Association between dental caries and sociodemographic factors in children aged 5-12years

ABSTRACT

Dental caries is a global public health issue, particularly in children, and is influenced bysociodemographic conditions.

Aim

To assess the association between dental caries and sociodemographic factors in children aged5-12 years in Victoria, Cabañas, El Salvador.

Materials and Method

A cross-sectional, correlational study was conducted among 137 children (48.2% boys, 51.8%girls), measuring dental caries using DMFT and dmft indices, and oral hygiene using the O’LearyPlaque Index. Sociodemographic data were obtained through caregiver questionnaires.

Results

Caries prevalence was 63.5%. DMFT increased with age (highest in 10–12-year-old girls: mean =5.44), while dmft declined (lowest in 12-year-old girls: mean = 0.56). Most children (98.5%) hadpoor oral hygiene. A weak but significant negative correlation was found between income and dmft(ρ = -0.189; p = 0.027). No other significant association was observed between caries indicesand maternal education, income, or plaque index.

Conclusions

Caries patterns differed significantly according to age and sex. Despite the high prevalence,most sociodemographic variables showed no strong statistical association with caries. Preventiveinterventions should focus on age- and sex-specific risks.

INTRODUCTION

Oral health is a global public health concern, particularly among vulnerable pediatric populations inlow- and middle-income countries, where economic and social inequalities exacerbate disparities inaccess to care ¹ . Despite being largely preventable, dental caries is the most prevalent chroniccondition in childhood, impacting overall well-being and quality of life ² .

According to the 2022 Global Oral Health Status Report from the World Health Organization (WHO), 514million children suffer from caries in primary teeth and over 2 billion people have untreated caries inpermanent teeth ³ . Urbanization, high consumption of sugary foods, limited fluoride exposure,inadequate oral hygiene practices, and insufficient access to dental services contribute significantlyto this burden ^4-5 .

Research from around the world has consistently demonstrated a strong association betweensociodemographic determinants and caries prevalence in children. For example, studies from Japan, Braziland Latin America have shown that low maternal education, household income, and socioeconomic status areassociated with higher prevalence of dental caries ^6-8 . Similarly, Vasireddy et al. found that childrenin the lowest income bracket in the U.S. were more than twice as likely to develop caries compared tothose from higherincome families ⁹ . In Latin America, Ojeda and Villavicencio identified income as abarrier to access to dental care ¹⁰ . Recent evidence from Syria and Ethiopia highlights that schooltype, household size, parental education, and dietary behaviors are significantly associated with cariesexperience ^11-12

A recent national study in Peru reported that 76.2% of children aged 3 to 5 had dental caries ¹³ . InChina, research in 2024 showed that 97% of caries in preschoolers were untreated, underscoring limitedaccess to early treatment ¹⁴ . All these studies highlight the importance of identifying vulnerablepopulations in order to plan preventive interventions.

Despite the growing body of international literature, localized data are essential to guide effectivecommunity-based public health strategies. In El Salvador, data on dental caries and their associationwith social determinants are scarce. This study addresses this gap by examining the association betweensociodemographic factors and dental caries in children aged 5-12 in Victoria, Cabañas, a region withlimited access to healthcare and high poverty levels.

This study employed validated caries indices DMFT (Decayed, Missing, and Filled Teeth for permanentdentition) and dmft (for primary dentition), as well as a structured caregiver questionnaire to explorehow age, sex, maternal education and family income influence oral health status in children.

Research question: What is the association between dental caries diagnosis andsociodemographic factors such as age, sex, maternal education and family income among children aged 5 to12 years in Victoria, Cabañas, El Salvador?

Hypothesis:Sociodemographic factors, including age, sex, maternal education level andfamily income significantly influence the prevalence and severity of dental caries in children aged 5 to12 years in Victoria, Cabañas, El Salvador.

Null hypothesis (H₀) : There is no significant association between dental caries diagnosis and the sociodemographic factorsunder study.

Alternative hypothesis (H₁) : There is a significant association between dental caries diagnosis and at least one of thesociodemographic factors (age, sex, maternal education or family income).

MATERIALS AND METHOD

Ethical considerations

Participants’ rights were protected through informed consent signed by parents or guardians, verbalassent from children, voluntary participation, and guaranteed confidentiality. The study wasapproved by the Health Research Ethics Committee (ACTA No. 339).

Study design

This was a quantitative, cross-sectional, observational, correlational study conducted in 2023 inVictoria, Cabañas, El Salvador. A total of 137 children aged 5 to 12 years (66 boys and 71 girls)participated. A non-probabilistic convenience sampling method was used, justified by logisticallimitations in rural areas and the inability to construct a comprehensive sampling frame within thestudy timeframe.

Inclusion and exclusion criteria

Children aged 5 to 12 years residing in Victoria, Cabañas, El Salvador, whose parents or guardianssigned the informed consent, were included. Exclusion criteria were presence of systemic diseases,ongoing orthodontic treatment, or absence on the day of the dental evaluation.

Clinical assessment and data collection

Dental caries were diagnosed using the DMFT (Decayed, Missing, and Filled Teeth) and dmft indices forpermanent and primary dentition, respectively, following the World Health Organizationguidelines ¹⁵ . Three trained, calibrated examiners performed the clinical assessments. Dentalplaque was assessed using the O’Leary Plaque Index ¹⁶ by four trained observers, applying a plaquedisclosing agent to smooth tooth surfaces.

Instruments

The instruments included standardized clinical records, dental charts, plaque index forms, and avalidated questionnaire designed by experts in pediatric dentistry, public health and research. Thequestionnaire recorded the following sociodemographic information: age (in years), sex(male/female), maternal education level (no schooling, primary, secondary, higher), and monthlyhousehold income (<$100, $100-199, $200299, $300-499 USD).

Quality control and bias reduction

Calibration among observers was conducted before data collection. Kappa coefficients showed highinterobserver agreement: 0.852 and 0.855 for caries diagnosis; 0.855 and 0.719 for plaqueevaluation. To reduce information bias, surveys were administered by trained personnel. Selectionbias was minimized by including all children who met inclusion criteria and were available duringthe evaluation period.

Statistical analysis

Data were analyzed using IBM SPSS Statistics version 23 ¹⁷ . The Kolmogorov-Smirnov test was appliedto assess the normality of the data, revealing non-normal distribution for most variables.Consequently, non-parametric tests were used: Mann-Whitney U for two-group comparisons,Kruskal-Wallis for multiple groups, and Spearman’s rank correlation for ordinal variables.Descriptive statistics included frequency distributions, measures of central tendency, andcross-tabulations to assess the distribution of caries and plaque indices by age and sex.

RESULTS

Descriptive Analysis

DMFT, dmft, and plaque index values according to age and sex in children aged 5 to 12 years

A progressive increase in the DMFT index was observed with age, particularly among girls, whereasthe dmft index decreased in older children, showing higher prevalence among those under 7 yearsof age. The plaque index remained high across all age groups, with worse outcomes in girls,reflecting a generalized pattern of poor oral hygiene ( Table1).

Table 1. Mean, median, and 75th percentile values of DMFT, dmft and plaque index according toage and sex (n = 137) .

Age Group	Sex	Mean DMFT	Mean dmft	Mean PI (%)	Median DMFT	Median dmft	Median PI (%)	75th Perc. DMFT	75th Perc. dmft	75th Perc. PI (%)
5-6	Male	0.36	8.05	65.18	0	7.5	65.18	0.25	11.25	81.34
5-6	Female	0.43	7.93	77.71	0	8.5	86.16	0.25	10.25	90.31
7-9	Male	1.91	5.22	75.72	1	5	83.7	3	8	90
7-9	Female	2.43	6.1	80.57	2	6	89.13	3.5	9	99.38
10-11	Male	3.85	2.15	78.17	4	1	83.33	4.5	3.5	96.21
10-11	Female	5.44	1.41	82.75	4	1	87.5	8	2	95.65
12	Male	3.13	1	67.47	3	0	78.82	5.5	1.5	89.9
12	Female	4.33	0.56	71.26	4	0	78.13	5.5	1.5	96.88

Note. DMFT = decayed, missing and filled teeth in permanent dentition; dmft= same index for primary teeth. PI = plaque index based on stained surfaces. Dataobtained from clinical assessments of 137 children in Victoria, Cabañas (2023).

The prevalence of caries in permanent teeth was 63.5%, with a Significant Caries Index (SCI) of6.82, indicating higher rates in girls. The average plaque index was 74.97%, and the 75thpercentile revealed a subgroup with alarmingly high values across all indices, suggesting theneed for specialized dental care.

Association among age, sex and DMFT index

In the 5-6-year age group, 24.1% of the children had very low DMFT indices. In the 7-9-yeargroup, 14.6% maintained low DMFT values, although 1.5% reached very high levels. Among childrenaged 10–12 years, the proportion of moderate to very high DMFT indices increased markedly,especially in girls, indicating a progression of caries in permanent teeth with age andsex-related differences in prevalence (Table 2).

Table 2. Distribution of DMFT index categories according to age group and sex in children aged5 to 12 years (n = 137) .

Age Group	Sex	Very Low (0-1.1)	Low (1.2-2.6)	Moderate (2.7-4.4)	High (4.5-6.5)	Very High (≥6.6)	Total
5-6	Male	20 (30.3%)	1 (1.5%)	1 (1.5%)	0 (0.0%)	0 (0.0%)	22
7-9	Male	12 (18.2%)	3 (4.5%)	5 (7.6%)	2 (3.0%)	1 (1.5%)	23
10-11	Male	1 (1.5%)	3 (4.5%)	6 (9.1%)	2 (3.0%)	1 (1.5%)	13
12	Male	2 (3.0%)	1 (1.5%)	3 (4.5%)	2 (3.0%)	0 (0.0%)	8
Total	35 (53.0%)	8 (12.1%)	15 (22.7%)	6 (9.1%)	2 (3.0%)	66
5-6	Female	13 (18.3%)	0 (0.0%)	1 (1.4%)	0 (0.0%)	0 (0.0%)	14
7-9	Female	8 (11.3%)	5 (7.0%)	5 (7.0%)	2 (2.8%)	1 (1.4%)	21
10-11	Female	5 (7.0%)	2 (2.8%)	7 (9.9%)	5 (7.0%)	8 (11.3%)	27
12	Female	1 (1.4%)	1 (1.4%)	3 (4.2%)	3 (4.2%)	1 (1.4%)	9
Total	27 (38.0%)	8 (11.3%)	16 (22.5%)	10 (14.1%)	10 (14.1%)	71
Overall Total	62 (45.3%)	16 (11.7%)	31 (22.6%)	16 (11.7%)	12 (8.8%)	137

Note. DMFT = Decayed, Missing and Filled Teeth index in permanent dentition.Categories were classified following WHO guidelines for epidemiological comparisons.Percentages refer to the proportion of the total sample within each sex and age group.

Association among age, sex and dmft index

In the 5–6-year age group, a high proportion of children had very high dmft scores, with 21.2% ofboys and 14.1% of girls being included in this category. Similarly elevated values were observedin the 7–9-year age group, although a slight reduction was noted. Among older children,especially 12-yearold girls, the dmft index declined progressively, reflecting the naturalshedding of primary teeth and a reduced impact of caries on deciduous dentition at older ages(Table 3).

Table 3. Distribution of dmft index categories according to age group and sex inchildren aged5 to 12 years .

Age Group	Sex	Very Low (0–1.1)	Low (1.2–2.6)	Moderate (2.7–4.4)	High (4.5–6.5)	Very High (≥6.6)	Total
5-6	Male	1 (1.5%)	2 (3.0%)	0 (0.0%)	5 (7.6%)	14 (21.2%)	22
7-9	Male	5 (7.6%)	1 (1.5%)	5 (7.6%)	2 (3.0%)	10 (15.2%)	23
10-11	Male	7 (10.6%)	0 (0.0%)	4 (6.1%)	2 (3.0%)	0 (0.0%)	13
12	Male	7 (10.6%)	1 (1.5%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	8
Total	20 (30.3%)	4 (6.1%)	9 (13.6%)	9 (13.6%)	24 (36.4%)	66
5-6	Female	1 (1.4%)	0 (0.0%)	1 (1.4%)	2 (2.8%)	10 (14.1%)	14
7-9	Female	4 (5.6%)	0 (0.0%)	1 (1.4%)	6 (8.5%)	10 (14.1%)	21
10-11	Female	16 (22.5%)	5 (7.0%)	4 (5.6%)	2 (2.8%)	0 (0.0%)	27
12	Female	7 (9.9%)	2 (2.8%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	9
Total	28 (39.4%)	7 (9.9%)	6 (8.5%)	10 (14.1%)	20 (28.2%)	71
Overall Total	48 (35.0%)	11 (8.0%)	15 (10.9%)	19 (13.9%)	44 (32.1%)	137

Note. dmft = decayed, missing and filled teeth in primary dentition.Classification follows WHO criteria. Percentages represent the proportion of the totalsample in each subgroup.

Association between DMFT index and mother’s education level

Children whose mothers had only received primary education exhibited the highest proportion ofvery low DMFT indices (31.4%). However, paradoxically, this group also concentrated the mostcases of caries, indicating a high overall prevalence of caries despite the distribution. Adownward trend in DMFT values was observed as maternal education level increased, suggestingthat higher maternal education may have a protective influence on permanent dentition.Nonetheless, statistical analysis did not reveal a significant correlation between maternaleducation and DMFT index (Rho = 0.070, p = 0.418). This may be influenced bythe homogeneity of the study population or by uncontrolled confounding variables ( Table 4 ).

Table 4. Distribution of DMFT index categories according to mother’s education level (n =137) .

DMFT Category	No Schooling	Early Childhood Education	Primary Education	Secondary Education	Higher Education	Total
Very Low (0-1.1)	5 (3.6%)	1 (0.7%)	43 (31.4%)	11 (8.0%)	2 (1.5%)	62
Low (1.2-2.6)	0 (0.0%)	1 (0.7%)	7 (5.1%)	8 (5.8%)	0 (0.0%)	16
Moderate (2.7-4.4)	2 (1.5%)	3 (2.2%)	19 (13.9%)	5 (3.6%)	2 (1.5%)	31
High (4.5-6.5)	0 (0.0%)	1 (0.7%)	12 (8.8%)	3 (2.2%)	0 (0.0%)	16
Very High (≥6.6)	1 (0.7%)	0 (0.0%)	5 (3.6%)	5 (3.6%)	1 (0.7%)	12
Total	8 (5.8%)	6 (4.4%)	86 (62.8%)	32 (23.4%)	5 (3.6%)	137

Note. DMFT = decayed, missing and filled teeth in permanent dentition.Education levels were self-reported by parents and categorized according to nationalstandards. Percentages represent proportions relative to the total sample.

Association between dmft index and mother’s education level

Children whose mothers had no schooling or only basic education exhibited higher dmft indices.Specifically, 22.6% of the children in the primary education group had a “very high” dmft index,indicating greater vulnerability to dental caries in primary dentition when maternal educationis limited. In contrast, children whose mothers had secondary, or university-level educationtended to have lower dmft values. However, the statistical analysis revealed no significantcorrelation between maternal education level and the dmft index (Rho = 0.070; p =0.418). This lack of association could be attributed to the limited variability in educationallevels within the sample or to the influence of other unmeasured factors such as access todental care or hygiene habits supervised by caregivers ( Table5).

Table 5. Distribution of dmft index categories according to mother’s education level (n =137) .

dmft Category	No Schooling	Early Childhood Education	Primary Education	Secondary Education	Higher Education	Total
Very Low (0–1.1)	1 (0.7%)	1 (0.7%)	28 (20.4%)	15 (10.9%)	3 (2.2%)	48
Low (1.2–2.6)	0 (0.0%)	2 (1.5%)	6 (4.4%)	3 (2.2%)	0 (0.0%)	11
Moderate (2.7–4.4)	2 (1.5%)	0 (0.0%)	11 (8.0%)	2 (1.5%)	0 (0.0%)	15
High (4.5–6.5)	2 (1.5%)	1 (0.7%)	10 (7.3%)	6 (4.4%)	0 (0.0%)	19
Very High (≥6.6)	3 (2.2%)	2 (1.5%)	31 (22.6%)	6 (4.4%)	2 (1.5%)	44
Total	8 (5.8%)	6 (4.4%)	86 (62.8%)	32 (23.4%)	5 (3.6%)	137

Note. dmft = decayed, missing and filled teeth in primary dentition. Thedata reflects maternal education levels and their association with caries prevalence inchildren. Percentages indicate the proportion of each group relative to the full sample.

Association between DMFT index and family income

Children from families earning less than $100 per month showed a higher proportion of moderate(22.6%) and very high (8.8%) DMFT indices, indicating a greater burden of caries in permanentdentition among lower-income groups. In contrast, children from families with monthly incomesbetween $300 and $500 demonstrated significantly better oral health, with 45.3% presenting a“very low” DMFT index. Nevertheless, Spearman’s correlation analysis showed no statisticallysignificant association between family income and DMFT index (Rho = 0.030; p = 0.730). This lackof significance may reflect the relative homogeneity of the sample or the influence ofconfounding factors such as dietary habits or previous access to preventive dental care (Table 6 ).

Table 6. Distribution of DMFT index categories according to family monthly income (n = 137).

DMFT Category	< $100.00	$100.00–$199.99	$200.00–$299.99	$300.00–$499.99	Total
Very Low (0–1.1)	27 (19.7%)	20 (14.6%)	10 (7.3%)	5 (3.6%)	62
Low (1.2–2.6)	8 (5.8%)	3 (2.2%)	5 (3.6%)	0 (0.0%)	16
Moderate (2.7–4.4)	13 (9.5%)	11 (8.0%)	4 (2.9%)	3 (2.2%)	31
High (4.5–6.5)	6 (4.4%)	2 (1.5%)	2 (1.5%)	6 (4.4%)	16
Very High (≥6.6)	7 (5.1%)	3 (2.2%)	2 (1.5%)	0 (0.0%)	12
Total	61 (44.5%)	39 (28.5%)	23 (16.8%)	14 (10.2%)	137

Note. DMFT = decayed, missing and filled teeth in permanent dentition. Thistable shows how caries severity varies according to household monthly income. The “VeryLow” category was most prevalent among children from families earning $300.00–$499.99.

Association between dmft index and family income

The cross-tabulation between the dmft index and monthly income ranges showed that children fromfamilies earning less than $100 per month exhibited higher rates of “very high” (18.2%) and“high” (6.6%) dmft indices, indicating a greater prevalence of caries in primary teeth. Incontrast, children from families with incomes between $300 and $500 showed lower caries levels,with a greater proportion falling into the “very low” category (5.1%).

Overall, 35.0% of the children had a “very low” dmft index, while 32.1% showed a “very high”value, suggesting a polarized distribution of caries in primary dentition. Spearman’scorrelation revealed a weak but statistically significant negative association between familyincome and the dmft index (Rho = –0.189; p = 0.027), indicating that lower income levels areassociated with higher caries prevalence in deciduous teeth. These findings underscore theimportance of implementing targeted preventive strategies for low-income populations (Table 7 ).

Table 7. Distribution of dmft index categories according to family monthly income (n = 137).

dmft Category	< $100.00	$100.00–$199.99	$200.00–$299.99	$300.00–$499.99	Total
Very Low (0–1.1)	15 (10.9%)	18 (13.1%)	8 (5.8%)	7 (5.1%)	48
Low (1.2–2.6)	3 (2.2%)	3 (2.2%)	2 (1.5%)	3 (2.2%)	11
Moderate (2.7–4.4)	9 (6.6%)	3 (2.2%)	3 (2.2%)	0 (0.0%)	15
High (4.5–6.5)	9 (6.6%)	6 (4.4%)	3 (2.2%)	1 (0.7%)	19
Very High (≥6.6)	25 (18.2%)	9 (6.6%)	7 (5.1%)	3 (2.2%)	44
Total	61 (44.5%)	39 (28.5%)	23 (16.8%)	14 (10.2%)	137

Note. dmft = decayed, missing and filled teeth in primary dentition. Thedata indicates a higher prevalence of severe caries in children from lowerincomefamilies, particularly those earning less than $100.00 per month.

Association between DMFT index and plaque index

Analysis of the association between the DMFT index and plaque index revealed that most children(98.5%) had poor oral hygiene, as defined by plaque coverage exceeding 30%. Among thesechildren, although 45.3% presented “very low” DMFT values, a considerable proportion alsoexhibited moderate (22.6%), high (11.7%) and very high (8.8%) DMFT indices (Table 8 ).

Table 8. Distribution of DMFT index categories according to oral hygiene (plaque index) inchildren aged 5–12 years (n = 137) .

DMFT Category	Good (<20%)	Acceptable (20%–30%)	Poor (>30%)	Total
Very Low (0–1.1)	0 (0.0%)	0 (0.0%)	62 (45.3%)	62
Low (1.2–2.6)	0 (0.0%)	0 (0.0%)	16 (11.7%)	16
Moderate (2.7–4.4)	0 (0.0%)	0 (0.0%)	31 (22.6%)	31
High (4.5–6.5)	1 (0.7%)	1 (0.7%)	14 (10.2%)	16
Very High (≥6.6)	0 (0.0%)	0 (0.0%)	12 (8.8%)	12
Total	1 (0.7%)	1 (0.7%)	135 (98.5%)	137

Note. DMFT = decayed, missing and filled permanent teeth. Poor oral hygienewas nearly universal (98.5%), with the highest caries burden found in children withplaque indices above 30%.

These results indicate a heterogeneous distribution of caries severity despite uniformly poorhygiene conditions. The absence of a statistically significant correlation between DMFT and theplaque index (Rho = 0.148; p = 0.084) suggests that while plaque accumulation is widespread, itmay not fully explain the variation in caries prevalence in permanent dentition. Additionalfactors such as diet, fluoride exposure and previous access to dental care may contribute tothis disparity and should be further explored in future research.

Association between dmft index and plaque index

Among children with poor oral hygiene (defined as a plaque index greater than 30%), 32.1%exhibited a “very high” dmft index, indicating high prevalence of dental caries in primary teethwithin this group. However, 33.6% of these children had a “very low” dmft index despite poorhygiene (Table 9).

Table 9. Distribution of dmft Index Categories According to Oral Hygiene (Plaque Index) inChildren Aged 5–12 Years (n = 137) .

dmft Category	Good (<20%)	Acceptable (20%–30%)	Poor (>30%)	Total
Very Low (0–1.1)	1 (0.7%)	1 (0.7%)	46 (33.6%)	48
Low (1.2–2.6)	0 (0.0%)	0 (0.0%)	11 (8.0%)	11
Moderate (2.7–4.4)	0 (0.0%)	0 (0.0%)	15 (10.9%)	15
High (4.5–6.5)	0 (0.0%)	0 (0.0%)	19 (13.9%)	19
Very High (≥6.6)	0 (0.0%)	0 (0.0%)	44 (32.1%)	44
Total	1 (0.7%)	1 (0.7%)	135 (98.5%)	137

Note. dmft = decayed, missing and filled primary teeth. Although most children had poorhygiene (98.5%), nearly one-third (33.6%) had a low dmft index, indicating that hygienealone does not explain caries patterns in primary teeth.

These findings suggest that while oral hygiene is an important factor, it is not the soledeterminant of caries prevalence in primary dentition. The lack of a significant correlationbetween plaque index and dmft (Rho = –0.005; p = 0.953) reinforces thisinterpretation. Other variables such as sugar intake frequency, fluoride use, caregivers’educational level, and access to dental care probably play a critical role in shaping children’scaries experience.

Inferential Analysis

Statistical analysis of DMFT and dmft indices according to sociodemographic variables and plaqueindex.

This inferential analysis explored how the DMFT and dmft indices vary in relation to keysociodemographic factors, including sex, age, maternal education level, family income and plaqueindex (PI) ( Table 10).

Table 10. Inferential statistical analysis of DMFT and dmft indices according tosociodemographic and clinical variables in children aged 5–12 years (n = 137) .

Analysis	Dependent Variable	Independent Variable	Test Used	Statistic	p-value	Interpretation
Mann–Whitney U Test	DMFT	Sex	U	1813	0.019	Significant difference: girls had higher DMFT than boys.
Mann–Whitney U Test	dmft	Sex	U	1977	0.112	No significant difference in dmft according to sex.
Kruskal–Wallis Test	DMFT	Age group	H	52.588	<0.001	Significant increase in DMFT with age.
Kruskal–Wallis Test	dmft	Age group	H	65.686	<0.001	Significant decrease in dmft with age.
Spearman’s Correlation	DMFT	Mother’s education level	Rho	0.07	0.418	No significant correlation.
Spearman’s Correlation	dmft	Mother’s education level	Rho	0.07	0.418	No significant correlation.
Spearman’s Correlation	DMFT	Monthly family income	Rho	0.03	0.73	No significant correlation.
Spearman’s Correlation	dmft	Monthly family income	Rho	–0.189	0.027	Weak but significant negative correlation: lower income linked to higherdmft.
Spearman’s Correlation	DMFT	Plaque Index	Rho	0.148	0.084	No significant correlation.
Spearman’s Correlation	dmft	Plaque Index	Rho	–0.005	0.953	No significant correlation.

Note. DMFT = decayed, missing and filled permanent teeth; dmft = primaryteeth; p-values < 0.05 considered statistically significant. The results indicateassociations of sex and age with DMFT, and a weak inverse association between income anddmft.

• Sex

  The Mann-Whitney U test revealed a statistically significant difference in DMFT scoresaccording to sex (U = 1813.000; p= 0.019), with girls showing highercaries experience in permanent teeth. In contrast, differences in dmft scores were notsignificant (U = 1977.000; p = 0.112), suggesting that sex did not playa major role in caries prevalence in primary teeth.

• Age

  The Kruskal-Wallis test indicated statistically significant differences in both DMFT (H =52.588; p < 0.001) and dmft (H = 65.686; p <0.001) across age groups. These results reflect the expected pattern: DMFT increaseswith age, while dmft decreases, consistently with the natural shift from primary topermanent dentition.

• Maternal education level

  Spearman’s correlation showed no statistically significant association between themother’s level of education and either the DMFT (Rho = 0.070; p = 0.418) or the dmftindex (Rho = 0.070; p = 0.418). These findings suggest that, withinthis sample, maternal education was not a key determinant of caries status.

• Family icome

  The correlation between family income and DMFT was not significant (Rho = 0.030; p =0.730). However, a weak but statistically significant inverse correlation was observedbetween family income and dmft (Rho = –0.189; p = 0.027), indicatingthat children from lower-income families were more likely to present higher cariesprevalence in their primary teeth.

• Plaque index

  No significant correlation was found between the plaque index and DMFT (Rho = 0.148; p = 0.084) or between PI and dmft (Rho = –0.005; p = 0.953). Thus,in this sample, oral hygiene measured by plaque accumulation did not have a directstatistical association with caries prevalence in either dentition.

Summary of Findings

• DMFT indexwas significantly influenced by sex and age, with highervalues in girls and older children.

• dmft indexwas significantly affected by age and showed a weak but meaningfulassociation with family income.

• Maternal educationand plaque index were not significantly associated with either the DMFT ordmft caries index.

• The lack of statistical significance in some variables (e.g., DMFT vs. income) may beexplained by sample homogeneity or unmeasured confounders.

These results underscore the importance of targeted interventions for specific ageand socioeconomic groups. Future studies should consider additional determinants such as dietaryhabits, fluoride exposure, and access to dental services to better understand and reduce cariesrisk in children.

DISCUSSION

The results of this study are consistent with global trends described by the World Health Organization,which report a high prevalence of dental caries, particularly in low- and middle-income countries. Asubstantial burden of caries was identified in both primary and permanent dentition, aligning with WHOestimates indicating that 514 million children are affected by caries in primary teeth and more than 2billion people have untreated caries in permanent teeth. ³ Factors such as urbanization, limitedfluoride exposure, and restricted access to dental care continue to be major contributors to thisburden. ³

Socioeconomic factors and caries prevalence

Our findings are consistent with studies highlighting the influence of parental income andeducational level on dental caries prevalence. ^4-8 However, no statistically significantassociations were observed between these variables and caries indices in the present study, whichmay be partially explained by the relative homogeneity of the study population. In contrast,international reports, including the 2024 CDC Oral Health Surveillance Report ¹⁸ and analyses basedon U.S. national data ¹⁹ , have documented strong associations between low income, gaps in healthinsurance coverage, and higher prevalence of untreated dental caries.

Regional differences in caries prevalence

This study observed an increase in caries prevalence with age. Fernández-de-Quezada et al. ²⁰ reported similar patterns among 12-year-old children in El Salvador, while Danke et al. ²¹ documented declining trends in caries-free prevalence among younger children in Chile. Additionally,high caries rates reported in Syria ¹¹ and Ethiopia ¹² support the notion that regional andcontextual factors play a significant role in shaping the burden of dental caries acrosspopulations.

Maternal education and caries prevalence

Although no statistically significant association between maternal education level and cariesprevalence was observed in the present study, previous research has consistently demonstrated itsrelevance. Studies by Kumar et al. ⁵ , Kato et al. ⁶ , Lopes et al. ⁷ , and Carvalho et al. ⁸ haveshown that higher maternal education levels are associated with a reduced risk of dental caries inchildren. The absence of such an association in this study may be partly explained by limitedvariability in educational attainment among participants, suggesting that the influence of maternaleducation may be less evident in relatively homogeneous populations, despite its recognizedimportance in other settings.

Parental income and caries prevalence

The pattern observed in this study—namely, a higher prevalence of dental caries among children fromlower-income families—is consistent with population-based evidence reported in national oral healthsurveys and epidemiological research. In Spain, data from the 2020 Oral Health Survey revealedpersistent social gradients in caries experience among children, with a higher burden observed insocially disadvantaged groups.22 Similar patterns have been documented in local studies conducted inlow socioeconomic settings. ²³

Furthermore, de Lucena et al. ²⁴ and Folayan et al. ²⁵ demonstrated that structural socioeconomicinequalities significantly influence children’s oral health, although the magnitude of these effectsmay vary across contexts. In Peru, a national study published in 2024 reported that 76.2 % ofchildren aged 3 to 5 years presented dental caries, underscoring the need for public programstargeting vulnerable populations. ¹³

Likewise, an ecological analysis conducted across European Union countries found that the burden ofearly childhood caries was significantly associated with structural socioeconomic indicators,including poverty risk, gross domestic product per capita, urbanization, and sugar consumption. ²⁶ These findings highlight that socioeconomic inequities act as upstream determinants of cariesburden, even within highly developed regions.

Complementarily, evidence from Mexico indicates that socioeconomic and contextual factorssignificantly influence oral health–related behaviors among schoolchildren. A multi-site studyconducted in several Mexican cities reported significant associations between socioeconomic positionand toothbrushing frequency in children aged 6 to 12 years, highlighting the role of familyenvironments and broader structural conditions in shaping daily oral hygiene practices. ²⁷ Thesefindings support the concept that socioeconomic inequities operate as upstream determinants ofcaries risk and reinforce the need for comprehensive interventions that address both individualbehaviors and the social context in which they occur.

Similarly, a study conducted in Chile reported a higher prevalence of dental caries amongschoolchildren from more disadvantaged social backgrounds, particularly among those attending publicschools and with limited access to dental services. ²¹ This pattern is consistent with the findingsof the present study and further underscores the influence of social determinants and institutionalsettings on children’s oral health outcomes.

Oral hygiene and dental caries

Although the statistical correlations observed in the present study were weak, from a clinicalperspective an association between oral hygienerelated behaviors and caries prevalence was evidentin the studied population. Similar patterns have been described in pediatric populations, wheredental caries remains highly prevalent and is associated with plaque accumulation, the age atinitiation of toothbrushing, and inadequate oral hygiene practices. ^{14, 28}

Plaque index and associated factors

In the present study, higher plaque accumulation was observed among girls. Although sex-relateddifferences in plaque accumulation have been inconsistently reported in the literature, this findingmay reflect variations in oral hygiene practices, caregiver supervision, and family-relatedcontextual factors within the study population. From a clinical perspective, these differences couldalso be influenced by behavioral patterns and household dynamics that shape daily oral hygieneroutines during childhood.

Public health implications

In agreement with the findings of the present study, Borrell and Talih29 demonstrated the usefulnessof equity-sensitive indicators, such as the symmetrized Theil index, to quantify disparities indental caries among children and adolescents in the United States. Their work highlights thatsocioeconomic inequalities constitute a measurable and persistent determinant of oral health, evenin high-income countries. This evidence supports the importance of monitoring health inequalities asa basis for guiding targeted public health interventions.

Similarly, Almerich-Torres et al. ³⁰ reported that dental caries in schoolchildren was notsignificantly associated with body mass index when analyses were stratified by social class,suggesting that socioeconomic conditions may play a more relevant role than nutritional statusalone. This finding is consistent with the results of the present study, in which dental cariesprevalence was higher among children from economically disadvantaged families, reinforcing the needfor integrated, equity-focused public health strategies.

Overall, this study highlights the need for targeted oral health interventions, particularly insettings with limited resources. The importance of improving education, access to care, andpreventive strategies is consistent with evidence from multicenter and population-based studiesconducted in low- and middle-income countries. ^24-26 To be effective, future programs should bedesigned with careful consideration of the sociodemographic context in which children live.

Limitations and future directions

This study presents certain limitations that should be acknowledged. First, the use of anon-probabilistic sampling strategy restricts the external validity of the findings and limits theirgeneralizability to other populations. Additionally, the relative socioeconomic homogeneity of thestudy sample may have reduced the ability to detect statistically significant associations betweensociodemographic variables and caries outcomes. The reliance on selfreported information fromcaregivers also introduces the possibility of information bias.

Future research should prioritize longitudinal study designs to better assess caries progression overtime and to establish temporal relationships between sociodemographic factors and oral healthoutcomes. Moreover, the inclusion of additional variables—such as dietary patterns, fluorideexposure, access to preventive dental services, and communitylevel determinants—would contribute toa more comprehensive understanding of caries risk and inform the development of targeted publichealth interventions.

CONCLUSIONS

This study confirmed a high prevalence of dental caries among children aged 5–12 years in Victoria,Cabañas, with a higher burden observed among girls and children from lower-income families. Although nostatistically significant associations were identified between maternal education or family income andcaries prevalence, the observed trends suggest that socioeconomic vulnerability may still influence oralhealth outcomes in this population. In addition, a clinically relevant association was identifiedbetween poor oral hygiene, as measured by the plaque index, and dental caries occurrence, particularlyamong girls, despite the absence of statistically significant correlations.

From a public health perspective, these findings underscore the importance of implementing early, age-and sex-specific preventive strategies. The results highlight the need for culturally adaptedinterventions targeting vulnerable sociodemographic groups, with particular emphasis on oral hygieneeducation and community-based prevention programs.

The study’s limitations include the use of a non-probabilistic sample and the relative sociodemographichomogeneity of the population, which may have limited the detection of statistically significantassociations. Consequently, future research should employ longitudinal designs and include more diversepopulations to better elucidate the relationships between socioeconomic conditions, oral hygienepractices, and caries progression.

In light of these findings, expanding access to preventive oral health services through schoolbasedprograms, mobile dental units, and public health campaigns is essential to reduce caries risk inunderserved communities. Policymakers should prioritize the integration of oral health into primaryhealthcare and educational settings.

Furthermore, longitudinal research exploring genetic, environmental, and behavioral determinants isneeded to strengthen the evidence base for the development of sustainable, equitable, and effective oralhealth interventions.