Validity and accuracy of three radiographic dental age estimation methods in Brazilians

Accepted Manuscript Title: Validity and accuracy of three radiographic dental age estimation methods in Brazilians Authors: Eduardo Novaes Benedicto, Alana C´assia Silva Azevedo, Edgard Michel-Crosato, Maria Gabriela Haye Biazevic PII: DOI: Reference:

S0379-0738(17)30521-2 https://doi.org/10.1016/j.forsciint.2017.12.014 FSI 9096

To appear in:

FSI

Received date: Revised date: Accepted date:

30-5-2017 23-10-2017 6-12-2017

Please cite this article as: Eduardo Novaes Benedicto, Alana C´assia Silva Azevedo, Edgard Michel-Crosato, Maria Gabriela Haye Biazevic, Validity and accuracy of three radiographic dental age estimation methods in Brazilians, Forensic Science International https://doi.org/10.1016/j.forsciint.2017.12.014 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Title: Validity and accuracy of three radiographic dental age estimation methods in Brazilians Type of manuscript: Forensic Anthropology Population Data.

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Authors: Eduardo Novaes Benedicto (Benedicto EN)a Alana Cássia Silva Azevedo (Azevedo ACS)a Edgard Michel-Crosato (Michel-Crosato E)a Maria Gabriela Haye Biazevic (Biazevic MGH) a,* a

author ([email protected])

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*Corresponding

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Community Dentistry Department, School of Dentistry, Universidade de São Paulo – FO-USP, São Paulo, SP, Brazil. Community Dentistry Department. School of Dentistry, Universidade de São Paulo (FOUSP). Address: Avenida Professor Lineu Prestes, 2227 - 05508-000 - São Paulo – SP. Phone: +55-11-30917891 / fax: +55-11-30917874.

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Highlights  Application of three methods of age estimation by mineralization stages in Brazilians  Liliequist and Lundberg method is more suitable to the Brazilian population  Experts can use the equations for faster age estimation in Brazilians

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ABSTRACT

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Objective: To validate, analyse accuracy, and construct multiple regression formulae of three age estimation methods— Liliequist and Lundberg, Haavikko, and Mornstad—using mineralization stages of permanent teeth in Brazilians. Methods: Panoramic radiographs of 1009 Brazilian children and adolescents (387 males and 622 females) aged 8-15.99 years were analysed using the aforementioned methodologies. Results: The overall accuracy (Absolute Difference = AD and Dental AgeChronological Age = DA-CA) of the methods was as follows: Liliequist and Lundberg, AD = 0.97 and DA-CA = -0.58; Haavikko, AD = 1.42 and DA-CA = -1.35; and Mornstad, AD = 2.48 and DA-CA = 0.78. After sex-based stratification, the values for males were as follows: Liliequist and Lundberg, AD = 0.91 and DA-CA = -0.45; Haavikko, AD = 1.80 and DA-CA = -1.75; and Mornstad, AD = 2.74 and DA-CA = 1.17. In females, the values were as follows: Liliequist and Lundberg, DA = 1.01 and DA-CA = -0.67; Haavikko, AD = 1.17 and DA-CA = -1.09; and Mornstad, AD = 2.31 and DA-CA = 0.53. The Liliequist and Lundberg technique predominated, followed by Haavikko and Mornstad when distinction was present between sex and age. Multiple

2 regression formulae were constructed from the data presented for the Liliequist and Lundberg, Haavikko and Mornstad methodologies. Conclusions: The validation of the methodologies in the Brazilian population is possible. The Liliequist and Lundberg method most closely represented the Brazilian sample.

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Keywords: Forensic Anthropology Population Data; Forensic dentistry; Radiography, Panoramic; Age estimation; Tooth Calcification. Introduction

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Human identification is a science of great importance to settle social problems of civil and criminal orders. Age estimation is among the most common techniques to initiate identification, and can be performed using physiologic indices such as dental and somatic features and sexual and skeletal maturity [1]. The stages of mineralization of the permanent teeth are nothing more than periods of maturation of the dental tissues. The degree of maturation of tissues, according to Demirjian et al. [2] can be considered the basis for the concept of physiological age. From this foundation, many biological aspects were used to define age of the subjects. The degree of maturation is determined by applying these aspects together or separately. Thus, it is possible to estimate the age of a deceased subject from certain parts of the body. Among the main methods of estimating age that make use of the analysis of stage of mineralization of permanent teeth, we can mention the methods developed by Nolla [3], Demirjian et al. [2], Willems et al. [4] and Cameriere et al. [5]. Willems et al. [4] carried out a research in order to verify the possible overestimation of age in the method of Demirjian et al. [2] in Belgian children. After some necessary adjustments, the results led the authors to affirm that it is not always possible to use data from age estimation by the stage of mineralization of permanent teeth from one population to another. For this reason, there is a tendency to adapt the data for each population in order to improve their age estimation [6-19]. Many authors [12,20-22] have investigated the possibility of validation of the Haavikko [23] (HKK) method in their populations. However, this validation has not yet been tried for Brazilians, which would allow a new alternative for improving age estimation in this population. This improvement would occur either by validation, accuracy assessment, or by developing a self-regression model following the parameters of the author. Besides Haavikko [23], other authors have also developed estimation methods for staging the mineralization of permanent teeth, which have not yet been validated for the Brazilian population. These include Liliequist and Lundberg [24], with the table adapted from Hägg and Matsson [25] (LLH), and Mornstad et al. [26] (MSD). Brazil has a population of 204.450.649, and according to the Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatística - IBGE), in 2011, this population was composed of 47.76% brancos (Caucasians), 43.07%

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pardos (Browns), 8.21% pretos (Africans), 0.4% indigenous, and 0.56% amarelos (Asians) [27]. The pardos population is defined by the same institute [27] as a population that involves mixtures such as the Mulata (Caucasians + Africans), Cabocla (Indigenous + Caucasians), Cafuza (Africans + Indigenous), Mameluca (Caucasians + Indigenous), or Mestiça (Africans with any other ethnicity). The presence of this large percentage of pardos justifies the importance of validating foreign techniques in Brazilians. This study aims to validate, analyse the accuracy of, and construct regression formulas for age estimation by means of mineralization stages of permanent teeth using the methods made by Liliequist and Lundberg [24], Haavikko [23], and Mornstad et al. [26] in Brazilians.

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Materials and methods

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The research was approved by the University of São Paulo (FOUSP) Ethics and Research Committee (process number 697.620) and follows the ethical standards on human experimentation defined in the Helsinki Declaration. The study included 1.009 panoramic radiographs of Brazilians (Table 1), with ages ranging from 8 to 15.99 years, in 387 males and 622 females. Table 1 shows slightly different total numbers in each method because one panoramic radiograph may be suitable for one, but not for other. The radiographs were acquired at a private radiology clinic located in the city of Florianópolis-SC, Brazil. Radiographs of individuals with the following information were selected: Sex, date of birth, and date of the panoramic radiograph. Panoramic radiographs with low quality, distorted and/or elongated images were excluded from the study. Moreover, images of patients with systemic diseases, growth deficiencies, syndromes, and absence of the teeth needed to perform the techniques were excluded as well. After selecting the radiographs, the methods of age estimation were applied. The method of Liliequist and Lundberg [24] consists of the separation of the panoramic radiographs by sex (male and female), and then the assignment of individual scores (0; 0.5; 1; 2; 3; 4; 5; 6) defined by the authors, according to eight stages of development in seven lower left permanent teeth (41, 42, 43, 44, 45, 46, and 47). The numbers obtained for each tooth are summed and the result inserted in a pre-established table published later [28], thus obtaining an estimated age range for the individual. Originally, the method was developed by means of full-mouth radiographs composed of two oblique lateral extra-oral radiographs and four intraoral radiographs (three of the upper and one of the lower arch). However, several authors [25,29-32] studied this method by means of panoramic radiographs with success. Obtaining or estimating age by an age range is not very common among estimation methods because it does not present a specific age. The most common goal in the studies [2,3,23,25,29,33] is the "determination of an individual age", also known as the Dental Age (DA) [9,11,18,19,34-44]. For this reason, both the studies by Crossner and Mansfeld [29] and the studies by Hägg and Matsson [25] sought to adapt the method of Liliequist and Lundberg [24] for the determination of a dental

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age. The present study used the table developed by Hägg and Matsson [25] as reference in obtaining dental age after the sum of all the teeth was used. In the method of Haavikko [23], the participants were divided into two groups based on sex (male and female), which were again divided into two subgroups according to age. The first subgroup of each sex comprised individuals with chronological age (CA) up to 10 years and only teeth 47, 46, (16), 44, and 41 were counted; the tooth 16 was included in the evaluation only if the apex was not closed. Although the author does not directly state this condition of using the tooth 16 throughout her article, she reports that the "closed apex" phase is not reliable because although the chronological age increases, this stage will always estimate the same age [23]. The second group consisted of individuals aged over 10 years and only teeth 47, 44, 13, and 43 were analysed. The aforementioned teeth were analysed according to the mineralization stage in which they were found. After identifying this phase, it was located in the table previously published by the author [45], and the estimated mean age for the analysed tooth was presented. At the end, all the individual means of the teeth were submitted to a new "final mean age", that is, the values obtained from each evaluated tooth were summed and divided by the number of permanent teeth involved, resulting in a mean of final age (final dental age). The age estimation method proposed by Mornstad et al. [26] is a methodology based on regression models constructed from measurements of the eight lower right permanent teeth of children aged 6 to 14 years. To calculate the measurements, the authors calculated seven measurements for permanent molar teeth and three measurements for the premolars and permanent single-rooted teeth. At the end, two "general" regression equations (one for each sex) covering all the ages involved and the other six "specific" (three for each sex) equations were obtained, according to the value obtained in the first equation (groups: ages 6-10, 8-12, and 10-14 years). To validate this study, only the measures present in the equations developed by the authors were measured on the radiographs using the ruler tool on Adobe ® Photoshop® CS6 (Adobe Systems, San Jose, California, USA). After measuring, the values were inserted into the general equation and later in the proper specific equation to obtain the dental age. Equations created for the methodologies were constructed using the Enter and the Stepwise methods. The former consists of application of the variables in the order of entry, without excluding any variable from the equation. The latter consists of application of the significant independent variables in sequence and after their insertion, the program checks and removes the variables with no statistical significance in the model. Before the initiation of this study, two examiners (ENB and ACSA) underwent training and calibration of the techniques involved. After the calibration phase, the first step of the study was started by verifying the reliability of the methods. To verify the intra- and inter-examiner agreement, 20% of the proposed sample was analysed in a 2-week interval by them using Intraclass Correlation Coefficient (ICC). To verify the validity of the study, the dental (DA) and chronological age (CA) analyses were checked for normality by the Skewness and Kurtosis test. After the normality test, the results of the comparison between the DA and CA analyses were

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checked by the Paired t test when normal values were found, and the Wilcoxon test when abnormal values were found. Following some authors [12,38], if the sample presented less than 30 subjects and abnormal distribution, both the tests were performed. The methodologies were also evaluated by the difference of means (Dental Age - Chronological Age = DA - CA). Absolute accuracy was obtained by absolute differences (AD) of DA-CA for several groups, which were evaluated depending on the observed methodology, sex, and age range of the cohort. The last step was the construction of specific formulae for this population by creating multiple regression equations. Data from the present study were analysed using Microsoft Excel ® (Microsoft, Redmond, WA, USA), STATA® 13.0. (StataCorp, College Station, Texas, USA), and Medcalc® (Medcalc® Software, Mariakerke, Belgium). The level of significance for all tests used in the present study was 95%. Results

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The inter-examiner evaluation for the HKK, LLH, and MSD methods resulted respectively in: Intraclass Correlation Coefficient (ICC) of 0.9559, 0.9174, and 0.9105. In the intra-examiner evaluation, the results were respectively: ICC of 0.9277, 0.9263, and 0.9772 indicating excellent reliability [46] in both exams. Table 2 shows the results for the various methods without distinction of sex or age. In this classification, the method that best approached the chronological age was LLH, which underestimated the age in -0.58 years following MSD (0.78 years overestimated) and HKK (-1.35 years - underestimated the age). In terms of accuracy (AD), LLH was again the best among the methods (0.97 years), followed by HKK (1.42 years) and MSD (2.48 years). The estimated dental age was statistically significant for both sexes in relation to chronological age. There was an underestimation of the age for males in the methods HKK (-1.75 years) and LLH (-0.45 years). For females, the same methods presented, respectively, -1.09 and -0.67 years. The ages were overestimated in the MSD method for males by 1.17 years, and females by 0.53 years. The accuracy of the methods according to the sex presented, for the male sex was 1.80 years for HKK, 0.91 years for LLH, and 2.74 years for MSD. For females, the values were 1.17, 1.01, and 2.31, respectively. The comparison between the methodologies according to the DA and CA in relation to sex can be observed in Table 2 and Figure 1. The same comparison, in relation to the male age groups can be observed in Table 3 and Figure 2. The female data are presented in Table 4 and Figure 3. The accuracy between the methods according to sex and the age group can be observed in Table 5 and Table 6. As mentioned in Materials and Methods, multiple regression equations for the Brazilian population were developed for the ages studied in the LLH, HKK and MSD methods (Supplementary 1 and Supplementary 2).

Discussion

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Age estimation and the various ways of measuring its accuracy are important resources for forensic sciences. When studying the methodologies of age estimation by the stage of tooth mineralization, one fact becomes constant i.e., it is possible to classify the methodologies as subjective or objective [47]. The subjective form can be defined as those present in the precursory studies of several techniques that began in the 1960s and 1970s, such as Nolla [3], Demirjian et al. [2], Gustafson and Koch [48], and Haavikko [23]. These techniques have in common the characteristic of presenting different stages of each phase of mineralization of the teeth from a visual inspection (depends on the subjectivity of the evaluator), to classify the tooth as to the stage in which the tooth is. This posture remains the same for all "subjective" techniques that end up differing only in the quantities of stages, tables, and the way the data are operated. The objective form is a modality that came with the apparent purpose of eliminating the "subjective" factor, i.e., one evaluator might differ from another, with the use of radiographic measurements to extract the data. Of this modality, we can highlight the methods of Mornstad et al. [26] and the exhaustively used method of Cameriere et al. [5]. The three techniques studied were considered by the evaluators (ENB and ACSA) because of easy execution, which was proven by the good values presented by inter and intra-examiner analysis. Both of them (Haavikko [23] and Liliequist and Lundberg [24]) were originally created to determine teeth development stages and only later these techniques were used for forensic dental age estimation. Most age estimation studies attempt to reach a certain individual value (dental age) and even, in some cases, a confidence interval in which this age may vary, as can be seen from Haavikko's study [23]. However, for the Liliequist and Lundberg [24], this application can only be performed with the additional table created by Hägg and Matsson [25], which adds details to obtain the dental age. The absence of equal age groups did not interfere with our study, since most of the group of ages was almost equal until the age of 13years. Furthermore, no comparison was made at ages without equal number of subjects. Our objective was simply to analyze accuracy, validate and construct multiple regression formulas (for each method). The most complex methodology studied was the Liliequist and Lundberg [24]. This method was presented in 1971 to estimate age at a time interval. However, only in 1975, Lysell and Myberg [28] published in their book, with the permission of the authors, the missing table to complete the original technique. Currently, it is only possible to apply this method by referring to later literature [25,28,29]. The Haavikko method is a well-studied methodology [12,2022,30,32,34,37,40,42,49,50] and validated in different populations. It can be found in two separate works, one that presents the technique [23] and the other [45] that contains the necessary tables to apply this technique. In relation to the method of Mornstad et al. [26], the authors developed a technique briefly studied in the literature, in our study; only one article was found applying this technique [51].

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We looked at some possible hierarchical divisions of how accuracy data could be worked out. In some of them, the methodology was applied without any distinction as to sex [20,22,34,38,42]. In other studies, both sexes (male and female) were analysed separately [12,20-22,25,30,32,34,38,42,49-51]. Finally, it is very common to find studies [12,20,21,38,51] that besides analysing the sexes separately, created subgroups within each sex according to a predetermined age range (cohort). Our study divided the data following the main divisions found in the literature, since it was applied: a) without any distinction as to sex [20,22,34,38,42]; b) dividing the subjects into two groups in relation to sex [12,20-22,25,30,32,34,38,42,49-51]; and c) dividing the subjects into two groups according to sex, and subgroups as to age [12,20,21,38,51]. Another more unusual classification found in the studies [12,22], analyses the accuracy of individual tooth types. However, this modality generates a greater range of new branches, since the teeth can be evaluated separately according to the methodology and the sex. Our study defined only working the validations and accuracy in a general context, concerning to work with the teeth together and exclude the less representative teeth in the study. Regarding the lack of sex distinction, only a few studies [20,22,34,38,42] have investigated the methodology of Haavikko [23] in this aspect. Our study is in agreement with all literature consulted [20,22,34,38,42], in which the Haavikko technique underestimates the age. The study that most closely approached ours was conducted by Kumaresan et al. [34], with a value of 0.4 year between differences of means between the two studies and difference of SD of 0.05 year. In the study Kırzıoğlu and Ceyhan [12], for differences of means and SD, a certain caution is required when comparing these data. The article only mention the study that presents the stages of mineralization of the teeth of Haavikko of the year of 1970 [45] and do not present any citation on the later study, in which its methodology for estimating age was effectively disclosed [23]. In this aspect, the authors found only one study [52], which determine if all the lower teeth of the left side (except the third molar) can be used instead of the other teeth defined by the methodology of Haavikko. At the same time, our study performed a quick simulation comparing the original Haavikko method with an adaptation that would only use the lower teeth used in this same methodology (47, 46, 44, 43 and 41). In this simulation, we found that the accuracy of this adaptation would determine an absolute difference of 0.82 year; thus, a superior result compared to 1.42 of the original technique. However, the difference between the means determined an underestimation of -2.14 years, which resulted in a lower underestimation but with a higher accuracy. The Haavikko methodology in our study underestimated the age in both males (-1.75) and females (-1.09), like most of the studies consulted [12,2022,34,38,42,50]. Only the study of Staaf et al. [32]. and the study of Rai and Anand [49] overestimated the age in males, whereas in females, in addition to the previous studies, Mornstad et al. [30] overestimated as well. Regarding the accuracy, the study of Kumaresan et al. [34] (♂ 1.13 and ♀ 1.69) presented the closest absolute difference for both sexes in comparison to the present study (♂ 1.80 and ♀ 1.17). Our study was able to be confronted with two others [25,32] found in the literature, in relation to the Liliequist and Lundberg method (adapted by Hägg and

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Matsson). For males, our study underestimated the age (DA – CA = -0.45 and SD = 1.05), following the results found by Hägg and Matsson [25] (DA – CA = -0.14 and SD = 0.8 year, presented in the study in months as DA - CA = -1.7 and SD = 9.6 months). In addition, our study contradicted the result of Staaf et al. [32], which overestimated the age (DA - CA = 0.58 year and SD = 0.97, although in the original article the values presented different signs, since the authors subtracted the CA by the DA). However, in the females, all the studies underestimated the age, and the study closest to ours was Hägg and Matsson [25], which resulted in a mean difference (MD) of -0.46 (SD = 0.75, presented in months as mean difference of -5.6 years and 9.1 of SD), whereas in our study the value was -0.67 (DA-CA) and SD = 1.08. This methodology was the first, so far, following this hierarchical order of evaluation, in which an author [32] was able to affirm that the result of the age estimation of one of the sexes (in the case, the female sex) did not have statistical significance when compared with the chronological age. In relation to Mornstad et al. [26], the study of Liversidge et al. [51] was the only study found that attempted to validate this methodology. In comparison, our study overestimated the age for both sexes while Liversidge et al. [51] underestimated the same. For females, the value of 0.53 year (SD = 2.89) and accuracy of 2.31 years was obtained, while Liversidge et al. [51] obtained the mean difference of -0.67 (SD = 0.76). In males, mean differences were respectively 1.17 (SD = 3.32) and -0.83 (SD = 0.96). When analysing the Haavikko method at the age of 8 years in the males, all studies, as well as ours (-0.45 years, SD = 0.44 and AD = 0.53) underestimated the ages of the study participants. The study with the best difference in means was conducted by Butti et al. [20] with -0.18 years, and the study with the closest results to ours was conducted by Galić et al. [21], with -0.32 years and SD=0.65, however, although this study had a better result when the mean difference is analysed, the absolute difference determined a lower accuracy (0.7 year). In females, all studies underestimated the age (except LLH), and the best result in terms of mean difference was found in the Galić et al. [21], with -0.23 year and AD = 0.59 and the study closest to Brazilians (-0.31, SD = 0.61 and AD = 0.55) is Butti et al. [20] with -0.34 and SD = 0.57. In the 9-year age range, all studies underestimated the ages in both the sexes for HKK method. In males, the best result was given by the Italian population [20], with mean difference of -0.39 year (SD = 0.65) and the closest result of the Brazilian population (-1.30 and SD = 0.46) was that of the Turkish population [12] although the difference between these two is 0.65 year. For the female population, the Turkish population obtained the best mean difference among all the studies (-0.23 and SD = 1.10). The population that comprised the region of Bosnia and Herzegovina [21] showed greater proximity to the Brazilian population (-0.55 and SD = 0.53). At 10 years of age, the age of the Brazilian male population was underestimated by -1.61 years (SD = 0.66 and AD = 1.61), agreeing to all other studies, and one of them [21] with a higher accuracy (AD = 0.73). For the female sex, only the latter overestimated the age (0.10 year), while the study of Staaf et al. [32] underestimated the age of the Brazilian population and the other [12,20,21,32,38] with a difference of 0.03 year (MD) and 0.08 year of AD between the two populations.

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At age 11 years and above, age was underestimated for Brazilians of both the sexes with averages close to and greater than 1.0 year. The study of Hägg and Matsson [25], perhaps because it was one of the pioneer articles that evaluated the difference of means, did not present its results in "years" and also did not follow the current tendency to evaluate the cohort with a single age range, but with a combination between 9.5 -12.5 years. To compare this study, the difference of means for this age range was also calculated in parallel and gave the following results: In the male sex, our study underestimated at -0.12 a year while Hägg and Matsson [25] overestimated in 0.23 year. In females, Hägg and Matsson [25] and our study underestimated the age in, respectively -0.63 and -0.35 year. When researching a methodology, researchers should carefully consider how to read the original work on age estimation methodologies. Another fundamental point would be the need for greater attention by the authors when describing and citing correctly the original studies in their "materials and methods". This greater attention would prevent the studies being carried out differently than the authors. Structuring an analytical study of accuracy of age estimation methods is not a simple process and involves several ramifications that must be defined by the study according to the objectives initially established. It is recommended that a study should present the largest possible number of calculations and statistical information, as well as Hägg and Matsson [25] already stated that the knowledge of the accuracy and precision of age estimation methods is necessary in the routine application of the clinic. Among them, we can mention absolute difference, difference of means, paired tests, value of ρ, and others. This information is essential to compare studies with the same methodologies in different populations with better consistency. This study could not be applied in individuals with absence of one of the teeth necessary for the application of the methodologies. As a justification, we believed that the final dental age would be compromised in the HKK method for the calculation of the final mean, for LLH during the total sum and location in the table, and for MSD during the application of the equations. Another limitation of the study is the application of the MSD methodology. The measurement of the opening of the apices in the MSD methodology is difficult near to the closing. Although the software currently collaborates in allowing a high zoom view of the images, new technologies could be developed to improve the technique. Agreeing with Willems et al. [4], in relation to the need to create specific data for each population, this study reinforces the idea that new surveys of age estimation are fundamental to evaluate a certain population. In Brazil, and in other colonized countries, the study evaluates the changes resulting from this historical miscegenation. In other countries with low miscegenation, future studies may better reflect the reality, especially reflecting the migratory flow of refugees in these countries.

CONCLUSIONS

10 The Liliequist & Lundberg method with the adaptation proposed by Hagg and Matsson was the most appropriate for the Brazilian population in practically all forms of comparison, followed by Haavikko and Mornstad et al. It was also possible to develop regression equations by the Stepwise method and by the Enter mode for all methods. Source of funding: The authors recognize the scholarship support provided by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Demanda Social (CAPES DS) and the program CAPES Ciências Forenses (25/2014)

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Conflicts of interest: none.

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Acknowledgements The authors would like to express their gratitude to Andrea Carro and Åsa Zetterling for providing information aiding this present study. This investigation was funded by Coordination of Improvement of Higher Education Personnel (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES DS 2014-2017; and CAPES Ciências Forenses 25/2014).

11 References

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[1] Demirjian A, Buschang PH, Tanguay R, Patterson DK. Interrelationships among measures of somatic, skeletal, dental, and sexual maturity. Am J Orthod. 1985;88:433-8. [2] Demirjian A, Goldstein H, Tanner JM. A new system of dental age assessment. Hum Biol. 1973;45:211-27. [3] Nolla CM. The development of the permanent teeth. J Dent Child. 1960;27:25466. [4] Willems G, Van Olmen A, Spiessens B, Carels C. Dental age estimation in Belgian children: Demirjian's technique revisited. J Forensic Sci. 2001;46:893-5. [5] Cameriere R, Ferrante L, Cingolani M. Age estimation in children by measurement of open apices in teeth. Int J Legal Med. 2006;120:49-52. [6] Bagherpour A, Imanimoghaddam M, Bagherpour MR, Einolghozati M. Dental age assessment among Iranian children aged 6–13 years using the Demirjian method. Forensic Sci Int. 2010;197:121.e1-.e4. [7] Birchler FA, Kiliaridis S, Combescure C, Vazquez L. Dental age assessment on panoramic radiographs in a Swiss population: a validation study of two prediction models. Dentomaxillofac Radiol. 2016;45:20150137. [8] Chen JW, Guo J, Zhou J, Liu RK, Chen TT, Zou SJ. Assessment of dental maturity of western Chinese children using Demirjian's method. Forensic Sci Int. 2010;197:119.e1-.e4. [9] Djukic K, Zelic K, Milenkovic P, Nedeljkovic N, Djuric M. Dental age assessment validity of radiographic methods on Serbian children population. Forensic Sci Int. 2013;231:398.e1-.e5. [10] Feijóo G, Barbería E, De Nova J, Prieto JL. Permanent teeth development in a Spanish sample. Application to dental age estimation. Forensic Sci Int. 2012;214:213.e1-.e6. [11] Franco A, Thevissen P, Fieuws S, Souza PHC, Willems G. Applicability of Willems model for dental age estimations in Brazilian children. Forensic Sci Int. 2013;231:401.e1-.e4. [12] Kırzıoğlu Z, Ceyhan D. Accuracy of different dental age estimation methods on Turkish children. Forensic Sci Int. 2012;216:61-7. [13] Koshy S, Tandon S. Dental age assessment: The applicability of Demirjian's method in South Indian children. Forensic Sci Int. 1998;94:73-85. [14] Lee S-S, Kim D, Lee S, Lee UY, Seo JS, Ahn YW, et al. Validity of Demirjian's and modified Demirjian's methods in age estimation for Korean juveniles and adolescents. Forensic Sci Int. 2011;211:41-6. [15] Maia MCG, Martins MdGA, Germano FA, Neto JB, Silva CABd. Demirjian's system for estimating the dental age of northeastern Brazilian children. Forensic Sci Int. 2010;200:177.e1-.e4. [16] Nik-Hussein NN, Kee KM, Gan P. Validity of Demirjian and Willems methods for dental age estimation for Malaysian children aged 5–15 years old. Forensic Sci Int. 2011;204:208.e1-.e6. [17] Tunc ES, Koyuturk AE. Dental age assessment using Demirjian's method on northern Turkish children. Forensic Sci Int. 2008;175:23-6. [18] Yusof MYPM, Thevissen PW, Fieuws S, Willems G. Dental age estimation in Malay children based on all permanent teeth types. Int J Legal Med. 2013:1-5. [19] Zhai Y, Park H, Han J, Wang H, Ji F, Tao J. Dental age assessment in a northern Chinese population. J Forensic Leg Med. 2016;38:43-9.

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[20] Butti AC, Clivio A, Ferraroni M, Spada E, Testa A, Salvato A. Häävikko's method to assess dental age in Italian children. Eur J Orthod. 2009;31:150-5. [21] Galić I, Vodanović M, Cameriere R, Nakaš E, Galić E, Selimović E, et al. Accuracy of Cameriere, Haavikko, and Willems radiographic methods on age estimation on Bosnian–Herzegovian children age groups 6–13. Int J Legal Med. 2011;125:315-21. [22] Maber M, Liversidge HM, Hector MP. Accuracy of age estimation of radiographic methods using developing teeth. Forensic Sci Int. 2006;159, Supplement:S68-S73. [23] Haavikko K. Tooth formation age estimated on a few selected teeth. A simple method for clinical use. Proc Finn Dent Soc. 1974;70:15-9. [24] Liliequist B, Lundberg M. Skeletal and tooth development. A methodologic investigation. Acta Radiol Diagn (Stockh). 1971;11:97-112. [25] Hägg U, Matsson L. Dental maturity as an indicator of chronological age: the accuracy and precision of three methods. Eur J Orthod. 1985;7:25-34. [26] Mornstad H, Staaf V, Welander U. Age estimation with the aid of tooth development: a new method based on objective measurements. Scand J Dent Res. 1994;102:137-43. [27] IBGE. Instituto Brasileiro de Geografia e Estatística. In: Ministério do Planejamento DeG, editor. Séries históricas e estatísticas. Brasília2017. [28] Lysell L, Myberg N. Postnatal bettutveckling. In: Lundström A, editor. Nordisk lärobok i ortodonti. 4 ed. Stockholm: Sveriges Tandläkarforbunds Förlagsförening; 1975. p. 111-35. [29] Crossner CG, Mansfeld L. Determination of dental age in adopted non-European children. Swed Dent J. 1983;7:1-10. [30] Mornstad H, Reventlid M, Teivens A. The validity of four methods for age determination by teeth in Swedish children: a multicentre study. Swed Dent J. 1995;19:121-30. [31] Reventlid M, Mornstad H, Teivens AA. Intra- and inter-examiner variations in four dental methods for age estimation of children. Swed Dent J. 1996;20:133-9. [32] Staaf V, Mornstad H, Welander U. Age estimation based on tooth development: a test of reliability and validity. Scand J Dent Res. 1991;99:281-6. [33] Willems G. A review of the most commonly used dental age estimation techniques. J Forensic Odontostomatol. 2001;19:9-17. [34] Kumaresan R, Cugati N, Chandrasekaran B, Karthikeyan P. Reliability and validity of five radiographic dental-age estimation methods in a population of Malaysian children. J Investig Clin Dent. 2016;7:102-9. [35] Jayaraman J, Wong HM, King NM, Roberts GJ. Development of a Reference Data Set (RDS) for dental age estimation (DAE) and testing of this with a separate Validation Set (VS) in a southern Chinese population. J Forensic Leg Med. 2016;43:26-33. [36] Tandon A, Agarwal V, Arora V. Reliability of India-specific regression formula for age estimation of population in and around Bahadurgarh, Haryana (India). J Oral Biol Craniof Res. 2015;5:193-7. [37] Pinchi V, Pradella F, Vitale G, Rugo D, Nieri M, Norelli G-A. Comparison of the diagnostic accuracy, sensitivity and specificity of four odontological methods for age evaluation in Italian children at the age threshold of 14 years using ROC curves. Med Sci Law. 2015. [38] Mohammed RB, Sanghvi P, Perumalla KK, Srinivasaraju D, Srinivas J, Kalyan US, et al. Accuracy of Four Dental Age Estimation Methods in Southern Indian Children. J Clin Diagn Res. 2015;9:HC01-HC8.

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[39] Kiran CHS, Reddy RS, Ramesh T, Madhavi NS, Ramya K. Radiographic evaluation of dental age using Demirjian's eight-teeth method and its comparison with Indian formulas in South Indian population. J Forensic Dent Sci. 2015;7:44-8. [40] Woods E, Parekh S, Evans R, Moles DR, Gill D. The dental development in patients with Aperts syndrome. Int J Paediatr Dent. 2014;25:136-43. [41] Pechníková M, De Angelis D, Gibelli D, Vecchio V, Cameriere R, Zeqiri B, et al. Twins and the paradox of dental-age estimations: A caution for researchers and clinicians. HOMO. 2014;65:330-7. [42] Patnana AK, Vabbalareddy RS, V Vanga NR. Evaluating the Reliability of Three Different Dental Age Estimation Methods in Visakhapatnam Children. Int J Clin Pediatr Dent. 2014;7:186-91. [43] AlQahtani SJ, Hector MP, Liversidge HM. Accuracy of dental age estimation charts: Schour and Massler, Ubelaker and the London Atlas. Am J Phys Anthropol. 2014;154:70-8. [44] Galić I, Vodanović M, Janković S, Mihanović F, Nakaš E, Prohić S, et al. Dental age estimation on Bosnian–Herzegovinian children aged 6–14 years: Evaluation of Chaillet's international maturity standards. J Forensic Leg Med. 2013;20:40-5. [45] Haavikko K. The formation and the alveolar and clinical eruption of the permanent teeth. An orthopantomographic study. Suom Hammaslaak Toim. 1970;66:103-70. [46] Fleiss JL. Reliability of Measurement. The Design and Analysis of Clinical Experiments: John Wiley & Sons, Inc.; 1999. p. 1-32. [47] Kullman L, Tronje G, Teivens A, Lundholm A. Methods of reducing observer variation in age estimation from panoramic radiographs. Dentomaxillofac Radiol. 1996;25:173-8. [48] Gustafson G, Koch G. Age estimation up to 16 years of age based on dental development. Odontol Revy. 1974;25:297-306. [49] Rai B, Anand S. Tooth developments: an accuracy of age estimation of radiographic methods. World J Med Sci. 2006;1:130-2. [50] Pinchi V, Norelli GA, Pradella F, Vitale G, Rugo D, Nieri M. Comparison of the applicability of four odontological methods for age estimation of the 14 years legal threshold in a sample of Italian adolescents. J Forensic Odontostomatol. 2012;30:1725. [51] Liversidge HM, Lyons F, Hector MP. The accuracy of three methods of age estimation using radiographic measurements of developing teeth. Forensic Sci Int. 2003;131:22-9. [52] Hedge S, Shah K, Dixit U. A comparative evaluation of the applicability of two adapted Haavikko methods for age estimation of 5-15 year old Indian children. J Forensic Odontostomatol. 2016;2:21-34.

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Figure 1 - Box-plot of the difference between the Dental Age (DA) and Chronological Age (CA) for males and females according to the methodologies of Haavikko (HKK), Liliequist and Lundberg (LLH) and Mornstad et al. (MSD). The Boxplot shows the median and interquartile interval, and the whiskers represent the reach. A) HKK Male; B) LLH Male; C) MSD Male; D) HKK Female; E) LLH Female; and F) MSD Female.

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Figure 2 - Box-plot of the difference between the Dental Age (DA) and Chronological Age (CA) for males in groups of 8-15 years according to the methodologies of Haavikko (HKK), Liliequist and Lundberg (LLH), and Mornstad et al. (MSD). The Boxplot shows the median and interquartile interval, and the whiskers represent the reach.

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Figure 3 - Box-plot of the difference between the Dental Age (DA) and Chronological Age (CA) for females in groups of 8-15 years according to the methodologies of Haavikko (HKK), Liliequist and Lundberg (LLH), and Mornstad et al. (MSD). The Boxplot shows the median and interquartile interval, and the whiskers represent the reach.

17 Table 1 - Distribution of children in methodologies according to age and sex.

M 28 32 40 51 70 90 0 0 311

HKK F 34 33 62 94 119 121 0 0 463

Total 62 65 102 145 189 211 0 0 774

M 28 32 40 51 70 87 48 21 377

LLH F 34 33 62 94 118 115 86 58 600

Total 62 65 102 145 188 202 134 79 977

M 28 32 40 51 70 90 55 0 366

MSD F 34 33 62 93 119 121 101 0 563

Total 62 65 102 144 189 211 156 0 929

IP T

Age Groups 8.00-8.99 9.00-9.99 10.00-10.99 11.00-11.99 12.00-12.99 13.00-13.99 14.00-14.99 15.00-15.99 Totala

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U

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M, Male; F, Female; HKK, Method of Haavikko; LLH, Method of Liliequist and Lundberg; MSD, Method of Mornstad et al.; a Of the total sample 1009, the total referred to this line corresponds to the X-rays that could be used by each technique, respecting the exclusion/inclusion criteria.

18 Table 2 - Mean differences in years (DA - CA) between the Dental Age (DA) and Chronological Age (CA) for each technique, and Absolute difference (AD) for the sexes analysed together and separately. DACA

CA

DA

Mea SD n

Mea SD n

M+ F M+ F M+ F

77 4 97 7 92 9

11.7 1.5 2 8 12.3 1.9 7 1 12.1 1.7 7 6

10.3 1.5 6 3 11.7 1.7 9 2 12.9 3.6 6 9

HKK

M

31 1

11.6 1.6 8 5

9.93

LLH

M

MSD

M

37 7 36 6

12.2 1.9 3 2 12.1 1.8 0 2

11.7 1.6 8 9 13.2 4.0 7 4

HKK

F

46 3

11.7 1.5 4 3

LLH

F

60 0

MSD

F

56 3

t Statistics/

-1.35

1.05 -1.43 - -1.28

-0.58

1.07

0.78

3.08

-1.75

1.29

22.87c

-0.65 - 0.52

0.97

0.83

15.38c

0.58 - 0.98

2.48

2.09

-6.76c

1.22 -1.89 - -1.62

1.80

1.73

-0.45

1.05 -0.56 - -0.35

0.91

0.75

1.17

3.32

2.74

2.28

10.6 1.3 5 4

-1.09

0.82 -1.16 - -1.01

1.17

1.11

12.4 1.9 6 0

11.7 1.7 9 4

-0.67

1.08 -0.75 - -0.58

1.01

0.84

12.2 1.7 2 3

12.7 3.4 5 4

0.53

2.89

2.31

2.00

1.6 9

PT CC E

Pb

Pc

0.83 - 1.51

0.29 - 0.77

< < 0.0001 0.0001 < < 0.0001 0.0001 < < 0.0001 0.0001

IP T

1.42

N, Number of subjects; HKK, Method of Haavikko; LLH, Method of Liliequist and Lundberg; MSD, Method of Mornstad et al.; CI, Confidence interval; M, Male; F, Female; a Median; b Paired t Test; c Paired Wilcoxon test.

A

ADa

25.33b/14. 79c 8.35b/7.63c 6.72b/5.84c 28.40b/17. 39c 15.09b/13. 43c 4.37b/3.92c

SC R

MSD

SD

M

LLH

AD

test statistic Z

Mean

ED

HKK

95% CI of DA - CA

U

N

N

Sex

A

Metho d

< < 0.0001 0.0001 < < 0.0001 0.0001 < < 0.0001 0.0001 < < 0.0001 0.0001 < < 0.0001 0.0001 < = 0.0001 0.0001

19

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Table 3 - Comparison between the Dental Age (DA) and Chronological Age (CA) in relation to the age groups of the male sex.

20

N

Meth od

CA

DA

DACA

Me SD an

Me SD an

Mea SD n

t Statistics / test statistic Z -0.6209 to 5.37a/3.8 0.2777 2b 0.06350 to 0. 2.48a/6694 2.33b 0.6448 to 0.1 1.14a/0.5 841 9b -1.4620 to 16.05a/4. 1.1324 94b 0.86a/0.1645 to 0.4 1.58b 064 0.27ª/0.1 0.4393 to 0.5 3b 712 -1.8189 to 15.30a/5. 1.3941 51b 0.38a/0.1880 to 0.2 0.28b 760 0.36a/0.1 0.5315 to 0.7 9b 595 -2.2665 to 13.29ª/6. 1.6715 03b 0.3323 to 0.1 0.85ª/1.3 355 0b 0.2142 to 1.8 2.52a/807 2.16b -2.3318 to 11.86ª/6. 1.6602 72b -0.6690 to 3.84a/3.9 0.2118 6b 95% CI of DA-CA

2 8

HKK

8.4 0.3 9 0

8.0 0.4 4 5

0.4 0.45 4

8.008.99

2 8

LLH

8.4 0.3 9 0

8.8 0.7 6 8

0.37

8.008.99

2 MSD 8

8.4 0.3 9 0

8.2 1.0 6 2

1.0 0.23 7

9.009.99

3 2

HKK

9.5 0.3 5 0

8.2 0.4 5 2

0.4 1.30 6

9.009.99

3 2

LLH

9.5 0.3 5 0

9.6 0.8 7 7

0.12

0.7 9

9.009.99

3 MSD 2

9.5 0.3 5 0

9.6 1.4 2 5

0.07

1.4 0

10.0010.99

4 0

HKK

10. 0.2 57 4

8.9 0.7 6 2

0.6 1.61 6

10.0010.99

4 0

LLH

10. 0.2 57 4

10. 0.7 61 7

0.04

10.0010.99

4 MSD 0

10. 0.2 57 4

10. 2.1 68 1

11.0011.99

5 1

HKK

11. 0.3 43 0

9.4 1.1 6 3

11.0011.99

5 1

LLH

11. 0.3 43 0

11.0011.99

5 MSD 1

11. 0.3 43 0

12. 3.0 48 8

1.05

12.0012.99

7 0

HKK

12. 0.3 45 0

10. 1.4 45 3

1.4 2.00 1

12.0012.99

7 0

LLH

12. 0.3 45 0

12. 0.9 01 7

0.9 0.44 6

12.0012.99

7 MSD 0

12. 0.3 45 0

14. 3.4 43 7

1.98

13.0013.99

9 0

HKK

13. 0.2 48 9

11. 1.4 41 7

1.3 2.07 8

-2.3556 to 1.7795

13.0013.99

8 7

LLH

13. 0.2 47 9

12. 1.0 71 1

0.9 0.76 7

-0.9719 to 0.5580

13.0013.99

9 MSD 0

13. 0.2 48 9

15. 3.5 81 4

2.33

14.0014.99

4 8

14. 0.2 46 8

13. 1.0 53 3

1.0 0.93 2

A

2.0 2

M 0.11

U

N

A

0.7 3

1.0 1.97 6

ED 11. 0.9 33 1

PT

CC E LLH

0.7 8

0.8 0.10 3 2.9 6

27 27

Pc

< 0.000 1 0.019 6

3.5 1.1410 to 2.8 3 224

3.5 1.5956 to 3.0 2 708 -1.2264 to 0.6340

4.70ª/4.00b 14.26ª/8. 11b 7.35ª/5.7 7b

Pd

0.000 1 0.019 6

0.264 1

0.556 1

< 0.000 1

< 0.000 1

31

0.394 2

0.114 1

31

0.791 9

0.895 9

39

< 0.000 1

< 0.000 1

39

0.703 3

0.780 2

39

0.722 9

0.850 7

50

< 0.000 1

< 0.000 1

50

0.402 0

0.192 7

0.014 8 < 0.000 1

0.031 1 < 0.000 1

0.000 3

0.000 1

27

31

SC R

8.008.99

D. F.

IP T

Age groups

50 69

69

69

89

86

6.28a/4.81b

89

6.32ª/4.6

47

< 0.000 1 < 0.000 1 < 0.000 1 < 0.000 1 < 0.000

0.000 1 < 0.000 1 < 0.000 1 < 0.000 1 < 0.000

21 9b 14.0014.99

5 MSD 5

14. 0.2 47 7

14. 4.3 92 1

4.3 0.45 0.7337 to 1.6 9 388

15.0015.99

2 1

15. 0.2 46 8

13. 1.0 57 9

1.1 1.88 0

LLH

-2.3846 to 1.3840

54

0.447 7

0.615 2

7.86ª/4.0 1b

20

< 0.000 1

0.000 1

IP T SC R U N A M

ED PT CC E

1

0.77ª/0.50b

HKK, Method of Haavikko; LLH, Method of Liliequist and Lundberg; MSD, Method of Mornstad et al.; SD, Standard deviation; CI, Confidence interval; D.F., Degrees of freedom; N, Number of subjects. a t Statistics, b Test statistic Z, c Paired t Test, d Paired Wilcoxon test.

A

1

22 Table 4 - Comparison between the Dental Age (DA) with Chronological Age (CA) in relation to the age groups of the female sex.

8.008.99

N

Meth od

34 HKK

CA

DA

DACA

95% CI of DA-CA

Me SD an

Me SD an

Mea SD n

8.3 0.2 9 7

8.0 0.5 8 2

-0.31

0.6 1

-0.5211 to 0.0936

t Statistics / test statistic Z 2.93ª/3.04

Pc

Pd

0.006 2

0.002 3

0.957 0

0.421 4

0.559 9

0.053 4

< 0.000 1

< 0.000 1

0.206 4

0.061 9

0.559 3

0.655 1

< 0.000 1

< 0.000 1

0.489 5

0.354 7

0.008 0 < 0.000 1 < 0.000 1 0.043 9 < 0.000 1 < 0.000 1

0.001 4 < 0.000 1 < 0.000 1 0.050 7 < 0.000 1 < 0.000 1

1.82a/1.44b

0.070 9

0.148 8

32.30ª/9.5 4b 7.58ª/6.17

< 0.000 1 < 0.000 1 0.027 8

< 0.000 1 < 0.000 1 0.053 2

b

34

LLH

8.3 0.2 9 7

8.4 0.7 0 7

0.01

0.8 5

8.008.99

34 MSD

8.3 0.2 9 7

8.2 1.2 6 1

-0.13

1.2 4

9.009.99

33 HKK

9.5 0.2 0 8

8.5 0.5 9 8

-0.91

0.6 7

9.009.99

33

LLH

9.5 0.2 0 8

9.2 0.9 6 7

-0.24

1.0 6

9.009.99

33 MSD

9.5 0.2 0 8

9.6 1.2 3 9

0.13

1.3 0

10.0010.99

62 HKK

10. 0.3 48 0

9.9 0.9 0 0

-0.58

0.8 4

62

LLH

10. 0.3 48 0

10. 1.1 39 4

-0.09

10.0010.99

62 MSD

10. 0.3 48 0

11. 2.0 18 2

11.0011.99

94 HKK

11. 0.2 53 8

10. 0.8 59 2

-0.94

0.8 1

94

11. 0.2 53 8

11. 0.8 07 8

-0.46

0.8 8

0.3651 to 0.1 0.70a/0.93 b 767 0.1896 to 1.2 2.74ª/088 3.19b -1.1017 to 11.26a/7.6 0.7715 4b -0.6390 to 5.03a/4.50 0.2773 b

LLH

93 MSD

12.0012.99

11 HKK 9

12.0012.99

11 8

12.0012.99 13.0013.99

A 13.0013.99 13.0013.99

A

N

U

0.6112 to 0.1 1.29ª/1.87 b 373 0.59a/0.3270 to 0.5 0.45 936 -0.7942 to 5.46a/4.56 0.3684 b

M

1.0 7 2.0 1

11. 0.2 53 8

12. 2.4 05 7

0.52

2.4 7

0.01474 to 1. 0321

12. 0.2 44 7

11. 0.7 27 2

-1.16

0.7 4

-1.2990 to 1.0286

12. 0.2 44 7

11. 1.0 94 3

-0.50

1.0 3

-0.6856 to 0.3085

11 MSD 9

12. 0.2 44 7

12. 2.7 90 8

0.46

2.7 8

0.04008 to 0. 9683

12 HKK 1

13. 0.2 42 6

11. 0.5 76 4

-1.65

0.5 6

-1.7562 to 1.5533

11 5

13. 0.2 41 6

12. 1.0 65 7

-0.76

1.0 8

-0.9601 to 0.5621

13. 0.2 42 6

14. 3.2 09 9

0.67

3.3 2

0.07454 to 1. 2686

CC E

11.0011.99

0.70

ED

11.0011.99

PT

10.0010.99

0.05ª/0.80 0.2892 to 0.3 b 051 0.5582 to 0.3 0.59ª/1.93 b 076 -1.1422 to 7.79a/4.63 0.6687 b

SC R

8.008.99

LLH

LLH

12 MSD 1

IP T

Age groups

2.04a/1.95b 17.04ª/9.3 9b 5.22ª/4.80 b

b

2.23a/1.93b

23 14.0014.99

86

LLH

14. 0.2 38 9

13. 0.8 36 4

-1.02

0.8 3

-1.1998 to 0.8420

11.35ª/8.0 5b

< 0.000 1

< 0.000 1

14.0014.99

10 MSD 1

14. 0.3 40 0

15. 3.9 10 3

0.71

3.9 2

0.06815 to 1. 4800

1.81a/1.35b

0.073 4

0.176 5

15.0015.99

58

15. 0.2 44 6

13. 0.7 57 6

-1.87

0.7 8

-2.0794 to 1.6692

18.30ª/6.6 2b

< 0.000 1

< 0.000 1

LLH

A

CC E

PT

ED

M

A

N

U

SC R

IP T

HKK, Method of Haavikko; LLH, Method of Liliequist and Lundberg; MSD, Method of Mornstad et al.; SD, Standard deviation; CI, Confidence interval; N, Number of subjects; a t Statistics; b Test statistic Z; c Paired t Test; d Paired Wilcoxon test.

24 Table 5 - Accuracy of the age estimation techniques according to the age groups of the male sex.

SD

SE

8.00-8.99 8.00-8.99 8.00-8.99

HKK LLH MSD HKK LLH MSD HKK LLH MSD HKK LLH MSD HKK LLH MSD HKK LLH MSD LLH MSD LLH

28 28 28 32 32 32 40 40 40 51 51 51 70 70 70 90 87 90 48 55 21

0.53 0.79 0.65 1.30 0.60 1.06 1.61 0.62 1.49 2.07 0.64 2.42 2.10 0.87 3.39 2.09 1.06 3.56 1.06 3.82 1.88

0.35 0.32 0.87 0.46 0.52 0.90 0.66 0.37 1.35 0.85 0.53 1.98 1.25 0.59 2.18 1.35 0.62 2.26 0.88 2.14 1.10

0.07 0.06 0.16 0.08 0.09 0.16 0.10 0.06 0.21 0.12 0.07 0.28 0.15 0.07 0.26 0.14 0.07 0.24 0.13 0.29 0.24

11.00-11.99 11.00-11.99 11.00-11.99 12.00-12.99 12.00-12.99 12.00-12.99 13.00-13.99 13.00-13.99 13.00-13.99 14.00-14.99 14.00-14.99 15.00-15.99

A

CC E

PT

ED

HKK, Method of Haavikko; LLH, Method of Liliequist and Lundberg; MSD, Method of Mornstad et al.; SD, Standard deviation; CI, Confidence interval; N, Number of subjects; AD, Absolute difference; SE, Standard error.

A

10.00-10.99 10.00-10.99 10.00-10.99

M

9.00-9.99 9.00-9.99 9.00-9.99

95% CI of AD 0.39 0.67 0.32 1.13 0.41 0.73 1.40 0.50 1.05 1.83 0.49 1.86 1.80 0.73 2.87 1.81 0.93 3.09 0.80 3.24 1.38

-

0.66 0.91 0.99 1.46 0.79 1.38 1.82 0.74 1.92 2.31 0.79 2.98 2.40 1.01 3.91 2.37 1.20 4.03 1.32 4.40 2.39

AD median 0.54 0.80 0.35 1.34 0.59 1.02 1.72 0.67 1.28 2.21 0.49 1.99 2.22 0.75 3.00 2.16 0.92 3.23 0.80 4.37 1.59

IP T

AD

SC R

N

U

Method

N

Age groups

25 Table 6 - Accuracy of the age estimation techniques according to the age groups of the female sex.

SD

SE

8.00-8.99 8.00-8.99 8.00-8.99

HKK LLH MSD HKK LLH MSD HKK LLH MSD HKK LLH MSD HKK LLH MSD HKK LLH MSD LLH MSD LLH

34

0.55

0.39

0.07

34 34 33 33 33 62 62 62 94 94 93 119 118 119 121 115 121 86 101 58

0.57 0.85 1.01 0.86 1.04 0.84 0.84 1.68 1.03 0.83 2.05 1.18 0.95 2.30 1.66 1.06 2.78 1.01 3.28 1.87

0.62 0.90 0.50 0.64 0.76 0.57 0.66 1.29 0.69 0.55 1.46 0.72 0.64 1.61 0.56 0.78 1.91 0.86 2.24 0.78

0.11 0.15 0.09 0.11 0.13 0.07 0.08 0.16 0.07 0.06 0.15 0.07 0.06 0.15 0.05 0.07 0.17 0.10 0.22 0.10

14.00-14.99 14.00-14.99 15.00-15.99

A

CC E

PT

ED

HKK, Method of Haavikko; LLH, Method of Liliequist and Lundberg; MSD, Method of Mornstad et al.; SD, Standard deviation; CI, Confidence interval; n, Number of subjects; AD, Absolute difference; SE, Standard error.

A

10.00-10.99 10.00-10.99 10.00-10.99 11.00-11.99 11.00-11.99 11.00-11.99 12.00-12.99 12.00-12.99 12.00-12.99 13.00-13.99 13.00-13.99 13.00-13.99

M

9.00-9.99 9.00-9.99 9.00-9.99

0.69

AD median 0.49

0.78 1.15 1.18 1.09 1.31 0.99 1.00 2.01 1.17 0.94 2.35 1.31 1.07 2.59 1.76 1.20 3.13 1.20 3.72 2.08

0.33 0.58 1.03 0.67 0.94 0.74 0.71 1.43 0.93 0.75 1.83 1.04 0.91 2.29 1.55 0.84 3.06 0.75 3.77 1.63

95% CI of AD 0.42 0.37 0.54 0.83 0.64 0.77 0.70 0.67 1.35 0.88 0.71 1.75 1.05 0.84 2.01 1.56 0.91 2.44 0.83 2.84 1.67 -

IP T

AD

SC R

N

U

Method

N

Age groups