|Year : 2018 | Volume
| Issue : 3 | Page : 244-249
Sociodemographic and behavioral factors associated with dental caries in preschool children: Analysis using a decision tree
Ágata Sabine Brito1, Marayza Alves Clementino2, Monalisa Cesarino Gomes2, Érick Tássio Barbosa Neves2, Aline de Sousa Barbosa1, Camila Andurandy de Medeiros1, Mayra Macedo de Aquino1, Ana Flávia Granville-Garcia2, Valdenice Aparecida de Menezes2
1 Department of Social and Preventive Dentistry, University of Pernambuco, Recife, Pernambuco, Brazil
2 Department of Dentistry, State University of Paraíba, Campina Grande, Paraíba, Brazil
|Date of Web Publication||24-Sep-2018|
Dr. Ana Flávia Granville-Garcia
State University of Paraíba, Street Juvêncio Arruda S/N, Bodoncogó, Campina Grande, Paraíba
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: Untreated dental caries can result a negative impact on oral health-related quality of life. Aims: The aim of the present study was to determine the prevalence of dental caries and associated factors in children enrolled in public preschools in the city of Recife, Brazil. Settings and Design: A descriptive, analytical, cross-sectional study was conducted with a representative random sample of 556 children aged 3–5 years. Materials and Methods: Data were collected through clinical examinations using the International Caries Detection and Assessment System. The parents answered a questionnaire addressing sociodemographic and behavioral characteristics. Two examiners underwent training and calibration exercise for the calculation of interexaminer agreement (Kappa index of 0.83). Statistical Analysis Used: In addition to descriptive data, an inductive decision tree was constructed to analyze the results (Algorithm J48; α = 5%). Results: The prevalence of dental caries was 92.1%. The following factors were associated with dental caries: brushing performed by the child (prevalence ratio [PR] = 4.39, 95% confidence interval [CI]: 2.57–7.51 P < 0.001), household income less than the minimum wage (PR = 1.79; 95% CI: 1.18–2.72, P = 0.005), brushing frequency (PR = 1.50; CI 95%: 0.50–4.49; P = 0.001), and parent's/caregiver's school equal to an incomplete elementary school education (PR = 1.65, 95% CI: 1.56–1.74, P < 0.001). Conclusions: The occurrence of dental caries in children was high and was associated with brushing performed by the child, household income less than the monthly minimum wage, low brushing frequency, and low parent's/caregiver's schooling.
Keywords: Dental caries, epidemiology, preschool, prevalence
|How to cite this article:|
Brito ÁS, Clementino MA, Gomes MC, Barbosa Neves ÉT, Barbosa Ad, de Medeiros CA, de Aquino MM, Granville-Garcia AF, de Menezes VA. Sociodemographic and behavioral factors associated with dental caries in preschool children: Analysis using a decision tree. J Indian Soc Pedod Prev Dent 2018;36:244-9
|How to cite this URL:|
Brito ÁS, Clementino MA, Gomes MC, Barbosa Neves ÉT, Barbosa Ad, de Medeiros CA, de Aquino MM, Granville-Garcia AF, de Menezes VA. Sociodemographic and behavioral factors associated with dental caries in preschool children: Analysis using a decision tree. J Indian Soc Pedod Prev Dent [serial online] 2018 [cited 2020 Oct 26];36:244-9. Available from: https://www.jisppd.com/text.asp?2018/36/3/244/241965
| Introduction|| |
Dental caries is a public health problem that affects all ages, especially children. The prevalence of dental caries is high among preschoolers (44.34%–66.3%)., Untreated dental caries can result in pain, difficulties regarding the performance of activities of daily living, difficulty chewing, problems regarding self-esteem, esthetic concerns, and trouble sleeping, which exert a negative impact on oral health-related quality of life.,,
The prevalence of caries is substantially higher among poorer and disadvantaged populations in both developed and developing countries. Thus, socioeconomic status is an important determinant of oral health., Studies have addressed socioeconomic factors, such as household income and lack of access to a dentist, as relevant to the understanding of oral diseases.,,, Such investigations have shown that a more unfavorable socioeconomic status generally translates to a poorer oral health status among children, which underscores the importance of the social component in the occurrence of prevalent oral conditions.
Dental caries has a multifactor etiology involving factors associated with the individual and the environment., The development of dental caries is related to lifestyle and behavioral factors, particularly frequent sugar intake, inadequate oral hygiene, and lack of exposure to fluoride. Toothbrushing with a fluoride toothpaste is the cost-effective and the most commonly employed method to control dental caries.,, Brushing the teeth twice a day has been demonstrated to be better than brushing once a day, and for young children, supervised toothbrushing at school is better than unsupervised brushing at home.,
The aim of the present study was to determine the prevalence of dental caries and associated factors in children enrolled at public preschools in the city of Recife, Brazil. For such, a decision tree was used, which is a statistical resource of high reliability and precision that remain underexplored in the health field. This type of analysis has only recently been introduced in the field of dentistry., This study is therefore intended to provide a useful, relevant decision tree for oral health researchers, health professionals, and administrators.
| Materials and Methods|| |
Sample characteristics and study design
A descriptive, analytical, cross-sectional study was conducted with a representative random sample of children aged 3–5 years enrolled at public preschools in the city of Recife, Brazil.
The sample size was calculated using a procedure stratified by administration districts of the city and schools. Preschools were randomly selected from each district, and a random sample proportional to the number of students was selected per school. The sample size was calculated with a 5% margin of error, 95% confidence level, and 50% prevalence rate (PR) of dental caries. A correction factor of 1.2 was applied to compensate for the design effect. The minimum sample size was estimated to be 502 schoolchildren, to which an additional 20% was added to compensate for possible dropouts, giving a total sample of 602 schoolchildren.
Calibration and training exercise
Images of the teeth were used for the diagnosis of dental caries by the examiners and an experienced dentist (gold standard) and the results were discussed. Clinical oral examinations were then performed on a number of children by the examiners and the experienced dentist. In cases of disagreement in the diagnosis, doubts were discussed and a new examination was performed. The Kappa statistic was used for the determination of interexaminer agreement (K = 0.83) and demonstrated very good reliability.
Data were collected through clinical examinations and a questionnaire administered to parents/caregivers. The clinical examination was performed with relative isolation, artificial lighting, mouth mirror, and WHO periodontal probe.
The dependent variable was the presence/absence of dental caries, which was evaluated using the diagnostic criteria of the International Caries Detection and Assessment System (ICDAS II). The ICDAS II is used to diagnose lesions with and without cavities and is therefore a useful tool for the detection of early carious lesions (white spots), which can facilitate preventive strategies. The scoring system ranges from 0 to 6:
- 0 = Sound
- 1 = First visual change in enamel, seen dry (compressed air drying required)
- 2 = Distinct visual change in enamel
- 3 = Localized enamel breakdown
- 4 = Underlying dark shadow from dentin
- 5 = Distinct cavity with visible dentin
- 6 = Extensive distinct cavity with visible dentin.
Due to the epidemiological nature of the present study, code 1 ( first visual change in enamel) was not used, as drying of the teeth was performed with gauze rather than compressed air.
Two dichotomies were used for dental caries: (1) “presence of caries” (code 0 = absence; codes ≥2); “presence of cavity” (codes 0 and 2 = absence of caries; codes 3–6 = presence). The second dichotomy was used in the decision-tree analysis.
A pilot study was conducted at a preschool to test the methods and understanding of the questionnaires. The 40 children in the pilot study were not included in the main sample. No changes to the data collection procedures were deemed necessary.
The second stage was an interview with the parents/caregivers to investigate the independent variables: child's age, child's sex, parent's/caregiver's education, socioeconomic factors, dietary habits, oral hygiene, and frequency of dental visits.
The sample was characterized using descriptive analysis. The dependent variable was dental caries (ICDAS-II index). For the decision-tree analysis, the codes used to evaluate the presence of caries were dichotomized. Codes 0, 1 and 2 were considered indicative of the absence of caries (absence of cavity) and codes 3–6 were considered indicative of the presence of caries (with cavity). For the inferential analysis, a classifying, inductive decision tree (α =5%) generated with the J48 algorithm was employed using the top-down approach, which consists of dividing more complex problems into simpler subproblems (divide and conquer) and provides probabilistic rules of the type “if…then.” The algorithm J48 produces an inverted tree that hierarchically organizes factors related to the condition of interest starting with the problem and evaluating the explanatory variables generated during the construction of the tree. The tree structure consists of a root node (main attribute/key problem), internal nodes (intermediate attributes/problems that need to be evaluated in relation to the key problem), and terminal nodes (terminal attributes/solution that will aid in the final decision-making). Decision trees allow the evaluation of multiple variables, have high effectiveness, and provide both useful information and easy visualization. Moreover, decision trees have been highly effective at explaining complex problems and facilitating the visualization of the interaction between the independent variables involved. The interpretation of a decision tree depends on the identification of its components and the probabilistic evaluation of the dependence established among its basic elements. PRs and confidence intervals (CIs) using a 5% significance level were calculated for each node generated in the decision tree. The probabilistic, classificatory decision tree was developed in the Waikato Environment for Knowledge Analysis, version 3.8.0, produced by the University of Waikato, New Zealand.
This study received approval from the Human Research Ethics Committee of the University of Pernambuco, Brazil (registration: CEP/UPE 111/12). The principles established by the World Medical Association in the Declaration of Helsinki (2008) were respected. All legal guardians agreed to the participation of the children by signing a statement of informed consent. Children diagnosed with oral health problems received an official statement issued by the dentists with orientation regarding the services to be sought for care in the city.
| Results|| |
A total of 556 children were examined. Forty-six (8.2%) children were considered dropouts due to incomplete questionnaires or absence from school on the day scheduled for the clinical examination. The male sex accounted for the majority of the sample (56.7%); 30.6% were 3 years of age, 38.7% were 4 years of age, and 30.8% were 5 years of age. The prevalence of caries (ICDAS code ≥2) was 92.1% and the prevalence of caries with cavitation (ICDAS code ≥3) was 61.7% [Table 1].
|Table 1: Distribution of children analyzed according to prevalence of caries|
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[Figure 1] displays the decision tree. The factors that participated in the formation of the decision tree regarding the occurrence of dental caries in preschoolers were the responsibility for brushing, household income, visits to the dentist, brushing frequency, and parent's/caregiver's schooling. The rules taken from the decision tree are as follows:
Responsibility for toothbrushing (PR = 4.39, CI 95%: 2.57–7.51; P < 0.001) was the main factor influencing the occurrence of dental caries. If the child is responsible for brushing, he/she is more likely to have tooth decay. If the adult is responsible for toothbrushing, then the presence of dental caries will depend on the household income (PR = 1.79; CI 95%: 1.18–2.72; P = 0.005). Children who have parents with a household income less than the monthly minimum wage are more likely to have tooth decay. If household income is greater than or equal to the monthly minimum wage, the presence of dental caries will also depend on whether the child has visited a pediatric dentist (PR = 2.15; CI 95%: 1.38–3.37; P = 0.001). Caries among children who have not visited a dentist will depend on the parent's/caregiver's schooling. Children whose parents/guardians who do not complete elementary school education are more likely to have tooth decay (PR = 1.65; CI 95%: 1.56–1.74; P < 0.001). If the child has visited a pediatric dentist, then the presence of caries will depend on toothbrushing frequency. Children who brush their teeth less than twice a day may develop more cavities than children who brush two or more times a day (PR = 1.50; CI 95%: 0.50–4.49; P = 0.001).
| Discussion|| |
The prevalence of cavitated lesions was high in the present investigation (61.7%). Previous studies using the index recommended by the World Health Organization (dmft) conducted with a similar population report rates ranging from 34.3% to 49.2%.,, When the evaluation of dental caries included white spots (ICDAS II), the prevalence increased to 92.1%. This is in agreement with findings described in previous studies, which report rates ranging from 66% to 100% in preschool children., Beginning with code 3 of the ICDAS (detection of cavitated lesions), the results are comparable to those obtained when the dmft index is used. Therefore, the higher prevalence determined with the ICDAS is due to the fact that this index enables the determination of caries in an initial stage (noncavitated), at which reversal of the condition is possible. Another factor to consider was that all children in the present study were enrolled at public preschools, which is an indicator of a low income in Brazil. Studies have shown that children from low-income families have less access to dental services and prevention programs and consequently have a greater frequency of dental caries.,
Toothbrushing is considered fundamental self-care behavior for the maintenance of oral health., In the present study, the person responsible for brushing was associated with the presence of dental caries as children who brushed their teeth alone were more likely to have tooth decay. González Martínez et al. reported that when parents/caregivers do not monitor their children during toothbrushing, there is a greater risk of the development of oral diseases. This is probably due to the fact that preschool children do not yet have sufficient coordination to perform oral hygiene alone.
When brushing was performed by parents/caregivers, the prevalence of dental caries was associated with household income, with a greater frequency of caries in children from families with a lower income. Socioeconomic variables have been used in epidemiological studies on dental caries and household income is considered a good indicator of this condition, as children from families with different income levels tend to exhibit significant differences in the levels of the disease. Indeed, several studies reported that the number of children with caries is higher in families with an income below a minimum earnings threshold.,,
Among children from families with a larger income, the prevalence of caries was higher among children who had not visited a dentist. The preschool age group is generally a challenge for dental care providers due to problems with controlling the patient's behavior. These conditions influence the risk of early childhood caries beginning with the eruption of the first teeth and increase the likelihood of developing caries in both dentitions.,, Visits to health services are the result of many factors, such as health needs of the population, availability or accessibility of service providers, and other factors related to the organization of the health system. Both the organizational structure and factors related to the parent's/caregiver's perception of dental services are associated with the utilization of such services.,
Brushing frequency also influenced the occurrence of caries in children from families with higher incomes. The habit of brushing at least twice a day is a social norm, and it is common practice for dentists and professional organizations to offer such advice. Indeed, the US Centers for Disease Control and Prevention has recommended brushing twice a day specifically for preventing dental caries. A Cochrane review also concluded that brushing at least twice a day increases the effectiveness of fluoridated toothpaste in reducing the occurrence of caries.
Among children from families with a larger household income who had not been to the dentist, the prevalence of caries depended on the parent's/caregiver's schooling. The fact that a low educational level was associated with dental caries is consistent with data reported in previous studies that have investigated the influence of socioeconomic factors on the oral health of the pediatric population., Studies reported that children whose mothers did not complete primary education are more prone to dental caries. A low educational level may also lead to a low income, unemployment, and poor occupational status. Financial costs and a low level of information regarding the importance of oral hygiene could be a hindrance to oral health.,,
The main limitations of this study regard the very nature of the cross-sectional design, which does not enable establishing a causal relationship among the variables, and a possible bias of the information obtained through the administration of the questionnaires. However, methodological procedures were used to increase the power of the study, such as obtaining a representative sample, performing a pilot study, and calibrating the examiners. The use of a decision tree can also be considered a positive aspect due to the possibility of a differentiated interpretation and its usefulness with regard to public oral health planning by administrators. The present findings highlight specific risk factors for the occurrence of dental caries in preschool children and can assist in the establishment of more effective prevention actions.
| Conclusions|| |
The occurrence of dental caries in children aged 3–5 years was high in the present study and was associated with brushing performed by the child, household income less than the monthly minimum wage, low brushing frequency, and low parent's/caregiver's schooling.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Rahman SS, Rasul CH, Kashem MA, Biswas SS. Prevalence of dental caries in the primary dentition among under five children. Bang Med J 2010;43:7-9.
Pinto-Sarmento TC, Abreu MH, Gomes MC, Costa EM, Martins CC, Granville-Garcia AF, et al
. Determinant factors of untreated dental caries and lesion activity in preschool children using ICDAS. PLoS One 2016;11:e0150116.
Ferraz NK, Nogueira LC, Pinheiro ML, Marques LS, Ramos-Jorge ML, Ramos-Jorge J, et al
. Clinical consequences of untreated dental caries and toothache in preschool children. Pediatr Dent 2014;36:389-92.
Clementino MA, Pinto-Sarmento TC, Costa EM, Martins CC, Granville-Garcia AF, Paiva SM, et al
. Association between oral conditions and functional limitations in childhood. J Oral Rehabil 2015;42:420-9.
Vieira-Andrade RG, Gomes GB, Pinto-Sarmento TC, Firmino RT, Pordeus IA, Ramos-Jorge ML, et al
. Oral conditions and trouble sleeping among preschool children. J Public Health 2016;24:395.
Usha C, Sathyanarayanan R. Dental caries – A complete changeover (Part I). J Conserv Dent 2009;12:46-54.
] [Full text]
Marya C, Kataria S, Nagpal R, Oberoi SS, Dhingra C, Arora D, et al
. A cross-sectional study for assessment of untreated dental caries and its consequences among slum-dwelling children. Int J Clin Pediatr Dent 2017;10:29-33.
Bonanato K, Pordeus IA, Moura-Leite FR, Ramos-Jorge ML, Vale MP, Paiva SM, et al
. Oral disease and social class in a random sample of five-year-old preschool children in a Brazilian city. Oral Health Prev Dent 2010;8:125-32.
Sufia S, Chaudhry S, Izhar F, Syed A, Mirza BA, Khan AA, et al
. Dental caries experience in preschool children: Is it related to a child's place of residence and family income? Oral Health Prev Dent 2011;9:375-9.
Singh A, Purohit B, Sequeira P, Acharya S. Oral health status of 5-year-old aborigine children compared with similar aged marginalised group in South Western India. Int Dent J 2011;61:157-62.
Borges HC, Garbín CA, Saliba O, Saliba NA, Moimaz SA. Socio-behavioral factors influence prevalence and severity of dental caries in children with primary dentition. Braz Oral Res 2012;26:564-70.
Fejerskov O. Changing paradigms in concepts on dental caries: Consequences for oral health care. Caries Res 2004;38:182-91.
Baelum V, van Palenstein Helderman W, Hugoson A, Yee R, Fejerskov O. A global perspective on changes in the burden of caries and periodontitis: Implications for dentistry. J Oral Rehabil 2007;34:872-906.
Nguyen L, Häkkinen U, Knuuttila M, Järvelin MR. Should we brush twice a day? Determinants of dental health among young adults in Finland. Health Econ 2008;17:267-86.
Jürgensen N, Petersen PE. Promoting oral health of children through schools – results from a WHO global survey 2012. Community Dent Health 2013;30:204-18.
Tarvonen PL, Sipilä K, Ri YS, Jang JH, Kim JH, Suominen AL, et al
. Awareness of and compliance with healthy oral habits reported by children and their parents in Democratic People's Republic of Korea after a preventive programme. Acta Odontol Scand 2016;74:525-31.
Tamošiūnas V, Kay E, Craven R. A preliminary study applying decision analysis to the treatment of caries in primary teeth. Stomatologija 2013;15:84-91.
da Nóbrega LM, Bernardino ÍM, Barbosa KGN, E Silva JAL, Massoni ACLT, d'Avila S, et al
. Pattern of oral-maxillofacial trauma from violence against women and its associated factors. Dent Traumatol 2017;33:181-8.
Ismail AI, Sohn W, Tellez M, Amaya A, Sen A, Hasson H, et al
. The international caries detection and assessment system (ICDAS): An integrated system for measuring dental caries. Community Dent Oral Epidemiol 2007;35:170-8.
Song YY, Lu Y. Decision tree methods: Applications for classification and prediction. Shanghai Arch Psychiatry 2015;27:130-5.
Carvalho FS, Carvalho CA, Bastos RS, Xavier A, Merlini SP, Bastos JR. Dental caries experience in preschool children of Bauru, SP, Brazil. Braz J Oral Sci 2009;8:97-100.
Piovesan C, Pádua MC, Ardenghi TM, Mendes FM, Bonini GC. Can type of school be used as an alternative indicator of socioeconomic status in dental caries studies? A cross-sectional study. BMC Med Res Methodol 2011;11:37.
Martínez R, Andrés C. Pre-school child oral health in a rural Chilean community. Int J Odontostomatol 2011;5:83-6.
Ardenghi TM, Vargas-Ferreira F, Piovesan C, Mendes FM. Age of first dental visit and predictors for oral healthcare utilisation in preschool children. Oral Health Prev Dent 2012;10:17-27.
Granville-Garcia AF, Clementino MA, Gomes MC, Costa EM, Pinto-Sarmento TC, Paiva SM, et al
. Influence of oral problems and biopsychosocial factors on the utilization of dental services by preschool children. J Dent Child (Chic) 2015;82:76-83.
Poklepovic T, Worthington HV, Johnson TM, Sambunjak D, Imai P, Clarkson JE, et al
. Interdental brushing for the prevention and control of periodontal diseases and dental caries in adults. Cochrane Database Syst Rev 2013;18:CD009857.
Kumar S, Tadakamadla J, Johnson NW. Effect of toothbrushing frequency on incidence and increment of dental caries: A Systematic review and meta-analysis. J Dent Res 2016;95:1230-6.
González Martínez F, Sierra Barrios CC, Morales Salinas LE. Knowledge, attitudes and practices in oral health of parents and caregivers in children's homes in colombia. Salud Publica Mex 2011;53:247-57.
Carvalho DM, Salazar M, Oliveira BH, Coutinho ES. Fluoride varnishes and decrease in caries incidence in preschool children: A systematic review. Rev Bras Epidemiol 2010;13:139-49.
Alcântra TL, Batista MJ, Gibilini C, Ferreira NP, Sousa ML. Factors associated with the oral health of preschool children enrolled in a preventive educational program in the city of Piracicaba/SP. RPG Rev Post Grad 2011;18:102-7.
Li Y, Zhang Y, Yang R, Zhang Q, Zou J, Kang D, et al
. Associations of social and behavioural factors with early childhood caries in Xiamen city in China. Int J Paediatr Dent 2011;21:103-11.
Piovesan C, Ardenghi TM, Mendes FM, Agostini BA, Michel-Crosato E. Individual and contextual factors influencing dental health care utilization by preschool children: A multilevel analysis Braz Oral Res 2017;31:e27.
Splieth CH, Bünger B, Pine C. Barriers for dental treatment of primary teeth in East and West Germany. Int J Paediatr Dent 2009;19:84-90.
Camargo MB, Barros AJ, Frazão P, Matijasevich A, Santos IS, Peres MA, et al
. Predictors of dental visits for routine check-ups and for the resolution of problems among preschool children. Rev Saude Publica 2012;46:87-97.
Marinho VC, Higgins JP, Sheiham A, Logan S. Fluoride toothpastes for preventing dental caries in children and adolescents. Cochrane Database Syst Rev 2003;1:CD002278.
Barriuso Lapresa L, Sanz Barbero B. Multilevel analysis of the use of oral health services by the pediatric population. Gac Sanit 2011;25:391-6.
Guedes RS, Piovesan C, Antunes JL, Mendes FM, Ardenghi TM. Assessing individual and neighborhood social factors in child oral health-related quality of life: A multilevel analysis. Qual Life Res 2014;23:2521-30.