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| Pàgina inicial > Articles > Articles publicats > Catalonia Suicide Risk Code Epidemiology (CSRC-Epi) study : |
(Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública) (Universitat Autònoma de Barcelona) (Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública) (Parc Taulí Hospital Universitari. Institut d'Investigació i Innovació Parc Taulí (I3PT)) (Institut Hospital del Mar d'Investigacions Mèdiques) (Institut d'Investigació Biomèdica Sant Pau) (Institut Català de la Salut) (University of Oslo) (Harvard Medical School) (Parc Taulí Hospital Universitari. Institut d'Investigació i Innovació Parc Taulí (I3PT)) (Institut Hospital del Mar d'Investigacions Mèdiques) (Universitat Pompeu Fabra)
| Data: | 2020 |
| Resum: | 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. 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. 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. |
| Ajuts: | Ministerio de Educación, Cultura y Deporte FPU15/05728 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017-SGR-134 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017-SGR-452 Instituto de Salud Carlos III ISCIII CD18/00049 Instituto de Salud Carlos III ISCIII FI18/00012 Instituto de Salud Carlos III ISCIII FJCI-2017-31738 Instituto de Salud Carlos III ISCIII/FEDER PI17/00521 Instituto de Salud Carlos III ISCIII/FEDER PI17/01205 |
| Drets: | Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original.  |
| Llengua: | Anglès |
| Document: | Article ; recerca ; Versió publicada |
| Matèria: | Suicide & self-harm ; Epidemiology ; Mental health ; Psychiatry ; Public health ; Statistics & research methods |
| Publicat a: | BMJ open, Vol. 10 (july 2020) , ISSN 2044-6055 |
