Anciennes et nouvelles aristocraties

Sans rien ignorer des spécificités de différentes aristocraties, anciennes et nouvelles, et des groupes qui les constituent, les auteurs de cet ouvrage tentent plutôt de les confronter, de rechercher les caractères communs qui les soudent, les différences qui les séparent et, plus encore, les fondements de clivages souvent ambigus entre aristocrates et non-aristocrates. Leur ambition est tout à la fois de présenter des études de cas précises réalisées en France, Grande-Bretagne, Allemagne, Italie, Hongrie, Finlande et Suède, un état des lieux ainsi que des recherches sur les noblesses menées par historiens, anthropologues et sociologues et enfin de proposer une analyse critique et comparative des évolutions de ces noblesses. Quel est le poids du symbolique mais aussi des décrochements politiques dans les transformations: disparition, désagrégation et parfois recomposition des anciennes aristocraties et constitution de nouvelles aristocraties ? Comment appréhender le phénomène aristocratique dans son extension européenne ? Comment articuler étude des tensions entre appartenances contradictoires - nationale et européenne, nobiliaire et démocratique - dans lesquelles sont parfois enserrés les aristocrates, et analyse des modes de reproduction de ces groupes ? Ce sont quelques-unes des questions majeures abordées dans ce livre

Catalonia Suicide Risk Code Epidemiology (CSRC-Epi) study: protocol for a population-representative nested case-control study of suicide attempts in Catalonia, Spain

Introduction: Suicide attempts represent an important public health burden. Centralised electronic health record (EHR) systems have high potential to provide suicide attempt surveillance, to inform public health action aimed at reducing risk for suicide attempt in the population, and to provide data-driven clinical decision support for suicide risk assessment across healthcare settings. To exploit this potential, we designed the Catalonia Suicide Risk Code Epidemiology (CSRC-Epi) study. Using centralised EHR data from the entire public healthcare system of Catalonia, Spain, the CSRC-Epi study aims to estimate reliable suicide attempt incidence rates, identify suicide attempt risk factors and develop validated suicide attempt risk prediction tools. Methods and analysis: The CSRC-Epi study is registry-based study, specifically, a two-stage exposure-enriched nested case-control study of suicide attempts during the period 2014-2019 in Catalonia, Spain. The primary study outcome consists of first and repeat attempts during the observation period. Cases will come from a case register linked to a suicide attempt surveillance programme, which offers in-depth psychiatric evaluations to all Catalan residents who present to clinical care with any suspected risk for suicide. Predictor variables will come from centralised EHR systems representing all relevant healthcare settings. The study's sampling frame will be constructed using population-representative administrative lists of Catalan residents. Inverse probability weights will restore representativeness of the original population. Analysis will include the calculation of age-standardised and sex-standardised suicide attempt incidence rates. Logistic regression will identify suicide attempt risk factors on the individual level (ie, relative risk) and the population level (ie, population attributable risk proportions). Machine learning techniques will be used to develop suicide attempt risk prediction tools. Ethics and dissemination: This protocol is approved by the Parc de Salut Mar Clinical Research Ethics Committee (2017/7431/I). Dissemination will include peer-reviewed scientific publications, scientific reports for hospital and government authorities, and updated clinical guidelines.This project was supported by ISCIII/FEDER PI17/00521, ISCIII/FEDER PI17/01205, and Generalitat de Catalunya (2017 SGR 452). The Catalonia Suicide Risk Code surveillance programme is an initiative of the Mental Health and Addictions Plan of the Department of Health of the Catalan Government. PM has a Sara Borrell research contract awarded by the ISCIII (CD18/00049). ME has a Juan de la Cierva research contract awarded by the ISCIII (FJCI-2017–31738). VPS and ME want to thank unrestricted research funding from Secretaria d′Universitats i Recerca del Departament d′Economia i Coneixement (2017 SGR 134 to ‘Mental Health Research Group’), and Generalitat de Catalunya (Government of Catalonia). BPG and ADIT received funding from ISCIII FI18/00012 and FPU2017-06447, respectively. LBC received funding by Ministerio de Educación, Cultura y Deporte (FPU15/05728). DP and JA received funding by ISCIII/FEDER PI17/01205

The catalonia suicide risk code Epi-Study - A population-representative nested case-control study of suicide attempts in Catalonia, Spain

Introduction: Suicide attempts represent an important public health burden. Centralised electronic health record (EHR) systems have high potential to provide suicide attempt surveillance, to inform public health action aimed at reducing risk for suicide attempt in the population, and to provide data-driven clinical decision support for suicide risk assessment across healthcare settings. To exploit this potential, we designed the Catalonia Suicide Risk Code Epidemiology (CSRC-Epi) study. Using centralised EHR data from the entire public healthcare system of Catalonia, Spain, the CSRC-Epi study aims to estimate reliable suicide attempt incidence rates, identify suicide attempt risk factors and develop validated suicide attempt risk prediction tools. Methods and analysis: The CSRC-Epi study is registry-based study, specifically, a two-stage exposure-enriched nested case-control study of suicide attempts during the period 2014-2019 in Catalonia, Spain. The primary study outcome consists of first and repeat attempts during the observation period. Cases will come from a case register linked to a suicide attempt surveillance programme, which offers in-depth psychiatric evaluations to all Catalan residents who present to clinical care with any suspected risk for suicide. Predictor variables will come from centralised EHR systems representing all relevant healthcare settings. The study's sampling frame will be constructed using population-representative administrative lists of Catalan residents. Inverse probability weights will restore representativeness of the original population. Analysis will include the calculation of age-standardised and sex-standardised suicide attempt incidence rates. Logistic regression will identify suicide attempt risk factors on the individual level (ie, relative risk) and the population level (ie, population attributable risk proportions). Machine learning techniques will be used to develop suicide attempt risk prediction tools. Ethics and dissemination: This protocol is approved by the Parc de Salut Mar Clinical Research Ethics Committee (2017/7431/I). Dissemination will include peer-reviewed scientific publications, scientific reports for hospital and government authorities, and updated clinical guidelines