Mental Health Network of Gipuzkoa is structured by thirteen small health areas. In these catchment areas, mental health services were standardized by using the DESDE-LTC codification tool. Mental health services were classified according to the main type of care provided (outpatient, day and residential). In the analysis, 57 variables were included, which were classified in resources –inputs- (availability, placement and workforce capacity) and results –outputs- (service utilization, readmissions, discharges and length of stay). A hybrid decision support system, that integrates statistical, operational and artificial intelligence techniques, has been used to analyze the indicators. The main statistical procedure was a Monte-Carlo simulation engine to include the uncertainty of real contexts. The data envelopment analysis, an operational technique, was utilized to assess the RTE. In addition, a prototype of fuzzy inference engine was included for interpreting expert knowledge according to the basic community mental health care model. The stability was calculated by analyzing the frequency distributions of the RTE and, finally, the Shannon’s entropy to estimate system disorder. The main structure of the real policy was identified by developing structured interviews to senior managers and planners of Mental Health System of Gipuzkoa. Results provided information about the changes of the selected indicators throughout three years (2012-2015). The impact of the policy developed can be considered positive but the stability remains poor as new real interventions have to take into account that small changes in data values can result in a change, positive or negative, in the indicators´ value. The methodology presented can be considered appropriate for analyzing mental health services and systems performance. Variations in the indicator values can also be considered as a consequence of the policy impact and, because of that, the decision support system could analyze the evolution of the system. As future research, it is suggested to assess indicator variations throughout the time span and assess the impact of new organizational interventions and policies.
This is a multicenter prospective observational study that included a large cohort (n = 397) of allogeneic (allo‐HSCT; (n = 311) and autologous (ASCT) hematopoietic stem cell transplant (n = 86) ...recipients who were monitored for antibody detection within 3–6 weeks after complete severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) vaccination from February 1, 2021, to July 20, 2021. Most patients (n = 387, 97.4%) received mRNA‐based vaccines. Most of the recipients (93%) were vaccinated more than 1 year after transplant. Detectable SARS‐CoV‐2‐reactive antibodies were observed in 242 (78%) of allo‐HSCT and in 73 (85%) of ASCT recipients. Multivariate analysis in allo‐HSCT recipients identified lymphopenia < 1 × 109/ml (odds ratio OR 0.33, 95% confidence interval 95% CI 0.16–0.69, p = .003), active graft versus host disease (GvHD; OR 0.51, 95% CI 0.27–0.98, p = .04) and vaccination within the first year of transplant (OR 0.3, 95% CI 0.15–0.9, p = .04) associated with lower antibody detection whereas. In ASCT, non‐Hodgkin's lymphoma (NHL; OR 0.09, 95% CI 0.02–0.44, p = .003) and active corticosteroid therapy (OR 0.2, 95% CI 0.02–0.87, p = .03) were associated with lower detection rate. We report an encouraging rate of SARS‐CoV‐2‐reactive antibodies detection in these severe immunocompromised patients. Lymphopenia, GvHD, the timing of vaccine, and NHL and corticosteroids therapy should be considered in allo‐HSCT and ASCT, respectively, to identify candidates for SARS‐CoV‐2 antibodies monitoring.
Seaweeds are rich in different bioactive compounds with potential uses in drugs, cosmetics and the food industry. The objective of this study was to analyze macromolecular antioxidants or ...nonextractable polyphenols, in several edible seaweed species collected in Chile (Gracilaria chilensis, Callophyllis concepcionensis, Macrocystis pyrifera, Scytosyphon lomentaria, Ulva sp. and Enteromorpha compressa), including their 1st HPLC characterization. Macromolecular antioxidants are commonly ignored in studies of bioactive compounds. They are associated with insoluble dietary fiber and exhibit significant biological activity, with specific features that are different from those of both dietary fiber and extractable polyphenols. We also evaluated extractable polyphenols and dietary fiber, given their relationship with macromolecular antioxidants. Our results show that macromolecular antioxidants are a major polyphenol fraction (averaging 42% to total polyphenol content), with hydroxycinnamic acids, hydroxybenzoic acids and flavonols being the main constituents. This fraction also showed remarkable antioxidant capacity, as determined by 2 complementary assays. The dietary fiber content was over 50% of dry weight, with some samples exhibiting the target proportionality between soluble and insoluble dietary fiber for adequate nutrition. Overall, our data show that seaweed could be an important source of commonly ignored macromolecular antioxidants.
Practical Application
In this study, the composition of several edible seaweeds from Chile, in terms of macromolecular antioxidants and dietary fiber, was evaluated. All the seaweeds showed relevant content of these constituents. Given the nutritional interest of these compounds, the consumption of these seaweeds might be promoted within the frame of a healthy diet and they can also be used as sources of macromolecular antioxidants and dietary fiber for the production of new ingredients.
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.
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