Abstract:

Objective　To study the status and the risk factors of dental caries in high-risk elderly people in Liaoning，in order to provide basic research data for establishing Logistic model of high-risk elderly people with dental caries. Methods　From May to December 2015，1144 elderly people aged 65～74 were randomly selected to take an oral examination， and a questionnaire survey was also performed. Chi square tests and multifactor Logistic regression analysis were used to analyze the results by SPSS13.0 software. Results　The dental caries prevalence was 68.53%. Elderly people living in urban areas had no significant difference with those living in rural areas on caries prevalence（73.12% vs. 63.92%，P > 0.05）. The same status existed in gender difference（67.78% vs. 69.27%，P > 0.05）. A multiple Logistic regression analysis showed that rural residence （OR = 0.676，95% CI：0.503-0.908），seldom smoking （OR = 0.681，95% CI：0.502-0.923），and seldom drinking （OR = 0.628，95% CI：0.446-0.885） were negatively associated with dental caries in elderly people. The experience of toothache in the recent one year （OR = 1.550，95% CI：1.164-2.063） was the risk factor of the dental caries. Conclusion　The prevalence of dental caries is high in elderly people in Liaoning，with experience of toothache in the past 1 year being positive determints and rural residence，seldom smoking，and seldom drinking being negative determints.

Key words: dental caries in the elderly, high-risk people, Logistic regression model

摘要：

目的　了解辽宁省老年人群患龋状况，分析影响龋病发生的危险因素，为建立老年龋病高危人群Logistic模型提供基线研究资料。方法　2015年5—12月，采用随机抽样方法对辽宁省65 ~ 74岁年龄组的城乡居民1144名进行龋病临床调查，并进行问卷调查。采用SPSS13.0统计软件对患病情况和相关因素进行χ2检验及多因素Logistic回归分析。结果　辽宁省老年人群患龋率为68.53%，城市患龋率为73.12%，农村患龋率为63.92%，城乡间差异无统计学意义（P＞0.05）；男性患龋率为67.78%，女性患龋率为69.27%，性别间差异无统计学意义（P＞0.05）。Logistic回归分析显示：过去1年有牙痛史（OR = 1.550，95%CI：1.164-2.063）是老年人易患龋的危险因素；农村户口（OR = 0.676，95%CI：0.503-0.908）、不吸烟（OR = 0.681，95%CI：0.502-0.923）、不饮酒（OR = 0.628，95%CI：0.446-0.885）是老年人患龋的保护因素。结论    辽宁省老年人患龋率较高，口腔保健意识不强；过去一年有牙痛史、城市居民、有饮酒和吸烟习惯者更易患龋。

关键词: 老年龋, 高危人群, Logistic回归模型