Risk factors for suicide in Japan: A model of predicting suicide in 2008 by risk factors of 2007

Ishii, Nobuyoshi; Terao, Takeshi; Araki, Yasuo; Kohno, Kentaro; Mizokami, Yoshinori; Arasaki, Masano; Iwata, Noboru

doi:10.1016/j.jad.2012.11.038

Cited by 16 publications

(6 citation statements)

References 15 publications

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“…Also, a range of mental health diagnoses were, as previously shown, predictive of future attempts during follow-up [10-12]. Social factors such as postcode change are strongly associated with suicide risk, concordant with recent findings [13,33]. We document that 66.8% of patients undergoing suicide assessment attended ED and that 41.8% of patients had been admitted to hospital in the year before their assessment.…”

Section: Discussionsupporting

confidence: 89%

Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments

et al. 2014

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BackgroundTo date, our ability to accurately identify patients at high risk from suicidal behaviour, and thus to target interventions, has been fairly limited. This study examined a large pool of factors that are potentially associated with suicide risk from the comprehensive electronic medical record (EMR) and to derive a predictive model for 1–6 month risk.Methods7,399 patients undergoing suicide risk assessment were followed up for 180 days. The dataset was divided into a derivation and validation cohorts of 4,911 and 2,488 respectively. Clinicians used an 18-point checklist of known risk factors to divide patients into low, medium, or high risk. Their predictive ability was compared with a risk stratification model derived from the EMR data. The model was based on the continuation-ratio ordinal regression method coupled with lasso (which stands for least absolute shrinkage and selection operator).ResultsIn the year prior to suicide assessment, 66.8% of patients attended the emergency department (ED) and 41.8% had at least one hospital admission. Administrative and demographic data, along with information on prior self-harm episodes, as well as mental and physical health diagnoses were predictive of high-risk suicidal behaviour. Clinicians using the 18-point checklist were relatively poor in predicting patients at high-risk in 3 months (AUC 0.58, 95% CIs: 0.50 – 0.66). The model derived EMR was superior (AUC 0.79, 95% CIs: 0.72 – 0.84). At specificity of 0.72 (95% CIs: 0.70-0.73) the EMR model had sensitivity of 0.70 (95% CIs: 0.56-0.83).ConclusionPredictive models applied to data from the EMR could improve risk stratification of patients presenting with potential suicidal behaviour. The predictive factors include known risks for suicide, but also other information relating to general health and health service utilisation.

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Section: Discussionsupporting

confidence: 89%

Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments

et al. 2014

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“…Prior studies have predominantly examined the clinical and statistical relationship among these risk factors and suicide attempts through univariate analysis or simple logistic regression. Epidemiological research has provided predictive analyses regarding demographic features, symptoms, and diagnoses 3,[46][47][48] . Several authors have shown that it is possible to predict suicide risk for the patients in the Veterans Health Administration based on their EHR 3,46 .…”

Section: Discussionmentioning

confidence: 99%

Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records

Zheng

Wang²,

Hao

et al. 2020

Transl Psychiatry

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Suicide is the tenth leading cause of death in the United States (US). An early-warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and analyzing the contribution of repeated attempts to the risk of eventual death by suicide. In this study we sought to develop an EWS for high-risk suicide attempt patients through the development of a population-based risk stratification surveillance system. Advanced machine-learning algorithms and deep neural networks were utilized to build models with the data from electronic health records (EHRs). A final risk score was calculated for each individual and calibrated to indicate the probability of a suicide attempt in the following 1-year time period. Risk scores were subjected to individual-level analysis in order to aid in the interpretation of the results for health-care providers managing the at-risk cohorts. The 1-year suicide attempt risk model attained an area under the curve (AUC ROC) of 0.792 and 0.769 in the retrospective and prospective cohorts, respectively. The suicide attempt rate in the "very high risk" category was 60 times greater than the population baseline when tested in the prospective cohorts. Mental health disorders including depression, bipolar disorders and anxiety, along with substance abuse, impulse control disorders, clinical utilization indicators, and socioeconomic determinants were recognized as significant features associated with incident suicide attempt.

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“…Furthermore, the diurnal temperature variation associated with spring peak of suicides in Helsinki metropolitan area [3]. However, contradictive data on the associations between suicide and ambient temperature exist as well [4,5], and recent studies found inverse associations between annual ambient temperature and suicide rate [6,7], and a study concerning monthly mean temperature and suicide rate, no association whatsoever [8]. Moreover, the analysis of long series of monthly data on suicide and weather in Switzerland concluded that the association between temperature and suicide is not due to warm temperatures but due to the lack of low temperatures, or in other words, it is not heat but the lack of cold that contributes to this association [9].…”

Section: Introductionmentioning

confidence: 99%

Local daily temperatures, thermal seasons, and suicide rates in Finland from 1974 to 2010

Hiltunen

Haukka

Ruuhela

et al. 2014

Environ Health Prev Med

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Risk factors for suicide in Japan: A model of predicting suicide in 2008 by risk factors of 2007

Cited by 16 publications

References 15 publications

Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments

Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments

Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records

Local daily temperatures, thermal seasons, and suicide rates in Finland from 1974 to 2010

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