Measuring follow-up time in routinely-collected health datasets: Challenges and solutions

A key requirement for longitudinal studies using routinely-collected health data is to be able to measure what individuals are present in the datasets used, and over what time period. Individuals can enter and leave the covered population of administrative datasets for a variety of reasons, including both life events and characteristics of the datasets themselves. An automated, customizable method of determining individuals' presence was developed for the primary care dataset in Swansea University's SAIL Databank. The primary care dataset covers only a portion of Wales, with 76% of practices participating. The start and end date of the data varies by practice. Additionally, individuals can change practices or leave Wales. To address these issues, a two step process was developed. First, the period for which each practice had data available was calculated by measuring changes in the rate of events recorded over time. Second, the registration records for each individual were simplified. Anomalies such as short gaps and overlaps were resolved by applying a set of rules. The result of these two analyses was a cleaned set of records indicating start and end dates of available primary care data for each individual. Analysis of GP records showed that 91.0% of events occurred within periods calculated as having available data by the algorithm. 98.4% of those events were observed at the same practice of registration as that computed by the algorithm. A standardized method for solving this common problem has enabled faster development of studies using this data set. Using a rigorous, tested, standardized method of verifying presence in the study population will also positively influence the quality of research

Infection and Risk of Parkinson\u27s Disease

Parkinson\u27s disease (PD) is thought to be caused by a combination of genetic and environmental factors. Bacterial or viral infection has been proposed as a potential risk factor, and there is supporting although not entirely consistent epidemiologic and basic science evidence to support its role. Encephalitis caused by influenza has included parkinsonian features. Epidemiological evidence is most compelling for an association between PD and hepatitis C virus. Infection with Helicobacter pylori may be associated not only with PD risk but also response to levodopa. Rapidly evolving knowledge regarding the role of the microbiome also suggests a role of resident bacteria in PD risk. Biological plausibility for the role for infectious agents is supported by the known neurotropic effects of specific viruses, particular vulnerability of the substantia nigra and even the promotion of aggregation of alpha-synuclein. A common feature of implicated viruses appears to be production of high levels of cytokines and chemokines that can cross the blood-brain barrier leading to microglial activation and inflammation and ultimately neuronal cell death. Based on multiple avenues of evidence it appears likely that specific bacterial and particularly viral infections may increase vulnerability to PD. The implications of this for PD prevention requires attention and may be most relevant once preventive treatments for at-risk populations are developed

Sustainability effects of next-generation intersection control for autonomous vehicles

Transportation sustainability is adversely affected by recurring traffic congestions, especially at urban intersections. Frequent vehicle deceleration and acceleration caused by stop-and-go behaviours at intersections due to congestion adversely impacts energy consumption and ambient air quality. Availability of the maturing vehicle technologies such as autonomous vehicles and Vehicle-To-Vehicle (V2V) / Vehicle-To-Infrastructure (V2I) communications provides technical feasibility to develop solutions that can reduce vehicle stops at intersections, hence enhance the sustainability of intersections. This paper presents a next-generation intersection control system for autonomous vehicles, which is named ACUTA. ACUTA employs an enhanced reservation-based control algorithm that controls autonomous vehicles’ passing sequence at an intersection. Particularly, the intersection is divided into n-by-n tiles. An intersection controller reserves certain time-space for each vehicle, and assures no conflict exists between reservations. The algorithm was modelled in microscopic traffic simulation platform VISSIM. ACUTA algorithm modelling as well as enhancement strategies to minimize vehicle intersection stops and eventually emission and energy consumption were discussed in the paper. Sustainability benefits offered by this next-generation intersection were evaluated and compared with traditional intersection control strategies. The evaluation reveals that multi-tile ACUTA reduces carbon monoxide (CO) and Particulate Matter (PM) 2.5 emissions by about 5% under low to moderate volume conditions and by about 3% under high volume condition. Meanwhile, energy consumption is reduced by about 4% under low to moderate volume conditions and by about 12% under high volume condition. Compared with four-way stop control, single-tile ACUTA reduces CO and PM 2.5 emissions as well as energy consumption by about 15% under any prevailing volume conditions. These findings validated the sustainability benefits of employing next-generation vehicle technologies in intersection traffic control. In addition, extending the ACUTA to corridor level was explored in the paper

Programmatically encrypting data linkage fields at a project level within the Secure Anonymised Information Linkage (SAIL) databank

ABSTRACT Introduction/Background The ability to link datasets within the Secure Anonymised Information Linkage (SAIL) databank provides researchers with a powerful tool to analyse multiple datasets. The ability to combine several datasets also has the adverse effect of potential identification of an individual. Further encrypting linkage fields at a project level limits the links to datasets specific to the project only. This presentation discusses the opensource web based administration tool that programmatically applies project encryption in a consistent and timely manner, logging administrator actions. Objectives 1). Identify encryption methodology 2). Programme encryption steps and log steps 3). Design and implement web based user administration tool Approach Utilising existing Secure Anonymised Information Linkage (SAIL) databank security, providing researchers with a view of their data, separating data linkage fields into a separate secure lookup table. Using Python programming language to automate the Structured Query Language (SQL) scripts required to accomplish this, as well as Python packages to interact with the databank and web based administration tool. Results Project encrypted views created for several projects and scores of datasets. Encrypted linkage fields unique to each project ensuring views across projects can not be linked either to each other or the original datasets. Conclusion Encryption process is programmable and administered through web tool

Solution Structure and Backbone Dynamics of the Functional Cytoplasmic Subdomain of Human Ephrin B2, a Cell-Surface Ligand with Bidirectional Signaling Properties

NRC publication: Ye

Solution Structure and Backbone Dynamics of the Functional Cytoplasmic Subdomain of Human Ephrin B2, a Cell-Surface Ligand with Bidirectional Signaling Properties

NRC publication: Ye