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      Suicide Among Older Adults Living in or Transitioning to Residential Long-term Care, 2003 to 2015

      research-article
      , PhD 1 , 2 , , , MPH 2 , , MPH, MSW, MA 2 , , PhD 3
      JAMA Network Open
      American Medical Association

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          Key Points

          Question

          How common is suicide in residential long-term care (LTC) settings, and is transitioning into or out of LTC associated with suicide among older adults?

          Findings

          In this cross-sectional epidemiologic study of 47 759 deaths in the National Violent Death Reporting System (NVDRS) (2003-2015), a natural language processing algorithm identified 1037 suicides among adults 55 years and older associated with LTC. The algorithm identified far more deaths associated with LTC in comparison with the existing NVDRS injury location codes.

          Meaning

          Suicide occurs among older adults living in or transitioning to LTC settings, and the mental health and well-being of older adults should be addressed in these settings.

          Abstract

          This cross-sectional epidemiologic study of deaths in the US National Violent Death Reporting System (2003-2015) estimates the number of suicides associated with residential long-term care among adults 55 and older and identifies whether machine learning tools could improve the quality of suicide surveillance data.

          Abstract

          Importance

          Almost 25% of Medicare beneficiaries live in residential long-term care (LTC) (eg, independent or assisted living facility or nursing home). There are few reliable statistics on completed suicide in LTC, in part because of data limitations.

          Objectives

          To estimate the number of suicides associated with residential LTC (ie, among persons in a facility, transitioning into or out of a facility, or otherwise associated with LTC) among adults 55 and older and, secondarily, to identify whether machine learning tools could improve the quality of suicide surveillance data.

          Design, Setting, and Participants

          Cross-sectional epidemiologic study (conducted in 2018) of restricted-access data from the National Violent Death Reporting System (NVDRS) (2003-2015) using restricted-access case narratives from suicides and undetermined deaths among adults 55 years and older in 27 states. Participants were all suicides and undetermined deaths (N = 47 759) among persons 55 years and older.

          Exposure

          Long-term care cited in the coroner/medical examiner case narrative, whether as a reason for self-harm or the injury location, identified using machine learning natural language processing (NLP) algorithms plus manual review of texts.

          Main Outcomes and Measures

          Number and characteristics (eg, demographics, health history, and means of injury) of suicides associated with LTC. The κ statistic was used to estimate the reliability of the existing NVDRS injury location codes relative to cases identified by the algorithm.

          Results

          Among 47 759 persons 55 years and older (median age, 64 years; 77.6% male; 90.0% non-Hispanic white), this study identified 1037 suicide deaths associated with LTC, including 428 among older adults living in LTC, 449 among older adults transitioning to LTC, and 160 otherwise associated with LTC. In contrast, there were only 263 cases coded with the existing NVDRS location code “supervised residential facility,” which had poor agreement with cases that the algorithm identified as occurring in LTC (κ statistic, 0.30; 95% CI, 0.26-0.35).

          Conclusions and Relevance

          Over a 13-year period, approximately 2.2% of suicides among adults 55 years and older were associated with LTC in some manner. Clinicians, administrators, and policy makers should consider ways to promote the mental health and well-being of older adults experiencing functioning limitations and their families. Natural language processing may be a useful way to improve abstraction of variables in the NVDRS.

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          Most cited references31

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          Natural language processing in mental health applications using non-clinical texts

          Natural language processing (NLP) techniques can be used to make inferences about peoples’ mental states from what they write on Facebook, Twitter and other social media. These inferences can then be used to create online pathways to direct people to health information and assistance and also to generate personalized interventions. Regrettably, the computational methods used to collect, process and utilize online writing data, as well as the evaluations of these techniques, are still dispersed in the literature. This paper provides a taxonomy of data sources and techniques that have been used for mental health support and intervention. Specifically, we review how social media and other data sources have been used to detect emotions and identify people who may be in need of psychological assistance; the computational techniques used in labeling and diagnosis; and finally, we discuss ways to generate and personalize mental health interventions. The overarching aim of this scoping review is to highlight areas of research where NLP has been applied in the mental health literature and to help develop a common language that draws together the fields of mental health, human-computer interaction and NLP.
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            • Record: found
            • Abstract: found
            • Article: not found

            Moving in: adjustment of people living with dementia going into a nursing home and their families.

            Most people with dementia in Western societies will eventually be placed in a nursing home. This can be stressful to those with dementia and to their families. The adjustment to this new caring environment by both residents and their family caregivers and the factors that influence this are the focus of this review.
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              • Record: found
              • Abstract: not found
              • Article: not found

              Factors that impact residents’ transition and psychological adjustment to long-term aged care: A systematic literature review

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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                14 June 2019
                June 2019
                14 June 2019
                : 2
                : 6
                : e195627
                Affiliations
                [1 ]Institute for Social Research, University of Michigan, Ann Arbor
                [2 ]Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor
                [3 ]School of Information, University of Michigan, Ann Arbor
                Author notes
                Article Information
                Accepted for Publication: April 25, 2019.
                Published: June 14, 2019. doi:10.1001/jamanetworkopen.2019.5627
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2019 Mezuk B et al. JAMA Network Open.
                Corresponding Author: Briana Mezuk, PhD, Department of Epidemiology, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109 ( bmezuk@ 123456umich.edu ).
                Author Contributions: Dr Mezuk had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Mezuk, Jurgens.
                Acquisition, analysis, or interpretation of data: All authors.
                Drafting of the manuscript: Mezuk, Kalesnikava, Jurgens.
                Critical revision of the manuscript for important intellectual content: Mezuk, Ko.
                Statistical analysis: Ko, Kalesnikava, Jurgens.
                Obtained funding: Mezuk.
                Supervision: Mezuk, Jurgens.
                Conflict of Interest Disclosures: None reported.
                Funding/Support: This study was supported by grant R21-MH108989 from the National Institute of Mental Health (Dr Mezuk).
                Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
                Disclaimer: The findings and conclusions of this study are those of the authors alone and do not necessarily represent the official position of the Centers for Disease Control and Prevention or of participating National Violent Death Reporting System (NVDRS) states.
                Additional Contributions: The NVDRS is administered by the Centers for Disease Control and Prevention by participating NVDRS states.
                Article
                zoi190230
                10.1001/jamanetworkopen.2019.5627
                6575144
                31199445
                b51b4527-c17c-49f0-9db6-78832777e7c4
                Copyright 2019 Mezuk B et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 11 February 2019
                : 25 April 2019
                Categories
                Research
                Original Investigation
                Featured
                Online Only
                Public Health

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