Impact of different health systems on the burden of type 2 diabetes in two populations

Objectives: Health policy directs the management of patients with chronic disease in a country, but evaluating nationwide policies is difficult, not least because of the absence of suitable comparators. This paper examines the management of patients with type 2 diabetes in two demographically comparable populations with different health care systems to see if this represents a viable approach to evaluation.

Methods: A secondary analysis of centralized prescribing databases for 2010 was undertaken to compare the levels and costs of care of patients with type 2 diabetes in Northern Ireland’s National Health Service (NHS) (NI, n = 1.8 million) which has structured care, financial incentives related to diabetes care and an emphasis on generic prescribing, with that of the Republic of Ireland (ROI, n = 4.3 million) where management of diabetes care is guided solely by clinical and other guidelines.

Results: The prevalence of treated type 2 diabetes was 3.59% in NI and 3.09% in ROI, but there were similar and high levels of prescribing of secondary cardiovascular medications. Medication costs per person for anti-diabetic, anti-obesity and cardiovascular medication were 46% higher in ROI than NI, due to differences in levels of generic prescribing.

Conclusions: These different health care systems appear to be producing similar levels of care for patients with type 2 diabetes, although at different levels of cost. The findings question the need for financial incentives in NI and highlight the large cost savings potentially accruing from a greater shift to generic prescribing in ROI. Cross-country comparison, though not without difficulties, may prove a useful adjunct to within-country analysis of policy impact.

An experimental study of UWB device-free person detection and ranging

Passive person detection and localization is an emerging area in UWB localization systems, whereby people are not required to carry any UWB ranging device. Based on experimental data, we propose a novel method to detect static persons in the absence of template waveforms, and to compute distances to these persons. Our method makes very little assumptions on the environment and can achieve ranging performances on the order of 50 cm, using off-the-shelf UWB devices. © 2013 IEEE.

Data-Augmentation for Reducing Dataset Bias in Person Re-identification

In this paper we explore ways to address the issue of dataset bias in person re-identification by using data augmentation to increase the variability of the available datasets, and we introduce a novel data augmentation method for re-identification based on changing the image background. We show that use of data augmentation can improve the cross-dataset generalisation of convolutional network based re-identification systems, and that changing the image background yields further improvements.

Person Re-Identification using Deep Convnets with Multi-task Learning

Person re-identification involves recognizing a person across non-overlapping camera views, with different pose, illumination, and camera characteristics. We propose to tackle this problem by training a deep convolutional network to represent a person’s appearance as a low-dimensional feature vector that is invariant to common appearance variations encountered in the re-identification problem. Specifically, a Siamese-network architecture is used to train a feature extraction network using pairs of similar and dissimilar images. We show that use of a novel multi-task learning objective is crucial for regularizing the network parameters in order to prevent over-fitting due to the small size the training dataset. We complement the verification task, which is at the heart of re-identification, by training the network to jointly perform verification, identification, and to recognise attributes related to the clothing and pose of the person in each image. Additionally, we show that our proposed approach performs well even in the challenging cross-dataset scenario, which may better reflect real-world expected performance.