Abstract
Background and objective
Dental caries remains a significant public health challenge with marked socioeconomic inequalities. However, the underlying mechanisms remain poorly understood. This study examined whether oral health literacy or oral health behaviors mediate the relationship between socioeconomic status and dental caries outcomes, and assessed the differential contributions of these two pathways.
Methods
A population-based cross-sectional study was conducted from September 2021 to July 2022 among 35- to 44-year-old adults in Guangdong Province, China. Structural equation modeling tested relationships among socioeconomic status (SES: education, urban/rural residence, insurance), oral health literacy (OHL: 8 knowledge items on caries etiology and prevention; 4 attitude items on oral health importance), oral health behaviors (OHB: preventive behaviors including brushing, mouthwash, interdental cleaning; treatment completion behaviors including Care Index reflecting caries treatment completion rate and Restorative Index reflecting treatment coverage), and oral health outcomes (OHO: caries status, DMFT).
Results
Among 401 participants, the structural model demonstrated acceptable fit (χ2/df = 2.88, CFI = 0.927, TLI = 0.902, RMSEA = 0.068, SRMR = 0.074). While SES significantly influenced both OHL (β = 0.452, p < 0.001) and OHB (β = 0.329, p < 0.001), a critical knowledge translation failure emerged: OHL showed no pathway to OHB (β = -0.094, p = 0.303), indicating that oral health knowledge and positive attitudes did not translate into health behaviors. Neither SES (β = -0.003, p = 0.932) nor OHL (β = -0.051, p = 0.254) directly affected oral health outcomes. Only OHB, particularly treatment completion indicators (CI: λ = 0.998; RI: λ = 0.647), predicted OHO (β = 0.788, p < 0.001), explaining 61.8% of variance. The SES-OHO relationship was completely mediated through OHB (β = 0.259, p < 0.001), with no mediation through OHL.
Conclusion
Socioeconomic inequalities in dental caries operate exclusively through differential oral health behaviors, particularly treatment-seeking completion, rather than through differences in oral health knowledge or attitudes. The disconnection between oral health literacy and behaviors suggests that interventions should prioritize enabling treatment completion capacity over knowledge enhancement to reduce oral health disparities.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12903-026-08034-x.
Keywords: Dental caries, Socioeconomic factors, Health inequalities, Structural equation modeling
Introduction
Untreated dental caries is the most prevalent disease affecting humans [1]. According to recent global estimates, 2.4 billion people have untreated dentinal cavities in permanent teeth [2]. Untreated dental caries and dental trauma are the primary causes of premature loss of deciduous teeth [3, 4]. In adults, both dental caries and periodontitis are major contributors to tooth loss [5]. Therefore, untreated dental caries stands out as a critical cause of tooth loss across all age groups, warranting significant attention.
Dental caries is a dynamic process that manifests as a multifactorial disease, mediated by oral biofilms and fueled by dietary sugars. Its pathogenesis is characterized by an imbalance between the phasic demineralization and remineralization of dental hard tissues, ultimately leading to a net mineral loss [6]. Various models of the causes of dental caries have been developed [7], with the most widely accepted being the four-factor model encompassing bacteria, host factors, oral environment (particularly fermentable carbohydrates), and time. Within this framework, there is clear evidence that diet strongly influences dental caries through providing substrate for bacterial acid production [8, 9], and indications that smoke exposure is associated with caries [10, 11]. However, considerable evidence suggests that low socioeconomic status (SES), via traditional individual risk factors as mediators and through additional independent pathways, is another significant cause [12]. Generally, caries are recognized as more severe among people with low socioeconomic status [13, 14]. A systematic review of European regions summarized 17 reports, with 13 studies showing lower caries prevalence in urban areas, 2 studies showing no difference between rural and urban areas, and 2 studies finding lower caries prevalence in rural areas [15].
Recent studies have increasingly used structural equation modeling to explore the complex relationships between SES and oral health outcomes. Lyu et al. examined the associations between SES, tooth loss, and oral health-related quality of life in Chinese elderly, finding that SES indirectly affected quality of life through tooth loss [16]. Celeste et al. used longitudinal data to compare life course models, demonstrating that SES affects oral health through chains of risk and accumulation effects [17]. Broadbent et al. suggested that socioeconomic status may affect oral health outcomes through beliefs and behaviors across the life course [18]. Building on these SEM-based approaches [16–18], the present study used cross-sectional oral health survey data to examine the pathways through which SES may influence oral health outcomes. The SEM approach allows for testing complex interrelationships, including both direct and indirect effects, which may help clarify the mechanisms underlying oral health disparities.
Based on our cross-sectional data and research objectives, we categorized the collected variables into three dimensions: socioeconomic status, oral health literacy (OHL), and oral health behaviors (OHB). OHL refers to the degree to which individuals can obtain, process, and understand basic oral health information needed to make appropriate health decisions [19, 20]. In this study, OHL was assessed through knowledge items (understanding of caries etiology and prevention) and attitude items (perceived importance of oral health), following the questionnaire design of the Fourth National Oral Health Survey of China questionnaire [21]. OHB encompasses both preventive behaviors (daily oral hygiene practices such as toothbrushing, mouthwash use, and interdental cleaning) and treatment completion behaviors (reflected by Care Index and Restorative Index), following Broadbent et al.'s framework [18]. Oral health outcomes (OHO) included caries status and DMFT index.
However, previous studies have not specifically examined the distinct mediating roles of OHL and OHB within a single analytical framework. Theoretically, SES may influence health outcomes through two potential pathways: (1) a cognitive pathway, where higher SES leads to better health knowledge and attitudes, which may then promote healthier behaviors; and (2) a behavioral pathway, where SES directly affects access to and utilization of health services regardless of knowledge levels. It should be noted that the translation from knowledge to behavior may not occur automatically, particularly among socioeconomically disadvantaged populations facing structural barriers to healthcare access [19, 20]. If socioeconomic inequalities in caries are primarily mediated through behaviors rather than through knowledge and attitudes, interventions focusing solely on health education may be insufficient, and efforts to remove structural barriers to treatment access may be needed.
Accordingly, this study aims to explore whether OHL or OHB mediates the relationship between SES and caries outcomes, and to assess the relative contribution of these two potential pathways among adults aged 35–44 in Guangdong Province.
Materials and methods
Study design and setting
The present study was conducted and reported following the guidelines of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE statement). A population-based cross-sectional study was conducted from September 2021 to July 2022 in Guangdong Province, China.
Ethical approval
This study was conducted in accordance with the ethical precepts stipulated in the Declaration of Helsinki and received approval from the Stomatological Ethics Committee of the Chinese Stomatological Association (Approval No.: 2014–003). All participants provided written informed consent before enrollment.
Participants and sampling
Our model included 401 participants and 19 free parameters (11 factor loadings, 6 structural paths, and 2 residual variances). The sample-to-parameter ratio was 21.1:1, exceeding the recommended minimum of 10:1. Post hoc power analysis confirmed statistical power above 0.99 for the weakest significant path. The sample was selected following the national Disease Surveillance Points (DSP) system. Based on stratified random sampling principles, oral health surveillance was conducted at 267 disease surveillance points across 31 provinces nationwide. This study focused on the 35–44 age group, employing a community-based sampling approach. Three village (resident) committees were selected as survey units from each surveillance point, with 10 residents aged 35–44 years randomly recruited from each committee. Eligible participants were required to have at least 6 months of residence in the survey area. Selected residents were invited to designated oral health examination sites where trained interviewers conducted face-to-face questionnaire administration followed by clinical examinations. According to the national survey design, 30 individuals in this age group were surveyed at each surveillance point. This study ultimately included 401 adult participants aged 35–44 years from 13 multiple surveillance points in Guangdong Province. The distribution of surveyed individuals across the monitoring points is depicted in Fig. 1. Urban and rural classifications followed the "Regulations on the Division of Urban and Rural Areas for Statistical Purposes" issued by the National Bureau of Statistics of China. This classification system integrates multiple criteria including administrative structure (districts of cities, county-level cities, or towns), determination of government seat location, and identification of actual built-up connection areas. Based on these criteria, sampling areas were classified as either urban or rural.
Fig. 1.
The number of individuals and the gender distribution for each sampling point
Variables and measurements
The selection of variables in this cross-sectional study was based on theoretical frameworks and previous population-based oral health surveys. Our study followed the methodological framework of recent epidemiological studies from the same region [22–24] and referred to the classification approach of Broadbent et al. [18], a longitudinal study exploring the pathways between socioeconomic status and oral health outcomes.In this study, Oral Health Behaviors (OHB) were categorized according to their conceptual framework into preventive behaviors (such as toothbrushing frequency, use of dental floss or interdental brushes, and mouthwash use) and treatment-seeking behaviors (such as the Care Index and Restorative Index).This variable structure follows established theoretical models suggesting that socioeconomic status (SES) affects health outcomes through pathways involving knowledge, attitudes, and behaviors [18].
Socioeconomic Status (SES)
Socioeconomic status was operationalized through three indicators: education level (years of education: ≤ 9 years, > 9 to ≤ 12 years, > 12 years), urban/rural residence classification, and oral health insurance coverage status. Insurance coverage was categorized as high-level (Urban Employee Basic Medical Insurance, commercial insurance, or public medical benefits with self-payment proportions of less than 50% for dental caries treatment) or low-level (Urban Resident Basic Medical Insurance, Rural Cooperative Medical Insurance, or no insurance coverage). Household income was assessed using the question: "What was your approximate total household income in the past 12 months?" Income was categorized as low (≤ ¥80,000/year) or high (> ¥80,000/year). Participants had the option to decline answering this question. Due to the sensitivity of income-related information, 245 participants (61.1%) had missing data for this variable, resulting in its exclusion from the final SEM model to maintain construct reliability.
Oral Health Literacy (OHL)
Oral health literacy was assessed using two components(see Supplementary Table 1 for complete questionnaire items):
Oral health attitudes: Four questions assessing attitudes toward oral health. Responses were coded 1 for positive attitudes and 0 for negative attitudes or neutral responses. Total scores ranged from 0–4, with a score of 4 categorized as "high" attitudes.
Oral health knowledge: Eight true/false questions covering oral health concepts. Correct answers were scored as 1, while incorrect or "don't know" responses were scored as 0. Total scores ranged from 0–8, with scores > 6 categorized as "high" knowledge.
Oral Health Behaviors (OHB)
Oral health behaviors comprised five indicators: frequency of toothbrushing (less than twice daily vs twice daily or more), mouthwash use (yes/no), interdental cleaning tool use (including dental floss, interdental brushes, or oral irrigators; categorized as less than once daily vs once or more daily, Care Index (CI = FT/(DT + FT)), and Restorative Index (RI = FT/DMFT). Additional health-related behaviors (sweet food/drink consumption frequency and smoking history) were assessed for comprehensive population characterization. However, these variables were excluded from the final structural equation model due to poor factor loadings on the OHB latent construct, suggesting they represent distinct behavioral dimensions from oral hygiene practices and treatment-seeking behaviors. The variables included in the final model are those specified in the OHB construct description above. The excluded variables are retained in Table 1 for descriptive purposes to provide a complete profile of the study population.
Table 1.
Descriptive Statistics and Associations with Caries and DMFT (N = 401)
| Variable | n (%) | Caries (%) | DMFT Mean (SD) | Caries p value | DMFT p value |
|---|---|---|---|---|---|
| Gender | 0.095 | 0.002 | |||
| Male | 189 (47.1) | 54.5 | 3.44 (3.51) | ||
| Female | 212 (52.9) | 63.2 | 4.57 (3.89) | ||
| Age (years) | 0.742 | 0.305 | |||
| 35–37 | 144 (35.9) | 56.9 | 4.11 (4.00) | ||
| 38–40 | 130 (32.4) | 61.5 | 3.65 (3.15) | ||
| 41–44 | 127 (31.7) | 59.1 | 4.35 (4.01) | ||
| Residence | < 0.001 | 0.896 | |||
| Rural | 211 (52.6) | 68.7 | 4.01 (3.74) | ||
| Urban | 190 (47.4) | 48.4 | 4.06 (3.77) | ||
| Education Level | 0.003 | 0.979 | |||
| ≤ 9 years | 150 (37.4) | 66 | 4.09 (3.56) | ||
| > 9, ≤ 12 years | 84 (20.9) | 66.7 | 4.00 (3.76) | ||
| > 12 years | 167 (41.6) | 49.1 | 4.01 (3.93) | ||
| Oral Health Insurance | 0.01 | 0.494 | |||
| Low | 191 (47.6) | 66 | 4.17 (4.00) | ||
| High | 210 (52.4) | 52.9 | 3.91 (3.51) | ||
| Toothbrushing Frequency | 0.188 | 0.072 | |||
| < Twice daily | 131 (32.7) | 64.1 | 4.54 (4.04) | ||
| ≥ Twice daily | 270 (67.3) | 56.7 | 3.79 (3.58) | ||
| Fluoride toothpaste use | 0.541 | 0.488 | |||
| Yes | 195 (83.7) | 60.5 | 3.98 (3.82) | ||
| No | 38 (16.3) | 65.8 | 4.45 (3.37) | ||
| Dental Visit | 0.356 | < 0.001 | |||
| Yes | 254 (63.3) | 61 | 4.76 (4.01) | ||
| No | 147 (36.7) | 55.8 | 2.80 (2.86) | ||
| Mouthwash Use | 0.723 | 0.579 | |||
| No | 353 (88.0) | 58.6 | 4.08 (3.75) | ||
| Yes | 48 (12.0) | 62.5 | 3.75 (3.81) | ||
| Interdental Cleaning Tool Use | 0.047 | 0.966 | |||
| < Once daily | 178 (44.4) | 53.4 | 4.03 (4.22) | ||
| ≥ Once daily | 223 (55.6) | 63.7 | 4.04 (3.33) | ||
| Sweet Food/Drink Consumption | 0.411 | 0.817 | |||
| < Once daily | 296 (73.8) | 60.5 | 4.01 (3.64) | ||
| ≥ Once daily | 105 (26.2) | 55.2 | 4.11 (4.07) | ||
| History of Smoking | 0.481 | 0.01 | |||
| Yes | 123 (30.7) | 56.1 | 3.33 (3.53) | ||
| No | 278 (69.3) | 60.4 | 4.35 (3.81) | ||
| Oral Health Attitudes | 0.159 | 0.067 | |||
| Low | 132 (32.9) | 53.8 | 3.53 (4.00) | ||
| High | 269 (67.1) | 61.7 | 4.29 (3.61) | ||
| Oral Health Knowledge | 0.307 | 0.758 | |||
| Low | 231 (57.6) | 61.5 | 4.09 (3.84) | ||
| High | 170 (42.4) | 55.9 | 3.97 (3.63) |
Caries %: Percentage of participants with caries (based on binary caries variable). Statistical tests: Chi-square or Fisher’s exact test for caries prevalence (binary variables), independent t-test for DMFT (binary variables), ANOVA for DMFT (categorical variables with more than two levels). p < 0.05 indicates statistical significance
DMFT Decayed, Missing, and Filled Teeth index
p < 0.05 indicates statistical significance
Oral Health Outcomes (OHO)
Oral health outcomes were measured using caries status and DMFT index. To ensure consistent directionality across all indicators, caries status was reverse-coded (1 = no caries, 0 = caries present) and DMFT scores were transformed using the formula: DMFT_transformed = max(DMFT)—DMFT.
Training and Calibration
14 examiners were experienced dentists with a minimum of three years of clinical practice. Prior to the survey, examiners underwent training comprising theoretical instruction on caries examination standards and clinical calibration. Inter-examiner reliability was assessed, with Kappa values exceeding 0.8 for caries examinations and 0.6 for periodontal examinations. Clinical examinations were performed under artificial lighting using plane mouth mirrors and Community Periodontal Index (CPI) probes, with cotton swabs available for removing soft deposits when necessary. Dental caries was diagnosed according to World Health Organization (WHO) criteria. The DMFT index was calculated as the sum of decayed teeth (DT), missing teeth due to caries (MT), and filled teeth (FT).
Statistical Analysis
Descriptive statistics and bivariate analyses were performed using R statistical software (version 4.2.0; R Foundation for Statistical Computing, Vienna, Austria) with the lavaan package (version 0.6–19). As DMFT scores showed approximately normal distribution, t-tests were used for binary variables and ANOVA for categorical variables with more than two levels to examine associations with DMFT scores. Caries prevalence (binary variable, indicating presence or absence of caries) was analyzed using Chi-square tests or Fisher's exact test when appropriate. Statistical significance was set at p < 0.05.
Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were performed to examine the relationships among socioeconomic factors, oral health literacy, oral health behaviors, and oral health outcomes. Model parameters were estimated using maximum likelihood with robust standard errors (MLR) and the Satorra-Bentler scaled chi-square test. To ensure model identification, the factor loading for the knowledge indicator on OHL was fixed to 1.0.
The following goodness-of-fit indices were used to evaluate the model: chi-square test statistic (χ2/df < 3.0), Comparative Fit Index (CFI > 0.90), Tucker-Lewis Index (TLI > 0.90), standardized root mean square residual (SRMR < 0.08), and root mean square error of approximation (RMSEA < 0.08). Indirect effects were calculated using the product of coefficients method. Composite reliability (CR > 0.70) and average variance extracted (AVE > 0.50) were calculated to assess construct reliability and validity. Statistical significance was set at p < 0.05.
Results
Participant Characteristics
A total of 401 adults aged 35–44 years participated in this study. Females accounted for 52.9% of the sample (n = 212). The age distribution was: 35–37 years (35.9%, n = 144), 38–40 years (32.4%, n = 130), and 41–44 years (31.7%, n = 127). The sample was nearly equally distributed between urban (47.4%, n = 190) and rural (52.6%, n = 211) residents. Regarding educational attainment, 37.4% (n = 150) had ≤ 9 years of education, 20.9% (n = 84) had > 9 to ≤ 12 years, and 41.6% (n = 167) had > 12 years. Oral health insurance coverage was distributed between low (47.6%, n = 191) and high (52.4%, n = 210) categories (Table 1).
Most participants (67.3%, n = 270) reported brushing their teeth twice daily or more, and 63.3% (n = 254) had visited a dentist in the past year. Only 12.0% (n = 48) used mouthwash regularly, and 55.6% (n = 223) used interdental cleaning tools at least once daily. Most participants (73.8%, n = 296) consumed sweet foods or drinks less than once daily. A history of smoking was reported by 30.7% (n = 123) of participants. Oral health attitudes were high in 67.1% (n = 269) of participants, while oral health knowledge was high in 42.4% (n = 170) (Table 1).
Comparative analysis of caries prevalence and mean DMFT scores across demographic subgroups revealed significant disparities. Individuals residing in urban areas exhibited significantly lower caries prevalence (48.4%) compared to their rural counterparts (68.7%) (p < 0.05). Statistically significant differences in caries prevalence were also observed across education levels, oral health insurance coverage, and frequency of interdental cleaning tool usage. Regarding mean DMFT scores, significant variations emerged between genders (Male: 3.44, Female: 4.57), dental visit history groups (Visited: 4.76, Not visited: 2.80), and smoking history groups (Smokers: 3.33, Non-smokers: 4.35) (Table 1).
The measurement model demonstrated varying degrees of construct validity. Standardized factor loadings for SES indicators were: education (λ = 0.746, SE = 0.039, p < 0.001), urban residence (λ = 0.639, SE = 0.044, p < 0.001), and insurance coverage (λ = 0.780, SE = 0.038, p < 0.001). OHL indicators showed moderate loadings: attitudes (λ = 0.536, SE = 0.042, p < 0.001) and knowledge (λ = 0.541, SE = 0.042, p < 0.001) (Table 2). The OHB construct revealed heterogeneous indicator performance. Treatment-related indices demonstrated strong associations (CI: λ = 0.998, SE = 0.022, p < 0.001; RI: λ = 0.647, SE = 0.034, p < 0.001), while preventive behaviors showed weak and non-significant loadings (brushing: λ = 0.080, SE = 0.050, p = 0.108; mouthwash: λ = −0.022, SE = 0.050, p = 0.665; interdental cleaning: λ = −0.064, SE = 0.050, p = 0.197).For OHO, caries status showed a loading of λ = 1.084 (SE = 0.052, p < 0.001), while DMFT showed λ = 0.333 (SE = 0.045, p < 0.001). The standardized loading for caries status slightly exceeded unity, which, while theoretically concerning, was retained given the minimal violation and to avoid compromising overall model performance through artificial constraints.
Table 2.
Measurement model results
| Latent Variable → Indicator | Standardized Loading | SE | p-value |
|---|---|---|---|
| SES → | |||
| Education | 0.746 | 0.039 | < 0.001 |
| Urban residence | 0.639 | 0.044 | < 0.001 |
| Insurance | 0.780 | 0.038 | < 0.001 |
| OHL → | |||
| Attitudes | 0.536 | 0.042 | < 0.001 |
| Knowledge | 0.541 | 0.042 | < 0.001 |
| OHB → | |||
| Brushing | 0.08 | 0.05 | 0.108 |
| Mouthwash | −0.022 | 0.05 | 0.665 |
| Interdental | −0.064 | 0.05 | 0.197 |
| CI | 0.998 | 0.022 | < 0.001 |
| RI | 0.647 | 0.034 | < 0.001 |
| OHO → | |||
| Caries (reversed) | 1.084 | 0.052 | < 0.001 |
| DMFT (reversed) | 0.333 | 0.045 | < 0.001 |
SES Socioeconomic Status, OHL Oral Health Literacy, OHB Oral Health Behaviors, OHO Oral Health Outcomes, CI Care Index, RI Restorative Index, DMFT Decayed, Missing, and Filled Teeth index, SE Standard Error
p < 0.001
The structural equation model achieved acceptable fit: χ2 = 141.02 (df = 49, p < 0.001), χ2/df = 2.88, CFI = 0.927, TLI = 0.902, RMSEA = 0.068 (90% CI: 0.055–0.082), SRMR = 0.074 (Table 3). Path analysis revealed that SES had a significant positive effect on OHL (β = 0.452, SE = 0.083, z = 5.427, p < 0.001), explaining 20.5% of the variance. SES also showed a significant direct effect on OHB (β = 0.329, SE = 0.072, z = 4.546, p < 0.001). However, OHL did not significantly influence OHB (β = −0.094, SE = 0.091, z = −1.031, p = 0.303). The model explained 8.9% of the variance in OHB. Neither SES (β = −0.003, SE = 0.038, z = −0.085, p = 0.932) nor OHL (β = −0.051, SE = 0.044, z = −1.140, p = 0.254) showed significant direct effects on OHO. OHB demonstrated a strong positive effect on OHO (β = 0.788, SE = 0.050, z = 15.647, p < 0.001). The model explained 61.8% of the variance in OHO.
Table 3.
Model fit indices and structural path coefficients
| Model Fit Indices | χ2 | df | p | χ2/df | CFI | TLI | RMSEA | 90% CI | SRMR |
|---|---|---|---|---|---|---|---|---|---|
| Value | 141.02 | 49 | < 0.001 | 2.88 | 0.927 | 0.902 | 0.068 | [0.055, 0.082] | 0.074 |
| Direct Effects | β | SE | z | p | R2 | ||||
| SES → OHL | 0.452 | 0.083 | 5.427 | < 0.001 | 0.205 | ||||
| SES → OHB | 0.329 | 0.072 | 4.546 | < 0.001 | 0.089 | ||||
| OHL → OHB | −0.094 | 0.091 | −1.031 | 0.303 | |||||
| SES → OHO | −0.003 | 0.038 | −0.085 | 0.932 | 0.618 | ||||
| OHL → OHO | −0.051 | 0.044 | −1.14 | 0.254 | |||||
| OHB → OHO | 0.788 | 0.05 | 15.647 | < 0.001 | |||||
SES Socioeconomic Status, OHL Oral Health Literacy, OHB Oral Health Behaviors, OHO Oral Health Outcomes, CFI Comparative Fit Index, TLI Tucker-Lewis Index, RMSEA Root Mean Square Error of Approximation, SRMR Standardized Root Mean Square Residual, SE Standard Error; R2 = proportion of variance explained
p < 0.001
The total indirect effect of SES on OHO was statistically significant (β = 0.203, SE = 0.053, z = 3.796, p < 0.001), with a total effect of β = 0.200 (SE = 0.053, z = 3.803, p < 0.001). The proportion mediated was 101.6% (SE = 0.194, z = 5.234, p < 0.001), indicating complete mediation. Analysis of specific indirect pathways revealed that only the pathway through oral health behaviors (SES → OHB → OHO) was significant (β = 0.259, SE = 0.061, z = 4.235, p < 0.001) (Table 4). The pathways through oral health literacy were not significant: SES → OHL → OHO (β = −0.023, SE = 0.021, z = −1.088, p = 0.276) and SES → OHL → OHB → OHO (β = −0.033, SE = 0.034, z = −0.974, p = 0.330). Construct reliability assessment yielded satisfactory values for SES (CR = 0.767, AVE = 0.525) and OHO (CR = 0.737, AVE = 0.642). (Supplementary Table S2).
Table 4.
Indirect and total effects
| Effect | β | SE | z | p-value |
|---|---|---|---|---|
| Specific Indirect Effects | ||||
| SES → OHL → OHO | −0.023 | 0.021 | −1.088 | 0.276 |
| SES → OHB → OHO | 0.259 | 0.061 | 4.235 | < 0.001 |
| SES → OHL → OHB → OHO | −0.033 | 0.034 | −0.974 | 0.33 |
| Total Effects | ||||
| Total indirect effect | 0.203 | 0.053 | 3.796 | < 0.001 |
| Total effect (SES → OHO) | 0.2 | 0.053 | 3.803 | < 0.001 |
| Proportion mediated | 1.016 | 0.194 | 5.234 | < 0.001 |
Proportion mediated > 1.0 indicates complete mediation with suppression effects
SES Socioeconomic Status, OHL Oral Health Literacy, OHB Oral Health Behaviors, OHO Oral Health Outcomes, SE Standard Error
p < 0.001
Figure 2 visually presents the structural relationships among study variables. As shown, SES has significant positive direct effects on both OHL and OHB (solid lines), but the path coefficient from OHL to OHB is not significant (dashed line), confirming the existence of a knowledge translation failure. The path from OHB to OHO shows the strongest standardized coefficient (β = 0.788), emphasizing the critical role of behavior in determining oral health outcomes. Notably, neither SES nor OHL has significant direct paths to OHO (dashed lines), further supporting the conclusion of a complete mediation model.
Fig. 2.
Structural Equation Model of Oral Health Determinants. Significant path (p < 0.001). Non-significant path (ns). Note: SES = Socioeconomic Status; OHL = Oral Health Literacy; OHB = Oral Health Behaviors; OHO = Oral Health Outcomes; CI = Caries Index; RI = Restoration Index; DMFT = Decayed, Missing, and Filled Teeth index. ***p < 0.001; Standardized coefficients are shown
Discussion
In this study, we employed structural equation modeling to analyze the pathways connecting socioeconomic status (SES) with oral health outcomes (OHO), examining the intermediary roles of oral health literacy (OHL) and oral health behaviors (OHB). The results demonstrate that socioeconomic disparities in dental caries are principally transmitted through behavioral mechanisms, particularly through the completion of recommended dental treatments, rather than through differences in knowledge or attitudes. The observed disconnection between oral health literacy and actual behaviors implies that to effectively mitigate oral health inequalities, interventional efforts should prioritize enhancing patients' capacity to follow through with prescribed care, moving beyond a singular focus on educational approaches.
Epidemiological research has sought to characterize caries prevalence disparities among diverse populations across various regions, aiming to elucidate regional caries burdens and identify potential influencing factors [25]. The influencing factors of dental caries in adults may attributed to gender, smoking, age, number of teeth, daily medication, single living oral, health awareness, treatment-seeking behavior and socio-economic profiles [26–28]. This epidemiological survey revealed a persistently high caries burden among 35–44 year-old adults in Guangdong Province in 2021, with an overall prevalence of 59.1%. Notably, this figure aligns remarkably with documented historical trends for the same demographic cohort: 67.7% in 1995, 57.0% in 2005, and 60.7% in 2015 [29]. This persistent pattern highlights the need for local health authorities to potentially reassess current caries prevention strategies.
Socioeconomic inequalities in oral health represent an important global public health challenge, but the underlying mechanisms through which SES affects caries outcomes remain unclear. Like other lifestyle diseases, dental caries has become disproportionately a disease of poverty and disadvantaged groups in many countries. The consequences have been documented in various populations [18, 30, 31]. Various familial socioeconomic indicators with lower scores are associated with lower oral health-related quality of life scores in children [32]. Childhood low SES, along with poor dental attendance and behaviors, is consistently associated with poor dental health and behaviors in mid-adulthood [33]. Adults from low-income groups are more likely to acknowledge and rate their oral health as fair or poor compared to high-income groups [34]. Childhood SES-related poor dental health is associated with significant dental health and quality of life consequences extending into later life, with oral health outcome disparities between high and low SES groups typically widening over time [35].
Building on these findings, this study further explored the specific pathways through which SES affects caries outcomes. Although recent studies have used structural equation modeling to explore the complex relationship between SES and oral health, such as Lyu et al. finding that SES indirectly affects oral health-related quality of life through tooth loss [36], Celeste et al. confirming that SES affects oral health through risk chains and cumulative effects [37], and Zhao et al. finding that oral health attitudes and behaviors are related to quality of life [38]. Additionally, Bittencourt et al. explored the relationship between psychosocial factors and childhood caries [39], Heaton et al. found associations between SES and oral health behaviors [40], and Stein et al. found mediating effects of dietary patterns [41]. While these studies have provided valuable insights into the SES-oral health relationship, few have simultaneously examined and compared the potential mediating roles of oral health literacy (knowledge and attitudes) and oral health behaviors (preventive and treatment completion behaviors) within a single analytical framework. The present study attempted to address this gap by quantifying the relative contributions of these two pathways. Individuals with lower health literacy often face difficulties in utilizing preventive services, experience delayed disease diagnosis, show poor adherence to medical advice, exhibit limited self-management skills, and consequently have higher mortality risk, poorer overall health, and increased healthcare costs [38]. Oral health literacy is a combination of knowledge and skills acquired over time through oral health experiences [19, 20]. Many studies indicate that SES, behaviors, and cognition may all influence oral health outcomes [39–41].
This study provides systematic evidence regarding the mechanisms of socioeconomic inequalities in dental caries. We found that urban participants showed lower caries prevalence and severity, reflecting rural areas' disadvantages in infrastructure, medical service accessibility, and socioeconomic conditions [23, 42]. A study by Jiang et al. on the Guangdong-Hong Kong-Macao Greater Bay Area revealed that residents in less-developed regions had to travel significantly longer to reach a hospital—an additional 20 min on average—compared to those in developed areas. These regions also exhibited a lower density of registered healthcare professionals per 1,000 people [43]. Education and income, as important socioeconomic indicators, are significantly associated with caries occurrence [44, 45]. Previous studies in Guangdong Province also found that socioeconomic inequalities were more prominent in treatment completion than in caries experience [46], which is consistent with our model's finding that SES completely mediates health outcomes through oral health behaviors.
The key finding of this study is that although SES significantly influenced both the cognitive-attitudinal dimension (including 8 oral health knowledge items and 4 attitude assessments, β = 0.452, p < 0.001) and the behavioral dimension (including preventive behaviors and treatment completion behaviors represented by Care Index and Restorative Index, β = 0.329, p < 0.001), the cognitive-attitudinal dimension could not translate into the behavioral dimension (β = −0.094, p = 0.303). Only the behavioral dimension—particularly treatment completion behavior indicators CI (λ = 0.998) and RI (λ = 0.647)—could directly translate into actual health improvement (β = 0.788, p < 0.001), completely mediating the impact of socioeconomic inequality on caries outcomes, while the cognitive-attitudinal dimension played no role in this mediation pathway. This finding aligns with Albarracín et al.'s meta-meta-analysis [47], which demonstrated that interventions targeting knowledge and beliefs have minimal effects on behavior change, whereas policies increasing access to behaviors show the strongest impact. Notably, in our measurement model, treatment completion indicators loaded strongly on the behavioral dimension (CI: λ = 0.998; RI: λ = 0.647), while preventive behaviors showed non-significant loadings (brushing: λ = 0.080, p = 0.108; mouthwash: λ = −0.022, p = 0.665; interdental cleaning: λ = −0.064, p = 0.197). This indicates that the behavioral pathway is driven primarily by access to and completion of dental treatment, as opposed to daily self-care practices—a finding that supports Albarracín et al.'s conclusion regarding the prioritization of enabling access over targeting cognitive determinants. This indicates that even when individuals possess adequate oral health knowledge (such as knowing that caries are caused by bacteria and sugar consumption leads to caries) and positive attitudes (such as believing oral health is important and regular check-ups are necessary), these cognitive factors cannot automatically translate into effective oral health behaviors, nor can they directly improve oral health outcomes.
This study has several limitations, including the cross-sectional design limiting causal inference, potential social desirability bias in self-reported behaviors, and high missing rate of income data (61.1%) affecting the comprehensiveness of SES assessment. Information on fluoride toothpaste use was collected but excluded from the structural equation model due to a high proportion of missing or indeterminate responses. Of 401 participants, 168 (41.9%) responded either "don't know" (n = 165, 41.1%) or "don't use toothpaste" (n = 3, 0.7%), resulting in only 233 (58.1%) valid responses. Although fluoride promotes remineralization and protects against caries [6], the high proportion of "don't know" responses may itself reflect oral health literacy gaps. This variable is reported descriptively for population characterization purposes. Sweet food/drink consumption was also excluded due to poor factor loadings on the OHB construct. While dietary sugars contribute to caries development [8, 9], dietary behaviors may represent a distinct dimension from oral hygiene and treatment-seeking behaviors, and their inclusion would compromise construct validity. Additionally, this study was conducted only among 35–44 year-old adults in Guangdong Province, which may limit the generalizability of the results. Additionally, this study was conducted only among 35–44 year-old adults in Guangdong Province, which may limit the generalizability of the results. Despite these limitations, our findings provide important insights for reducing oral health inequalities. Studies have shown that among 19 countries, the most common preventive policy was free dental services for children (78.95%), with increased oral hygiene expenditure associated with a 4.42% reduction in DMFT, and the presence of mandatory child dental care laws associated with a mean DMFT score reduction of 1.32 [48].
Therefore, unlike traditional health education approaches that focus on knowledge dissemination, future interventions should prioritize helping low-SES populations translate health cognition into behaviors, particularly by removing barriers to oral treatment access. This includes implementing affordable healthcare programs, improving service accessibility, establishing behavioral support systems, and reducing structural barriers through policy reforms. Only through comprehensive, behavior change-oriented intervention strategies can we effectively narrow the socioeconomic gap in oral health.
Supplementary Information
Acknowledgements
We express our gratitude for the financial funds provided by the Science Research Cultivation Program of Stomatological Hospital, Southern Medical University (No.: PY2023035) and COHF (China Oral Health Foundation, No.: A2021-053).
Authors’ contributions
Shaohong Huang and Linmei Wu had participated in proposing the conception and design; Jingxin Weng, Yanan Chen and Zeng Fan had carried the survey, collected and organized the data, conducted statistical analysis; Jingxin Weng had written the manuscript; Shaohong Huang and Linmei Wu had revised the manuscript; All authors had read and approved the final manuscript.
Funding
The research was supported by the Science Research Cultivation Program of Stomatological Hospital, Southern Medical University (No.: PY2023035) and China Oral Health Foundation (No.: A2021-053).
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This study was approved by the Stomatological Ethics Committee of the Chinese Stomatological Association (Approval No.: 2014–003). All participants provided written informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Shaohong Huang, Email: gd920@21cn.com.
Linmei Wu, Email: 124277475@qq.com.
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Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.