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This ecological study utilized 16 districts across 8 prefecture-level cities within Shanxi Province, China, as units of analysis. It included 3721 children aged 12 years. The study’s protocol and consent forms were rigorously reviewed and approved by the Ethics Committee of Shanxi Medical University.
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The analysis incorporated data from two recent surveys to examine oral health status, sociodemographic characteristics, and oral health-related behaviors of 3,721 children: (1) The First Epidemiological Survey on Oral Health in Shanxi Province in 2018 (N = 2,847), a government initiative assessing oral health across different age groups in 12 districts of eight cities in Shanxi Province. (2) The Fourth National Oral Health Epidemiological Survey conducted by the National Health Commission of the People’s Republic of China in 2015 (N = 874), covering four districts in four cities within the Province. The methodologies of each survey have been detailed in prior descriptions. Data collection involved oral examinations by trained and certified dentists, assessing the dental caries status of all permanent teeth under artificial light using a plane dental mirror and community periodontal index probe, adhering to diagnostic criteria of World Health Organization (WHO). The inter-examiner reliability, indicated by a kappa value exceeding 0.9, was substantial. Dental caries prevalence was computed as the proportion of affected individuals within the respondent pool. Prior to data collection, written informed consent was obtained from the children’s parents or legal guardians. Utilizing these two cross-sectional surveys, a multifactorial regression model predicted the 2018 caries prevalence among 12-year-olds in the 11 cities, enhancing the sample size. The model’s formulation is the Logistic function model:
$$ y=\frac{k}{1+e^{a x+b}} $$ (1) Equation 1: In this equation, a and b represent parameters to be determined, while k denotes the predicted upper limit of prevalence.
Table 1 presents the raw and computed sample data. Natural and socioeconomic factors were derived from the Shanxi Statistical Yearbook - 2019 and Shanxi Statistical Yearbook - 2016, respectively, published by the Shanxi Statistical Bureau.
City District The number of children sampled The caries prevalence rate in districts The caries prevalence rate in cities Taiyuan Xiaodian area 234 52.10 49.22 Qinxu county 239 46.40 Datong Pingcheng area 196 29.60 34.13 Zuoyun county 229 38.00 Yangquan urban area 236 39.00 32.19 Pending county 240 25.50 Changzhi 53.32 Jincheng urban area 239 45.50 38.99 Qinshui county 240 32.50 Shuozhou 31.62 Jinzhong Yuci area 239 36.70 39.99 Pingyao county 229 43.43 Yuncheng Yanhu area 238 43.80 46.61 Wenxi county 214 49.74 Xinzhou Xinfu area 235 49.28 49.19 Dai county 234 49.10 Linfen 52.65 Lvliang Lishi area 240 43.00 42.65 Liulin county 239 42.30 Total 3721 Note. The red data in the table represents the calculated data. Table 1. The Number of Children Sampled and the Caries Prevalence in Each City
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Prior research on the spatial distribution of dental caries has predominantly concentrated on individual factors, overlooking the complex interplay among multiple ecological determinants that could influence spatial disparities[19-21]. Thus, our investigation delves into the correlation between caries rates and various factors, including socioeconomic factors, medical resources and environmental conditions. Guided by the extant literature and data availability from local authorities, we identified eighteen pertinent variables, illustrated in Figure 1. To measure the social economy of a city, we should not only rely on the economic aggregate and total population, but also pay attention to the characteristics of its population structure, so we selected nine variables: urban population percentage (UPP), gross domestic product per capita (GDP-PC), the proportion of tertiary industry (PTI), arable land area per capita (ALA-PC), farm corn area per capita (FCA-PC), proportion of pupils (PP), annual output of fruits (AOF), annual output of meat (AOM) and annual output of vegetables (AOV). The healthcare system’s state was represented by two variables: the number of medical institutions (NMI) and the percentage of medical technicians (PMT). Additionally, we integrated seven natural environmental factors, based on data from the local authority: fluoride concentration of water(FW), fine inhalable particles (PM2.5) (µg/m3), inhalable particles (PM10) (µg/m3), sulfur dioxide (µg/m3), nitrogen dioxide (NO2) concentrations (µg/m3), average elevation (m) and geologic diversity.
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In this research, the Geo-Detector model, introduced by Wang in 2010, was employed to generate a map illustrating the spatial distribution of dental caries, aiming to delve deeper into the spatial heterogeneity and influential patterns of dental caries, with a focus on the interaction among various factors.
The Geo-Detector model[22] quantifies the extent to which an independent variable X elucidates variations in the spatial distribution of Y using the q value. The q value is defined as follows:
$$ \begin{array}{r} \mathrm{q}=1-\dfrac{\sum_{h=1}^{L} \sum_{i=1}^{N_{h}}\left(Y_{h i}-\bar{Y}_{h}\right)^{2}}{\sum_{i=1}^{N}\left(Y_{i}-\bar{Y}\right)^{2}} \times 100 \text{%} \\ =1-\dfrac{\sum_{h=1}^{1} N_{h} \sigma_{h}^{2}}{N \sigma^{2}} \times 100 \text{%} \end{array} $$ (2) In this context, N represents the total number of spatial lattice pixels across Shanxi province, categorized into h = 1, 2, …, L strata based on influencing factors (Xs). Each stratum h encompasses Nh spatial statistical pixels, with Yi and Yhi indicating the caries prevalence rate for the ith pixel and within stratum h of influencing factors (Xs), respectively. The terms Y and σ2 denote the overall mean and variance of the caries retention rate across the province. It is pertinent to note that q-values range from 0 to 1, where a higher q-value signifies a more robust explanatory power of X on Y.
The q-value was computed from the cross-classified strata of two distinct factors: X1 and X2, denoted as q (X1∩X2). This metric is instrumental in discerning the interaction effects of X1 and X2 on the dependent variable (Y). The Geo-Detector model delineates criteria to evaluate the nature of the interaction effects exerted by X1 and X2 on Y (Table 2)[ 23-25]. Furthermore, the model ascertains whether the factors (X1 and X2) attenuate or intensify their impact on Y. These interaction effects are categorized into five types of interactive relationships, with the corresponding criteria detailed in Table 2.
Judging rules Types of interaction effects q(X1∩X2)<Min(q(X1),q(X2)) Non-linearly weakened Min(q(X1),q(X2))<q(X1∩X2)<Max(q(X1),q(X2)) Univariate non-linearly weakened q(X1∩X2)>Max(q(X1),q(X2)) Bivariate enhanced q(X1∩X2)=q(X1)+q(X2) Independent q(X1∩X2)>q(X1)+q(X2) Non-linearly enhanced Table 2. The interactive categories of two factors and their relationship
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Figure 2. showed the flow diagram of the whole research process.
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The analyzed sample included 3,721 children aged 12, comprising 1,856 boys (49.9%) and 1,865 girls (50.1%), residing in 8 cities across Shanxi Province. Table 3 details the participants’ demographic characteristics and the prevalence of dental caries. In Shanxi Province, the dental caries rate among 12-year-olds (DMFT > 0) stood at 41.1%. Notably, there were significant gender differences in caries prevalence, with girls exhibiting a higher rate according to Table 3. Conversely, the variation in caries prevalence between urban and rural settings was not statistically significant (P = 0.10).
Variable Number Dental caries (%) χ2 P 12 years 3,721 1,528 (41.1) Sex Boy 1,856 689 (37.1) 23.769 0.00 Girl 1,865 839 (45.0) Area Urban 1,857 788 (42.4) 2.763 0.10 Rural 1,864 740 (39.7) Table 3. Baseline Characteristics of the Samples
To elucidate regional disparities, the caries prevalence rate was categorized into four levels (< 0.35, 0.35-0.43, 0.43-0.51, and 0.51-1.00).
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The Geo-Detector model assessed three categories of ecological factors: socioeconomic, medical resources, and the natural environment. Figure 3 depicts the spatial heterogeneity of these ecological factors alongside the caries prevalence rate, and Table 4 enumerates the q-values for the eighteen distinct ecological factors. The findings reveal that socioeconomic factors (Q = 0.30, P < 0.05) exert a more substantial impact compared to the natural environment (Q = 0.19, P < 0.05) and medical resource accessibility (Q = 0.25, P < 0.05). The UPP has the highest explanatory power (51%) for caries prevalence heterogeneity, as indicated by its q-value (q = 0.51, P < 0.05). Both the PTI and FCA-PC exhibit equal q-values. Moreover, natural environmental factors like SO2 concentration and FW are influential, with q-values of 0.39 (P < 0.05) and 0.27 (P < 0.05), respectively.
Figure 3. He spatial heterogeneity of ecological factors. (source of the base map: https://map.baidu.com). UPP, urban population percentage; GDP-PC, gross domestic product per capita; PTI, the proportion of tertiary industry; ALA-PC, arable land area per capita; FCA-PC, farm corn area per capita; PP, proportion of pupils; AOF, annual output of fruits; AOM, annual output of meat; AOV, annual output of vegetables; NMI, the number of medical institutions; PMT, the percentage of medical technicians; FW, fluoride concentration of water.
Factors Q P Socioeconomic factors 0.30 0.04 X1(UPP) 0.51 0.04 X2(GDP-PC) 0.38 0.02 X3(PTI) 0.39 0.02 X4(ALA-PC) 0.17 0.03 X5(FCA-PC) 0.39 0.04 X6(PP) 0.26 0.04 X7(AOF) 0.14 0.03 X8(AOM) 0.19 0.04 X9(AOV) 0.16 0.03 Medical resources factors 0.25 0.03 X10(NMI) 0.12 0.02 X11 (PMT) 0.08 0.03 Natural environmental factors 0.19 0.04 X12(FW) 0.27 0.04 X13(PM2.5) 0.14 0.03 X14(PM10) 0.22 0.04 X15(Sulfur dioxide) 0.39 0.02 X16(Nitrogen dioxide) 0.14 0.03 X17(Average elevation) 0.10 0.03 X18(Geologic diversity) 0.17 0.04 Note. Q in the table represents strength of interpretation of dental caries distribution by individual factors of socioeconomic, medical resources, and the natural environment, q represents strength of interpretation of dental caries distribution by individual factors of eighteen distinct ecological factors, The larger the value, the greater the ability to explain the spatial disparities of dental caries; UPP ,urban population percentage; GDP-PC, gross domestic product per capita; PTI, the proportion of tertiary industry; ALA-PC, arable land area per capita; FCA-PC, farm corn area per capita; PP, proportion of pupils; AOF, annual output of fruits; AOM, annual output of meat; AOV, annual output of vegetables; NMI, the number of medical institutions; PMT, the percentage of medical technicians; FW, fluoride concentration of water. Table 4. Q-values of the Three Ecological Factors and the Eighteen Specific Ecological Factors
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This study employed the Geo-Detector model to explore how interactions among socioeconomic, medical resources and natural environmental factors influence caries prevalence. Table 5 presents the q-values for the interaction effects among ecological factors. The analysis identified two interaction types: bivariate and nonlinear enhancement effects. Notably, the interactions between FCA-PC and PP., and between FW and PM10, demonstrated the highest explanatory power, at 100%, for the spatial heterogeneity of the caries prevalence rate (P < 0.05), among ninety-six interaction pairs exhibiting a nonlinear enhanced effect.
X5∩X6
(1)X12∩X14
(1)X2∩X12
(0.96)X2∩X15
(0.96)X5∩X11
(0.89)X11∩X12
(0.82)X1∩X16
(0.78)X4∩X16
(0.67)X4∩X9
(0.59)X4∩X17
(0.49)X2∩X16
(0.96)X6∩X15
(0.96)X12∩X17
(0.96)X2∩X13
(0.93)X5∩X14
(0.89)X3∩X14
(0.86)X5∩X18
(0.78)X3∩X13
(0.67)X7∩X18
(0.53)X9∩X16
(0.49)X5∩X15
(0.96)X6∩X16
(0.96)X2∩X14
(0.93)X3∩X4
(0.86)X6∩X9
(0.86)X1∩X9
(0.81)X3∩X10
(0.71)X4∩X6
(0.67)X9∩X14
(0.59)X10∩X13
(0.49)X6∩X8
(0.96)X6∩X14
(0.96)X6∩X13
(0.93)X12∩X16
(0.86)X12∩X13
(0.82)X10∩X18
(0.78)X4∩X8
(0.67)X4∩X10
(0.59)X9∩X11
(0.48)X1∩X5
(0.95)X6∩X12
(0.96)X7∩X14
(0.93)X12∩X18
(0.86)X1∩X17
(0.82)X3∩X6
(0.81)X13∩X18
(0.78)X2∩X11
(0.64)X4∩X11
(0.48)X1∩X2
(0.92)X1∩X7
(0.67)X16∩X17
(0.93)X2∩X8
(0.82)X2∩X9
(0.82)X1∩X10
(0.81)X4∩X14
(0.78)X7∩X12
(0.71)X8∩X10
(0.52)X2∩X3
(0.86)X1∩X13
(0.67)X2∩X7
(0.59)X16∩X18
(0.93)X2∩X18
(0.82)X14∩X15
(0.82)X5∩X8
(0.77)X3∩X8
(0.77)X5∩X16
(0.67)X17∩X18
(0.43)X2∩X5
(0.86)X1∩X18
(0.67)X3∩X17
(0.58)X15∩X16
(0.96)X14∩X17
(0.82)X3∩X9
(0.81)X7∩X8
(0.77)X4∩X15
(0.71)X10∩X17
(0.52)X3∩X5
(0.86)X2∩X6
(0.67)X5∩X12
(0.67)X2∩X17
(0.49)X3∩X16
(0.82)X10∩X14
(0.81)X9∩X15
(0.77)X8∩X17
(0.71)X8∩X12
(0.71)X9∩X13
(0.51)X1∩X6
(0.82)X5∩X13
(0.64)X2∩X10
(0.57)X5∩X9
(0.49)X4∩X18
(0.82)X1∩X4
(0.81)X9∩X17
(0.71)X9∩X12
(0.67)X8∩X18
(0.67)X1∩X14
(0.82)X3∩X7
(0.66)X15∩X17
(0.63)X5∩X7
(0.57)X6∩X18
(0.49)X5∩X17
(0.82)X13∩X15
(0.77)X11∩X18
(0.67)X4∩X13
(0.64)X6∩X10
(0.64)X1∩X15
(0.82)X7∩X15
(0.66)X10∩X15
(0.57)X3∩X18
(0.48)X8∩X14
(0.48)X1∩X11
(0.73)X10∩X12
(0.71)X11∩X15
(0.64)X7∩X16
(0.64)X1∩X8
(0.77)X1∩X3
(0.61)X2∩X4
(0.54)X6∩X11
(0.46)X8∩X16
(0.38)X8∩X9
(0.38)X9∩X18
(0.73)X14∩X18
(0.71)X10∩X11
(0.64)X3∩X15
(0.77)X4∩X5
(0.60)X6∩X7
(0.53)X11∩X14
(0.46)X11∩X16
(0.38)X6∩X17
(0.35)X7∩X17
(0.35)X13∩X17
(0.73)X3∩X11
(0.63)X4∩X7
(0.61)X1∩X12
(0.71)X7∩X10
(0.42)X13∩X14
(0.46)X8∩X13
(0.37)X7∩X11
(0.35)X8∩X11
(0.35)X14∩X16
(0.35)X15∩X18
(0.71)X7∩X13
(0.60)X3∩X12
(0.71)X5∩X10
(0.60)X7∩X11
(0.42)X4∩X12
(0.35)X9∩X10
(0.30)X13∩X16
(0.28)X11∩X13
(0.24)X11∩X17
(0.20)0.24)X10∩X16
(0.60)X8∩X15
(0.71)X12∩X15
(0.71)Bivariate enhancement effect Nonlinear enhancement effect 0.4-0.5 0.5-0.6 0.6-0.7 0.7-0.8 0.8-0.9 0.9-1 Note. X1 ∩ X2 represents the q-value of the interaction between variables X1 and X2, The resulting q-value in parentheses of the interactive factor is used to characterize the relationship between the two factors and their joint impact on dental caries; The table show that there are only two types of interaction infuence: the bivariate and nonlinear enhancement effects. Bivariate enhancement effect indicates that the combined influence of two factors on caries was more significant than the expected effect of each individual factor alone; Nonlinear enhancement effect indicates that the combined influence of two factors on caries greater than the sum of the expected effects of the two factors alone, and give different colors according to the q-value size. X1:UPP, urban population percentage; X2:GDP-PC, gross domestic product per capita; X3:PTI, the proportion of tertiary industry; X4:ALA-PC, arable land area per capita; X5:FCA-PC, farm corn area per capita; X6:PP, proportion of pupils; X7:AOF, annual output of fruits; X8:AOM, annual output of meat; X9:AOV, annual output of vegetables; X10:NMI, the number of medical institutions; X11:PMT, the percentage of medical technicians; X12:FW, fluoride concentration of water; X13:PM2.5, fine inhalable particles; X14:PM10, inhalable particles; X15:sulfur ioxide; X16:nitrogen dioxide; X17:average elevation; X18:geologic diversity. Table 5. Q-values of the Interaction Influence between Two Factors
A network map illustrating the factors with interactive q-values exceeding 0.90 is depicted in Figure 4. The analysis revealed that PP exhibited the highest frequency of nonlinear enhanced interactions with other factors (AOM, FW, PM2.5, PM10, NO2, SO2), all above 90.0%.
Figure 4. The Network Diagram of the Fnteractive Factors. Whose Interactive q-values were Greater Than 0.90. X2:GDP-PC, gross domestic product per capita; X5:FCA-PC, farm corn area per capita; X6:PP, proportion of pupils; X7:AOF, annual output of fruits; X8:AOM, annual output of meat; X12:FW, fluoride concentration of water; X13:PM2.5,fine inhalable particles; X14:PM10,inhalable particles; X15:sulfur dioxide; X16:nitrogen dioxide; X17:average elevation; X18:geologic diversity.
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DC: Dental caries; DMFT: Decayed, missing, and filled teeth; SEP: Socioeconomic position; GDP: Gross domestic product; WHO: World Health Organization; UPP, urban population percentage; GDP-PC, gross domestic product per capita; PTI, the proportion of tertiary industry; ALA-PC, arable land area per capita; FCA-PC, farm corn area per capita; PP, proportion of pupils; AOF, annual output of fruits; AOM, annual output of meat; AOV, annual output of vegetables; NMI, the number of medical institutions; PMT, the percentage of medical technicians; FW, fluoride concentration of water.
City District The names of the
sampled schoolsThe categories of the sampled schools Taiyuan Xiaodian area Taiyuan No.38 Middle School public school Xiaodian No.3 Middle School public school Beige No.1 Middle School public school Qinxu county Qing Xu Ji Township Middle School public school Qingyuan Town Middle School public school Xugou Town Middle School public school Datong Zuoyun county Zuoyun County No.1 Middle School public school Zuoyun County No.2 Middle School public school Xinyuan Middle School private school Yangquan urban area laboratory middle school public school Yangquan No.6 Middle School public school Yangquan No.2 Middle School public school Pending county Niangziguan Middle School public school Pingding County No.3 Middle School public school Dongguan Middle School public school Jincheng urban area Love thing school private school Jincheng No.9 Middle School public school Mining middle school public school Qinshui county Town junior high school public school Duan junior high school private school Longgang Town Junior High School public school Jinzhong Yuci area Yuci District No.10 Middle School public school Yuci District No.4 Middle School public school High School affiliated to Jinzhong Normal Junior College public school Yuncheng Yanhu area Hedong No.1 Middle School public school Yuncheng joined East School private school Beicheng junior high school public school Xinzhou Dai county Yang Mingbao Middle School public school No.4 Middle School public school No.5 Middle School public school Lyuliang Lishi area TingLiang middle school private school West belongs to Ba Middle School public school Affiliated to Lyuliang University public school Liulin county Liulin County Mu Cun Middle School public school Liulin No.2 Middle School public school Liulin Liansheng Middle School private school Table S1. The names and categories of the sampled middle schools in the first oral health epidemiological survey in Shanxi Province
Model Unstandardized coefficients Standardization coefficient Collinearity statistics β Standard error Beta t Significance Allowance VIF (Constant) 0.625 0.185 3.382 0.077 Urban population percentage −0.022 0.035 −0.246 −0.648 0.583 0.352 2.839 GDP per capita 0.017 0.031 0.259 0.552 0.636 0.230 4.354 Arable land area per capita −0.032 0.035 −0.361 −0.927 0.452 0.334 2.990 Farm corn area per capita 0.001 0.034 0.013 0.033 0.977 0.345 2.897 The proportion of pupil) −0.018 0.019 −0.250 −0.935 0.448 0.711 1.407 Annual output of fruits −0.036 0.039 −0.428 −0.935 0.448 0.242 4.140 Annual output of meat 0.036 0.054 0.353 0.678 0.568 0.186 5.368 Annual output of vegetables −0.045 0.034 −0.395 −1.312 0.320 0.560 1.785 Note. Dependent variable: Dental caries. Table 7. Multiple Covariance Diagnostics of Socioeconomic Factors
Spatial Heterogeneity and Risk Factors of Dental Caries in 12-Year-Old Children in Shanxi Province, China
doi: 10.3967/bes2024.102
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Key words:
- Caries /
- Socioeconomic factors /
- Natural environment /
- Oral health services /
- Spatial heterogeneity analysis /
- Risk factors
Abstract:
Citation: | Ru Xia HOU, Ting Ting YANG, Jia Jia LIU, Hao CHEN, Wen KANG, Jun Ming LI, Xiao Tong SHI, Yi LIANG, Jun Yu LIU, Bin ZHAO, Xiang Yu WANG. Spatial Heterogeneity and Risk Factors of Dental Caries in 12-Year-Old Children in Shanxi Province, China[J]. Biomedical and Environmental Sciences. doi: 10.3967/bes2024.102 |