Predictive models of weakness among older adults: the contribution of oral health indicators

老年人虚弱预测模型:口腔健康指标的作用

阅读:3

Abstract

Poor oral health can negatively impact overall health and quality of life. Understanding how oral health predicts weakness in older adults is critical, since weakness increases the risk of health outcomes. However, the predictive role of oral health indicators in weakness among older adults remains unclear. This study assessed the ability of oral health indicators to predict weakness using data from Brazil's EpiFloripa Aging cohort study. Predictive validity was evaluated in a sample of older adults participating in the cohort's second (n = 440) and third (n = 347) waves. Self-reported sociodemographic, general health, and oral health variables were analyzed, with weakness diagnosed using cut-off points for handgrip strength. Predictive models incorporating sociodemographic, general health, and oral health variables were tested. Receiver operating characteristic curves, sensitivity and specificity, and positive and negative predictive values were calculated. Approximately 45.9% of the participants had two to three compromised oral health indicators during the second wave, and the five-year incidence of weakness was 31.9%. Oral health indicators and the oral frailty score did not enhance the prediction of weakness compared to models based solely on demographic, socioeconomic, and general health variables. However, models including oral health indicators demonstrated predictive accuracy comparable to those with demographic, socioeconomic, and general health variables. Sensitivity values were low (3.70-6.48%), while specificity values were high (>99%), with accuracy ranging from 0.64 to 0.71. These findings suggest that oral health indicators offer comparable predictive validity for weakness as sociodemographic and general health models, potentially serving as useful tools for health teams in screening older adults for weakness.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。