The Role of Big Data Analytics in Predicting Suicide

Kessler, Ronald C.; Bernecker, Samantha L.; Bossarte, Robert M.; Luedtke, Alex; McCarthy, John F.; Nock, Matthew K.; Pigeon, Wilfred R.; Petukhova, Maria; Sadikova, Ekaterina; VanderWeele, Tyler J.; Zuromski, Kelly L.; Zaslavsky, Alan M.

doi:10.1007/978-3-030-03553-2_5

Cited by 35 publications

(28 citation statements)

References 97 publications

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“…Manualization is no longer regarded as the most effective aspect of delivering larger effect sizes with fidelity to the treatment modality (121). Real-world data analytic studies may be a better approach to identifying what may be effective (8, 87, 101, 102, 124). RCTs in psychiatry will require the capacity to quantify complex TAU (32), and this will depend on aggregation of measures (77, 78, 86, 116–119).…”

Section: Discussionmentioning

confidence: 99%

“…Deciphering this variability is therefore necessary. The Horizon 2020 advisory group put forward personalized medicine as a model that uses characterization of individual phenotypes and genotypes (molecular profiling, imaging, and lifestyle data) to tailor the correct individual therapeutic strategy, determining the predisposition to disease, or to deliver timely and targeted prevention (123, 124). The JPND is a trans-national network that is an exemplar of the type of initiative that could substantially improve outcomes for the patients of FMHS.…”

Section: Application To Fmhsmentioning

confidence: 99%

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Perspective On Excellence in Forensic Mental Health Services: What We Can Learn From Oncology and Other Medical Services

2019

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We propose that excellence in forensic and other mental health services can be recognized by the abilities necessary to conduct randomized controlled trials (RCTs) and equivalent forms of rigorous quantitative research to continuously improve the outcomes of treatment as usual (TAU). Forensic mental health services (FMHSs) are growing, are high cost, and increasingly provide the main access route to more intensive, organized, and sustained pathways through care and treatment. A patient newly diagnosed with a cancer can expect to be enrolled in RCTs comparing innovations with the current best TAU. The same should be provided for patients newly diagnosed with severe mental illnesses and particularly those detained and at risk of prolonged periods in a secure hospital. We describe FMHSs in four levels 1 to 4, basic to excellent, according to seven domains: values or qualities, clinical organization, consistency, timescale, specialization, routine outcome measures, and research. Excellence is not elitism. Not all centers need to achieve excellence, though all should be of high quality. Services can provide each population with a network of centers with access to one center of excellence. Excellence is the standard needed to drive the virtuous circle of research and development that is necessary for teaching, training, and the pursuit of new knowledge and better outcomes. Substantial advances in treatment of severe mental disorders require a drive at a national and international level to create services that meet these standards of excellence and are focused, active, and productive to drive better functional outcomes for service users.

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Section: Discussionmentioning

confidence: 99%

Section: Application To Fmhsmentioning

confidence: 99%

Perspective On Excellence in Forensic Mental Health Services: What We Can Learn From Oncology and Other Medical Services

2019

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“…Even though the PTR developed here is only provisional, the results are useful in suggesting that substantial variation exists in the relative effectiveness of CAMS and E‐CAU. As discussed in more detail elsewhere (Kessler, ; Kessler, Bernecker et al, ; Kessler, Bossarte et al, ), such heterogeneity of treatment effects (HTE) is likely to exist in many interventions focused on prevention of suicide‐related ideation and behaviors (SRIBs). This is true because SRIBs occur in conjunction with manifold mental disorders and because SRIB‐focused interventions target intermediate outcomes that might be issues for only a subset of patients, leading inevitably to weak aggregate effects even if large effects exist among the subset of patients who are helped by the intervention.…”

Section: Discussionmentioning

confidence: 99%

A Preliminary Precision Treatment Rule for Remission of Suicide Ideation

Kessler

Chalker

Luedtke

et al. 2019

Suicide & Life Threat Behav

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Objective There is growing interest in the development of composite precision treatment rules (PTRs) to guide the selection of the treatments most likely to be helpful for individual patients. We present here the results of an effort to develop a preliminary PTR for Collaborative Assessment and Management of Suicidality (CAMS) relative to enhanced‐care as usual based on secondary analysis of the Operation Worth Living (OWL) randomized controlled trial. The outcome of interest is eliminating suicide ideation (SI) within 3 months of initiating treatment. Method A state‐of‐the‐art ensemble machine learning method was used to develop the PTR among the n = 148 U.S. Soldiers (predominately male and White, age range 18–48) OWL patients. Results We estimated that CAMS was the better treatment for 77.8% of patients and that treatment assignment according to the PTR would result in a 13.6% (95% CI: 0.9%–26.3%) increase in 3‐month SI remission compared to random treatment assignment. Conclusions Although promising, results are limited by the small sample size, restrictive baseline assessment, and inability to evaluate effects on suicidal behaviors or disaggregate based on history of suicidal behaviors. Replication is needed in larger samples with comprehensive baseline assessments, longer‐term follow‐ups, and more extensive outcomes.

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“…These lists constitute annual censuses of individuals with access to public healthcare, which, by law, includes every Catalan resident. These lists include sociodemographic variables (ie, sex, age, nationality, a range of socioeconomic indicators and healthcare catchment region) as well as associated small area geocode data (ie, data available on the healthcare catchment region level), 33 the dates of immigration and emigration in/out of Catalonia, the date of death, as well as a range of healthcare summary variables that are constructed to monitor healthcare needs in the Catalan population (ie, 12-month depression, 12-month complex mental disease and the number of 12-month healthcare contacts for each healthcare setting). These lists will be used to construct a sampling frame when conducting the nested case–control sampling, as further explained below.…”

Section: Methods and Analysismentioning

confidence: 99%

“… 32 When routinely implemented at the healthcare system level, such data-driven decision support tools could guide the adequate allocation of clinical resources, such as in-depth suicide risk assessments and tailored treatment interventions in multistage screening approaches. 33 34 …”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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The Role of Big Data Analytics in Predicting Suicide

Cited by 35 publications

References 97 publications

Perspective On Excellence in Forensic Mental Health Services: What We Can Learn From Oncology and Other Medical Services

Perspective On Excellence in Forensic Mental Health Services: What We Can Learn From Oncology and Other Medical Services

A Preliminary Precision Treatment Rule for Remission of Suicide Ideation

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

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