Small fibre neuropathy in patients with latent autoimmune diabetes in adults

The prevalence of latent autoimmune diabetes in adults (LADA) in patients diagnosed with type 2 diabetes mellitus (T2DM) ranges from 7 to 10% (1). They present at a younger age and have a lower BMI but poorer glycemic control, which may increase the risk of complications (2). However, a recent analysis of the Collaborative Atorvastatin Diabetes Study (CARDS) has demonstrated no difference in macrovascular or microvascular events between patients with LADA and T2DM, but neuropathy was not assessed (3). Previous studies quantifying neuropathy in patients with LADA are limited. In this study, we aimed to accurately quantify neuropathy in subjects with LADA compared with matched patients with T2DM.

Research update: LANDMark study: Longitudinal assessment of neuropathy in diabetes using novel ophthalmic MARKers

The eye is a simple, non-invasive location for screening, diagnosing and follow up of diabetic peripheral neuropathy.

The lived experience of diagnosis delivery in motor neurone disease: A sociological-phenomenological study

Relatively few previous studies of individuals receiving a diagnosis of Motor Neurone Disease within the UK health care system have employed qualitative approaches to examine the diagnostic journey from a patient perspective. A qualitative sociological study was undertaken, involving interviews with 42 participants diagnosed with MND, to provide insight into their experiences of undergoing testing and receiving a diagnosis. Adopting a sociological-phenomenological perspective, this article examines key themes that emerged from participant accounts surrounding the lived experience of the diagnostic journey. The key themes that emerged were: The diagnostic quest; living with uncertainty; hearing bad news; communication difficulties; and a reified body of medical interest. In general, doctor-patient communication both at pre and post diagnosis was experienced as highly stressful, distressing and profoundly upsetting. Participants reported such distress as being due to the mode of delivery and communication strategies used by health professionals. We therefore suggest that professional training needs to emphasize the importance to health professionals of fostering greater levels of tact, sensitivity and empathy towards patients diagnosed with devastating, life-limiting illnesses such as MND.

A Bayesian latent class safety performance function for identifying motor vehicle crash blackspots

The current state of the practice in Blackspot Identification (BSI) utilizes safety performance functions based on total crash counts to identify transport system sites with potentially high crash risk. This paper postulates that total crash count variation over a transport network is a result of multiple distinct crash generating processes including geometric characteristics of the road, spatial features of the surrounding environment, and driver behaviour factors. However, these multiple sources are ignored in current modelling methodologies in both trying to explain or predict crash frequencies across sites. Instead, current practice employs models that imply that a single underlying crash generating process exists. The model mis-specification may lead to correlating crashes with the incorrect sources of contributing factors (e.g. concluding a crash is predominately caused by a geometric feature when it is a behavioural issue), which may ultimately lead to inefficient use of public funds and misidentification of true blackspots. This study aims to propose a latent class model consistent with a multiple crash process theory, and to investigate the influence this model has on correctly identifying crash blackspots. We first present the theoretical and corresponding methodological approach in which a Bayesian Latent Class (BLC) model is estimated assuming that crashes arise from two distinct risk generating processes including engineering and unobserved spatial factors. The Bayesian model is used to incorporate prior information about the contribution of each underlying process to the total crash count. The methodology is applied to the state-controlled roads in Queensland, Australia and the results are compared to an Empirical Bayesian Negative Binomial (EB-NB) model. A comparison of goodness of fit measures illustrates significantly improved performance of the proposed model compared to the NB model. The detection of blackspots was also improved when compared to the EB-NB model. In addition, modelling crashes as the result of two fundamentally separate underlying processes reveals more detailed information about unobserved crash causes.