348 research outputs found
sj-docx-1-sjp-10.1177_14034948211048050 – Supplemental material for Seroprevalence of anti-SARS-CoV-2 IgG antibodies, risk factors for infection and associated symptoms in Geneva, Switzerland: a population-based study
No full textSupplemental material, sj-docx-1-sjp-10.1177_14034948211048050 for Seroprevalence of anti-SARS-CoV-2 IgG antibodies, risk factors for infection and associated symptoms in Geneva, Switzerland: a population-based study by Aude Richard, Ania Wisniak, Javier Perez-Saez, Henri Garrison-Desany, Dusan Petrovic, Giovanni Piumatti, Hélène Baysson, Attilio Picazio, Francesco Pennacchio, David De Ridder, François Chappuis, Nicolas Vuilleumier, Nicola Low, Samia Hurst, Isabella Eckerle, Antoine Flahault, Laurent Kaiser, Andrew S. Azman, Idris Guessous and Silvia Stringhini in Scandinavian Journal of Public Health</p
Socioeconomic position, lifestyle habits and biomarkers of epigenetic aging: a multi-cohort analysis
No full textDifferences in health status by socioeconomic position (SEP) tend to be more evident at older ages, suggesting the involvement of a biological mechanism responsive to the accumulation of deleterious exposures across the lifespan. DNA methylation (DNAm) has been proposed as a biomarker of biological aging that conserves memory of endogenous and exogenous stress during life.We examined the association of education level, as an indicator of SEP, and lifestyle-related variables with four biomarkers of age-dependent DNAm dysregulation: the total number of stochastic epigenetic mutations (SEMs) and three epigenetic clocks (Horvath, Hannum and Levine), in 18 cohorts spanning 12 countries.The four biological aging biomarkers were associated with education and different sets of risk factors independently, and the magnitude of the effects differed depending on the biomarker and the predictor. On average, the effect of low education on epigenetic aging was comparable with those of other lifestyle-related risk factors (obesity, alcohol intake), with the exception of smoking, which had a significantly stronger effect.Our study shows that low education is an independent predictor of accelerated biological (epigenetic) aging and that epigenetic clocks appear to be good candidates for disentangling the biological pathways underlying social inequalities in healthy aging and longevity
Dataset for "Who Let The Trolls Out? Towards Understanding State-Sponsored Trolls"
No full textThis is the dataset used for the study "Who Let The Trolls Out? Towards Understanding State-Sponsored Trolls". Savvas Zannettou, Tristan Caulfield, William Setzer, Michael Sirivianos, Gianluca Stringhini, Jeremy Blackburn. Arxiv, 2019. DOI: 10.5281/zenodo.2558560
The dataset consists of the data released by Twitter on October 2018 for Russian and Iranian state-sponsored troll accounts, which is available at https://about.twitter.com/en_us/values/elections-integrity.html#data as well as intermediate data that we generated after processing the raw data.
For instance, we include trained Word2Vec and LDA models, the output of our influence estimation experiments via Hawkes Processes, and a lot of other data necessary to reproduce the results in the paper.
To use the provided data simply download the compressed file from and make sure that the uncompressed data folder is in the same directory as the IPython Notebook.
The code used for this study can be found here: https://github.com/zsavvas/trolls_analysis
Please cite our paper if any publication, of any form and kind results of you using this data:
@article{zannettou2018let,
title={Who let the trolls out? towards understanding state-sponsored trolls},
author={Zannettou, Savvas and Caulfield, Tristan and Setzer, William and Sirivianos, Michael and Stringhini, Gianluca and Blackburn, Jeremy},
journal={arXiv preprint arXiv:1811.03130},
year={2018}
}</pre
Software for "Who Let The Trolls Out? Towards Understanding State-Sponsored Trolls"
No full textThis repository contains the source code for reproducing the results from the paper "Who Let The Trolls Out? Towards Understanding State-Sponsored troll accounts on Twitter" (see https://arxiv.org/abs/1811.03130 for the detailed description on the results). DOI: 10.5281/zenodo.2558560
The data collected and used for this study can be found here: DOI 10.5281/zenodo.2558433
Please appropriately cite the "Who Let The Trolls Out? Towards Understanding State-Sponsored Trolls" paper in any publication, of any form and kind, using this software:
@article{zannettou2018let,
title={Who let the trolls out? towards understanding state-sponsored trolls},
author={Zannettou, Savvas and Caulfield, Tristan and Setzer, William and Sirivianos, Michael and Stringhini, Gianluca and Blackburn, Jeremy},
journal={arXiv preprint arXiv:1811.03130},
year={2018}
}
Acknowledgments:
This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under the Marie Skłodowska-Curie ENCASE project (Grant Agreement No. 691025).
</ul
Anxiety Disorders are Associated with Low Socioeconomic Status in Women but Not in Men
No full textObjectives We investigated to what extent the lifetime prevalence of anxiety disorders relates to negative economic changes, taking important lifestyle factors and unexpected life events into consideration. Methods We included 3,695 participants recruited in the city of Lausanne (Switzerland), from the population-based CoLaus/PsyCoLaus study. The association between anxiety disorders, lifestyle factors, and life events related to income was investigated using binary logistic regression analyses correcting for demographic and clinical confounders. Results Compared with men, women with anxiety disorders showed a significantly lower socioeconomic status (Mann-Whitney U = 56,318; p < .001) and reported a higher negative impact of substantial reduction of income (Mann-Whitney U = 68,531; p = .024). When performing adjusted analyses, low socioeconomic status (odd ratio, 0.87; p = .001) and negative impact of reduction of income (odd ratio, 1.01; p = .004) were associated significantly with anxiety disorders in women but not in men. Conclusion Our results suggest that anxiety disorders aggravate already existing gender differences in economic conditions, and that women with anxiety need additional support to attain socioeconomic security similar to that of men
Software for "Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior"
No full textThis repository consists of the custom external platform for the annotation process of CrowdFlower, used on the "Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior" paper, published in ICWSM 2018. Full text of the paper can be found here:
Please cite the paper in any published work that uses any of these resources.
@article{founta2018large,
title={Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior},
author={Founta, Antigoni-Maria and Djouvas, Constantinos and Chatzakou, Despoina and Leontiadis, Ilias and Blackburn, Jeremy and Stringhini, Gianluca and Vakali, Athena and Sirivianos, Michael and Kourtellis, Nicolas},
journal={arXiv preprint arXiv:1802.00393},
year={2018}
}
For any further questions contact a.m.founta at gmail dot com.</p
Association between education and quality of diabetes care in Switzerland
No full textAline Flatz, Alejandra Casillas, Silvia Stringhini, Emilie Zuercher, Bernard Burnand, Isabelle Peytremann-BridevauxInstitute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, SwitzerlandPurpose: Low socioeconomic status is associated with higher prevalence of diabetes, worse outcomes, and worse quality of care. We explored the relationship between education, as a measure of socioeconomic status, and quality of care in the Swiss context.Patients and methods: Data were drawn from a population-based survey of 519 adults with diabetes during fall 2011 and summer 2012 in a canton of Switzerland. We assessed patients and diabetes characteristics. Eleven indicators of quality of care were considered (six of process and five of outcomes of care). After bivariate analyses, regression analyses adjusted for age, sex, and diabetic complications were performed to assess the relationship between education and quality of care.Results: Of 11 quality-of-care indicators, three were significantly associated with education: funduscopy (patients with tertiary versus primary education were more likely to get the exam: odds ratio, 1.8; 95% confidence interval [CI], 1.004–3.3) and two indicators of health-related quality of life (patients with tertiary versus primary education reported better health-related quality of life: Audit of Diabetes-Dependent Quality of Life: β=0.6 [95% CI, 0.2–0.97]; SF-12 mean physical component summary score: β=3.6 [95% CI, 0.9–6.4]).Conclusion: Our results suggest the presence of educational inequalities in quality of diabetes care. These findings may help health professionals focus on individuals with increased needs to decrease health inequalities.Keywords: primary care, education, quality of care, diabete
The role of intermediate factors and biological mechanisms in life-course socioeconomic differences in cardiometabolic disorders
No full textDataset: Raiders of the Lost Kek: 3.5 Years of Augmented 4chan Posts from the Politically Incorrect Board
No full textThis is the dataset released with the paper titled: "Raiders of the Lost Kek: 3.5 Years of Augmented 4chan Posts from the Politically Incorrect Board".
The dataset is a single Newline delimited JSON file. Each line in the file consists of a JSON object which is a full 4chan /pol/ thread. The JSON objects contain all the key/values returned by the 4chan API, along with three additional keys (entities, perspectives, and extracted_poster_id).
For each JSON object we complement the data with the list of the named entities we detect for each post, using the spaCy Python library. In addition, for each post we add scores returned by the Google’s Perspective API, and more specifically seven scores in the [0; 1] interval.
For the detailed description of every key in the JSON structure, along with the type of the value, please read the readme.pdf file provided with this dataset.
If you find our dataset useful, please cite our paper:
@article{papasavva2020raiders,
title={Raiders of the Lost Kek: 3.5 Years of Augmented 4chan Posts from the Politically Incorrect Board},
author={Antonis Papasavva, Savvas Zannettou, Emiliano De Cristofaro, Gianluca Stringhini, Jeremy Blackburn},
journal={arXiv preprint arXiv:2001.07487},
year={2020}
}
</p
Mécanismes des inégalités sociales de mortalité : analyse comparative des études Whitehall II et GAZEL
No full textLes différences de morbidité et de mortalité entre les groupes socioéconomiques constituent un des résultatsle plus cohérent de la recherche épidémiologique. Cependant, les mécanismes qui sous-tendent cetteassociation demeurent encore mal compris. Les données de deux grandes cohortes européennes ont étéutilisées pour décrire l'ampleur des différences socioéconomiques de mortalité toutes causes et spécifique, etexaminer le rôle des comportements de santé et du soutien social dans ces inégalités. Les indicateurs de lasituation socioéconomique dans l’enfance se sont révélés liés à la mortalité à l'âge adulte, même si toutefoisles trois mesures examinées – position socioprofessionnelle du père, niveau d’études et taille – étaientdifféremment liées à la mortalité. Les indicateurs de la position socioéconomique à l'âge adulte, catégoriesocioprofessionnelle et revenu, étaient associés à la mortalité toutes causes et cardiovasculaire dans les deuxcohortes. Dans l'étude Whitehall II, les comportements de santé étaient socialement distribués etexpliquaient une grande partie des inégalités sociales de mortalité, en particulier lorsque les changementsdans ces comportements au cours du suivi étaient pris en compte. Les mêmes comportements expliquaienttrès peu les inégalités sociales de mortalité dans l'étude GAZEL, leur répartition sociale étant faible danscette cohorte. Parmi les mesures de soutien social considérées, le statut marital expliquait également unepartie du gradient socioéconomique de mortalité dans l'étude Whitehall II, mais pas dans GAZEL, tandis quele rôle de la participation sociale et du réseau social était négligeable dans les deux cohortes. Différentsmécanismes semblent jouer un rôle dans les inégalités sociales de santé dans ces deux pays européensvoisins. Cela implique que des recherches comparatives visant à comprendre les déterminants communs etspécifiques des différences sociales de santé sont nécessaires. D’autres recherches visant davantage lescauses fondamentales des inégalités sociales de santé sont également souhaitables.Differences in morbidity and mortality between socioeconomic groups constitute one of the most consistentfindings of epidemiologic research. However, research on social inequalities in health has yet to provide acomprehensive understanding of the mechanisms underlying this association. Data from two large Europeancohorts were used to examine socioeconomic differences in all-cause and cause-specific mortality in twopopulations in early old age, as well as the role played by health behaviours and social support in shapingthose inequalities. Indicators of socioeconomic circumstances in early life were found to be related tomortality in adulthood, even though the association of the three measures examined, father’s occupationalposition, education and height, with mortality did not have the same shape and depended on the cause ofmortality being examined. Indicators of socioeconomic position in adulthood, occupational position andincome, were strongly associated with all-cause and cardiovascular mortality in both cohorts. In theWhitehall II study, health behaviours - smoking, alcohol consumption, diet and physical activity - werestrongly socially patterned, and were found to contribute to a large part of social inequalities in mortality,particularly when changes in these behaviours over time were taken into account. The same behaviourscontributed little to explaining social inequalities in mortality in the GAZEL cohort, as their socialpatterning was weak in this cohort. Of the measures of social support examined, marital status alsoaccounted for part of the socioeconomic gradient in mortality in the Whitehall II cohort but not in GAZEL,while the role of social participation and network size was negligible in both cohorts. Different mechanismsmay be driving social inequalities in health in two neighbouring European countries. This finding calls forfurther comparative research to understand the common and unique determinants of social differences inhealth within and between countries, and for additional research addressing the fundamental causes of socialinequalities in health
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