Compliance with the Swiss Society for Nutrition's dietary recommendations in the population of Geneva, Switzerland: a 10-year trend study (1999-2009).

The trends in compliance with the dietary recommendations of the Swiss Society for Nutrition in the Geneva population were assessed for the period from 1999 to 2009 using 10 cross-sectional, population-based surveys (Bus Santé study) with a total of 9,320 participants aged 35 to 75 years (50% women). Dietary intake was assessed using a self-administered, validated, semi-quantitative food frequency questionnaire. Trends were assessed by logistic regression adjusting for age, smoking status, education, and nationality using survey year as the independent variable. After excluding participants with extreme intakes, the percentage of participants with a cholesterol intake of <300 mg/day increased from 40.8% in 1999 to 43.6% in 2009 for men (multivariate-adjusted P for trend=0.04) and from 57.8% to 61.4% in women (multivariate-adjusted P for trend=0.06). Calcium intake >1 g/day decreased from 53.3% to 46% in men and from 47.6% to 40.7% in women (multivariate-adjusted P for trend<0.001). Adequate iron intake decreased from 68.3% to 65.3% in men and from 13.3% to 8.4% in women (multivariate-adjusted P for trend<0.001). Conversely, no significant changes were observed for carbohydrates, protein, total fat (including saturated, monounsaturated, and polyunsaturated fatty acids), fiber, and vitamins D and A. We conclude that the quality of the Swiss diet did not improve between 1999 and 2009 and that intakes deviate substantially from expert recommendations for health promotion and chronic disease risk reduction.

The Individualized Genetic Barrier Predicts Treatment Response in a Large Cohort of HIV-1 Infected Patients.

The success of combination antiretroviral therapy is limited by the evolutionary escape dynamics of HIV-1. We used Isotonic Conjunctive Bayesian Networks (I-CBNs), a class of probabilistic graphical models, to describe this process. We employed partial order constraints among viral resistance mutations, which give rise to a limited set of mutational pathways, and we modeled phenotypic drug resistance as monotonically increasing along any escape pathway. Using this model, the individualized genetic barrier (IGB) to each drug is derived as the probability of the virus not acquiring additional mutations that confer resistance. Drug-specific IGBs were combined to obtain the IGB to an entire regimen, which quantifies the virus' genetic potential for developing drug resistance under combination therapy. The IGB was tested as a predictor of therapeutic outcome using between 2,185 and 2,631 treatment change episodes of subtype B infected patients from the Swiss HIV Cohort Study Database, a large observational cohort. Using logistic regression, significant univariate predictors included most of the 18 drugs and single-drug IGBs, the IGB to the entire regimen, the expert rules-based genotypic susceptibility score (GSS), several individual mutations, and the peak viral load before treatment change. In the multivariate analysis, the only genotype-derived variables that remained significantly associated with virological success were GSS and, with 10-fold stronger association, IGB to regimen. When predicting suppression of viral load below 400 cps/ml, IGB outperformed GSS and also improved GSS-containing predictors significantly, but the difference was not significant for suppression below 50 cps/ml. Thus, the IGB to regimen is a novel data-derived predictor of treatment outcome that has potential to improve the interpretation of genotypic drug resistance tests.

Foods, nutrients and the risk of oral and pharyngeal cancer.

Background:Besides tobacco and alcohol, dietary habits may have a relevant role in oral cavity and pharyngeal (OCP) cancer.Methods:We analysed the role of selected food groups and nutrients on OCP cancer in a case-control study carried out between 1997 and 2009 in Italy and Switzerland. This included 768 incident, histologically confirmed squamous cell carcinoma cases and 2078 hospital controls. Odds ratios (ORs) were estimated using logistic regression models including terms for tobacco, alcohol and other relevant covariates.Results:Significant inverse trends in risk were observed for all vegetables (OR=0.19, for the highest vs the lowest consumption) and all fruits (OR=0.39), whereas significant direct associations were found for milk and dairy products (OR=1.50), eggs (OR=1.71), red meat (OR=1.55), potatoes (OR=1.85) and desserts (OR=1.68), although trends in risk were significant only for potatoes and desserts. With reference to nutrients, significant inverse relations were observed for vegetable protein (OR=0.45, for the highest vs the lowest quintile), vegetable fat (OR=0.54), polyunsaturated fatty acids (OR=0.53), α-carotene (OR=0.51), β-carotene (OR=0.28), β-cryptoxanthin (OR=0.37), lutein and zeazanthin (OR=0.34), vitamin E (OR=0.26), vitamin C (OR=0.40) and total folate (OR=0.34), whereas direct ones were observed for animal protein (OR=1.57), animal fat (OR=2.47), saturated fatty acids (OR=2.18), cholesterol (OR=2.29) and retinol (OR=1.88). Combinations of low consumption of fruits and vegetables, and high consumption of meat with high tobacco and alcohol, led to 10- to over 20-fold excess risk of OCP cancer.Conclusion:Our study confirms and further quantifies that a diet rich in fruits and vegetables and poor in meat and products of animal origin has a favourable role against OCP cancer.

Family history of cancer and the risk of cancer: a network of case-control studies.

BACKGROUND: The risk of many cancers is higher in subjects with a family history (FH) of cancer at a concordant site. However, few studies investigated FH of cancer at discordant sites. PATIENTS AND METHODS: This study is based on a network of Italian and Swiss case-control studies on 13 cancer sites conducted between 1991 and 2009, and including more than 12 000 cases and 11 000 controls. We collected information on history of any cancer in first degree relatives, and age at diagnosis. Odds ratios (ORs) for FH were calculated by multiple logistic regression models, adjusted for major confounding factors. RESULTS: All sites showed an excess risk in relation to FH of cancer at the same site. Increased risks were also found for oral and pharyngeal cancer and FH of laryngeal cancer (OR = 3.3), esophageal cancer and FH of oral and pharyngeal cancer (OR = 4.1), breast cancer and FH of colorectal cancer (OR = 1.5) and of hemolymphopoietic cancers (OR = 1.7), ovarian cancer and FH of breast cancer (OR = 2.3), and prostate cancer and FH of bladder cancer (OR = 3.4). For most cancer sites, the association with FH was stronger when the proband was affected at age <60 years. CONCLUSIONS: Our results point to several potential cancer syndromes that appear among close relatives and may indicate the presence of genetic factors influencing multiple cancer sites.

Derivation and validation of a prognostic model for pulmonary embolism.

RATIONALE: An objective and simple prognostic model for patients with pulmonary embolism could be helpful in guiding initial intensity of treatment. OBJECTIVES: To develop a clinical prediction rule that accurately classifies patients with pulmonary embolism into categories of increasing risk of mortality and other adverse medical outcomes. METHODS: We randomly allocated 15,531 inpatient discharges with pulmonary embolism from 186 Pennsylvania hospitals to derivation (67%) and internal validation (33%) samples. We derived our prediction rule using logistic regression with 30-day mortality as the primary outcome, and patient demographic and clinical data routinely available at presentation as potential predictor variables. We externally validated the rule in 221 inpatients with pulmonary embolism from Switzerland and France. MEASUREMENTS: We compared mortality and nonfatal adverse medical outcomes across the derivation and two validation samples. MAIN RESULTS: The prediction rule is based on 11 simple patient characteristics that were independently associated with mortality and stratifies patients with pulmonary embolism into five severity classes, with 30-day mortality rates of 0-1.6% in class I, 1.7-3.5% in class II, 3.2-7.1% in class III, 4.0-11.4% in class IV, and 10.0-24.5% in class V across the derivation and validation samples. Inpatient death and nonfatal complications were <or= 1.1% among patients in class I and <or= 1.9% among patients in class II. CONCLUSIONS: Our rule accurately classifies patients with pulmonary embolism into classes of increasing risk of mortality and other adverse medical outcomes. Further validation of the rule is important before its implementation as a decision aid to guide the initial management of patients with pulmonary embolism.

Extraintestinal manifestations in inflammatory bowel disease: frequency and associated risk factors in the nationwide Swiss IBD cohort study (SIBDCS)

Background: Data on the frequency of extraintestinal manifestations (EIM) in Crohnʼs disease (CD) and ulcerative colitis (UC) are scarce. Goal: to evaluate prevalences, forms of EIM and risk factors in a large nationwide IBD cohort. Methods: Data from validated physician enrolment questionnaires of the adult Swiss IBD cohort were analyzed. Logistic regression models were used to identify EIM risk factors. Results: 950 patients were included, 580 (61%) with CD (mean age 43yrs) and 370 (39%) with UC (mean age 49yrs), of these, 249 (43%) of CD and 113 (31%) of UC patients had one to 5 EIM. The following EIM were found: arthritis (CD 33%, UC 21%), aphthous stomatitis (CD 10%, UC 4%), uveitis (CD 6%, UC 4%), erythema nodosum (CD 6%, UC 3%), ankylosing spondylitis (CD 6%, UC 2%), psoriasis (CD 2%, UC 1%), pyoderma gangrenosum (CD and UC each 2%), primary sclerosing cholangitis (CD 1%, UC 4%). Logistic regression in CD identified the following items as risk factors for ongoing EIM: active disease (OR 1.95, 95% CI 1.17-3.23, P=0.01), positive IBD family history (OR 1.77, 95% CI 1.07-2.92, P=0.025). No risk factors were identified in UC patients. Conclusions: EIM are a frequent problem in CD and UC patients. Active disease and positive IBD family history are associated with ongoing EIM in CD patients. Identification of EIM prevalence and associated risk factors may result in increased awareness for this problem and thereby facilitate their diagnosis and management.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Chronic obstructive pulmonary disease in never-smoking animal farmers working inside confinement buildings

BACKGROUND: In animal farming, respiratory disease has been associated with indoor air contaminants and an excess in FEV1 decline. Our aim was to determine the characteristics and risk factors for chronic obstructive pulmonary disease (COPD) in never-smoking European farmers working inside animal confinement buildings. METHODS: A sample of participants in the European Farmers' Study was selected for a cross-sectional study assessing lung function and air contaminants. Dose-response relationships were assessed using logistic regression models. RESULTS: COPD was found in 18 of 105 farmers (45.1 SD 11.7 years) (17.1%); 8 cases (7.6%) with moderate and 3 cases (2.9%) with severe disease. Dust and endotoxin showed a dose-response relationship with COPD, with the highest prevalence of COPD in subjects with high dust (low=7.9%/high=31.6%) and endotoxin exposure (low=10.5%/high=20.0%). This association was statistically significant for dust in the multivariate analysis (OR 6.60, 95% CI 1.10-39.54). CONCLUSION: COPD in never-smoking animal farmers working inside confinement buildings is related to indoor dust exposure and may become severe. [Authors]

Finite mixture analysis of beauty-contest data using generalised beta distributions

This paper introduces a mixture model based on the beta distribution, without preestablishedmeans and variances, to analyze a large set of Beauty-Contest data obtainedfrom diverse groups of experiments (Bosch-Domenech et al. 2002). This model gives a bettert of the experimental data, and more precision to the hypothesis that a large proportionof individuals follow a common pattern of reasoning, described as iterated best reply (degenerate),than mixture models based on the normal distribution. The analysis shows thatthe means of the distributions across the groups of experiments are pretty stable, while theproportions of choices at dierent levels of reasoning vary across groups.

Tuberculosis screening in patients with psoriasis before antitumour necrosis factor therapy: comparison of an interferon-gamma release assay vs. tuberculin skin test.

BACKGROUND: Antitumour necrosis factor (anti-TNF) treatments may reactivate latent tuberculosis infection (LTBI). For detecting LTBI, the tuberculin skin test (TST) has low sensitivity and specificity. Interferon-gamma release assays (IGRA) have been shown to be more sensitive and specific than TST. OBJECTIVE: To compare the TST and the T-SPOT.TB IGRA for identifying LTBI in patients with psoriasis before anti-TNF treatment. METHODS: A retrospective study was carried out over a 4-year period on patients with psoriasis requiring anti-TNF treatment. All were subjected to the TST, T-SPOT.TB and chest X-ray. Risk factors for LTBI and history of bacillus Calmette-Guérin (BCG) vaccination were recorded. The association of T-SPOT.TB and TST results with risk factors for LTBI was tested through univariate logistic regression models. Agreement between tests was quantified using kappa statistics. Treatment for LTBI was started 1 month before anti-TNF therapy when indicated. RESULTS: Fifty patients were included; 90% had prior BCG vaccination. A positive T-SPOT.TB was strongly associated with a presumptive diagnosis of LTBI (odds ratio 7.43; 95% confidence interval 1.38-39.9), which was not the case for the TST. Agreement between the T-SPOT.TB and TST was poor, kappa = 0.33 (SD 0.13). LTBI was detected and treated in 20% of the patients. In 20% of the cases, LTBI was not retained in spite of a positive TST but a negative T-SPOT.TB. All patients received an anti-TNF agent for a median of 56 weeks (range 20-188); among patients with a positive TST/negative T-SPOT.TB, no tuberculosis was detected with a median follow-up of 64 weeks (44-188). One case of disseminated tuberculosis occurred after 28 weeks of adalimumab treatment in a patient with LTBI in spite of treatment with rifampicin. CONCLUSION: This study is the first to underline the frequency of LTBI in patients with psoriasis (20%), and to support the use of IGRA instead of the TST for its detection. Nevertheless, there is still a risk of tuberculosis under anti-TNF therapy, even if LTBI is correctly diagnosed and treated.

Social factors related to the clinical severity of influenza cases in Spain during the A(H1N1)2009 virus pandemic

Background During the 2009 influenza pandemic, a change in the type of patients most often affected by influenza was observed. The objective of this study was to assess the role of individual and social determinants in hospitalizations due to influenza A (H1N1) 2009 infection. Methods We studied hospitalized patients (cases) and outpatients (controls) with confirmed influenza A (H1N1) 2009 infection. A standardized questionnaire was used to collect data. Variables that might be related to the hospitalization of influenza cases were compared by estimation of the odds ratio (OR) and 95% confidence intervals (CI) and the variables entered into binomial logistic regression models. Results Hospitalization due to pandemic A (H1N1) 2009 influenza virus infections was associated with non-Caucasian ethnicity (OR: 2.18, 95% CI 1.17 − 4.08), overcrowding (OR: 2.84, 95% CI 1.20 − 6.72), comorbidity and the lack of previous preventive information (OR: 2.69, 95% CI: 1.50 − 4.83). Secondary or higher education was associated with a lower risk of hospitalization (OR 0.56, 95% CI: 0.36 − 0.87) Conclusions In addition to individual factors such as comorbidity, other factors such as educational level, ethnicity or overcrowding were associated with hospitalization due to A (H1N1) 2009 influenza virus infections.

Factors associated with 56-day non-return rate in dairy cattle

The objective of this work was to identify factors associated with the 56-day non-return rate (56-NRR) in dairy herds in the Galician region, Spain, and to estimate it for individual Holstein bulls. The experiment was carried out in herds originated from North-West Spain, from September 2008 to August 2009. Data of the 76,440 first inseminations performed during this period were gathered. Candidate factors were tested for their association with the 56-NRR by using a logistic model (binomial). Afterwards, 37 sires with a minimum of 150 first performed inseminations were individually evaluated. Logistic models were also estimated for each bull, and predicted individual 56-NRR rate values were calculated as a solution for the model parameters. Logistic regression found four major factors associated with 56-NRR in lactating cows: age at insemination, days from calving to insemination, milk production level at the time of insemination, and herd size. First-service conception rate, when a particular sire was used, was higher for heifers (0.71) than for lactating cows (0.52). Non-return rates were highly variable among bulls. Asignificant part of the herd-level variation of 56-NRR of Holstein cattle seems attributable to the service sire. High correlation level between observed and predicted 56-NRR was found.

Dental service utilization by Europeans aged 50 plus.

OBJECTIVES: To describe variations in the utilization of dental services by persons aged 50+ from 14 European countries and to identify the extent to which such variations are attributable to differences in oral health need and in accessibility of dental care. METHODS: We use data from the Survey of Health, Ageing, and Retirement in Europe (SHARE Waves 2 and 3) and estimate a series of multivariate logistic regression models to analyze variations in dental service utilization (overall dental attendance, preventive treatment and/or operative treatment, dental attendance in early life years) RESULTS: Overall dental attendance and incidence of solely preventive treatment are comparatively high in the Netherlands, Sweden, Denmark, Germany, and Switzerland. In contrast, overall dental attendance is relatively low in Spain, Italy, France, Greece, Poland, and Ireland. Moreover, a high incidence of solely operative treatment is observed in Austria, Italy, and France, whereas in the Netherlands, Sweden, Denmark, Switzerland, and Ireland, the incidence of solely operative treatment is comparably low. By and large, these variations persist even when controlling for cross-country differences in oral health need and in accessibility of dental care. CONCLUSIONS: In comparison with other European regions, there is a tendency toward more frequent and preventive dental treatment of the elderly populations residing in Scandinavia and Western Europe. Such utilization patterns appear only partially attributable to differences in need for and accessibility of dental care.

Neighbourhood socio-economic position, late presentation and outcomes in people living with HIV in Switzerland.

OBJECTIVES: Inequalities and inequities in health are an important public health concern. In Switzerland, mortality in the general population varies according to the socio-economic position (SEP) of neighbourhoods. We examined the influence of neighbourhood SEP on presentation and outcomes in HIV-positive individuals in the era of combination antiretroviral therapy (cART). METHODS: The neighbourhood SEP of patients followed in the Swiss HIV Cohort Study (SHCS) 2000-2013 was obtained on the basis of 2000 census data on the 50 nearest households (education and occupation of household head, rent, mean number of persons per room). We used Cox and logistic regression models to examine the probability of late presentation, virologic response to cART, loss to follow-up and death across quintiles of neighbourhood SEP. RESULTS: A total of 4489 SHCS participants were included. Presentation with advanced disease [CD4 cell count <200 cells/μl or AIDS] and with AIDS was less common in neighbourhoods of higher SEP: the age and sex-adjusted odds ratio (OR) comparing the highest with the lowest quintile of SEP was 0.71 [95% confidence interval (95% CI) 0.58-0.87] and 0.59 (95% CI 0.45-0.77), respectively. An undetectable viral load at 6 months of cART was more common in the highest than in the lowest quintile (OR 1.52; 95% CI 1.14-2.04). Loss to follow-up, mortality and causes of death were not associated with neighbourhood SEP. CONCLUSION: Late presentation was more common and virologic response to cART less common in HIV-positive individuals living in neighbourhoods of lower SEP, but in contrast to the general population, there was no clear trend for mortality.

Associations between mood, anxiety or substance use disorders and inflammatory markers after adjustment for multiple covariates in a population-based study.

Inflammation is one possible mechanism underlying the associations between mental disorders and cardiovascular diseases (CVD). However, studies on mental disorders and inflammation have yielded inconsistent results and the majority did not adjust for potential confounding factors. We examined the associations of several pro-inflammatory cytokines (IL-1β, IL-6 and TNF-α) and high sensitive C-reactive protein (hsCRP) with lifetime and current mood, anxiety and substance use disorders (SUD), while adjusting for multiple covariates. The sample included 3719 subjects, randomly selected from the general population, who underwent thorough somatic and psychiatric evaluations. Psychiatric diagnoses were made with a semi-structured interview. Major depressive disorder was subtyped into "atypical", "melancholic", "combined atypical-melancholic" and "unspecified". Associations between inflammatory markers and psychiatric diagnoses were assessed using multiple linear and logistic regression models. Lifetime bipolar disorders and atypical depression were associated with increased levels of hsCRP, but not after multivariate adjustment. After multivariate adjustment, SUD remained associated with increased hsCRP levels in men (β = 0.13 (95% CI: 0.03,0.23)) but not in women. After multivariate adjustment, lifetime combined and unspecified depression were associated with decreased levels of IL-6 (β = -0.27 (-0.51,-0.02); β = -0.19 (-0.34,-0.05), respectively) and TNF-α (β = -0.16 (-0.30,-0.01); β = -0.10 (-0.19,-0.02), respectively), whereas current combined and unspecified depression were associated with decreased levels of hsCRP (β = -0.20 (-0.39,-0.02); β = -0.12 (-0.24,-0.01), respectively). Our data suggest that the significant associations between increased hsCRP levels and mood disorders are mainly attributable to the effects of comorbid disorders, medication as well as behavioral and physical CVRFs.