2003 年至 2015 年，居住在或过渡到长期居住的老年人中的自杀。

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

机构信息

Institute for Social Research, University of Michigan, Ann Arbor.

Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor.

出版信息

JAMA Netw Open. 2019 Jun 5;2(6):e195627. doi: 10.1001/jamanetworkopen.2019.5627.

DOI:10.1001/jamanetworkopen.2019.5627

PMID:31199445

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6575144/

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.

摘要

重要性：近 25%的医疗保险受益人居住在长期护理机构（LTC）中（例如，独立或辅助生活设施或疗养院）。关于长期护理机构中完成的自杀事件，几乎没有可靠的统计数据，部分原因是数据限制。

目的：估计与长期护理机构相关的自杀人数（即在设施中居住、转入或转出设施或与长期护理机构有其他关联的人），其中包括 55 岁及以上的成年人，其次，确定机器学习工具是否可以提高自杀监测数据的质量。

设计、设置和参与者：2018 年进行的一项横截面流行病学研究，使用来自 27 个州的国家暴力死亡报告系统（NVDRS）的受限访问案例叙述，对 55 岁及以上成年人中的自杀和未确定死因进行了限制访问。参与者均为 55 岁及以上的自杀和未确定死因（N=47759）。

暴露：在验尸官/法医的案例叙述中引用的长期护理，无论是作为自残的原因还是受伤地点，都使用机器学习自然语言处理（NLP）算法和对文本的手动审查来识别。

主要结果和措施：与长期护理机构相关的自杀人数及其特征（例如，人口统计学，健康史和伤害方式）。κ 统计量用于评估与算法识别的病例相比，现有的 NVDRS 伤害地点代码的可靠性。

结果：在 47759 名 55 岁及以上的人中（中位数年龄为 64 岁；77.6%为男性；90.0%为非西班牙裔白人），本研究确定了 1037 例与长期护理相关的自杀死亡，其中 428 例发生在长期居住在 LTC 的老年人中，449 例发生在过渡到 LTC 的老年人中，160 例与 LTC 有其他关联。相比之下，只有 263 例被归类为现有 NVDRS 地点代码“受监督的居住设施”，与算法确定的发生在 LTC 中的病例相比，该代码的一致性较差（κ统计量，0.30；95%置信区间，0.26-0.35）。

结论和相关性：在 13 年期间，55 岁及以上成年人中约有 2.2%的自杀与某种形式的长期护理有关。临床医生、管理人员和政策制定者应考虑促进有功能障碍的老年人及其家庭的心理健康和福祉。自然语言处理可能是改进 NVDRS 中变量抽象的有用方法。