Behavioral and Microbial Differences in Dental Caries among 5-Year-Old Children in Urban and Rural Yunnan: A Multifactorial Machine Learning Study

Behavioral and Microbial Differences in Dental Caries among 5-Year-Old Children in Urban and Rural Yunnan: A Multifactorial Machine Learning Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Behavioral and Microbial Differences in Dental Caries among 5-Year-Old Children in Urban and Rural Yunnan: A Multifactorial Machine Learning Study Yuexiao Li, Yifan Chen, Ruiyi Pu, Zhengxian Zhu, Tingru Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8112291/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Objective Yunnan, southwestern China, experiences uneven economic development and an unbalanced distribution of medical resources. Herein, 5-year-old children from Hongta District (urban) and Fengqing County (mountainous) were recruited to investigate oral health conditions. After integrating oral health behavior surveys with plaque microbiome analysis, machine learning identified influencing factors and microbial communities, facilitating diagnostic model construction and revealing how regional disparities affect dental caries in children. Methods We randomly selected 30 caries-free (CF group) and 30 caries-affected (DC group) 5-year-old children from each location and conducted oral epidemiological examinations, oral health behavior questionnaires, and 16S rRNA sequencing of dental plaque samples. Behavioral differences and genus-level microbial abundance were compared across locations. Random forest models analyzed high-risk factors, identified key microbial communities, and assessed diagnostic performance. Results Questionnaire analysis revealed significant differences between the DC and CF groups in location, dessert consumption frequency, and nighttime postbrushing sugar intake (NPSI). By location, Fengqing County and Hongta District differed significantly in NPSI and fluoridated toothpaste use. Plaque analysis showed significant phylum-level differences between the DC and CF groups for Bacteroidetes , Fusobacteria , and Proteobacteria . A phylum-level diagnostic model highlighted Fusobacteria as a diagnostic marker [area under the curve (AUC): 0.737]. Least absolute shrinkage and selection operator analysis identified three key genera—namely Capnocytophaga , Haemophilus , and Comamonas —with Capnocytophaga aiding diagnosis (AUC: 0.720). Adding location, dessert consumption, and NPSI to the model further improved diagnostic performance (AUC: 1). Conclusion Regional socioeconomic disparities influenced dental caries prevalence in 5-year-old children, reflected in behavioral and microbial differences. Dental caries Children Microbiology Machine learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Background Oral health is a key indicator of a nation’s overall health and quality of life. Poor oral health not only causes pain but also increases society’s economic burden [ 1 ]. Studies have indicated that 60% of dental caries occur in 36% of children [ 2 ], suggesting that childhood caries distribution is uneven, with some children at higher risk. In China, the Fourth National Oral Health Epidemiological Survey (2017) found that 71.9% of 5-year-old children had caries. The prevalence of childhood caries remains high and continues to rise in some regions and populations. With the release of the “Healthy China 2030” plan, national policies have established stricter goals for dental caries prevention and treatment [ 3 ]. Yunnan, in southwestern China, is characterized by mountainous terrain, poor transportation, slow economic growth, and limited medical resources. It has many ethnic minority groups, often living in remote mountainous areas with inadequate infrastructure. The prevalence of dental diseases such as caries makes Yunnan a weak point in national oral health maintenance [ 4 , 5 ]. Yao et al. found that the caries rate (and mean caries experience) in the deciduous teeth of 5-year-old children in rural Yunnan was 80.2% (5.0 ± 4.8), higher than national results for this age group [71.9% (4.1 ± 4.4)] [ 6 , 7 ]. Similarly, Yi et al. reported a 78.3% caries rate among 5-year-old Lahu children in Linxiang District, Lincang City, which also exceeded national results [ 8 ]. Dental caries development is influenced by multiple factors, including host traits, microbial composition, and environmental exposure. Dental plaque is a critical precursor to cavities, with studies demonstrating that complex plaque microbiota play a central role in early childhood caries progression [ 9 ]. However, the effects of regional, socioeconomic, or behavioral differences on plaque microbiota remain unclear. To address this gap, our study integrates oral behavior data, microbiome analysis, and machine learning models for caries risk assessment among children in Yunnan Province. Using this approach, we identify key correlations among specific microbial profiles, disease progression, and oral health behaviors, providing insights to guide targeted microbial diagnostics and personalized prevention strategies for high-risk pediatric groups in Yunnan. Methods Subject Selection Sampling method A random selection of 30 caries-free (CF group) and 30 caries-affected (DC group) 5-year-old children was made from Hongta District, Yuxi City, and from Fengqing County, Lincang City. This study was approved by the Medical Ethics Committee of Kunming Medical University Affiliated Stomatological Hospital (No. KYKQ2021MEC0093). Inclusion and exclusion criteria Inclusion criteria included individuals who signed informed consent, CF group participants with no decayed teeth, and DC group participants with all or partially decayed primary molars or decayed lower anterior teeth. Exclusion criteria included individuals without informed consent, those not meeting DC or CF group criteria, and those with systemic diseases. Questionnaire survey A questionnaire was administered to eligible participants. This instrument was specifically developed for the present research. It covered the following: risk factors for oral disease; knowledge, attitudes, and behaviors related to oral health; history of oral diseases; and use of oral health services. Plaque collection Children included had no systemic disease, dental hard-tissue developmental disorders, or oral mucosal/periodontal disease; had not taken any antibiotics within 2 weeks before sampling; and had not received dental pulp treatment or fluoride application. Sampling began after a 2-hour fast, followed by rinsing with clean water. Finger contact was avoided to prevent cross-contamination. The same examiner used a sterile scaler to collect dental plaque from the labial and buccal surfaces of all teeth in the CF group and from surfaces adjacent to decayed tissue in the DC group. The collected plaque was applied to sterile cotton swabs, placed into 15-mL sterile EP tubes, preserved on dry ice, transported to the laboratory within 2 hours under ice bath conditions, and stored at − 80°C for later use. Analysis of Correlations and Differences in Microbial Abundance Dental plaque from 60 children was subjected to 16S rRNA sequencing. Based on the relative abundance of the top 10 phyla and top 30 genera, Spearman correlation coefficients were calculated among phyla and genera. Differences in microbial abundance between the CF and DC groups were also compared using the top 10 phyla and top 30 genera, with results visualized in group comparison charts. Random Forest and Least Absolute Shrinkage and Selection Operator Analysis of Microbiota With the seed set to 142, the createDataPartition function in the R package caret (v7.0.1) divided samples equally into a training set (DC = 15, CF = 15) and a validation set (DC = 15, CF = 15). Random forest [ 10 ], an ensemble learning algorithm integrating multiple decision trees through bagging (bootstrap aggregation), was used for model building. When a sample prediction is required, predictions from all decision trees in the forest are aggregated, and the final output is determined through majority voting. The randomForest package (v4.7-1.2) was used to construct models with DC samples and microbiota that showed significant abundance differences between DC and CF samples, selecting five microbial taxa based on the mean decrease in Gini (ntree = 500). The five taxa were then analyzed via the least absolute shrinkage and selection operator (LASSO) method to identify key microbiota. Machine Learning Four machine learning models were used for training and validation: random forest, logistic regression, support vector machine (SVM), and extreme gradient boosting (XGBoost). Random forest improves model accuracy and robustness by constructing multiple decision trees using random samples and features, making it effective for classification. Logistic regression, a generalized linear model, is widely used and easily interpreted. SVM, a supervised algorithm for regression and classification, partitions data into categories with decision boundaries while maximizing the margins between these boundaries and the nearest data instances, improving model performance and generalizability. XGBoost, an ensemble learning method based on gradient boosting trees [ 11 ], reduces prediction errors by constructing multiple decision trees, thereby enhancing model accuracy. XGBoost also supports various loss functions and regularization options, offering high performance and scalability. The pROC package (v1.18.0) in R was used to plot receiver operating characteristic (ROC) curves and calculate the area under the ROC curve (AUC) to assess model diagnostic performance for dental caries. AUC values range from 0.5 to 1.0: 0.5–0.7 indicates low accuracy, 0.7–0.9 moderate accuracy, and > 0.9 high accuracy. Precision-recall curves (PRCs) were also plotted using pROC (v1.18.0), with recall and precision on the x-axis and y-axis, respectively (curves closer to the upper right corner reflected superior model performance). Statistical Analysis Figure 1 presents the study’s technical roadmap. All data were processed in R (v4.2.2). Unless otherwise stated, comparisons of two groups of continuous variables were made using the independent Student’s t -test for normally distributed data and the Mann–Whitney U test (Wilcoxon rank sum test) for non-normal data. For comparisons of three or more groups, the Kruskal–Wallis test was applied. Spearman correlation analysis was used to determine correlation coefficients. Unless specified, all p -values were two-tailed, with p < 0.05 considered significant. Results Demographic and Oral Health-related Questionnaire Survey A questionnaire survey was conducted on the oral health status of 60 children. Based on the results, a contingency table (Table 1 ) was created for the DC and CF samples. The results revealed that location, frequency of sugary food/drink consumption (desserts), and nighttime postbrushing sugar intake (NPSI) were significantly associated with dental caries ( p < 0.05). Table 1 Demographic and behavioral characteristics of the dental caries and caries-free groups. Characteristics Dental caries group Caries-free group P-value Statistic Method n 30 30 ​​Location, n (%)​ 0.015 5.934 Chi-square test Fengqing 24 (40%) 15 (25%) Yuxi 6 (10%) 15 (25%) ​​Height (cm), mean ± SD​ 114.87 ± 4.9601 115.07 ± 5.7592 0.886 −0.144 T-test ​​Weight (kg), median (interquartile range)​ 18.8 (17.188, 20.375) 19.3 (17.5, 22.925) 0.267 Wilcoxon ​​Feeding method within the first 6 months after birth, n (%)​ 0.391 4.113 Yates’ correction Breastfeeding exclusively 8 (13.3%) 14 (23.3%) Breastfeeding is the primary method 13 (21.7%) 8 (13.3%) Completely formula-fed 0 (0%) 1 (1.7%) Formula-fed is the primary method 1 (1.7%) 1 (1.7%) Half breastfed and half formula-fed 8 (13.3%) 6 (10%) Frequency of sugary food/drink consumption: desserts, n (%)​ 0.007 15.881 Yates’ correction ≥ 2 times/day 2 (3.3%) 0 (0%) 1 time/day 3 (5%) 9 (15%) 2–6 times/week 1 (1.7%) 7 (11.7%) 1 time/week 11 (18.3%) 10 (16.7%) 1–3 times/month 8 (13.3%) 4 (6.7%) Never 5 (8.3%) 0 (0%) ​​Frequency of sugary food/drink consumption: sweetened beverages, n (%)​ 0.354 5.539 Yates’ correction ≥ 2 times/day 1 (1.7%) 3 (5%) 1 time/day 8 (13.3%) 14 (23.3%) 2–6 times/week 7 (11.7%) 6 (10%) 1 time/week 8 (13.3%) 5 (8.3%) 1–3 times/month 4 (6.7%) 1 (1.7%) Never 2 (3.3%) 1 (1.7%) ​​Frequency of sugary food/drink consumption: sweetened dairy products, n (%)​ 0.603 3.634 Yates’ correction ≥ 2 times/day 4 (6.7%) 4 (6.7%) 1 time/day 9 (15%) 10 (16.7%) 2–6 times/week 5 (8.3%) 8 (13.3%) 1 time/week 7 (11.7%) 2 (3.3%) 1–3 times/month 4 (6.7%) 5 (8.3%) Never 1 (1.7%) 1 (1.7%) ​​Nighttime postbrushing sugar intake, n (%)​ 0.020 11.697 Yates’ correction 1 time/day 4 (6.7%) 4 (6.7%) 2–6 times/week 10 (16.7%) 3 (5%) 1 time/week 5 (8.3%) 1 (1.7%) 1–3 times/month 5 (8.3%) 5 (8.3%) Never 6 (10%) 17 (28.3%) ​​Age at which toothbrushing was initiated, n (%)​ 0.410 5.048 Yates’ correction 6–12 month 1 (1.7%) 4 (6.7%) 1–2 years old 9 (15%) 9 (15%) 2–3 years old 10 (16.7%) 11 (18.3%) 3–4 years old 6 (10%) 4 (6.7%) 4–5 years old 4 (6.7%) 1 (1.7%) 5 years and older 0 (0%) 1 (1.7%) ​​Daily toothbrushing frequency, n (%)​ 0.856 0.311 Yates’ correction > 2 times 19 (31.7%) 17 (28.3%) 1 times 9 (15%) 11 (18.3%) Not every day 2 (3.3%) 2 (3.3%) ​​Parent-assisted toothbrushing frequency, n (%)​ 0.089 8.073 Yates’ correction 1–2 times/day 9 (15%) 9 (15%) 2–6 times/week 12 (20%) 4 (6.7%) 1 time/week 1 (1.7%) 4 (6.7%) 1–3 times/month 5 (8.3%) 5 (8.3%) Never 3 (5%) 8 (13.3%) ​​Use of fluoridated toothpaste, n (%)​ 0.853 0.317 Chi-square test Yes 8 (13.3%) 10 (16.7%) No 11 (18.3%) 10 (16.7%) Unknown 11 (18.3%) 10 (16.7%) ​​Parent-assisted dental flossing frequency, n (%)​ 0.601 1.020 Yates’ correction Not used 26 (43.3%) 25 (41.7%) Occasionally 4 (6.7%) 4 (6.7%) Weekly 0 (0%) 1 (1.7%) A contingency table comparing Fengqing and Yuxi samples (Table 2 ) revealed significant differences in NPSI and fluoridated toothpaste use between the groups ( p < 0.05). This indicates that regional differences in caries may be related to these behavioral factors. Table 2 Demographic and behavioral characteristics of Fengqing and Yuxi samples. Characteristics Fengqing Yuxi P-value Statistic Method n 39 21 ​​Height (cm), mean ± SD​ 114.54 ± 5.0982 115.76 ± 5.7784 0.400998 −0.84606 T-test ​​Weight (kg), median (IQR)​ 18.9 (17.375, 22.1) 19.8 (17.5, 22.6) 0.587349 Wilcoxon rank sum test ​​Feeding method within the first 6 months after birth, n (%)​ 0.553084 3.0284 Yates’ correction Breastfeeding exclusively 13 (21.7%) 9 (15%) Breastfeeding is the primary method 15 (25%) 6 (10%) Completely formula-fed 0 (0%) 1 (1.7%) Formula-fed is the primary method 1 (1.7%) 1 (1.7%) Half breastfed and half formula-fed 10 (16.7%) 4 (6.7%) Frequency of sugary food/drink consumption: desserts, n (%)​ 0.327444 5.787546 Yates’ correction ≥ 2 times/day 2 (3.3%) 0 (0%) 1 time/day 8 (13.3%) 4 (6.7%) 2–6 times/week 4 (6.7%) 4 (6.7%) 1 time/week 14 (23.3%) 7 (11.7%) 1–3 times/month 6 (10%) 6 (10%) Never 5 (8.3%) 0 (0%) ​​Frequency of sugary food/drink consumption: sweetened beverages, n (%)​ 0.06155 10.52947 Yates’ correction ≥ 2 times/day 2 (3.3%) 2 (3.3%) 1 time/day 10 (16.7%) 12 (20%) 2–6 times/week 8 (13.3%) 5 (8.3%) 1 time/week 12 (20%) 1 (1.7%) 1–3 times/month 4 (6.7%) 1 (1.7%) Never 3 (5%) 0 (0%) ​​Frequency of sugary food/drink consumption: sweetened dairy products, n (%)​ 0.711225 2.927091 Yates’ correction ≥ 2 times/day 5 (8.3%) 3 (5%) 1 time/day 12 (20%) 7 (11.7%) 2–6 times/week 8 (13.3%) 5 (8.3%) 1 time/week 8 (13.3%) 1 (1.7%) 1–3 times/month 5 (8.3%) 4 (6.7%) Never 1 (1.7%) 1 (1.7%) ​​Nighttime postbrushing sugar intake, n (%)​ 0.000759 19.07825 Yates’ correction 1 time/day 8 (13.3%) 0 (0%) 2–6 times/week 11 (18.3%) 2 (3.3%) 1 time/week 3 (5%) 3 (5%) 1–3 times/month 9 (15%) 1 (1.7%) Never 8 (13.3%) 15 (25%) ​​Age at which toothbrushing was initiated, n (%)​ 0.25355 6.582941 Yates’ correction 6–12 month 1 (1.7%) 4 (6.7%) 1–2 years old 12 (20%) 6 (10%) 2–3 years old 13 (21.7%) 8 (13.3%) 3–4 years old 8 (13.3%) 2 (3.3%) 4–5 years old 4 (6.7%) 1 (1.7%) 5 years and older 1 (1.7%) 0 (0%) ​​Daily toothbrushing frequency, n (%)​ 0.415148 1.758242 Yates’ correction > 2 times 21 (35%) 15 (25%) 1 times 15 (25%) 5 (8.3%) Not every day 3 (5%) 1 (1.7%) ​​Parent-assisted toothbrushing frequency, n (%)​ 0.140553 6.913087 Yates’ correction 1–2 times/day 12 (20%) 6 (10%) 2–6 times/week 14 (23.3%) 2 (3.3%) 1 time/week 2 (3.3%) 3 (5%) 1–3 times/month 6 (10%) 4 (6.7%) Never 5 (8.3%) 6 (10%) ​​Use of fluoridated toothpaste, n (%)​ 0.021213 7.706262 Chi-square test Yes 7 (11.7%) 11 (18.3%) No 16 (26.7%) 5 (8.3%) Unknown 16 (26.7%) 5 (8.3%) ​​Parent-assisted dental flossing frequency, n (%)​ 0.60529 1.004094 Yates’ correction Not used 32 (53.3%) 19 (31.7%) Occasionally 6 (10%) 2 (3.3%) Weekly 1 (1.7%) 0 (0%) Correlation and Differential Analysis of Microbial Abundance Based on the relative abundance of the top 10 phyla and top 30 genera in all samples, correlations among phyla (Fig. 2A) and among genera (Fig. 2B) were calculated and visualized as heatmaps. Figure 2 Correlation analysis between microbial communities The relative abundance of the top 10 phyla was also compared between the DC and CF samples. Bacteroidetes , Fusobacteria , and Proteobacteria showed significant differences between the groups (Fig. 3 A). In Fengqing, Proteobacteria abundance differed significantly between the DC and CF groups (Fig. 3 B). In Yuxi, Fusobacteria differed significantly between these groups (Fig. 3 C). Bacteroidetes abundance also differed significantly between DC samples from Fengqing and Yuxi (Fig. 3 D). Differences in the relative abundances of the top 30 bacterial genera were then compared between DC and CF. Significant differences were found in the relative abundances of Capnocytophaga , Comamonas , Fusobacterium , Haemophilus , Leptotrichia , Ralstonia , unidentified_Veillonellaceae, and Veillonella between the two groups (Fig. 4 A). In Fengqing, Actinobacillus , Campylobacter , Capnocytophaga , Haemophilus , Porphyromonas , Prevotella , unidentified_Prevotellaceae, unidentified_Veillonellaceae, and Veillonella showed significant differences between DC and CF (Fig. 4 B). In Yuxi, significant differences were observed in Candidatus_Ishikawaella , Fusobacterium , Leptotrichia , and Ralstonia between DC and CF (Fig. 4 C). Between DC samples from Fengqing and Yuxi, significant differences occurred in Alloprevotella , Bergeyella , Campylobacter , Candidatus_Ishikawaella , Capnocytophaga , Gemella , Kingella , Neisseria , Prevotella , Streptococcus , unidentified_Prevotellaceae, Veillonella , and Wolbachia (Fig. 4 D)). Construction of Diagnostic Models With the seed set to 142, we used the createDataPartition function in the R package caret (v7.0-1) to divide samples equally into a training set (DC = 15, CF = 15) and a validation set (DC = 15, CF = 15). Using training data, four machine learning models (random forest, logistic regression, SVM, and XGBoost) were built based on the relative abundances of three phyla differing significantly between DC and CF ( Bacteroidetes , Fusobacteria , and Proteobacteria ). The models were validated using the validation set, and diagnostic ROC curves (Figs. 5 A and B) and PRCs (Figs. 5 C and D) were plotted for each model. SVM achieved the best validation performance. The diagnostic ROC curve of the three phyla (Fig. 5 E) showed that Fusobacteria had moderate diagnostic ability for DC samples (AUC = 0.737), whereas Bacteroidetes (AUC = 0.696) and Proteobacteria (AUC = 0.671) showed lower diagnostic ability. Based on significant differences in eight microbial genera ( Capnocytophaga , Comamonas , Fusobacterium , Haemophilus , Leptotrichia , Ralstonia , unidentified_Veillonellaceae, and Veillonella ) between all DC and CF samples, the random forest algorithm was applied to analyze their relative abundances (Figs. 6 A and B). The five genera with the greatest mean decrease in Gini impurity were selected for LASSO regression (Figs. 6 C and D), which identified three key taxa: Capnocytophaga , Haemophilus , and Comamonas . Using the training set data, four machine learning models (random forest, logistic regression, SVM, and XGBoost) were again constructed using the relative abundances of the three key genera, i.e., Capnocytophaga , Haemophilus , and Comamonas . The validation set was used for model testing, and ROC curves (Figs. 7 A and B) and PRCs (Figs. 7 C and D) were generated. The random forest model performed best in the validation set. Feature importance analysis (Fig. 7 E) showed that Capnocytophaga contributed most to model accuracy. The diagnostic ROC curve (Fig. 7 F) indicated that Capnocytophaga exhibited moderate diagnostic ability for DC samples (AUC = 0.720), whereas Haemophilus (AUC = 0.666) and Comamonas (AUC = 0.682) were less predictive. Construction of Clinical Diagnostic Models Using the validation set data, an SVM model was constructed combining the relative abundances of three phyla ( Bacteroidetes , Fusobacteria , and Proteobacteria ) with three features associated with dental caries (location, frequency of sugary food/drink consumption [desserts], and NPSI). The resulting diagnostic ROC curve (Fig. 8 A) and PRC (Fig. 8 B) indicated improved model performance (AUC = 1). Similarly, a random forest model was developed combining the relative abundances of the three key genera ( Capnocytophaga , Haemophilus , and Comamonas ) in the validation set with the same three features. The diagnostic ROC curve (Fig. 9 A) and PRC (Fig. 9 B) confirmed enhanced model performance (AUC = 0.933). Discussion Dental caries is a major public health issue worldwide, particularly affecting children. Its etiology is complex and influenced by multiple factors [ 12 ]. Combining microbiomics with machine learning enables more precise identification of disease-associated factors, improving early diagnostic accuracy. The present study identified several taxa at the phylum and genus levels significantly associated with dental caries, leading to the successful development of a machine learning model with strong diagnostic performance. Microbiomics analysis revealed significant differences in the abundance of Bacteroidetes , Fusobacteria , and Proteobacteria between the DC and CF groups at the phylum level. In Fengqing, Proteobacteria abundance differed significantly between the DC and CF samples, whereas in Yuxi, Fusobacteria showed a similar pattern. Bacteroidetes abundance also differed significantly between DC samples from Fengqing and those from Yuxi. At the genus level, Capnocytophaga , Haemophilus , and Comamonas were identified as key taxa using random forest and LASSO analyses. Questionnaire analysis indicated that location, frequency of sweet consumption, and eating after brushing teeth at night were associated with dental caries risk. When grouped by location, significant differences were observed in NPSI and fluoride toothpaste use between Fengqing County and Hongta District. Regarding diagnostic model construction, models based on bacterial taxa performed better when combined with questionnaire-derived features, suggesting that dental caries research should integrate microbiological and behavioral factors. The two study regions, namely Fengqing County in Lincang City and Hongta District in Yuxi City, differ markedly in socioeconomic background. Fluoride toothpaste use varies significantly between these regions, reflecting differences in economic conditions and oral health awareness. Prior studies showed that children from higher-income families are more likely to use fluoride toothpaste and maintain good oral hygiene [ 13 ]. A previous survey [ 14 ] found that although most parents know brushing should begin when teeth erupt, many lack awareness of proper fluoride concentration and usage. Fluoride toothpaste substantially reduces caries incidence [ 15 ] by lowering oral levels of cariogenic bacteria, including Haemophilus and Neisseria [ 16 ], consistent with our identified key taxa. Fluoride acts by inhibiting bacterial metabolism and disrupting biofilm formation. Once in the oral environment, it binds to bacterial membranes, alters membrane permeability, suppresses acid production, and interferes with carbohydrate metabolism, thereby reducing acid generation [ 17 , 18 ]. The regional differences in fluoride toothpaste use likely stem from varying socioeconomic and educational factors, although this requires further research. Dietary habits strongly influence caries development. High-sugar intake is a major risk factor, especially among children who frequently consume sugary snacks and beverages, markedly increasing caries susceptibility [ 19 ]. Sugars provide nutrients for cariogenic bacteria in the mouth, which ferment them into acids that demineralize enamel [ 20 , 21 ]. For example, Streptococcus mutans rapidly proliferates in sugar-rich environments, forming biofilms that promote caries [ 22 , 23 ]. Such proliferation is closely related to oral microbiome imbalance, with sugar intake being a major contributing factor [ 24 ]. Frequent sugar consumption promotes rapid adaptation of these cariogenic bacteria, creating a community dominated by acid-producing species [ 25 ]. High-sugar diets reduce microbial diversity in the oral microbiome while increasing the abundance of certain bacteria. A systematic review showed that excessive sugar intake decreases oral microbiome richness and diversity while elevating S. mutans , Scardovia , and Veillonella abundance [ 26 ]. Our study identified three key genera through LASSO regression: Capnocytophaga (higher in DC), Haemophilus (higher in CF), and Comamonas (higher in CF). Although this finding is not fully explained in the literature, it provides a foundation for future research. Brushing teeth before bed is essential for controlling plaque formation. Research has shown that nighttime brushing markedly reduces levels of cariogenic bacteria in the mouth, including S. mutans and Lactobacillus , key contributors to dental caries [ 27 ]. Shortly after brushing, the oral microbiota remains stable, helping resist pathogen invasion. Brushing not only lowers bacterial counts but also maintains a neutral oral environment that promotes remineralization, allowing tooth surfaces to self-repair and resist demineralization [ 28 ]. Eating after nighttime brushing leaves food residues that serve as substrates for bacterial metabolism. Because saliva secretion decreases at night, the mouth loses its natural cleansing function, allowing plaque to form more easily and increasing caries risk [ 29 ]. Our findings showed significant behavioral differences in nighttime eating after brushing between the DC and CF groups; however, further research is needed to determine whether this behavior affects plaque biodiversity. When constructing the machine learning–based clinical diagnostic model, we found that combining key bacterial taxa with children’s oral health behaviors effectively predicted dental caries. This result validates the utility of machine learning in public health and introduces new tools for early caries diagnosis and risk assessment. The constructed model demonstrated strong predictive ability (AUC = 0.933), providing valuable guidance for clinical practice, especially in resource-limited areas, by helping healthcare workers identify high-risk children and develop personalized interventions [ 30 ]. This successful application may encourage broader application of machine learning in other public health domains, including epidemic monitoring and chronic disease management [ 31 ]. Despite its strengths, this study has limitations. First, the small sample size may restrict the generalizability of the findings. Additionally, no laboratory-based experiments were conducted to explore underlying mechanisms. Future research should include larger, more regionally and demographically diverse samples to improve representativeness and reliability. Longitudinal study designs would also help clarify the relationship between behavioral and microbiome changes in caries development, supporting more effective public health strategies. Overall, this study highlights behavioral and microbial differences related to dental caries among 5-year-old children in urban and rural Yunnan, emphasizing the importance of integrating behavioral interventions with microbiome research to inform future oral health programs. Conclusion This study identified distinct behavioral habits and microbial community characteristics associated with dental caries in 5-year-old children from urban and rural areas of Yunnan, providing strong evidence for targeted oral health interventions. The findings highlight the need to strengthen oral health education and implement focused prevention strategies to improve children’s oral health. With continued research, these insights may support the development of broader, evidence-based interventions to reduce childhood caries and enhance population-level oral health outcomes. Abbreviations AUC: area under the curve CF: caries-free LASSO: least absolute shrinkage and selection operator NPSI: nighttime postbrushing sugar intake PRC: Precision-recall curve ROC: receiver operating characteristic SVM: support vector machine XGBoost: extreme gradient boosting Declarations Ethics approval and consent to participate This study was adhered to the Declaration of Helsinki and was approved by the Medical Ethics Committee of Kunming Medical University Affiliated Stomatological Hospital (No. KYKQ2021MEC0093). Informed consent to participate was obtained from all of the participants in the study. Informed consent was obtained from the parent or legal guardian of all minors under 16. Availability of data and materials The dataset supporting the conclusions of this article is available in the Sicence Data Bank , [DOI:10.57760/sciencedb.32617, https://www.scidb.cn/s/I7fI32]. Consent for publication: Not Applicable Clinical trial number : Not applicable Funding This work was supported by the Youth Research Fund of Yunnan Provincial Clinical Research Center for Oral Diseases [2022QN001]; and the Xingdian Talent Support Plan of Yunnan Province-Medical and Health Talents Special Project (XDYC-YLWS-2023-0047). References World Health Organization, Oral health. 2020. https://www.who.int/health-topics/oral-health/#tab=tab_1 . Accessed 1 Sept 2020. Wu XY, Wang JX, Cai T. Analysis of dental caries status and influencing factors in deciduous teeth of preschool children in Chongqing. West China J Stomatol. 2019;37:81–6. Healthy Oral Care Action Plan. (2019–2025). J Oral Care Prod Ind. 2019;29:35–6. Zhang S, Li Y, Liu J, Wang W, Ito L, Li SKY, et al. 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Additional Declarations No competing interests reported. Supplementary Files Questionnaire.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Jan, 2026 Reviews received at journal 30 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviews received at journal 16 Dec, 2025 Reviewers agreed at journal 16 Dec, 2025 Reviewers agreed at journal 16 Dec, 2025 Reviewers invited by journal 15 Dec, 2025 Editor assigned by journal 04 Dec, 2025 Submission checks completed at journal 03 Dec, 2025 First submitted to journal 03 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":83157,"visible":true,"origin":"","legend":"\u003cp\u003eTechnical Roadmap\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8112291/v1/29ecba6b17052d94e9b61230.png"},{"id":98778903,"identity":"6adad199-0d40-468e-b277-6ee06a13b8d4","added_by":"auto","created_at":"2025-12-22 12:29:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":194234,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between microbial communities\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8112291/v1/7ad8671c634af7b0ec440402.png"},{"id":98756993,"identity":"1d3c33b0-b382-4762-ae68-ad715f2ed148","added_by":"auto","created_at":"2025-12-22 09:36:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":142109,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of Differences in Phylum Level Microbial Abundance\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8112291/v1/8ba062fd26d788a8a5a17a42.png"},{"id":98756994,"identity":"0b4f9213-534c-4925-b6f9-a7ecb5ff5f26","added_by":"auto","created_at":"2025-12-22 09:36:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":416516,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the differences in the abundance of microbial communities at the genus level\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8112291/v1/d4a03837db308f384356e1f9.png"},{"id":98756999,"identity":"a372c90c-5145-4385-b985-3bfd6abf0dab","added_by":"auto","created_at":"2025-12-22 09:36:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":128759,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic Effect of the Hierarchical Diagnosis Model\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8112291/v1/d3562fd6eab5d02d7dc82e94.png"},{"id":98757003,"identity":"924da66f-839d-4e83-b376-3f918ef06b5f","added_by":"auto","created_at":"2025-12-22 09:36:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":103495,"visible":true,"origin":"","legend":"\u003cp\u003eSelection of Key Bacterial Communities at the Genus Level\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8112291/v1/9a1c0b5034c04333fa13c80d.png"},{"id":98757007,"identity":"63536cb2-c148-4064-b708-2db2131e89a9","added_by":"auto","created_at":"2025-12-22 09:36:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":134445,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic Effect of the Hierarchical Diagnosis Model\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8112291/v1/d6784b8e4bb6d20520c994ba.png"},{"id":98757018,"identity":"ccbb83a0-1fed-438a-8c0d-7b7dfe7153d0","added_by":"auto","created_at":"2025-12-22 09:36:16","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":42891,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic Effect of the Hierarchical Clinical Diagnosis Model\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8112291/v1/bf3de6e816c813df6730f420.png"},{"id":98780581,"identity":"a9e25622-be35-47be-8d74-d3038423c289","added_by":"auto","created_at":"2025-12-22 12:31:29","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":44997,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic Effect of Hierarchical Clinical Diagnosis Model\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-8112291/v1/79f0911cf27d7e5851eb7493.png"},{"id":99306825,"identity":"e6f3c38b-1b3c-48aa-b9a2-083a74960dbd","added_by":"auto","created_at":"2025-12-31 15:59:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2412278,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8112291/v1/9716250c-6cb4-4300-a293-07b0163d0b07.pdf"},{"id":98780597,"identity":"2803b872-fd8b-48f7-b790-b8f68954ecc3","added_by":"auto","created_at":"2025-12-22 12:31:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24925,"visible":true,"origin":"","legend":"","description":"","filename":"Questionnaire.docx","url":"https://assets-eu.researchsquare.com/files/rs-8112291/v1/1fbb7a0a40e5f88f0da3a0e3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Behavioral and Microbial Differences in Dental Caries among 5-Year-Old Children in Urban and Rural Yunnan: A Multifactorial Machine Learning Study","fulltext":[{"header":"Background","content":"\u003cp\u003eOral health is a key indicator of a nation\u0026rsquo;s overall health and quality of life. Poor oral health not only causes pain but also increases society\u0026rsquo;s economic burden [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Studies have indicated that 60% of dental caries occur in 36% of children [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], suggesting that childhood caries distribution is uneven, with some children at higher risk. In China, the Fourth National Oral Health Epidemiological Survey (2017) found that 71.9% of 5-year-old children had caries. The prevalence of childhood caries remains high and continues to rise in some regions and populations. With the release of the \u0026ldquo;Healthy China 2030\u0026rdquo; plan, national policies have established stricter goals for dental caries prevention and treatment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eYunnan, in southwestern China, is characterized by mountainous terrain, poor transportation, slow economic growth, and limited medical resources. It has many ethnic minority groups, often living in remote mountainous areas with inadequate infrastructure. The prevalence of dental diseases such as caries makes Yunnan a weak point in national oral health maintenance [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Yao et al. found that the caries rate (and mean caries experience) in the deciduous teeth of 5-year-old children in rural Yunnan was 80.2% (5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8), higher than national results for this age group [71.9% (4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4)] [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Similarly, Yi et al. reported a 78.3% caries rate among 5-year-old Lahu children in Linxiang District, Lincang City, which also exceeded national results [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDental caries development is influenced by multiple factors, including host traits, microbial composition, and environmental exposure. Dental plaque is a critical precursor to cavities, with studies demonstrating that complex plaque microbiota play a central role in early childhood caries progression [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the effects of regional, socioeconomic, or behavioral differences on plaque microbiota remain unclear. To address this gap, our study integrates oral behavior data, microbiome analysis, and machine learning models for caries risk assessment among children in Yunnan Province. Using this approach, we identify key correlations among specific microbial profiles, disease progression, and oral health behaviors, providing insights to guide targeted microbial diagnostics and personalized prevention strategies for high-risk pediatric groups in Yunnan.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubject Selection\u003c/h2\u003e \u003cp\u003eSampling method\u003c/p\u003e \u003cp\u003eA random selection of 30 caries-free (CF group) and 30 caries-affected (DC group) 5-year-old children was made from Hongta District, Yuxi City, and from Fengqing County, Lincang City. This study was approved by the Medical Ethics Committee of Kunming Medical University Affiliated Stomatological Hospital (No. KYKQ2021MEC0093).\u003c/p\u003e \u003cp\u003eInclusion and exclusion criteria\u003c/p\u003e \u003cp\u003eInclusion criteria included individuals who signed informed consent, CF group participants with no decayed teeth, and DC group participants with all or partially decayed primary molars or decayed lower anterior teeth. Exclusion criteria included individuals without informed consent, those not meeting DC or CF group criteria, and those with systemic diseases.\u003c/p\u003e \u003cp\u003eQuestionnaire survey\u003c/p\u003e \u003cp\u003eA questionnaire was administered to eligible participants. This instrument was specifically developed for the present research. It covered the following: risk factors for oral disease; knowledge, attitudes, and behaviors related to oral health; history of oral diseases; and use of oral health services.\u003c/p\u003e \u003cp\u003ePlaque collection\u003c/p\u003e \u003cp\u003eChildren included had no systemic disease, dental hard-tissue developmental disorders, or oral mucosal/periodontal disease; had not taken any antibiotics within 2 weeks before sampling; and had not received dental pulp treatment or fluoride application. Sampling began after a 2-hour fast, followed by rinsing with clean water. Finger contact was avoided to prevent cross-contamination. The same examiner used a sterile scaler to collect dental plaque from the labial and buccal surfaces of all teeth in the CF group and from surfaces adjacent to decayed tissue in the DC group. The collected plaque was applied to sterile cotton swabs, placed into 15-mL sterile EP tubes, preserved on dry ice, transported to the laboratory within 2 hours under ice bath conditions, and stored at \u0026minus;\u0026thinsp;80\u0026deg;C for later use.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalysis of Correlations and Differences in Microbial Abundance\u003c/h3\u003e\n\u003cp\u003eDental plaque from 60 children was subjected to 16S rRNA sequencing. Based on the relative abundance of the top 10 phyla and top 30 genera, Spearman correlation coefficients were calculated among phyla and genera. Differences in microbial abundance between the CF and DC groups were also compared using the top 10 phyla and top 30 genera, with results visualized in group comparison charts.\u003c/p\u003e\n\u003ch3\u003eRandom Forest and Least Absolute Shrinkage and Selection Operator Analysis of Microbiota\u003c/h3\u003e\n\u003cp\u003eWith the seed set to 142, the createDataPartition function in the R package \u003cem\u003ecaret\u003c/em\u003e (v7.0.1) divided samples equally into a training set (DC\u0026thinsp;=\u0026thinsp;15, CF\u0026thinsp;=\u0026thinsp;15) and a validation set (DC\u0026thinsp;=\u0026thinsp;15, CF\u0026thinsp;=\u0026thinsp;15). Random forest [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], an ensemble learning algorithm integrating multiple decision trees through bagging (bootstrap aggregation), was used for model building. When a sample prediction is required, predictions from all decision trees in the forest are aggregated, and the final output is determined through majority voting. The \u003cem\u003erandomForest\u003c/em\u003e package (v4.7-1.2) was used to construct models with DC samples and microbiota that showed significant abundance differences between DC and CF samples, selecting five microbial taxa based on the mean decrease in Gini (ntree\u0026thinsp;=\u0026thinsp;500). The five taxa were then analyzed via the least absolute shrinkage and selection operator (LASSO) method to identify key microbiota.\u003c/p\u003e\n\u003ch3\u003eMachine Learning\u003c/h3\u003e\n\u003cp\u003eFour machine learning models were used for training and validation: random forest, logistic regression, support vector machine (SVM), and extreme gradient boosting (XGBoost). Random forest improves model accuracy and robustness by constructing multiple decision trees using random samples and features, making it effective for classification. Logistic regression, a generalized linear model, is widely used and easily interpreted. SVM, a supervised algorithm for regression and classification, partitions data into categories with decision boundaries while maximizing the margins between these boundaries and the nearest data instances, improving model performance and generalizability. XGBoost, an ensemble learning method based on gradient boosting trees [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], reduces prediction errors by constructing multiple decision trees, thereby enhancing model accuracy. XGBoost also supports various loss functions and regularization options, offering high performance and scalability.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003epROC\u003c/em\u003e package (v1.18.0) in R was used to plot receiver operating characteristic (ROC) curves and calculate the area under the ROC curve (AUC) to assess model diagnostic performance for dental caries. AUC values range from 0.5 to 1.0: 0.5\u0026ndash;0.7 indicates low accuracy, 0.7\u0026ndash;0.9 moderate accuracy, and \u0026gt;\u0026thinsp;0.9 high accuracy. Precision-recall curves (PRCs) were also plotted using \u003cem\u003epROC\u003c/em\u003e (v1.18.0), with recall and precision on the x-axis and y-axis, respectively (curves closer to the upper right corner reflected superior model performance).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the study\u0026rsquo;s technical roadmap. All data were processed in R (v4.2.2). Unless otherwise stated, comparisons of two groups of continuous variables were made using the independent Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test for normally distributed data and the Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e test (Wilcoxon rank sum test) for non-normal data. For comparisons of three or more groups, the Kruskal\u0026ndash;Wallis test was applied. Spearman correlation analysis was used to determine correlation coefficients. Unless specified, all \u003cem\u003ep\u003c/em\u003e-values were two-tailed, with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and Oral Health-related Questionnaire Survey\u003c/h2\u003e \u003cp\u003eA questionnaire survey was conducted on the oral health status of 60 children. Based on the results, a contingency table (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was created for the DC and CF samples. The results revealed that location, frequency of sugary food/drink consumption (desserts), and nighttime postbrushing sugar intake (NPSI) were significantly associated with dental caries (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and behavioral characteristics of the dental caries and caries-free groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDental caries group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaries-free group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e​​Location, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChi-square test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFengqing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYuxi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e​​Height (cm), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.87\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115.07\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eT-test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e​​Weight (kg), median (interquartile range)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.8 (17.188, 20.375)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.3 (17.5, 22.925)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWilcoxon\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e​​Feeding method within the first 6 months after birth, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYates\u0026rsquo; correction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreastfeeding exclusively\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e14 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreastfeeding is the primary method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompletely formula-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormula-fed is the primary method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHalf breastfed and half formula-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eFrequency of sugary food/drink consumption: desserts, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYates\u0026rsquo; correction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2 times/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 time/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;6 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 time/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e10 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 times/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e​​Frequency of sugary food/drink consumption: sweetened beverages, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYates\u0026rsquo; correction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2 times/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 time/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e14 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;6 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 time/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 times/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e​​Frequency of sugary food/drink consumption: sweetened dairy products, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYates\u0026rsquo; correction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2 times/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 time/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e10 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;6 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 time/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 times/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e​​Nighttime postbrushing sugar intake, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYates\u0026rsquo; correction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 time/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;6 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 time/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 times/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e17 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e​​Age at which toothbrushing was initiated, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYates\u0026rsquo; correction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;12 month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;3 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e11 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;5 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 years and older\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e​​Daily toothbrushing frequency, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYates\u0026rsquo; correction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e17 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e11 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot every day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e​​Parent-assisted toothbrushing frequency, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYates\u0026rsquo; correction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2 times/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;6 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 time/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 times/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e​​Use of fluoridated toothpaste, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChi-square test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e10 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e10 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e10 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e​​Parent-assisted dental flossing frequency, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYates\u0026rsquo; correction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (43.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e25 (41.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA contingency table comparing Fengqing and Yuxi samples (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed significant differences in NPSI and fluoridated toothpaste use between the groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This indicates that regional differences in caries may be related to these behavioral factors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and behavioral characteristics of Fengqing and Yuxi samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFengqing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYuxi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e​​Height (cm), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.54\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115.76\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.400998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.84606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eT-test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e​​Weight (kg), median (IQR)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.9 (17.375, 22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.8 (17.5, 22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.587349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWilcoxon rank sum test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e​​Feeding method within the first 6 months after birth, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.553084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.0284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYates\u0026rsquo; correction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreastfeeding exclusively\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreastfeeding is the primary method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompletely formula-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormula-fed is the primary method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHalf breastfed and half formula-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eFrequency of sugary food/drink consumption: desserts, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.327444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.787546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYates\u0026rsquo; correction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2 times/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 time/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;6 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 time/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 times/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e​​Frequency of sugary food/drink consumption: sweetened beverages, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.52947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYates\u0026rsquo; correction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2 times/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 time/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;6 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 time/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 times/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e​​Frequency of sugary food/drink consumption: sweetened dairy products, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.711225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.927091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYates\u0026rsquo; correction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2 times/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 time/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;6 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 time/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 times/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e​​Nighttime postbrushing sugar intake, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.07825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYates\u0026rsquo; correction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 time/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;6 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 time/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 times/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e​​Age at which toothbrushing was initiated, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.582941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYates\u0026rsquo; correction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;12 month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;3 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;5 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 years and older\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e​​Daily toothbrushing frequency, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.415148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.758242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYates\u0026rsquo; correction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot every day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e​​Parent-assisted toothbrushing frequency, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.140553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.913087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYates\u0026rsquo; correction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2 times/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;6 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 time/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 times/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e​​Use of fluoridated toothpaste, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.021213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.706262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChi-square test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e11 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e​​Parent-assisted dental flossing frequency, n (%)​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.004094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYates\u0026rsquo; correction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (53.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e19 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCorrelation and Differential Analysis of Microbial Abundance\u003c/h3\u003e\n\u003cp\u003eBased on the relative abundance of the top 10 phyla and top 30 genera in all samples, correlations among phyla (Fig.\u0026nbsp;2A) and among genera (Fig.\u0026nbsp;2B) were calculated and visualized as heatmaps.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;2 Correlation analysis between microbial communities\u003c/p\u003e \u003cp\u003eThe relative abundance of the top 10 phyla was also compared between the DC and CF samples. \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eFusobacteria\u003c/em\u003e, and \u003cem\u003eProteobacteria\u003c/em\u003e showed significant differences between the groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In Fengqing, \u003cem\u003eProteobacteria\u003c/em\u003e abundance differed significantly between the DC and CF groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In Yuxi, \u003cem\u003eFusobacteria\u003c/em\u003e differed significantly between these groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). \u003cem\u003eBacteroidetes\u003c/em\u003e abundance also differed significantly between DC samples from Fengqing and Yuxi (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDifferences in the relative abundances of the top 30 bacterial genera were then compared between DC and CF. Significant differences were found in the relative abundances of \u003cem\u003eCapnocytophaga\u003c/em\u003e, \u003cem\u003eComamonas\u003c/em\u003e, \u003cem\u003eFusobacterium\u003c/em\u003e, \u003cem\u003eHaemophilus\u003c/em\u003e, \u003cem\u003eLeptotrichia\u003c/em\u003e, \u003cem\u003eRalstonia\u003c/em\u003e, unidentified_Veillonellaceae, and \u003cem\u003eVeillonella\u003c/em\u003e between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). In Fengqing, \u003cem\u003eActinobacillus\u003c/em\u003e, \u003cem\u003eCampylobacter\u003c/em\u003e, \u003cem\u003eCapnocytophaga\u003c/em\u003e, \u003cem\u003eHaemophilus\u003c/em\u003e, \u003cem\u003ePorphyromonas\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, unidentified_Prevotellaceae, unidentified_Veillonellaceae, and \u003cem\u003eVeillonella\u003c/em\u003e showed significant differences between DC and CF (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In Yuxi, significant differences were observed in \u003cem\u003eCandidatus_Ishikawaella\u003c/em\u003e, \u003cem\u003eFusobacterium\u003c/em\u003e, \u003cem\u003eLeptotrichia\u003c/em\u003e, and \u003cem\u003eRalstonia\u003c/em\u003e between DC and CF (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Between DC samples from Fengqing and Yuxi, significant differences occurred in \u003cem\u003eAlloprevotella\u003c/em\u003e, \u003cem\u003eBergeyella\u003c/em\u003e, \u003cem\u003eCampylobacter\u003c/em\u003e, \u003cem\u003eCandidatus_Ishikawaella\u003c/em\u003e, \u003cem\u003eCapnocytophaga\u003c/em\u003e, \u003cem\u003eGemella\u003c/em\u003e, \u003cem\u003eKingella\u003c/em\u003e, \u003cem\u003eNeisseria\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, unidentified_Prevotellaceae, \u003cem\u003eVeillonella\u003c/em\u003e, and \u003cem\u003eWolbachia\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eD)).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of Diagnostic Models\u003c/h2\u003e \u003cp\u003eWith the seed set to 142, we used the createDataPartition function in the R package \u003cem\u003ecaret\u003c/em\u003e (v7.0-1) to divide samples equally into a training set (DC\u0026thinsp;=\u0026thinsp;15, CF\u0026thinsp;=\u0026thinsp;15) and a validation set (DC\u0026thinsp;=\u0026thinsp;15, CF\u0026thinsp;=\u0026thinsp;15). Using training data, four machine learning models (random forest, logistic regression, SVM, and XGBoost) were built based on the relative abundances of three phyla differing significantly between DC and CF (\u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eFusobacteria\u003c/em\u003e, and \u003cem\u003eProteobacteria\u003c/em\u003e). The models were validated using the validation set, and diagnostic ROC curves (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and B) and PRCs (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and D) were plotted for each model. SVM achieved the best validation performance. The diagnostic ROC curve of the three phyla (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eE) showed that \u003cem\u003eFusobacteria\u003c/em\u003e had moderate diagnostic ability for DC samples (AUC\u0026thinsp;=\u0026thinsp;0.737), whereas \u003cem\u003eBacteroidetes\u003c/em\u003e (AUC\u0026thinsp;=\u0026thinsp;0.696) and \u003cem\u003eProteobacteria\u003c/em\u003e (AUC\u0026thinsp;=\u0026thinsp;0.671) showed lower diagnostic ability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on significant differences in eight microbial genera (\u003cem\u003eCapnocytophaga\u003c/em\u003e, \u003cem\u003eComamonas\u003c/em\u003e, \u003cem\u003eFusobacterium\u003c/em\u003e, \u003cem\u003eHaemophilus\u003c/em\u003e, \u003cem\u003eLeptotrichia\u003c/em\u003e, \u003cem\u003eRalstonia\u003c/em\u003e, unidentified_Veillonellaceae, and \u003cem\u003eVeillonella\u003c/em\u003e) between all DC and CF samples, the random forest algorithm was applied to analyze their relative abundances (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and B). The five genera with the greatest mean decrease in Gini impurity were selected for LASSO regression (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eC and D), which identified three key taxa: \u003cem\u003eCapnocytophaga\u003c/em\u003e, \u003cem\u003eHaemophilus\u003c/em\u003e, and \u003cem\u003eComamonas\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing the training set data, four machine learning models (random forest, logistic regression, SVM, and XGBoost) were again constructed using the relative abundances of the three key genera, i.e., \u003cem\u003eCapnocytophaga\u003c/em\u003e, \u003cem\u003eHaemophilus\u003c/em\u003e, and \u003cem\u003eComamonas\u003c/em\u003e. The validation set was used for model testing, and ROC curves (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eA and B) and PRCs (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eC and D) were generated. The random forest model performed best in the validation set. Feature importance analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eE) showed that \u003cem\u003eCapnocytophaga\u003c/em\u003e contributed most to model accuracy. The diagnostic ROC curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eF) indicated that \u003cem\u003eCapnocytophaga\u003c/em\u003e exhibited moderate diagnostic ability for DC samples (AUC\u0026thinsp;=\u0026thinsp;0.720), whereas \u003cem\u003eHaemophilus\u003c/em\u003e (AUC\u0026thinsp;=\u0026thinsp;0.666) and \u003cem\u003eComamonas\u003c/em\u003e (AUC\u0026thinsp;=\u0026thinsp;0.682) were less predictive.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of Clinical Diagnostic Models\u003c/h2\u003e \u003cp\u003eUsing the validation set data, an SVM model was constructed combining the relative abundances of three phyla (\u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eFusobacteria\u003c/em\u003e, and \u003cem\u003eProteobacteria\u003c/em\u003e) with three features associated with dental caries (location, frequency of sugary food/drink consumption [desserts], and NPSI). The resulting diagnostic ROC curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eA) and PRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eB) indicated improved model performance (AUC\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilarly, a random forest model was developed combining the relative abundances of the three key genera (\u003cem\u003eCapnocytophaga\u003c/em\u003e, \u003cem\u003eHaemophilus\u003c/em\u003e, and \u003cem\u003eComamonas\u003c/em\u003e) in the validation set with the same three features. The diagnostic ROC curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eA) and PRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eB) confirmed enhanced model performance (AUC\u0026thinsp;=\u0026thinsp;0.933).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDental caries is a major public health issue worldwide, particularly affecting children. Its etiology is complex and influenced by multiple factors [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Combining microbiomics with machine learning enables more precise identification of disease-associated factors, improving early diagnostic accuracy. The present study identified several taxa at the phylum and genus levels significantly associated with dental caries, leading to the successful development of a machine learning model with strong diagnostic performance.\u003c/p\u003e \u003cp\u003eMicrobiomics analysis revealed significant differences in the abundance of \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eFusobacteria\u003c/em\u003e, and \u003cem\u003eProteobacteria\u003c/em\u003e between the DC and CF groups at the phylum level. In Fengqing, \u003cem\u003eProteobacteria\u003c/em\u003e abundance differed significantly between the DC and CF samples, whereas in Yuxi, \u003cem\u003eFusobacteria\u003c/em\u003e showed a similar pattern. \u003cem\u003eBacteroidetes\u003c/em\u003e abundance also differed significantly between DC samples from Fengqing and those from Yuxi. At the genus level, \u003cem\u003eCapnocytophaga\u003c/em\u003e, \u003cem\u003eHaemophilus\u003c/em\u003e, and \u003cem\u003eComamonas\u003c/em\u003e were identified as key taxa using random forest and LASSO analyses.\u003c/p\u003e \u003cp\u003eQuestionnaire analysis indicated that location, frequency of sweet consumption, and eating after brushing teeth at night were associated with dental caries risk. When grouped by location, significant differences were observed in NPSI and fluoride toothpaste use between Fengqing County and Hongta District. Regarding diagnostic model construction, models based on bacterial taxa performed better when combined with questionnaire-derived features, suggesting that dental caries research should integrate microbiological and behavioral factors.\u003c/p\u003e \u003cp\u003eThe two study regions, namely Fengqing County in Lincang City and Hongta District in Yuxi City, differ markedly in socioeconomic background. Fluoride toothpaste use varies significantly between these regions, reflecting differences in economic conditions and oral health awareness. Prior studies showed that children from higher-income families are more likely to use fluoride toothpaste and maintain good oral hygiene [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A previous survey [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] found that although most parents know brushing should begin when teeth erupt, many lack awareness of proper fluoride concentration and usage. Fluoride toothpaste substantially reduces caries incidence [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] by lowering oral levels of cariogenic bacteria, including \u003cem\u003eHaemophilus\u003c/em\u003e and \u003cem\u003eNeisseria\u003c/em\u003e [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], consistent with our identified key taxa. Fluoride acts by inhibiting bacterial metabolism and disrupting biofilm formation. Once in the oral environment, it binds to bacterial membranes, alters membrane permeability, suppresses acid production, and interferes with carbohydrate metabolism, thereby reducing acid generation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The regional differences in fluoride toothpaste use likely stem from varying socioeconomic and educational factors, although this requires further research.\u003c/p\u003e \u003cp\u003eDietary habits strongly influence caries development. High-sugar intake is a major risk factor, especially among children who frequently consume sugary snacks and beverages, markedly increasing caries susceptibility [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Sugars provide nutrients for cariogenic bacteria in the mouth, which ferment them into acids that demineralize enamel [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. For example, \u003cem\u003eStreptococcus mutans\u003c/em\u003e rapidly proliferates in sugar-rich environments, forming biofilms that promote caries [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Such proliferation is closely related to oral microbiome imbalance, with sugar intake being a major contributing factor [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Frequent sugar consumption promotes rapid adaptation of these cariogenic bacteria, creating a community dominated by acid-producing species [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. High-sugar diets reduce microbial diversity in the oral microbiome while increasing the abundance of certain bacteria. A systematic review showed that excessive sugar intake decreases oral microbiome richness and diversity while elevating \u003cem\u003eS. mutans\u003c/em\u003e, \u003cem\u003eScardovia\u003c/em\u003e, and \u003cem\u003eVeillonella\u003c/em\u003e abundance [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our study identified three key genera through LASSO regression: \u003cem\u003eCapnocytophaga\u003c/em\u003e (higher in DC), \u003cem\u003eHaemophilus\u003c/em\u003e (higher in CF), and \u003cem\u003eComamonas\u003c/em\u003e (higher in CF). Although this finding is not fully explained in the literature, it provides a foundation for future research.\u003c/p\u003e \u003cp\u003eBrushing teeth before bed is essential for controlling plaque formation. Research has shown that nighttime brushing markedly reduces levels of cariogenic bacteria in the mouth, including \u003cem\u003eS. mutans\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e, key contributors to dental caries [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Shortly after brushing, the oral microbiota remains stable, helping resist pathogen invasion. Brushing not only lowers bacterial counts but also maintains a neutral oral environment that promotes remineralization, allowing tooth surfaces to self-repair and resist demineralization [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Eating after nighttime brushing leaves food residues that serve as substrates for bacterial metabolism. Because saliva secretion decreases at night, the mouth loses its natural cleansing function, allowing plaque to form more easily and increasing caries risk [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Our findings showed significant behavioral differences in nighttime eating after brushing between the DC and CF groups; however, further research is needed to determine whether this behavior affects plaque biodiversity.\u003c/p\u003e \u003cp\u003eWhen constructing the machine learning\u0026ndash;based clinical diagnostic model, we found that combining key bacterial taxa with children\u0026rsquo;s oral health behaviors effectively predicted dental caries. This result validates the utility of machine learning in public health and introduces new tools for early caries diagnosis and risk assessment. The constructed model demonstrated strong predictive ability (AUC\u0026thinsp;=\u0026thinsp;0.933), providing valuable guidance for clinical practice, especially in resource-limited areas, by helping healthcare workers identify high-risk children and develop personalized interventions [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This successful application may encourage broader application of machine learning in other public health domains, including epidemic monitoring and chronic disease management [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite its strengths, this study has limitations. First, the small sample size may restrict the generalizability of the findings. Additionally, no laboratory-based experiments were conducted to explore underlying mechanisms. Future research should include larger, more regionally and demographically diverse samples to improve representativeness and reliability. Longitudinal study designs would also help clarify the relationship between behavioral and microbiome changes in caries development, supporting more effective public health strategies. Overall, this study highlights behavioral and microbial differences related to dental caries among 5-year-old children in urban and rural Yunnan, emphasizing the importance of integrating behavioral interventions with microbiome research to inform future oral health programs.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identified distinct behavioral habits and microbial community characteristics associated with dental caries in 5-year-old children from urban and rural areas of Yunnan, providing strong evidence for targeted oral health interventions. The findings highlight the need to strengthen oral health education and implement focused prevention strategies to improve children\u0026rsquo;s oral health. With continued research, these insights may support the development of broader, evidence-based interventions to reduce childhood caries and enhance population-level oral health outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC: area under the curve\u003c/p\u003e\n\u003cp\u003eCF: caries-free\u003c/p\u003e\n\u003cp\u003eLASSO: least absolute shrinkage and selection operator\u003c/p\u003e\n\u003cp\u003eNPSI: nighttime postbrushing sugar intake\u003c/p\u003e\n\u003cp\u003ePRC: Precision-recall curve\u003c/p\u003e\n\u003cp\u003eROC: receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eSVM: support vector machine\u003c/p\u003e\n\u003cp\u003eXGBoost: extreme gradient boosting\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was adhered to the Declaration of Helsinki and was approved by the Medical Ethics Committee of Kunming Medical University Affiliated Stomatological Hospital (No. KYKQ2021MEC0093).\u003c/p\u003e\n\u003cp\u003eInformed consent to participate was obtained from all of the participants in the study.\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from the parent or legal guardian of all minors under 16.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset supporting the conclusions of this article is available in the Sicence Data Bank , [DOI:10.57760/sciencedb.32617, https://www.scidb.cn/s/I7fI32].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Youth Research Fund of Yunnan Provincial Clinical Research Center for Oral Diseases [2022QN001]; and the Xingdian Talent Support Plan of Yunnan Province-Medical and Health Talents Special Project (XDYC-YLWS-2023-0047).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization, Oral health. 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/health-topics/oral-health/#tab=tab_1\u003c/span\u003e\u003cspan address=\"https://www.who.int/health-topics/oral-health/#tab=tab_1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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Front Oral Health. 2024;5:1322733. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/froh.2024.1322733\u003c/span\u003e\u003cspan address=\"10.3389/froh.2024.1322733\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dental caries, Children, Microbiology, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-8112291/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8112291/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eYunnan, southwestern China, experiences uneven economic development and an unbalanced distribution of medical resources. Herein, 5-year-old children from Hongta District (urban) and Fengqing County (mountainous) were recruited to investigate oral health conditions. After integrating oral health behavior surveys with plaque microbiome analysis, machine learning identified influencing factors and microbial communities, facilitating diagnostic model construction and revealing how regional disparities affect dental caries in children.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe randomly selected 30 caries-free (CF group) and 30 caries-affected (DC group) 5-year-old children from each location and conducted oral epidemiological examinations, oral health behavior questionnaires, and 16S rRNA sequencing of dental plaque samples. Behavioral differences and genus-level microbial abundance were compared across locations. Random forest models analyzed high-risk factors, identified key microbial communities, and assessed diagnostic performance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eQuestionnaire analysis revealed significant differences between the DC and CF groups in location, dessert consumption frequency, and nighttime postbrushing sugar intake (NPSI). By location, Fengqing County and Hongta District differed significantly in NPSI and fluoridated toothpaste use. Plaque analysis showed significant phylum-level differences between the DC and CF groups for \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eFusobacteria\u003c/em\u003e, and \u003cem\u003eProteobacteria\u003c/em\u003e. A phylum-level diagnostic model highlighted \u003cem\u003eFusobacteria\u003c/em\u003e as a diagnostic marker [area under the curve (AUC): 0.737]. Least absolute shrinkage and selection operator analysis identified three key genera\u0026mdash;namely \u003cem\u003eCapnocytophaga\u003c/em\u003e, \u003cem\u003eHaemophilus\u003c/em\u003e, and \u003cem\u003eComamonas\u003c/em\u003e\u0026mdash;with \u003cem\u003eCapnocytophaga\u003c/em\u003e aiding diagnosis (AUC: 0.720). Adding location, dessert consumption, and NPSI to the model further improved diagnostic performance (AUC: 1).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eRegional socioeconomic disparities influenced dental caries prevalence in 5-year-old children, reflected in behavioral and microbial differences.\u003c/p\u003e","manuscriptTitle":"Behavioral and Microbial Differences in Dental Caries among 5-Year-Old Children in Urban and Rural Yunnan: A Multifactorial Machine Learning Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 09:36:10","doi":"10.21203/rs.3.rs-8112291/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-09T17:06:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-30T14:55:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229577558075032187422242856569834494827","date":"2025-12-18T14:15:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T13:06:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102269621136434445304568484483332557085","date":"2025-12-16T10:32:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74090201059511662832196678273626618265","date":"2025-12-16T08:55:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-15T15:06:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-04T11:11:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-04T03:33:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2025-12-04T03:28:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2011ea96-8f0a-4f3a-b2be-82905c2facde","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T14:39:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 09:36:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8112291","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8112291","identity":"rs-8112291","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}