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使用线性回归和结构方程建模的老年人中自我感知口腔功能的评估

Linxin Jiang¹, Simin Li², Daniel R Reissmann³

¹Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, Guangdong, PR China; Department of Prosthodontics, University of Leipzig, Liebigstr. 12, 04103 Leipzig, Germany.

Journal of dentistry

|July 16, 2025

概括

口腔实践是识别老年人口腔功能下降的关键. 这项研究开发了一个预测模型,使用人口统计数据,牙科诊所,知识和健康状况来改善口腔健康管理.

科学领域:

老年学是指老年学的学科.
口腔健康口腔健康
流行病学流行病学

背景情况:

全球人口老龄化增加了老年人口腔功能下降的患病率.
对口腔功能的自我感知对于早期检测和干预至关重要.
需要有效的预测模型,以更好地管理老龄化人口的口腔健康.

研究的目的:

确定与老年人自我感知口腔功能相关的因素.
验证口腔低功能的预测模型的结构完整性.
提升口腔功能下降的早期识别和个性化预防策略.

主要方法:

一项大规模的横截面调查,对423名65-74岁的中国成年人 (2021年).
分析包括斯皮尔曼相关性,曼-惠特尼U,克鲁斯卡尔-瓦利斯测试和多重线性回归.
结构方程建模 (SEM) 和确认因素分析 (CFA) 用于验证该模型.

主要成果:

回归模型解释了自我感知口腔功能变异的21.6% (p < 0.001).
有意义的预测因素包括性别,去看牙医的原因,饮食习惯,刷牙频率,牙,结石,失恋和全身性疾病 (p < 0.05).
在SEM中,可接受的模型匹配 (χ2/df=1.865,RMSEA=0.045,GFI=0.982,CFI=0.823).

结论:

口腔实践是自我感知口腔功能最重要的预测因素.
SEM证实了回归模型的有效性,提供了一种可靠的方法来确定口腔功能缺陷风险.
纳入自我感知措施可以提高口腔功能下降的早期检测和个性化预防策略.

关键词:

流行病学流行病学线性回归分析线性回归分析年长的成年人.风险评估风险评估风险评估结构方程建模结构方程建模

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