The influence of lifestyle, diet, and reproductive history on age at natural menopause in Spain: Analysis from the EPIC-Spain sub-cohort.

OBJECTIVE: To determinate the role of lifestyle factors, recent diet, menstrual factors, and reproductive history in age at natural menopause in adult Spanish women. METHODS: In total, 12 562 pre-menopausal women were available for analysis from the EPIC-Spain sub-cohort. Women were recruited between 1992 and 1996 in five regions of Spain (Asturias, Granada, Murcia, Navarra, and San Sebastian) and, for these analyses, were followed for 3 years. Questionnaires on diet, lifestyle, anthropometric measurements, and reproductive and exogenous hormones history were collected at baseline. Menopause status was updated at a median of 3 years of follow-up. RESULTS: After a median of 3 years of follow-up 1166 women became postmenopausal. An earlier age at menopause was observed in current smokers (HR: 1.29; 95%CI 1.08-1.55) and in non-users of oral contraceptives (HR: 1.32; 95%CI 1.01-1.57). A later age at menopause was observed in women with irregular menses (HR: 0.71; 95%CI 0.56-0.91) and in women with a higher number of pregnancies (HR: 0.74; 95%CI 0.56-0.94). CONCLUSIONS: Our results confirm that women who smoked had an earlier age at natural menopause, while use of oral contraceptives, higher number of pregnancies, and irregularity of menses were associated with a prolonged reproductive lifespan. No associations were observed for dietary habits assessed after the age of 40 years.

In-patient costs of agitation and containment in a mental health catchment area.

BACKGROUND: There is a scarce number of studies on the cost of agitation and containment interventions and their results are still inconclusive. We aimed to calculate the economic consequences of agitation events in an in-patient psychiatric facility providing care for an urban catchment area. METHODS: A mixed approach combining secondary analysis of clinical databases, surveys and expert knowledge was used to model the 2013 direct costs of agitation and containment events for adult inpatients with mental disorders in an area of 640,572 adult inhabitants in South Barcelona (Spain). To calculate costs, a seven-step methodology with novel definition of agitation was used along with a staff survey, a database of containment events, and data on aggressive incidents. A micro-costing analysis of specific containment interventions was used to estimate both prevalence and direct costs from the healthcare provider perspective, by means of a mixed approach with a probabilistic model evaluated on real data. Due to the complex interaction of the multivariate covariances, a sensitivity analysis was conducted to have empirical bounds of variability. RESULTS: During 2013, 918 patients were admitted to the Acute Inpatient Unit. Of these, 52.8% were men, with a mean age of 44.6 years (SD = 15.5), 74.4% were compulsory admissions, 40.1% were diagnosed with schizophrenia or non-affective psychosis, with a mean length of stay of 24.6 days (SD = 16.9). The annual estimate of total agitation events was 508. The cost of containment interventions ranges from 282€ at the lowest level of agitation to 822€ when verbal containment plus seclusion and restraint have to be used. The annual total cost of agitation was 280,535€, representing 6.87% of the total costs of acute hospitalisation in the local area. CONCLUSIONS: Agitation events are frequent and costly. Strategies to reduce their number and severity should be implemented to reduce costs to the Health System and alleviate patient suffering.

Model Choice for Quantitative Health Impact Assessment and Modelling: An Expert Consultation and Narrative Literature Review.

BACKGROUND: Health impact assessment (HIA) is a widely used process that aims to identify the health impacts, positive or negative, of a policy or intervention that is not necessarily placed in the health sector. Most HIAs are done prospectively and aim to forecast expected health impacts under assumed policy implementation. HIAs may quantitatively and/ or qualitatively assess health impacts, with this study focusing on the former. A variety of quantitative modelling methods exist that are used for forecasting health impacts, however, they differ in application area, data requirements, assumptions, risk modelling, complexities, limitations, strengths, and comprehensibility. We reviewed relevant models, so as to provide public health researchers with considerations for HIA model choice. METHODS: Based on an HIA expert consultation, combined with a narrative literature review, we identified the most relevant models that can be used for health impact forecasting. We narratively and comparatively reviewed the models, according to their fields of application, their configuration and purposes, counterfactual scenarios, underlying assumptions, health risk modelling, limitations and strengths. RESULTS: Seven relevant models for health impacts forecasting were identified, consisting of (i) comparative risk assessment (CRA), (ii) time series analysis (TSA), (iii) compartmental models (CMs), (iv) structural models (SMs), (v) agent-based models (ABMs), (vi) microsimulations (MS), and (vii) artificial intelligence (AI)/machine learning (ML). These models represent a variety in approaches and vary in the fields of HIA application, complexity and comprehensibility. We provide a set of criteria for HIA model choice. Researchers must consider that model input assumptions match the available data and parameter structures, the available resources, and that model outputs match the research question, meet expectations and are comprehensible to end-users. CONCLUSION: The reviewed models have specific characteristics, related to available data and parameter structures, computational implementation, interpretation and comprehensibility, which the researcher should critically consider before HIA model choice.

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.

Leveraging Data Science for a Personalized Haemodialysis.

BACKGROUND: The 2019 Science for Dialysis Meeting at Bellvitge University Hospital was devoted to the challenges and opportunities posed by the use of data science to facilitate precision and personalized medicine in nephrology, and to describe new approaches and technologies. The meeting included separate sections for issues in data collection and data analysis. As part of data collection, we presented the institutional ARGOS e-health project, which provides a common model for the standardization of clinical practice. We also pay specific attention to the way in which randomized controlled trials offer data that may be critical to decision-making in the real world. The opportunities of open source software (OSS) for data science in clinical practice were also discussed. SUMMARY: Precision medicine aims to provide the right treatment for the right patients at the right time and is deeply connected to data science. Dialysis patients are highly dependent on technology to live, and their treatment generates a huge volume of data that has to be analysed. Data science has emerged as a tool to provide an integrated approach to data collection, storage, cleaning, processing, analysis, and interpretation from potentially large volumes of information. This is meant to be a perspective article about data science based on the experience of the experts invited to the Science for Dialysis Meeting and provides an up-to-date perspective of the potential of data science in kidney disease and dialysis. KEY MESSAGES: Healthcare is quickly becoming data-dependent, and data science is a discipline that holds the promise of contributing to the development of personalized medicine, although nephrology still lags behind in this process. The key idea is to ensure that data will guide medical decisions based on individual patient characteristics rather than on averages over a whole population usually based on randomized controlled trials that excluded kidney disease patients. Furthermore, there is increasing interest in obtaining data about the effectiveness of available treatments in current patient care based on pragmatic clinical trials. The use of data science in this context is becoming increasingly feasible in part thanks to the swift developments in OSS.

Knowledge discovery about quality of life changes of spinal cord injury patients: clustering based on rules by states.

In this paper, an integral Knowledge Discovery Methodology, named Clustering based on rules by States, which incorporates artificial intelligence (AI) and statistical methods as well as interpretation-oriented tools, is used for extracting knowledge patterns about the evolution over time of the Quality of Life (QoL) of patients with Spinal Cord Injury. The methodology incorporates the interaction with experts as a crucial element with the clustering methodology to guarantee usefulness of the results. Four typical patterns are discovered by taking into account prior expert knowledge. Several hypotheses are elaborated about the reasons for psychological distress or decreases in QoL of patients over time. The knowledge discovery from data (KDD) approach turns out, once again, to be a suitable formal framework for handling multidimensional complexity of the health domains.

Decision support systems (DSS) for wastewater treatment plants - A review of the state of the art.

The use of decision support systems (DSS) allows integrating all the issues related with sustainable development in view of providing a useful support to solve multi-scenario problems. In this work an extensive review on the DSSs applied to wastewater treatment plants (WWTPs) is presented. The main aim of the work is to provide an updated compendium on DSSs in view of supporting researchers and engineers on the selection of the most suitable method to address their management/operation/design problems. Results showed that DSSs were mostly used as a comprehensive tool that is capable of integrating several data and a multi-criteria perspective in order to provide more reliable results. Only one energy-focused DSS was found in literature, while DSSs based on quality and operational issues are very often applied to site-specific conditions. Finally, it would be important to encourage the development of more user-friendly DSSs to increase general interest and usability.

Knowledge engineering as a support for building an actor profile ontology for integrating Home-Care systems.

One of the tasks towards the definition of a knowledge model for home care is the definition of the different roles of the users involved in the system. The roles determine the actions and services that can or must be performed by each type of user. In this paper the experience of building an ontology to represent the home-care users and their associated information is presented, in a proposal for a standard model of a Home-Care support system to the European Community.

Inherited disorder phenotypes: controlled annotation and statistical analysis for knowledge mining from gene lists.

BACKGROUND: Analysis of inherited diseases and their associated phenotypes is of great importance to gain knowledge of underlying genetic interactions and could ultimately give clinically useful insights into disease processes, including complex diseases influenced by multiple genetic loci. Nevertheless, to date few computational contributions have been proposed for this purpose, mainly due to lack of controlled clinical information easily accessible and structured for computational genome-wise analyses. To allow performing phenotype analyses of inherited disorder related genes we implemented new original modules within GFINDer http://www.bioinformatics.polimi.it/GFINDer/, a Web system we previously developed that dynamically aggregates functional annotations of user uploaded gene lists and allows performing their statistical analysis and mining. RESULTS: New GFINDer modules allow annotating large numbers of user classified biomolecular sequence identifiers with morbidity and clinical information, classifying them according to genetic disease phenotypes and their locations of occurrence, and statistically analyzing the obtained classifications. To achieve this we exploited, normalized and structured the information present in textual form in the Clinical Synopsis sections of the Online Mendelian Inheritance in Man (OMIM) databank. Such valuable information delineates numerous signs and symptoms accompanying many genetic diseases and it is divided into phenotype location categories, either by organ system or type of finding. CONCLUSION: Supporting phenotype analyses of inherited diseases and biomolecular functional evaluations, GFINDer facilitates a genomic approach to the understanding of fundamental biological processes and complex cellular mechanisms underlying patho-physiological phenotypes.

Impact of data encoding and thyroids dysfunctions.

In medical environments it is usual to previously encode some variables following medical criteria. In the context of thyroids dysfunctions, the levels of the hormones T3, T4 or TSH are usually treated as low, normal or high instead of using their numerical form, and this is a very common practice in other items. In medical environments it is also frequent to need a clustering process to analyze data. Clus-tering algorithms use always some distance or similarity coefficient to decide which elements have to be grouped in a cluster. The nature of the data determines which distance/similarity can be used, and can change results. In this paper, the impact of preprocessing numerical levels of hormones into categorical labels for clustering is studied, by means of a real sample of patients from a Hospital in Zagreb (Croatia).

The strategy of combining antidepressants in the treatment of major depression: clinical experience in spanish outpatients.

Introduction. The combination of antidepressants is a useful tool in the treatment of major depression, especially in cases where there is a partial response to antidepressant monotherapy. However, the use of this strategy is a matter of controversy, and its frequency of use in clinical practice is not clear. The aim of our study is to assess the use of antidepressants combination in Spain by reviewing three databases used between 1997 and 2001. Methods. Databases pertain to patients who are study subjects of major depression treatment. These databases are a result of studies performed in Spain and in which 550 psychiatrists participated. The total studied sample was comprised of N = 2, 842 patients, aged over 18, fitting DSM-IV criteria for Major Depressive Episode. The percentage of patients who received more than one antidepressant and the types of combinations used was described. Subsequently, a comparative study between the group which received a combination of antidepressants (N = 64) and the group which received antidepressant monotherapy (N = 775) was performed. Results. 27.1% of patients were on antidepressive monotherapy treatment, and 2.2% were on combination therapy. In the comparison of patients on combination therapy and monotherapy, there were significant differences only in episode duration (P = 0.001). The most frequent combinations are SSRIs and tricyclic antidepressants. The active principle most widely combined is fluoxetine. Conclusions. The prevalence of use of antidepressant combination therapy is 2.2% of the global sample and 8.3% of treated patients. Other than duration of the depressive episode, no clinical characteristics exclusive to patients who received combination rather than monotherapy were found. Our study found that the most frequent combination is SSRIs + TCAs, also being the most studied.

Using ontologies for structuring organizational knowledge in Home Care assistance.

PURPOSE: Information Technologies and Knowledge-based Systems can significantly improve the management of complex distributed health systems, where supporting multidisciplinarity is crucial and communication and synchronization between the different professionals and tasks becomes essential. This work proposes the use of the ontological paradigm to describe the organizational knowledge of such complex healthcare institutions as a basis to support their management. The ontology engineering process is detailed, as well as the way to maintain the ontology updated in front of changes. The paper also analyzes how such an ontology can be exploited in a real healthcare application and the role of the ontology in the customization of the system. The particular case of senior Home Care assistance is addressed, as this is a highly distributed field as well as a strategic goal in an ageing Europe. MATERIALS AND METHODS: The proposed ontology design is based on a Home Care medical model defined by an European consortium of Home Care professionals, framed in the scope of the K4Care European project (FP6). Due to the complexity of the model and the knowledge gap existing between the - textual - medical model and the strict formalization of an ontology, an ontology engineering methodology (On-To-Knowledge) has been followed. RESULTS: After applying the On-To-Knowledge steps, the following results were obtained: the feasibility study concluded that the ontological paradigm and the expressiveness of modern ontology languages were enough to describe the required medical knowledge; after the kick-off and refinement stages, a complete and non-ambiguous definition of the Home Care model, including its main components and interrelations, was obtained; the formalization stage expressed HC medical entities in the form of ontological classes, which are interrelated by means of hierarchies, properties and semantically rich class restrictions; the evaluation, carried out by exploiting the ontology into a knowledge-driven e-health application running on a real scenario, showed that the ontology design and its exploitation brought several benefits with regards to flexibility, adaptability and work efficiency from the end-user point of view; for the maintenance stage, two software tools are presented, aimed to address the incorporation and modification of healthcare units and the personalization of ontological profiles. CONCLUSIONS: The paper shows that the ontological paradigm and the expressiveness of modern ontology languages can be exploited not only to represent terminology in a non-ambiguous way, but also to formalize the interrelations and organizational structures involved in a real and distributed healthcare environment. This kind of ontologies facilitates the adaptation in front of changes in the healthcare organization or Care Units, supports the creation of profile-based interaction models in a transparent and seamless way, and increases the reusability and generality of the developed software components. As a conclusion of the exploitation of the developed ontology in a real medical scenario, we can say that an ontology formalizing organizational interrelations is a key component for building effective distributed knowledge-driven e-health systems.

A preliminary taxonomy and a standard knowledge base for mental-health system indicators in Spain.

BACKGROUND: There are many sources of information for mental health indicators but we lack a comprehensive classification and hierarchy to improve their use in mental health planning. This study aims at developing a preliminary taxonomy and its related knowledge base of mental health indicators usable in Spain. METHODS: A qualitative method with two experts panels was used to develop a framing document, a preliminary taxonomy with a conceptual map of health indicators, and a knowledge base consisting of key documents, glossary and database of indicators with an evaluation of their relevance for Spain. RESULTS: A total of 661 indicators were identified and organised hierarchically in 4 domains (Context, Resources, Use and Results), 12 subdomains and 56 types. Among these the expert panels identified 200 indicators of relevance for the Spanish system. CONCLUSIONS: The classification and hierarchical ordering of the mental health indicators, the evaluation according to their level of relevance and their incorporation into a knowledge base are crucial for the development of a basic list of indicators for use in mental health planning.

Response to traumatic brain injury neurorehabilitation through an artificial intelligence and statistics hybrid knowledge discovery from databases methodology.

PURPOSE: Develop a classificatory tool to identify different populations of patients with Traumatic Brain Injury based on the characteristics of deficit and response to treatment. WORK METHOD: A KDD framework where first, descriptive statistics of every variable was done, data cleaning and selection of relevant variables. Then data was mined using a generalization of Clustering based on rules (CIBR), an hybrid AI and Statistics technique which combines inductive learning (AI) and clustering (Statistics). A prior Knowledge Base (KB) is considered to properly bias the clustering; semantic constraints implied by the KB hold in final clusters, guaranteeing interpretability of the resultis. A generalization (Exogenous Clustering based on rules, ECIBR) is presented, allowing to define the KB in terms of variables which will not be considered in the clustering process itself, to get more flexibility. Several tools as Class panel graph are introduced in the methodology to assist final interpretation. WORK RESULTS: A set of 5 classes was recommended by the system and interpretation permitted profiles labeling. From the medical point of view, composition of classes is well corresponding with different patterns of increasing level of response to rehabilitation treatments. DISCUSSION: All the patients initially assessable conform a single group. Severe impaired patients are subdivided in four profiles which clearly distinct response patterns. Particularly interesting the partial response profile, where patients could not improve executive functions. CONCLUSIONS: Meaningful classes were obtained and, from a semantics point of view, the results were sensibly improved regarding classical clustering, according to our opinion that hybrid AI & Stats techniques are more powerful for KDD than pure ones.

Qualitative profiles of disability.

This study identified profiles of functional disability (FD) paralleled by increasing levels of disability. We assessed 96 subjects using the World Health Organization Disability Assessment Schedule II (WHODAS II). Clustering Based on Rules (ClBR) (a hybrid technique of Statistics and Artificial Intelligence) was used in the analysis. Four groups of subjects with different profiles of FD were ordered according to an increasing degree of disability: "Low," self-dependent subjects with no physical or emotional problems; "Intermediate I," subjects with low or moderate physical and emotional disability, with high perception of disability; "Intermediate II," subjects with moderate or severe disability concerning only physical problems related to self-dependency, without emotional problems; and "High," subjects with the highest degree of disability, both physical and emotional. The order of the four classes is paralleled by a significant difference (<0.001) in the WHODAS II standardized global score. In this paper, a new ontology for the knowledge of FD, based on the use of ClBR, is proposed. The definition of four classes, qualitatively different and with an increasing degree of FD, helps to appropriately place each patient in a group of individuals with a similar profile of disability and to propose standardized treatments for these groups.