Catalonia Suicide Risk Code Epidemiology (CSRC-Epi) study: protocol for a population-representative nested case-control study of suicide attempts in Catalonia, Spain.
epidemiology
mental health
psychiatry
public health
statistics & research methods
suicide & self-harm
Journal
BMJ open
ISSN: 2044-6055
Titre abrégé: BMJ Open
Pays: England
ID NLM: 101552874
Informations de publication
Date de publication:
12 07 2020
12 07 2020
Historique:
entrez:
15
7
2020
pubmed:
15
7
2020
medline:
16
3
2021
Statut:
epublish
Résumé
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. NCT04235127.
Identifiants
pubmed: 32660952
pii: bmjopen-2020-037365
doi: 10.1136/bmjopen-2020-037365
pmc: PMC7359191
doi:
Banques de données
ClinicalTrials.gov
['NCT04235127']
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e037365Informations de copyright
© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: DP has received grants and also served as consultant or advisor for Angelini, Janssen, Lundbeck and Servier. The other authors have no competing interests to declare.
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