Refugees’ Transition from Welfare to Work: A Quasi-Experimental Approach of the Impact of the Neighbourhood Context

This study analyses the impact of the neighbourhood context on the likelihood that refugees move from social assistance to paid employment. It makes use of Dutch policy that resulted in an exogenous placement of refugees in their first regular housing. This natural quasi-experiment allows us to estimate intent-to-treat effects of initial neighbourhood characteristics on the likelihood of transitioning from welfare to work. We consider the impact of the employment share and the median level of income among natives and co-ethnics, using Dutch longitudinal administrative data and discrete time event-history modelling. Our findings indicate that refugees are more likely to enter the labour market when the neighbourhood’s employment share among natives is higher. A similar effect for employment among co-ethnics is not found. There is also no evidence that the placement of refugees in an area with a higher median income among co-ethnics or natives facilitates the transition from welfare to work

Brain atrophy and white matter hyperintensities are independently associated with plasma neurofilament light chain in an Asian cohort of cognitively impaired patients with concomitant cerebral small vessel disease

Introduction: Plasma neurofilament light chain (NfL) is a potential biomarker for neurodegeneration in Alzheimer's disease (AD), ischemic stroke, and non-dementia cohorts with cerebral small vessel disease (CSVD). However, studies of AD in populations with high prevalence of concomitant CSVD to evaluate associations of brain atrophy, CSVD, and amyloid beta (Aβ) burden on plasma NfL are lacking. Methods: Associations were tested between plasma NfL and brain Aβ, medial temporal lobe atrophy (MTA) as well as neuroimaging features of CSVD, including white matter hyperintensities (WMH), lacunes, and cerebral microbleeds. Results: We found that participants with either MTA (defined as MTA score ≥2; neurodegeneration [N]+WMH−) or WMH (cut-off for log-transformed WMH volume at 50th percentile; N−WMH+) manifested increased plasma NfL levels. Participants with both pathologies (N+WMH+) showed the highest NfL compared to N+WMH−, N−WMH+, and N−WMH− individuals. Discussion: Plasma NfL has potential utility in stratifying individual and combined contributions of AD pathology and CSVD to cognitive impairment

Clustering of Alzheimer's and Parkinson's disease based on genetic burden of shared molecular mechanisms

One of the visions of precision medicine has been to re-define disease taxonomies based on molecular characteristics rather than on phenotypic evidence. However, achieving this goal is highly challenging, specifically in neurology. Our contribution is a machine-learning based joint molecular subtyping of Alzheimer’s (AD) and Parkinson’s Disease (PD), based on the genetic burden of 15 molecular mechanisms comprising 27 proteins (e.g. APOE) that have been described in both diseases. We demonstrate that our joint AD/PD clustering using a combination of sparse autoencoders and sparse non-negative matrix factorization is reproducible and can be associated with significant differences of AD and PD patient subgroups on a clinical, pathophysiological and molecular level. Hence, clusters are disease-associated. To our knowledge this work is the first demonstration of a mechanism based stratification in the field of neurodegenerative diseases. Overall, we thus see this work as an important step towards a molecular mechanism-based taxonomy of neurological disorders, which could help in developing better targeted therapies in the future by going beyond classical phenotype based disease definitions

Changes in the incidence of occupational disability as a result of back and neck pain in the Netherlands

BACKGROUND: Back pain (including neck pain) is one of the most prevalent health problems for which physicians are consulted. Back pain can decrease the quality of life considerably during a great part of the lives of those who suffer from it. At the same time it has an enormous economic impact, mainly through sickness absence and long-term disability. The objective of this paper is to compare the incidence of occupational disability as a result of back and neck pain in 1980–1985 to 1999–2000 and to explain the findings. METHODS: A descriptive study was performed at population level of changes in incidence of occupational disability as a result of back and neck pain. Statistics from the National Institute of Social Insurance in the Netherlands are used to calculate age and gender specific incidence rates for back pain diagnoses based on the ICD-classification. Incidence rate ratios stratified according to gender and adjusted for age were calculated to indicate changes over time. RESULTS: The incidence of occupational disability as a result of back pain decreased significantly by 37% (95% CI 37%–38%) in men and with 21% (95% CI 20%–24%) in women, after adjustment for age. For overall occupational disability as a result of all diagnoses this was 18% (95% CI 18%–19%) and 34% (95% CI 33%–35%) respectively. Changes were not homogeneous over diagnostic subcategories and age groups. Spondylosis decreased most in men by 59% (95% CI 57%–61%). The incidence of non-specific back pain and neck pain increased most by 196% (95% CI 164%–215%). Post-laminectomy syndrome increased over all age categories both for men (85%, 95% CI 61%–113%) and women (113%, 95% CI 65%–179%). CONCLUSION: The decrease in occupational disability as a result of back pain was larger than the decrease in occupational disability over all diagnoses. However, time trends were not homogeneous over age-, nor over sex- nor back pain categories. Most of this decrease was due to general changes such as legal and economic changes. One of several additional explanations for a decrease is the changed view on management of back pain

MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi) automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.This study was financially supported by IMDI Grant 104002002 (Brainbox) from ZonMw, the Netherlands Organisation for Health Research and Development, within kind sponsoring by Philips, the University Medical Center Utrecht, and Eindhoven University of Technology. The authors would like to acknowledge the following members of the Utrecht Vascular Cognitive Impairment Study Group who were not included as coauthors of this paper but were involved in the recruitment of study participants and MRI acquisition at the UMC Utrecht (in alphabetical order by department): E. van den Berg, M. Brundel, S. Heringa, and L. J. Kappelle of the Department of Neurology, P. R. Luijten and W. P. Th. M. Mali of the Department of Radiology, and A. Algra and G. E. H. M. Rutten of the Julius Center for Health Sciences and Primary Care. The research of Geert Jan Biessels and the VCI group was financially supported by VIDI Grant 91711384 from ZonMw and by Grant 2010T073 of the Netherlands Heart Foundation. The research of Jeroen de Bresser is financially supported by a research talent fellowship of the University Medical Center Utrecht (Netherlands). The research of Annegreet van Opbroek and Marleen de Bruijne is financially supported by a research grant from NWO (the Netherlands Organisation for Scientific Research). The authors would like to acknowledge MeVis Medical Solutions AG (Bremen, Germany) for providing MeVisLab. Duygu Sarikaya and Liang Zhao acknowledge their Advisor Professor Jason Corso for his guidance. Duygu Sarikaya is supported by NIH 1 R21CA160825-01 and Liang Zhao is partially supported by the China Scholarship Council (CSC).info:eu-repo/semantics/publishedVersio

Deep learning for clustering of multivariate clinical patient trajectories with missing values

BACKGROUND: Precision medicine requires a stratification of patients by disease presentation that is sufficiently informative to allow for selecting treatments on a per-patient basis. For many diseases, such as neurological disorders, this stratification problem translates into a complex problem of clustering multivariate and relatively short time series because (i) these diseases are multifactorial and not well described by single clinical outcome variables and (ii) disease progression needs to be monitored over time. Additionally, clinical data often additionally are hindered by the presence of many missing values, further complicating any clustering attempts. FINDINGS: The problem of clustering multivariate short time series with many missing values is generally not well addressed in the literature. In this work, we propose a deep learning-based method to address this issue, variational deep embedding with recurrence (VaDER). VaDER relies on a Gaussian mixture variational autoencoder framework, which is further extended to (i) model multivariate time series and (ii) directly deal with missing values. We validated VaDER by accurately recovering clusters from simulated and benchmark data with known ground truth clustering, while varying the degree of missingness. We then used VaDER to successfully stratify patients with Alzheimer disease and patients with Parkinson disease into subgroups characterized by clinically divergent disease progression profiles. Additional analyses demonstrated that these clinical differences reflected known underlying aspects of Alzheimer disease and Parkinson disease. CONCLUSIONS: We believe our results show that VaDER can be of great value for future efforts in patient stratification, and multivariate time-series clustering in general