Depressive symptoms in asymptomatic stage B heart failure with Type II diabetic mellitus.

BackgroundThe presence of concomitant Type II diabetic mellitus (T2DM) and depressive symptoms adversely affects individuals with symptomatic heart failure (HF).HypothesisIn presymptomatic stage B HF, this study hypothesized the presence of greater inflammation and depressive symptoms in T2DM as compared to non-T2DM Stage B patients.MethodsThis cross-sectional study examined clinical parameters, inflammatory biomarkers, and depressive symptoms in 349 T2DM and non-T2DM men with asymptomatic stage B HF (mean age 66.4 years ±10.1; range 30-91).ResultsFewer diabetic HF patients had left ventricular (LV) systolic dysfunction (P < .05) although more had LV diastolic dysfunction (P < .001). A higher percentage of T2DM HF patients were taking ACE-inhibitors, beta-blockers, calcium channel blockers, statins, and diuretics (P values < .05). T2DM HF patients had higher circulating levels of interleukin-6 (IL-6) (P < .01), tumor necrosis factor-alpha (P < .01), and soluble ST2 (sST2) (P < .01) and reported more somatic/affective depressive symptoms (Beck Depression Inventory II) (P < .05) but not cognitive/affective depressive symptoms (P = .20). Among all patients, in a multiple regression analysis predicting presence of somatic/affective depressive symptoms, sST2 (P = .026), IL-6 (P = .010), B-type natriuretic peptide (P = .016), and sleep (Pittsburgh Sleep Quality Index [P < .001]) were significant predictors (overall model F = 15.39, P < .001, adjusted R2 = .207).ConclusionsSomatic/affective but not cognitive/affective depressive symptoms are elevated in asymptomatic HF patients with T2DM patients. Linkages with elevated inflammatory and cardiac relevant biomarkers suggest shared pathophysiological mechanisms among T2DM HF patients with somatic depression, and these conditions are responsive to routine interventions, including behavioral. Copyright © 2019 John Wiley & Sons, Ltd

Associations between disease severity, coping and dimensions of health-related quality of life in patients admitted for elective coronary angiography – a cross sectional study

<p>Abstract</p> <p>Background</p> <p>In patients with suspected coronary artery disease (CAD), the overall aim was to analyse the relationships between disease severity and both mental and physical dimensions of health related quality of life (HRQOL) using a modified version of the Wilson and Cleary model.</p> <p>Methods</p> <p>Using a cross-sectional design, 753 patients (74% men), mean age 62 years, referred for elective cardiac catheterisation were included. The measures included 1) physiological factors 2) symptoms (disease severity, self-reported symptoms, anxiety and depression 3) self-reported functional status, 4) coping, 5) perceived disease burden, 6) general health perception and 7) overall quality of life. To analyse relationships, we performed linear and ordinal logistic regressions.</p> <p>Results</p> <p>CAD and left ventricular ejection fraction (LVEF) were significantly associated with symptoms of angina pectoris and dyspnea. CAD was not related to symptoms of anxiety and depression, but less depression was found in patients with low LVEF. Angina pectoris and dyspnea were both associated with impaired physical function, and dyspnea was also negatively related to social function. Overall, less perceived burden and better overall QOL were observed in patients using more confronting coping strategy.</p> <p>Conclusion</p> <p>The present study demonstrated that data from cardiac patients to a large extent support the suggested model by Wilson and Cleary.</p

Risk of antidepressant initiation among users of cardiovascular agents and metformin. Findings from the Trøndelag Health Study (HUNT) and Norwegian Prescription Database (NorPD), Norway

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Geographic transmission hubs of the 2009 influenza pandemic in the United States

A key issue in infectious disease epidemiology is to identify and predict geographic sites of epidemic establishment that contribute to onward spread, especially in the context of invasion waves of emerging pathogens. Conventional wisdom suggests that these sites are likely to be in densely-populated, well-connected areas. For pandemic influenza, however, epidemiological data have not been available at a fine enough geographic resolution to test this assumption. Here, we make use of fine-scale influenza-like illness incidence data derived from electronic medical claims records gathered from 834 3-digit ZIP (postal) codes across the US to identify the key geographic establishment sites, or “hubs”, of the autumn wave of the 2009 A/H1N1pdm influenza pandemic in the United States. A mechanistic spatial transmission model is fit to epidemic onset times inferred from the data. Hubs are identified by tracing the most probable transmission routes back to a likely first establishment site. Four hubs are identified: two in the southeastern US, one in the central valley of California, and one in the midwestern US. According to the model, 75% of the 834 observed ZIP-level outbreaks in the US were seeded by these four hubs or their epidemiological descendants. Counter-intuitively, the pandemic hubs do not coincide with large and well-connected cities, indicating that factors beyond population density and travel volume are necessary to explain the establishment sites of the major autumn wave of the pandemic. Geographic regions are identified where infection can be statistically traced back to a hub, providing a testable prediction of the outbreak's phylogeography. Our method therefore provides an important way forward to reconcile spatial diffusion patterns inferred from epidemiological surveillance data and pathogen sequence data. Keywords: Pandemic influenza, Transmission hubs, Metapopulation, Gravity model, Phylogeograph

Mental Health, Academic Self-Efficacy and Study Progress Among College Students – The SHoT Study, Norway

Student life can be stressful and for some students it may cause mental distress. Besides being a major public health challenge, mental distress can influence academic achievement. The main objectives of the current study were to examine associations of mental distress with academic self-efficacy and study progress. A secondary aim was to examine mental health help seeking for students with mental distress. Data was derived from the Norwegian Students’ health and welfare survey 2014 (SHOT 2014) which is the first major survey comprising questions of both mental health, academic self-efficacy and psychosocial factors amongst students. Utilizing these data for a Norwegian region, we found that 749 (31%) of the 2430 Norwegian full-time students under the age of 35 responded to the survey. Symptoms of mental distress were measured using the Hopkins Symptom Checklist (HSCL-25) and academic self-efficacy was measured using a Norwegian version of the General Self-Efficacy Scale (GSE) tailored to the academic setting. Demographic-, social, lifestyle, and study-related variables were included in the analyses. Logistic regression analyses were performed to assess the relationship between mental distress, academic self-efficacy, and academic performance. Seventeen percent reported severe symptoms of psychological distress which is similar to the overall prevalence among students in Norway. Students reporting severe mental distress were four times as likely to report low academic self-efficacy and twice as likely to report delayed study progress compared to students reporting few or moderate symptoms of mental distress. 27% of those reporting severe mental distress had sought professional help whereas 31% had considered seeking help. The study showed that there was a strong association between symptoms of mental distress, academic self-efficacy and study progress. Prospective studies should evaluate whether improved help-seeking and psychological treatment can promote students mental health and ultimately improve academic self-efficacy and study progress

Spatial Transmission of 2009 Pandemic Influenza in the US

The 2009 H1N1 influenza pandemic provides a unique opportunity for detailed examination of the spatial dynamics of an emerging pathogen. In the US, the pandemic was characterized by substantial geographical heterogeneity: the 2009 spring wave was limited mainly to northeastern cities while the larger fall wave affected the whole country. Here we use finely resolved spatial and temporal influenza disease data based on electronic medical claims to explore the spread of the fall pandemic wave across 271 US cities and associated suburban areas. We document a clear spatial pattern in the timing of onset of the fall wave, starting in southeastern cities and spreading outwards over a period of three months. We use mechanistic models to tease apart the external factors associated with the timing of the fall wave arrival: differential seeding events linked to demographic factors, school opening dates, absolute humidity, prior immunity from the spring wave, spatial diffusion, and their interactions. Although the onset of the fall wave was correlated with school openings as previously reported, models including spatial spread alone resulted in better fit. The best model had a combination of the two. Absolute humidity or prior exposure during the spring wave did not improve the fit and population size only played a weak role. In conclusion, the protracted spread of pandemic influenza in fall 2009 in the US was dominated by short-distance spatial spread partially catalysed by school openings rather than long-distance transmission events. This is in contrast to the rapid hierarchical transmission patterns previously described for seasonal influenza. The findings underline the critical role that school-age children play in facilitating the geographic spread of pandemic influenza and highlight the need for further information on the movement and mixing patterns of this age group

Measuring Inequalities in the Distribution of Health Workers: The case of Tanzania.

The overall human resource shortages and the distributional inequalities in the health workforce in many developing countries are well acknowledged. However, little has been done to measure the degree of inequality systematically. Moreover, few attempts have been made to analyse the implications of using alternative measures of health care needs in the measurement of health workforce distributional inequalities. Most studies have implicitly relied on population levels as the only criterion for measuring health care needs. This paper attempts to achieve two objectives. First, it describes and measures health worker distributional inequalities in Tanzania on a per capita basis; second, it suggests and applies additional health care needs indicators in the measurement of distributional inequalities. We plotted Lorenz and concentration curves to illustrate graphically the distribution of the total health workforce and the cadre-specific (skill mix) distributions. Alternative indicators of health care needs were illustrated by concentration curves. Inequalities were measured by calculating Gini and concentration indices.\ud There are significant inequalities in the distribution of health workers per capita. Overall, the population quintile with the fewest health workers per capita accounts for only 8% of all health workers, while the quintile with the most health workers accounts for 46%. Inequality is perceptible across both urban and rural districts. Skill mix inequalities are also large. Districts with a small share of the health workforce (relative to their population levels have an even smaller share of highly trained medical personnel. A small share of highly trained personnel is compensated by a larger share of clinical officers (a middle-level cadre) but not by a larger share of untrained health workers. Clinical officers are relatively equally distributed. Distributional inequalities tend to be more pronounced when under-five deaths are used as an indicator of health care needs. Conversely, if health care needs are measured by HIV prevalence, the distributional inequalities appear to decline. The measure of inequality in the distribution of the health workforce may depend strongly on the underlying measure of health care needs. In cases of a non-uniform distribution of health care needs across geographical areas, other measures of health care needs than population levels may have to be developed in order to ensure a more meaningful measurement of distributional inequalities of the health workforce

The Chromosome 9p21 CVD-and T2D-Associated Regions in a Norwegian Population (The HUNT2 Survey)

Background. Two adjacent regions upstream CDKN2B on chromosome 9p21 have been associated with type 2 diabetes (T2D) and progression of cardiovascular disease (CVD). The precise location and number of risk variants have not been completely delineated and a possible synergistic relationship between the adjacent regions is not fully addressed. By a population based cross-sectional case-control design, we genotyped 18 SNPs upstream of CDKN2B tagging 138 kb in and around two LD-blocks associated with CVD and T2D and investigated associations with T2D, angina pectoris (AP), myocardial infarction (MI), coronary heart disease (CHD; AP or AMI), and stroke using 5,564 subjects from HUNT2. Results. Single point and haplotype analysis showed evidence for only one common T2D risk haplotype (rs10757282|rs10811661: OR = 1.19, = 2.0 × 10 −3 ) in the region. We confirmed the strong association between SNPs in the 60 kb CVD region with AP, MI, and CHD ( &lt; 0.01). Conditioning on the lead SNPs in the region, we observed two suggestive independent single SNP association signals for MI, rs2065501 ( = 0.03) and rs3217986 ( = 0.04). Conclusions. We confirmed the association of known variants within the 9p21 interval with T2D and CHD. Our results further suggest that additional CHD susceptibility variants exist in this region

Multivariate Statistical Analyses Demonstrate Unique Host Immune Responses to Single and Dual Lentiviral Infection

Feline immunodeficiency virus (FIV) and human immunodeficiency virus (HIV) are recently identified lentiviruses that cause progressive immune decline and ultimately death in infected cats and humans. It is of great interest to understand how to prevent immune system collapse caused by these lentiviruses. We recently described that disease caused by a virulent FIV strain in cats can be attenuated if animals are first infected with a feline immunodeficiency virus derived from a wild cougar. The detailed temporal tracking of cat immunological parameters in response to two viral infections resulted in high-dimensional datasets containing variables that exhibit strong co-variation. Initial analyses of these complex data using univariate statistical techniques did not account for interactions among immunological response variables and therefore potentially obscured significant effects between infection state and immunological parameters.Here, we apply a suite of multivariate statistical tools, including Principal Component Analysis, MANOVA and Linear Discriminant Analysis, to temporal immunological data resulting from FIV superinfection in domestic cats. We investigated the co-variation among immunological responses, the differences in immune parameters among four groups of five cats each (uninfected, single and dual infected animals), and the "immune profiles" that discriminate among them over the first four weeks following superinfection. Dual infected cats mount an immune response by 24 days post superinfection that is characterized by elevated levels of CD8 and CD25 cells and increased expression of IL4 and IFNgamma, and FAS. This profile discriminates dual infected cats from cats infected with FIV alone, which show high IL-10 and lower numbers of CD8 and CD25 cells.Multivariate statistical analyses demonstrate both the dynamic nature of the immune response to FIV single and dual infection and the development of a unique immunological profile in dual infected cats, which are protected from immune decline