Faculty of Dentistry, National University of Singapore, Singapore.
Chief Dental Officer's Office, Ministry of Health, College of Medicine Building, Singapore.
J Dent Res. 2020 Jul;99(7):787-796. doi: 10.1177/0022034520913476. Epub 2020 Apr 20.
Despite development of new technologies for caries control, tooth decay in primary teeth remains a major global health problem. Caries risk assessment (CRA) models for toddlers and preschoolers are rare. Among them, almost all models use dental factors (e.g., past caries experience) to predict future caries risk, with limited clinical/community applicability owing to relatively uncommon dental visits compared to frequent medical visits during the first year of life. The objective of this study was to construct and evaluate risk prediction models using information easily accessible to medical practitioners to forecast caries at 2 and 3 y of age. Data were obtained from the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) mother-offspring cohort. Caries was diagnosed using modified International Caries Detection and Assessment System criteria. Risk prediction models were constructed using multivariable logistic regression coupled with receiver operating characteristic analyses. Imputation was performed using multiple imputation by chained equations to assess effect of missing data. Caries rates at ages 2 y ( = 535) and 3 y ( = 721) were 17.8% and 42.9%, respectively. Risk prediction models predicting overall caries risk at 2 and 3 y demonstrated area under the curve (AUC) (95% confidence interval) of 0.81 (0.75-0.87) and 0.79 (0.74-0.84), respectively, while those predicting moderate to extensive lesions showed 0.91 (0.85-0.97) and 0.79 (0.73-0.85), respectively. Postimputation results showed reduced AUC of 0.75 (0.74-0.81) and 0.71 (0.67-0.75) at years 2 and 3, respectively, for overall caries risk, while AUC was 0.84 (0.76-0.92) and 0.75 (0.70-0.80), respectively, for moderate to extensive caries. Addition of anterior caries significantly increased AUC in all year 3 models with or without imputation (all < 0.05). Significant predictors/protectors were identified, including ethnicity, prenatal tobacco smoke exposure, history of allergies before 12 mo, history of chronic maternal illness, maternal brushing frequency, childbearing age, and so on. Integrating oral-general health care using medical CRA models may be promising in screening caries-susceptible infants/toddlers, especially when medical professionals are trained to "lift the lip" to identify anterior caries lesions.
尽管在控制龋齿方面有了新技术的发展,但乳牙龋齿仍然是一个全球性的健康问题。对于幼儿和学龄前儿童的龋齿风险评估(CRA)模型很少见。其中,几乎所有模型都使用牙齿因素(例如,过去的龋齿经历)来预测未来的龋齿风险,但由于与生命第一年相比,牙科就诊相对较少,因此临床/社区适用性有限。本研究的目的是构建和评估使用易于医疗从业者获得的信息来预测 2 岁和 3 岁时龋齿的风险预测模型。数据来自新加坡幼儿成长至健康结果(GUSTO)母婴队列。龋齿的诊断采用改良的国际龋齿检测和评估系统标准。使用多变量逻辑回归结合受试者工作特征分析构建风险预测模型。使用链式方程的多重插补来评估缺失数据的影响。2 岁(=535)和 3 岁(=721)时的龋齿发生率分别为 17.8%和 42.9%。预测 2 岁和 3 岁时总体龋齿风险的风险预测模型显示曲线下面积(AUC)(95%置信区间)分别为 0.81(0.75-0.87)和 0.79(0.74-0.84),而预测中度至广泛病变的模型分别为 0.91(0.85-0.97)和 0.79(0.73-0.85)。插补后结果显示,2 岁和 3 岁时总体龋齿风险的 AUC 分别降低至 0.75(0.74-0.81)和 0.71(0.67-0.75),而中度至广泛龋齿的 AUC 分别为 0.84(0.76-0.92)和 0.75(0.70-0.80)。在所有有或没有插补的 3 岁模型中,添加前牙龋齿都会显著增加 AUC(均<0.05)。确定了有意义的预测因子/保护因子,包括种族、产前吸烟、12 个月前过敏史、慢性母亲疾病史、母亲刷牙频率、生育年龄等。使用医疗 CRA 模型整合口腔-全身健康护理可能有望筛选出龋齿易感婴儿/幼儿,尤其是当医疗专业人员接受“抬起嘴唇”以识别前牙龋齿病变的培训时。