Biostatistics, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC, USA.
Division of Comprehensive Oral Health University of North Carolina at Chapel Hill, Adams School of Dentistry, Chapel Hill, NC, USA.
JDR Clin Trans Res. 2023 Oct;8(4):384-393. doi: 10.1177/23800844221112062. Epub 2022 Aug 9.
Edentulism affects health and quality of life.
Identify factors that predict older adults becoming edentulous over 12 y in the US Health and Retirement Study (HRS) by developing and validating a prediction model.
The HRS includes data on a representative sample of US adults aged >50 y. Selection criteria included participants in 2006 and 2018 who answered, "Have you lost all of your upper and lower natural permanent teeth?" Persons who answered "no" in 2006 and "yes" in 2018 experienced incident edentulism. Excluding 2006 edentulous, the data set ( = 4,288) was split into selection (70%, = 3,002) and test data (30%, = 1,286), and Monte Carlo cross-validation was applied to 500 random partitions of the selection data into training ( = 1,716) and validation ( = 1,286) data sets. Fitted logistic models from the training data sets were applied to the validation data sets to obtain area under the curve (AUC) for 32 candidate models. Six variables were included in all models (age, race/ethnicity, gender, education, smoking, last dental visit) while all combinations of 5 variables (income, alcohol use, self-rated health, loneliness, cognitive status) were considered for inclusion. The best parsimonious model based on highest mean AUC was fitted to the selection data set to obtain a final prediction equation. It was applied to the test data to estimate AUC and 95% confidence interval using 1,000 bootstrap samples.
From 2006 to 2018, 9.7% of older adults became edentulous. The 2006 mean (SD) age was 66.7 (8.7) for newly edentulous and 66.3 (8.4) for dentate ( = 0.31). The baseline 6-variable model mean AUC was 0.740. The 7-variable model with cognition had AUC = 0.749 and test data AUC = 0.748 (95% confidence interval, 0.715-0.781), modestly improving prediction. Negligible improvement was gained from adding more variables.
Cognition information improved the 12-y prediction of becoming edentulous beyond the modifiable risk factors of smoking and dental care use, as well as nonmodifiable demographic factors.
This prediction modeling and validation study identifies cognition as well as modifiable (dental care use, smoking) and nonmodifiable factors (race, ethnicity, gender, age, education) associated with incident complete tooth loss in the United States. This information is useful for the public, dental care providers, and health policy makers in improving approaches to preventive care, oral and general health, and quality of life for older adults.
牙缺失会影响健康和生活质量。
通过开发和验证预测模型，确定美国健康与退休研究（HRS）中 12 年内老年人成为无牙的预测因素。
HRS 包括对美国 50 岁以上成年人的代表性样本的数据。入选标准包括 2006 年和 2018 年回答“你是否失去了所有的上、下天然恒牙？”的参与者。在 2006 年回答“否”，而在 2018 年回答“是”的人经历了无牙事件。排除 2006 年无牙的参与者，数据集（=4288）分为选择数据（70%，=3002）和测试数据（30%，=1286），并对选择数据的 500 个随机分区应用蒙特卡罗交叉验证，分为训练数据（=1716）和验证数据（=1286）。从训练数据集拟合的逻辑模型应用于验证数据集，以获得 32 个候选模型的曲线下面积（AUC）。所有模型都包含 6 个变量（年龄、种族/民族、性别、教育、吸烟、最近的牙科就诊），同时考虑了所有 5 个变量（收入、饮酒、自我评估健康、孤独、认知状况）的所有组合。基于最高平均 AUC 的最佳简约模型拟合选择数据集，以获得最终预测方程。将其应用于测试数据，使用 1000 个引导样本估计 AUC 和 95%置信区间。
从 2006 年到 2018 年，9.7%的老年人出现牙缺失。新无牙者的 2006 年平均（SD）年龄为 66.7（8.7），有牙者为 66.3（8.4）（=0.31）。基线 6 变量模型的平均 AUC 为 0.740。具有认知功能的 7 变量模型 AUC=0.749，测试数据 AUC=0.748（95%置信区间，0.715-0.781），适度提高了预测能力。增加更多变量几乎没有带来改善。
认知信息除了可改变的风险因素（吸烟和口腔护理使用）以及不可改变的人口统计学因素（种族、民族、性别、年龄、教育）之外，还可以提高 12 年成为无牙的预测。
这项预测模型建立和验证研究确定了认知以及可改变的（口腔护理使用、吸烟）和不可改变的因素（种族、民族、性别、年龄、教育）与美国全口牙缺失事件的相关性。这些信息对公众、口腔护理提供者和卫生政策制定者都有用，可以改善对老年人的预防保健、口腔和整体健康以及生活质量的方法。
