日本长期护理认证风险因素的年龄相关差异:跨越3年的决策树分析
Kenji Tsuchiya1, Kazuki Kitazawa1, Tomomi Furukawa1
1Nagano University of Health and Medicine, Nagano, Japan.
概括
这项研究确定了日本长期护理认证的主要风险因素.
科学领域:
- 老年学是一门学科.
- 公共卫生 公共卫生
- 生物统计学 生物统计学
背景情况:
- 老龄化社会面临着确保健康老龄化和延长健康寿命的挑战.
- 长期护理 (LTC) 认证是老年人群健康状况的关键指标.
- 识别LTC认证的早期风险因素对于及时干预至关重要.
研究的目的:
- 在三年内分析长期护理 (LTC) 认证的风险模式.
- 在日本快速老龄化的人口中调查这些模式.
- 利用Kihon检查清单 (KCL) 和机器学习进行风险预测.
主要方法:
- 分析来自日本Iiyama市65岁以上成年人的数据.
- 详尽的千平方自动交互探测器 (CHAID) 决策树的应用.
- 独立变量包括年龄,性别和6个KCL领域 (身体力量,营养,口腔功能,隔离,记忆,情绪).
主要成果:
- 年龄是LTC认证的最强有力的预测因素.
- 在80岁以上的个体中,认知功能低下和抑郁症是显著的危险因素.
- 在年轻的老年人中,身体虚弱与LTC认证的相关性更强.
- 在数据集中发现了三个一致的风险模式.
结论:
- 针对年龄的风险分层对于有效的干预措施至关重要.
- 基于与年龄相关的风险概况,可以制定有针对性的策略.
- 防止LTC认证需要针对老年人群中不同年龄组的量身定制方法.
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