Big Data Analytics + Virtual Clinical Semantic Network (vCSN): An Approach to Addressing the Increasing Clinical Nuances and Organ Involvement of COVID-19.

ASAIO J. 2021 01 01;67(1):18-24

Authors: Rahman F, Meyer R, Kriak J, Goldblatt S, Slepian MJ

Abstract
The coronavirus disease 2019 (COVID-19) pandemic has revealed deep gaps in our understanding of the clinical nuances of this extremely infectious viral pathogen. In order for public health, care delivery systems, clinicians, and other stakeholders to be better prepared for the next wave of SARS-CoV-2 infections, which, at this point, seems inevitable, we need to better understand this disease-not only from a clinical diagnosis and treatment perspective-but also from a forecasting, planning, and advanced preparedness point of view. To predict the onset and outcomes of a next wave, we first need to understand the pathologic mechanisms and features of COVID-19 from the point of view of the intricacies of clinical presentation, to the nuances of response to therapy. Here, we present a novel approach to model COVID-19, utilizing patient data from related diseases, combining clinical understanding with artificial intelligence modeling. Our process will serve as a methodology for analysis of the data being collected in the ASAIO database and other data sources worldwide.

PMID: 32796159 [PubMed - indexed for MEDLINE]

Utilization of text mining as a big data analysis tool for food science and nutrition.

Compr Rev Food Sci Food Saf. 2020 Mar;19(2):875-894

Authors: Tao D, Yang P, Feng H

Abstract
Big data analysis has found applications in many industries due to its ability to turn huge amounts of data into insights for informed business and operational decisions. Advanced data mining techniques have been applied in many sectors of supply chains in the food industry. However, the previous work has mainly focused on the analysis of instrument-generated data such as those from hyperspectral imaging, spectroscopy, and biometric receptors. The importance of digital text data in the food and nutrition has only recently gained attention due to advancements in big data analytics. The purpose of this review is to provide an overview of the data sources, computational methods, and applications of text data in the food industry. Text mining techniques such as word-level analysis (e.g., frequency analysis), word association analysis (e.g., network analysis), and advanced techniques (e.g., text classification, text clustering, topic modeling, information retrieval, and sentiment analysis) will be discussed. Applications of text data analysis will be illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer-opinion mining, new-product development, food knowledge discovery, food supply-chain management, and online food services. The goal is to provide insights for intelligent decision-making to improve food production, food safety, and human nutrition.

PMID: 33325182 [PubMed - in process]

Web-based interactive mapping from data dictionaries to ontologies, with an application to cancer registry.

BMC Med Inform Decis Mak. 2020 Dec 15;20(Suppl 10):271

Authors: Tao S, Zeng N, Hands I, Hurt-Mueller J, Durbin EB, Cui L, Zhang GQ

Abstract
BACKGROUND: The Kentucky Cancer Registry (KCR) is a central cancer registry for the state of Kentucky that receives data about incident cancer cases from all healthcare facilities in the state within 6 months of diagnosis. Similar to all other U.S. and Canadian cancer registries, KCR uses a data dictionary provided by the North American Association of Central Cancer Registries (NAACCR) for standardized data entry. The NAACCR data dictionary is not an ontological system. Mapping between the NAACCR data dictionary and the National Cancer Institute (NCI) Thesaurus (NCIt) will facilitate the enrichment, dissemination and utilization of cancer registry data. We introduce a web-based system, called Interactive Mapping Interface (IMI), for creating mappings from data dictionaries to ontologies, in particular from NAACCR to NCIt.
METHOD: IMI has been designed as a general approach with three components: (1) ontology library; (2) mapping interface; and (3) recommendation engine. The ontology library provides a list of ontologies as targets for building mappings. The mapping interface consists of six modules: project management, mapping dashboard, access control, logs and comments, hierarchical visualization, and result review and export. The built-in recommendation engine automatically identifies a list of candidate concepts to facilitate the mapping process.
RESULTS: We report the architecture design and interface features of IMI. To validate our approach, we implemented an IMI prototype and pilot-tested features using the IMI interface to map a sample set of NAACCR data elements to NCIt concepts. 47 out of 301 NAACCR data elements have been mapped to NCIt concepts. Five branches of hierarchical tree have been identified from these mapped concepts for visual inspection.
CONCLUSIONS: IMI provides an interactive, web-based interface for building mappings from data dictionaries to ontologies. Although our pilot-testing scope is limited, our results demonstrate feasibility using IMI for semantic enrichment of cancer registry data by mapping NAACCR data elements to NCIt concepts.

PMID: 33319710 [PubMed - in process]

Friend of a Friend with Benefits ontology (FOAF+): extending a social network ontology for public health.

BMC Med Inform Decis Mak. 2020 Dec 15;20(Suppl 10):269

Authors: Amith M, Fujimoto K, Mauldin R, Tao C

Abstract
BACKGROUND: Dyadic-based social networks analyses have been effective in a variety of behavioral- and health-related research areas. We introduce an ontology-driven approach towards social network analysis through encoding social data and inferring new information from the data.
METHODS: The Friend of a Friend (FOAF) ontology is a lightweight social network ontology. We enriched FOAF by deriving social interaction data and relationships from social data to extend its domain scope.
RESULTS: Our effort produced Friend of a Friend with Benefits (FOAF+) ontology that aims to support the spectrum of human interaction. A preliminary semiotic evaluation revealed a semantically rich and comprehensive knowledge base to represent complex social network relationships. With Semantic Web Rules Language, we demonstrated FOAF+ potential to infer social network ties between individual data.
CONCLUSION: Using logical rules, we defined interpersonal dyadic social connections, which can create inferred linked dyadic social representations of individuals, represent complex behavioral information, help machines interpret some of the concepts and relationships involving human interaction, query network data, and contribute methods for analytical and disease surveillance.

PMID: 33319708 [PubMed - in process]

Selected articles from the Fourth International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019).

BMC Med Inform Decis Mak. 2020 Dec 14;20(Suppl 4):315

Authors: He Z, Tao C, Bian J, Zhang R

Abstract
In this introduction, we first summarize the Fourth International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019) held on October 26, 2019 in conjunction with the 18th International Semantic Web Conference (ISWC 2019) in Auckland, New Zealand, and then briefly introduce seven research articles included in this supplement issue, covering the topics on Knowledge Graph, Ontology-Powered Analytics, and Deep Learning.

PMID: 33317524 [PubMed - in process]

Conversational ontology operator: patient-centric vaccine dialogue management engine for spoken conversational agents.

BMC Med Inform Decis Mak. 2020 Dec 14;20(Suppl 4):259

Authors: Amith M, Lin RZ, Cui L, Wang D, Zhu A, Xiong G, Xu H, Roberts K, Tao C

Abstract
BACKGROUND: Previously, we introduced our Patient Health Information Dialogue Ontology (PHIDO) that manages the dialogue and contextual information of the session between an agent and a health consumer. In this study, we take the next step and introduce the Conversational Ontology Operator (COO), the software engine harnessing PHIDO. We also developed a question-answering subsystem called Frankenstein Ontology Question-Answering for User-centric Systems (FOQUS) to support the dialogue interaction.
METHODS: We tested both the dialogue engine and the question-answering system using application-based competency questions and questions furnished from our previous Wizard of OZ simulation trials.
RESULTS: Our results revealed that the dialogue engine is able to perform the core tasks of communicating health information and conversational flow. Inter-rater agreement and accuracy scores among four reviewers indicated perceived, acceptable responses to the questions asked by participants from the simulation studies, yet the composition of the responses was deemed mediocre by our evaluators.
CONCLUSIONS: Overall, we present some preliminary evidence of a functioning ontology-based system to manage dialogue and consumer questions. Future plans for this work will involve deploying this system in a speech-enabled agent to assess its usage with potential health consumer users.

PMID: 33317519 [PubMed - in process]

A semantic relationship mining method among disorders, genes, and drugs from different biomedical datasets.

BMC Med Inform Decis Mak. 2020 Dec 14;20(Suppl 4):283

Authors: Zhang L, Hu J, Xu Q, Li F, Rao G, Tao C

Abstract
BACKGROUND: Semantic web technology has been applied widely in the biomedical informatics field. Large numbers of biomedical datasets are available online in the resource description framework (RDF) format. Semantic relationship mining among genes, disorders, and drugs is widely used in, for example, precision medicine and drug repositioning. However, most of the existing studies focused on a single dataset. It is not easy to find the most current relationships among disorder-gene-drug relationships since the relationships are distributed in heterogeneous datasets. How to mine their semantic relationships from different biomedical datasets is an important issue.
METHODS: First, a variety of biomedical datasets were converted into RDF triple data; then, multisource biomedical datasets were integrated into a storage system using a data integration algorithm. Second, nine query patterns among genes, disorders, and drugs from different biomedical datasets were designed. Third, the gene-disorder-drug semantic relationship mining algorithm is presented. This algorithm can query the relationships among various entities from different datasets.
RESULTS AND CONCLUSIONS: We focused on mining the putative and the most current disorder-gene-drug relationships about Parkinson's disease (PD). The results demonstrate that our method has significant advantages in mining and integrating multisource heterogeneous biomedical datasets. Twenty-five new relationships among the genes, disorders, and drugs were mined from four different datasets. The query results showed that most of them came from different datasets. The precision of the method increased by 2.51% compared to that of the multisource linked open data fusion method presented in the 4th International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019). Moreover, the number of query results increased by 7.7%, and the number of correct queries increased by 9.5%.

PMID: 33317518 [PubMed - in process]

Antibiotic prescribing in UK care homes 2016-2017: retrospective cohort study of linked data.

BMC Health Serv Res. 2020 Jun 18;20(1):555

Authors: Smith CM, Williams H, Jhass A, Patel S, Crayton E, Lorencatto F, Michie S, Hayward AC, Shallcross LJ, Preserving Antibiotics through Safe Stewardship group

Abstract
BACKGROUND: Older people living in care homes are particularly susceptible to infections and antibiotics are therefore used frequently for this population. However, there is limited information on antibiotic prescribing in this setting. This study aimed to investigate the frequency, patterns and risk factors for antibiotic prescribing in a large chain of UK care homes.
METHODS: Retrospective cohort study of administrative data from a large chain of UK care homes (resident and care home-level) linked to individual-level pharmacy data. Residents aged 65 years or older between 1 January 2016 and 31 December 2017 were included. Antibiotics were classified by type and as new or repeated prescriptions. Rates of antibiotic prescribing were calculated and modelled using multilevel negative binomial regression.
RESULTS: 13,487 residents of 135 homes were included. The median age was 85; 63% residents were female. 28,689 antibiotic prescriptions were dispensed, the majority were penicillins (11,327, 39%), sulfonamides and trimethoprim (5818, 20%), or other antibacterials (4665, 16%). 8433 (30%) were repeat prescriptions. The crude rate of antibiotic prescriptions was 2.68 per resident year (95% confidence interval (CI) 2.64-2.71). Increased antibiotic prescribing was associated with residents requiring more medical assistance (adjusted incidence rate ratio for nursing opposed to residential care 1.21, 95% CI 1.13-1.30). Prescribing rates varied widely by care home but there were no significant associations with the care home-level characteristics available in routine data.
CONCLUSIONS: Rates of antibiotic prescribing in care homes are high and there is substantial variation between homes. Further research is needed to understand the drivers of this variation to enable development of effective stewardship approaches that target the influences of prescribing.

PMID: 32552886 [PubMed - indexed for MEDLINE]

Cochrane's Linked Data Project: How it Can Advance our Understanding of Surrogate Endpoints.

J Law Med Ethics. 2019 09;47(3):374-380

Authors: Mavergames C, Beecher D, Becker LA, Last A, Ali A

Abstract
Cochrane has developed a linked data infrastructure to make the evidence and data from its rich repositories more discoverable to facilitate evidence-based health decision-making. These annotated resources can enhance the study and understanding of biomarkers and surrogate endpoints.

PMID: 31560633 [PubMed - indexed for MEDLINE]

Model-Driven Decision Making in Multiple Sclerosis Research: Existing Works and Latest Trends.

Patterns (N Y). 2020 Nov 13;1(8):100121

Authors: Alshamrani R, Althbiti A, Alshamrani Y, Alkomah F, Ma X

Abstract
Multiple sclerosis (MS) is a neurological disorder that strikes the central nervous system. Due to the complexity of this disease, healthcare sectors are increasingly in need of shared clinical decision-making tools to provide practitioners with insightful knowledge and information about MS. These tools ought to be comprehensible by both technical and non-technical healthcare audiences. To aid this cause, this literature review analyzes the state-of-the-art decision support systems (DSSs) in MS research with a special focus on model-driven decision-making processes. The review clusters common methodologies used to support the decision-making process in classifying, diagnosing, predicting, and treating MS. This work observes that the majority of the investigated DSSs rely on knowledge-based and machine learning (ML) approaches, so the utilization of ontology and ML in the MS domain is observed to extend the scope of this review. Finally, this review summarizes the state-of-the-art DSSs, discusses the methods that have commonalities, and addresses the future work of applying DSS technologies in the MS field.

PMID: 33294867 [PubMed]

Analyzing the Influence of Hyper-parameters and Regularizers of Topic Modeling in Terms of Renyi Entropy.

Entropy (Basel). 2020 Mar 30;22(4):

Authors: Koltcov S, Ignatenko V, Boukhers Z, Staab S

Abstract
Topic modeling is a popular technique for clustering large collections of text documents. A variety of different types of regularization is implemented in topic modeling. In this paper, we propose a novel approach for analyzing the influence of different regularization types on results of topic modeling. Based on Renyi entropy, this approach is inspired by the concepts from statistical physics, where an inferred topical structure of a collection can be considered an information statistical system residing in a non-equilibrium state. By testing our approach on four models-Probabilistic Latent Semantic Analysis (pLSA), Additive Regularization of Topic Models (BigARTM), Latent Dirichlet Allocation (LDA) with Gibbs sampling, LDA with variational inference (VLDA)-we, first of all, show that the minimum of Renyi entropy coincides with the "true" number of topics, as determined in two labelled collections. Simultaneously, we find that Hierarchical Dirichlet Process (HDP) model as a well-known approach for topic number optimization fails to detect such optimum. Next, we demonstrate that large values of the regularization coefficient in BigARTM significantly shift the minimum of entropy from the topic number optimum, which effect is not observed for hyper-parameters in LDA with Gibbs sampling. We conclude that regularization may introduce unpredictable distortions into topic models that need further research.

PMID: 33286169 [PubMed]

Biomedica. 2020 Dec 2;40(4):702-721. doi: 10.7705/biomedica.5182.

ABSTRACT

Introduction: As an initiative to improve the quality of health care, the trend in biomedical research focused on health disparities and sex has increased. Objective: To carry out a characterization of the scientific evidence on health disparity defined as the gap between the distribution of health and the possible gender bias for access to medical services. Materials and methods: We conducted a simultaneous search of two fundamental descriptors in the scientific literature in the Medline PubMed database: healthcare disparities and sexism. Subsequently, a main semantic network was built and some structural subunits (communities) were identified for the analysis of information organization patterns. We used open-source software: Cytoscape to analyze and visualize the semantic network, and MapEquation for community detection, as well as an ad hoc code available in a public access repository. Results: The core network corpus showed that the terms on heart disease were the most common among the descriptors of medical conditions. Patterns of information related to public policies, health services, social determinants, and risk factors were identified from the structural subunits, but with a certain tendency to remain indirectly connected to the nodes of medical conditions. Conclusions: Scientific evidence indicates that gender disparity does matter for the care quality in many diseases, especially those related to the circulatory system. However, there is still a gap between the medical and social factors that give rise to possible disparities by sex.

PMID:33275349 | PMC:PMC7808772 | DOI:10.7705/biomedica.5182

HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images.

Med Image Anal. 2020 Oct 29;68:101890

Authors: van Rijthoven M, Balkenhol M, Siliņa K, van der Laak J, Ciompi F

Abstract
We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional neural networks. Concentric patches at multiple resolutions with different fields of view, feed different branches of HookNet, and intermediate representations are combined via a hooking mechanism. We describe a framework to design and train HookNet for achieving high-resolution semantic segmentation and introduce constraints to guarantee pixel-wise alignment in feature maps during hooking. We show the advantages of using HookNet in two histopathology image segmentation tasks where tissue type prediction accuracy strongly depends on contextual information, namely (1) multi-class tissue segmentation in breast cancer and, (2) segmentation of tertiary lymphoid structures and germinal centers in lung cancer. We show the superiority of HookNet when compared with single-resolution U-Net models working at different resolutions as well as with a recently published multi-resolution model for histopathology image segmentation. We have made HookNet publicly available by releasing the source code1 as well as in the form of web-based applications2,3 based on the grand-challenge.org platform.

PMID: 33260110 [PubMed - as supplied by publisher]

Effects of physical exercise on executive function in cognitively healthy older adults: A systematic review and meta-analysis of randomized controlled trials: Physical exercise for executive function.

Int J Nurs Stud. 2020 Oct 24;114:103810

Authors: Xiong J, Ye M, Wang L, Zheng G

Abstract
OBJECTIVE: To assess the effect of physical exercise interventions on executive function in cognitively healthy adults aged 60 years and older.
METHODS: Four electronic databases, the Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, Web of Science and Embase, were comprehensively searched from their inception to November 25, 2019. Randomized controlled trials (RCTs) examining the effect of physical exercise on executive function in cognitively healthy older adults were included.
RESULTS: Twenty-five eligible trials with fair methodological quality were identified. Compared to a no-exercise intervention, physical exercise had positive effect on working memory (Hedge's g=0.127, p<0.01, I2= 0%), cognitive flexibility (Hedge's g=0.511; p=0.007, I2=89.08%), and inhibitory control (Hedge's g=0.136; p=0.001, I2=0%) in cognitively healthy older adults. The moderator analysis indicated that more than 13 weeks of aerobic exercise significantly improved working memory and cognitive flexibility, and intervention lasting more than 26 weeks significantly improved inhibition; mind-body exercise significantly improved working memory. No significant effect on planning or semantic verbal fluency (SVF) was found.
CONCLUSION: Regular physical exercise training, especially aerobic exercise and mind-body exercise, had positive benefit for improving working memory, cognitive flexibility and inhibitory control of executive function in cognively healthy older adults. Further well-designed RCTs should focus on the impact of specific exercise forms with a standardized exercise scheme on executive function in cognitively healthy older adults.

PMID: 33248291 [PubMed - as supplied by publisher]

Detection of Suicidality Among Opioid Users on Reddit: Machine Learning-Based Approach.

J Med Internet Res. 2020 Nov 27;22(11):e15293

Authors: Yao H, Rashidian S, Dong X, Duanmu H, Rosenthal RN, Wang F

Abstract
BACKGROUND: In recent years, both suicide and overdose rates have been increasing. Many individuals who struggle with opioid use disorder are prone to suicidal ideation; this may often result in overdose. However, these fatal overdoses are difficult to classify as intentional or unintentional. Intentional overdose is difficult to detect, partially due to the lack of predictors and social stigmas that push individuals away from seeking help. These individuals may instead use web-based means to articulate their concerns.
OBJECTIVE: This study aimed to extract posts of suicidality among opioid users on Reddit using machine learning methods. The performance of the models is derivative of the data purity, and the results will help us to better understand the rationale of these users, providing new insights into individuals who are part of the opioid epidemic.
METHODS: Reddit posts between June 2017 and June 2018 were collected from r/suicidewatch, r/depression, a set of opioid-related subreddits, and a control subreddit set. We first classified suicidal versus nonsuicidal languages and then classified users with opioid usage versus those without opioid usage. Several traditional baselines and neural network (NN) text classifiers were trained using subreddit names as the labels and combinations of semantic inputs. We then attempted to extract out-of-sample data belonging to the intersection of suicide ideation and opioid abuse. Amazon Mechanical Turk was used to provide labels for the out-of-sample data.
RESULTS: Classification results were at least 90% across all models for at least one combination of input; the best classifier was convolutional neural network, which obtained an F1 score of 96.6%. When predicting out-of-sample data for posts containing both suicidal ideation and signs of opioid addiction, NN classifiers produced more false positives and traditional methods produced more false negatives, which is less desirable for predicting suicidal sentiments.
CONCLUSIONS: Opioid abuse is linked to the risk of unintentional overdose and suicide risk. Social media platforms such as Reddit contain metadata that can aid machine learning and provide information at a personal level that cannot be obtained elsewhere. We demonstrate that it is possible to use NNs as a tool to predict an out-of-sample target with a model built from data sets labeled by characteristics we wish to distinguish in the out-of-sample target.

PMID: 33245287 [PubMed - as supplied by publisher]

Multiscale Cross-Domain Thermochemical Knowledge-Graph.

J Chem Inf Model. 2020 Nov 26;:

Authors: Mosbach S, Menon A, Farazi F, Krdzavac N, Zhou X, Akroyd J, Kraft M

Abstract
In this paper, we develop a set of software agents which improve a knowledge-graph containing thermodynamic data of chemical species by means of quantum chemical calculations and error-canceling balanced reactions. The knowledge-graph represents species-associated information by making use of the principles of linked data, as employed in the Semantic Web, where concepts correspond to vertices and relationships between the concepts correspond to edges of the graph. We implement this representation by means of ontologies, which formalize the definition of concepts and their relationships, as a critical step to achieve interoperability between heterogeneous data formats and software. The agents, which conduct quantum chemical calculations and derive the estimates of standard enthalpies of formation, update the knowledge-graph with newly obtained results, improving data values, and adding nodes and connections between them. A key distinguishing feature of our approach is that it extends an existing, general-purpose knowledge-graph, called J-Park Simulator (http://theworldavatar.com), and its ecosystem of autonomous agents, thus enabling seamless cross-domain applications in wider contexts. To this end, we demonstrate how quantum calculations can directly affect the atmospheric dispersion of pollutants in an industrial emission use-case.

PMID: 33242243 [PubMed - as supplied by publisher]

Visualization Environment for Federated Knowledge Graphs: Development of an Interactive Biomedical Query Language and Web Application Interface.

JMIR Med Inform. 2020 Nov 23;8(11):e17964

Authors: Cox S, Ahalt SC, Balhoff J, Bizon C, Fecho K, Kebede Y, Morton K, Tropsha A, Wang P, Xu H

Abstract
BACKGROUND: Efforts are underway to semantically integrate large biomedical knowledge graphs using common upper-level ontologies to federate graph-oriented application programming interfaces (APIs) to the data. However, federation poses several challenges, including query routing to appropriate knowledge sources, generation and evaluation of answer subsets, semantic merger of those answer subsets, and visualization and exploration of results.
OBJECTIVE: We aimed to develop an interactive environment for query, visualization, and deep exploration of federated knowledge graphs.
METHODS: We developed a biomedical query language and web application interphase-termed as Translator Query Language (TranQL)-to query semantically federated knowledge graphs and explore query results. TranQL uses the Biolink data model as an upper-level biomedical ontology and an API standard that has been adopted by the Biomedical Data Translator Consortium to specify a protocol for expressing a query as a graph of Biolink data elements compiled from statements in the TranQL query language. Queries are mapped to federated knowledge sources, and answers are merged into a knowledge graph, with mappings between the knowledge graph and specific elements of the query. The TranQL interactive web application includes a user interface to support user exploration of the federated knowledge graph.
RESULTS: We developed 2 real-world use cases to validate TranQL and address biomedical questions of relevance to translational science. The use cases posed questions that traversed 2 federated Translator API endpoints: Integrated Clinical and Environmental Exposures Service (ICEES) and Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP). ICEES provides open access to observational clinical and environmental data, and ROBOKOP provides access to linked biomedical entities, such as "gene," "chemical substance," and "disease," that are derived largely from curated public data sources. We successfully posed queries to TranQL that traversed these endpoints and retrieved answers that we visualized and evaluated.
CONCLUSIONS: TranQL can be used to ask questions of relevance to translational science, rapidly obtain answers that require assertions from a federation of knowledge sources, and provide valuable insights for translational research and clinical practice.

PMID: 33226347 [PubMed - as supplied by publisher]

Disengagement from mental health treatment and re-offending in those with psychosis: a multi-state model of linked data.

Soc Psychiatry Psychiatr Epidemiol. 2020 Dec;55(12):1639-1648

Authors: Hwang YIJ, Albalawi O, Adily A, Hudson M, Wand H, Kariminia A, O'Driscoll C, Allnutt S, Grant L, Sara G, Ogloff J, Greenberg DM, Butler T

Abstract
BACKGROUND AND AIMS: Individuals with psychosis are over-represented in the criminal justice system and, as a group, are at elevated risk of re-offending. Recent studies have observed an association between increased contacts with mental health services and reduced re-offending, as well as reduced risk of re-offending in those who are ordered to mental health treatment rather than punitive sanctions. In furthering this work, this study examines the effect of disengagement from mental health treatment on probability of re-offence in offenders with psychosis over time.
METHODS: Data linkage was conducted with judicial, health and mortality datasets from New South Wales, Australia (2001-2015). The study population included 4960 offenders with psychosis who received non-custodial sentences and engaged with community-based mental health treatment. Risk factors for leaving treatment and/or reconviction were examined using multivariate cox regression. Further, a multi-state model was used to observe the probabilities associated with individuals moving between three states: conviction, disengagement from mental health treatment and subsequent re-conviction.
RESULTS: A threefold increase was observed in the risk of re-offending for those who disengaged from treatment compared to those who did not (aHR = 2.76, 95% CI 1.65-4.62, p < 0.001). The median time until re-offence was 195 days, with the majority (67%) being convicted within one year of leaving treatment. A higher risk of leaving treatment was found for those born outside of Australia, with substance-related psychosis, and a history of violent offence.
CONCLUSIONS: The findings argue for an emphasis on continued engagement with mental health services following release for offenders with psychosis and identify subgroups within this population for whom concentrated efforts regarding treatment retention should be targeted.

PMID: 32390094 [PubMed - indexed for MEDLINE]

Association of Vascular Endothelial Growth Factor Subtypes with Melanoma Patients' Characteristics and Survival: A Semantic Connectivity Map Analysis.

Acta Derm Venereol. 2020 Jan 07;100(1):adv00019

Authors: Cazzaniga S, Wiedmer C, Frangež Ž, Shafighi M, Beltraminelli H, Weber B, Simon D, Naldi L, Simon HU, Hunger RE, Seyed Jafari SM

PMID: 31742647 [PubMed - indexed for MEDLINE]

West J Emerg Med. 2020 Oct 27;21(6):141-145. doi: 10.5811/westjem.2020.8.48305.

ABSTRACT

INTRODUCTION: The American Hospital Association (AHA) has hospital-level data, while the Centers for Medicare & Medicaid Services (CMS) has patient-level data. Merging these with other distinct databases would permit analyses of hospital-based specialties, units, or departments, and patient outcomes. One distinct database is the National Emergency Department Inventory (NEDI), which contains information about all EDs in the United States. However, a challenge with merging these databases is that NEDI lists all US EDs individually, while the AHA and CMS group some EDs by hospital network. Consolidating data for this merge may be preferential to excluding grouped EDs. Our objectives were to consolidate ED data to enable linkage with administrative datasets and to determine the effect of excluding grouped EDs on ED-level summary results.

METHODS: Using the 2014 NEDI-USA database, we surveyed all New England EDs. We individually matched NEDI EDs with corresponding EDs in the AHA and CMS. A "group match" was assigned when more than one NEDI ED was matched to a single AHA or CMS facility identification number. Within each group, we consolidated individual ED data to create a single observation based on sums or weighted averages of responses as appropriate.

RESULTS: Of the 195 EDs in New England, 169 (87%) completed the NEDI survey. Among these, 130 (77%) EDs were individually listed in AHA and CMS, while 39 were part of groups consisting of 2-3 EDs but represented by one facility ID. Compared to the individually listed EDs, the 39 EDs included in a "group match" had a larger number of annual visits and beds, were more likely to be freestanding, and were less likely to be rural (all P<0.05). Two grouped EDs were excluded because the listed ED did not respond to the NEDI survey; the remaining 37 EDs were consolidated into 19 observations. Thus, the consolidated dataset contained 149 observations representing 171 EDs; this consolidated dataset yielded summary results that were similar to those of the 169 responding EDs.

CONCLUSION: Excluding grouped EDs would have resulted in a non-representative dataset. The original vs consolidated NEDI datasets yielded similar results and enabled linkage with large administrative datasets. This approach presents a novel opportunity to use characteristics of hospital-based specialties, units, and departments in studies of patient-level outcomes, to advance health services research.

PMID:33207159 | PMC:PMC7673880 | DOI:10.5811/westjem.2020.8.48305