在COVID-19大流行之前,预测社区居住退伍军人中长期制度化风险
Bruce Kinosian1,2,3,4, Susan Schmitt1,5,6, Matthew Augustine1,7,8
1Geriatrics and Extended Care Data Analysis Center (GECDAC), Bronx, New York, USA.
Health services research
|July 27, 2025
概括
一个新的模型预测了退伍军人的长期制度化 (LTI) 风险. PLI评分识别了高风险个体,使得有针对性的干预措施能够支持社区生活.
科学领域:
- 老年学是指老年学的学科.
- 医疗保健服务研究 医疗服务研究
- 生物统计学 生物统计学
背景情况:
- 识别面临长期制度化 (LTI) 风险的退伍军人对于资源分配至关重要.
- 退伍军人健康管理局 (VA) 旨在优化护理提供,并为其受益者支持社区居住.
研究的目的:
- 开发和验证退伍军人长期制度化 (LTI) 风险的预测模型.
- 确定需要有针对性的干预措施的退伍军人,以防止安置在养老院.
主要方法:
- 预测长期制度化 (PLI) 风险模型是使用VA和Medicare索赔和最小数据集数据开发的.
- 该模型结合了人口统计数据,诊断,先前的医疗保健利用率,以及死亡和脆弱性风险指数.
- 使用年龄规范化和差异性风险层值来提高预测准确度并最大限度地减少偏差.
主要成果:
- 最终的PLI模型实现了0.87的c-统计,确定3.5%的退伍军人患有LTI的高风险.
- 高风险退伍军人 (PLI-high) 的LTI率为13%,占所有新增LTI的41%.
- 与基线模型相比,PLI模型表现出更好的灵敏度 (41%),具有96.6%的特异性.
结论:
- PLI评分有效地识别了长期住院风险较高的退伍军人.
- 这种风险分层使得有针对性的评估和资源分配能够支持退伍军人继续在社区居住.
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