dental histology and age estimation at damdama: an
TRANSCRIPT
DENTAL HISTOLOGY AND AGE ESTIMATION
AT DAMDAMA: AN INDIAN MESOLITHIC SITE
Gwendolyn M. Robbins
A MASTERS PAPER
Presented to the Department of Anthropology And the Graduate School of the University of Oregon
In partial fulfillment of the requirements for the degree of Master of Science
December 2000
Approved by:
Dr. John R. Lukacs
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CURRICULUM VITA
NAME OF THE AUTHOR: Gwendolyn Meredith Robbins
PLACE OF BIRTH: Marietta, Georgia
DATE OF BIRTH: 26 November 1972
GRADUATE AND UNDERGRADUATE SCHOOLS ATTENDED:
1996 2000 University of Oregon 1993 1995 Lane Community College 1992 1993 University of Utah
DEGREES AWARDED:
Master of Science in Anthropology, 2000, University of Oregon
Bachelor of Science in Anthropology, with departmental honors, 1998, University of Oregon
AREAS OF SPECIAL INTEREST:
Dental Anthropology, Human Osteology, Histology, Bioarchaeology, Biocultural Interactions, South Asia
PROFESSIONAL EXPERIENCE:
Jan Feb 2000 Research Assistant: Nutritional Stress in Modern and Prehistoric Populations in Maharashtra, India. Principle investigators Dr. J.R. Lukacs and Dr. S. Walimbe
July August 1999 Assistant Director: U of O Archaeological Field School, Tutuila, American Samoa. Principle investigators Dr. W. Ayres and Joan Wozniak
July August 1998 Archaeological Field Assistant: U of O Archaeological Field
School, Tutuila, American Samoa. Principle investigator Dr. W. Ayres, Epi Suafo'a, and Joan Wozniak
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Spring 1998 Artifact Illustration: Carlon Village Project, Oregon State Museum of Anthropology. Principle investigator Dr. George Wingard
Fall 1997 Archaeological Lab Technician: faunal analysis for the Carlon Village Project, Oregon State Museum of Anthropology. Principle investigator Dr. George Wingard
August 1997 Archaeological Field Technician: CRM for Oregon Department of Transportation Project, Hines, Oregon State Museum of Anthropology. Principle investigators Dr. T. Connolly and Dr. D. Jenkins
July 1997 Archaeological Field Technician: CRM for Oregon Department of Transportation Project, Salem, Oregon State Museum of Anthropology. Principle investigators Dr. T. Connolly and Dr. D. Jenkins
1997 U of O Archaeological Field School: Carlon Village Site, Eastern Oregon. Principle investigators Dr. M. Aikens and Dr. D Jenkins.
1996 Archaeological Lab Technician: Nunivak Island, Alaska Project, University of Oregon. Principle investigator Dennis Griffen
FELLOWSHIPS AND HONORS:
Summer 2000 Graduate Administrative Fellowship: Web Master for the Anthropology Department, University of Oregon.
19992000 Graduate Teaching Fellowship: Faculty Advisor for the General Science Department, University of Oregon.
19981999 Graduate Teaching Fellowship: Faculty Advisor for the General Science Department, University of Oregon.
GRANTS:
20002001 Fulbright Scholarship: Paleodemography in India at the Rise of Agriculture, Deccan College, Pune, India.
Spring 2000 Graduate Student Research Award: Dental Histological Section Preparation, Graduate School, University of Oregon ($400)
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ACKNOWLEDGEMENTS
Appreciation is extended to Dr. J.R. Lukacs of the University of Oregon for the opportunity to work on the material from Damdama, for continued support, and guidance throughout the project. Sincere thanks are also extended to the members of the Department of Ancient History, Culture and Archaeology at the University of Allahabad, India for granting permission for this project and for the sample collection.
I thank Dr. Jeanne Selker of the Electron Microscopy Lab, University of Oregon for providing consultation and materials for preparing and sectioning the teeth. A special thank you is extended to Dr. Murray Marks of the University of Tennessee, Knoxville for sharing his expertise concerning preparation and sectioning.
Sincere appreciation is extended to the National Geographic Society for funding parts of the training that went into the completion of this project. Thanks also to the University of Oregon Graduate School for partially funding a trip to the University of Knoxville, Tennessee and to the Department of Anthropology at the University of Oregon for providing funding for equipment necessary to the completion of this research.
I wish to thank Barbara Robbins for providing funding and support to me for the duration of my academic studies. Finally, I thank my partner Michael Boyer for supporting and encouraging me in all of my endeavors.
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DEDICATION
What is known about the ultrastructure, the surface characteristics, and the function of mineralized dental tissues is due to visionary researchers in the field of human biology who saw the potential of the electron microscope in examining the structure of mineralized tissues and in using those tissues for age estimation. Dr. Edward Reith was an anatomist and cell biologist who braved the hard world of mineralized tissues to study their development, structure and function. He was a pioneer in the study of developmental timing in enamel mineralization, the functional morphology of the dentine, and the ultrastructure of cementum. Dr. Reith was committed to these topics throughout his career. He authored many books and articles, inspired students through his teaching, and his memory energized colloquia and additional volumes of important research. Dr. Reith also happened to be the father of a good friend of mine, a member of my family. Marianne Reith M.S., R.N. encouraged me to pursue higher education and her undying commitment to science, to knowledge, and to healing continue to inspire me. This project is dedicated to the memories and the work of Marianne Reith and her father, Dr. Edward Reith.
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TABLE OF CONTENTS
INTRODUCTION 1
ESTIMATING AGE AT DEATH FOR PREHISTORIC HUMAN REMAINS 3
DAMDAMA: THE ARCHAEOLOGICAL CONTEXT 7
THE INDIAN MESOLITHIC 7 BURIAL SITES ON THE GANGETIC PLAIN 8 DAMDAMA 12
CEMENTUMANNULATIONSANDMULTIVARIATEMETHODSOFAGE ESTIMATION 16
THEULTRASTRUCTURALNATURE OF CEMENTUM ANNULATIONS 18 CEMENTUMAPPOSITION AND THE ESTIMATION OF AGE AT DEATH 22 REVIEW OF THE LITERATURE ON AGE ESTIMATION 26 SUMMARY 36
ROOTDENTINE TRANSLUCENCYANDSECONDARYDENTINEDEPOSITION IN AGEESTIMATION 38
THEULTRASTRUCTURALNATURE OF ROOTDENTINE TRANSLUCENCY 38 REVIEW OF THE LITERATURE ON AGE ESTIMATION FOR INTACT TEETH 40 REVIEW OF THE LITERATURE ON AGE ESTIMATION FOR SECTIONED TEETH 42 SUMMARY 44
MATERIALS ANDMETHODS 46
SAMPLE DERIVATION AND CHARACTERISTICS 46 PROTOCOL 49
RESULTS 56
OBSERVER ERROR AND COMPARISONS WITHIN INDIVIDUALS 56 AGE ESTIMATES FROM DENTALHISTOLOGICALMETHODS 59 DISCUSSION AND INTERPRETATION 68 SUMMARY OF RESULTS AND CONCLUSIONS 73 IMPLICATIONS FOR FUTURERESEARCH 73
PALEODEMOGRAHIC PROFILE FOR DAMDAMA 76
TABLES 82
FIGURES 85
BIBLIOGRAPHY 100
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LIST OF TABLES
TABLE 1: RADIOCARBON DATES FOR INDIAN MESOLITHIC SITES 7 TABLE 2: CLASS OF TOOTH AVAILABLE FOR ANALYSIS FROM DAMDAMA 47 TABLE 3:DENTAL PATHOLOGICAL PROFILE FOR DAMDAMA SAMPLE 49 TABLE 4:MACROSCOPICMETHODS FOR AGE ESTIMATION 81 TABLE 5:MACROSCOPICMETHODS FOR SEX ESTIMATION 82 TABLE 6: PROTOCOL FOR PREPARING AND SECTIONING TEETH 83 TABLE 7: REGRESSION FORMULAS FOR AGE ESTIMATION (JOHANSON 1970) 52 TABLE 8: REGRESSION FORMULAS FOR AGE ESTIMATION (MAPLES 1978) 53 TABLE 9: REGRESSION FORMULAE FOR AGE ESTIMATION FROMAREA OF
TRANSLUCENT DENTINE (LORENTSEN AND SOLHEIM 1989) 54 TABLE 10:DENTAL EMERGENCE TIMING FOR CHILDREN IN CHANDIGARH, INDIA
(YEARS) 55 TABLE 11:MACROSCOPICAGE AND SEX ESTIMATES FOR DAMDAMA SAMPLE 56 TABLE 12:DESCRIPTIVE STATISTICS FOR CEMENTUM ANNULATION COUNTS 57 TABLE 13:AGE ESTIMATES FOR INDIVIDUALS WITH MULTIPLE TEETH AVAILABLE 57 TABLE 14: PAIRED TTEST FOR SIGNIFICANTDIFFERENCES FOR INDIVIDUALS
WITHMULTIPLE TEETH 58 TABLE 15:MEAN ESTIMATES FOR AGE AT DEATH 60 TABLE 16: PAIRED TTEST FOR MEANS OF THE MACROSCOPIC ANDHISTOLOGICAL
METHODS 61 TABLE 17:ANALYSIS OF VARIANCE (ANOVA) FOR METHODS 62 TABLE 18: PEARSON CORRELATIONS AND PVALUES FOR METHODS 63 TABLE 19: EUCLIDEAN DISTANCE MATRIX 64 TABLE 20: TEST FOR SIGNIFICANTDIFFERENCES IN INDIVIDUALS 1629 YEARS OLD 64 TABLE 21: TEST FOR SIGNIFICANTDIFFERENCES IN INDIVIDUALS 3055 YEARS OLD 65 TABLE 22:DESCRIPTIVE STATISTICS FOR AGE ESTIMATES FOR MALES AND FEMALES 67 TABLE 23:AGE ESTIMATES FOR THE SKELETAL POPULATION FROM DAMDAMA 75 TABLE 24:DAMDAMA, MAHADAHA, AND SARAI NAHAR RAI AGE DISTRIBUTION 77 TABLE 25: STATIONARY POPULATION LIFE TABLE FOR DAMDAMA 79
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LISTOF FIGURES
FIGURE 1:MAP OF MESOLITHIC SITES ON THE GANGETIC PLAIN 9 FIGURE 2:MEASUREMENTS DEVELOPED FOR MULTIVARIATE AGE
ESTIMATION (KASHYAP AND RAO 1990) 51 FIGURE 3: JOHANSON’S SCORING CRITERIA 84 FIGURE 4:AREA OF ROOTTRANSLUCENCY (LORENTSEN AND SOLHEIM 1989) 85 FIGURE 5: LENGTH OF ROOTTRANSLUCENCY (DRUSINI 1990) 86 FIGURE 6: CEMENTUM ANNULATIONS 87 FIGURE 7: COMPARISON OF MULTIPLE TEETH FROM THE SAME
INDIVIDUAL 88 FIGURE 8: RANGE OF AGE ESTIMATES PER METHOD AND PER INDIVIDUAL 61 FIGURE 9:HISTOGRAMS FOR ESTIMATES FOR EACHMETHOD 90 FIGURE 10:HISTOGRAMS FOR ESTIMATES FOR EACH INDIVIDUAL 92 FIGURE 11:NORMALQUANTILE PLOTS FOR METHODS 95 FIGURE 12: SCATTERPLOTS FOR EACHMETHOD 97 FIGURE 13:DIFFERENCES BETWEEN MACROSCOPIC AND HISTOLOGICAL AGE
ESTIMATES 67 FIGURE 14:AGE PROFILE FOR DAMDAMA 76 FIGURE 15:DISTRIBUTION OF AGE ESTIMATES FOR FEMALES AND MALES 77 FIGURE 16:ADULT MORTALITY CURVE AND LIFE EXPECTANCY FOR DAMDAMA
ADULTS 79
INTRODUCTION
Bioarchaeological research must be built upon a foundation of accurate age and
sex estimates. Unfortunately there are inherent biases which guarantee a certain amount
of error in the assessment of these fundamental variables, certain biases that are
seemingly unavoidable using any relative standard for age estimation. This research is
an attempt to avoid some of the pitfalls of relative standards by using a method that has
potential to be a chronometric measure of age in the human dentition. Cementum
annulations may represent chronological age in a manner analogous to
dendrochronology, however biologists have obtained mixed results in their attempts to
understand the phenomenon of their accumulation and anthropologists have similarly
obtained mixed results in their application. Whether or not the annulations represent
strictly annual structures, they are highly correlated with age at death in modern
samples (Condon et al. 1986). Here the biological basis for the annulations is explored
and an attempt was made to use a count of acellular annulations in an archaeologically
derived sample.
Relative standards for age estimation using dentine translucency and various
combinations of attrition, root resorption, and secondary dentine apposition were also
used in this study. As histological research is a destructive process, it seemed important
to test any other established histological aging methods that could be practically applied
to this sample. As the sample was not of documented age, it seemed further that
convergence (or lack of convergence) of age estimates from all the available methods
would be necessary to fully test the relative accuracy of the cementum annulations. In
the end, a combination of standards for macroscopic morphological changes,
microscopic degenerative processes, and cementum annulations were compared with
one another in an attempt to construct the estimates for age at death. Patterns within the
sets of estimates were also considered in an attempt to discover methodological biases
and explore questions about the relative usefulness of certain biological changes for age
estimation.
Relative standards for age estimation, on a macroscopic or microscopic scale,
are inherently flawed due to an interesting set of problems in human biology as well as
anthropology. Some of the most important and most frequently discussed
considerations include: 1.) the distinction between chronological and biological age, 2.)
uniformitarian assumptions about aging through time and space, 3.) environmental and
genetic differences between reference and sample populations, 4.) archaeological
processes of preservation, diagenetic change, and recovery, and 5.) variance between
dental and skeletal age determinations.
The disparity between chronological and biological age has been the subject of
research by both human biologists (Bittles and Collins 1986) and paleodemographers
(Paine 1997). Bioarchaeologists use the term biological age in recognition of the
discrepancy between an individual’s morphology and their chronological age, which is
inaccessible for most skeletal populations. The discrepancy between chronological and
biological age is a product of both measurable systemic reasons as well as significant
idiosyncratic and seemingly random differences. There are three main sets of problems
responsible for the differences between chronological and biological age: individual
variation, pathological conditions, and methodological issues.
The methods subject to the most individual variability are those developed for
adults based on the process of senescence and consequent degenerative changes in the
dentition and in the skeleton. Standards for dental attrition are among the most
commonly used methods for estimating age at death for adults. There are multiple
causes for inconsistencies in individual dentitions as well as between standard and
reference populations including age, sex, diet, occlusal patterns, temporomandibular
joint form, mineralization differences, bruxism, and cultural practices such as non
dietary uses of teeth (Hillson 1996). If attrition is to be used to estimate age at death,
onequarter of one jaw would be the ideal minimum, careful consideration of context is
warranted, and a population specific wear rate should be calibrated (Walker, Dean and
Shapiro 1991).
It is generally considered easier to determine the biological age of children
because estimates are based on a sequence of interrelated developmental stages, which
are more predictable than senescence. However variation in rates of development and
maturity caused by both genetic and environmental factors, continue to maintain the
gap between chronological and biological age. Deprivation and disease add further to
these difficulties. For example, even in situations where researchers are aware that a
group of modern children are developing in conditions of severe environmental stress,
with chronic mild to moderate proteinenergy malnutrition and moderate to high levels
of infectious disease, age may still be underestimated by one to three years (Lampl and
Johnson 1996).
The generalization (or averaging) of complex morphological changes in order to
create reference standards introduces an inherent amount of inaccuracy in all relative
methods of age estimation. This bias is due not only to the generalization itself, but also
to observation error, scoring differences, level of experience using these discrete
reference categories, and finally to the differences between the reference and the
sample populations themselves. When age estimation methods are applied to
archaeological populations, the number of biases are further compounded by
uniformitarian assumptions about aging across time and space. Genetic and
environmental differences between reference and sample populations lead to an
uncertain level of error in the application of any method to an archaeological sample.
Archaeological processes of diagenesis, preservation, and decomposition are
primary considerations for age estimation and demographic inferences in
archaeological populations. The elements of the skeleton that are present, recovered
during excavation, and well preserved must primarily determine the method used for
age estimation. Teeth are the most frequently represented and often the bestpreserved
elements in an archaeologically derived population but there can be significant
differences between the estimates (and their standard errors) derived from dental
remains versus estimates derived from skeletal material.
In subadults, moderate to severe malnutrition can cause retardation of long
bone lengths and consequently lower age estimates in comparison with estimates
derived from dental development and eruption timing. The disparity is compounded by
the fact that sex cannot yet be accurately determined for subadult remains. Female
juveniles will give older estimates than males of equivalent age because their teeth tend
to develop and erupt at younger ages. Skeletal growth patterns also differ for males and
females and these standards may tend to underage female subadults. Adult standards
for age estimation will also vary between the dentition and the skeleton. Attrition,
epiphyseal fusion, cranial suture closure, pelvic morphology, and degenerative
conditions are all subject to different lurking variables.
Despite these difficulties, some methods of age estimation are considered to be
quite accurate, especially for juvenile remains. Some have claimed a margin of error of
less than 2 weeks for estimates derived from long bone lengths for neonates (Fazekas
and Kosa 1978; Scheuer 1980) though these estimates are obviously highly dependent
on maternal and infant nutritional status and subject to questions about the
comparability of reference and archaeological samples. Age estimates from tooth
height (Liversedge 1993, 1998), or the numerous standards for dental mineralization
and eruption timing, generally have a standard error of plus or minus three to six
months under the age of five years. These techniques are useful because again teeth are
often the most numerous and bestpreserved elements in a skeletal collection, especially
for juveniles whose small and gracile bones are frequently destroyed after burial.
However dental development and eruption are still relative standards and thus suffer
from the set of problems outline above.
Bioarchaeologists commonly use a suite of methods, as determined by the
elements preserved for each individual, in order to converge upon the most accurate
estimate possible. As the chronological age at death is not known in an archaeologically
derived population, accuracy does not here refer to the level of concordance between
chronological and biological age. Here accuracy is defined only in the relative sense as
the degree to which an estimate for biological age resembles estimates given by other
methods and the degree to which they converge on a “true” biological age. Precision is
here defined as the amount of error between observations made on different elements
from a single individual, as well as the amount of error between observers, or whether
the method can be precisely applied.
In addition to applying as many methods as possible to ensure the greatest
possible amount of accuracy in estimation, many researchers have looked at age related
transformation on a different scale, generally with relatively successful results. Several
methods of age estimation for juvenile dentitions have been developed based on
observations of microscopic age related phenomena. Boyde (1963, 1990) suggested that
in juveniles, age at death could be calculated by counting the crossstriations in enamel
from the neonatal line to the completion of the crown and crossreferencing the brown
straie between the first incisor and the lower first molar (cited in Hillson 1996).
Bromage and Dean (1985) developed an aging method using a count of perikymata
grooves on the enamel surface, to which Dean and Beynon (1991) added the count of
crossstriations (cited in Hillson 1996).
In adults, age has a positive (though sometimes weak) linear relationship with
several microscopic degenerative variables including root dentine translucency and
sclerosis, the formation of secondary dentine, the apposition of cementum at the root
apex, cementum annulations, and periodontitis or gingival recession (Alt 1998).
Gustafson (1950) devised a multivariate method of age estimation using six of these
measures of histological change. His method combined scores from measurements of
attrition, periodontitis, secondary dentine, cementum apposition (overall thickness), and
root dentine translucency. He developed a four stage grading technique for each
characteristic and calculated regression formulas for estimating age at death from the
scores. Gustafson’s method relied on simple linear regression for each characteristic
with all characters being given the same weight. He reported a 98% correlation between
estimates from his technique and known age at death. He calculated the average error
for his estimates to be plus or minus 3.63 years.
Johanson (1970) tested Gustafson’s method on a larger, independent sample and
he added two major refinements: intermediate grades for scoring each variable and
multiple regression. Johanson found that the accuracy of age estimates improved when
more than one tooth was available for analysis, but he concluded that there was no
benefit to using more than five teeth per individual. Johanson’s formulas predicted age
at death within five years 78.3% of the time, within ten years 95.7% of the time, and
within fifteen years 97.8% of the time. The age of 26.1% of the individuals in his
sample (12/46) were predicted with an accuracy of plus or minus one year. When only
dentine translucency, root resorption and cementum apposition were used, his
predictions yielded a correlation of 86% with known age at death (+/ 6.55 years).
The Gustafson (1950) and Johanson (1970) methods have been tested, critiqued
and improved upon for use in forensics over the subsequent 50 years (Burns and
Maples 1976; Maples 1978; Maples and Rice 1979; Solheim and Sundnes 1980;
Kashyap and Rao 1990; LopezNicolas 1989, 1990; LopezNicolas and Luna 1991;
Lamendin et al. 1981 and 1992; Solheim 1993; Lucy et al. 1995, 1996; Akroyd et al.
1997). The methods have not been widely adopted for archaeologically derived
samples, partially because of the destructive and cumbersome process of preparing the
sections. The destructive nature of histological analysis is somewhat justified by the
presence of the antimere and through proper documentation including photographs and
casts. It is also possible that the methods are not suitable for archaeological material
because the effects of uncontrolled temporal, geographic, genetic, cultural,
environmental, and taphonomic variables limit the accuracy. This study is an attempt to
assess the feasibility of using these histological methods on an archaeological sample.
The Present Research
This project represents an initial attempt to apply dental histological aging
methods to a prehistoric skeletal sample from India. For this analysis, age at death was
assessed using methods based on dental attrition, root dentine translucency, and
cementum annulations (Johanson 1971; Maples 1978; Charles et al 1986, Lorentsen
and Solheim 1989; Kashyap and Rao 1990; Drusini 1990). To determine whether
histological methods that were developed from forensic samples and dental extractions,
are applicable to prehistoric archaeological material, the following research questions
were posed:
1.) Are the methods relatively accurate in relationship to one another and to the
multifactorial macroscopic age estimates made previously?
2.) Are the methods internally consistent in tests of observer error?
3.) Are there significant differences between multiple teeth available from the same
individuals?
4.) Are there detectable systematic biases within the methods, such as overaging
young individuals and underaging older individuals?
5.) As all of these methods use the same few anatomical structures, what do any
differences between the resulting estimates say about the methods themselves?
6.) Given diagenesis, are the original protocols directly applicable to this sample or are
there necessary modifications?
7.) Can any of these methods improve the accuracy of the paleodemographic profile for
Damdama?
8.) Can the demographic profile be expanded through the inclusion of individuals for
whom age could not previously be estimated specifically?
It is expected that if the methods are accurate, the age estimates will closely
resemble one another in statistical tests and there will be no obvious trend or bias in
estimation. Similarly, if the methods are precise, age estimates from independent
observations will not be significantly different and multiple teeth from the same
individual will yield similar estimates. The original published protocols were used in
the test for each method, followed as closely as possible and without major
modifications.
If a combination of histological and macroscopic techniques can be used to
converge on more precise estimates of age at death, the paleodemographic
characteristics of Damdama’s skeletal population can be more accurately assessed. The
paleodemographic profile for Damdama is an integral component to understanding
Mesolithic culture on the Gangetic Plain in India. The burial sites on the Gangetic Plain
have yielded the most numerous and best preserved human remains for this period in
India. The hunting and gathering cultures of the Mesolithic period provide an
opportunity for reconstructing relationships between environment, subsistence, and
settlement prior to the development of agriculture in the Chalcolithic and Neolithic
periods in Indian prehistory. These materials can potentially provide clues to the nature
of relationships between the Mesolithic cultures of the Gangetic Plain and
contemporaneous populations in surrounding areas, as well as affinities with later
peoples. Chapter 1 summarizes some of the context and the issues involved with the
archaeological record for this period in India.
Age estimation techniques should be developed and applied with an understanding of
the biological basis for those age related changes. Unfortunately there are many
questions remaining about the ultrastructural nature and the biological processes
underlying the phenomenon of cementum annulations. Despite a long history of
research into this subject, cementum is one of the least understood mineralized tissues.
Chapter 2 summarizes what is known about the biological basis of cementum, the
nature of the increments, and the effects of pathology on age estimation. Chapter 2 also
summarizes the literature on age estimation using cementum annulations including the
protocols used, the success and/or the relative accuracy in estimating age at death for
known age individuals.
The process of root dentine formation, mineralization, sclerosis and other degenerative
processes are better understood. Chapter 3 summarizes the biological basis for the
sclerosis, dentine translucency, and secondary dentine deposition. Although these
processes are more fully understood, the most biologically relevant protocol for using
dentine translucency in age estimation has yet to be determined. The ideal section
thickness, the use of stains, and the most accurate criteria for measuring the
translucency vary considerably per study and have yet to be standardized. This
variability reduces the possibility for comparative analysis and increases the likelihood
of interobserver error. Chapter 3 also summarizes the various protocols that have been
developed and their relative accuracy for known aged samples.
Chapter 4 provides a discussion of the materials and methods used in this study. The
ideal sample selection criteria and the real characteristics of this sample are examined,
the methods and protocols used here are also detailed. The results of comparisons
between the estimates from all the histological and the macroscopic methods are
presented in Chapter 5 and interpretations follow in Chapter 6, including a discussion
of problems encountered and implications for further research. Chapter 7 is a revision
of the paleodemographic profile for Damdama for the 39 individuals whose age at
death could be estimated either macroscopically or histologically.
DAMDAMA: THE ARCHAEOLOGICAL CONTEXT
The archaeological record for the Mesolithic period on the Gangetic plain has
been extensively investigated by several institutions including the University of
Allahabad, Deccan College, the University of Pune, and the Archaeological Survey of
India (Misra 1996). Several hundred Mesolithic sites have been discovered in the
Vindhyan hills and the Gangetic Plains region of Uttar Pradesh in Northern India,
including rockshelter and open air sites (Misra 1977). The Indian Mesolithic is a
generally defined as a transitional period between the Upper Paleolithic and the
Neolithic and specific dates for this period are varied and controversial (see Table 1).
The Mesolithic in northern India stretches from approximately 80003000 BP, if one of
the earliest date from Sarai Nahar Rai (10,050 +/ 110 BP) is excluded because it was
obtained from a sample of calcified unburned bone (Kennedy et al.1986; Misra 1977).
TABLE 1: RADIOCARBON DATES FOR INDIAN MESOLITHIC SITES
Damdama 8865 +/ 65 1 ; 5550 +/ 60 2 (st 1) ;
5250 +/ 70 2 (st 6) ; 5430 +/ 60 2 (st 8)
Lekhahia, Phase I 8,000 +/ 75 1 ; 5410 +/ 115; 4240 +/ 110 ;
3710 +/ 110 ; 3560 +/ 105
Mahadaha 2880 +/ 250 ; 3840 +/ 130 ; 4010 +/120 ; 4680 +/ 80 2 (st
1) ; 4110 +/ 60 2 (st 2) ; 6160 +/ 60 2 (st 4)
Sarai Nahar Rai 2860 +/ 120 ; 10050 +/ 110 ;
10395 +/ 110 ; 5040 +/ 50 2 (surface);
Taken from Misra, V.D. (1977: 67); Kennedy et al. (1992); Allchin and Allchin (1982: 79), Kennedy et al. (1986: 52); Lukacs et al. (1996); Lukacs n.d.. 1 Dates obtained from Accelerated Mass Spectrometry (AMS) techniques on bioapatite from bone 2 Dates obtained from AMS dating techniques on bioapatite from bovid teeth
Mesolithic sites in India generally consist of the lithic scatters and artifacts,
hearth features (sometimes with burned clay inclusions), faunal remains, some include
burials and occasionally grave goods. Microlithic tools are considered the diagnostic
artifact for this era but this period also saw the appearance of composite tools such as
knives and sickles (Kennedy 1992). Tools were also produced from new raw materials
as well including wood, bamboo, shell, bone, horn, ivory, and leather (Kennedy 1996).
Cultural information about the Indian Mesolithic has also been derived from
paintings and carvings on the walls of rock shelters at Bhimbetka and Lekhahia. The
rock art has been interpreted as further evidence for a hunting and gathering lifestyle,
with depictions of animals and hunting scenes most commonly represented (Varma
1996). Kennedy (1996) has suggested that the paintings may also indicate technologies
not represented in the archaeological record including shelters, watercraft, storage
vessels, animal traps, and cordage in the Mesolithic.
Burial Sites on the Gangetic Plain
Damdama (8750m 2 ), Mahadaha (3900 m 2 ), and Sarai Nahar Rai (2800 m 2 ) were
located on the banks of oxbow lakes on the Gangetic Plains (Figure 1). They are large
sites with deep cultural deposits, rich in artifacts and are considered to be semi
sedentary sites (Pal 1992). Most Mesolithic sites are considered temporary campsites
because they are composed of a thin lens of artifacts confined to a small area. These
sites are also unique in having yielded a large number of human burials, among the best
preserved for this period (Misra 1977). The similarities between the artifact
assemblages, burial customs, and skeletal morphology of the remains at these sites may
indicate cultural (and possibly genetic) interaction between this community of sites.
Lekhahia is located 160 km south of Mahadaha at latitude 24 0 47’ N and
longitude 82 0 8’ E in the Mirzapur district (Misra 1977; Lukacs, 1993b). The site
consists of five rock shelters, excavated in the 1960’s by Allahabad University.
Seventeen graves were discovered and tentatively attributed to eight burial phases
based on stratigraphic associations and relative dates (Sharma 1965; Lukacs 1997).
These associations are tentative because there is evidence for repeated disturbance of
Figure 1: Map of Mesolithic Sites on the Gangetic Plain
the site (Lukacs 1997). The graves contained the remains of 27 individuals, some
represented by as little as a single bone or tooth (Lukacs 1997).
Radiocarbon dates from Lekhahia range between 5410 +/ 115 and 3560 +/ 105
(See Table 1). The site was also dated using Accelerated Mass Spectrometry on
bioapatite from bone samples, this technique yielded dates for the Mesolithic layers
between 8,3708000 BP (margin of error was +/ 75 years for both dates) (Lukacs
1996). These dates support contemporaneity between Lekhahia and Damdama. The
dates also increase the temporal difference between the Mesolithic huntergatherers and
later agriculturist groups, which may call into question theories about the relationships
between the two groups (Lukacs et al. 1996).
Mahadaha is located along the banks of an oxbow lake on the Gangetic plain
proximate to Damdama (25 0 59’ N latitude and 82 0 11’ E longitude) (Misra 1977). The
3900 m 2 site was discovered during the construction of a canal that disturbed several
burials and was excavated by Allahabad University (Kennedy 1992). The site contained
28 graves and 32 burials associated with 35 pit hearths, microliths, burned clay lumps,
rubbed ochre, quern and muller fragments, hammer stone fragments, and rubbed
hematite pieces (Ibid.; Pal 1992).
The skeletons from Mahadaha are relatively numerous and well preserved. The
burials have been assigned to four phases of the Mesolithic, based on stratigraphic
associations (Kennedy 1992; Pal 1992a, b). The graves were oblong and shallow and
the skeletons were in a supine position (Ibid.). The graves were filled with material
from the hearths and there was a 46 cm thick soil cushion lining (Ibid.). All of the
burials were single except for burial I, which contained a male and a female buried side
by side and burial V, which contained two individuals, one laying in a prone position
on top of the other in a supine position (Pandey 1996). The Mahadaha skeletons were
oriented east to west or southeast to northwest, with the skull at either end. Some
graves included bone rings and jewelry, rubbed ochre, microliths and animal bones
(Kennedy 1992).
Sarai Nahar Rai is located 15 km southwest of Pratapgarh on the banks of a
fossil oxbow lake at latitude 25 0 48’ N and 81 0 51’ E longitude, proximate to
Damdama and Mahadaha (Misra 1977). The surface of the 2800 m 2 site was “littered”
with chalcedony and carnelian geometric microliths (Ibid.; Kennedy 1986). The site
was excavated by Anthropological Survey of India in 1970 and Allahabad University
beginning in 1972 (Misra 1996). Sarai Nahar Rai was the first Mesolithic site on the
Gangetic Plain in which burials were discovered (Lukacs 1993b). The undisturbed
oblong graves were lined with a cushion of soil 3 cm deep and filled with ash from the
hearths that included bone fragments and microliths (Kennedy 1986). Microliths and
shells may also have been offered as grave goods (Misra 1977). The graves contained
fifteen adult skeletons associated with eight pit hearths (Ibid.). The skeletons were in
extended and supine position, oriented east to west (Kennedy 1986). Most individuals
were buried singly but one grave (VII) contained four individuals, two males and two
females (Misra 1977; Kennedy 1986).
Damdama
Damdama is a habitation site located at 26 0 10’ N latitude and 82 0 10’ E
longitude on the Gangetic plain, approximately 25 km northwest of the Mesolithic site
of Mahadaha (Lukacs and Pal 1993b). The site is situated on the banks of an oxbow
lake formed by two tributaries of the Tambura nala, north of where they meet the Sai
River (Pal 1992). The site covers an area of 8750 m 2 , with a 1.5 m deep cultural deposit
(Pal 1992). There are 10 stratigraphic layers in the Mesolithic period, the uppermost is
postMesolithic deposit. Excavations were conducted under the direction of J.N. Pal of
Allahabad University and continued over five field seasons (19821987) (Pal 1985;
Lukacs and Pal 1993b). Burials, microliths, bone objects, querns, mullers, hammer
stones, burned clay lumps, charred grains and faunal remains were recovered (Ibid.).
Four phases of burial activity were discovered at Damdama and 41 graves were
excavated from the western and central areas of the site (Pal 1988). The graves are
within the habitation area and were generally near hearths, material from which was
used as fill in the graves (Pal 1992a). Grave goods included microliths (in graves VII,
XVI, and XVIII) and a perforated ivory pendant in grave VII (Ibid.). The graves are
shallow and oblong resembling burial customs at Sarai Nahar Rai and Mahadaha. The
orientation, positioning and contents of the graves at Damdama, Mahadaha, and Sarai
Nahar Rai are similar and may represent a cultural tradition common to the region in
the Mesolithic (Kennedy 1986).
The majority of burials were single, oriented east to west with the skull to the
west. However, Damdama is exceptional in that there were five double burials (in grave
nos. VI, XX, XXVI, XXX, and XXXVI) and one grave (XVIII) contained a triple
burial with two males and one female (Pal 1988). The majority of the individuals were
buried in a flexed supine position. Two individuals (in graves I and XXVIII) were
buried in a flexed prone position, a custom previously unknown in the Indian
Mesolithic (Pal 1996). Some researchers have suggested an ethnoarchaeological
interpretation for the presence of the flexed position at Damdama and Mahadaha
(Ibid.).
The microlithic assemblage is considered typical for the Indian Mesolithic
period. The collection includes retouched blades, scrapers, points, awls, triangles, and
trapezes (Ibid.). Blade tools made up the largest percentage of the assemblage (58.93
%), followed by triangular microliths (13.17 %) (Pal 1985). The tools were primarily
manufactured from chalcedony and chert (Ibid.) Pal (1985) has suggested that the raw
material for stone tool production is found at a distance of 100 km from the site and
economy was vital to their production, with every workable fragment having been used
as a tool. Heavily worn grinding stones and sickles were recovered and the usewear
analysis of the heavily polished blades suggests use in cutting grasses and plants (Ibid.;
Pal 1996).
Kajale (1996) conducted a preliminary analysis of the floral remains from
Damdama. Floral remains are scarce in the Mesolithic sites of North India (Kajale
1996). Six plant taxa were identified at Damdama, three to species, including:
buckwheat (Polygonaceae sp.), mint (Labiatae), nightshade (Solanaceae sp.), wild
grasses (Heteropogon contortus, H. sp.), goosefoot (Chenopodium album), and
Purslane (Portulaca oleracia) (Ibid.; Lukacs and Pal 1993b). There is also evidence for
the presence of the Indian jujube (Ziziphus). Impressions of caryopsis, glumes,
Ziziphus, and charcoal from bamboo have been discovered at Mahadaha and
impressions of rice husks (Oryza rufipogon and O. spontanea) have been found at both
Lekhahia and Mahadaha (Kajale 1996).
The faunal remains (21,000 bone fragments) were analyzed at Deccan College
in Pune. Much of the material was not identifiable, but 27% of the bones were
identified as belonging to over 30 species of animals (Thomas 1995). Of the
identifiable bone, 77% were mammals, almost all of which were species of deer,
tortoise, gaur (Indian bison), wild buffalo and wild pigs (Sus scrofa) comprised the
majority of the rest of the material (Ibid.). Approximately 90% of the bone fragments
(identified or not) were charred, most completely and some were calcined (Ibid.).
Most of the faunal remains were extremely fragmentary due to processing
activities. Processing activities were localized around the site in discrete butchering,
refuse, and dwelling areas (Ibid.). Bone was the most readily available local source
material and a bone tool processing area may be indicated at Damdama by the presence
of unburned long bones from large mammals in a location separate from food refuse
(Ibid.). Intact bone tools recovered include bifacial points, blades, knives, chisels,
scrapers, saws, and harpoons (Ibid.).
The skeletal morphology of the Gangetic Plains populations of Damdama,
Mahadaha, and Sarai Nahar Rai appears to be relatively homogeneous: tall, robust
people with well developed musculature on the appendicular skeleton. The cranial
morphology and odontometric profiles further indicate that they may share genetic
affinities (Lukacs 1993b). Gene flow between these groups has not been positively
established but it is possible, given that they are thought to be roughly
contemporaneous (Ibid.; Lukacs 1996). There are few apparent biological affinities
between these populations and later Indian peoples, with the possible exception of the
people who inhabited Neolithic Merhgarh (Ibid.; Lukacs 1992b, 1993b).
At both Mahadaha and Sarai Nahar Rai, pathological lesions were limited to
vertebral exostoses and osteoarthritis (Kennedy 1986, 1992). Squatting facets on the
male tibiae, perforation of the olecranon fossa, and hypertrophy of muscle attachments,
especially on the right arm, in individuals from Mahadaha and Sarai Nahar Rai have
been interpreted as evidence for habitual activities (Ibid.). Some slight bowing of limb
bones may suggest rickets or osteomalacia, but may also be a product of individual
variation. These findings and the lack of evidence for communicable disease, parasites,
nutritional deficiencies, and dental caries are generally consistent with the profile
expected to accompany huntergatherer subsistence.
Lukacs and Pal (1993b) analyzed the dental pathological profile for Damdama
and found that females had higher prevalence of caries, abscesses, AMTL, and pulp
exposure, whereas males had higher prevalence of calculus, alveolar resorption, and
enamel hypoplasia. The rates for pulp exposure, abscess, AMTL, enamel hypoplasia,
calculus, and resorption were lower for the skeletons from Mahadaha, caries rates were
higher. All of the carious lesions at Damdama and Mahadaha were on molar teeth.
The dental pathological profile at Mahadaha and Sarai Nahar Rai could be
interpreted as supporting the inference of huntergatherer subsistence as well. Lukacs
and colleagues found a pattern of few cavities but heavy attrition and hypoplastic
defects, indicating episodic dietary stress (Lukacs 1982; Lukacs 1991; Lukacs and Pal
1993b). The skeletal populations also had relatively high rates of antemortem tooth loss
(AMTL) and subsequent alveolar resorption, resulting from attrition rather than caries.
Dental size has also associated with subsistence, and Lukacs (1993b) found a pattern of
large tooth size consistent with huntergatherer populations.
Lukacs, Pal and Misra (1996) attempted dietary reconstruction based on the
carbon and nitrogen stable isotopes in bioapatite samples from Damdama and
Lekhahia. The change in Carbon13 ratios for Damdama were less negative than those
for Lekhahia, meaning that the people at Damdama were eating more C4 foods (grassy
plants), while the population at Lekhahia was concentrating mainly on meat and C3
plants (trees, shrubs, and tubers).
Dental and isotopic evidence that the primary mode of subsistence for these
Mesolithic plains communities was hunting and gathering, supplemented with
exploitation of aquatic resources, is supported by floral, faunal remains, and rock art at
Lekhahia. At Mahadaha, faunal remains included B. gaurus, rhinoceros, S. scrofa,
elephant, stag, deer, antelope, gazelle, turtle, fish and birds (Alur 1980; Kennedy 1992).
From the hearths at Sarai Nahar Rai excavators recovered charred and uncharred faunal
remains of the following species: Bos indicus, B. bubulus, B. gaurus, Ovis sp., Capra
sp., Elephas indicus, as well as tortoise and fish bones (Misra 1977; Alur 1980). Alur
(1980) has analyzed the faunal remains and has suggested that the species were wild
types, though incipient domestication is possible.
The faunal remains from Damdama, Mahadaha, and Sarai Nahar Rai have been
interpreted as evidence of huntergatherer subsistence with possible incipient
domestication (Alur 1980; Thomas 1996). The presence of bone harpoons, aquatic bird,
fish, and turtle remains suggest exploitation of aquatic resources. The temporal and
spatial distributions of faunal remains indicate that the size of mammalian species
tended to decrease through time (Thomas 1995). In addition, the quantity of
mammalian species hunted varied inversely with the quantity of avian and reptilian
species exploited, the latter may have been exploited in times of scarcity (Thomas
1996). Time averaging seems unlikely because of the distinct and undisturbed
stratigraphic layers, but preservation bias or cultural processes may play a role in these
patterns.
The paleodemographic profile has yet to be published for Damdama, though the
site is crucial for any reconstruction of Mesolithic culture on the Gangetic Plain in
India. Estimates of age at death will form the foundation for future biocultural research
on this important period in Indian prehistory. The Mesolithic is well represented by
numerous, well preserved burial sites, and thus provides an opportunity for population
studies. As a transitional period between the Late Paleolithic and the development of
agriculture in the Chalcolithic and Neolithic, the site is a key component to
characterizing the development of agriculture and initial sedentary cultural systems in
India.
CEMENTUM ANNULATIONS AND
MULTIVARIATE METHODS OF AGE ESTIMATION
This chapter has four main intentions, 1.) to summarize the ontogeny and
function of the cementum, 2.) to examine some of the issues surrounding the
ultrastructural properties of the annulations 3.) to examine the basis for using
cementum annulations for estimating age at death, and 4.) to review the literature on
age estimation using cementum apposition in univariate and multivariate analyses. The
ultrastructural nature of cementum annulations is unknown and has been described as
an artifact of sample preparation, section thickness, and as an optical illusion (discussed
further below). However, the number of annulations in the cementum has been
correlated with known age at death as high as 98% (Charles et al, 1986, 1989). Thus an
examination of what is known about the development and structure of cementum, and a
thorough review of the protocols used for age estimation is crucial to successfully
applying this method.
The Ontogeny and Physiology of Cementum Annulations 1
Cementum is an extracellular matrix composed of calcified collagenous
Sharpey’s fibrils, collagen, glycosaminoglycans, proteoglycans, and inorganic
hydroxyapatite. Cementum initially develops in utero upon the disintegration of the
epithelial sheath of Hertwig surrounding the tooth germ. The disintegration of the
sheath exposes the root dentine to the follicle, stimulating the differentiation of the
cementoblasts from mesenchymal cells. The surface of the root dentine is initially
covered with a nonmineralized hyalin layer formed from ectomesenchymal and
epithelial products. This hyalin layer is thought to bind the cementum to the dentine.
Once the cementoblasts have differentiated, they insert cytoplasmic processes into the
hyalin layer and begin to deposit collagen fibrils and extracellular matrix as they move
away from the root surface.
As the tooth erupts, acellular cementum slowly develops, eventually covering
1 This discussion of the ontogeny and biology of cementum is based on Ten Cate (1998)
the coronal twothirds of the root surface. The cellular cementum around the apical
portion of the root is formed more rapidly, trapping cementoblasts within lacunae that
eventually become cementocytes. Once the tooth has reached occlusion the
cementoblasts in the acellular region are resorbed and additional layers of cementum
are formed by the fibroblasts in the periodontal ligament. Although cementum can be
complex and patchily distributed, in general the outermost bands (closest to the
periodontal ligament) are termed intermediate cementum. At the innermost (closest to
the dentine), youngest levels, the cementum is unmineralized and is termed the
cementoid (or precementum).
Acellular cementum meets the enamel at the CEJ and gets progressively thicker
towards the apex of the root, where most of the cementum is cellular. Acellular
cementum serves as the tooth’s anchor within the alveolus whereas the primary
function of cellular cementum is to adapt to tooth movement and wear and keep the
tooth in the occlusal plane (Ten Cate 1998). The main difference in the ultrastructure of
cellular versus acellular cementum, is the inclusion of cementoblasts within lacunae
and their subsequent development onto cementocytes.
There are extrinsic and intrinsic collagen fibers present in both cellular and
acellular cementum. The collagen fibers synthesized by the cementoblasts during
eruption are labeled intrinsic. The fibers produced by the fibroblasts in the periodontal
ligament are labeled extrinsic. This collagen is organized into long Sharpey’s fibers
running between the periodontal ligament and the root dentine. Cementoblasts deposit
intermediate cementum around the fibers that, once mineralized within the cementum,
anchor the tooth root to the periodontal ligament. This process occurs slowly until the
tooth erupts to occlusion, at which point the cementoblasts stop producing the
intermediate cementum.
The correlation between age and apposition is considered strongest in the
cementum 1/3 of the distance from the root’s apex, where it is less compressed than the
cementum near the CEJ but has less cellular cementum than the root apex (Naylor
1985; Charles 1986). In general, the thickness of healthy cementum increases threefold
between the ages of 1176 years (Kvaal et al. 1996). The deposition of acellular
cementum is thought to be seasonally controlled. The annulations grow with regularity
throughout the life span and are more easily distinguishable in the acellular component
(Lieberman 1992). The bands in acellular cementum are better predictors of age than
bands in the cellular cementum because environmental factors and the stresses that
cause passive eruption and hypercementosis influence the acellular cementum to a
lesser extent.
Cellular cementum also forms in increments around the apical portion of the
root. However, the bands in acellular cementum are uneven in width and distribution
partially because it is deposited more rapidly than the acellular component. The width
of cellular cementum also varies by proximity to the dentine, younger layers being
thinner as the rate of tooth eruption slows. The cellular cementum has additional rest
lines representing periods of slow growth and differential degrees of mineralization.
The precise mechanism and regulation underlying this process is not well understood.
However, eruption, occlusal stress, tooth size, and attrition appear to influence the rate
and amount of cellular cementum deposition (Lieberman 1992). For example, “passive
eruption” or hypercementosis can result from a combination of these factors. In this
condition, the cementum deposition increases at the root apex in an attempt to keep the
clinical crown in occlusion.
The Ultrastructural Nature of Cementum Annulations
Acellular cementum annulations are visible in transmitted and polarized light
microscopy, and in microradiography as alternating translucent and opaque bands.
Cementum is a complex and patchy tissue and the exact nature of the incremental
structures is unknown. Hypotheses concerning the nature of the annulations include
differential matrix deposition rates across the translucent and opaque bands
(determining the amount of intrinsic fibers and cells), changes in mineralization or
extrinsic fiber orientation. Spinage (1973) suggested that the bands represented
hypercalcified areas, periods of hydroxyapatite crystal formation, in contrast to areas of
decreased matrix production. Grue and Jensen (1979) suggested that the hypercalcified
bands formed during arrested matrix production. The opacity difference between the
increments is often attributed to degree of calcification, but microprobe analysis has
shown a consistent ratio across the bands (Kvaal 1995). Schroeder (1986) attributed the
annulations to changes in the orientation of collagenous fibres. Gordon (1993) has
suggested that metabolic differences in the way that the matrix itself is laid down may
determine the shape, size and orientation of the crystals.
Renz and colleagues have published two attempts to discover the ultrastructural
nature of the cementum increments (1997, 1999). These two studies bring up some
interesting issues in histology as well as human biology. Their 1997 research paper can
be analyzed in terms of the importance of histological protocol for preservation of
ultrastructure. Using protocols given in Stott et al (1982), the premolars from clinical
extractions were fixed in formalin and then rinsed in running water for several hours.
100150 um sections were taken from the middle third of the root using a diamond
edged saw and polished with sandpaper. The sections were postfixed with Osmium,
dehydrated with ethanol replacement, and polymerized in resin. This sequence is a
standard EM protocol minus one ingredient (glutaraldehyde) in the fixing process.
Semi (0.52.0 um) and ultrathin (50100 nm) sections were then cut with the
microtome using a diamond knife.
This protocol suffers from problems that will destroy ultrastructure as well as
poor sampling choices. The authors may have chosen to fix the teeth in formalin
because they wanted to decalcify in addition to fixing the tooth’s ultrastructure at the
moment of extraction. However, formalin creates only weak crosslinks and does not
preserve as much ultrastructure as the more commonly used glutaraldehyde. When
formalin is used, the tissue should not be washed for several hours in water. This step
would reverse the crosslinks and buffer solution should be used instead. “The presence
of weak crosslinks introduced into the tissue by formaldehyde necessitates rather rapid
washing and dehydration.” (Hayat, 1970; italics mine) Glutaraldehyde could have been
used in combination with the formalin. Renz (1997) dried the specimens, if that drying
occurred prior to fixation and dehydration, the ultrastructure could also be damaged.
In terms of the location where Renz (1997) chose to make their microtome
sections, taking sections from both the cementum nearest the CEJ and nearest the apical
portion would have helped to clarify the cellular vs. acellular issue in terms of
ultrastructure and potential for age estimation. The authors did not specify what type of
cementum was used or where exactly the tooth was sectioned. The sections were
examined using Bright Field Light Microscopy (LM), Confocal Laser Scanning,
Transmission Electron Microscope (TEM), and Xray electrondispersive analysis in a
Scanning Electron Microscope (SEM). In the bright field LM, the authors found that
the main factors influencing the resolvability of the incremental lines were the
thickness of the sections, the medium in which they were examined, the focus plane (or
amount of overlapping information), and the illumination. Resin infiltration also
enhanced the visibility presumably because of the refractive difference.
The TEM sections were stained only with uranyl acetate and no lead citrate was
added. Usually, the two stains are used together as uranyl acetate stains have the most
affinity for phosphate groups and stain nucleic acids, membranes, and proteins well.
Lead citrate has the greatest affinity for glycogens, staining membranes and proteins
best. Renz (1997) published micrographs from the TEM showing oblique sections of
the Sharpey’s fibers but no specific ultrastructures corresponding to the increments
were seen at higher magnification. The reported thickness of the thin sections is too
thick to yield decent resolution in the Xray SEM analysis so the reported figures about
mineralization may not be accurate.
In 1999, Renz and colleagues published another attempt to discern the
ultrastructure of cementum increments. Transverse sections were removed from the
middle third of the root from five premolars. The same section was examined by LM,
CSLM, TEM, and in an SEM with an energy dispersive xray analysis attachment
(EDX). For this study, sections100150 um were cut on a Buehler saw and polished to
100 um. The sections were rinsed in water for an unspecified amount of time. The teeth
were postfixed after sectioning in glutaraldehyde and osmium tetraoxide, then
progressively dehydrated in ethanol. The ground sections were then embedded in
Spurr’s resin. Once the thin sections were embedded, the ultrathin sections were made
on a microtome using a diamond knife.
The ground sections (100 um) were stained with toluidine blue or methylene
blue. The cementum increments were clearly discernible in the photographs from the
light microscope in the 100 um sections. The structures seemed to disappear in the
published photograph of the 1 um sections, leaving only the granular textured substrate.
However, inspection of the caption beneath the two photographs reveals that the one
section was left unstained, probably heavily contributing to the absence of discernable
structures. The author’s state that whether or not the semithin sections were stained,
the annulations never materialized.
The CSLM picked up lines that had been visible in the bright field light
microscopy, as well as the presence of cementocytes, indicating that the region
analyzed was at least partially composed of cellular cementum. The TEM micrographs
of the ultra thin sections (80 nm) show no evidence of distinct annulations. There were
crosssectioned fiber bundles, however they had no spatial relationship to the location
of the annulations seen in bright field. The EDX analysis recorded small fluctuations in
the calciumphosphorous ratio across the cementum, however the authors attribute
these small variations to changes in the signal intensities of the beam current. The
authors conclude that the nature of the structures is still unknown and that the
possibility remains that they represent an artifact of section thickness and optical
properties.
As the latter study used a properly designed histological protocol, their case is
certainly strengthened but more research is obviously necessary in this area. The
disappearance of the incremental structures in section 1 um thick is questionable given
the success of Charles and colleagues (1986, 1989) in evaluating cementum annulations
in demineralized sections 7 um thick. The sections examined in the TEM may have
been located in areas of ground substance matrix between mineralized areas and may
simply have bypassed any ultrastructural evidence. If the increments represent changes
in mineralization, then an unmodified TEM may be inappropriate for observing the
ultrastructure of the increments.
Studies of ultrastructure in dental tissues are difficult due to the hardness of the
materials and the need for ultrathin microtome sections, requiring the use of expensive
diamond knives. There are communication and interdisciplinary barriers preventing
studies using biologically relevant protocols by researchers specifically interested in
aging and other archaeological applications. The studies are crucially important and
electron microscopic investigations, elemental signature analyses, and microprobe
studies will eventually unlock the biological basis behind this seemingly agerelated
phenomenon. The relationship between the cementum and the periodontal ligament,
biochemistry, biological cycles, and dietary stress are other factors considered
important to better understanding the increments.
Cementum Apposition and the Estimation of Age at Death
Although the structure is not well understood, in almost all mammalian species
the number of bands in the dental cementum are correlated with the age at death
(Gordon 1993). Studies of incremental structures in the dental cementum can be traced
back to Malpighi’s study of cementum in the 1600’s (cited in Gordon 1993). Retzius
(1837) investigated the ‘striae’ in cementum and Tomes (1904) identified the granular
inclusions that bear his name (cited in Gordon 1993). During this century there has
been considerable research on these annulations in mammals (Bosshardt and Schroeder
1996 for rodents; Beasley 1992 for Bos taurus) and in humans (Gustafson 1950; Dalitz
1963; Johanson 1971; Maples 1978; Solheim and Sundnes 1980; Stott 1982; Naylor
1985; Charles et al. 1986; Condon et al. 1986; Lipsinc 1986; Miller 1988; Lorentsen
and Solheim 1989; Kashyap and Rao 1990; LopezNicolas et al. 1990, 1991, 1996;
Solheim 1990; Solheim and Kvaal 1993; Stein and Corcoran 1994; Kvaal and Solheim
1995; Kvaal 1996).
Cementum annulations are used by ecologists to study demographics in modern
mammalian species. The annulations are commonly used as an aging method for
archaeologically derived and forensic human skeletal remains, and to determine age
and season at death in archaeologically derived faunal remains (Lieberman and
Meadow 1992, for Gazella gazelle). In many mammalian species, the refractive nature
of the outermost cementum band is considered representative of season at death
(Beasley 1992; Lieberman 1992; Gordon 1993). When the outermost bands are opaque,
they indicate death in the nongrowth season (rest lines). Translucent bands are used to
indicate that the organism died during the growth season. Archaeologists use cementum
increments on faunal collections to ascertain human subsistence patterns. The history of
cementum studies as presented here is focused on aging methods for humans.
Cementum is an attractive tissue for age estimation in human skeletal
populations because: 1.) The thickness of acellular cementum is proportional to the age
of the tooth (Stein 1994). The number of cementum annulations added to the age at root
completion, or age of eruption, has an approximately linear relationship with age at
death. The tissue is continuously deposited throughout the life span of the tooth and is
rarely remodeled or destroyed in vivo in the absence of other pathological conditions
(Lieberman 1992). Unlike bone, cementum is not regularly resorbed and there are no
cementoclasts in the permanent dentition. 2.) Cementum has a protected location within
the alveolus, which makes this delicate material less sensitive to the oral environment
and pathological conditions. This protective function is negated in cases of
periodontitis, caries, and abscess. SEM analyses of the cementum surface have shown
significant modifications to its structure in those situations.
The linear relationship between cementum annulations and age at death is not
simple and has been questioned by many authors (Solheim and Sundnes 1980; Miller
1988; Lucy 1995, 1996). There is a tendency for overestimation of age in younger
individuals and underestimation in older individuals (Lucy 1996). Several investigators
have noted an opposite tendency toward underestimation in individuals over thirty
years of age (Solheim and Sundnes 1980; Miller 1988). Some of these problems may
be attributed to inappropriate analyses such as the use of linear regression for discrete
data. The use of linear regression requires several assumptions about the data, 1.) that
the variables have a linear relationship to age, 2.) the data is normally distributed, 3.)
the error is normally distributed about the mean, 4.) and that all variables are weighted
equally (Hillson 1992; Lucy et al. 1996).
Many studies have used age as the dependent variable, a tendency that may
cause systematic, agedependent errors in least squares regression analysis (Akroyd
1997). If the data is collected in a discrete, or categorical rather than an ordinal scale, a
nonparametric Bayesian approach may be a better measure of accuracy (Lucy 1996).
Instead of giving an age estimate and confidence interval, this method gives the
probability that an individual falls within a 10 year range around the estimate (Hillson
1992; Lucy 1996). Thus the linear relationship between age at death and the number of
annulations is not straightforward and there is some evidence to the contrary. Condon
and colleagues (1986) found that in a reduced major axis analysis the slope of their
regression line was less than one, indicating that age was accumulating faster than the
annulations. When analyzed by sex, the males in the sample were more responsible for
the decreased number of annulations as compared to known age (Charles 1989). There
are anomalous cases in which individuals appear to have no discernable annulations or
the number is obviously doubled (Condon 1986). The reasons for these anomalies are
thus far unknown.
Questions have also arisen regarding other statistical methods used in
histological aging studies. Gustafson’s (1950) work in particular has been criticized
because he tested his model using 19 individuals from the original sample used to
develop the model. He has also been criticized for using the average difference between
estimates and known age, an “average error of estimation,” rather than standard error or
dispersion of the estimates about the regression line (Lucy 1995). Gustafson (1950)
calculated his error to be +/ 4.5 years, a figure that has never been reproduced by any
researcher using his method on an independent sample. Gustafson published his
original data, which Maples and Rice (1979) used to calculate a standard error of 7.03
years for his regression line. Johanson’s (1971) method has been determined to be more
accurate and his regression formulae give estimates closer to known age at death in
independent tests (Lucy 1995).
The use of cementum annulations to estimate age at death is further complicated
by the presence of pathological conditions, particularly periodontal disease.
Periodontitis has been shown to have a significant effect on the number of annulations
present in human teeth (Solheim and Sundnes 1980; Condon 1986; Charles 1989).
Periodontal disease exposes cementum to the oral environment, weakens periodontal
attachments, and increases cementum apposition at the root apex. Charles and
colleagues (1989) found that eliminating 18 cases of periodontal disease from their
sample greatly increased the correlation between the number of annulations and the
known age at death, moving the slope of the regression line in a reduced major axis
analysis closer to one.
Periodontitis not only effects cementum thickness, it impacts the ultrastructure
and elemental composition as well. In contact microradiographs, healthy cementum has
alternating layers of high and low mineralization (Simon 1981). When periodontal
disease exposes the cementum to the oral environment, a thick outer layer (10 um) of
hypermineralization develops, characterized by dense “tabletshaped” hydroxyapatite
crystals (Ibid.).
In the SEM, teeth affected by periodontal disease show a haphazard
arrangement of the cellular cementum matrix. There are more calcification projections
and lacunae depressions, and decreased numbers of fibers overall. This indicates an
increase in the level of reparative and regenerative activity. There is also an increase in
collagen destruction with exposure to the oral environment, and mineralization of
remaining collagen fibers increases as the tooth loosens. The increase in projections
and depressions may be due to increased calcification resulting from inflammation.
Quickly formed cementum, caused by both faster paced mineralization at the
‘resorption bays’ along exposed areas (Hillson 1986) as well as hypercementosis at the
apex in numbers of fibers, could also cause a less organized structure to be laid down.
In xray spectrometry analysis, healthy cementum has a relatively equal
proportion of phosphorous and calcium (Simon 1981). Periodontally diseased
cementum has a preponderance of calcium and a deficit of phosphorous. This increased
level of calcification has been found in periodontally diseased acellular cementum as
well. The incremental bands are observed in healthy and periodontally diseased
cementum, indicating that the increments are not solely a product of changes in mineral
content, though they might still represent changes in the crystalline structure across the
bands, as Gordon (1993) suggested.
Two major ultrastructural changes have been observed on periodontally
diseased cementum examined using electron microscopy: tabletshaped crystals at the
surface of exposed cementum that have an xray diffraction pattern consistent with
hydroxyapatite, and hypermineralization (Armitage and Christie 1973b). The surface
area of the exposed cementum also often has microorganisms from calculus deposits
and destruction of the remnants of periodontal ligaments (Ibid.). In light microscopy,
no major changes have been observed with the exception of granular structures on the
surface of exposed cementum (Bass 1951, cited in Armitage and Christie 1973b).
These granular structures may be explained by the vacuoles discovered by
Armitage and Christie (1973b). The ‘vacuoles’ were not observed in any of the sections
from unexposed cementum. There were four common patterns for the ‘vacuoles’: 1.)
isolated occurances, 2.) grapelike clusters, 3.) chainlike aggregates, and 4.) long
fissured areas in one specimen. These ‘vacuoles’ tend to follow the pattern, orientation,
and configuration of the collagen bundles. The ‘vacuoles’ may represent demineralized
areas caused by periodontal disease. This speculation is supported by xray diffraction
studies (Simon 1981) in which hypermineralization occurred at the surface in exposed
cementum, but overall there was a lack phosphorous. The hypercalcification at the
surface of exposed cementum may be a product of the oral environment and a response
to an overall decrease in mineralization.
The regenerative and other fluid processes in cementum that occur within the
life span warrant further investigation. The formation rate of cementum has been
calculated for certain mammals and has been found to accumulate at a constant rate
barring pathological disruption. In cases of pathological disturbance, the cellular
cementum may regenerate and the overall width may be maintained. If the root has
been exposed to the oral environment, the tooth should be included in histological
analyses with caution, if at all. A better understanding of the pathological changes to
the ultrastructure is needed to understand any impact on age estimations.
Review of the Literature on Age Estimation
Despite the fact that the biological basis for cementum annulations in humans is
not well understood, and though Gustafson (1950) and Johanson’s (1971) methods have
been criticized, they are still considered the most accurate methods for aging adults
from dental histology in forensic circumstances (Lamendin 1992). A summary of
studies employing cementum increments, or thickness, is presented here to demonstrate
the variety of protocols employed and the range of success correlating cementum with
known age at death. Multivariate methods are included in this section as well.
Dalitz (1963) successfully applied Gustafson’s (1950) scale for attrition,
periodontitis, secondary dentine, and translucency to an independent sample of known
age at death (r = 0.88). Johanson (1970) refined the Gustafson technique and added a
multivariate analysis (discussed in the introduction). However, Burns and Maples
(1976), Maples (1978) and Maples and Rice (1979) were among the first investigators
to suggest that tooth class, pathology, and ethnicity influenced age estimates from
cementum annulations. These authors were also the first to critique Gustafson’s
statistical methods.
Burns and Maples (1976) applied the Gustafson method to a sample of 355
individuals of known age, sex, race, and socioeconomic class. They found that these
variables had significant effects on the results, as did periodontal disease. Maples
(1978) found that root resorption had the worst correlation with age. This result is not
surprising given that in general age estimation methods that rely on degenerative
processes are less useful than are those that rely on developmental or even appositional
processes.
Maples (Ibid.) found that the two variables with the highest correlation with
known age at death were root translucency and secondary dentine. He suggested that
periodontitis should be eliminated as a criterion because it is difficult to determine the
extent of periodontal disease after decomposition of soft tissues. He also tested the
correlation between estimated and known age by tooth class and found that the most
accurate results came from estimates made using central incisors, lateral incisors, and
second molars.
Solheim and Sundnes (1980) compared the age estimates obtained from
traditional macroscopic observations with those obtained using the intact tooth method
of observing root translucency (Bang and Ramm 1970) and the Gustafson method
(Dalitz 1963; Miles 1963; Johanson 1971). They found that Johanson’s (1971) method
was the most accurate among the histological techniques, and compared most closely
with traditional macroscopic estimates coming within one or two years. The standard
error was calculated at 10 years, much less than the error for the Bang and Ramm
(1970) method. The authors found no significant differences in accuracy of estimates
from pathological specimens, by sex or tooth class.
Stott (1982) was the first researcher to test the correlation between known age at
death and a count of the number of increments, rather than the thickness of cementum.
He made serial sections of 80 um thickness from a sample of 19 canines from cadavers.
He stained the sections with 2% alizarin red for 4 minutes. His correlation coefficient
was very low (r = 0.263). The standard error was 4 years in his examination of 10 teeth
derived from three cadavers, aged 57, 67, and 76.
Naylor (1985) conducted a study to determine the optimal section thickness and
stain for observing the increments. He did not report his sample size nor did he give full
details about his technique. He determined that transverse sections should be taken
from 1545% down the root from the CEJ, in order to avoid both cellularity and
compression. He sectioned the teeth to thicknesses of 50, 75, and 100 um. He also
tested the following stains: Mayer’s haematoxylin, luxol fast blue, celestine blue,
Congo red, Harris’ haematoxylin, thionin, eosin, Bibrich scarlet acid fuchsin, chlorazol
black E, nuclear fast red, aniline blue, alcian blue, alizarin red S with ammonium
hydroxide, gallocyanin, toluidine blue, and silver. He found that 0.1% Cresyl fast violet
for 3 minutes provided the best resolution in mineralized sections. His annulation count
was made from black and white photographs at 200x magnification. He found that the
annulations were clearest on the labiomesial and linguodistal aspects of the root.
Condon and colleagues (1986) focused on calculating the inaccuracy and bias of
cementum annulation studies. They demineralized 80 premolars from clinical
extractions and sectioned them to 7 um thickness. The sections were stained with
haematoxylin and photographed at 400x magnification. The increments were counted
from a slide projection of the section. Their sample had originally included extracted
canines but these teeth were eliminated from the final analysis because they
systematically overestimated age at death by 10 years.
The authors performed a least squares regression of cementum increments (y)
on known age (x). Measures of inaccuracy (average absolute error) and bias (mean over
or under prediction) were calculated for each decade of known age. The standard error
was calculated using a jackknife technique, in which one tooth was eliminated from the
sample in a series of regression analyses, and these teeth were used as the independent
sample. The authors found that 4% of their sample teeth showed no evidence of
annulations. It is likely that the increments were not distinct enough to be counted, or
did not stain properly. The individuals without annulations, 2 females (24 and 37 years
old) and one male (63 years old) were eliminated from the sample. The overall
thickness of the cementum was not measured; it is unknown whether it was correlated
with known age for these individuals.
An additional four individuals were eliminated from the sample because they
showed clear evidence that the number of annulations had been doubled. The authors
do not indicate if the thickness of the cementum was doubled as well. This doubling
phenomenon is difficult to detect unless the individual is older and the count exceeds
100, making that case an extreme outlier. The reasons behind doubling are unknown,
however it is also found in indigenous tropical species and is thought to be related to
seasonal fluctuations (Charles 1989). Cementum annulations are absent in domesticated
animals whose exposure to seasonal fluctuations has been lessened (Grue and Jensen
1979). Exposure to seasonal and environmental fluctuations in modern human forensic
samples could generally be considered extremely modified and reduced.
Of the 73 individuals remaining in this sample, the authors achieved a
correlation of 97.3% between their estimates and the known age at death (the standard
error was 9.6 years). There were no significant differences between the sexes in terms
of the accuracy of the technique. Condon and colleagues (1986) also found that
periodontal disease had a significant effect on the number of annulations. There were
18 cases of periodontal disease, when these individuals were removed from the sample,
the correlation surprisingly decreased to 96.5% between estimates and known age but
the standard error was also significantly reduced to 7.4 years. The authors found that
the residuals increased in individuals over 30 years of age, meaning that the number of
increments represented biological age rather than chronological age, which was
accumulating faster than the increments. The authors suggest the difference may be
attributed to population differences in the rate of annulation formation.
Charles and associates (1986, 1989) attempted to define a standard for
estimating age at death using cementum annulations in response to criticisms of
paleodemography discussed above (BouquetAppel and Masset 1982). The authors
used 52 canines and premolars sampled from a forensic population with known age at
death. They tested several methods for preparing the sections using decalcification,
different thicknesses and stains (Condon 1986; Charles 1986; Charles 1989). The first
method most closely resembles the technique employed in this study: the teeth
remained mineralized, they were sectioned to a thickness between 80150 um and
stained with a 12 % solution of Alizarin Red for 14 minutes, washed in ETOH,
cleared in xylene, and mounted on glass slides.
Charles and colleagues (1986) also examined mineralized sections that had been
left unstained and others stained with Cresyl Fast Violet. Demineralized sections were
also prepared and sectioned to 7 um using a microtome. These sections were stained
with haematoxylin. The authors found that a magnification of 400x was necessary to
clearly delineate and count the cementum annulations. Magnification of 100x is
associated with underestimation unless the overall thickness of the cementum is being
measured. Color photomicrographs were used to analyze the sections because of the
condensed nature of human cementum. The slides were projected to a size of 22 x 28
cm and counts were made in short transects in the area of the root with the clearest
definition, and then the transects were linked to form a total cross section. The authors
only had access to canines and premolars; they found the latter to be better indicators of
age at death because the canine cementum was generally thicker. The canines
systematically overestimated the age at death by 10 years.
In this study variability created by interobserver error accounted for 17% of the
total variation between the estimates and known age. Intratooth and intraobserver error
only accounted for 35% of the variation. Only 58% of the sections in the mineralized
sample and 76% of the demineralized sections were scorable. Since sectioning a tooth
perfectly perpendicular to the cementum increments is not possible, it is preferable to
use a demineralized section of 7 um because there will be less overlapping information.
However, the researchers also tried both techniques on an archaeologically derived
tooth and the sections from the demineralized sample appeared to be macerated
(Condon 1986).
Lipsinc (1986) examined mineralized and demineralized sections from 31 teeth
that had been extracted in clinical practice. Formic acid (10% solution) was used to
decalcify sections. The sections were made onethird down the length of the root in the
transverse plane to 5 um and stained with haematoxylin and eosin. The sections were
examined at 100x magnification at two loci. The counts from three investigators were
averaged and added to the age at eruption. The correlation was only 50.98% between
known age (y) and the count of cementum annulations (x). However, when the one
individual over the age of thirty was eliminated from the sample, the correlation was
increased to 84.47% (standard error was 8.04 years). When age was used as the
independent variable (x) and the count of cementum annulations as the dependent
variable (y), the accuracy was improved significantly (r = 0.9294, SE = 2.2). Lipsinc
(1986) concluded that the technique was unreliable because of the systematic
underestimation of age in individuals over the age of thirty.
Miller (1988) examined cementum annulations in 100 clinically extracted teeth
from individuals between 978 years old. Teeth were sectioned to 200400 um
thickness in the transverse plane through the middle of the root. The sections were
polished using 6 um diamond paste. Black and white photomicrographs were taken at
90x magnification and the observations of four researchers were averaged and added to
the age of eruption. Twentynine percent of the teeth had obscured or indistinct
annulations and were eliminated from the sample. Only 5.7% of the age estimates were
accurate within 5 years. The standard error was 10 years for 85% of the age estimates.
The regression line did not account for most of the variability in the estimates and there
was a large amount of scatter about the regression line. Ages were most accurate in
individuals under the age of 35.
Lorentsen and Solheim (1989) used a sample of 500 teeth, 50 of each type
excluding molars. They tested the Gustafson (1950) technique with teeth that were
longitudinally sectioned once in the buccolingual plane. The specimens were
photographed and the images projected for scoring each characteristic. Rather than
using Gustafson’s (1950) subjective ordinal scale for the root dentine translucency, the
authors created an index using the area of translucency, the total root area and the total
tooth area. They found that dentine translucency, secondary dentine formation and
cementum thickness were the variables most correlated with age.
Kashyap and Rao (1990) conducted a study designed to minimize the
difficulties in quantification of attrition, secondary dentine, translucency and cementum
annulations. They collected 25 teeth from cadavers in Hyderabad, India. The
individuals were between 1845 years of age. The authors applied the Gustafson (1950)
technique to their sample and obtained an error in estimation of +/ 8.13 years. The
authors also developed their own method of age estimation using indices for four age
related changes. Their index for attrition was the width of the worn area divided by the
width of the tooth at the CEJ. The index for secondary dentine deposition was the
length of the secondary dentine divided by the length of the entire pulp cavity.
Translucency was indexed by dividing the length of the translucent area by the length
of the entire tooth. The thickness of cementum was measured at the thickest point and
divided by the width of the tooth at that point.
The idea was to create indices for each measurement, to make the technique
more specific to individual variation. The method was highly accurate and precise in
their study. The index values showed a linear relationship with known age at death. The
cementum index required a square root transformation. Their estimates, derived from
the mean of the four indices, were very highly correlated with known age at death (r =
0.998). The standard error was 1.59 years, a smaller number than any produced thus far
by any other study. The precision of the technique warrants further testing of this
method on samples of known age, particularly given that the original sample size was
25 individuals.
LopezNicolas and associates (1990) used digital image analysis in their test of
the Gustafson method. They collected 173 incisors from clinical extractions from
individuals between 1383 years. The teeth were halved and a central section 1 mm
thick was removed. The sections were photographed with color film at a magnification
between 918x through a binocular microscope. The following measurements were
estimated using the IBAS image analysis program: crown hemidiameter at the CEJ,
root hemidiameter at half the length from the apex to the CEJ, the width of the pulp
cavity at the CEJ, the thickness of the pulp cavity at half the length of the root, the
distance from the end of the pulp cavity to the apical foramen. The authors also
estimated the following areas using the IBAS image analysis program: secondary
dentine, root transparency, complete pulp area, pulp area from the apex to the pulp
cementum limit, apical resorption, cementum thickness, ideal crown area, and attrition.
The individual measures (y) were regressed on the known age at death (x) and
the following variables were found to have a significant positive correlation with age at
death: width of the pulp canal at the CEJ (x1), area of root transparency (x2), complete
pulp area (x3), the area of secondary dentine, and the area of estimated attrition. Their
regression formula is: Age = 56.4837 – 0.2757 (x1) + 07.3547 (x2) – 4.632 (x3) –
0.844 (x4) + 1.0461 (x5). The crown length (x4) and the estimated distance from the
periodontal attachment to the CEJ (x5) did not produce significant correlations in
univariate analyses. Unfortunately, the measurement of periodontitis is not reliable
once the soft tissue has decomposed, making this method less useful for archaeological
teeth.
Solheim (1990) tested Gustafson (1950) and Johanson’s (1971) methods for
measuring cementum thickness and root dentine translucency on a sample of 1000
teeth, 100 of each type excluding molars. In addition, the authors included a
measurement of root color. The teeth were halved and stained with carbol fuchsin.
They found a poor correlation between estimates and age at death (r = 0.31 to r = 0.72).
The authors provided no possible explanations for the poor correlation, though it might
be partially attributed to the use of cementum thickness at 1/3 the length of the root
from the apex, without regard to the cellularity of the cementum at that point. A count
of increments in acellular cementum may have produced better results.
Solheim (1993) published a list of critiques of the original Gustafson method
beginning with the subjectivity of scoring each of the criteria into discrete categories. In
fact, to account for this subjectivity, Gustafson had recommended that each worker
calculate his or her own regression formula from an independent sample (1950). Most
of Solheim’s critiques were focused on Gustafson’s statistical procedures. The use of
linear regression requires that the variables are truly independent of one another and
that there is a direct linear correlation between the independent and the dependent
variables. Solheim (1993) questions whether the variables are independent, but offers
no alternatives to this dilemma (see Lucy et al. 1995, 1996; Akroyd 1997).
Solheim's main critique of Gustafson is the method by which he derived his
estimate of the error of his technique. Gustafson used teeth from his original sample to
calculate the error of his method. Had he used an independent sample, the amount of
error would have been greater. In fact, Gustafson calculated his regression formula
incorrectly and the correlation has since been corrected to 91% from the reported 98%
(Maples and Rice 1978). Solheim (1993) describes the corrections and improvements
on the Gustafson method that have been published by Burns and Maples (1976),
Maples and Rice (1978), and Maples (1979). Unfortunately many of these
improvements scattered through the literature have not been widely adopted because a
standardized protocol has yet to be developed and tested.
Stein and Corcoran (1994) found several problems using cementum increments
as an age estimation tool. Their sample consisted of 52 teeth from 42 patients at the
University of Michigan Veterans Affairs Hospital. All of the teeth were mandibular
premolars and central incisors. The sections were stained with 1% Alizarin Red for 2
minutes, examined with a transmitted light microscope, and photographed using a
green filter. The increments were counted from 10X photomicrographs and the count
was added to the average age of eruption in Gray’s Anatomy 35 ed. (1973). The
regression line for individuals under 55 was Age = x – 1.8. Their results indicated that
there is a onethird decrease in cementum apposition in individuals over 60 years of
age, which makes the technique less valuable as a tool for aging elderly individuals.
They also found that the distinction between light and dark bands seems to disappear at
higher magnifications which can lead to double counting (see also Kay 1984 and Miller
1988).
Kvaal and Solheim (1995) removed the apical portion of the root and
longitudinally sectioned in the mesiodistal plane. The teeth were demineralized,
sections made between 57 um thick and stained with Cresyl violet (Voigt’s method).
They used a fluorescence microscope with a green excitation light. The cementum
fluoresced bright red and the incremental lines did not fluoresce. The authors also
tested several methods of counting the increments. 1.) All of the lines were counted
using a video monitor, beginning in one area and following the line to a new area
occasionally. The lines were counted in three different areas for each tooth and the
highest count was recorded. 2.) The lines were counted where they were judged easiest
to see, whether the cementum was cellular or acellular. 3.) The lines were counted in an
area where they appeared to run approximately parallel to one another. The width of the
cementum was measured from the dentine border to the outermost layer and the width
of 46 of the most easily recognizable increments was measured at the same location.
These measurements were taken on the projection screen. The authors estimated the
number of increments within the total width of the cementum.
A Pearson’s correlation was calculated for the estimates from each procedure
and the known age at death. The number of incremental lines counted in the first
method had an 84% correlation with chronological age. The correlation was only 74 %
from the second method. The correlation between estimates made by the two
researchers was 74 % for the first two methods, though this test may not be as
appropriate as a student's ttest for paired samples for evaluating repeatability. In the
third method, the number of increments was estimated from cementum thickness, these
estimates had a 73 % correlation with known age.
The best correlation (88 %) was obtained for teeth extracted for non
pathological reasons when the lines were counted and not estimated. The accuracy of
counting (not estimating) the number of annulations was greatest for the lower fourth
premolar at 96 %. The accuracy of the counting method by age group was 93% for
those aged less than 30, 54% for those individuals between 3049, 33% for those
between the ages of 5069, and 45% for those over 69. The regression formula for
estimating tooth age from increment counts is tooth age = 3.4 + 1.8C. The predictions
from this formula had a 78 % correlation with known age.
Kvaal and associates (1996) tested a variety of methods, stains, and
microscopes in their study of cementum annulations in 27 molars and premolars from
individuals of known age (1685 years old). They made sections from decalcified and
mineralized teeth. The teeth were demineralized in either HCL or Nitric acid and
sections were cut at a thickness of 6 um. The mineralized sample was sectioned to 15
100 um in the longitudinal plane and 1535 um thick in the transverse plane. An
additional sample was half sectioned and acid etched for examination in the SEM (at
5000x magnification).
The cementum in the demineralized sections tended to separate from the dentine
or to shred into ribbons. The increments were most visible in demineralized sections
stained with contrast haematoxylin (PAS) and observed in a transmitted light
microscope. The mineralized sections were examined using phase contrast, interference
contrast, and confocal laser scanning microscopy. The best contrast in the annulations
was obtained in the mineralized sections stained with Cresyl fast violet and examined
with a fluorescence microscope using a green excitation light.
Summary
The Gustafson (1950) method of age determination has undergone a substantial
amount of testing and revision. Kashyap and Rao (1990) and LopezNicolas (1998)
have obtained the most accurate results for a multivariate approach. The authors of
revisions have developed methods that standardize each criterion using indices and
both studies employed digital analysis as well. However, the technique developed by
the latter researchers included periodontitis. Periodontal disease is no longer considered
a useful criteria by many researchers because it will vary per tooth and is most
influenced by diet, oral and internal environmental factors.
Studies of cementum in isolation have been most successful when the number
of annulations is recorded, rather than the overall thickness of the tissue. There is
disagreement as to which tooth class provides the most accurate estimates, though the
anterior teeth may be easier to position for longitudinal sections as they are single
rooted. Some researchers have avoided the use of the posterior teeth altogether because
the roots tend to be twisted and bent. However, Maples (1978) found that the age
estimates from the Gustafson method on second molars were well correlated with
known age at death (r = 0.89 +/ 8.0). In an archaeological sample, the use of molars
with straight roots may be necessary in order to achieve a reasonable sample size.
Most researchers have discovered that the section should be 100150 um in
thickness if the teeth remain mineralized. The section should be stained with Alizarin
Red and observed at a magnification greater than 100x. Cementum annulations should
be observed in the acellular region; the area 1/3 the distance from the root apex seems
to be the best compromise between cellularity and compression. Periodontal disease has
a significant negative impact on the number of annulations present, teeth with obvious
evidence of exposure to the oral environment should be eliminated. The sections should
be photographed and projected, or analyzed digitally to avoid interobserver error. The
number of annulations must be added to the mean age at which the root is completely
formed for the given tooth class.
Researchers have had varying success estimating age at death from the
increments. The annulations are easiest to observe in demineralized sections, and
studies using decalcified teeth have obtained high correlations with known age at death.
However, those researchers who have attempted to demineralize archaeologically
derived teeth have discovered that the teeth tend to become macerated upon sectioning.
Correlations between the number of annulations and known age at death range from 88
% for mineralized teeth (Kvaal and Solheim 1995) to 97.3 % for demineralized teeth
(Condon et al. 1986). Despite these difficulties, many researchers consider the
cementum annulations to be the most promising method in dental histology for age
estimation especially for forensic samples.
ROOT DENTINE TRANSLUCENCY AND
SECONDARY DENTINE DEPOSITION IN AGE ESTIMATION
Ontogeny and Physiology of Root Dentine Translucency 2
Late in the bell stage of tooth development, the cells of the dental papilla
adjacent to the internal dental epithelia differentiate to form odontoblasts, which will
form the coronal dentine. The root dentine begins to form upon the disintegration of
Hertwig’s sheath. Odontoblasts lay down the dentine’s organic matrix of collagen and
ground substance, as the collagen fibers are secreted they increase in diameter until the
ground substance between them is obliterated. The odontoblasts move towards the
center of the papilla and the odontoblast process begins to form.
The mineralized matrix begins to form as hydroxyapatite crystals are deposited.
Calcium and phosphorous ions are present in the cytoplasm of the tissue. Calcium
channels on the dentinal cell membranes are activated by the production of ALP
(alkaline phosphatase) and CaATPase. Mineralization proceeds by “globular
calcification” by which crystals are deposited in discrete areas, which are enlarged until
they eventually fuse. Dentine mineralizes incrementally at a rate of approximately 4 um
per day and small shifts in the orientation of the fibers are visible at these increments.
Greater changes in orientation occur on a five day cycle (approximately 20 um apart)
and are known as von Ebner’s lines. The rate of deposition for root dentine is slower,
the orientation of collagen is different from that of the coronal dentine, and this dentine
is mineralized to a lesser degree.
The Ultrastructural Nature of Root Dentine Translucency
Root dentine becomes increasingly translucent with age, the process
commencing at the root apex and progressing towards the CEJ (Nalbandian and
Sognnaes 1960). Dentine normally appears opaque because of different refractive
indices of the crystalline ‘fundamental matter’ and the intratubular organic matrix
(Drusini 1989). With advancing age, the continued deposition of intratubular dentine
2 This discussion of the ontogeny and physiology of Root translucency is based on Ten Cate (1998)
can lead to the complete obstruction of tubules (Ten Cate 1998). The dentinal tubules in
the mat dentine are approximately 3.2 um in diameter and are reduced to 1.0 um as they
become increasingly calcified with age (Williams 1985). As the tubules calcify, the
refractive index of the fundamental matter becomes increasingly similar to the
intratubular matrix (Drusini 1989; Ten Cate 1998). Eventually the dentine appears
transparent in the transmitted light microscope. Secondary ion microscopy studies have
shown that sclerosed tubules have a higher content of calcium, phosphorous,
magnesium, potassium, and sodium than the intertubular dentine (Berkovitz 1989).
The sclerosis usually begins at the root apex after an individual has reached the
age of 20 and the teeth are fully erupted (Drusini, 1990; Ten Cate 1998). The amount of
sclerosis of the dentinal tubules is linearly correlated with age and is generally assumed
to be unaffected by tooth function, pathology, or other external processes (Vasiliadis
1983a). However, Johanson (1971) found some translucency in individuals between the
ages of 1519 years old. Lamendin (1992) found that translucency was rarely present in
individuals under the age of 30. There are clearly some factors other than age
responsible for this variation, although they are unclear at this time.
Secondary dentine and reparative, or tertiary dentine formation is a function of
age, attrition, and injury. Secondary dentine forms in the same manner as described
above once the root is completed. However, secondary dentine has been found in
unerupted teeth, indicating that its production is not solely a product of functional
requirements (Ten Cate 1998). This type of dentine is also produced by odontoblasts
but there are indications that the secondary dentine does not mineralize fully and has a
different affinity for stains. The rate at which secondary dentine forms is dependent
upon the extent of the attrition or injury, when dentine is deposited quickly it is laid
down in an increasingly disorganized fashion (Ibid.).
Root dentine translucency, in combination with secondary dentine formation, is
considered by some researchers to be the most accurate bivariate technique for age
estimation from Gustafson’s original method (Maples 1978; Hillson 1992; Lucy 1995;
Sengupta 1998). However, there are difficulties specific to this technique which can
limit its usefulness: 1.) within the constraints of the root morphology, the sections must
be positioned in such a way that the translucency is visible from apex to CEJ and 2.) the
most useful section thickness has yet to be standardized.
Most researchers have reported that it is difficult to make sections that are
representative of the real level of translucency in the root. The translucency may
develop in a butterfly shaped pattern in the transverse plane, which would be
misrepresented by longitudinal sections (Darling and Levers 1983). This difficulty is
also partially due to twisted and bent roots that can complicate sectioning through the
‘center’ of the root. Workers do not agree on the optimal section thickness for accurate
use of this method and thus there has been a wide range of variation in the accuracy and
precision of age estimations made using root dentine translucency.
Another difficulty for the purposes of this study, is that the methods have
mostly been tested on samples derived from clinical extractions and cadavers. The
effects of long term burial have yet to be explored. Some workers have tested the
method on recent archaeological material (Drusini 1989, 1991; Sengupta 1998) and
others have avoided ancient remains because of discoloration in the dentine (Lucy
1995). In the case of archaeologically derived material which is heavily mineralized, or
in which the root surface has suffered postdepositional destruction, there may not be
evidence of translucency in thicker sections (1 mm to 300 um). In this study, the extent
of the translucent zone was not apparent until the sections were ground to a thickness of
250 um.
Review of the Literature on Age Estimation for Intact Teeth
It is possible in to evaluate the dentine translucency of modern intact teeth by
rotating the tooth in front of a diffuse light source (Bang and Ramm 1970; Drusini
1989; Lamendin 1992) or through the use of radiographs (Ikeda 1985; Kvaal and
Solheim 1994). Bang and Ramm (1970) developed a method of judging translucency in
intact teeth in order to eliminate the sectioning difficulties of Gustafson’s (1950)
method. However, their method was not highly accurate, the correlation between
estimate and known age at death was between 61 and 83%. The technique is not precise
and there is a large amount of interobserver error in measuring the length of the
translucent zone in whole teeth (Solheim and Sundnes 1980; Solheim 1989; Drusini
1989; Lucy 1995).
The standard error in studies of intact teeth is also higher than that achieved in
sectioned teeth. The lowest estimate of standard error is +/ 10 years, achieved by
Lamendin et al. (1992). Lamendin’s (1992) technique uses root dentine translucency
and periodontitis, indexed by root length from the CEJ to the apex. The use of
periodontitis as a criterion has been criticized because the amount of gingival recession
varies between teeth and is more influenced by diet and hygiene than age (Sperber
1992). In addition, periodontitis cannot be accurately measured in archaeological
material in comparison to a forensic reference population.
Drusini and colleagues (1989) also tested the macroscopic method of evaluating
dentine translucency developed by Bang and Ramm (1970). The teeth were examined
under a tungsten light and sliding calipers were used to measure the zone of
transparency. The length of the transparent zone was not symmetrical on two sides of
the root so an average was calculated. An average was also calculated for all sides of
the roots in multirooted teeth. They tested the method on 382 teeth (32 anterior, 33
premolars, 81 molars) from 311 individuals of known age and sex. The samples were
from both living people and 100 of the individuals were from a skeletal population. The
root translucency was measured, multiplied by 100 and then divided by the total root
length. Their standard error was comparable to that of Lamendin (1992) in 50% of
observations (+/ 10 years). However, 50% of the measurements had a standard error of
greater than 20 years. They found the method showed the best correlation with known
age at death in second molars (r = 91%).
Drusini et al. (1991) based their method on that of Lamendin (Lamendin and
Cambray 1981). The authors created four indices 1.) translucent area/length of
translucent zone; 2.) root area/translucent area; 3.) translucent area x length of
translucent zone; and 4.) translucent area x translucent zone. They made their
measurements on 366 intact teeth from clinical extractions and buried remains 100
years old. The correlation between known age and estimated age was 86% with a
standard error of 7.10 years. Drusini (1997) studied secondary dentine formation in a
sample of intact teeth using panoramic radiographs. The radiographs were collected
from 433 living individuals of known age and sex. They measured the secondary
dentine in 846 teeth. They found a relatively high correlation between known age and
estimated age at death (r = –89.5), the standard error was 5 years in 60% of the cases.
Baccino et al. (1998) collected 19 teeth from autopsied individuals ages 1954.
The authors used Lamendin’s (1992) method and measured root translucency in
addition to periodontitis. They compared the age estimates derived from these criteria
with those from the pubic symphysis using SucheyBrooks (1990), methods developed
by Iscan and Loth for the sternal end of the fourth rib, and Kerley’s (1965, 1978)
method based on cortical bone remodeling (all cited in Baccino, et al. 1998). They
found that the Lamendin method was the best single technique, with negligible intra
and interobserver error, however the method was most accurate when combined with
the other techniques.
Review of the Literature on Age Estimation for Sectioned Teeth
The accuracy and precision of age estimates from root translucency also varies
widely for sectioned teeth. Much of this variability is probably related to differences in
section location and thickness, as well as tooth type. Johanson (1971) suggested that
sections 250 um thick gave the most accurate resolution. Mineralized sections at
between 350100 um thickness are most commonly used (Gustafson 1950; Miles 1963;
Johanson 1971; Burns and Maples 1976; Maples 1978; Solheim and Sundnes 1980;
Sengupta 1998). Age estimates from thin sections are associated with reduced margins
of error, ranging from +/ 3 to 5 years (Miles 1963; Solheim and Sundnes 1980; and
Drusini et al. 1989).
Metzger and colleagues (1980) suggested that sections 250 um through the
center of the root would bypass large areas of translucency. He suggested that the
sections be made 1 mm thick. LopezNicolas et al. (1990) measured transparency,
among other variables, in 1 mm sections. Their age estimates had a prediction error of
2.05 years with a confidence interval of 95%. However, 1mm thick sections have been
criticized as difficult to examine due to overlapping information. This danger is
supported by studies of halfsectioned teeth that have produced low correlations
between known age and estimates, ranging from 3172%. (Solheim 1989, 1990, 1993;
Lorentsen and Solheim 1989).
Solheim (1989, 1990, and 1993) conducted a series of tests using the Gustafson
(1950) and Johanson (1971) criteria on 1000 teeth, 100 from each tooth class except for
the molars. Johanson (1971) had found that root dentine translucency was the single
variable that was most correlated with age at death (r = 0.84). In a test of root dentine
translucency alone, the authors measured the length of the translucent zone for intact
and halfsectioned teeth (1989). They found the sectioned teeth to be more useful, as
the measurements made on intact teeth had large interobserver error. The sectioned
teeth showed little variation between tooth class and the influence of pathological
conditions was negligible. The same sample was used to test the correlation of
secondary dentine deposition, the width of the pulp chamber, and known age (1992).
The authors only found a 60% correlation using these two criteria.
Drusini et al. (1990) tested the idea that root dentine translucency shows the
best correlation with age of all the criteria in the Gustafson (1950) method. The authors
looked at 70 teeth (33 premolars, 37 molars) from 46 adults of known age and sex.
They sectioned one root from each tooth to 600 um thickness (in the buccolingual
plane). The authors used a light microscope at 6 times magnification and measured total
root length as well as root transparency. Two observers took the measurements and
there was no significant difference between the two scores.
The authors noted a tendency to overestimate age in young individuals and to
underestimate in older individuals. The margin of error around their estimates was +/5
years in 21% of cases, +/ 10 years in 26% of cases, and the other half of the estimates
had a margin of error between 1020 years. The best correlation between the estimate
and known age was 58 %. The authors attribute their low success rate to the difficulties
of getting the full zone of transparency included in the section. The average correlation
for the premolars was 49%, for the molars it was slightly higher at 55%.
Sengupta and colleagues (1998) tested the applicability of root dentine
translucency studies for archaeologically derived teeth. They tested several methods on
both modern, clinically extracted teeth and on two historic period skeletal assemblages.
The teeth were examined intact, following Bang and Ramm (1970) and then embedded.
They collected three buccolingual sections 250150 um thick from the center of each
tooth. A test of six stains showed unstained teeth to provide the most resolution. The
authors took monochrome digital photographs of each section over a light box with a
macrolens. They used Microscale TM/TC program for digital analysis, tracing all of the
areas to be measured using the mouse. They measured the length and area of root
transparency, the percent length of root translucency (length of translucency divided by
the total root length), and the percent area of root translucency (translucent area divided
by the total root area).
Sengupta et al. (1998) found that intra and interobserver error were not
significant in measurements of sectioned teeth, interobserver error was significant for
intact teeth. The canine was the preferred tooth class for sectioning because the roots
are generally broad and straight. The teeth from the archaeological sample tended to
fracture and fragment upon sectioning unless they were infiltrated with the methyl
methacrylate. The authors also found that the stains that they tested did not improve
resolution of the translucent area so teeth were examined without the aid of stain. The
paper is a preliminary report and the authors do not report here the age estimates
obtained or the resulting correlation with known age at death.
Summary
A review of the literature on root translucency reveals that the method is
employed with varying success using a variety of protocols. The methods developed for
intact teeth by Band and Ramm (1970) and Lamendin et al. (1981) are useful in that
they do not require sectioning. However, the standard error for age estimates made
using these techniques is greater than ten years and interobserver error is generally
highly significant. The methods have not been fully tested on archaeological material.
Sectioning has also been employed for the study of root translucency using a plethora
of techniques, thickness’ and measures. The most accurate measures seem to have been
derived from teeth sectioned to 250150 um, left unstained, examined using digital
analysis. The most accurate results have been derived from measures of translucent
area rather than the length of the translucent zone.
Almost all of the tests of root translucency in age estimation were conducted
using teeth derived from forensic contexts or from clinical extraction. Archaeological
samples present additional challenges. Heavy amounts of postdepositional
mineralization and damage to the root surface could make observations on intact
prehistoric teeth difficult. In addition, archaeologically derived teeth tend to fracture
and fragment upon sectioning. It is unknown how much the standard error increases for
age estimates based on methods developed from modern forensic reference samples
and used on teeth derived from different temporal and geographical contexts.
Periodontitis cannot be accurately measured on teeth when soft tissue has decomposed,
so multivariate methods employing measures of periodontal disease in addition to
translucency are less useful.
MATERIALS AND METHODS
This section provides detailed description of the sample available for this study,
including description of the sample selection criteria, stratigraphic context, pathological
profile and sex estimates. Each method used in this analysis had different specifications
for section thickness, stains, and data collection. The protocols for preparing the
sections for each phase of analysis, equipment, methods for data collection, and
regression formulas for predicting age at death are included in this section. Discussion
of the statistical methods is reserved for the following chapter on results.
Sample Derivation and Characteristics
Fortyone graves were excavated at Damdama from a deposit 1.5 m in depth,
comprised of ten stratigraphic layers. Layers 2 and 3 were composed of hard, ashy
black soil and the rest of the layers were black clay with occasional bands of yellow
clay (Varma et al. 1985). The Damdama skeletal collection consists of the remains
from fortyeight individuals. The sample is comprised of 2 juveniles (age 22.5), 9
adolescents (1419), 10 young adults (2029), 10 adults (3045), 6 older adults (4560).
Due to poor preservation 6 individuals were aged only as “adult” and age estimation
was not attempted macroscopically for 3 additional individuals. J.R. Lukacs collected
twentynine teeth from 18 adult individuals from the Damdama skeletal collection with
permission from the Department of Ancient History, Culture, and Archaeology,
University of Allahabad. Table 2 shows the sample collected and the stratigraphic
context. Five out of the eighteen individuals included in this analysis (28%) were
recovered from double burials, individual 18a was in a triple burial.
The selection of teeth for histological aging should ideally be determined by
preservation, homogeneity, root morphology, and absence of pathology. Teeth that
have remained within the alveolus throughout the depositional period are protected, and
thus less likely to suffer damage from soil chemistry and other taphonomic processes.
The depositional environment may affect dentine translucency through postmortem
mineralization. Tears and other damage to the periodontal ligament and to the
cementum are also more likely to occur in isolated teeth. Stott (1982) recommended
TABLE 2: CLASS OF TOOTH AVAILABLE FOR ANALYSIS FROM DAMDAMA
Individual Teeth Stratigraphic Orientation 1
6b 2 LRP3, URM2 VII North to South 7 LLC, URM2, URM3 VII WestEast 8 LLI2, ULM3 VII WestEast 11 URM2 IV WestEast 12 ULM3 I WestEast 13 LRC, LRM3 IX WestEast 15 URM3 VI WestEast 16a 2 ULI2 III WestEast 17 ULM1, ULM3 III EastWest 18a 3 ULM2 VII WestEast 20a 2 ULM1 V WestEast 28 URI1, LRM3 VII NWSE 30a 2 URM3 VIII WestEast 30b 2 ULC, LRM1 VIII WestEast 32 ULI1, ULP4, LRM3 VIII SWNE 34 ULM2, ULM3 IX SWNE 36a 2 LRP4 VIII WestEast 37 URM3 VIII WestEast Total # Ind Total # Teeth 18 29 Information on stratigraphic context derived from Pal 1988 and 1992. Individuals are numbered using the original grave numbers. 1 Orientation is given with the position of the skull listed first 2 Individuals/graves numbered 6, 16, 20, 30 and 36 were double burials 3 Grave XVIII contained three individuals
sectioning and examining teeth within the alveolar bone because extraction may
damage the cementum. This advice seems especially relevant for archaeological
samples. The Damdama skeletons were heavily encrusted with a calcareous matrix,
making teeth difficult to extract. This concretion had the greatest influence on the
selection of teeth available for analysis. Eight teeth had portions of alveolar bone
protecting the root surface (30%), the rest of the sample consists of isolated teeth
(Table 3).
The sample is not large and it is not homogenous in the sense that the
individuals were recovered from nine different stratigraphic layers and the teeth are not
of the same tooth class. Small sample size is unfortunate but it is one of the common
limitations faced in bioanthropological research. The small size of this sample may be
considered part of the test to see if the methods are feasible for archaeological
populations. The sample size is also not so small as to be unprecedented, even for tests
of dental histological aging methods in forensic contexts (Cook 1984). In order to
maximize samples size, the temporal context of the nine stratigraphic layers is
considered roughly contemporaneous.
The teeth were originally collected for paleodietary analysis. The sample is thus
biased toward molar teeth, particularly third molars. Single rooted teeth are preferred
for histological study because they are generally easier to position and section, though
there may be few differences in accuracy between anterior and posterior teeth in
evaluating root dentine translucency (Drusini 1989) and cementum annulations (Maples
1978). Most of the methods employed in this study do not differentiate between tooth
types and in order to maximize sample sizes for archaeologically derived material, it is
not always possible to consider tooth class as a primary selection criteria. For the
purposes of this study, the main question involved the feasibility of employing
histological techniques on an archaeologically derived sample. All of the teeth available
were used.
The teeth should also be free of pathological conditions such as periodontitis, to
the extent that the condition can be judged in archaeological remains. Periodontitis,
alveolar resorption, passive eruption, AMTL, and caries can expose the tooth root to the
oral environment. Exposure of the root surface to the oral environment can cause
hypermineralization of exposed surfaces, resorption bays on the cementum, and may
have a significantly negative impact on age estimation. In this sample most of the teeth
were missing portions of the periodontal ligament, making periodontitis impossible to
judge. The cementum annulations were only counted in areas where the ligament
remained intact. However, the possible effects of periodontitis on the results of this
study are unfortunately unknown.
Table 3 gives details on the condition and the pathological profile of the teeth
used in this sample. Attrition had reached the level of dentine exposure in 10 teeth
(66.67%) and had resulted in pulp exposure in 7 teeth (25.93%). There were 13 teeth
(48.15%) which had large interproximal wear facets, and 1 tooth (3.7%) the LRP3 from
individual 6b had an antemortem interproximal groove located at the CEJ, probably
resulting from some habitual idiosyncratic behavior such as tooth picking (not included
in Table 3). There was clear evidence of postmortem damage to the enamel of 8
TABLE 3: DENTAL PATHOLOGICAL PROFILE FOR DAMDAMA SAMPLE
Ind # Alveolar bone
caries dentine exposure
pulp exp interprox. wear
p.m. damage
6b URM2 LRP3, URM2 LRP3, URM2 URM2 7 URM2 URM2 LLC 8 ULM3 LLI2, ULM3 LLI2, ULM3 LLI2, ULM3 11 URM2 URM2 12 ULM3 13 ULI1, LRM3 ULI1, LRM3 LRM3 ULI1 15 URM3 17 ULI1, ULM3 18a ULM2 ULM2 28 LRM3 URI1, LRM3 LRM3 URI1 30a URM3 LRM3 30b ULC, LRM1 ULC, LRM1 LRM1 32 ULP4, LRM3 ULI1 ULI1 ULI1, ULI1 34 ULM2, ULM3 ULM2, ULM3 36a LRP4 LRP4 LRP4 37 URM3 URM3 Tl # 8 2 10 7 13 8 % 30% 7% 67% 26% 48% 30%
additional teeth (29.63%). Two teeth had occlusal caries (7.4%), however the root was
unaffected.
Protocol
Age at death was estimated macroscopically by dental eruption timing, attrition,
changes in pelvic morphology (the auricular surface and the pubic symphysis), cranial
sutures, epiphyseal suture closure, and degenerative changes to postcranial morphology
(Lukacs n.d.). Sex was estimated using the shape of the sciatic notch when available,
the diameter of the acetabulum, mandibular and cranial morphology, as well as metric
and morphological observations of the postcranial skeleton (Lukacs n.d.). Tables 4 and
5 give details of the macroscopic methods used for age and sex estimation for each
individual. For comparative purposes, the mean for each range was used.
Estimating age at death from cementum increments requires thin sectioning and
histological analysis. Root translucency can be judged from observations on whole
teeth (Bang and Ramm 1970; Lamendin and Cambray 1981; Drusini 1989). The whole
tooth technique may be less accurate than observations made on sectioned teeth and is
subject to higher intra and interobserver error (Lucy 1995; Sengupta 1998).
Translucency is difficult to observe at a thickness greater than 200 um in archaeological
samples, which may have suffered from postdepositional mineralization.
Six histological methods of age determination were used in this study. The
multivariate methods include those developed by Kashyap and Rao (1990), Johanson
(1970), and Maples (1978). Two methods based solely on root translucency were used,
those developed by Lorentsen and Solheim (1990) and Drusini (1989). Finally, age was
estimated using a count of cementum annulations, following Charles and colleagues
(1986, 1989).
Development of standardized protocols for embedding, sectioning, staining and
observing the age related changes could reduce the margin of interobserver error (Table
6). This protocol for preparing and sectioning the teeth was developed with Dr. Jeanne
Selker, a biologist who runs the electron microscope facilities at the University of
Oregon. In discussing the problems of working with a hard and intractable material, Dr.
Selker recommended using Spurr’s resin (Buehler, Ltd., Lake Bluff, Ill.). Spurr’s resin
is an embedding material with very low viscosity, in this case mixed for medium
hardness (through the addition of a smaller amount of catalyst). The use of Spurr’s
resin also leaves open the possibility of work with the cementum on the EM.
The teeth used in this study did not require fixation because of their
archaeological derivation. In terms of demineralization, staining and sectioning, the
various age estimation techniques have different requirements. The multivariate
methods, and those based on root translucency do not require decalcification or
staining. The section thickness varies per method, so the sections in this study were
ground thinner for each succeeding method to be tested, as will be discussed below.
For studying cementum increments, many authors feel that the best method is to
microtome section decalcified teeth and to stain them with haematoxylin (Condon
1986; Hillson 1996). Though this protocol produces the best resolution of the
increments, Lieberman and Meadow (1992) have suggested that this procedure is often
too harsh for archaeological remains and other researchers (Condon 1986; Charles
1989) have found that decalcification tends to produce macerated sections in
archaeological material. For the purposes of this study, several methods for estimating
age at death were applied to each section and the teeth were not demineralized prior to
dehydration and embedding.
Each tooth was placed in 100% acetone for 24 hours, then 75% acetone mixed
with 25% Spurr’s resin for 24 hours. The concentration of resin was increased to a 50
50% solution for the next 24 hours, then increased to 75% for 24 hours. Finally each
tooth was placed in 100% Spurr’s resin for 24 hours. The teeth were embedded in the
resin in a plastic mold and polymerized at 60 degrees for 24 hours. This protocol is
similar to that used by Stein and Corcoran (1994).
Each tooth was longitudinally sectioned in the buccolingual plane to an initial
thickness of 1 mm using a Buehler Isomet low speed saw with a diamond impregnated
blade. The section that passed though the center of the root was used to test each
method of age estimation. Some authors section in the transverse plane for studies of
cementum annulations, however the multivariate methods and those for root
translucency required longitudinal sections. Kashyap and Rao (1990) sectioned teeth to
1 mm thickness; in the interests of repeatability, their method was tested on the sections
at their original 1 mm thickness (Figure 2). A trial run also indicated that the teeth were
easily fractured and occasionally disintegrated when sectioned less than 1 mm.
FIGURE 2:MEASUREMENTS FOR MULTIVARIATE AGE ESTIMATION (KASHYAP AND RAO 1990)
T
t
TABLE 7:REGRESSION FORMULAS FOR AGE ESTIMATION (JOHANSON 1970)
Johanson’s formulae Regression equations Correlation St. dev. Attrition 23.25 + 14.96(A) .49 10.75 Dentine translucency 25.32 + 14.91(T) .86 7.1 Secondary dentine 25.32 + 13.13(S) .66 10.26 Root resorption 48.28 + 11.04(R) .24 13.32 Attrition & translucency 19.07 + 7.74(A) + 11.17(T) .88 6.14
The sections were not stained for the multivariate methods, nor for measuring
the translucent area, as Sengupta (1998) found a decrease in the contrast between
translucent and opaque dentine in comparison with unstained sections in their test of
the following stains: Ammoniated Indian ink, Solochrome cyanin, Schmorl’s picro
thionin, Methylene blue, von Kossa, and P.A.S. Each observation was repeated after
one week to test intraobserver error. The individual ages at death were calculated using
the following formula: [(a/A) x 100] + [(d/D) x 100] + [(t/T) x 100] + [(c1 + c2)/C] x
100/ 4. Kashyap and Rao (1990) obtained a correlation of 99.8 % between their
estimates and known age at death. The standard error was +/ 1.59 years.
The sections were subsequently ground to a thickness of 200 um using a series
of sandpapers (grit 200600) and finally 9 um diamond paste on a Buehler Minimet
automatic polisher. The sections were examined using Johanson’s (1970) criteria for
age estimation (Figure 3). For this study, four of Johanson’s six original criteria were
used: attrition, secondary dentine, root dentine translucency, and root resorption.
Periodontitis was excluded as it is not reliably measured in archaeological specimens.
Cementum thickness was also excluded because it is measured at the thickest point, an
area generally found in the cellular region that is heavily influenced by environment
and is not an accurate indicator of age. Each observation was repeated after one week to
test intraobserver error. Age at death was calculated using four of Johanson’s (1970)
univariate and one multivariate regression formulae (Table 7).
The 200 um sections were also evaluated using the regression formulae from
Maples (1978) with Gustafson’s (1950) criteria for scoring secondary dentine and root
translucency (Figure 3). Maples (1978) found that these two criteria had the best
TABLE 8: REGRESSION FORMULAS FOR AGE ESTIMATION (MAPLES 1978)
Tooth Class Regression formula Correlation S.E. I1 3.89S + 14.23T + 15.28 .89 9.1 I2 6.51S + 12.55T + 25.16 .88 9.6 C 18.67S + 11.72T + 21.94 .76 11.0 P3 2.82S + 15.25T + 19.65 .77 12.2 P4 4.79S + 15.53T + 17.99 .83 7.6 M1 11.28S + 5.32T + 10.86 .85 11.1 M2 6.99S + 10.86T + 19.31 .88 6.8 M3 4.71S + 12.30T + 24.57 .83 12.0
correlation with age when weighted by tooth class (Table 8). He achieved an overall
weighted correlation of r = 0.86 with known age at death. These formulas also include
estimates of standard error by tooth class, which range from +/ 6.8 to +/ 12.2 years.
Each observation was repeated after one week to test intraobserver error.
Recently digital imaging analysis has been employed to measure the area of
dentine translucency. The use of digital image analysis can reduce the level of intra and
interobserver error as the imaging program can be used to precisely measure the area of
dentine translucency, in relationship to the length and total area of the root (Lorentsen
and Solheim 1989; Drusini 1991). The area is an important unit of measurement if the
root translucency does not proceed linearly from the apex, but in a butterfly shape in
the transverse plane (Darling and Levers 1983).
To measure the area and length of root translucency, the 200 um sections were
photographed through a Zeiss 2000C stereomicroscope at 6.5 x magnification and a
Polaroid digital camera (DMC1) at 1600 x 1200 resolution. The images were analyzed
using the SigmaScan Image program on a PC running Windows 98. Following
Lorentsen and Solheim (1989) the area of translucency and total root area were
calculated in pixels using the mouse to demarcate the area to be measured (Figure 4).
Measurements were converted to millimeters using the calibration function on a 0.5
mm scale included in the photographs. Each observation was repeated after one week.
Lorentsen and Solheim (1989) developed their regression formulae for each
anterior tooth class by jaw, producing a total of ten formulae (Table 9). They calculated
the correlation between the translucent area and known age at death and the standard
TABLE 9: REGRESSION FORMULAS FOR AGE ESTIMATION FROM AREA OF TRANSLUCENT DENTINE (LORENTSEN AND SOLHEIM 1989)
Tooth class Regression formulae Correlation SD for Age Maxilla I1 37.27 + 2.71ATD – 0.03ATD 2 – TA 82 9.0 I2 31.14 + 1.30ATD – 8.28S 84 9.2 C 19.19 + 1.46ATD – 0.01ATD 2 86 8.7 P3 21.90 + 0.69ATD 75 13.1 P4 22.89 + 0.90ATD 70 12.1 Mandible I1 23.96 + 2.06ATD – 0.02ATD 2 – 9.74S 76 10.9 I2 25.44 + 1.68ATD – 0.02ATD 2 64 12.9 C 23.51 + 1.60ATD – 0.02ATD 2 78 12.0 P3 27.85 + 1.04ATD 68 12.0 P4 21.60 + 1.59 ATD – 0.01ATD 2 81 11.4 ATD = Area of translucent dentine; TA = Total area; S = Sex (male = 1, female = 0)
error for each formula. The correlation coefficients ranged from r = 0.64 for mandibular
lateral incisors (S.E. = 12.9) to r = 0.86 for maxillary canines (S.E. = 8.7). Only ten
individuals (11 teeth) could be included in this analysis, as the authors did not publish
regression formula for molar teeth.
The root length and the length of the translucent area were also used to calculate
age at death following Drusini (1991). The age estimates are based on an index
designed by Lamendin and Cambray (1981). The length of the translucent area (h) was
multiplied by 100 and then divided by the length of the root (H). Both measurements
were made using a ruler function in SigmaScan on the digital images, they were
calibrated using a scale included in the photograph (see Figure 5). Age at death was
estimated using two regression formulae, one developed for premolars and one for
molars. For this study, the anterior teeth were evaluated using the premolar formula, a
compromise somewhat justified by the single straight roots for these classes of teeth.
The formula for premolars (Age = 23.7329 + 0.1262x +0.0089 x 2 – 0.1046 x 3 ) had a
58% correlation with known age at death and the standard error was 8.4. The formula
for premolars (Age = 23.7329 + 0.1262x +0.0089 x 2 – 0.1046 x 3 ) had a 57% correlation
with known age at death with a standard error of 6.1 years.
The sections were subsequently ground to a final thickness of 100 um using a
TABLE 10:DENTAL EMERGENCE TIMING FOR CHILDREN IN CHANDIGARH, INDIA (YEARS)
Maxilla Males Females Mandible Males Females Central incisor 7.08 6.92 Central incisor 6.61 6.96 Lateral incisor 8.13 8.13 Lateral incisor 7.59 7.59 Canine 10.97 10.47 Canine 10.71 9.77 Third premolar 10.47 10.23 Third premolar 10.91 10.00 Fourth premolar 11.48 11.22 Fourth premolar 11.75 11.22 First molar 6.61 6.03 First molar 8.17 5.62 Second molar 12.02 11.22 Second molar 11.18 10.72
Table based on original data published in Kaul et al. (1975), taken from ElNofely and Iscan (1989).
series of sandpapers (grit 200600) and finally 9 um diamond paste on a Buehler
Minimet automatic polisher. Each section was stained with 2% Alizarin Red to
examine the cementum increments, following Charles (1986, 1989). Image processing
software may also be used to provide a more objective analysis of the annulations
through contrasting pixels (Drusini 1990; Lieberman and Meadow 1992; Stein and
Corcoran 1995; LopezNicolas 1996). The sections were photographed at 200x
magnification through a Zeiss transmitted light microscope using a Polaroid digital
camera (DMC1) at 1600 x 1200 resolution. The number of cementum annulations was
counted on a 14” Sony digital monitor (see Figure 6). The measurements were collected
twice a month apart to test for intraobserver error. Measurements were also collected by
Dr. J. Lukacs to test for interobserver error. Following Charles (1986, 1989), the
number of annulations was added to the age at which the tooth generally completes
eruption and reaches the occlusal plane. Eruption times by tooth class for children from
Chandigarh, India were chosen and are given in Table 10.
RESULTS
This study is a comparison of age estimates from macroscopic and microscopic
methods in a sample of 29 teeth from 18 individuals from Damdama. Macroscopic age
estimates were made by Lukacs (n.d.) based dental eruption times and attrition, changes
in the pelvic morphology (the auricular surface and the pubic symphysis), cranial
sutures and epiphyseal closure, and degenerative changes to postcranial morphology.
Sex was estimated using the shape of the sciatic notch when available, the diameter of
the acetabulum, mandibular and cranial morphology, as well as metric and
morphological observations of the postcranial skeleton (Table 11).
Observer Error and Comparisons within Individuals
Intraobserver error indicates the precision with which estimates are made from
each method on two separate occasions; interobserver tests the precision with which the
estimates can be collected between two observers. Intraobserver error was tested for
TABLE 11:MACROSCOPICAGE AND SEX ESTIMATES FOR DAMDAMA SAMPLE
Age Male Female Unknown total Age Male Female Total 1.52.5 4 1 > 30 2 1 2.53.5 5 1 3035 6b 1 1418 36b 1 3040 27 1 1618 17, 35 2 3045 6a 1 1620 32 36a 2 3539 30a 1 1720 20a 1 3545 11 12, 13 3 1820 7 1 3650 1 1 1822 33 1 3743 40 1 19 19 1 4045 20b 1 2023 8 1 4351 39 1 2024 22 1 4550 28 23 2 2025 18a 1 4555 18c 1 21 16a 1 4560 26 1 2225 18b 1 4555 37 1 2529 34 1 5060 3 1 2535 16b 29 2 2733 30b 1 Indet 21 14,31 3 Adult 9, 24, 25 10, 15 6 total 24 18 4 46
TABLE 12:DESCRIPTIVE STATISTICS FOR CEMENTUM ANNULATION COUNTS
N Min Max Mean S.E. S.d. t p Annulation count 1 (Robbins) 17 18 38 26.95 1.55 6.38 .226 .824 Annulation count 2 (Robbins) 16 17 38 26.81 1.57 6.30 .392 .700 Annulation count (Lukacs) 12 11 38 21.33 2.08 7.20 2.813 .017
all of the methods and was found to be insignificant (∝ = 0.05). Intraobserver error
between the two sets of age estimates from my counts of cementum annulations were
not significantly different (p = 0.597). Interobserver error was tested only for the
cementum annulation method because it is the method that appears to produce the most
accurate results (see below). The cementum increments were counted independently by
Dr. J.R. Lukacs and a student’s ttest for paired samples shows that both sets of
observations that I collected were significantly different from the count made by Dr.
Lukacs (p = 0.000 and p = 0.003 respectively). There were no significant differences
between the two sets of observations that I collected for the cementum annulations and
the macroscopic methods (Table 12).
TABLE 13:AGE ESTIMATES FOR INDIVIDUALS WITH MULTIPLE TEETH AVAILABLE
Ind Tooth N MinimumMaximum Mean S.E. Std. Dev. Variance 6b LRP3 9 20.00 56.14 40.53 4.06 12.18 148.30 6b URM2 7 24.00 56.14 43.93 4.63 12.24 149.84 7 LLC 9 16.00 83.67 47.98 8.60 25.80 665.83 7 URM2 7 14.00 70.05 44.36 8.14 21.54 464.07 7 URM3 7 15.00 70.89 43.31 7.54 19.96 398.41 8 LLI2 9 19.00 68.13 45.86 5.97 17.90 320.31 8 ULM3 8 5.00 70.89 45.03 8.37 23.66 559.98 13 ULI1 9 22.00 88.66 57.75 6.74 20.21 408.53 13 LRM3 8 38.00 75.60 57.29 5.80 16.40 269.00 17 ULI1 8 19.00 58.88 40.75 5.33 15.07 227.05 17 ULM3 7 19.00 47.69 35.29 4.22 11.15 124.38 28 URI1 8 22.00 70.05 53.56 5.41 15.28 233.35 28 LRM3 7 32.00 68.82 53.81 5.17 13.69 187.35 30b ULC 9 30.00 75.77 56.53 5.73 17.18 295.02 30b LRM1 8 23.00 74.78 48.18 7.36 20.83 433.83 32 ULI1 9 7.00 62.60 39.99 6.85 20.55 422.41 32 ULP4 8 18.00 75.97 46.25 6.82 19.29 372.09 32 LRM3 7 18.00 55.14 39.81 5.80 15.35 235.61 34 ULM2 8 24.00 40.20 32.91 2.02 5.70 32.51 34 ULM3 7 12.00 55.14 39.93 5.91 15.63 244.26
TABLE 14: PAIRED TTEST FOR SIGNIFICANTDIFFERENCES FOR INDIVIDUALS WITH MULTIPLE TEETH
Ind Teeth n Mean Diff s.d. S.E. t Sig. 6b LRP3 URM2 6 2.58 3.99 1.51 1.708 .138 7 LLC URM2 6 1.95 11.23 4.25 .459 .663
LLCURM3 3.00 5.73 2.17 1.384 .216 URM2 URM3 1.05 9.20 3.48 .302 .773
8 LLI2 ULM3 7 1.38 15.37 5.44 .254 .807 13 ULI1 LRM3 7 3.40 12.84 4.54 .749 .478 17 ULI1 ULM3 6 7.28 8.00 3.02 2.408 .053 28 URI1 LRM3 6 .87 8.72 3.30 .263 .802 30b ULC LRM1 7 7.68 17.41 6.16 1.248 .252 32 ULI1 ULP4 6 .20 15.34 5.80 .035 .974
ULP4 LRM3 2.19 7.09 2.68 .818 .445 ULI1 LRM3 1.99 10.20 3.86 .517 .624
34 ULM2 ULM3 6 5.75 15.20 5.74 1.001 .355
Dr. Lukacs eliminated five sections because they appeared unscorable. This indicates
that the method cannot be applied reliably by two different observers with a different
level of familiarity with cementum annulations in general.
There were seven individuals with two teeth available, and two individuals with
three teeth available (Table 13). The precision of all of the estimation methods was also
tested by comparing estimates for different teeth from the same individual. The
estimates from the Lorentsen and Solheim method (1989) and the cementum
annulations were eliminated from the comparisons between teeth unless they were
present for both, or all three teeth. There were only two individuals (13 and 30b) for
whom the cementum could be included in the analysis; none of the Lorentsen and
Solheim estimates could be included. The results of the paired ttest for significant
differences in the mean estimates for age at death are given in Table 14. The largest
difference in the mean estimates was 5 years, the smallest difference was 0.18 years.
For all of the individuals with multiple teeth available, the differences in the estimates
from those teeth were not significant. The relationship between teeth was also plotted in
scatterplots (Figure 7).
Had there been significant differences between the estimates derived for
different teeth, possible biases between tooth classes would have been indicated for
each method. However, similar ages were derived despite tooth class for all of the
methods. The test of differences “within” individuals also speaks to the small amount
of intraobserver error for observations within each method. As there were no
significant differences between the estimates from different teeth for the same
individual it is possible to state that 1.) The methods can be applied to the same
sections with an insignificant level of intraobserver error, 2.) estimates for different
teeth from the same individuals are internally precise, whether or not they are accurate,
and thus 3.) tooth classes do not seem to influence the accuracy of results.
Age Estimates from Dental Histological Methods
As the true age at death is not known, the accuracy of the histological methods
can only be approximated by the significance of differences between the means,
analysis of variance, and the significance of correlation coefficients. Age estimates for
the macroscopic and all the histological methods are given in Table 15. Boxplots were
created for an initial, rough comparison of the range of age estimates for each method
and for each individual (Figure 8). To examine the density and distribution of the age
estimates, histograms were produced for each method (Figure 9) and each individual
(Figure 10). Normal quantile plots were produced for each method to test the normality
of their distribution (Figure 11). Scatterplots were produced comparing each
histological method with the macroscopic age estimates (Figure 12).
To evaluate the similarities between the histological methods, a student’s ttest
for paired samples was used to compare the mean age estimate from each method with
the mean from the macroscopic methods (Table 16). An analysis of variance (one way
ANOVA) was calculated to measure the amount of variance between estimates as
opposed to the amount of variance within the estimates from each method (Table 17).
A Pearson’s correlation matrix was used to evaluate the significance of similarities
between sets of estimates from the different methods (Table 18). The Euclidean
distance was also calculated between the methods (Table 19). The estimates from the
Kashyap and Rao method and from the cementum annulations were the most closely
TABLE 15:MEAN ESTIMATES FOR AGE AT DEATH
Ind 1 2 3 4 5 6 7 8 9 10 6b 3035 32.5 22 53.2 55.1 56.1 39.4 41.3 40.2 34 7 1820 19 15 43.4 62.6 57.6 70.8 83.7 44.4 24 8 2023 21.5 31 56.9 62.6 63.8 67.1 41.5 44.2 27 11 3545 40 64 68.1 55.1 62.9 58.9 . 49.6 12 3545 40 26 53.2 70.1 68.1 58.6 . 45.1 30 13 3545 40 50.5 68.1 70.1 74.8 62.4 88.7 48.9 30 15 30 30 20 45.7 62.6 58.8 66.2 . 41.6 27 16a 21.00 21 20 30.7 32.8 28.2 44.2 34.1 36.0 17 1618 19 24 38.2 51.4 46.5 50.2 . 43.2 28 18a 2025 22.5 16 38.2 55.1 49.4 48.0 . 40.0 23 20a 1720 18.5 27 38.2 55.1 49.4 32.8 . 34.1 28 28 4550 47.5 27 60.7 66.3 68.5 57.9 57.8 45.9 30a 3539 37 25 68.1 70.1 74.8 66.2 . 46.4 28 30b 2733 30 48.5 68.1 70.1 74.8 51.6 61.9 42.6 31 32 1620 18 20 40.7 60.1 54.5 53.4 62.6 41.7 18 34 2529 27 23.5 38.2 47.7 43.5 42.0 . 37.0 24 36a 1620 18 19 45.7 62.6 58.8 69.4 62.2 41.6 19 37 4555 50 28 68.1 70.1 74.8 36.9 . 46.1 N 18 18 18 18 18 18 10 18 17 Mean 29.72 28.14 51.31 59.97 59.17 54.22 59.30 42.70 26.94 s.d. 10.95 13.07 13.04 9.80 12.75 11.81 18.59 4.20 6.37 Min 18.0 15.0 30.7 32.8 28.2 32.8 34.1 34.1 18.0 Max 50.0 64.0 68.1 70.1 74.8 70.8 88.7 49.6 38.0
1.) Macroscopic age estimate range, 2.) Mean macroscopic estimate, 3.) Kashyap and Rao, 4.) Johanson (Attrition), 5.) Johanson (Translucency), 6.) Johanson (Attrition and Translucency), 7.) Maples, 8.) Lorentsen and Solheim, 9.) Drusini, 10.) Cementum.
correlated with the macroscopic estimates. These two methods were further tested by
dividing the sample into young (1629) and older (3055) adult groups to test for
systematic biases related to age (Tables 2021).
The mean age estimates from the Kashyap and Rao and the Charles method for
counting cementum annulations were not significantly different from the mean age of
the macroscopic methods (Table 14). The difference between the means was 1.37
between Kashyap and Rao and the macroscopic estimates (p = 0.670); the mean
difference was –0.44 between the cementum annulation estimates and the macroscopic
FIGURE 8: RANGE OF AGE ESTIMATES PER METHOD AND PER INDIVIDUAL
Methods
C harles
Drus ini
Lorentson
M aples
Att & Trans
Translu cency
Attrition
Kashy ap & Rao
Mac roscopic
Age E
stimate
s
100
80
60
40
20
0
Indiv iduals
37 36A 34
32 30B 30A
28 20A
18A 17 16A
15 13 12 11 8 7 6B
Age E
stimate
s
100
90
80
70
60
50
40
30
20
10
0
age estimates (p = 0.824). All of the other methods tested here, produced age estimates
that were significantly different from the macroscopic estimates, in fact they
substantially overestimated age at death (Figure 8).
The paired ttest only tests for significant differences between the means of two
samples. A sample could be highly internally variable and the ttest will allow those
differences to be averaged out. The standard deviation around the mean gives some
idea of the variability within methods. A oneway ANOVA was calculated to compare
the variability between methods (Table 15). The one way analysis of variance measures
the amount of variation between methods in comparison to the amount of variation
within methods. Quantile plots were made for each method because the ANOVA
requires that the estimates have a normal distribution. Again, the age estimates from
TABLE 16: PAIRED TTEST FOR MEANS OF THE MACROSCOPIC ANDHISTOLOGICAL METHODS
Method Mean Difference Std. dev. t df Sig. K & R 1.37 12.71 .43 15 .670 Attrition 21.47 7.88 10.89 15 .000 Translucency 29.80 10.28 11.59 15 .000 Attrition & Trans 29.06 9.75 11.92 15 .000 Maples 22.82 15.56 5.87 15 .000 L & S 33.28 20.73 4.54 7 .000 Drusini 12.83 8.56 6.00 15 .000 Cementum .4412 8.05 .226 16 .824
TABLE 17:ANALYSIS OF VARIANCE (ANOVA) FOR METHODS
Method SOS df MS F Sig. Kashyap & Rao SSG 2985.007 12 248.751 1.676 .166
SSE 2375.200 16 148.450 SST 5360.207 28
Attrition SSG 3721.340 12 310.112 3.742 .008 SSE 1326.025 16 82.877 SST 5047.365 28
Translucency SSG 1784.064 12 148.672 3.031 .020 SSE 784.919 16 49.057 SST 2568.984 28
Attrition & SSG 3391.613 12 282.634 6.306 .000 Translucency SSE 717.094 16 44.818
SST 4108.707 28 Maples SSG 2686.684 12 223.890 1.115 .412
SSE 3214.038 16 200.877 SST 5900.722 28
Drusini SSG 379.973 12 31.664 2.040 .092 SSE 248.385 16 15.524 SST 628.358 28
Cementum SSG 265.775 12 22.148 .230 .979 SSE 385.167 4 96.292 SST 650.941 16
SSG: Variance between groups (between mean for each method and overall mean); SSE: Variance within groups (between mean and individual observations); SST: Total variance (within and between groups); SS: Sum of Squares; MS: Mean Square
Lorentsen and Solheim’s method for area of root translucency were excluded from this
ANOVA because there were too many missing variables.
The variance within each method (MSE) exemplifies the range of ages
produced by each method. The level of variance between groups (MSG) represents of
the amount of variance between the methods. The estimates from the count of
cementum annulations had the least significant amount of within and between method
variance. The estimates from the Kashyap and Rao and Maples’ method also had
insignificant levels of variation within and between methods. The rest of the methods
produced significant results for the analysis of variance between groups. The variation
between groups was highest for the estimates from Johanson’s methods for attrition and
translucency, and Drusini’s estimates from length of root translucency.
The level of correlation between age estimates from the various methods and
the macroscopic methods was also calculated using Pearson’s correlation for similarity
(Table 16). Euclidean distances between the methods illustrate the relative differences
(Table 17). These correlation and distance statistics confirm close associations between
the macroscopic methods and both Kashyap and Rao as well as estimates from
cementum annulations. The cementum annulations were most closely related to the
macroscopic estimates and were not significantly correlated with any other methods.
The methods based on categorical measures of root translucency and attrition
consistently cluster together. These similarity and distance statistics also show the
divergence between the Maples method and those of Johanson and Drusini. While
Maples method is not an accurate predictor in comparison with the macroscopic,
Kashyap and Rao, and Charles methods, it does appear to be more precise than the
other multivariate methods. Maples’ method has a smaller standard deviation, a less
significant amount of variance, and a higher correlation coefficient than Johanson and
Drusini’s methods.
Though the mean age from the metric measurement of root translucency
(Drusini’s method) was significantly different from the mean age for the macroscopic
estimates, the method also produced a significant Pearson correlation to and a relatively
small Euclidean distance from the macroscopic estimates. Thus it appears that while
translucency may not be a good measure of age at death for this sample, it is somewhat
TABLE 18: PEARSON CORRELATIONS AND PVALUES FOR METHODS
MacroK & R Attrition Trans A & T Maples Drusini Cement Macro .009** .000** .012** .000** .356 .011** .043* K & R .434 000** .174 .011* .344 .136 .281 Attrition .695 .619 .001** .000** .235 .007** .270 Trans. .417 .181 .561 .000** .017* .000** .108 A & T .609 .422 .852 .911 .046* .000** .143 Maples .071 .078 .139 .396 .319 .007** .128 Drusini .424 .211 .454 .616 .616 .449 .156 Cement .428 .151 .160 .316 .275 .292 .261 * Correlation (1tailed) is significant at the 0.05 level ** Correlation (1tailed) is significant at the 0.01 level
TABLE 19: EUCLIDEAN DISTANCE MATRIX
Macro K & R Attrition Trans A & T Maples Drusini
K & R 48.485 Attrition 114.948 109.742 Trans. 147.632 147.753 51.795 A & T 147.244 144.789 41.668 18.093 Maples 142.028 147.214 76.189 64.676 68.995 Drusini 66.266 75.005 62.686 86.690 88.560 84.467 Cement 32.261 58.975 121.006 149.488 151.005 138.473 66.968
more accurate to measure the translucency than to use discrete categories for data
collection. The categorical measures of attrition in the Johanson method also
substantially overestimated age at death. Johanson had only achieved a correlation of
less than 50% from his univariate formula for attrition, thus it is not surprising that the
method was not accurate in this study.
The Kashyap and Rao method also used attrition and translucency in age
estimation. However, their method was based on a metric measurements of these
variables, rather than a discrete score. In addition, their method standardized attrition
TABLE 20: TEST FOR SIGNIFICANTDIFFERENCES IN INDIVIDUALS 1629 YEARS OLD
Ind Macro. Range Macro. Mean Kashyap & Rao Cementum 7 1820 19.0 15.00 24.00 8 21.50 21.5 31.00 27.00 16a 21.00 21.0 20.00 . 17 1618 19.0 24.00 28.00 18a 2025 22.5 16.00 23.00 20a 1720 18.5 27.00 28.00 32 1620 18.0 20.00 24.00 34 2529 27.5 23.50 19.00 36a 1620 18.0 19.00 24.00 Mean (St. Dev.) 20.56 (3.07) 21.72 (5.17) 24.63 (3.02) t .619 2.014 p .553 .084 Correlation (r) .131 .693 p .736 .056
by dividing the measure by the width of the CEJ. Their measure of the length of the
translucent zone was standardized by tooth height. The standardized nature of their
method, and the metric system of data collection may contribute to the small margin of
error and to the accuracy in relation to the macroscopic estimates. The Kashyap and
Rao method was based on samples from Hyderabad, India and environmental
similarities may also partially account for some of this increased level of accuracy.
In younger individuals, for whom the macroscopic age estimates were based on
eruption of the third molar and on epiphyseal union, the estimates have a smaller
standard error and might be expected to be the most accurate. A separate comparison of
the estimates from Kashyap and Rao and the cementum annulations was made with the
macroscopic estimates for individuals aged 1629 and 3055 (Tables 20, 21). For this
comparison, the sample sizes were small and a student’s ttest was used though this test
is not specifically designed for small samples and some error must be expected in tests
of these subsets. A Pearson correlation coefficient was also calculated to compare the
significance of similarities of estimates between methods.
TABLE 21: TEST FOR SIGNIFICANTDIFFERENCES IN INDIVIDUALS 3055 YEARS OLD
Ind Macroscopic Range
Macroscopic Mean
Kashyap & Rao
Cementum Annulations
6b 3035 32.5 22.00 34.00 11 3545 40.0 64.00 . 12 3545 40.0 26.00 30.00 13 3545 40.0 50.50 30.00 15 30.00 30.0 20.00 27.00 28 4550 50.0 27.00 28.00 30a 3539 37.5 25.00 31.00 30b 2733 30.0 48.50 18.00 37 4555 50.0 28.00 . Mean (St. Dev.) 38.89 (7.51) 34.56 (15.61) 28.29 (5.06) t .745 3.149 p .478 .020 Correlation (r) .018 .292 p .963 .525
For the young adult category, the estimates from both Kashyap and Rao and
from the cementum annulations were not significantly different from the macroscopic
estimates (p = 0.553 and 0.084 respectively). The estimates from the cementum
increments were significantly correlated with the macroscopic estimates (r = 0.693, p =
0.056). For the older adult age category, the cementum estimates were significantly
different from the macroscopic estimates (p = 0.020), the estimates from Kashyap and
Rao’s method were not (0.478). For this subsample, the Kashyap and Rao method had
the highest standard deviation, reflecting a broad range of estimates for the older
sample (2250.5). Neither of the histological methods produced a significant correlation
coefficient with the macroscopic estimates for the older age category.
Though the mean of the estimates from the Kashyap and Rao method were not
significantly different from the macroscopic mean in either age category, the variability
for this method increases in the older age category (Figure 13). There was no dominant
trend in the direction of the differences (towards over or under estimation) for the
Kashyap and Rao method for the young or older adult samples. The estimates from the
cementum annulations had a smaller range of difference than the estimates from
Kashyap and Rao. However, three individuals in the older adult category have age
estimates differing by 10 years or more from the macroscopic estimates. The cementum
annulations tended to overestimate the age of younger individuals and underestimate
the age of older individuals (Figure 13). Charles and colleagues (1986) found a similar
centrist trend in the age estimates from cementum for known aged individuals. Despite
this trend, they found that the estimates were highly correlated with known age at
death. As the cementum has a margin of error less than +/ 10 years for older
individuals, an error similar to the macroscopic estimation methods, a mean of the two
estimates will be used to revise the demographic profile for this population.
The sample was also subdivided into male (n = 9) and female (n= 9) sub
samples to test for significant differences between the sexes (Table 22). The mean age
at death is slightly higher for females than for males, but the results of the ttest for
independent samples show that the differences between the sexes for the macroscopic
estimates are not significant (p = 0.286). There were no significant sex differences in
either histological method, Kashyap and Rao (p = 0.289) and the estimates from the
FIGURE 13: DIFFERENCES BETWEEN MACROSCOPIC AND HISTOLOGICAL AGE ESTIMATES
Indiv iduals
37 28 13 12 11 30a 6b 30b 15 34 18a 8 16a 17 7 20a 36a 32
Age E
stimate
s
70
60
50
40
30
20
10
Macro
Kashyap & Rao
Individuals
37 28 13 12 11 30a 6b 30b 15 34 18a 8 16a 17 7 20a 36a 32
Age E
stimate
s
60
50
40
30
20
10
Macro
C ement
cementum annulations (p = 0.107). An examination of the estimates from all three
methods for males and females shows that the amount of variance in the estimates was
again greater for the method developed by Kashyap and Rao than for the cementum
annulations. The variance was equivalent for both males and females. Thus there
appear to be no significant differences in the mean ages at death and no significant sex
based systematic biases within these age estimation methods.
The Damdama sample was also divided by anterior (n = 11) and posterior (n =
18) tooth classes. Anterior teeth were defined as incisors, canines, and premolars.
Posterior teeth included all molars. The two groups were tested using a student’s ttest
for independent samples. There were no significant differences between anterior and
posterior tooth class in the estimates from the macroscopic (p = 0.306), Kashyap and
Rao (p = 0.948), or cementum (p = 0.318) methods. The cementum and the Kashyap
TABLE 22:DESCRIPTIVE STATISTICS FOR AGE ESTIMATES FORMALES AND FEMALES
N Min Max Mean s.d Variance Female Macroscopic 9 18.00 50.00 32.56 11.70 136.84 Kashyap & Rao 9 19.00 64.00 31.50 15.28 233.357 Cementum 7 24.00 31.00 28.29 2.36 5.57 Male Macroscopic 9 18.00 50.00 26.89 10.00 99.99 Kashyap & Rao 9 15.00 48.50 24.79 10.21 104.26 Cementum 8 18.00 34.00 24.63 5.13 26.27
and Rao method are thus not biased by tooth class because the macroscopic methods
were also based on skeletal observations. The Kashyap and Rao method was the least
sensitive to real differences between the ages of individuals included in both samples.
In comparisons between teeth from the right and left sides, the macroscopic
estimates were significantly different (p = 0.043), with a difference of 7.71 years
between the means for the two sides. This indicates only a coincidental difference in
age at death between individuals for whom right and left teeth were available. Neither
the Kashyap and Rao method, nor the cementum recorded significant differences
between the right and left sides (p = 0.986, and 0.575 respectively), indicating that
these methods are not biased by side. The sample was also divided by maxillary teeth
(n = 21) and mandibular teeth (n = 8). In a ttest for independent samples, there were no
significant differences between macroscopic, Kashyap and Rao, and cementum
estimates from either jaw (p = 0.971, 0.588, and 0.724 respectively).
Discussion and Interpretation
The histological methods compared in this study are based on attrition, root
translucency, secondary dentine, and cementum annulations. The Kashyap and Rao
method and the cementum annulations produced the most promising results. Methods
based on root translucency produced results very similar to one another and very
different from the macroscopic age estimates. The multivariate method developed by
Kashyap and Rao (1990) produced a mean that was not significantly different from that
of the macroscopic estimates and the correlation coefficient was significant. However,
an analysis of the variance within and between the estimates from the macroscopic and
the Kashyap and Rao method showed that there was a large amount of variance for
estimates from this method, which the mean difference could not demonstrate. Eleven
estimates from this method differed from the macroscopic estimate by greater than plus
or minus fifteen years.
The amount of variation in the estimates from this method may be related to the
way the indices were constructed. Some of the measurements used as fixed variables
are not themselves independent of age related changes. The length of the secondary
dentine was divided by the length of the pulp cavity. It might be better to divide the
length of the secondary dentine deposit by the length of the root, which is a more
independent variable. The length of the translucent area is divided by the length of the
entire tooth, which is also effected by the amount of attrition, root resorption, and
cellular cementum. The method might be more accurate if the area of translucency were
divided by a variable more independent of age. Their method used the width of the
cementum deposit at the thickest point, which would generally be at the level of the
cellular cementum that is not solely deposited as a function of age. A count of
cementum annulations also might be more accurate.
It should be mentioned that the method was precise within 1.59 years in modern
teeth. It remains to be seen whether these changes would provide more precision to the
method for prehistoric material. This method was originally developed on a small
sample of 25 individuals. It has not been independently tested on modern teeth of
known age at death or extraction. Thus the standard error for independent samples and
the repeatability of this method are not known. Another possible contributing factor to
the variability of this method is that the sections were examined at a thickness of 1 mm.
This thickness may be more useful in clinically extracted or forensic material, but may
be too thick to represent the full extent of translucency in prehistoric archaeological
samples.
It appears that the rest of multivariate and univariate methods based on root
translucency are not useful for this sample. Johanson’s methods for attrition and
translucency consistently overestimated the age at death in individuals from Damdama.
Though Johanson’s univariate formula for attrition was only correlated with known age
around 50% in his original study, the method combining attrition with translucency was
correlated at 86%. Not only was Johanson’s method overestimating age at death in
comparison with the macroscopic, Kashyap and Rao and cementum methods, it was
strongly clustering with the other methods based on multivariate methods utilizing
categorical scores (Maples) and the methods based on root translucency (Lorentsen and
Solheim and Drusini).
One explanation for the lack of accuracy for these methods in this sample then
involves the application of a discrete, or categorical scale. Although tooth types did not
contribute to a lack of precision in the test of individuals with multiple teeth available,
the lack of distinction between tooth types may complicate the data collection in
discrete categories. In anterior teeth, wear can be easily scored on one cusp. In posterior
teeth the wear does not generally affect the entire occlusal surface evenly. If for
example, the lingual cusps of a maxillary molar are worn to dentine exposure and the
buccal cusp is worn to half the height of the enamel, there is no intermediate grade to
average the scores.
Methods that do not distinguish tooth type can have problems caused by
differential amount of wear being complicated by eruption timing. If eruption timing is
not considered in scores of attrition, there can be a wear gradient caused not by
differences in the age of onset of wear, or the age at which the tooth reached the
occlusal plane. However, differences in tooth type are not supported in this study, given
the close resemblance of estimates for different teeth available from the same
individual for all of the methods.
For individuals with two or three teeth available for analysis, there were no
significant differences between the estimates based on root translucency. Thus the
methods based on root translucency were as internally consistent as the other methods.
This indicates several things: 1.) the methods were consistently applied in independent
assessments and between tooth classes, 2.) while the accuracy of the methods may be
questionable in this study, the methods are precise. In other words, whether or not the
estimate is accurate, the method yields estimates within a narrow margin of error for
teeth from different classes from the same individual.
There may be an effect in Johanson’s attrition scores from the obvious
differences in diet between the reference and the sample populations. Attrition will
proceed much slower in Johanson’s sample of modern Norwegians than hunter
gatherers from Mesolithic India. Lukacs’ study of dental pathology profiles in
Mesolithic India has demonstrated that these groups tend to have high levels of
attrition, most likely due to a coarse textured wild food and excess grit in the diet in
comparison to processed agricultural foods. Dietary differences in childhood may also
be implicated in differential degrees of mineralization. These problems with attrition
are recognized in macroscopic age estimation methods as well and are responsible for
the general consensus that specific rates should be calibrated for each sample, based on
comparisons between attrition and independent age estimates.
Johanson’s criteria for root dentine translucency suffers from similar problems.
All of the methods based on root translucency were inaccurate whether or not a
categorical system was employed for scoring. The age estimates from methods
developed by Maples (1978) from Gustafson’s (1950) criteria, Lorentsen and Solheim
(1990) for translucent area, and Drusini (1989) for the length of the translucent zone
consistently and substantially overestimated age at death in comparison with the
macroscopic methods. Maples’ method produced results that were more closely
correlated with the macroscopic estimates than the other methods, but the method still
most closely resembled this cluster of methods.
The fact that all of the methods based on root translucency closely resembled
one another suggests that the use of categorical scores only partially explains problems
in using root translucency in this prehistoric sample. Much more work is needed to
understand the effects of diagenesis on root dentine translucency. The teeth in this
sample had been affected by postdepositional processes and were heavily mineralized.
Vlcek and Mrklas (1975) found that neither periodontitis nor root translucency could be
scored in archaeological samples. In their test of the Gustafson method, root resorption
and attrition were not useful for estimating age at death either.
The count of cementum increments, based on the method developed by Charles
and colleagues (1986, 1989; Condon et al 1986) produced the most promising results.
The mean estimate was not significantly different from the mean for the macroscopic
method and the variance between the two groups was not significant. The Pearson’s
correlation coefficient for similarities between macroscopic estimates and cementum
counts was 42.8%, significant at the 5% level (p = 0.043). The Pearson correlation is
much higher for young adults, 1629 year olds (r = 0.693, p = 0.056) and is not
significant in the 30 55 year old category (r = 0.292, p = 0.525). The differences in the
mean age estimates were not significant for the young adult category (1629) but there
was a significant difference for the older adult category (3055). In comparison with the
macroscopic estimates, the cementum tends to overestimate age for individuals in the
younger category and underestimate age in the older adults, demonstrated by the
reduced amount of variance within the cementum estimates as compared to the
macroscopic estimates. However, Charles and colleagues (1986) also observed this
trend and their age estimates from the cementum were still highly correlated with
known age at death.
The method for cementum annulations does not pose the same problems as the
other dental histology methods. The method is metric, not based on categorical scores,
thus the full range of variation is represented in the observations. Dental eruption
timing was taken into consideration because the estimates represent the sum of
increment counts and eruption times by tooth class. Differences between sample
populations are controlled by using eruption timing specific for a modern Indian
population. There will still be an unavoidable amount of error introduced by differences
between eruption times in modern individuals and a Mesolithic population. This error is
common to all bioanthropological estimates for age at death.
The amount of interobserver error was statistically significant for the cementum
annulation counts. The level of error between observers was higher than the level of
intraobserver error. The criteria whereby sections were eliminated as unscorable were
highly variable between observers as well. The significant amount of interobserver
error indicates that the accuracy of the method is highly dependent on the experience of
the observer. The insignificant amount of intraobserver error demonstrates that an
experienced observer can precisely apply the method. These results for observer error
are also consistent with those obtained by Charles and colleagues (1986).
Implications for Further Research
In the next phase of this research, the methods developed by Kashyap and Rao
and the Charles method for counting cementum annulations in mineralized teeth will be
tested on an independent sample of modern teeth from India. As the age of extraction
will be known, regression formulas can be developed for each tooth class for the
Kashyap and Rao method and changes outlined above can be tested as well. The
original method will be tested on thinner section, as the 1 mm thickness is less
appropriate for prehistoric than for modern samples. New indices used for
standardizing age related changes will also be tested in the modern sample.
As was mentioned in the conclusions section, some of the measurements used
as fixed variables in the Kashyap and Rao method are not themselves independent of
age related change. The following modifications will be tested: dividing the length of
the secondary dentine deposit and the length of the translucent area by the length of the
root. The cementum will be measured at onethird the distance from the apex, rather
than at the thickest point. If the revised method is accurate at a thickness of 100 um, a
count of cementum annulations may also be factored in instead of the thickness. The
centrist tendency noted in the cementum annulations will be tested as well. The Lopez
Nicolas methods (1990, 1991, 1996) described above will also be tested and a
regression formula can hopefully be developed that does not include periodontitis.
These histological methods will also be tested on archaeological samples from a
different chronocultural context, the Indian Chalcolithic (35002700 BP). As this
material is derived from a different deposit, it has suffered very different diagenetic
changes. Testing these methods on Chalcolithic samples for trends in the variance
between macroscopic and microscopic methods will hopefully provide a more detailed
understanding of the effects of taphonomic processes as well as the effects of temporal
distortion in histological aging.
Summary of Results and Conclusions
1. The Kashyap and Rao (1990) and Charles (1986) methods produced estimates most
closely resembling the macroscopic estimates. The other multivariate methods
developed by Johanson (1971) and Maples (1978), and the methods based solely on
root translucency developed by Lorentsen and Solheim (1989) and Drusini (1990)
consistently and substantially overestimated age at death by comparison.
2. The mean for the method developed by Kashyap and Rao (1990) was not
significantly different from that of the macroscopic estimates. However, the method
had a large standard deviation, produced a substantial amount of internal variance,
and there were eleven estimates that differed from the mean macroscopic estimate
by 15 years or more. The method was developed using a small modern sample (25
individuals) of known age. This may have affected the accuracy of the regression
formulas for estimating age in an independent sample. The method has yet to be
tested on an independent sample of known age. The method shows promise for use
in archaeological samples, though the method might benefit from changing some of
the index variables that are not themselves independent of age at death. The 1mm
thickness of the sections may also negatively impacted the accuracy of the results.
3. The methods developed by Johanson (1971) and Maples’ (1978) revision of the
Gustafson method rely on categorical scores, rather than metric measurements. The
use of categorical scores may partially account for the inflated age estimates. The
scores for attrition are probably inflated for this sample due to the acceleration of
wear accompanying a huntergatherer diet in the Damdama sample. These methods
were developed from modern sample populations with a diet higher in processed
food. The attrition scores in Johanson’s original study were not highly correlated
with age at death unless combined with translucency in a bivariate formula.
4. The estimates from root translucency in the multivariate methods and the methods
developed by Lorentsen and Solheim (1989) and Drusini (1990) were all elevated to
a similar degree and strongly correlated with one another. This indicates that root
translucency, whether measured categorically, by area, or by length is not a useful
tool for age estimation in this sample. It is possible that the translucency was
increased by diagenesis. Soil samples were collected and await further analysis.
Increased levels of mineralization in prehistoric samples that precluded analysis of
root translucency have been reported elsewhere (Vlcek and Mrklas 1975; Lucy et
al. 1996). It is also possible that a section thickness of 500 um is not practical for
prehistoric, archaeologically derived material.
5. The age estimates derived from cementum annulations were not significantly
different from the macroscopic estimates. The macroscopic age estimates for
younger adults (1629) had a smaller margin of error than estimates for the older
adults (3055) in comparison with the macroscopic estimates. An analysis of the
variance within and between methods and an examination of the differences
between age estimates for the two age categories indicates that the cementum tends
to overestimate age at death in younger individuals and underestimate age for older
individuals. Despite finding a similar trend, Charles et al. (1986) found a strong
relationship between the estimates from the cementum and known age at death.
6. There were no significant differences between the macroscopic, Kashyap and Rao,
and cementum methods in comparisons between males and females, anterior and
posterior tooth classes, and upper and lower jaws. The macroscopic estimates were
significantly different between teeth from the right and left sides, reflecting real
differences in the age profiles for these two subsamples not bias in the macroscopic
methods. The macroscopic ages were derived from skeletal and dental age
estimates. The histological methods were not significantly different in this
comparison. This last comparison indicates that the histological methods were
insensitive to a real trend in age at death between two subsamples. The cementum
method was considered accurate enough to be included in the revised ages at death
for the demographic reconstruction of the Damdama sample.
PALEODEMOGRAPHIC PROFILE FOR DAMDAMA
The age estimates from the cementum annulations closely compared with the
mean estimates from the macroscopic methods. There was a tendency for the
cementum annulations to overestimate age at death for younger adults and to
underestimate age at death for older individuals, in comparison with the macroscopic
estimates. Charles and colleagues (1986) also noted this tendency, but they found that
the estimates from the cementum annulations were accurately predicting age at death
never the less. It was thereby determined that the most accurate age profile for the
Damdama skeletal sample would be derived by averaging the estimates from the
cementum annulations with the macroscopic estimates (Table 23). There were no
estimates from the cementum annulations available for three individuals (11, 16a, and
37). For those three individuals and for twentyone individuals for whom teeth were not
available for this analysis, the macroscopic estimates were used.
TABLE 23:AGE ESTIMATES FOR THE SKELETAL POPULATION FROM DAMDAMA
Age Male Female Unknown Age Male Female 1.52.5 4 32.537.5 30a 2.53.5 5 3040 27 12, 13 1418 36b 3540 6a 1618 35 3050 28 1822 33 40 11, 40 19 19 4045 20b 2024 22 3650 1 2025 18a 4351 39 21 7, 16a, 32 36a 4550 23 2225 18b,34 17, 20a 4555 18c 2425 8, 30b 4560 26 26.530.5 15 50 37 30 16b 2, 29 5060 3 3036 6b Adult 9, 24, 25 10
Indet 21 14,31 total (46) 25 17 4
FIGURE 14:AGE PROFILE FOR DAMDAMA
Age at Death
56 52 48 44 40 36 33 29 25 21 17 13 10 6 2
8
6
4
2
0
Std. Dev = 12.62 Mean = 30 N = 39.00
As is common in prehistoric skeletal samples, juveniles (defined here as birth to
16) are underrepresented in the Damdama population. There were only two subadults
recovered from excavations at Damdama, both young children aged 2 and 3 years old
based on dental development and eruption timing. The mean age at death for the entire
sample, aside from individuals aged only as adult or for whom age could not be
determined, was 30 years of age (n =39). The mode is 21 years of age (7 individuals,
15%) and the distribution appears fairly normally distributed between the ages of 16 to
55. The age distribution is biased towards younger adult individuals. Of the 39
individuals for whom age at death could be assessed, 24 individuals were in the young
adult category (1834), there were 11 older adults (3550). There are only 4 individuals
over the age of 50. Of these 39 individuals, 21 (53.85%) died before the age of 25.
The 37 adult individuals for whom age was estimated in the Damdama sample
were also divided by sex. The mean age at death for males (n = 22) was 29.25 years of
age (standard deviation = 9.94). The mean age at death for females (n = 15) was 35.63
years of age (standard deviation = 11.93). The distributions were significantly different
in a chisquare test (X 2 = 68.27, p = 0.015). An examination of the distribution of ages
shows this difference in the patterning of age at death for females and males (Figure
FIGURE 15:DISTRIBUTION OF AGE ESTIMATES FOR FEMALES AND MALES
Age at Death Females
55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0
4
3
3
2
2
1
1
0
Std. Dev = 11.93 Mean = 35.6
N = 15.00
Age at Death M ales
50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0
7
6
5
4
3
2
1
0
Std. Dev = 9.94 Mean = 29.3
N = 22.00
15). The age at death for males was skewed toward early adulthood, with peaks at 20
25 and a small standard deviation reflecting minimal spread about the mean. For
females however, there was more of a bimodal distribution with a substantial peak for
death between ages 2535 and a smaller peak at age 5055. The sample sizes are small
and unequal, so caution must be exercised in relying on the results of this comparison.
However, some broad trends can be extrapolated.
Life expectancy for this population in general was not long, with 30 years old
being the average age at death. It also appears that there was a sex related difference in
age at death, with more females living somewhat longer than males. Kennedy and
colleagues (1992) noted a similar trend in the skeletal population from Mahadaha
(Table 24). For Mahadaha, Kennedy attributes the young adult mean age at death to
TABLE 24:DAMDAMA, MAHADAHA, AND SARAI NAHAR RAI AGE DISTRIBUTION
n Subadult <18 yrs
Young adult
1834 yrs
Middle adult
3550 yrs
Older adult >50 yrs
Indet.
DDM 46 4 (8.7 %) 19 (41.3 %) 13 (28.3 %) 3 (6.5 %) 7 (15.2 %) MHD 26 3 (11.5 %) 11 (42.3 %) 1 (3.8 %) 2 (7.7 %) 9 (34.6 %) SNR 10 0 10 (100 %) 0 0 0 Total 82 7 (8.5 %) 40(48.8 %) 14 (17.1 %) 5 (6.1 %) 16 (19.5 %) DDM= Damdama (this study and Lukacs, n.d.); MHD = Mahadaha (Kennedy et al. 1992); SNR= Sarai Nahar Rai (Kennedy et al. 1986)
sampling error and/or age estimation bias. Damdama has a more evenly distributed
profile for age at death. A chisquare test shows that the distributions for the two groups
are significantly different at a 1% level (X 2 = 48.85, p = 0.0205).
The depth of deposits at Damdama has been interpreted as evidence for a
stationary lifestyle, though the depth could also certainly represent a series of
successive temporary occupations. The construction of a stationary life table (following
Storey 1992) assumes that the population was not only stationary, but stable in physical
size and in size of the population. Though it might be oversimplifying the evidence
from Damdama to construct such a table, it is also possible that interesting insights can
be tentatively derived from overstepping the boundaries of conservatism, as long as the
limitations and potential problems are addressed. The table is probably biased from
invisible demographic characteristics resulting from time averaging and the relatively
small sample size, so any inferences or conclusions drawn from the table should be
considered estimates about general patterns in somewhat faulty data. Further
extrapolations such fertility and fecundity are considered beyond the potential of the
data from this sample and will not be attempted.
The table was constructed for adult individuals and inference will be limited to
broad patterns, with the caveat that the results may be inaccurate if this sample
population did not crosssect a single temporal period of relative stability (Table 25).
Juveniles under 15 years of age were not included because they were obviously under
represented in the sample (there were only two individuals between the ages of 14).
There is no direct evidence that these individuals were interred elsewhere, although
separate burial customs for infants and juveniles have been recorded for Chalcolithic
sites in central Western India (Lukacs and Walimbe 1986). The calculations of life
expectancy are based only on individuals who had already survived to the age of
fifteen. Five year intervals were used to account for errors in age estimation.
To briefly explain the table: 1.) Dx represents the raw number of deaths in each
age class, 2.) dx is the simple proportion of all deaths in each age category, 3.) Ix is an
estimate of survivorship (the percentage of individuals surviving to the beginning of the
age class), 4.) qx is the percentage of individuals who reach a given age class and die
within that class (mortality), 5.) Lx is the total number of years lived in each age
TABLE 25: STATIONARY POPULATION LIFE TABLE FOR DAMDAMA
Age # deaths Dx
% deaths dx
% Survivors
lx
Prob of death qx
Yrs lived in class Lx
Yrs left in life Tx
Life Expect. ex
15 4 10.8108 100.000 .1081 472.975 1763.80 17.64 20 12 32.4324 89.1900 .3636 364.875 1290.83 14.48 25 1 2.7027 56.7600 .0476 277.050 925.953 16.31 30 4 10.8108 54.0600 .2000 243.275 648.903 12.00 35 5 13.5135 43.2500 .3125 182.475 405.628 9.38 40 5 13.5135 29.7400 .4544 114.925 223.153 7.50 45 2 5.4054 16.2300 .3331 67.625 108.228 6.67 50 3 8.1081 10.8200 .7494 33.825 40.6025 3.75 55 1 2.7027 2.7100 1.000 6.778 6.7775 2.50 60+ 0 0 0 0 0 0 0
total 37 100.00
category (assuming that the number of deaths per year is equivalent), 6.) Tx is the total
number of years left in life for individuals in a given age class until all individuals are
dead, and 7.) ex is the life expectancy at a given age (calculated from Tx) beginning
with eo or life expectancy at birth.
For individuals who had reached the age of fifteen life expectancy was 17.6
years. Fewer than 50% of the population lived beyond the age of thirty and 30% lived
past 40 years old. For those individuals who did make it to thirty, life expectancy was
FIGURE 16: ADULT MORTALITY CURVE AND LIFE EXPECTANCY FOR DAMDAMA ADULTS
Age (in years)
5155 4650 4145 3640 3135 2630 2125 1620
% Surviv
ors
100
90
80
70
60
50
40
30
20
10
0 11
16
30
43
54 57
89
AGE
55.00 50.00 45.00 40.00 35.00 30.00 25.00 20.00 15.00
Life E
xpectan
cy (y
ears)
20
10
0
3
4
7 8
9
12
16
14
18
only 12 years. As there are very few individuals in the age classes above fifty, a
particular bias involving underestimation of age at death in older individuals should be
mentioned. The four individuals over the age of fifty were all aged using macroscopic
estimates of degenerative change. There was one older adult (37) which was originally
intended to be included in the dental histology portion of this study, but the root surface
was damaged and the cementum annulations could not be counted.
If this individual could have been included in the cementum age estimations, the
method demonstrated a tendency to underestimate age in comparison with the
macroscopic estimates in individuals over the age of 35. The trend has also been noted
in studies of modern teeth, of known age, but the centrist trend has generally been
ascribed to the use of multiple regression when a Bayesian approach might be more
appropriate (Lucy et al 1996). A contradictory trend has also been noted, whereby the
annulations accumulate faster than age at death, leading to overestimation (Condon et
al. 1986). Further work on archaeological samples and in teeth of known age at death or
extraction will be required to determine if this trend is a permanent bias in the
annulations, or if there is a solution in statistical approaches. If age was underestimated
for these older individuals, the estimates for mortality per age category will be
overestimated and the life expectancy will be underestimated (Storey 1992).
The construction of demographic profiles and intraregional comparison allows
broad inferences to be made about the skeletal population from Damdama. The life
expectance was generally low, about 17.6 years for individuals who had survived to the
age of 15. The mean age at death was also low (30 years) but 30% of individuals lived
past the age of 40. Females tended to live longer, up to 5055 years of age, than males,
few of whom lived beyond the age of 35. These statistics are somewhat comparable to
the skeletal population from Mahadaha, though the middle adult (3550) category in
that sample is substantially underrepresented. Sarai Nahar Rai does not offer much
potential for comparisons of demographic characteristics as the collection consists of
10 young adults. As the Damdama sample was fairly normally distributed above the
age of 15, the profiles might be considered reasonable approximations of the actual
demography of the population and therefore partially representative of life in the Indian
Mesolithic.
TABLES
TABLE 4:MACROSCOPICMETHODS FOR AGE ESTIMATION
Ind Auricular Surface
Pubic Symph
Dental Eruption
Dental Attrition
Sutures & Epiphyses
Summary Age
1 3650 Adult 2249~35 3650 2 Adult > 30 > 30 3 5060 Adult 5060 4 2+/6mo 1.52.5 5 23 2.53.5 6a Adult 3045 Adult 3045 6b 3035 Adult 3035 3035 7 Adult 1820 ~20 ~20 8 Adult Adult Adult 9 Adult Adult 10 Adult Adult Adult 11 Adult 3545 Adult 12 4045 3047 Adult 3545 Adult 3545 13 4346 2247 Adult Adult 3545 14 Frgmt. 15 Adult Adult Adult 16a 2024 Adult 1824 1724 21 16b 3039 2735 Adult Adult 2535 17 1618 18 1618 18a 2024 >18 2022 2025 18b 2226 >18 2024 2225 18c 4555 adult 56.4 4555 19 1721 19 20a 1720 1720 1720 20b 4044 4045 4045 21 ind 22 2024 >20 2024 23 4550 4550 24 adult adult 25 adult adult 26 5256 4560 27 3040 3040 28 4550 4550 29 2535 2535 30a 3539 3539 3539 30b 2733 2733 31 ind 32 1618 1620 1620 33 2024 1822 1822 34 2529 2529 35 1618 1618 36a 1620 1620 1620 36b 1518 1418 37 4555 4555 39 4055 2376 4351 40 3743 3743
TABLE 5:MACROSCOPICMETHODS FOR SEX ESTIMATION
Ind Sciatic Notch
Diameter of Acetabulum
PC Measures
PC Morph
Cranial Morph
Mand Morph
Sex
1 F M/F F F F F Female 2 M/F F F F Female 3 F F F Female 4 Ind. 5 Ind. 6a F M/F M/F Female 6b M M M M Male 7 M F M Male 8 M/F M M M Male 9 M M Male 10 F M/F F Female 11 M M M/F Male 12 F M/F F F F Female 13 F F M/F Female 14 Frgmt. 15 F F Female 16a M M M Male 16b M F M F M M/F Male 17 F Female 18a F M M M M M/F Male 18b M M M M M Male 18c M M M M Male 19 M M M Male 20a F? F F? F? Female 20b M M M M Male 21 F F Female 22 M M/F Male 23 M/F M M M/F F F Female 24 M M/F M Male 25 M? M? Male? 26 F F F Female 27 M M M Female 28 M M M Male 29 F F F Female 30a F M/F F F F Female 30b M M/F M/F Male 31 ind 32 M? M? Male? 33 M M M Male 34 M M M/F F Male 35 F? Female 36a F F F F Female 36b M M/F M/F M/F Male 37 F Female 39 M M M M Male 40 M M M M M Male
TABLE 6: PROTOCOL FOR PREPARING AND SECTIONING TEETH
1. Sample selection • Teeth should be unaffected by caries, periodontitis, abscess, or other
pathological processes that expose the root to the oral environment • Anterior teeth are easiest to section given the tendencies for molar teeth to have
roots that are crooked, twisted, or bent. • Several teeth should be processed for each individual when possible
2. Documentation • A thorough dental anthropological analysis should be recorded prior to removal
from the jaw. • Photographs: black and white photographs, color slides, and/or digital photos of
each arcade from which samples will be derived, as well as 5 views of each individual tooth (occlusal, medial, distal, buccal, and lingual surfaces)
• Casts negative and positive molds should be made for each tooth 3. Embedding
• Labels with the tooth’s identification number (assigned from a random number table) should be embedded at a permanent face in the mold
• Teeth must be positioned carefully in embedding molds for buccolingual sections; teeth that are more difficult to position can be mounted with wire
• For light and electron microscopy, many embedding media will suffice. (In this study Buehler’s Spurr’s Resin (medium hardness formula) was used.)
• Infiltration 1. 25% resin and 75% acetone for 24 hours 2. 50% resin and 50% acetone for 24 hours 3. 75% resin and 25% acetone for 24 hours 4. 100% resin for 24 hours 5. Fresh 100% resin heated to 60 degrees Celsius for 24 hours, or until resin
has hardened completely. 4. Sectioning and polishing
• Serial sections should be made in the buccolingual plane using a diamond edged saw (Buehler Isomet) to a thickness of 200 um (for translucency) to 100 um (for cementum) based on method to be employed.
• The sections can be bathed in HCL to soften the edges of large scratches. The sections should be polished to remove fine scratches (Buehler Minimet).
• Sections should be dehydrated in 70% ETOH, attached to glass slides with methyl methacrylate or Canadian Balsam, labeled, and stored carefully.
5. Microscopy and section photography • No stain is necessary for observations of root translucency. For counting
cementum annulations, mineralized sections should be stained with Alizarin red • Root dentine translucency should be examined at 65100 x magnification.
Cementum annulations should be counted at 200400x magnification • Photographs of sections can be taken with a digital camera mounted on the light
microscope. Images should be saved at 1200x1600 resolution, in a high quality format (such as .tiff).
FIGURES
FIGURE 3: JOHANSON’S SCORING CRITERIA
FIGURE 4:AREA OF ROOT TRANSLUCENCY (LORENTSEN AND SOLHEIM 1989)
FIGURE 5: LENGTH OFROOT TRANSLUCENCY (DRUSINI 1990)
FIGURE 6:CEMENTUMANNULATIONS
FIGURE 7:COMPARISON OF MULTIPLE TEETH FROM THE SAME INDIVIDUAL 1
Individual 6B URM2
80 70 60 50 40 30 20 10
Indiv
idual 6B
LRP
3 80
70
60
50
40
30
20
10 Rsq = 0 .9154
10 8
7
6
5
4 3
2
1
Individual 7 URM2
90 80 70 60 50 40 30 20 10
Individual 7 L
LC
90
80
70
60
50
40
30
20
10 Rsq = 0 .6806
10
8
7
6 5
4
3
2 1
Individual 7 URM3
90 80 70 60 50 40 30 20 10
Individual 7 L
LC
90
80
70
60
50
40
30
20
10 Rsq = 0 .8542
10
8
7
6 5
4
3
2 1
Individual 8 ULM3
90 80 70 60 50 40 30 20
Individual 8 LLI2
90
80
70
60
50
40
30
20
10 Rsq = 0 .5956
11
10
8 7
6
5
4 3
2
1
Individual 13 LRM3
90 80 70 60 50 40 30
Individual 13 ULI1
90
80
70
60
50
40
30
20 Rsq = 0 .5117 11
10 8
7
6
5
4
3
2
1
Individual 17 ULM3
90 80 70 60 50 40 30 20 10
Individual 17 ULI1
90
80
70
60
50
40
30
20
10 Rsq = 0 .5474
10
8
7
6
5
4 3
2
1
1 1 = Macroscopic methods, 2 = Kashyap and Rao (1990), 3 = Johanson (1971) Attrition, 4 = Johanson (1971) Translucency, 5 = Johanson (1971) Attrition and Translucency, 6 = Maples (1978), 7 = Lorentsen and Solheim (1989), 8 = Drusini (1990), 9 = Charles (1986, 1989) method for cementum annulation counts
FIGURE 7:COMPARISON OF MULTIPLE TEETH FROM THE SAME INDIVIDUAL (CONTINUED) 1
Individual 28 LRM3
80 70 60 50 40 30
Individual 28 URI1
90
80
70
60
50
40
30
20 Rsq = 0 .7903
10
8
7
6
5
4
3
2
1
Individual 30B LRM1
80 70 60 50 40 30 20
Indiv
idual 30B UL
C
90
80
70
60
50
40
30
20 Rsq = 0 .4381
11
10
8 7
6
5
4
3
2
1
Individual 32 ULP4
90 80 70 60 50 40 30 20 10
Individual 32 ULI1
100
80
60
40
20
0 Rsq = 0 .4366
10
9 8
7
6
5
4
3
2
1
Individual 32 LRM3
90 80 70 60 50 40 30 20 10
Individual 32 ULI1
100
80
60
40
20
0 Rsq = 0 .8334
10
8
7
6
5
4
3
2
1
Individual 34 ULM3
70 60 50 40 30 20 10
Indiv
idual 34 ULM
2
60
50
40
30
20 Rsq = 0 .2318
10 8
7
6
5
4
3
2
1
1 1 = Macroscopic methods, 2 = Kashyap and Rao (1990), 3 = Johanson (1971) Attrition, 4 = Johanson (1971) Translucency, 5 = Johanson (1971) Attrition and Translucency, 6 = Maples (1978), 7 = Lorentsen and Solheim (1989), 8 = Drusini (1990), 9 = Charles (1986, 1989) method for cementum annulation counts
FIGURE 9:DISTRIBUTION OF ESTIMATES FOR EACH METHOD
Individuals
8
7
6b
37
36a
34
32
30b
30a
28
20a
18a
17
16a
15
13
12
11
Age E
stimate
s
70
60
50
40
30
20
10
Macroscopic
Kashy ap and Rao
C harles
MACROSCOPIC
43.8 41.4 39.0 36.5 34.1 31.7 29.3 26.9 24.5 22.0 19.6 17.2
3.5
3.0
2.5
2.0
1.5
1.0
.5
0.0
Std. Dev = 10.95 Mean = 29.7
N = 18.00
KASHYAP
65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0
7
6
5
4
3
2
1
0
Std. Dev = 13.07 Mean = 28.1 N = 18.00
ATTRITION
70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0
6
5
4
3
2
1
0
Std. Dev = 13.04 Mean = 51.3
N = 18.00
TRANSLUCENCY
70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0
6
5
4
3
2
1
0
Std. Dev = 9.80 Mean = 60.0 N = 18.00
ATTRITION & TRANSLUCENCY
75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0
5
4
3
2
1
0
Std. Dev = 12.76 Mean = 59.2
N = 18.00
FIGURE 9:DISTRIBUTION OF ESTIMATES FOR EACH METHOD (CONTINUED)
MAPLES
70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0
5
4
3
2
1
0
Std. Dev = 11.81 Mean = 54.2
N = 18.00
LORENTSEN
90.0 80.0 70.0 60.0 50.0 40.0 30.0
5
4
3
2
1
0
Std. Dev = 18.59
Mean = 59.3
N = 9.00
DRUSINI
50.0 48.0 46.0 44.0 42.0 40.0 38.0 36.0 34.0
5
4
3
2
1
0
Std. Dev = 4.19 Mean = 42.7
N = 18.00
CEMENTUM
35.0 32.5 30.0 27.5 25.0 22.5 20.0 17.5
6
5
4
3
2
1
0
Std. Dev = 4.37 Mean = 26.3 N = 15.00
FIGURE 10:HISTOGRAMS FOR ESTIMATES FOR EACH INDIVIDUAL
6B
55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0
4
3
3
2
2
1
1
0
Std. Dev = 11.51 Mean = 41.5
N = 9.00
7
80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0
3
2
2
1
1
0
Std. Dev = 24.97 Mean = 46.1
N = 9.00
8
70.0 60.0 50.0 40.0 30.0 20.0
4
3
3
2
2
1
1
0
Std. Dev = 17.19 Mean = 46.2
N = 9.00
11
70.0 65.0 60.0 55.0 50.0 45.0 40.0
3
2
2
1
1
0
Std. Dev = 9.64
Mean = 56.9
N = 7.00
12
70.0 60.0 50.0 40.0 30.0
3
2
2
1
1
0
Std. Dev = 16.49 Mean = 48.9
N = 8.00
13
90.0 80.0 70.0 60.0 50.0 40.0 30.0
4
3
3
2
2
1
1
0
Std. Dev = 18.45 Mean = 59.3
N = 9.00
FIGURE 10:HISTOGRAMS FOR ESTIMATES FOR EACH INDIVIDUAL (CONTINUED)
15
70.0 60.0 50.0 40.0 30.0 20.0
3
2
2
1
1
0
Std. Dev = 17.42 Mean = 44.0 N = 8.00
16A
45.0 40.0 35.0 30.0 25.0 20.0
4
3
3
2
2
1
1
0
Std. Dev = 7.94 Mean = 30.9 N = 8.00
17
50.0 45.0 40.0 35.0 30.0 25.0 20.0
3
2
2
1
1
0
Std. Dev = 12.44 Mean = 37.6 N = 8.00
18A
60.0 50.0 40.0 30.0 20.0
4
3
3
2
2
1
1
0
Std. Dev = 14.45 Mean = 36.5 N = 8.00
20A
60.0 50.0 40.0 30.0 20.0
5
4
3
2
1
0
Std. Dev = 12.03 Mean = 35.4 N = 8.00
28
70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0
4
3
3
2
2
1
1
0
Std. Dev = 15.29 Mean = 51.1
N = 9.00
FIGURE 10:HISTOGRAMS FOR ESTIMATES FOR EACH INDIVIDUAL (CONTINUED)
30A
70.0 60.0 50.0 40.0 30.0
5
4
3
2
1
0
Std. Dev = 19.70
Mean = 52.4
N = 8.00
30B
70.0 60.0 50.0 40.0 30.0 20.0
4
3
3
2
2
1
1
0
Std. Dev = 19.18 Mean = 51.7 N = 9.00
32
60.0 50.0 40.0 30.0 20.0
4
3
3
2
2
1
1
0
Std. Dev = 17.41 Mean = 41.7
N = 9.00
34
50.0 45.0 40.0 35.0 30.0 25.0 20.0
3
2
2
1
1
0
Std. Dev = 10.28 Mean = 34.8
N = 8.00
36A
70.0 60.0 50.0 40.0 30.0 20.0
4
3
3
2
2
1
1
0
Std. Dev = 20.14 Mean = 44.6 N = 9.00
37
70.0 60.0 50.0 40.0 30.0
4
3
3
2
2
1
1
0
Std. Dev = 17.95
Mean = 53.4
N = 7.00
FIGURE 11:NORMALQUANTILE PLOTS FOR METHODS
Normal Quantile Plot Estimates
from Macroscopic Methods
Observed Cum Prob
1.00 .75 .50 .25 0.00
Expected Cum Prob
1.00
.75
.50
.25
0.00
Normal Quantile Plot Estimates from
Kashyap and Rao Method (1990)
Observed Cum Prob
1.00 .75 .50 .25 0.00
Expected Cum Prob
1.00
.75
.50
.25
0.00
Normal Quantile Plot for Attrition
from Johanson's Method (1970)
Observed Cum Prob
1.00 .75 .50 .25 0.00
Expected Cum Prob
1.00
.75
.50
.25
0.00
Normal Quantile Plot for Translucency
from Johanson's Method (1970)
Observed CumProb
1.00 .75 .50 .25 0.00
Expected Cum Prob
1.00
.75
.50
.25
0.00
Normal Quantile Plot for A & T
from Johanson's Method (1970)
Observed Cum Prob
1.00 .75 .50 .25 0.00
Expected Cum Prob
1.00
.75
.50
.25
0.00
Normal Quantile Plot for Estimates
from Maples Method (1978)
Observed Cum Prob
1.00 .75 .50 .25 0.00
Expected Cum Prob
1.00
.75
.50
.25
0.00
FIGURE 11:NORMALQUANTILE PLOTS FOR METHODS (CONTINUED)
Normal Quantile Plot for Estimates
from Drusini's Method (1990)
Observed Cum Prob
1.00 .75 .50 .25 0.00
Expected Cum Prob
1.00
.75
.50
.25
0.00
Normal Quantile Plot for Estimates
Lorentson and Solheim Method (1989)
Observed Cum Prob
1.00 .75 .50 .25 0.00
Expected Cum Prob
1.00
.75
.50
.25
0.00
Normal Quantile Plot for Estimates
from Cementum Annulations
Observed Cum Prob
1.00 .75 .50 .25 0.00
Expected Cum Prob
1.00
.75
.50
.25
0.00
FIGURE 12: SCATTERPLOTS FOR EACH METHOD
Macroscopic
70 60 50 40 30 20 10
Kash
yap a
nd Rao
70
60
50
40
30
20
10 Rsq = 0.2046
Macroscopic
80 70 60 50 40 30 20 10
Attrition
(Joh
anso
n)
80
70
60
50
40
30
20
10 Rsq = 0.6093
Macroscopic
80 70 60 50 40 30 20
Tran
slucency (Joha
nson
)
80
70
60
50
40
30
20 Rsq = 0.2631
Macroscopic
80 70 60 50 40 30 20 10
Attrition
and
Trans
lucen
cy (Joh
anso
n)
80
70
60
50
40
30
20
10 Rsq = 0.4497
Macroscopic
80 70 60 50 40 30 20 10
Maple
s
80
70
60
50
40
30
20
10 Rsq = 0.0009
Macroscopic
95 85 75 65 55 45 35 25 15
Lorents
en and
Solh
eim
95
85
75
65
55
45
35
25
15 Rsq = 0.0262
FIGURE 12: SCATTERPLOTS FOR EACH METHOD (CONTINUED)
Macroscopic
60 50 40 30 20 10
Drus
ini
60
50
40
30
20
10 Rsq = 0.3911
Macroscopic
60 50 40 30 20 10
CHAR
LES
40
30
20
10 Rsq = 0.1557
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