Identifying Risk and Improving Care for Elder Abuse among Veterans

背景资料。虐待老人是指身体上、性上或心理上的虐待、经济剥削或忽视 一个≥60岁的成年人。在美国，十分之一的老年人每年都会经历电针，其中许多人经历过 多种类型。退伍军人的风险特别高，因为EA风险因素在这方面的高流行率 人口。经历电针与抑郁、受伤、增加医疗保健使用和死亡率有关，但尽管 由于其患病率和发病率，只有不到5%的病例被发现，从而限制了干预的机会。而当 筛查是改善类似情况检测的常见方法，但EA的筛查工具还没有 得到了很好的验证或广泛的研究。此外，EA筛查可能会遗漏重要的高危人群， 例如痴呆症患者，有必要开发额外的检测策略 放映。本研究旨在通过评估和优化现有EA来改进退伍军人管理局的EA风险检测 筛查方法和利用退伍军人医疗保健数据识别临床怀疑EA的退伍军人 他们可能会从进一步的评估中受益。重要性/影响力。随着老年人口的增长， 美国和1000多万美国退伍军人将≥年龄定为60岁，改善EA的检测和干预是一项 国家和退伍军人事务部公共卫生优先事项。通过更知情的筛查和改进对EA的检测 新的数据驱动工具，这项研究与退伍军人事务部HSR&D改善我国老龄化护理的优先事项一致 退伍军人和他们的照顾者。创新。这项研究融合了虐待老人和实施科学 开发改进EA检测的新方法的概念框架。这项研究将对测试进行评估 首个使用唯一VA数据元素的EA可疑数据标记的特征，并将采用 创新的数据信息学方法，如自然语言处理(NLP)，以解决复杂的 对健康有重大影响的社会问题。明确的目标。目标1是对当前 退伍军人医疗中心(VAMCs)EA筛查实践的概况和设施的定量评估 与筛选相关的水平因素。目标2是一项量化研究，将确定性能最佳的EA VA数据中的管理标记(AM)。目标3是一项定性研究，将阐明以下机会： 退伍军人事务部实施基于医疗保健的EA检测计划的促进者和障碍。方法论。 在与退伍军人事务部关怀管理和社会工作办公室的密切合作下，Aim 1将在全国范围内开展 对VAMC进行调查，以评估有关EA筛查和检测的当前做法；VA设施级别的数据将 用于评估与筛选相关的结构特征。目标2将检查三个潜在的EA 怀疑AMS，并通过与多组分参考标准进行比较来选择表现最好的 包括：a)简化的基于规则的进度说明内容净额规划；b)评价 AMS和NLP通过有针对性的病历审查。在AIM 3中，早期、近期和非EA筛选站点 目标1中确定的根据目标2中选择的AM而变化的EA案例数量将被招募用于在- 深入的定性访谈，阐明EA检测计划的机会、促进者和障碍 在弗吉尼亚州。实施/后续步骤。这项研究的结果将被用来推导和验证一种新的EA 退伍军人事务部的可疑工具(EAST)，然后开发和实施检测方法，以提高效率和 通过将改进的EA筛选与针对以下人员的全面EA评估相结合来产生影响 风险最高。候选人。Lena Makaroun博士是老年病学家，也是退伍军人健康中心的核心研究员 股权研究与推广。这个CDA的目标是获得培训和研究经验，以改善 通过以下方面的深入培训在老年退伍军人中检测电针：(1)真实世界电针评估和干预 计划；(2)实施科学；(3)框架导向的定性方法；(4)预测分析。 这个CDA将支持Makaroun博士成为独立的退伍军人健康服务机构的长期职业目标 研究人员专注于改善护理提供、干预，并最终预防老年人的EA。

Background. Elder abuse (EA) is the physical, sexual or psychological abuse, financial exploitation or neglect of an adult age ≥60 years. One in 10 older adults experience EA annually in the US, with many experiencing multiple types. Veterans are at particularly high risk due to the high prevalence of EA risk factors in this population. Experiencing EA is linked to depression, injury, increased healthcare use and mortality, but despite its prevalence and morbidity, fewer than 5% of cases are detected, limiting opportunities for intervention. While screening is a common approach to improving detection of similar conditions, screening tools for EA have not been well validated or widely studied. Furthermore, EA screening may miss important high-risk populations, such as those with dementia, necessitating the development of additional detection strategies that complement screening. This research aims to improve EA risk detection in VA by both evaluating and optimizing current EA screening approaches and by leveraging VA healthcare data to identify Veterans with clinical suspicion of EA who may benefit from further assessment. Significance/Impact. With the growing population of older adults in the US and over 10 million US Veterans age ≥60 years, improving detection of and interventions for EA is a national and VA public health priority. By improving detection of EA via both better-informed screening and novel data-driven tools, this research aligns with VA HSR&D’s priority to improve care for our nation’s aging Veterans and their caregivers. Innovation. This research integrates elder abuse and implementation science conceptual frameworks to develop new approaches to improving EA detection. This study will evaluate the test characteristics of the first-ever data marker for EA suspicion using unique VA data elements and will employ innovative data informatics approaches, such as natural language processing (NLP), to address a complex social problem with large health impacts. Specific Aims. Aim 1 is a national assessment of the current landscape of EA screening practices in VA medical centers (VAMCs) and a quantitative evaluation of facility level factors associated with screening. Aim 2 is a quantitative study that will identify the best performing EA administrative marker (AM) in VA data. Aim 3 is a qualitative study that will elucidate opportunities for, facilitators of and barriers to implementation of healthcare-based EA detection programs in VA. Methodology. In close partnership with the VA Office of Care Management and Social Work, Aim 1 will conduct a national survey of VAMCs to assess current practices around EA screening and detection; VA facility-level data will be used to assess structural characteristics associated with screening. Aim 2 will examine three potential EA suspicion AMs and select the best performing via comparison to a multi-component reference standard consisting of: a) simplified rule-based NLP of progress note content, and b) evaluation of discordance between AMs and NLP through targeted medical record review. In Aim 3, early-, recent-, and non-EA screener sites identified in Aim 1 varying in EA case volume according to the AM selected in Aim 2 will be recruited for in- depth qualitative interviews to elucidate opportunities for, facilitators of, and barriers to EA detection programs in VA. Implementation/Next steps. Findings from this research will be used to derive and validate a novel EA Suspicion Tool (EAST) in VA, then develop and implement a detection approach that improves efficiency and impact by combining improved EA screening with comprehensive EA assessments targeted towards those at highest risk. Candidate. Dr. Lena Makaroun is a geriatrician and Core Investigator at the VA Center for Health Equity Research and Promotion. The goal of this CDA is to gain training and research experience in improving EA detection among older Veterans through in-depth training in: (1) real-world EA evaluation and intervention programs; (2) implementation science; (3) framework-guided qualitative methods; and (4) prediction analytics. This CDA will support Dr. Makaroun’s long-term career goal of becoming an independent VA health services researcher focused on improving care delivery, intervention and, ultimately, prevention of EA in older adults.