Higher chronic stress and less satisfaction with GP communication in migrants with unemployment experience: data from the representative German DEGS1 and the GPCare-1 study.

BACKGROUND: The impact of unemployment on health is well studied. However, information on associations of unemployment, migration background and general practitioner-patient communication is scarce. METHODS: Data from the representative German Health Interview and Examination Survey for Adults (DEGS1) of individuals in working age (n = 5938) were analysed stratified by unemployment and migration background. Using official weighting factors, the prevalence of chronic stress, having ≥1 chronic disease, having a GP and GP visits in the last 12 months was determined. Multivariate regression models were analysed for associations between unemployment, migration background, and other socio-demographic characteristics with GP visits and chronic stress. Data from the General Practice Care-1 (GPCare-1) study (n = 813 patients) were analysed for differences in patient-physician communication between unemployed with and without migration background. Reverse proportional odds models were estimated for associations of unemployment and migration background with physician-patient communication. RESULTS: In the DEGS1, 21.5% had experienced unemployment (n = 1170). Of these, 31.6% had a migration background (n = 248). Compared to unemployed natives, unemployed with migration background had higher chronic stress (mean: 14.32 vs. 13.13, p = 0.02), while the prevalence of chronic disease was lower (21.7% vs. 30.2%, p = 0.03). They were less likely to have a GP (83.6% vs. 90%, p = 0.02), while GP visits were similar (mean: 3.7 vs. 3.3, p = 0.26). Migration background and unemployment experience were not associated with GP visits, while both factors were significantly associated with higher chronic stress (both: p < 0.01). In GPCare-1, 28.8% had ever experienced unemployment (n = 215). Of these, 60 had a migration background (28.6%). The unemployed with migration background reported less frequently that the GP gives them enough space to describe personal strains (46.5% vs. 58.2%; p = 0.03), and that their problems are taken very seriously by their GP (50.8% vs. 73.8%; p = 0.04). In multivariate analyses, migration background showed a lower probability of having enough space to describe personal strains and feeling that problems were taken very seriously. CONCLUSION: Unemployment experience and migration background were associated with higher chronic stress. Only migration background was associated with less satisfaction regarding physician-patient communication.

More GP contacts and poorer health of informal caregivers with low socioeconomic status in Germany: results from the population-based DEGS1 and the cross-sectional GPCare-1 study.

OBJECTIVES: Informal caregivers are known to have poorer mental health. Risk factors for caregiver burden include low education, female gender, cohabitation with the care recipient and lack of resources. General practitioners (GPs) have an important role in supporting caregivers. Drawing on data from two surveys, associations between caregivers' socioeconomic status (SES), psychophysical health and GP contacts are analysed. DESIGN: Cross-sectional study. The study draws on data from two surveys (German Health Interview and Examination Survey for Adults, DEGS1 and General Practice Care-1, GPCare-1). SETTING: Germany. PARTICIPANTS: DEGS1: German general population (18+ years) n=7987. GPCare-1: general practice patients (18+ years) n=813. PRIMARY OUTCOME: Psychophysical health, GP contacts and communication. METHODS: Using representative DEGS1 data, the prevalence of informal caregivers, caregivers' burden, chronic stress, various health conditions and frequency of GP contacts were evaluated stratified by SES. Data from the GPCare-1 study addressed caregivers' experiences and communication preferences with GPs. RESULTS: In the DEGS1, the prevalence of caregivers was 6.5%. Compared with non-caregivers, caregivers scored significantly higher for chronic stress (15.45 vs 11.90), self-reported poor health (37.6% vs 23.7%) and GP visits last year (3.95 vs 3.11), while lifestyle and chronic diseases were similar. Compared with caregivers with medium/high SES, those with low SES had a significantly lower prevalence of high/medium caregiver burden (47.9% vs 67.7%) but poorer self-reported health (56.9% vs 33.0%), while other characteristics did not differ. In the GPCare-1 study, the prevalence of caregivers was 12.6%. The majority of them felt that their GP takes their problems seriously (63.6%) without difference by SES. CONCLUSION: Caregivers with low SES constitute an especially high-risk group for psychological strain, requiring special GP attention to support their needs.

FatSegNet: A fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon MRI.

PURPOSE: Introduce and validate a novel, fast, and fully automated deep learning pipeline (FatSegNet) to accurately identify, segment, and quantify visceral and subcutaneous adipose tissue (VAT and SAT) within a consistent, anatomically defined abdominal region on Dixon MRI scans. METHODS: FatSegNet is composed of three stages: (a) Consistent localization of the abdominal region using two 2D-Competitive Dense Fully Convolutional Networks (CDFNet), (b) Segmentation of adipose tissue on three views by independent CDFNets, and (c) View aggregation. FatSegNet is validated by: (1) comparison of segmentation accuracy (sixfold cross-validation), (2) test-retest reliability, (3) generalizability to randomly selected manually re-edited cases, and (4) replication of age and sex effects in the Rhineland Study-a large prospective population cohort. RESULTS: The CDFNet demonstrates increased accuracy and robustness compared to traditional deep learning networks. FatSegNet Dice score outperforms manual raters on VAT (0.850 vs. 0.788) and produces comparable results on SAT (0.975 vs. 0.982). The pipeline has excellent agreement for both test-retest (ICC VAT 0.998 and SAT 0.996) and manual re-editing (ICC VAT 0.999 and SAT 0.999). CONCLUSIONS: FatSegNet generalizes well to different body shapes, sensitively replicates known VAT and SAT volume effects in a large cohort study and permits localized analysis of fat compartments. Furthermore, it can reliably analyze a 3D Dixon MRI in ∼1 minute, providing an efficient and validated pipeline for abdominal adipose tissue analysis in the Rhineland Study.