The Excess Costs of Depression and the Influence of Sociodemographic and Socioeconomic Factors: Results from the German Health Interview and Examination Survey for Adults (DEGS)

Introduction The aim of this study was to estimate excess costs of depression in Germany and to examine the influence of sociodemographic and socioeconomic determinants. Methods Annual excess costs of depression per patient were estimated for the year 2019 by comparing survey data of individuals with and without self-reported medically diagnosed depression, representative for the German population aged 18–79 years. Differences between individuals with depression (n = 223) and without depression (n = 4540) were adjusted using entropy balancing. Excess costs were estimated using generalized linear model regression with a gamma distribution and log-link function. We estimated direct (inpatient, outpatient, medication) and indirect (sick leave, early retirement) excess costs. Subgroup analyses by social determinants were conducted for sex, age, socioeconomic status, first-generation or second-generation migrants, partnership, and social support. Results Total annual excess costs of depression amounted to €5047 (95% confidence interval [CI] 3214–6880) per patient. Indirect excess costs amounted to €2835 (1566–4103) and were higher than direct excess costs (€2212 [1083–3341]). Outpatient (€498), inpatient (€1345), early retirement (€1686), and sick leave (€1149) excess costs were statistically significant, while medication (€370) excess costs were not. Regarding social determinants, total excess costs were highest in the younger age groups (€7955 for 18–29-year-olds, €9560 for 30–44-year-olds), whereas total excess costs were lowest for the oldest age group (€2168 for 65+) and first-generation or second-generation migrants (€1820). Conclusions Depression was associated with high excess costs that varied by social determinants. Considerable differences between the socioeconomic and sociodemographic subgroups need further clarification as they point to specific treatment barriers as well as varying treatment needs.Peer Reviewe

Data analytics for project delivery : unlocking the potential of an emerging field

Purpose: In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research. Design/methodology/approach: We propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice. Findings: The literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples. Originality/value: Based on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management

Modelling the penumbra in computed tomography

Background: In computed tomography (CT), the spot geometry is one of the main sources of error in CT images. Since X-rays do not arise from a point source, artefacts are produced. In particular there is a penumbra effect, leading to poorly defined edges within a reconstructed volume. Penumbra models can be simulated given a fixed spot geometry and the known experimental setup. Objective: This paper proposes to use a penumbra model, derived from Beer’s law, both to confirm spot geometry from penumbra data, and to quantify blurring in the image. Methods: Two models for the spot geometry are considered; one consists of a single Gaussian spot, the other is a mixture model consisting of a Gaussian spot together with a larger uniform spot. Results: The model consisting of a single Gaussian spot has a poor fit at the boundary. The mixture model (which adds a larger uniform spot) exhibits a much improved fit. The parameters corresponding to the uniform spot are similar across all powers, and further experiments suggest that the uniform spot produces only soft X-rays of relatively low-energy. Conclusions: Thus, the precision of radiographs can be estimated from the penumbra effect in the image. The use of a thin copper filter reduces the size of the effective penumbra

An assessment of candidate genes to assist prognosis in gastric cancer

Gastric cancer (GC) is the fourth commonest cancer worldwide, with the second highest mortality rate. Its poor mortality is linked to delayed presentation. There is a drive towards non-invasive biomarker screening and monitoring of many different types of cancer, although with limited success so far. We aimed to determine if any genes from a 32-gene panel could be used to determine GC prognosis. We carried out a retrospective study on the expression of 32 genes, selected for their proven or potential links to GC, on historic formalin fixed paraffin-embedded (FFPE) GC specimens from our unit. Gene expression was measured using quantitative nuclease protection assays (qNPA) technology. Following statistical analysis of the results, immunohistochemical staining for eight genes, both discriminating and non-discriminating, was conducted in seven age and sex matched non-metastatic: metastatic GC pairings. The stained samples were reviewed by two blinded consultant histopathologists. Multivariate Cox analysis of the gene expression data revealed metastatic status, age, sex and five genes appeared to influence GC survival. Genes negatively influencing survival included and (relative risks 2.20, 3.73 and 7.53 respectively). Genes conveying survival benefit included and (relative risks 0.10 and 0.24 respectively). Immunohistochemical staining of seven age and sex matched non-metastatic: metastatic pairs revealed no association between gene expression and protein expression. Our study found several genes whose expression may affect GC prognosis. However, immunohistochemical analysis revealed no association between gene expression and protein expression. It remains to be determined whether gene expression or protein expression are reliable means of assessing GC prognosis

Tele-monitoring of cancer patients’ rhythms during daily life identifies actionable determinants of circadian and sleep disruption

The dichotomy index (I < O), a quantitative estimate of the circadian regulation of daytime activity and sleep, predicted overall cancer survival and emergency hospitalization, supporting its integration in a mHealth platform. Modifiable causes of I < O deterioration below 97.5%—(I < O)low—were sought in 25 gastrointestinal cancer patients and 33 age- and sex-stratified controls. Rest-activity and temperature were tele-monitored with a wireless chest sensor, while daily activities, meals, and sleep were self-reported for one week. Salivary cortisol rhythm and dim light melatonin onset (DLMO) were determined. Circadian parameters were estimated using Hidden Markov modelling, and spectral analysis. Actionable predictors of (I < O)low were identified through correlation and regression analyses. Median compliance with protocol exceeded 95%. Circadian disruption—(I < O)low—was identified in 13 (52%) patients and four (12%) controls (p = 0.002). Cancer patients with (I < O)low had lower median activity counts, worse fragmented sleep, and an abnormal or no circadian temperature rhythm compared to patients with I < O exceeding 97.5%—(I < O)high—(p < 0.012). Six (I < O)low patients had newly-diagnosed sleep conditions. Altered circadian coordination of rest-activity and chest surface temperature, physical inactivity, and irregular sleep were identified as modifiable determinants of (I < O)low. Circadian rhythm and sleep tele-monitoring results support the design of specific interventions to improve outcomes within a patient-centered systems approach to health care

Genomic Regions Associated with Multiple Sclerosis Are Active in B Cells

More than 50 genomic regions have now been shown to influence the risk of multiple sclerosis (MS). However, the mechanisms of action, and the cell types in which these associated variants act at the molecular level remain largely unknown. This is especially true for associated regions containing no known genes. Given the evidence for a role for B cells in MS, we hypothesized that MS associated genomic regions co-localized with regions which are functionally active in B cells. We used publicly available data on 1) MS associated regions and single nucleotide polymorphisms (SNPs) and 2) chromatin profiling in B cells as well as three additional cell types thought to be unrelated to MS (hepatocytes, fibroblasts and keratinocytes). Genomic intervals and SNPs were tested for overlap using the Genomic Hyperbrowser. We found that MS associated regions are significantly enriched in strong enhancer, active promoter and strong transcribed regions (p = 0.00005) and that this overlap is significantly higher in B cells than control cells. In addition, MS associated SNPs also land in active promoter (p = 0.00005) and enhancer regions more than expected by chance (strong enhancer p = 0.0006; weak enhancer p = 0.00005). These results confirm the important role of the immune system and specifically B cells in MS and suggest that MS risk variants exert a gene regulatory role. Previous studies assessing MS risk variants in T cells may be missing important effects in B cells. Similar analyses in other immunological cell types relevant to MS and functional studies are necessary to fully elucidate how genes contribute to MS pathogenesis

Data management challenges for artificial intelligence in plant and agricultural research [version 2; peer review: 2 approved]

Artificial Intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful and usable ways to integrate, compare and visualise large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of Machine Learning (AI) which holds much promise for this domain