# Association of Prolonged Breastfeeding With Early Childhood Caries Using Propensity Score Matching in the French Longitudinal Study of Children (ELFE Cohort)

Untray Brown, Sylvie Azogui-levy, Cathy Nabet, Marie-noëlle Dufourg, Marie-aline Charles, Joséphine Kerguen, Monique Kaminski, Laetitia Marchand-martin, Alice Germa

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Untray Brown, Sylvie Azogui-levy, Cathy Nabet, Marie-noëlle Dufourg, Marie-aline Charles, et al. Association of Prolonged Breastfeeding With Early Childhood Caries Using Propensity Score Matching in the French Longitudinal Study of Children (ELFE Cohort). Community Dentistry and Oral Epidemiology, 2025, 10.1111/cdoe.70012. inserm-05242513

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ORIGINAL ARTICLE

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# Association of Prolonged Breastfeeding With Early Childhood Caries Using Propensity Score Matching in the French Longitudinal Study of Children (ELFE Cohort)

Untray Brown<fnref n="1" /> ID | Sylvie Azogui-Levy²,³ | Cathy Nabet⁴,⁵,⁶ | Marie-Noëlle Dufourg⁷ | Marie-Aline Charles<fnref n="1" />,⁷ | Joséphine Kerguen<fnref n="1" />,²,⁸ | Monique Kaminski<fnref n="1" /> | Laetitia Marchand-Martin<fnref n="1" /> | Alice Germa<fnref n="1" />,²,⁸

**Correspondence:** Untray Brown (untray.brown@inserm.fr)

**Received:** 11 September 2024 | **Revised:** 21 June 2025 | **Accepted:** 25 July 2025

**Funding:** The ELFE cohort is a joint project between the French Institute for Demographic Studies (INED) and the National Institute of Health and Medical Research (INSERM), in partnership with the French blood transfusion service (Etablissement français du sang, EFS), Santé publique France, the National Institute for Statistics and Economic Studies (INSEE), the Direction générale de la santé (DGS, part of the Ministry of Health and Social Affairs), the Direction générale de la prévention des risques (DGPR, Ministry for the Environment), the Direction de la recherche, des études, de l'évaluation et des statistiques (DREES, Ministry of Health and Social Affairs), the Département des études, de la prospective et des statistiques (DEPS, Ministry of Culture), and the Caisse nationale des allocations familiales (CNAF), with the support of the Ministry of Higher Education and Research and the Institut national de la jeunesse et de l'éducation populaire (INJEP). Via the RECONAI platform, it receives a government grant managed by the National Research Agency under the "Investissements d'avenir-" programme (ANR-11-EQPX-0038 and ANR19COHO-0001).

**Keywords:** birth cohort | breastfeeding | Early childhood caries | propensity score matching

## ABSTRACT

**Objectives:** The aim of this study was to investigate the link between prolonged breastfeeding (≥ 12 months) and early childhood caries (ECC) using propensity score matching (PSM) to account for observed confounders, reduce bias, and provide a more reliable estimate of this relationship.

**Methods:** This study utilised data from the French Longitudinal Study of Children (ELFE Cohort), comprising 11 718 participants. PSM was employed to pair children who were breastfed for 12 months or longer with those breastfed for less than 12 months or not at all, controlling for shared risk factors such as socioeconomic status and dietary habits. Logistic regression models were conducted to examine the association between ECC, reported by the parents at 3.5 years, and prolonged breastfeeding.

**Results:** Infants (7.6%) who were breastfed for 12 months or more exhibited twice the odds of developing ECC at 3.5 years compared to those breastfed for less than 12 months or not at all (OR = 2.20, 95% CI: 1.39, 3.47).

**Abbreviations:** aOR, adjusted OR; CCTIRS, Comité Consultatif sur le Traitement de l'Information en matière de Recherche dans le domaine de la Santé; CNIS, Conseil National de l'Information Statistique; CPP, Comité de Protection des Personnes; CRESS, Centre for Research in Epidemiology and Statistics; DAG, directed acyclic graph; ECC, early childhood caries; ELFE, French Longitudinal Study of Children; EPOPE, Obstetrical Perinatal and Pediatric Epidemiology Research Team; GEE, generalised estimating equations; INSERM, French National Institute of Health and Medical Research; M1, main analysis; MAR, missing at random; Matchets, nonparametric preprocessing for parametric causal inference; MatchThem, matching and weighting after multiple imputation; MICE, multiple imputation chained equations; N, population; *n*, sample population; NNT, number needed to treat; OR, odds ratio; PSM, propensity score matching; S1, sensitivity analysis 1; S2, sensitivity analysis 2; S3, sensitivity analysis 3; S4, sensitivity analysis 4; SMD, standardised mean difference.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). *Community Dentistry and Oral Epidemiology* published by John Wiley & Sons Ltd.

<footnote n="1">¹UMR 1153, CRESS, Obstetric, Perinatal, Paediatric Life Course Epidemiology, OPPaLE, INSERM, INRAE, Université Paris Cité, Paris, France | ²Faculty of Health, UFR Odontology, Université Paris Cité, Montrouge, France | ³UR 3412, Laboratory of Health Education and Promotion, LEPS, Sorbonne Paris Nord University, Bobigny Cedex, France | ⁴Faculty of Health, UFR Odontology, Toulouse Université, Toulouse, France | ⁵Department of Odontology, Toulouse University, Hospital (CHU de Toulouse), Toulouse University, Toulouse, France | ⁶UMR1295, Center for Epidemiology and Research in POPulation Health, CERPOP, INSERM, Toulouse University, Toulouse, France | ⁷Ined, Inserm Elfe Joint Unit, Aubervilliers, France | ⁸Department of Odontology, Charles Foix Hospital, AP-HP, Ivry sur Seine, France</footnote>
**Conclusion:** Children breastfed for 12 or more months are at increased risk of developing ECC. Further research is needed to investigate specific breastfeeding practices that may contribute to this increased risk, with the aim of promoting prolonged breastfeeding while ensuring optimal oral health.

# 1 | Introduction

Early childhood caries (ECC) is a significant global health challenge, affecting up to approximately 48% of children under 6 years old, based on data from 29 countries [1]. In France, the prevalence of ECC was estimated to be 16% at the age of 5-6 years [2]. ECC is a biofilm-mediated, sugar-driven, multifactorial, and dynamic disease characterised by alternating phases of demineralisation and remineralisation, affecting both primary and permanent dentitions throughout life [3]. Poor oral health in children imposes substantial societal burdens, with untreated ECC leading to severe complications such as chronic pain, infections, and nutritional deficiencies [4]. ECC is closely linked to socioeconomic factors, with a higher prevalence among disadvantaged populations [5-7]. Prolonged breastfeeding, defined as breastfeeding for 12 months or more, is also influenced by social, cultural, and economic factors, including breastfeeding norms, maternal employment and socioeconomic status [8-11].

Both the World Health Organization (WHO) and the American Academy of Pediatrics (AAP) recommend exclusive breastfeeding for the first 6 months, followed by the introduction of appropriate complementary foods thereafter, up to 2 years of age or beyond [12, 13]. The American Academy of Pediatric Dentistry (AAPD) supports breastfeeding prior to 12 months and advises minimising added sugar exposure in the diets of children under 24 months of age to promote optimal health and development [14]. While breastfeeding until 6 months during infancy may offer protection against ECC [15], further research is needed to fully understand the potential heightened risk of caries in children breastfed beyond 12 months [16, 17]. Several studies employing diverse methodologies have explored the association between prolonged breastfeeding and ECC [18-21]. ECC is associated with social and economic factors including poor oral hygiene, inappropriate infant feeding practices, and socioeconomic factors such as maternal education and household income [22].

While these studies provide valuable insights, variations in the consideration and retention of confounding factors between studies may affect association estimates. A gap exists in understanding breastfeeding beyond 12 months, with disparities likely due to shared and overlapping risk factors between breastfeeding and ECC. Given the impracticality of randomising breastfeeding conditions, this study aimed to investigate the association between breastfeeding beyond 12 months and the development of ECC, using the Propensity Score Matching (PSM). To address confounding factors [23] PSM approximated randomised conditions by matching individuals with similar characteristics in exposed and unexposed groups, minimising selection bias and enhancing comparability [24].

# 2 | Methods

## 2.1 | Setting

This study draws from the ELFE study, which is a French national birth cohort. The cohort's primary aim is to comprehensively investigate the determinants influencing children's development, health, and socialisation from birth through to adulthood, employing a multidisciplinary approach. Initiated in 2011, the cohort encompasses a diverse national sample of children continuously monitored since birth in France. Inclusion criteria required participants to be live births at 33 weeks' gestation or later, with mothers aged 18 years and above, and no intention to depart from France within the subsequent 3 years.

## 2.2 | Study Design

A total of 349 maternity units were randomly selected in metropolitan France and the children were recruited at birth from the 320 maternity units that agreed to participate. Over 96% (n=37494) of eligible mothers were approached during their maternity stay, with 51% consenting to participate, ultimately resulting in a cohort of 18329 babies [25]. Of the 18329 babies initially identified, exclusions were made due to missing data on the exposure or outcome, resulting in a final study population of 11718 infants.

## 2.3 | Data Collection

Baseline data were collected from hospital records (type of delivery, maternal birth location, smoking prior to pregnancy, and prenatal care) and maternal interviews at birth, followed by phone interviews conducted at 2 months, 1 year, 2 years, and 3.5 years. Other covariates were mainly collected at 2 months via phone interviews and questionnaires, including maternal age, education, employment status, household income, presence of grandparents, and the number of children in the household. Dietary variables, such as fruit juice and sugary drink consumption, were assessed through a self-administered questionnaire, completed monthly by the parents between 2 and 10 months.

## 2.4 | Exposure

Breastfeeding duration was assessed through maternal reports collected via structured questionnaires and interviews at multiple time points: at birth, 2 months, and 1 year. While the 1-year phone interview provided key data on breastfeeding practices, information on breastfeeding that continued beyond 12 months was obtained retrospectively during the 2-year follow-up interview. For children breastfed at 1 year but not at 2, mothers
reported the age of breastfeeding cessation. Breastfeeding was
defined as any instance of breast milk provision, regardless of
other dietary components. In this study, prolonged breastfeed-
ing—defined as breastfeeding for 12 months or more—was the
primary exposure variable. Children were categorised based on
whether they had been breastfed for 12 months or longer, com-
pared to those who stopped breastfeeding before 12 months or
were never breastfed.

2.5 | Outcome

The primary outcome, ECC, was assessed via a parent-reported
questionnaire during a phone interview at 3.5 years. Parents
were asked whether their child had ever had at least one dental
carious lesion.

2.6 | Covariates

Covariates were selected based on prior literature and their
relevance to both breastfeeding duration and ECC. A Directed
Acyclic Graph (DAG) was developed to inform the identification
of variables potentially associated with both the exposure and the
outcome (Figure 1). The selected covariates included: maternal

age [26], maternal education [27], maternal employment sta-
tus [28], maternal birth location [29], presence of grandparent
(grandmothers more often) [30], prenatal care (pregnancy infor-
mation received) [27], smoking prior to pregnancy [31, 32], num-
ber of children [33], type of delivery [34], and preterm birth [35].
Household income was the income per unit of consumption of
the household, categorised in quartiles [36]. Introduction of fruit
juice and sugary drinks prior to 6 months was also considered as
it is a risk factor for ECC. These variables were collected through
medical records at birth and through maternal phone interviews
and questionnaires at birth and 2 months postpartum.

2.7 | Analysis Strategy

Descriptive analyses were conducted for each covariate to char-
acterise the study population and examine differences between
breastfeeding groups.

2.8 | Propensity Score Matching

The propensity score (PS) was defined as each infant's proba-
bility of being breastfed for 12 months or more, estimated from
a logistic regression model using observed covariates. The final

<figure><img src="image_2.png" /><figcaption>FIGURE 1 | Directed acyclic graph of the relationship between prolonged breastfeeding and early childhood caries.</figcaption></figure>
study sample was compared to the entire initial cohort before exclusions of those lost to follow up or non-respondents. PSM was employed. PSM with logistic regression is a widely used method in observational research for controlling confounding by observed variables [37, 38].

All listed covariates, with the exception of introduction of fruit juice and sugary drinks, were considered potential predictors of both breastfeeding and ECC and were included in the PS model regression.

PSM was implemented utilising MatchIt (Nonparametric pre-processing for Parametric Causal Inference) software in R [39]. PSM facilitated the balancing of observed covariates by matching prolonged breastfed and those less than 12 months or not at all according to measured maternal characteristics (maternal age, maternal birth location, education, employment status, household income) [40, 41]. A nearest neighbour 1-1 matching within a 0.2-SD calliper without replacement was performed.

Infants breastfed for 12 months or more were matched with those breastfed for less than 12 months or not at all, based on similar PSs. After matching, the balance of covariates between groups was evaluated using standardised mean differences (SMD), with values less than 10% considered indicative of sufficient balance. The SMD is the difference in means or proportions divided by the pooled standard deviation. Matching quality was further assessed by examining whether key characteristics differed between matched infants and their counterparts. This process was repeated across multiple simulated datasets to evaluate the robustness of the matching.

## 2.9 | Main Analysis

A reference odds ratio (OR) on the overall cohort was calculated using logistic regression prior to PSM. The baseline OR from this model provided an initial estimate of their association in the unadjusted model, serving as a reference point for the main and sensitivity analyses. Odds ratios were then calculated to quantify the association between prolonged breastfeeding exposure and ECC outcome using logistic regression analysis fitted by generalised estimating equations (GEE), which account for paired data [42, 43].

## 2.10 | Sensitivity Analysis

First, logistic regression analyses were performed to calculate OR for the association of breastfeeding with ECC, adjusted for fruit juice and sugary drinks prior to 6 months. This adjustment ensured that the significant association between breastfeeding at 12 months and ECC at 3.5 years remained even after accounting for these dietary factors. Second and third, additional matching between infants breastfed 12 months or more and those breastfed less than 12 months or not breastfed at different ratios (1:2 and 1:3) was conducted to evaluate how changes in the matching ratio affected the estimated odds ratios and overall study findings. This explored the consistency of observed associations

between breastfeeding duration and ECC risk across different scenarios.

## 2.11 | Missing Data

For a sensitivity analysis, missing values on the ECC outcome were modelled and imputed using the Multiple Imputation Chained Equations (MICE) package in R [44]. Multiple imputation was performed using all previously described variables. A large number of predictors were included in order to make the assumption, missing at random, more plausible and ensure reliable imputed results. PS analysis was then performed on each imputed dataset to estimate the effect of exposure and estimates were pooled according to Rubin's rule, using the MatchThem package in R [45].

## 2.12 | Weighting

A weighting procedure was applied to calculate the prevalence of ECC in the study population. To correct for non-representativeness, statistical weights were calculated for each child based on the survey design, initial non-response, and their 3.5-year participation. This procedure ensured that the prevalence rates more accurately reflect the true occurrence of ECC in the entire population [25, 46]. Weights were not used for the matching.

# 3 | Results

## 3.1 | Study Population

Out of 18 329 infants, those with data on breastfeeding at 12 months, excluding 330 (1.8%) with missing data, totalled 17 999. (Figure 2) Among these, 6281 (35%) had missing data on the ECC outcome, resulting in a study population of 11 718 infants with data on both breastfeeding exposure and ECC outcome. Out of the 11 718 infants included in the main analysis, 10 946 were non-breastfed or breastfed for ≥ 12 months, and 772 were breastfed for 12 months or more. A comparison between the study population and the population with missing ECC data revealed differences in several demographic and socioeconomic characteristics. The study population tended to be older, more educated, and had higher employment and income levels compared to the group with missing data. They were also more likely to have been born in France, received prenatal care, and had a lower prevalence of smoking before pregnancy. In contrast, the group with missing data had a higher proportion of younger mothers, lower education and income levels, and a greater presence of grandparents in the home. In the study, the prevalence of breastfeeding at 12 months was 5.5%, while the weighted prevalence of ECC was estimated at 3.4% (Figure 3).

## 3.2 | Main Results

Mothers in the prolonged breastfeeding group were more likely to be older (31–40 years), foreign-born, have multiple children,
<figure><img src="image_3.png" /><figcaption>**FIGURE 2** | Study population.</figcaption></figure>

hold a university degree, and come from lower-income house-
holds (Table 1). In contrast, mothers who breastfed for a shorter
duration or not at all were more often professionally active and
had a higher prevalence of smoking before pregnancy. However,
gestational age showed minimal differences between the
groups. The main result of the study, based on the PSM, is that
infants breastfed for 12 months or more are at a higher risk of
ECC, OR = 2.20 and a 95% CI (1.39, 3.47), compared to those who
were not breastfed or breastfed for ≥ 12 months (Table 2).

**3.3 | Sensitivity Results**

The sensitivity analysis adjusting for fruit juice and sugary drinks
showed a slightly lower odds ratio (OR = 2.16, 95% CI: 1.36, 3.43)

compared to the primary matched analysis (OR=2.20). The
matching ratio 1:2 the OR=2.63 and 95% CI (1.78, 3.89) and for
1:3 OR=3.09, 95% CI (2.14, 4.45). After multiple imputation of
the 6281 missing data on the ECC outcome (35%), 992 infants
breastfed 12 months or more were matched to 992 not breastfed
at 12 months. The association estimated an OR of 1.98 (95% CI:
1.24, 3.17) (Table 2).

**4 | Discussion**

Overall, the findings indicate that infants breastfed for
12 months or more have a significantly higher risk of experi-
encing ECC compared to those not breastfed or breastfed for
less than 12 months. The association between breastfeeding
<figure><img src="image_4.png" /><figcaption>**FIGURE 3** | Study population after multiple imputation.</figcaption></figure>

duration and ECC was analysed using a PS approach to adjust for confounding factors and to control for baseline differences between exposure groups. This method helps reduce potential bias due to unmeasured confounders. Consistency in estimated odds ratios across different matching ratios further reinforces the robustness and reliability of the findings.

This study employed PSM within the French national birth cohort (ELFE). Participants were matched based on observed maternal characteristics—age, birth location, education, employment status, and household income—to balance the covariates across breastfeeding groups. This technique addresses methodological challenges common in observational studies and helps mitigate selection bias. While no definitive breastfeeding threshold can be pinpointed at which oral health is affected, studies often examine durations such as 12, 18 or 24 months. Among these, 12 months is a practical reference for both preventive strategies and research purposes, as it aligns with the timing of primary tooth eruption and early oral exposure.

The early introduction of sugary beverages—such as fruit juices, herbal teas, and syrups—before 6 months of age has been recently linked to ECC [47]. Infants breastfed for longer durations who were also exposed to these drinks earlier or in larger quantities may have a higher risk of ECC. Early introduction may not

only reduce breastfeeding duration by decreasing interest but also serve as an indicator of higher sugar consumption later in childhood. Although this variable could not be included in the PS due to its complex and not fully understood relationship with breastfeeding duration, it was considered as a potential marker of early sugar exposure (Table 3).

Previous studies investigating the association between prolonged breastfeeding and ECC have reported mixed findings, partly due to methodological differences. For instance, cohort studies by Abanto et al. [18], van Meijeren-van Lunteren et al. [20], Devenish et al. [21] and Peres et al. [48] utilised statistical approaches such as the G-formula, negative hurdle binomial models, and marginal structural models, producing odds ratios ranging from 1.13 to 1.9. Building on this literature, the present study applied PSM combined with logistic regression, offering a more precise estimate of the association while adjusting for a broader range of cultural, social, and economic factors that influence both breastfeeding practices and ECC risk.

A notable strength of this study lies in its methodological advancement over earlier work. Unlike prior studies that used unmatched comparisons or basic regression models, this analysis accounted for complex confounding structures through PSM, resulting in well-balanced comparison groups. This allowed
<table><caption>TABLE 1 | Population baseline characteristics: propensity scores before and after matching.</caption><thead><tr><th rowspan="2">Characteristics</th><th colspan="2">Initial sample</th><th>Before matching</th><th>Matched infants</th><th>After matching</th></tr><tr><th>BF ≥ 12m, N=772, n (%)</th><th>BF < 12m, N=10946, n (%)</th><th>SMD</th><th>BF < 12m, N=771, n (%)</th><th>SMD</th></tr></thead><tbody><tr><td>Maternal age (years)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>≤ 25</td><td>42 (5.4)</td><td>1069 (9.8)</td><td>4.4</td><td>40 (5.2)</td><td>0.2</td></tr><tr><td>26–30</td><td>200 (25.9)</td><td>3745 (34.2)</td><td>8.3</td><td>217 (28.1)</td><td>2.2</td></tr><tr><td>31–35</td><td>325 (42.1)</td><td>4066 (37.1)</td><td>5.0</td><td>315 (40.9)</td><td>1.2</td></tr><tr><td>36–40</td><td>162 (21.0)</td><td>1738 (15.9)</td><td>5.1</td><td>166 (21.5)</td><td>0.5</td></tr><tr><td>> 40</td><td>43 (5.6)</td><td>327 (3.0)</td><td>2.6</td><td>33 (4.3)</td><td>1.3</td></tr><tr><td>Maternal birth location</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>France</td><td>591 (76.6)</td><td>9980 (91.2)</td><td>14.6</td><td>612 (79.4)</td><td>2.8</td></tr><tr><td>Outside France</td><td>181 (23.4)</td><td>966 (8.8)</td><td>14.6</td><td>159 (20.6)</td><td>2.8</td></tr><tr><td>Maternal education</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Less than high school</td><td>44 (5.7)</td><td>488 (4.5)</td><td>1.2</td><td>42 (5.5)</td><td>0.2</td></tr><tr><td>High school</td><td>158 (20.5)</td><td>3081 (28.2)</td><td>7.7</td><td>148 (19.2)</td><td>1.3</td></tr><tr><td>Two-year college</td><td>143 (18.5)</td><td>2607 (23.8)</td><td>5.3</td><td>152 (19.7)</td><td>1.2</td></tr><tr><td>University</td><td>407 (52.7)</td><td>4590 (41.9)</td><td>10.8</td><td>408 (52.9)</td><td>0.2</td></tr><tr><td>Missing data</td><td>20 (2.6)</td><td>180 (1.6)</td><td>1.0</td><td>21 (2.7)</td><td>0.1</td></tr><tr><td>Maternal employment status</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Professionally active</td><td>482 (62.4)</td><td>8396 (76.7)</td><td>14.3</td><td>477 (61.9)</td><td>0.5</td></tr><tr><td>Unemployed</td><td>95 (12.3)</td><td>1094 (10.0)</td><td>2.3</td><td>87 (11.3)</td><td>1.0</td></tr><tr><td>Housewife</td><td>135 (17.5)</td><td>915 (8.4)</td><td>9.1</td><td>145 (18.8)</td><td>1.3</td></tr><tr><td>Student</td><td>40 (5.2)</td><td>360 (3.3)</td><td>1.9</td><td>41 (5.3)</td><td>0.1</td></tr><tr><td>Missing data</td><td>20 (2.6)</td><td>181 (1.6)</td><td>1.0</td><td>21 (2.7)</td><td>0.1</td></tr><tr><td>Household income</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low income</td><td>214 (27.7)</td><td>1974 (18.0)</td><td>9.7</td><td>232 (30.1)</td><td>2.4</td></tr><tr><td>Lower-middle income</td><td>165 (21.4)</td><td>2951 (27.0)</td><td>5.6</td><td>152 (19.7)</td><td>1.7</td></tr><tr><td>Upper-middle income</td><td>207 (26.8)</td><td>3101 (28.3)</td><td>1.5</td><td>213 (27.6)</td><td>0.8</td></tr><tr><td>High income</td><td>143 (18.5)</td><td>2434 (22.3)</td><td>3.8</td><td>137 (17.8)</td><td>0.7</td></tr><tr><td>Missing data</td><td>43 (5.6)</td><td>486 (4.4)</td><td>1.2</td><td>37 (4.8)</td><td>0.8</td></tr><tr><td>Grandparent in home</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>17 (2.2)</td><td>167 (1.5)</td><td>0.7</td><td>7 (0.9)</td><td>1.3</td></tr><tr><td>No</td><td>738 (95.6)</td><td>10612 (97.0)</td><td>1.4</td><td>747 (96.9)</td><td>1.3</td></tr><tr><td>Missing data</td><td>17 (2.2)</td><td>167 (1.5)</td><td>0.5</td><td>17 (2.2)</td><td>0.0</td></tr><tr><td>Smoking before pregnancy</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>211 (27.3)</td><td>4606 (42.1)</td><td>14.8</td><td>209 (27.1)</td><td>0.2</td></tr><tr><td>No</td><td>557 (72.2)</td><td>6252 (57.1)</td><td>15.1</td><td>561 (72.8)</td><td>0.6</td></tr><tr><td>Missing data</td><td>4 (0.5)</td><td>88 (0.8)</td><td>0.3</td><td>1 (0.1)</td><td>0.4</td></tr></tbody></table>

(Continues)
<table><caption>**TABLE 1** | (Continued)</caption><thead><tr><th rowspan="2">Characteristics</th><th colspan="2">Initial sample</th><th>Before matching</th><th>Matched infants</th><th>After matching</th></tr><tr><th>BF ≥ 12m,<br/>N=772, n (%)</th><th>BF < 12m,<br/>N=10946, n (%)</th><th>SMD</th><th>BF < 12m,<br/>N=771, n (%)</th><th>SMD</th></tr></thead><tbody><tr><td>Prenatal care</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>479 (62.0)</td><td>6865 (62.7)</td><td>0.7</td><td>469 (60.8)</td><td>1.2</td></tr><tr><td>No</td><td>287 (37.2)</td><td>3919 (35.8)</td><td>1.7</td><td>297 (38.5)</td><td>1.3</td></tr><tr><td>Missing data</td><td>6 (0.8)</td><td>162 (1.5)</td><td>0.7</td><td>5 (0.7)</td><td>0.1</td></tr><tr><td>Preterm birth</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>62 (8.1)</td><td>1208 (11.0)</td><td>3.1</td><td>51 (6.6)</td><td>1.5</td></tr><tr><td>No</td><td>702 (90.9)</td><td>9566 (87.4)</td><td>3.5</td><td>714 (92.6)</td><td>1.7</td></tr><tr><td>Missing data</td><td>8 (1.0)</td><td>172 (1.6)</td><td>0.6</td><td>6 (0.8)</td><td>0.2</td></tr><tr><td>Type of birth</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spontaneous vaginal</td><td>557 (72.2)</td><td>7327 (66.9)</td><td>5.3</td><td>575 (74.6)</td><td>2.4</td></tr><tr><td>Forceps/Spatulas/<br/>Vacuum</td><td>78 (10.1)</td><td>1346 (12.3)</td><td>2.2</td><td>68 (8.8)</td><td>1.3</td></tr><tr><td>Caesarean</td><td>116 (15.0)</td><td>1985 (18.1)</td><td>3.1</td><td>108 (14.0)</td><td>1.0</td></tr><tr><td>Missing data</td><td>21 (2.7)</td><td>288 (2.7)</td><td>0.0</td><td>20 (2.6)</td><td>0.1</td></tr><tr><td>Number of children</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Two</td><td>270 (35.0)</td><td>5040 (46.1)</td><td>11.1</td><td>281 (36.5)</td><td>1.5</td></tr><tr><td>Three or more</td><td>284 (36.8)</td><td>3919 (35.8)</td><td>1.0</td><td>270 (35.0)</td><td>1.8</td></tr><tr><td>One</td><td>210 (27.2)</td><td>1844 (16.8)</td><td>10.4</td><td>213 (27.6)</td><td>0.4</td></tr><tr><td>Missing data</td><td>8 (1.0)</td><td>143 (1.3)</td><td>0.3</td><td>7 (0.9)</td><td>0.1</td></tr></tbody></table>

Note: n (%), by column; mean.

Abbreviations: BF, breastfeeding; SMD, standardised mean difference.

<table><caption>**TABLE 2** | Summary effects: association of prolonged breastfeeding and early childhood caries.</caption><thead><tr><th>Analysis</th><th>Sample</th><th>N</th><th>OR</th><th>95% CI</th><th>p</th></tr></thead><tbody><tr><td rowspan="2">Reference</td><td>Initial</td><td>11 718</td><td>3.11</td><td>2.32, 4.15</td><td>&lt;0.001</td></tr><tr><td>Matched</td><td>1542</td><td>2.20</td><td>1.39, 3.47</td><td>&lt;0.001</td></tr><tr><td>SA 1</td><td>Matched (adjusted)<sup>a</sup></td><td>1538</td><td>2.16</td><td>1.36, 3.43</td><td>&lt;0.001</td></tr><tr><td>SA 2</td><td>Matched ratio 1:2</td><td>2308</td><td>2.63</td><td>1.78, 3.89</td><td>&lt;0.001</td></tr><tr><td>SA 3</td><td>Matched ratio 1:3</td><td>3052</td><td>3.09</td><td>2.14, 4.45</td><td>&lt;0.001</td></tr><tr><td>SA 4</td><td>After MI<sup>b</sup></td><td>1984</td><td>1.98</td><td>1.24, 3.17</td><td>0.004</td></tr></tbody></table>

Abbreviations: CI, confidence interval; OR, odds ratio; SA, sensitivity analysis (1-3).

for a more accurate estimation of ECC risk specifically attributable to prolonged breastfeeding, even beyond the 1-year mark. Furthermore, the study emphasised the potential mediating role of sugary drink consumption, offering a nuanced perspective rarely addressed in previous research. This approach highlights that prolonged breastfeeding might delay the introduction of sugary drinks, a potential confounder in the relationship with ECC.

The methodological rigour of this study offers multiple contributions. PSM with logistic regression is a widely used approach in observational research for controlling confounding by observed variables. Given the large sample size and availability of extensive sociodemographic data, this method allowed for matching on a wide range of factors despite the relatively small number of children breastfed at 12 months. Although PSM cannot fully eliminate the impact of unmeasured confounding or model misspecification,

<footnote>aAdjusted for the introduction of fruit juice and sugary drinks before 6 months.</footnote>

<footnote>bMultiple imputation.</footnote>
<table><caption>TABLE 3 | Comparison of the study population and the children with missing data on ECC.</caption><thead><tr><th>Characteristic</th><th>Study population (N=11,718)<sup>a</sup>, n (%)</th><th>Missing caries data (N=6281)<sup>a</sup>, n (%)</th><th>p<sup>b</sup></th></tr></thead><tbody><tr><td>Maternal age (years)</td><td></td><td></td><td></td></tr><tr><td>≤25</td><td>1111 (9.5)</td><td>1573 (25.0)</td><td>&lt;0.001</td></tr><tr><td>26–30</td><td>3945 (33.7)</td><td>2095 (33.4)</td><td></td></tr><tr><td>31–35</td><td>4391 (37.5)</td><td>1607 (25.6)</td><td></td></tr><tr><td>36–40</td><td>1900 (16.2)</td><td>810 (12.9)</td><td></td></tr><tr><td>&gt;40</td><td>370 (3.1)</td><td>191 (3.0)</td><td></td></tr><tr><td>Missing data</td><td>1 (0.01)</td><td>5 (0.1)</td><td></td></tr><tr><td>Maternal birth location</td><td></td><td></td><td></td></tr><tr><td>France</td><td>10 571 (90.2)</td><td>5055 (80.5)</td><td>&lt;0.001</td></tr><tr><td>Outside France</td><td>1147 (9.8)</td><td>1187 (18.9)</td><td></td></tr><tr><td>Missing data</td><td>0 (0.0)</td><td>39 (0.6)</td><td></td></tr><tr><td>Maternal education</td><td></td><td></td><td></td></tr><tr><td>Less than high school</td><td>532 (4.5)</td><td>757 (12.0)</td><td>&lt;0.001</td></tr><tr><td>High school</td><td>3239 (27.6)</td><td>2175 (34.6)</td><td></td></tr><tr><td>Two-year college</td><td>2750 (23.5)</td><td>815 (13.0)</td><td></td></tr><tr><td>University</td><td>4997 (42.6)</td><td>954 (15.2)</td><td></td></tr><tr><td>Missing data</td><td>200 (1.8)</td><td>1580 (25.2)</td><td></td></tr><tr><td>Maternal employment status</td><td></td><td></td><td></td></tr><tr><td>Professionally active</td><td>8878 (75.8)</td><td>2695 (42.9)</td><td>&lt;0.001</td></tr><tr><td>Unemployed</td><td>1189 (10.1)</td><td>780 (12.4)</td><td></td></tr><tr><td>Housewife</td><td>1050 (9.0)</td><td>1038 (16.5)</td><td></td></tr><tr><td>Student</td><td>400 (3.4)</td><td>173 (2.8)</td><td></td></tr><tr><td>Missing data</td><td>201 (1.7)</td><td>1595 (25.4)</td><td></td></tr><tr><td>Household income</td><td></td><td></td><td></td></tr><tr><td>Low income</td><td>2188 (18.7)</td><td>1786 (28.4)</td><td>&lt;0.001</td></tr><tr><td>Lower-middle income</td><td>3116 (26.6)</td><td>1220 (19.4)</td><td></td></tr><tr><td>Upper-middle income</td><td>3308 (28.2)</td><td>743 (11.8)</td><td></td></tr><tr><td>High income</td><td>2577 (22.0)</td><td>517 (8.3)</td><td></td></tr><tr><td>Missing data</td><td>529 (4.5)</td><td>2015 (32.1)</td><td></td></tr><tr><td>Grandparent in home</td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>184 (1.6)</td><td>204 (3.3)</td><td>&lt;0.001</td></tr><tr><td>No</td><td>11 350 (96.9)</td><td>4600 (73.2)</td><td></td></tr><tr><td>Missing data</td><td>184 (1.5)</td><td>1477 (23.5)</td><td></td></tr></tbody></table>

(Continues)

<table><caption>TABLE 3 | (Continued)</caption><thead><tr><th>Characteristic</th><th>Study population (N=11,718)<sup>a</sup>, n (%)</th><th>Missing caries data (N=6281)<sup>a</sup>, n (%)</th><th>p<sup>b</sup></th></tr></thead><tbody><tr><td>Smoking before pregnancy</td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>4817 (41.1)</td><td>2767 (44.0)</td><td>&lt;0.001</td></tr><tr><td>No</td><td>6809 (58.1)</td><td>3452 (55.0)</td><td></td></tr><tr><td>Missing data</td><td>92 (0.8)</td><td>62 (1.0)</td><td></td></tr><tr><td>Prenatal care</td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>7344 (62.7)</td><td>2443 (38.9)</td><td>&lt;0.001</td></tr><tr><td>No</td><td>4206 (35.9)</td><td>3690 (58.7)</td><td></td></tr><tr><td>Missing data</td><td>168 (1.4)</td><td>148 (2.4)</td><td></td></tr><tr><td>Premature birth</td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>1270 (10.9)</td><td>818 (13.0)</td><td>&lt;0.001</td></tr><tr><td>No</td><td>10 268 (87.6)</td><td>5353 (85.2)</td><td></td></tr><tr><td>Missing data</td><td>180 (1.5)</td><td>110 (1.8)</td><td></td></tr><tr><td>Type of birth</td><td></td><td></td><td></td></tr><tr><td>Spontaneous vaginal</td><td>7884 (67.3)</td><td>4158 (66.2)</td><td>0.5</td></tr><tr><td>Forceps/ Spatulas/ Vacuum</td><td>1424 (12.2)</td><td>778 (12.4)</td><td></td></tr><tr><td>Caesarean</td><td>2101 (17.9)</td><td>1165 (18.5)</td><td></td></tr><tr><td>Missing data</td><td>309 (2.6)</td><td>180 (2.9)</td><td></td></tr><tr><td>Number of children</td><td></td><td></td><td></td></tr><tr><td>One</td><td>5310 (45.3)</td><td>2796 (44.5)</td><td>&lt;0.001</td></tr><tr><td>Two</td><td>4203 (35.9)</td><td>2003 (31.9)</td><td></td></tr><tr><td>Three or more</td><td>2054 (17.5)</td><td>1382 (22.0)</td><td></td></tr><tr><td>Missing data</td><td>151 (1.3)</td><td>100 (1.6)</td><td></td></tr></tbody></table>

the inclusion of multiple sensitivity analyses helped strengthen the robustness of the findings.

Regarding outcome measurement, ECC was based on maternal report rather than clinical examination, which may introduce some misclassification. While maternal-reported data are less accurate than clinical assessments [49], they remain a practical and scalable method in large population-based cohorts where clinical data collection is often not feasible. Moreover, ECC may have been underestimated, as parents are more likely to report only visibly cavitated lesions, while posterior lesions (e.g., on molars) may go unnoticed. Anterior teeth—commonly affected at age 3.5—are more visible and thus more likely to be reported. This may have resulted in non-differential misclassification of the outcome. As a result, some children with ECC may have been misclassified as unaffected. However, there is no reason

<footnote><sup>a</sup>n (%), by column.</footnote>

<footnote><sup>b</sup>Pearson's chi-squared test was used to calculate all p-values.</footnote>
to believe that this underestimation of ECC would differ be-
tween the exposed and non-exposed groups. Consequently, the
observed association between prolonged breastfeeding and ECC
may underestimate the true risk.

Despite this limitation, the findings are valuable, especially in
the context of limited dental visits among young children in
France, where the first dental visit often occurs around age 6,
as part of a national prevention programme. In France, the esti-
mated prevalence is 6% among children aged 5–6, though data
on children under three are lacking [50]. Notably, when missing
ECC data were imputed, the odds ratio for the association with
prolonged breastfeeding was only slightly attenuated. This sug-
gests that missing outcome data had minimal influence on the
observed association.

The final sample sizes—1542 for complete cases and 1984 with imputed data—were sufficiently large to yield precise and reliable estimates, despite the sample size reduction due to unmatched cases. These findings align with and complement the results of Shrestha's 2024 systematic review, which identified prolonged and nocturnal breastfeeding as significant ECC risk factors [51]. While that review provided pooled estimates, the present study contributes a more detailed and contextually grounded analysis using advanced statistical techniques.

Ultimately, this study highlights the complexity of the relation-
ship between breastfeeding duration and ECC. While breast-
feeding provides numerous health benefits, the findings suggest
that breastfeeding beyond 12 months—especially in the context
of high sugar exposure or nocturnal feeding—may increase
ECC risk. Given that nocturnal breastfeeding is more common
than nocturnal bottle-feeding [52], future research should fur-
ther investigate specific practices, such as night-time feeding,
to inform targeted public health recommendations. These find-
ings underscore the importance of balancing the promotion of
breastfeeding with strategies to prevent ECC, supporting nu-
anced guidance that considers both nutritional and oral health
outcomes.

5 | Conclusion

Prolonged breastfeeding was associated with an increased risk of self-reported ECC for infants breastfed for 12 or more months. Further research should weigh the strengths and limitations of PSM alongside regression and consider complementary methods or sensitivity analyses to enhance the robustness of the findings. Additionally, it is essential to explore strategies that support mothers while mitigating the impact of breastfeeding on their child's oral health.

Ethics Statement

The ELFE study received approval from the national committee for
statistical information (CNIS). Ethical clearance for data collection in
maternity units and each follow-up wave was obtained from the na-
tional advisory committee on information processing in health research
(CCTIRS), the national data protection authority (CNIL), and, for in-
vasive data collection such as biological sampling, the committee for
protection of persons engaged in research (CPP).

Consent

Recruitment and data collection began after obtaining informed con-
sent from participating families, who planned to reside in metropolitan
France for at least 3 years. Consent was given by both parents or the
mother alone, with the father informed of his right to decline consent.
Information and consent materials were available in French, Arabic,
Turkish, and English, reflecting common languages among women
giving birth in France.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The EFLE studies follow a data-sharing policy accessible at https://plate
forme-acces-donnees-elfe-france.site.ined.fr/, with updates and further
details on the ELFE website https://www.ELFE-france.fr/en/. ELFE
implements an open-data policy, with an 18-month exclusivity period
for associated research teams. Study protocols, questionnaires, and
the data catalogue are available online. Data access requests must be
submitted through the platform for approval by the ELFE data-access
committee, following the ELFE data-access policy downloadable from
the platform.

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