Rate and predictors for non-attendance of patients undergoing hospital outpatient treatment for chronic diseases: a register-based cohort study.

Failure to keep medical appointments results in inefficiencies and, potentially, in poor outcomes for patients. The aim of this study is to describe non-attendance rate and to investigate predictors of non-attendance among patients receiving hospital outpatient treatment for chronic diseases. We conducted a historic, register-based cohort study using data from a regional hospital and included patients aged 18 years or over who were registered in ongoing outpatient treatment courses for seven selected chronic diseases on July 1, 2013. A total of 5895 patients were included and information about their appointments was extracted from the period between July 1, 2013 and June 30, 2015. The outcome measure was occurrence of non-attendance. The associations between non-attendance and covariates (age, gender, marital status, education level, occupational status, specific chronic disease and number of outpatient treatment courses) were investigated using multivariate logistic regression models, including mixed effect. During the two-year period, 35% of all patients (2057 of 5895 patients) had one or more occurrences of non-attendance and 5% of all appointments (4393 of 82,989 appointments) resulted in non-attendance. Significant predictors for non-attendance were younger age (OR 4.17 for 18 ≤ 29 years as opposed to 80+ years), male gender (OR 1.35), unmarried status (OR 1.39), low educational level (OR 1.18) and receipt of long-term welfare payments (OR 1.48). Neither specific diseases nor number of treatment courses were associated with a higher non-attendance rate. Patients undergoing hospital outpatient treatments for chronic diseases had a non-attendance rate of 5%. We found several predictors for non-attendance but undergoing treatment for several chronic diseases simultaneously was not a predictor. To reduce non-attendance, initiatives could target the groups at risk. This study was approved by the Danish Data Protection Agency (Project ID 18/35695 )

Geographical and temporal distribution of SARS-CoV-2 clades in the WHO European Region, January to June 2020

We show the distribution of SARS-CoV-2 genetic clades over time and between countries and outline potential genomic surveillance objectives. We applied three available genomic nomenclature systems for SARS-CoV-2 to all sequence data from the WHO European Region available during the COVID-19 pandemic until 10 July 2020. We highlight the importance of real-time sequencing and data dissemination in a pandemic situation. We provide a comparison of the nomenclatures and lay a foundation for future European genomic surveillance of SARS-CoV-2.Peer reviewe

Effects of eight neuropsychiatric copy number variants on human brain structure

peer reviewedMany copy number variants (CNVs) confer risk for the same range of neurodevelopmental symptoms and psychiatric conditions including autism and schizophrenia. Yet, to date neuroimaging studies have typically been carried out one mutation at a time, showing that CNVs have large effects on brain anatomy. Here, we aimed to characterize and quantify the distinct brain morphometry effects and latent dimensions across 8 neuropsychiatric CNVs. We analyzed T1-weighted MRI data from clinically and non-clinically ascertained CNV carriers (deletion/duplication) at the 1q21.1 (n = 39/28), 16p11.2 (n = 87/78), 22q11.2 (n = 75/30), and 15q11.2 (n = 72/76) loci as well as 1296 non-carriers (controls). Case-control contrasts of all examined genomic loci demonstrated effects on brain anatomy, with deletions and duplications showing mirror effects at the global and regional levels. Although CNVs mainly showed distinct brain patterns, principal component analysis (PCA) loaded subsets of CNVs on two latent brain dimensions, which explained 32 and 29% of the variance of the 8 Cohen’s d maps. The cingulate gyrus, insula, supplementary motor cortex, and cerebellum were identified by PCA and multi-view pattern learning as top regions contributing to latent dimension shared across subsets of CNVs. The large proportion of distinct CNV effects on brain morphology may explain the small neuroimaging effect sizes reported in polygenic psychiatric conditions. Nevertheless, latent gene brain morphology dimensions will help subgroup the rapidly expanding landscape of neuropsychiatric variants and dissect the heterogeneity of idiopathic conditions. © 2021, The Author(s)

The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape : A Large-Scale Genome-Wide Interaction Study

Genome-wide association studies (GWAS) have identified more than 100 genetic variants contributing to BMI, a measure of body size, or waist-to-hip ratio (adjusted for BMI, WHRadjBMI), a measure of body shape. Body size and shape change as people grow older and these changes differ substantially between men and women. To systematically screen for age-and/or sex-specific effects of genetic variants on BMI and WHRadjBMI, we performed meta-analyses of 114 studies (up to 320,485 individuals of European descent) with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Each study tested the association of up to similar to 2.8M SNPs with BMI and WHRadjBMI in four strata (men 50y, women 50y) and summary statistics were combined in stratum-specific meta-analyses. We then screened for variants that showed age-specific effects (G x AGE), sex-specific effects (G x SEX) or age-specific effects that differed between men and women (G x AGE x SEX). For BMI, we identified 15 loci (11 previously established for main effects, four novel) that showed significant (FDR= 50y). No sex-dependent effects were identified for BMI. For WHRadjBMI, we identified 44 loci (27 previously established for main effects, 17 novel) with sex-specific effects, of which 28 showed larger effects in women than in men, five showed larger effects in men than in women, and 11 showed opposite effects between sexes. No age-dependent effects were identified for WHRadjBMI. This is the first genome-wide interaction meta-analysis to report convincing evidence of age-dependent genetic effects on BMI. In addition, we confirm the sex-specificity of genetic effects on WHRadjBMI. These results may providefurther insights into the biology that underlies weight change with age or the sexually dimorphism of body shape.Peer reviewe