Experience sampling methods for the personalised prediction of mental health problems in Spanish university students: protocol for a survey-based observational study within the PROMES-U project

INTRODUCTION

There is a high prevalence of mental health problems among university students. Better prediction and treatment access for this population is needed. In recent years, short-term dynamic factors, which can be assessed using experience sampling methods (ESM), have presented promising results for predicting mental health problems.

METHODS AND ANALYSIS

Undergraduate students from five public universities in Spain are recruited to participate in two web-based surveys (at baseline and at 12-month follow-up). A subgroup of baseline participants is recruited through quota sampling to participate in a 15-day ESM study. The baseline survey collects information regarding distal risk factors, while the ESM study collects short-term dynamic factors such as affect, company or environment. Risk factors will be identified at an individual and population level using logistic regressions and population attributable risk proportions, respectively. Machine learning techniques will be used to develop predictive models for mental health problems. Dynamic structural equation modelling and multilevel mixed-effects models will be considered to develop a series of explanatory models for the occurrence of mental health problems.

ETHICS AND DISSEMINATION

The project complies with national and international regulations, including the Declaration of Helsinki and the Code of Ethics, and has been approved by the IRB Parc de Salut Mar (2020/9198/I) and corresponding IRBs of all participating universities. All respondents are given information regarding access mental health services within their university and region. Individuals with positive responses on suicide items receive a specific alert with indications for consulting with a health professional. Participants are asked to provide informed consent separately for the web-based surveys and for the ESM study. Dissemination of results will include peer-reviewed scientific articles and participation in scientific congresses, reports with recommendations for universities' mental health policy makers, as well as a well-balanced communication strategy to the general public.

STUDY REGISTRATION

osf.io/p7csq.

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.

TRIAL REGISTRATION NUMBER

NCT04235127.