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

Abstract

IntroductionSuicide 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 analysisThe 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 disseminationThis 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.Trial registration numberNCT04235127.

Suicide Interventions in Spain and Japan: A Comparative Systematic Review

“…And, in the Japanese sample, we found more articles without lost-to-follow-up participants [26,28,35,42,43]. In both samples, most studies were non-randomized for the convenience of accessible patient groups, except for four studies in the Spanish sample [24,34,44,45] and eight in the Japanese sample [37,38,[46][47][48][49][50][51]. Finally, as to the characteristics of the samples of the articles, they are explained in detail in the following section.…”

Variations in suicide attempts attended by an emergency service during the SARS-CoV-2 pandemic

“…En la Tabla 2 se muestran las variables correspondientes a los episodios atendidos en cada periodo. En el 50,2% de los casos se activó el código riesgo de suicidio, un programa protocolizado que proporciona atención urgente y seguimiento preventivo a las personas identificadas con elevado riesgo de suicidio 8 . El código se activó en mayor proporción durante el confinamiento (61,1%) y la nueva normalidad (57,0%).…”

Suicide, the main cause of referral of patients from a general hospital for psychiatric evaluation

“…The assessment of suicide risk [9] is really important to know it those patients can back home or need more assistance. This fact is related to the establishment of the "Codi risc de suïcidi" [10] by the Department of Health. It is a protocol of action in cases of suicide attempt, which through various scales (such as the "Sad Persons Scale"), will classify patients according to risk in their attempt.…”