Identifying Risk and Improving Care for Elder Abuse among Veterans

识别退伍军人中虐待老年人的风险并改善护理

• 批准号: 10620201
• 负责人: Lena Makaroun
• 金额: --
• 依托单位: VETERANS HEALTH ADMINISTRATION
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10620201
• 关键词: Address; Adopted; Adult; Affect; Age; Aging; Caregivers; Caring; Characteristics; Clinical; Clinical Pathways; Code; Cognitive; Complement; Complex; Country; Data; Data Element; Dedications; Dementia; Detection; Development; Diagnostic; Early identification; Elder Abuse; Elder abuse prevention; Elderly; Evaluation; Family Caregiver; Future; Geriatric Assessment; Geriatrics; Goals; Harm Reduction; Health; Health Care Visit; Health Services; Health system; Healthcare; Healthcare Systems; High Prevalence; Hospitalization; Hybrids; Informatics; Injury; Integrated Health Care Systems; International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10); Intervention; Interview; Knowledge; Leadership; Link; Long-Term Care for Elderly; Managed Care; Manuals; Medical Care Team; Medical Records; Medical center; Mental Depression; Mental Health; Methodology; Methods; Mission; Morbidity - disease rate; Natural Language Processing; Older Population; Outcome; Patients; Personal Satisfaction; Persons; Population; Positioning Attribute; Predictive Analytics; Prevalence; Process; Provider; Qualitative Methods; Quality of Care; Quantitative Evaluations; Questionnaires; Reference Standards; Research; Research Personnel; Resources; Risk; Risk Assessment; Risk Factors; Screening procedure; Services; Sexual abuse; Site; Social Problems; Social Work; Standardization; Surveys; Testing; Training; Trust; United States; Veterans; Work; aging population; candidate marker; care delivery; career; caregiving; effectiveness testing; emotional abuse; evidence base; experience; financial exploitation; future implementation; health equity; health service use; high risk; high risk population; implementation barriers; implementation science; implementation strategy; improved; informant; innovation; intervention program; mortality; neglect; novel; novel strategies; physical abuse; programs; public health priorities; recruit; risk mitigation; screening; skills; social; tool; whole health; Address; Adopted; Adult; Affect; Age; Aging; Caregivers; Caring; Characteristics; Clinical; Clinical Pathways; Code; Cognitive; Complement; Complex; Country; Data; Data Element; Dedications; Dementia; Detection; Development; Diagnostic; Early identification; Elder Abuse; Elder abuse prevention; Elderly; Evaluation; Family Caregiver; Future; Geriatric Assessment; Geriatrics; Goals; Harm Reduction; Health; Health Care Visit; Health Services; Health system; Healthcare; Healthcare Systems; High Prevalence; Hospitalization; Hybrids; Informatics; Injury; Integrated Health Care Systems; International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10); Intervention; Interview; Knowledge; Leadership; Link; Long-Term Care for Elderly; Managed Care; Manuals; Medical Care Team; Medical Records; Medical center; Mental Depression; Mental Health; Methodology; Methods; Mission; Morbidity - disease rate; Natural Language Processing; Older Population; Outcome; Patients; Personal Satisfaction; Persons; Population; Positioning Attribute; Predictive Analytics; Prevalence; Process; Provider; Qualitative Methods; Quality of Care; Quantitative Evaluations; Questionnaires; Reference Standards; Research; Research Personnel; Resources; Risk; Risk Assessment; Risk Factors; Screening procedure; Services; Sexual abuse; Site; Social Problems; Social Work; Standardization; Surveys; Testing; Training; Trust; United States; Veterans; Work; aging population; candidate marker; care delivery; career; caregiving; effectiveness testing; emotional abuse; evidence base; experience; financial exploitation; future implementation; health equity; health service use; high risk; high risk population; implementation barriers; implementation science; implementation strategy; improved; informant; innovation; intervention program; mortality; neglect; novel; novel strategies; physical abuse; programs; public health priorities; recruit; risk mitigation; screening; skills; social; tool; whole health

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.

背景。虐待老人（EA）是身体，性或心理虐待，财务剥削或忽视 成人年龄≥60岁。每年在美国每年有十分之一的老年人经验，拥有许多经验 多种类型。由于EA风险因素的高流行率，退伍军人处于特别高的风险 人口。 EA EA与抑郁症，伤害，医疗保健使用和死亡率增加有关，但需求 它的患病率和发病率不到5％的病例，限制了干预的机会。尽管 筛选是改善对类似条件的检测的常见方法，EA的筛选工具尚未 经过良好的验证或广泛研究。此外，EA筛查可能会错过重要的高风险人群， 例如那些患有痴呆症的人，有必要制定完成的其他检测策略 筛选。这项研究旨在通过评估和优化当前的EA来改善VA中的EA风险检测 筛选方法并利用VA医疗保健数据来识别具有临床怀疑的退伍军人 谁可以从进一步的评估中受益。意义/影响。随着老年人人数不断增长 美国和超过1000万美国退伍军人年龄≥60岁，改善EA的检测和干预措施是 国家和VA公共卫生优先事项。通过通过更有信息的筛选和 这项研究的新型数据驱动工具与VA HSR＆D的优先级相吻合，以改善对我们国家衰老的护理 退伍军人及其护理人员。创新。这项研究将虐待老年人和实施科学整合在一起 概念框架开发了改进EA检测的新方法。这项研究将评估测试 使用唯一的VA数据元素可疑EA的第一个数据标记的特征，并将采用 创新的数据信息信息方法，例如自然语言处理（NLP），以解决复杂的 社会问题带有巨大的健康影响。具体目标。 AIM 1是对当前的国家评估 VA医疗中心（VAMC）的EA筛查实践的景观和设施的定量评估 与筛查相关的水平因素。 AIM 2是一项定量研究，它将确定性能最佳的EA VA数据中的管理标记（AM）。 AIM 3是一项定性研究，将阐明机会 在VA中实施基于医疗保健的EA检测计划的促进者和障碍。方法论。 与VA护理管理和社会工作办公室密切合作，AIM 1将进行国家 对VAMC的调查，以评估围绕EA筛查和检测的当前实践； VA设施级数据将是 用于评估与筛选相关的结构特征。 AIM 2将检查三个潜在的EA 可疑AM并通过比较多组分参考标准选择最佳性能 由：a）进度注释内容的简化基于规则的NLP，b）评估不一致 AMS和NLP通过有针对性的病历审查。在AIM 3，早期，最近和非AEA筛查网站 在AIM 1中确定的在EA病例量中根据AIM 2中选定的AM的识别将被招募用于In- 深度定性访谈，以阐明EA检测计划的机会，促进者和障碍的机会 在弗吉尼亚州。实施/下一步。这项研究的发现将用于得出和验证新型EA VA中的可疑工具（东方），然后开发和实施一种检测方法，以提高效率和 通过将改进的EA筛查与针对AT的全面EA评估相结合的影响 最高风险。候选人。莉娜·马卡鲁恩（Lena Makaroun）博士是弗吉尼亚州卫生中心的老年医生和核心调查员 股票研究和促进。该CDA的目的是获得改进的培训和研究经验 通过深入的培训在：（1）现实世界中的EA评估和干预措施中，在老年退伍军人中检测到EA 程序； （2）实施科学； （3）框架引导定性方法； （4）预测分析。 该CDA将支持Makaroun博士成为独立VA卫生服务的长期职业目标 研究人员致力于改善医疗服务，干预措施，并最终预防老年人的EA。