|Year : 2017 | Volume
| Issue : 3 | Page : 143-148
Compare, evaluate, and estimate chronological age with dental age and skeletal age in 6–14-year-old Himachali children
Ajay K Kapoor, Seema Thakur, Parul Singhal, Deepak Chauhan, Cheranjeevi Jayam
Department of Paediatric and Preventive Dentistry, Himachal Pradesh Government Dental College and Hospital, Shimla, Himachal Pradesh, India
|Date of Web Publication||9-Aug-2017|
Ajay K Kapoor
Department of Paediatric and Preventive Dentistry, Himachal Pradesh Government Dental and College, Shimla - 171 001, Himachal Pradesh
Source of Support: None, Conflict of Interest: None
Introduction: Age estimation is of immense importance not only for personal identification but also for treatment planning in medicine and dentistry. Chronologic age conveys only a rough approximation of the maturational status of a person. Hence, dental and skeletal ages (SAs) have been explored as maturity indicators since decades.
Aims And Objectives: To test the applicability of Willem's dental age (DA) assessment methods as well as Mito T SA assessment method in Himachali children.
Materials and Methods: The study included 55 subjects (30 males and 25 females) ranging from 6 to 14 years age who require orthodontic intervention. DA estimation was performed from digital orthopantomogram of mandibular teeth of left quadrant using Willem's methods. SA estimation was done from lateral cephalogram using Mito T method. The differences between the chronological age (CA) and the estimated dental and SAs were statistically tested using paired t-test. The correlation between CA, dental, and SA estimation methods was confirmed statistically using Pearson's correlation.
Results: Among the age estimation methods used in this study, the Willem's DA estimation method proved to be the most accurate and consistent.
Conclusion: Although various age estimation methods do exist, the results are varied in different populations due to ethnic differences. However, till new tables are formulated using greater sample size, the Willem's method can be accurately applied to estimate CA for the population studied.
Keywords: Age estimation, dental age estimation, Mito T method, skeletal age estimation, Willem's method
|How to cite this article:|
Kapoor AK, Thakur S, Singhal P, Chauhan D, Jayam C. Compare, evaluate, and estimate chronological age with dental age and skeletal age in 6–14-year-old Himachali children. Int J Health Allied Sci 2017;6:143-8
|How to cite this URL:|
Kapoor AK, Thakur S, Singhal P, Chauhan D, Jayam C. Compare, evaluate, and estimate chronological age with dental age and skeletal age in 6–14-year-old Himachali children. Int J Health Allied Sci [serial online] 2017 [cited 2020 Aug 6];6:143-8. Available from: http://www.ijhas.in/text.asp?2017/6/3/143/212594
| Introduction|| |
Human growth shows considerable variation in the chronologic ages at which individual children reach similar developmental events. Chronologic age alone is not sufficient for assessing the stage of development of a growing child.,, Dental age (DA) is of particular interest to the pedodontist and orthodontist in the management of different types of malocclusions in relation to maxillofacial growth. It can be determined by the stage of tooth eruption or the stage of tooth calcification.
The most widely used method for DA estimation was described in 1973 by Demirjian et al. but has overestimated the age in various population studied.,,,, In 2001, Willems et al., evaluated the accuracy of Demirjian method in Belgian Caucasian population and modified the scoring system when a significant overestimation was reported.
Skeletal maturation is routinely evaluated to indicate the level of body maturation and to determine the remaining growth potential in children. Skeletal maturation can be assessed by evaluating the degree of ossification of certain bony markers located within the skeletal system.
Mito et al. conducted a study on Japanese girls to establish cervical vertebral bone age as a new index for objectively evaluating skeletal maturation on cephalometric by measuring various parameters on third and fourth cervical vertebrae.
However, no previous study exist to investigate the relationship between chronological age (CA), DA and skeletal age (SA) in Himachali population, and hence this study was to estimate SA, by measuring dimensions of the cervical vertebral bodies on lateral cephalometric radiographs and estimate DA on digital orthopantomogram (OPG) to investigate its correlation in Himachali population.
| Materials and Methods|| |
The present cross-sectional study was conducted in the Department of Paediatric and Preventive Dentistry at Himachal Pradesh Government College and Hospital, Shimla - 171 001, Himachal Pradesh, India.
The study included 55 patients of age group 6–14 years who require preventive and interceptive orthodontic treatment selected from routine patient OPD were included in the study. Ethical clearance was obtained from the Institutional Ethics Committee.
Criteria for case selection
- Patients were between the age group of 6 and 14 years
- Normal patients with no history of systemic medical diseases or any nutritional disorders
- Patients with no missing teeth (congenital or extraction)
- Patients appearing in the department for preventive/interceptive orthodontic treatment with no prior history of orthodontic treatment
- Patients with no history of previous pathology and trauma to the face and cervical vertebral region.
All the subjects were divided into two groups: Group 1 consisted of males and Group 2 consisted of females. Each group was further divided into four subgroups on the basis of age as shown in [Table 1].
|Table 1: The mean±standard deviation age of male and females in all age groups|
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The present study was based on lateral cephalometric radiographs and digital OPG of 55 samples. The sample comprised 30 males and 25 females (age ranges 6–14 years).
Chronological age estimation
CA was calculated by subtracting the date of birth from the date on which radiograph was taken.
Dental age estimation
Digital panoramic radiographs (OPGs) of all children were assessed the for maturation status on the basis of calcification of the permanent teeth in mandibular left side, from central incisor to the second molar, using Demirjian et al., method. To avoid observer bias, each digital OPG of an individual was coded with a numerical identity number (1–55) to ensure that the examiner was blind to sex, name, and age of subjects. Two evaluators (pedontists) were supplied with all 55 OPGs each and with written instructions for staging, including drawings and written descriptions of stages of tooth development of Demirjian et al., method [Table 2].
|Table 2: Description for developmental stages of teeth given by Demirjian et al. (1973)|
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Tooth formation is divided into eight stages (A–H) and criteria of these stages for each tooth were given separately. After noting all stages of teeth from central incisor to the second molar by the two examiners, the developmental status of a particular tooth was calculated in years on the basis of tables given by Willems et al., [Table 4].
|Table 4: Developmental tooth stages with corresponding age scores expressed directly in years for each of the seven left mandibular teeth in boys and girls (Willems 2002)|
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All the values from central incisor to the second molar thus obtained were summed to obtain an overall maturity score, which will indicate the DA of that particular patient.
Skeletal age estimation
SA assessment was done using Mito et al. regression formula on a lateral cephalogram [Figure 1]. To avoid bias, tracings and calculation were done by a different evaluator who was blinded to name, age, and sex of child patient. Two evaluators (pedontists) were supplied with all 55 lateral cephalometric radiographs with good clarity and contrast and written instructions for measurement of parameters, including drawings and written descriptions.
|Figure 1: Various parameters measured on third and fourth cervical vertebrae on lateral cephalogram|
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A lateral cephalogram was taken for each patient. Third and fourth cervical vertebrae were traced on 0.003” thickness matte acetate cephalometric tracing paper with 0.03 mm diameter lead pencil. Various parameters measured were: Anterior vertebral body height (AH), vertebral body height (H), posterior vertebral body height (PH) and antero-posterior vertebral body length (AP). The ratio of these parameters was then calculated, that is, AH3/AP3, AH4/PH 4, AH3/PH 4 [Figure 1].
The regression formula for cervical vertebral maturity (SA) in years as given by Toshinori Mito et al.
= −0.20+ ([6.20 × AH3/AP3] + [5.90 × AH4/AP4] + [4.74 × AH4/PH 4])
Mean, standard deviation (SD), and standard error were calculated for all the groups. Data were analyzed using Statistical Package for the Social Sciences computer software (SPSS, version 20.0, SPSS Inc., Chicago, IL, USA).
CA, estimated DA, and estimated SA was described by frequency distribution, means, and SD in both the genders and compared using independent unpaired t-test. The difference between the two genders was analyzed with independent unpaired t-test.
The correlation between CA, dental and SA estimation methods was confirmed statistically using Pearson's correlation (r value) [Table 3]. In all these tests, r value closest to 1 was considered to indicate the strongest relation between the comparisons.
|Table 3: Correlation coefficient between chronological and estimated dental and skeletal ages|
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| Results|| |
[Table 1] demonstrates a total sample of 55 children were included in the study. The children were distributed among both genders including 30 male and 25 female patients for the age group of 6–14 years. Correlations of dental and SAs with CAs were done. Pearson's correlation coefficients were used to measure the association between the skeletal maturity and dental maturity.
The mean ages of male and female subjects were determined as 9.70 ± 0.93 years and 9.39 ± 1.473 years, respectively.
[Table 5] shows the correlations between skeletal, dental, and CAs were determined by correlation coefficient for males and females separately. CA was found to be strongly correlated to DA and found to be statistically significant (P < 0.01) and Willems method overestimated age by 0.32 ± 0.93 years among males and an overestimation of 0.49 ± 1.02 years among females.
|Table 5: Comparison of chronological age and estimated dental age by Willems method|
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[Table 6] shows that chronologic age was weakly correlated with SA for male and female subjects and found to be statistically significant (P < 0.05) and Mito T. method overestimated age by 0.38 ± 1.40 years among males and an overestimation of 1.28 ± 1.32 years among females.
|Table 6: Comparison of chronological age and estimated skeletal age by Mito method|
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[Table 3] shows that for males, the correlation coefficient chronological between and DAs was 0.778 with P< 0.01 and between chronological and SAs was 0.398 with P< 0.05.
Correlation between SA and DA was found to be statistically significant with P< 0.001 and correlation coefficient of 0.502.
[Table 3] shows that for females, the correlation coefficient between chronological and DAs was 0.700 with P< 0.01and between chronological and SAs was 0.497 with P< 0.05. Correlation between skeletal and DAs was found to be statistically significant with P< 0.05 and correlation coefficient of 0.454.
| Discussion|| |
Determination of a child's growth and development are of great value from both the medical and odontologic points of view. Although various methods for the age determination exists, a universal system has not yet been developed due to the varying differences in different ethnic population groups. Hence, each method requires to be tested in different populations.
Age estimation by means of tooth development is a well-established predictor of age during childhood. Tooth development is an accurate measure than chronologic age and is independent of extrinsic factors, for example, diseases or malnutrition, unlike SA, body height, and weight measurement. Demirjian's method of DA estimation is most widely researched and applied technique, because of its simplicity as well as radiographic and schematic illustrations of the tooth development and accompanying description but has shown consistent overestimation in all populations studied. Hence, Willem's DA estimation method was tested in this study which is based on the stages of the tooth development described by Demirjian et al.
Bone age is frequently used as a diagnostic tool for the evaluation of endocrine, orthopedic, and various genetic disorders. Clavicle, knee, hand-wrist, and the cervical bone are the most frequently Lamparski, Baccetti et al. and Hassel and Farman, studied bone age from cervical vertebra by observation of the development of concavity in the lower border of vertebral bodies and their transition in shape from trapezoidal to rectangular., Most of these methods were subjective which may lead to error while studying the various stages to assess skeletal maturity. Hence, Mito et al. proposed a regression formula to derive SA using the measurement of cervical vertebrae C3 and C4 in a more objective manner.
Cervical vertebral maturity (SA) in years:
= −0.20+ ([6.20 × AH3/AP3] + [5.90 × AH4/AP4] + [4.74 × AH4/PH 4])
This study was a cross-sectional study, having an advantage of accumulating more information about growth at many ages in a short period. Cross-sectional studies provide the best data for establishing the national standards for growth and comparing growth in different populations.
The study was aimed to estimate, compare, and correlate the skeletal and DAs with the CAs of the children with mixed dentition. The overall mean difference between the estimated DA and CA for males was 0.32 ± 0.93 years (P > 0.05) while for females, it was 0.49 ± 1.02 years (P > 0.05) in the entire sample suggesting that there was no statistically significant difference between the CA and DA by Willem's method for almost all age groups and that this method was applicable to the population under study and were in accordance with the various studies mentioned in literature as mentioned in [Table 7].
|Table 7: Demonstrates the comparative studies for dental age estimated by Willems method among the male and female subjects|
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A strong correlation was found for both male (0.778) and female (0.700) subjects for comparison between CA and DA which were statistically significant.
Mito T. method revealed an overestimation of age by 0.38 ± 1.40 years among males and an overestimation of 1.28 ± 1.32 years among females when compared with CA in the entire sample with P< 0.05 suggesting the statistically significant difference between CA and SA. Hence, need for certain refinement would be required in Mito T. method to assess SA in the given population.
The correlation found between chronological and SA in our study is weak but significant (0.308). Furthermore, the significance is more for females (0.497) than males (0.398). This is in accordance with the studies conducted by Muthy et al. and Kumar et al.
The reason for the difference among different populations is unclear and could be due to population difference. The differences can also be explained by difference in sample size, method of age calculation, age groups, the age and sex distribution of the original study population, and statistical methodologies. Racial variations in the relationship have been suggested. Mappes et al. indicated that the predominant ethnic origin of the population, climate, nutrition, socioeconomic levels, and urbanization are causative factors of these racial variations.
| Conclusion|| |
It is essential for a pedodontist to study the growth of the child to compare with the normalcy and treatment planning. This study showed a strong correlation between the developmental ages in mixed dentition population; hence, DA can be considered a replacement in the study population. However, certain refinement would be required in Mito T. method to assess SA in the given population. Studies with a larger sample size should be carried out for more reliable results.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]