Out-of-hours care in western countries: assessment of different organizational models

Contains fulltext : 81655.pdf (publisher's version ) (Open Access)BACKGROUND: Internationally, different organizational models are used for providing out-of-hours care. The aim of this study was to assess prevailing models in order to identify their potential strengths and weaknesses. METHODS: An international web-based survey was done in 2007 in a sample of purposefully selected key informants from 25 western countries. The questions concerned prevailing organizational models for out-of-hours care, the most dominant model in each country, perceived weaknesses, and national plans for changes in out-of-hours care. RESULTS: A total of 71 key informants from 25 countries provided answers. In most countries several different models existed alongside each other. The Accident and Emergency department was the organizational model most frequently used. Perceived weaknesses of this model concerned the coordination and continuity of care, its efficiency and accessibility. In about a third of the countries, the rota group was the most dominant organizational model for out-of-hours care. A perceived weakness of this model was lowered job satisfaction of physicians. The GP cooperative existed in a majority of the participating countries; no weaknesses were mentioned with respect to this model. Most of the countries had plans to change the out-of-hours care, mainly toward large scale organizations. CONCLUSION: GP cooperatives combine size of scale advantages with organizational features of strong primary care, such as high accessibility, continuity and coordination of care. While specific patients require other organizational models, the co-existence of different organizational models for out-of-hours care in a country may be less efficient for health systems

Hierarchy measure for complex networks

Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges). Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy. Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network. The measure we introduce is based on a generalization of the m-reach centrality, which we first extend to directed/partially directed graphs. Then, we define the global reaching centrality (GRC), which is the difference between the maximum and the average value of the generalized reach centralities over the network. We investigate the behavior of the GRC considering both a synthetic model with an adjustable level of hierarchy and real networks. Results for real networks show that our hierarchy measure is related to the controllability of the given system. We also propose a visualization procedure for large complex networks that can be used to obtain an overall qualitative picture about the nature of their hierarchical structure.Comment: 29 pages, 9 figures, 4 table

Walk-ins seeking treatment at an emergency department or general practitioner out-of-hours service: a cross-sectional comparison

Background Emergency Departments (ED) in Switzerland are faced with increasing numbers of patients seeking non-urgent treatment. The high rate of walks-ins with conditions that may be treated in primary care has led to suggestions that those patients would best cared for in a community setting rather than in a hospital. Efficient reorganisation of emergency care tailored to patients needs requires information on the patient populations using the various emergency services currently available. The aim of this study is to evaluate the differences between the characteristics of walk-in patients seeking treatment at an ED and those of patients who use traditional out-of-hours GP (General Practitioner) services provided by a GP-Cooperative (GP-C). Methods In 2007 and 2009 data was collected covering all consecutive patient-doctor encounters at the ED of a hospital and all those occurring as a result of contacting a GP-C over two evaluation periods of one month each. Comparison was made between a GP-C and the ED of the Waid City Hospital in Zurich. Patient characteristics, time and source of referral, diagnostic interventions and mode of discharge were evaluated. Medical problems were classified according to the International Classification of Primary Care (ICPC-2). Patient characteristics were compared using non-parametric tests and multiple logistic regression analysis was applied to investigate independent determinants for contacting a GP-C or an ED. Results Overall a total of 2974 patient encounters were recorded. 1901 encounters were walk-ins and underwent further analysis (ED 1133, GP-C 768). Patients consulting the GP-C were significantly older (58.9 vs. 43.8 years), more often female (63.5 vs. 46.9%) and presented with non-injury related medical problems (93 vs. 55.6%) in comparison with patients at the ED. Independent determining factors for ED consultation were injury, male gender and younger age. Walk-in distribution in both settings was equal over a period of 24 hours and most common during daytime hours (65%). Outpatient care was predominant in both settings but significantly more so at the GP-C (79.9 vs. 85.7%). Conclusions We observed substantial differences between the two emergency settings in a non gate-keeping health care system. Knowledge of the distribution of diagnoses, their therapy, of diagnostic measures and of the factors which determine the patients' choice of the ED or the GP-C is essential for the efficient allocation of resources and the reduction of costs

The Deep Dementia Phenotyping (DEMON) Network: A global platform for innovation using data science and artificial intelligence.

This is the final version. Available from Wiley via the DOI in this record. BACKGROUND: The increasing availability of large high-dimensional data from experimental medicine, population-based and clinical cohorts, clinical trials, and electronic health records has the potential to transform dementia research. Our ability to make best use of this rich data will depend on utilisation of advanced machine learning and artificial intelligence (AI) techniques and collaboration across disciplinary and geographic boundaries. METHOD: The Deep Dementia Phenotyping (DEMON) Network launched in 20191 to support the growing interest in machine learning and AI. Led by Director Prof David Llewellyn and Deputy Director Dr Janice Ranson, the leadership team additionally includes 5 Theme Leads and 14 Working Group Leads, supported by an international Steering Committee of world-leading academics. Core funding is provided by Alzheimer's Research UK, the Alan Turing Institute and the University of Exeter, with additional support from strategic partners including the UK Dementia Research Institute and the Alzheimer's Society. Grand Challenges were established at a National Strategy Workshop in June 2020. Multidisciplinary Working Groups were formed to coordinate practical activities in seven key areas: Genetics and omics, experimental medicine, drug discovery and trials optimisation, biomarkers, imaging, dementia prevention, and applied models and digital health. Additional Special Interest Groups coordinate topic specific collaborations. RESULT: Membership on 4th February 2022 comprised 1,321 individuals from 61 countries across 6 continents (see Figure). Areas of expertise include dementia research (904; 68%), data science (692; 52%), clinical practice (244; 18%), industry (162; 12%), and regulation (26; 2%). Individual membership is free, and regular knowledge transfer events are provided including a monthly seminar series, talks and workshops, training, networking, and early career development. Each Working Group meets monthly, with multiple grants, reviews, and original research articles in progress. Eight state of the science position papers are in preparation, resulting from a Symposium held in April 2021. In January 2022, 110 early career researchers participated in the Network's flagship event 'NEUROHACK', a 4-day competitive global hackathon, with pilot grants awarded to those generating the most innovative solutions. CONCLUSION: The DEMON Network is a rapidly growing global platform for innovation that is supporting the global dementia research community to collaborate. Find out more at demondementia.com

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10−8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P &lt; 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.</p

A genome-wide association search for type 2 diabetes genes in African Americans

African Americans are disproportionately affected by type 2 diabetes (T2DM) yet few studies have examined T2DM using genome-wide association approaches in this ethnicity. The aim of this study was to identify genes associated with T2DM in the African American population. We performed a Genome Wide Association Study (GWAS) using the Affymetrix 6.0 array in 965 African-American cases with T2DM and end-stage renal disease (T2DM-ESRD) and 1029 population-based controls. The most significant SNPs (n = 550 independent loci) were genotyped in a replication cohort and 122 SNPs (n = 98 independent loci) were further tested through genotyping three additional validation cohorts followed by meta-analysis in all five cohorts totaling 3,132 cases and 3,317 controls. Twelve SNPs had evidence of association in the GWAS (P<0.0071), were directionally consistent in the Replication cohort and were associated with T2DM in subjects without nephropathy (P<0.05). Meta-analysis in all cases and controls revealed a single SNP reaching genome-wide significance (P<2.5×10(-8)). SNP rs7560163 (P = 7.0×10(-9), OR (95% CI) = 0.75 (0.67-0.84)) is located intergenically between RND3 and RBM43. Four additional loci (rs7542900, rs4659485, rs2722769 and rs7107217) were associated with T2DM (P<0.05) and reached more nominal levels of significance (P<2.5×10(-5)) in the overall analysis and may represent novel loci that contribute to T2DM. We have identified novel T2DM-susceptibility variants in the African-American population. Notably, T2DM risk was associated with the major allele and implies an interesting genetic architecture in this population. These results suggest that multiple loci underlie T2DM susceptibility in the African-American population and that these loci are distinct from those identified in other ethnic populations