ArticleScientific reports2025
Construction of a troublemaking risk assessment tool for patients with severe mental disorders in community of China.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Patient subgroups and shifting incidence trends: an epidemiological profile of registered severe mental disorders in Quzhou, China (1969-2022).Frontiers in public health · 2026Article
- Psychiatric emergency events and risk factors among patients with severe mental disorders: an 8-year retrospective analysis in an urban district of Beijing.Frontiers in psychiatry · 2026Article
- Prevalence and Associated Factors of Bullying Experienced by Community Patients With Serious Mental Disorders: Cross-Sectional Study.JMIR public health and surveillance · 2025Article
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6 authors.
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Abstract
objectiveConstruction a troublemaking risk assessment tool to predict the risk of troublemaking for patients with severe mental disorders in the community of China.
methods28,000 cases registered in the Jiangsu Provincial Severe Mental Disorder Management System from January 2017 to December 2019 were collected. The risk factors of troublemaking among patients with severe mental disorders in the community were analyzed through Logistic regression analysis, then the troublemaking risk assessment tool was established and verified.
resultsThe incidence of troublemaking among patients with severe mental disorders in the community was 7.15%. The results of multivariate logistic regression analysis showed that males, ≤ 44 years old, duration of disease ≤ 14 years, high school education and below, unemployed, subsistence allowances, schizophrenia, major symptoms > 1, psychiatric visits ≥ 1 time per year, unwilling to participate in community management and community rehabilitation activities, and delayed diagnosis < 2 months were risk factors for troublemaking. The above factors were incorporated into the nomogram model, and the area under the ROC curve of the nomogram model was 0.688 (95%CI: 0.563-0.726). The calibration curve proved that the probability predicted by the model was in good agreement with the actual probability.
conclusionThe established troublemaking risk assessment tool for patients with severe mental disorders in the community based on Logistic regression analysis had good predictive performance, which could be applied to assess the probability of troublemaking among patients with severe mental disorders in the community.
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