Literature DB >> 28483298

Using Hospitalization and Mortality Data to Identify Areas at Risk for Adolescent Suicide.

Kun Chen1, Robert H Aseltine2.   

Abstract

PURPOSE: The purpose of this study is to use statewide data on inpatient hospitalizations for suicide attempts and suicide mortality to identify communities and school districts at risk for adolescent suicide.
METHODS: Five years of data (2010-2014) from the Office of the Connecticut Medical Examiner and the Connecticut Hospital Inpatient Discharge Database were analyzed. A mixed-effects Poisson regression model was used to assess whether suicide attempt/mortality rates in the state's 119 school districts were significantly better or worse than expected after adjusting for 10 community-level characteristics.
RESULTS: Ten districts were at significantly higher risk for suicidal behavior, with suicide mortality/hospitalization rates ranging from 154% to 241% of their expected rates, after accounting for their community characteristics. Four districts were identified as having significantly lower risk for suicide attempts than expected after accounting for community-level advantages and disadvantages.
CONCLUSIONS: Data capturing hospitalization for suicide attempts and suicide deaths can inform prevention activities by identifying high-risk areas to which resources should be allocated, as well as low-risk areas that may provide insight into the best practices in suicide prevention.
Copyright © 2017 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Keywords:  Hospitalizations; Risk stratification; Suicide; Suicide attempts

Mesh:

Year:  2017        PMID: 28483298     DOI: 10.1016/j.jadohealth.2017.02.020

Source DB:  PubMed          Journal:  J Adolesc Health        ISSN: 1054-139X            Impact factor:   5.012


  4 in total

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Authors:  Sidra Goldman-Mellor; Kevin Kwan; Jonathan Boyajian; Paul Gruenewald; Paul Brown; Deborah Wiebe; Magdalena Cerdá
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Authors:  Wanwan Xu; Chang Su; Yan Li; Steven Rogers; Fei Wang; Kun Chen; Robert Aseltine
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3.  Trends in suicide rates in Brazil from 1997 to 2015.

Authors:  Cássio D Rodrigues; Débora S de Souza; Henrique M Rodrigues; Thais C R O Konstantyner
Journal:  Braz J Psychiatry       Date:  2019-02-18       Impact factor: 2.697

4.  Machine learning for suicide risk prediction in children and adolescents with electronic health records.

Authors:  Chang Su; Robert Aseltine; Riddhi Doshi; Kun Chen; Steven C Rogers; Fei Wang
Journal:  Transl Psychiatry       Date:  2020-11-26       Impact factor: 6.222

  4 in total

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