A total of 3 cities including 2 districts and 2 counties in Shandong were sampled. 1330 preschool children were intended to be invited. However, 18 children’s guardians decline to sign the informed consent, 6 questionnaires cover missing information, 5 children transfer to school, so the remaining 1301 children’s data were included in the final statistical analysis.
The caries rate (CR) of 3-5-year-old children was 64.6%, and mean dmft was 3.15, the proportion of dmft=2 was as high as 13.2%. The rates of 3, 4, and 5 age group were respectively 51.1%, 67.8%, 73.9%, the mean dmft were 2.16, 3.21, 4.01 (see Table 1). 96.4% of decayed teeth were waiting to be treated.
The characteristics and difference analysis results of the survey (including demographics andKAP variables) are shown in Table 1-3 and Additional Table S1.
The demographic characteristic shows in Table 1, ECC rates increasedwith age(51.1% vs 67.8% vs 73.9%, P=0.000), whereas no statistical differences were shown in different genders, regions, education background and income.
The distribution characteristic of KAP variables shows in Additional Table S1. 80.9% of the questionnaires were completed by parents and the rest were done by older people, and the CR between them showed no significant difference (data not shown). Analysis from perspective of OHC knowledge (Q22a-h), 66.0%-84.4% of people could correctly answer Q22b-22f, yet only 30.7% known that gum bleeding is not a normal phenomenon when brushing teeth (Q22a), and 16.0% and 27.7% known that fit and fissure sealing or fluoride can protect teeth (Q22g, h). The analysis found thatthe CR of Q22d, e, g variable show significant difference(P=0.026, 0.013, 0.030 respectively), which mainly involve in bacteria and sugar can induce the caries, as well as the pit and fissure sealing can protect teeth. However, after the dmft number was subdivided into two groups, 0<dmft≤3 group (Mild, MCG) and dmft≥4 group (Severe, SCG), only Q22g showed statistical difference (P=0.033). Curiously, among these difference variables, the CR of those who answered incorrectly were lower than those who answered correctly.
Analysis from perspective of OHC attitude (Q21a-f), 81.6%-98.8% of people answer Q21a-e correctly, but only 35.8% believe that the mother’s oral health affects the child’s oral health (21f). The children raised by those guardians who believe that whether the teeth are health is not related to their own protection unexpectedly have a slightly lower CR than those who do not think so (Q21c, 56.7% vs 66.4%, P=0.005). In addition, after respectively summing up the answers to the Q21 and Q22 (named “Q21/Q22_sum_group”), statistics showed that group 1 with high correct rates in Q21_sum_grouphad higher CR (67.0% vs 61.8%, P=0.049).
Analysis from perspective of OHC practices (Q3-11).When infants younger 6 months were fed in different ways, their oral condition would be different (Q3). The CR of the totally artificial feeding subgroup (45%) was significantly lower than other feeding methods (P=0.005). In addition, the different frequency and time of eating sweets, the child’s caries condition was significantly different (Q4-5). But the difference was only shown in the Q5 about the frequency of eating sugary before going to bed at night, which showed a positive correlation trend with the caries (73.8% vs 66.8% vs 58.6%; P=0.002). In Q10, the rates in children brushing with toothpaste were much higher than those children who do it without toothpaste (65.2% vs 25.0%, P=0.018). Similarly, children who brush their teeth with fluoride toothpaste also had higher rates (80.0% vs 68.1% vs 62.2%, P=0.016). Children who have frequent toothache or discomfort in the past 12 months (Q12) or who have seen a doctor to the hospital (Q13) usually have a higher caries rate (88.1%-100.0%, 81.0%, P=0.000). And the analysis results of Q6-9 showed no statistical difference.
Considering the age was a significant difference variable, we would separately analyze the differences in the distribution of covariates in each age group to eliminate the potential effects of age. Those variables with differences are shown in Table 2. In the 3-year-old group, there was 1 attitude variable (Q21f) and 5 practice variables (Q3_group, Q5, Q11-13) with statistical differences.In the 4-year-old group, gender and4 practice variables (Q5, Q8, Q12-13) have statistical differences.Among them, those children who didn’t brushing daily in Q8 showed the lowest CR in the SCG (18.4%, P=0.025). According to further statistics, 66.6% (n=6) of these children lived in Linyi district, a place where tough pancakes are the staple food. In the 5-year-old group, 1 knowledge variable (Q22d), 1 attitude variable (Q21c) and 5 practice variables (Q4a, 5, 11-13) have statistical differences.The difference trend of these variables are similar to the results of the overall analysis(P<0.05).
Based on the above results, we also found that the difference variables were different in the MCG and SCG. Therefore, a new difference analysis grouped by the degree of caries was implemented (Table 3). In the MCG, there are only 4 difference variables (Q22e, 21c, 3, 12). In the SCG, However, as much as 13 variables have significant difference, including age, Q22d, 22e, 22g, 21b, 21c, 21_sum_group, and Q3, 5, 7, 10-13 (P<0.05). All of these variables also showed similar trends to the overall analysis results (P<0.05).
The results of logistic regression analyzes based on the overall, age and the carieslevel (model 1-3) were shown in Table 4-6. Each model contains two sub-models (I and II).
In the overall analysis model (model 1, Table 4), age, Q22e, g and Q5 variables were the caries risk factors for all children (P=0.000), while age, Q21_sum_group and Q11 variables were the risk factors for those children with brushing habits (P=0.000). In the analysis model of the 3-5-year-old group (model 2, Table 5), model 2-1 of 3-year-old group reveals that Q21f, 21_sum_group, 22e and Q13 were the risk factors for all kids (P=0.000). If we added the Q7-11 variables, the Q22 in the model would lose meaning, so only Q21f, 21_sum_group and Q11 were the risk factors in sub-model II (P=0.000). In the model 2-2 of 4-year-old group, only Q5 and Q8, 13 respectively made these two sub-models meaningful (P=0.014 and 0.001). The model 2-3 of 5-year-old group showed that Q21c, Q4a, 5, 13 and Q5,11, 13 were risk factors for these two sub-models, respectively (P=0.000). Furthermore, model 3 (Table 6), which grouped based on the caries level, told us that in model 3-1 with mild caries, only age, Q21c and Q3, 12 were risk factors (P=0.000). Once the Q7-11 variables were added, the model or variable itself lost statistical significance. While in thesevere caries model 3-2, age, Q22e and Q5, 12-13 were caries risk factors for all children (P=0.000), and age, Q21b, Q5 and Q11-13 were risk factors for children with brushing habits (P=0.000).