Literature DB >> 32311276

Caries Risk Prediction Models in a Medical Health Care Setting.

T A Kalhan1, C Un Lam2, B Karunakaran1, P L Chay3, C K Chng3, R Nair4,5, Y S Lee6,7, M C F Fong8, Y S Chong2,9, K Kwek3, S M Saw8, L Shek10, F Yap11,12,13, K H Tan3,14, K M Godfrey15, J Huang16, C-Y S Hsu1.   

Abstract

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 (n = 535) and 3 y (n = 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 P < 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.

Entities:  

Keywords:  cohort studies; deciduous tooth; oral health; pediatricians; preschool; risk assessment

Mesh:

Year:  2020        PMID: 32311276      PMCID: PMC7343522          DOI: 10.1177/0022034520913476

Source DB:  PubMed          Journal:  J Dent Res        ISSN: 0022-0345            Impact factor:   6.116


  38 in total

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4.  Identification of caries risk factors in toddlers.

Authors:  M Fontana; R Jackson; G Eckert; N Swigonski; J Chin; A Ferreira Zandona; M Ando; G K Stookey; S Downs; D T Zero
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5.  Dental caries prevalence and distribution among preschoolers in Singapore.

Authors:  X L Gao; C Y S Hsu; T Loh; D Koh; H B Hwamg; Y Xu
Journal:  Community Dent Health       Date:  2009-03       Impact factor: 1.349

6.  Caries prevalence and associations with medications and medical comorbidities.

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8.  In vitro effect of tobacco on the growth of oral cariogenic streptococci.

Authors:  R G Lindemeyer; R H Baum; S C Hsu; R E Going
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9.  The effect of maternal smoking during pregnancy and postnatal household smoking on dental caries in young children.

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Journal:  J Pediatr       Date:  2009-06-24       Impact factor: 4.406

10.  Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016.

Authors: 
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2.  Caries risk assessment-related knowledge, attitude, and behaviors among Chinese dentists: a cross-sectional survey.

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3.  Prediction of Early Childhood Caries Based on Single Nucleotide Polymorphisms Using Neural Networks.

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