Indonesian Dental Association Journal of Indonesian Dental Association http://jurnal. id/index. php/jida ISSN: 2621-6183 (Prin. ISSN: 2621-6175 (Onlin. Research Article Prediction Model of Pre. Peri-, and Postnatal Factors for Early Childhood Caries in Stunted Children of Juwiring. Central Java. Indonesia (A Life Course Approac. Iwany Amalliah Badruddin1*. Lintang Andarini Putri2. Anton Rahardjo1. Atik Ramadhani1. Risqa Rina Darwita1 1Department of Dental Public Health. Faculty of Dentistry. Universitas Indonesia. Jakarta. Indonesia 2Faculty of Dentistry. Universitas Indonesia KEYWORDS Dental caries. ECC. Life course. Prediction model. Prenatal. Perinatal. Postnatal. Received: 31 August 2025 Revised: 25 September 2025 Accepted: 24 October 2025 Published: 26 October 2025 ABSTRACT Introduction: The life course approach highlights how early life, beginning with fetal growth, influences future disease risk. Oral health studies in Indonesia using this approach are still limited. This study examined the relationship between ECC status and retrospective data on stunted children under three and their mothersAo pregnancy history at Juwiring District Community Health Center. Objective: This study aims to examine the influence of pre, peri-, and postnatal factors on ECC occurrence in stunted children in Juwiring Regency. Central Java. Methods: Secondary data of pre, peri-, and postnatal information from 265 stunted children were obtained from the medical records. Oral examination was assisted by local dentists from the health center. Multiple logistic regression analysis was performed to construct a prediction model and determine the factors with the greatest influence on dental caries in stunted children in Juwiring District. Results: The prevalence of dental caries was 69. 1% in 246 subjects. The variables in the final prediction model for factors related to ECC were the mother's perception of her child's dental problems, the mother's education level, the motherAos knowledge of dental caries, and the childAos stunting status. The most influential factor was the mother's perception of her child's dental problem, with the highest odds ratio (OR) of 5. 83Ae Conclusion: This study revealed that prenatal factors related mothersAo education level and postnatal factors related to mothersAo perceived dental problems, poor knowledge of dental caries and childrenAos stunting status were included in the ECC prediction model for the stunted children of Juwiring District. * Corresponding Author E-mail address: iwanyamalliah@gmail. com (Iwany Amalliah Badruddi. DOI: 10. 32793/jida. Copyright: A2025 Iwany AB. Andarini L. Rahardjo A. Ramadhani A. Darwita RR. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium provided the original author and sources are credit Journal of Indonesian Dental Association 2025 8. , 78-89 Iwany AB et al. INTRODUCTION socioeconomic environment from conception. Several types of hard dental tissue abnormalities have been shown to be clinically indicative of systemic diseases, socioeconomic status, nutritional status, pregnancy conditions, genetic and hereditary conditions, and depictions of prehistoric societies, such as enamel Early development influences a person's health as an adult. The environment during an individual's infancy or childhood has a long-term impact on their health as an adult. Barker suggested that chronic diseases that a person develops in adulthood are the result of environmental influences programmed from the time of conception through adulthood. 1 This concept is known as the life course perspective or approach, which has been evident from longitudinal studies for psychosocial, etc. Dental caries has also been shown to be associated with social class and can interfere with children's growth and development. 14-16 Several crosectional studies have examined the relationships between early-life biomarkers such as birth weight and dental caries. 17 Children with a history of low birth weight had a higher average rate of dental caries. relationship between average caries experience and height was also observed, with taller children having lower caries scores. 18-19 A cross-sectional study in Beji District. Depok. West Java, revealed that the proportion of children with primary tooth decay among mothers with a history of undernutrition (LiLA <23. 5 c. during pregnancy was significantly greater . %) than that among mothers with a normal nutritional status . 1%). There are four common models of the life course approach, which are not mutually exclusive, and time is an important factor. The first model is the critical period model, also known as fetal programming or biological programming, known as Barker's hypothesis. Exposure during a specific period of growth or physical development can alter several underlying body structures or systems, resulting in damage or disease that emerges later in life. The second model is the critical period model with modifying effects. individual's risk of developing a disease due to exposure early in life can be modified by exposures received during development. These modifying effects can increase or decrease the risk of disease. The third model is the risk factor accumulation model. The accumulation of risk factors shapes a person's health trajectory from early life to adulthood. The fourth model is the risk factor chain model. This concept is the dynamic interaction between intrinsic individual factors and extrinsic factors, such as family factors, that results in an increased individual risk of developing a disease. Another life course model that has been introduced is the intergenerational life course approach. The intergenerational transmission of health inequalities from parents to offspring through the pre- and postnatal environments contributes to socioeconomic inequalities in adult health. The relative positions of parents and offspring in the social hierarchy are closely related, as observed for educational attainment, income, wealth, and occupational class, within time and place The Juwiring District in Klaten Regency Central Java has a special program, organized by the Juwiring Community Health Center for addressing stunting, called JUWITA 1000 Harta (Juwiring Responds to the First 1000 Day. This program started in 2013 to reduce stunting rates, low birth weight, and the risk of maternal and infant mortality. The program's framework has registered hundreds of children within the 19 villages of Juwiring District. The local health authorities administered medical records of mothers and children during their participation in the program. The availability of retrospective data about childrenAos growth and development medical records made it possible to conduct research that linked pre, peri-, and postnatal information to childrenAos current dental caries status. This study is the first in Indonesia to develop a life course prediction model of early childhood caries in stunted children, integrating pre, peri-, and postnatal Oral health research with this type of approach is still scarce, especially in Indonesia. The life course concept aligns with the development of oral and dental disease because of its chronic, cumulative, and socially patterned nature. 2 Oral and dental diseases are linked to social conditions, reflected in disparities, with lowerincome groups suffering more. 6 Social status is associated with clinical and subjective outcomes throughout a person's life. Poor oral health reflects low socioeconomic status and is strongly associated with general health. 7 Oral health can also be used to identify at-risk individuals, linking it to their general health and MATERIALS AND METHODS The Juwiring District Stunting Program The design of this study was cross-sectional and uses secondary data from the Juwiring Community Health Center from 2022--2024 for 264 stunted children aged under 3 years. A purposive sampling technique was applied for respondent selection with criteria such as residing in Juwiring District, being registered in the JUWITA 1000 Harta program, and Journal of Indonesian Dental Association 2025 8. , 78-89 Iwany AB et al. completing the mother and child health card (KMS) at Integrated Health Service Posts (Posyand. The children aged under 3 years were already diagnosed with stunting by the Juwiring Community Health Center medical team based on body length or height-for-age index (PB/U or TB/U), according to the anthropometric standards of the WHO. income, categorized relative to the regional minimum wage (UMR) of Klaten Regency in 2024, which was IDR 2,244,000. The parentsAo income was categorized as low if it was under IDR 2,244,000 and high if it was greater than or equal to IDR 2,244,000. 27 The Central Statistics Agency (BPS) defines employment status into formal and informal employment. Secondary data of pre, peri-, and postnatal information about the history of pregnancy, birth, and growth monitoring records were obtained from the health card. The health conditions during pregnancy variables were weight gain, sickness history, and prenatal check-ups. Pregnancy term, birthweight, birth helper, type of delivery, and crying at birth were recorded as perinatal information. For postnatal information, the program recorded nutritional status. sickness history, such as frequency and diagnosed and vaccination history. To maintain data validity and minimize bias, several steps were taken. Data completeness was ensured through systematic extraction of child health records at the Community Health Center (Puskesma. ncluding the JUWITA 1000 Harta register and KMS). Each entry was cross verified using the child's name, mother's name, and date of birth. Inconsistent data were verified directly with the original Puskesmas records. Child feeding patterns were categorized as follows: . poor= not consuming some complete nutrition from birth until the time the study was conducted, including exclusive breastfeeding for up to 2 years. sufficient= consuming complete nutrition from birth until the time the research was conducted, including exclusive breastfeeding for less than 2 years. good= consuming complete nutrition from birth until the time the research was conducted, including exclusive breastfeeding for 2 years. MothersAo perceptions of their childrenAos oral health are categorized as yes if they think their children have dental problems and no if the mothers feel that their children have no dental health problems. MothersAo knowledge of dental caries and preventive practices for dental caries were also measured. Recall bias was controlled by using a structured questionnaire. Parents' responses were verified whenever possible with community health center (Puskesma. or integrated health post (Posyand. Enumerators were trained to conduct standardized interviews and avoid suggestive questions. Clinical Examination and Interview Dental caries status was assessed via the dmft 23 This study used an intraoral camera for clinical Two operators were calibrated prior to the clinical examination, with substantial kappa agreement ranging from 0. 70 to 0. From 265 children under 3 years old, 246 children aged 6 to 36 months were included in the study according to the pattern of tooth eruption reported by the Journal of American Dental Association (JADA) in 2005. 24 Before the examination, parents were provided with an informed consent form and an assent form. Ethical approval was obtained from the Dentistry Research Ethics Committee. Faculty of Dentistry. University of Indonesia (Protocol Number: The research procedures were developed and reported on the basis of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Data Analysis Statistical analysis was conducted via SPSS Multivariate analysis aimed to assess the strength of the relationship between ECC and its influencing pre, peri-, and postnatal factors via multiple logistic regression. For a parsimonious predictive model, the modeling process followed the following steps: . determine the independent variables included in the model with p< 0. 250 and substantially related to the dental caries variable in the bivariate analysis. use the Enter method, all variables are entered into the model to control simultaneously the influence of potentially confounding variables. variables with the highest nonsignificant p values are removed one by one until all final variables reach partial significance of p< 0. interaction testing is intended to identify potential effect modifier variables. the model quality is tested via the HosmerAeLemeshow test, classification accuracy, and ROC test. the most independent variable with the greatest influence on ECC in the final model, along with other predictors that formed the last model, is identified. Additional information, such as socioeconomic and demographic status, child feeding patterns, mothersAo knowledge, preventive practices toward dental caries, and perceptions of their childrenAos oral health, was collected via structured questionnaires. The parental education category was based on a national educational system that sets nine years of study throughout elementary and junior high school as low education. Economic status was assessed on the basis of parental Journal of Indonesian Dental Association 2025 8. , 78-89 Iwany AB et al. RESULTS problems, poor knowledge about dental caries and the childAos stunting status in the prediction model were postnatal factors (Table . The most influential factor was the mother's perception of her child's dental health problem, with the highest odds ratio (OR) of 5. 83Ae14. Mothers with perceptions of their child having dental problems were 5 times more likely to have dental caries than mothers with no perceptions of oral health problems. Mothers with a low level of education were 3. 2 times more likely to have ECC than mothers with a higher level of Mothers with poor knowledge about dental caries are 1. 975 times more likely to have ECC than mothers with good knowledge. Severely stunted children were 2. 4 times more likely to have ECC than were milder stunted children. Prevalence and Variable Selection The prevalence of ECC in 246 subjects was 1%, with an overall mean number of dmfts of 2. teeth per person (Table . Prenatal factors included parentsAo education, income, age, height, child order, family size . umber of childre. , mothersAo weight gain during pregnancy, sickness history, and prenatal control during pregnancy (Table . The prenatal factors that were significantly related to ECC were parentsAo education and fathersAo income. The perinatal factors included in the study were term pregnancy, type of delivery, birth help, birth weight, and whether the child cried at birth. None of the perinatal factors were significantly related to ECC, as shown in Table 3. The postnatal factors included sex, childAos age at the time of ECC examination, wasting status, stunting status, sickness history . requency and diagnosed disease. , and A set of questionnaires to interview mothers about perceived dental problems, knowledge levels of dental caries, and preventive practices for the children were also carried out. Postnatal factors that were significantly related to ECC were the childAos age at examination time, stunting status, perceived dental problems, number of problematic teeth, motherAos knowledge of dental caries, and preventive practices for the children, as shown in Table 4. The model fit the data . <0. because it was significant according to the chi-square test . 2=58. The equation quality was assessed via the HosmerAeLemeshow test, classification accuracy, and receiver operating characteristic (ROC) test. The HosmerAeLemeshow test revealed that the LR model was well calibrated because p>0. =0. However, the quality of the prediction model is lacking from some other measures. The predictive accuracy value of this research model was only 69. 1%, which is considered fair. The AUC of the ROC curve analysis 775 or 77. 5%, indicating that the regression equation has a moderate ability to distinguish healthy and sick subjects. The Nagelkerke R square of 0. means that the factors in the final model can explain the ECC by only 29. 7%, and the model needs other variables to reach at least 50%. Tabel 1. ECC distribution in 246 stunted children in Juwiring District Early Childhood Caries Prevalence Yes Dmft index Frequency (%) Mean (SD) DISCUSSION A Global Burden of Oral Diseases study revealed that of the 530 million people with primary dental caries worldwide in 2017, approximately 267 million, or 7. were from lower-middle-income countries such as Indonesia. The global incidence of primary dental caries is 6,776 per 100,000 people . 78%). 31-32 IHME data on the burden of oral disease in Indonesia in 2018 revealed that the rate of primary dental caries in Indonesia was even higher. A total of 7,251 people per 100,000 people suffer from primary dental caries, or 7. The prevalence of ECC in O3-year-old stunted children in Juwiring District was 69. 1%, which was lower than the national prevalence of RISKESDAS 2018 . 5%). 34 The overall mean dmft of the O3-yearold stunted children in Juwiring District was 2. teeth per person, which was lower than the national dmft mean at 3--4 years of age from RISKESDAS 2018 . and SKI 2023 . 34--3 Prediction Modeling The variables included in the multiple logistic regression (LR) model are the variables from the pre, peri-, and postnatal periods that showed a significant relationship and had p values less than 0. 250 in the bivariate analysis. The variables in the final prediction model for factors related to ECC for children under 3 years of age in Juwiring District were mothersAo perceptions of their child's dental problems, mothersAo education level, mothersAo knowledge about dental caries, and the childAos stunting status. The prenatal factor that was included in the prediction model was only the motherAos education level. MothersAo perceived dental Journal of Indonesian Dental Association 2025 8. , 78-89 Iwany AB et al. Table 2. Prenatal factors and ECC in stunted children in Juwiring District Prenatal variables FatherAos education Junior High or less High School Bachelor or above MotherAos education Junior High or less High School Bachelor or above FatherAos occupation Unemployed Nonformal jobs Formal jobs MotherAos occupation Unemployed Employed FatherAos income No income 35 yo 20 to 35 yo MotherAos height <145 cm Ou145 cm MotherAos weight gain >16 kg <11. 5 kg 5-16 kg Prenatal check-ups Never Midwife GP/Gynecologist Sickness history Yes Chi square Simple logistic regression Total (N=. ECC 132. NSC NS# NS# NSC NSC NS# NS# NS# NSC NSC NS# Journal of Indonesian Dental Association 2025 8. , 78-89 Iwany AB et al. Table 3. Perinatal factors and ECC in stunted children in Juwiring District Perinatal Total N=246 Pregnancy term Preterm 19. Late term 10. Full term 217. Types of delivery Cesarean section 103. Normal 143. Birth helper Midwife 103. Gynecologist 127. Birth weight <2. 5 kg 54. 8 kg 185. >3. 8 kg 7. Crying at birth 6. Yes 240. Chi square Simple logistic regression ECC 13. Socioeconomic status is very important so that children who grow up in an unfavorable socioeconomic environment are more likely to have poor health status as Research using a life course approach has proven a consistent relationship. 6 ParentsAo education, occupation, income, number of children, and child order have been shown to be associated with ECC in several review 36-39 In this Juwiring District study, although parentsAo education and income were significant in the bivariate analysis, only mothersAo education was significantly related to ECC in the final model, with a greater risk of OR= 17. 8 times for those with low education levels and OR= 4. 3 times for those with moderate education levels to experience ECC in their This result was consistent with those of other . NSC NS# NSC NS# environment contribute to inequalities in fetal development and birth outcomes, with lifelong socioeconomic and health consequences. 5 Cohort studies in New Zealand and Brazil demonstrate the importance of socioeconomic status, which can even influence intergenerational health status. This also applies to parental knowledge and attitudes toward oral 44-45 Parental oral health knowledge and attitudes appear to underlie the continuity of oral health between 46 The variable of mothersAo knowledge about dental caries was one of the variables in the final model of multivariate analysis in this Juwiring District The OR was significant at 10. 3, indicating that mothers with poor knowledge levels are 10. 3 times more likely for their children to have ECC than are mothers with good knowledge levels. This result is in accordance with studies in Jordan,47 southern Brazil,48 Taiwan,43 and Indonesia49-50. Although this study was cross-sectionally conducted for ECC clinical examination and several postnatal risk factors, many other factors that occurred during the pre, peri- and postnatal periods were recorded in the stunting program of the Juwiring District Community Health Center. Therefore, we have the opportunity to establish a temporal relationship for the ECC risk factor prediction model. The final prediction models of ECC risk factors for 3-year-old stunted children in Juwiring District were the mother's education level, the mother's perception of her child's dental problems, the motherAos poor knowledge about dental caries, and the motherAos stunting status. Socioeconomic Socioeconomic status can determine health beliefs and the perceived need for the use of family dental health care, which can then affect children's oral health, including increased susceptibility to caries. There is a strong relationship between socioeconomic status and dental health behavior. 5 In this study, mothersAo perceptions of their childAos dental health problems were the most influential factor, with the highest OR of 36. 904--103. Mothers with poor perceptions of their child's dental health were 36. times more likely for their children to experience dental caries than mothers with good perceptions were. This result is in accordance with studies in Jordan and Journal of Indonesian Dental Association 2025 8. , 78-89 Iwany AB et al. Indonesia. A total of 650 pairs of mothers and children in Jordan reported that 25. 7% of mothers had perceptions that their children had poor dental health and that more than half . 3%) were not aware that their children had dental caries. 47 MothersAo perceptions of their childrenAos perceived susceptibility to dental health and the severity risk of dental caries to their childrenAos dental health are related to their attitudes toward their childAos oral Table 4. Postnatal factors and ECC in stunted children in Juwiring District Postnatal variables Sex Female Male Age at examination time 25-36 months 13-24 months O12 months Wasting status Overweight Thinness Normal Stunting status Severely stunted Stunted Sickness history Yes Sickness frequency per year >4 times 3-4 times 1-2 times Diagnosed disease Yes Vaccination history Not complete Complete MothersAo perceived dental problem Yes Number of problematic teeth None Eating habit pattern Poor Moderate Good MotherAos knowledge about dental caries Poor Moderate Good MotherAos preventive habit toward caries Poor Moderate Good #Chi square Simple logistic regression Total (N=. ECC NS# NSC NS# NSC NS# NS# NSC Journal of Indonesian Dental Association 2025 8. , 78-89 Iwany AB et al. Table 5. Prediction model of factors related to ECC in stunted children in Juwiring District Variabelc MotherAos perceived of dental problem 95% CI MotherAos education level Stunting status MotherAos knowledge of dental caries Multiple logistic regression. Constant -6. information bias. despite bounded recall, matching with community health center records and training of enumerators, recall bias and residual misclassification are still possible. Another limitation concerns the model The model quality assessment met the criteria as an adequate predictor model at some of the assessments, but with such a low coefficient of determination, the study should include more direct variables of dental caries determinants, especially those related to the biological aspects of stunting, which include salivary gland atrophy and nutritional deficiencies. Based on data from UNICEF and the WHO. Indonesia ranks 27th out of 154 countries with a relatively high prevalence of stunting. Although the country has made progress in reducing stunting, with the prevalence decreasing from 24. 4% in 2021 to 21. 6% in 2022, its rate is still considered notably high, placing it fifth among Asian countries. 51 The WHO stated that 1 in 5 Indonesian children under the age of 5 are stunted or too short for their 52 In 2024, the prevalence of stunting in Central Java Province reached 20. 8%, whereas in Klaten Regency, it was 11%. 53 In this Juwiring District study, severely stunted children were 2. 4 times more likely to have ECC than mildly stunted children were in the final prediction This result was in accordance with studies in Nigheria and China. 54-56 A study using RISKESDAS 2018 data that linked 5-year-old stunted children with their ECC status revealed that 92. 3% of stunted children had ECC. 57 Poor nutritional status, including stunting, is known to affect various aspects of health, including dental and oral health. Several studies have shown that stunting can cause a decrease in the salivary flow rate due to salivary gland atrophy associated with protein and vitamin A deficiency. This decrease in saliva production can weaken the oral cavity's ability to defend against infection, reduce the acid-neutralizing capacity of dental plaque, and therefore increase the risk of dental caries. Chronic malnutrition impedes tooth development, causing a weaker tooth structure due to disturbances in amelogenesis,58-61 thus increasing the likelihood of ECC. Regarding the type of life course model, this study more fits to the second model, which is critical period with effect modification. The critical period model with effect modification is a more nuanced version of the critical period concept. It recognizes that early exposures can be critical, but their long-term consequences are not fixed. they can be amplified or mitigated by other exposures or characteristics that act as effect modifiers. Stunting linked to motherAos pregnancy condition and can be corrected within 1000 days after birth. There is biological link from stunting to dental caries such as decrease of saliva flow rate, saliva content that could act as dental caries prevention, and tooth structure development. 62 Thus, if stunting status can be corrected during the critical window for intervention, the risk of dental caries occurrence can also be decreased. The growing new model of life course, intergeneration model, was can also be seen in this The variables motherAos perceived of dental health problems and knowledge about dental caries linked to their children oral health status. Parental behavior appears to underlie oral health continuity between generation, this includes habits such as oral hygiene practice, smoking, going to the dentist, etc. Socioeconomic status that usually an important factor in life course approach, unfortunately could not be proven in this study. Despite the use of a cross-sectional design, this study utilized prospectively collected pre, peri-, and postnatal data from the JUWITA 1000 Harta program to approximate temporal patterns across life stages. Therefore, the results are interpreted as predictive associations, not causal effects. However, this study has several limitations. Since it involves purposive sampling and a specific population of children who are diagnosed as stunted, the prediction is also limited in application. For the purpose of building a prediction model, a larger sample size is needed to avoid random error since there are variables with association values that are risk factors that are not statistically significant. Other limitations were still related to bias. Given that prenatal and perinatal information was collected by community health center staff as secondary data, there might be systematic bias for the research instrument. The use of secondary data and parent questionnaires has the potential to introduce ECC and stunting are still public health concerns, especially in Indonesia. Therefore, it is important to plan a multisectoral approach of health promotion and intervention program, especially for critical period of stunting . 0 days. Oral health improvement strategies need to focus on ecomanagement of the underlying social determinants of chronic conditions (NCD. , including oral diseases. Journal of Indonesian Dental Association 2025 8. , 78-89 Iwany AB et al. CONCLUSION Community Dent Oral Epidemiol. : 241Ae249. DOI: 10. 1111/j. Houweling TAJ. Grynberger I. Intergenerational transmission of health inequalities: toward a life course approach to socioeconomic inequalities in health - a review. J Epidemiol Community Health. 641Ae649. DOI: 10. 1136/jech-2022-220162. Hobdell MH. Oliveira ER. Bautista R. Myburgh NG. Narendran S. Johnson NW. Oral diseases and socioeconomic status (SES). Br Dent J. : 91Ae96. 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As the first study in Indonesia to develop a prediction model for early childhood caries (ECC) in stunted children via a life course approach encompassing pre, peri-, and postnatal factors by utilizing longitudinal data from the JUWITA 1000 Harta program, the model identified a combination of social factors, education, and nutritional status as predictors of ECC. The variables in the final prediction model for factors related to ECC for children under 3 years of age in Juwiring District were mothersAo education level as prenatal factors, mothersAo perception of their child's dental problems, mothersAo poor knowledge of dental caries, and the childAos stunting status as post-natal MothersAo education levels indicate that early-life exposure can shape mothersAo attitudes and ultimately affect their health. Stunting, which closely results from socioeconomic factors such as poverty, could also be a factor in a childAos health deterioration. Understanding these psychosocial determinants is critical for improving long-term oral health, especially ECC prevention. This provides new evidence that goes beyond previous studies, which generally assessed only single risk factors cross-sectionally without producing an integrated prediction model. ACKNOWLEDGMENT The authors would like to thank the Juwiring Community Health Center and the Klaten Regency Government. Central Java, for their support of this CONFLICT OF INTEREST There are no conflicts of interest related to the published article. REFERENCES