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Analysis of dental attrition and mortality in the Medieval village of Tirup Denmark.

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AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 126:169 –176 (2005)
Analysis of Dental Attrition and Mortality in the Medieval
Village of Tirup, Denmark
Jesper L. Boldsen*
Institute of Forensic Medicine, Department of Anthropology (ADBOU), University of Southern Denmark, DK 5000
Odense C, Denmark
KEY WORDS
dental attrition; whole mouth analysis; heterogeneity; selective mortality;
nutrition; Tirup
ABSTRACT
New directions and new questions raised
in the study of health in the past justify this reanalysis of
the pattern of dental attrition in the Medieval Danish
population of Tirup. Dental attrition was scored on all
permanent molars from the Tirup skeletal sample. Scores
were analyzed by means of logistic regression of the probability of having entered a given stage of wear for a given
tooth in a way that is very similar to transition analysis.
The primary determinant of dental attrition was age at
death. In addition to age, the effects of sex, side, and
dating were analyzed. In order to assess the homogeneity
of the process of wearing teeth down, a third-order polynomial in age-at-death was also fitted to the transition
probabilities. It was found that age is the single most
Dental attrition is a more direct consequence of
the action of the environment on human hard tissue
than any other marker. Teeth wear in the process of
chewing. Contrary to what happens in the skeleton,
there is no buildup of new mineralized tissue on the
crowns of worn teeth. These facts open unique possibilities for studying the processes of wear and tear
in the past. An analysis based on such individually
seen unidirectional series of transitions has a much
stronger possibility for revealing the effect of selective mortality and other aspects of heterogeneity of
health and living conditions than other types of osteological analyses. It is a natural consequence of
the unidirectionality of dental attrition that it is
frequently used as a means of individual age estimation. In the absence of selective mortality, dental
attrition can provide a population-specific means of
age estimation; but archaeologically relevant reference samples, i.e., samples of skeletons of known age
and sex coming from a population with the same
way of living as any given archaeological sample of
skeletons, are virtually nonexistent. Therefore, age
estimation of ancient individuals from dental attrition is difficult (cf. Brothwell, 1989; Lovejoy, 1985).
Skeletal samples are, at best, random samples of
the dead members of a once-living community (Wood
et al., 1992). At all ages, it was usually the most frail
in the population who died; possible exceptions
would be high-status warriors, but probably not the
©
2004 WILEY-LISS, INC.
important determinant of dental attrition, and that sex or
side did not differentiate the rate of attrition. In several
transitions, there was evidence of heterogeneity, indicating both random and systematic interpersonal differences
in the rate of attrition and an association between the rate
of attrition and age-at-death. It was found that attrition
proceeded more quickly after AD 1300 than prior to that
date. It is suggested that this was due to a possible general
deterioration of living conditions in Northern Europe and
an increased reliance on grain for food during the first half
of the 14th century. The temporal effect on attrition rate
accounts for some but not all the observed heterogeneity
wear. Am J Phys Anthropol 126:169 –176, 2005.
©
2004 Wiley-Liss, Inc.
kind of peasant soldiers or members of rebellions
who would have found their last resting place in
Tirup. This means that skeletal samples are, in comparison to the age matched living population, samples of the most frail. One of the challenges is to
utilize such systematically skewed samples to draw
conclusions about living conditions in the equally
but oppositely selected once-living population. Since
the publication of Wood et al. (1992), some work was
carried out to bridge the information gap between
the living and the dead and promote our understanding of health in the past. A few case studies,
within limited frames of reference, provided specific
solutions to the problems raised by the osteological
paradox (e.g., Boldsen, 1997; Poulsen et al., 2001);
most of the progress (be it from a slightly different
perspective) was synthesized by Hoppa and Vaupel
(2002). The purpose of this paper is to follow up on
*Correspondence to: Jesper L. Boldsen, Institute of Forensic Medicine, Department of Anthropology (ADBOU), SDU-Odense University, Campusvej 55, DK 5230 Odense M, Denmark.
E-mail: Jboldsen@health.sdu.dk
Received 4 September 2003; accepted 2 March 2004.
DOI 10.1002/ajpa.20057
Published online 26 July 2004 in Wiley InterScience (www.
interscience.wiley.com).
170
J.L. BOLDSEN
earlier analyses of dental attrition in Medieval Denmark (Helm and Prydsø, 1979; Boldsen, 1991), and
to reach an understanding of the processes in the
living that created the observed patterns in the
Tirup sample of Medieval Danish skeletons. The
attrition scores used in this paper were published
before (Boldsen, 1991), but they have not been analyzed in relationship to age estimates based on transition analysis. As there were some shortcomings in
the first paper on these data, it is justifiable to
reanalyze and republish the Tirup dental attrition
data.
MATERIALS AND METHODS
In total, 620 burials were identified and excavated
from the Tirup cemetery (Kieffer-Olsen et al., 1986).
Due to relatively few infant burials, this figure is
probably a little smaller than the total number people interred in the cemetery. During the data collection for another recent research project, two additional individuals were identified among the
scattered remains of bones from disturbed burials
(Boldsen, 2003). The cemetery appears to have been
in use from the middle of the 12th to the middle of
the 14th century AD (Kieffer-Olsen et al., 1986).
Based on the position of the forearms in relation to
the rest of the body, it is possible to divide the
burials into two only slightly overlapping temporal
groups. Burials with the arms stretched along the
side of the body are older than burials where the
forearms were placed over the body. According to
Jantzen et al. (1994), the transition between the two
types of arrangements was archaeologically instantaneous, i.e., it took only a few decades for one custom to replace the other. The transition took place
around AD 1300.
A ditch surrounded the cemetery. There were no
graves outside of the demarcated area, and an area
approximately 2 m wide just inside the ditch was
also empty of graves. Only a small area in the southern part of the cemetery was excavated prior to the
recognition of the site as a Medieval cemetery. The
excavator driver who discovered the site was very
thorough in collecting the human remains that led
him to the discovery. It seems likely that no adult
skeletons were lost due to his nonarchaeological excavation.
The skeletons in the analyses in this paper are
those of adults (age 18 years or more, as determined
by fusion of the spheno-occipital synchondrosis) giving information on dental attrition of at least one of
the 12 permanent molars, and sufficiently well-preserved to yield data for transition analysis age estimation.1 A total of 149 skeletons (58% of 258 skele1
Transition analysis age estimation consists of the collection of
standardized osteological observations (as described by Boldsen et al.,
2002) from the pubic symphysis, the iliac auricular surface, and five
different suture segments. These observations (scores) are transformed to estimates of age at death through a computer program
available by request from the author. The resulting age estimates
TABLE 1. Definition of dental attrition scores
Score
0
1
2
3
4
5
6
7
8
9
Description
No information; crown is missing due to reasons other
than attrition
Unworn tooth
Dental attrition affects only enamel
Dental attrition has exposed one “dentine island”
Dental attrition has exposed two “dentine islands”
Dental attrition has exposed three “dentine islands”
Dental attrition has exposed four “dentine islands”
Dental attrition has exposed confluent “dentine
island”
Dental attrition has removed all enamel from occlusal
surface of tooth
Dental attrition has removed crown altogether
tons aged 18 years or more) fulfilled the inclusion
criteria, giving attrition information on 1–12 permanent molars. On average, each skeleton provided
information about 7.9 teeth (SD ⫽ 3.5). Only being
able to utilize around half of the adult burials in the
analyses was a potential source of bias in the analyses. However, as lack of sufficient preservation was
the only exclusion criterion, this possible bias is
probably a minor source of systematic error in the
estimates.
The registration of stages of dental attrition was
carried out on each of the up to 12 permanent molars
separately. Scores were given based on naked eye
inspection of the teeth, and followed an old tradition
of concentrating on enamel exposure on the occlusal
surface (Murphy, 1959; but also used on Danish
Medieval skeletons by Helm and Prydsø, 1979; Boldsen 1991). Data on the angle of attrition were not
collected. The scores used in this paper are summarized in Table 1. The Murphy dental attrition scoring system is quite simple and thus facilitates a
relatively quick collection of attrition data on a large
number of individuals. This makes Murphy scores
more suited for analyses of the kind carried out here.
Each of the 12 permanent molars has an opposing
tooth, i.e., the molar with the same number from the
same side but from the other jaw. It follows from this
that there are up to six sites where molars can
occlude in the process of mastication. In order to get
a measure of the area available for functional chewing, the number of such intact positions (functional
chewing positions) was counted. The hypothesis behind this data collection scheme is that attrition
rates go up as the number of surviving functional
chewing positions go down. This number is a measure of total oral function. On an individual tooth
level, it is also important to know if the tooth opposing the tooth under study is functionally present or
not. The hypothesis behind this registration is that
the attrition rate declines if the opposing tooth is
lost.
consist of a point estimate (a maximum likelihood estimate of age at
death; MLA), a standard error for this estimate, and a 95% confidence
interval.
171
DENTAL ATTRITION AND MORTALITY IN MIDDLE AGES
TABLE 2. Numbers of molars from Tirup partitioned by degree of occlusal wear
Upper molar
Score
1
2
3
4
5
6
7
8
9
Lower molar
Description
1
2
3
1
2
3
Unworn
Enamel only
1 dentine island exposed
2 dentine islands exposed
3 dentine islands exposed
4 dentine islands exposed
Confluent dentine islands
No enamel left
No crown left
0
3
6
8
21
18
65
60
21
0
58
33
24
13
3
45
15
5
3
93
20
12
10
1
11
4
1
0
2
3
3
10
38
75
51
28
0
23
16
16
27
30
61
29
10
0
79
15
19
9
15
27
21
4
In addition to data on dental conditions, age at
death was estimated by means of transition analysis
(Boldsen et al., 2002). Usher (2000) made the scorings of the pubic symphysis, the iliac auricular surface, and the cranial suture segments that were
used for age estimation.
The analyses of attrition are carried out as logistic
regression analyses of the probability of having
reached a given stage of attrition in the same way
that Boldsen et al. (2002) did in defining the curves
for transition analysis age estimation. Logistic regression is regression analysis carried out on the
probability parameter of a dichotomous, i.e., a binomially distributed random variable. Logistic regression is particularly well-suited to analyze individually age-progressive traits, like dental occlusal
attrition in samples where individuals can only be
observed once and at a random age, namely, age at
death. Other types of general linear (GLM) models
(logistic regression analysis is a member of the GLM
family) might statistically be as suited for the analysis of data like those analyzed here, but the interpretation of results of a logistic regression analysis
is much more straightforward than the interpretation of results of other types of GLM analyses. In the
process of dichotomizing data for logistic regression
analysis, the focus is fixed on one particular event in
the dental attrition history of each individual. It is
the probability that the individual has reached
(and/or passed through) this stage of attrition that is
the object of the analysis.
Here let p̂i(kPc) denote the estimated probability
that tooth “i” has reached attrition score “k” or
higher for any given combination of covariates (age,
side, sex, presence of opposing tooth, number of
functional chewing positions, or dating) “c.” The statistic p̂i(kPc) is limited to values from 0 –1, so it does
not lend itself directly to regression analysis. Consequently, it is the logit-transformation of
p̂i(kPc) , i.e., l̂i(kPc)that is analyzed.
冉
l̂ i (kPc) ⫽ ln
冊
p̂ i (kPc)
1 ⫺ p̂ i (kPc)
ˆ
p̂ i (kPc) ⫽
e li (kPc)
ˆ
1 ⫹ e li (kPc)
(1)
l̂i(kPc) is analyzed using the following model:
l̂ i (kPc) ⫽ ␣ˆ ⫹ ␤ˆ c ⫹ ε ⫽ l̂ i (kPc) ⫹ ε
(2)
The ␣ and ␤ parameters are estimated through (logistic) regression, and ε is an independent error
term with mean 0. Here ␤ is a vector of parameters,
and not necessarily a single number. The estimated
␣ and ␤ values are used to reconstruct the attrition
transition curves in order to illustrate the different
effect that acted on dental wear in the Tirup population. In order to capture a possible effect of logistic
nonlinearity on the transition to higher stages of
dental attrition, and thus find evidence for selective
mortality, the age effect includes a third-order polynomial of the transition analysis maximum likelihood estimate of age at death.
The analyses are carried out as single tooth attrition stage transition estimation in two rounds. First,
each tooth position is analyzed on an individual
basis, with no concern given to the conditions of
other teeth. In the second round of analyses, data on
the number of functional molar chewing positions in
the mouth and on the presence of the opposing molar
are included. In the first round of analyses, all teeth
can be utilized; in the second round, only the teeth
coming from individuals with information on the
presence of all molars are analyzed.
RESULTS
Table 2 gives the number of molars classified by
number position and attrition stage. Table 2 clearly
reveals a gradient of attrition with, on average,
higher scores on the first molar and the lowest on
the third molar. Further, it is clear that attrition
stage 6 (dentine exposed in four islands) is rarely
seen in second and third upper molars. This is not a
reflection of a strange pattern of attrition, but is
rather caused by the fact that many of these molars
only have three cusps. To avoid any consequences of
such anatomical conditions and to reduce the correlations among the transition curve estimates, numerical restrictions were applied in choosing which
transitions to analyze. In the analyses, the observations were dichotomized into those who had not (the
“⫺” group) and those who had passed the transition
analyzed (the “⫹” group). To carry out the analysis
of a transition, it was required that both groups
172
J.L. BOLDSEN
TABLE 3. Test statistics (␹2 tests with 1 degree of freedom; significant values, P ⬍ 0.05, in bold) for transition
curves describing entry into attrition scores for six different types of molars
Entry into score
number
First upper molar
5
6
7
8
9
First lower molar
6
7
8
9
Second upper molar
3
4
7
8
Second lower molar
3
4
5
6
7
8
Third upper molar
3
4
Third lower molar
3
5
7
8
Side
Sex
Age
Age2
Age3
Sex-age
interaction
Median age
at transition
0.14
0.02
1.17
1.96
3.53
0.03
2.69
0.14
0.06
1.55
6.49
14.78
21.01
25.95
12.06
1.31
0.25
1.23
0.81
1.53
1.18
0.33
1.29
0.44
1.66
0.06
1.89
0.21
0.10
1.18
Age
Age
Age
Age
Age
15.7
21.1
24.2
45.6
95.0
0.57
0.28
0.58
1.33
2.91
0.76
0.51
3.09
5.46
15.20
16.56
1.22
0.06
2.21
1.47
0.34
0.06
1.87
1.24
0.26
0.11
0.33
1.29
2.59
Age
Age
Age
None
5.4
19.8
51.1
0.42
5.07
10.80
3.54
3.17
1.90
Age ⫹ sex
Model
M: 7.4
F: 25.0
26.9
63.1
109.8
0.22
1.04
0.46
0.02
0.06
1.59
9.12
9.25
7.10
6.51
1.31
0.01
4.97
0.90
0.00
0.72
0.25
0.98
Age ⫹ age ⫹ age
Age
Age
0.87
0.12
0.34
0.30
0.23
0.20
0.30
0.01
1.53
0.03
0.72
0.00
7.37
4.73
7.26
12.14
7.30
10.20
1.08
4.06
5.57
2.02
0.01
0.01
1.10
3.75
4.63
1.52
0.01
0.08
0.33
0.02
1.43
0.14
0.07
0.08
Age
Age ⫹ age2 ⫹ age3
Age ⫹ age2 ⫹ age3
Age
Age
Age
1.0
20.2
22.8
23.8
41.7
91.4
1.14
0.94
1.90
0.21
5.10
4.54
0.15
0.12
0.06
0.17
2.95
0.14
Age
Age
64.5
90.2
0.37
0.54
0.06
0.00
1.03
0.78
0.45
0.10
6.94
4.71
5.10
0.01
5.67
4.22
0.10
0.02
5.04
4.08
0.01
0.09
0.03
1.18
0.71
0.25
Age ⫹ age2 ⫹ age3
Age ⫹ age2 ⫹ age3
Age
None
26.7
66.1
86.6
-
2
3
consisted of more than 15 observations, and that
more that 15 observations had moved from the “⫹”
to the “⫺” group compared to the previous transition
analyzed. This means, for example, that only the
entry to stages 3, 5, 7, and 8 was analyzed for the
third lower molar.
Individual tooth analysis
Table 3 summarizes all tests for both discrete and
continuous effects acting on the probability of having entered a given attritional stage for each of the
six molar positions. Table 3 clearly reveals the general finding that age in all but two transitions plays
a significant part in the transition into a given attrition stage. This fact is illustrated in Figure 1,
which shows the transition curves for the first upper
molar. On the other hand, there is never a side
differentiation in the transition probability. This
finding is not unexpected, but it shows that molars
were not used extensively to perform other tasks
than chewing in Tirup. Sex and sex-age interaction
is only significant for one transition out of 25 tests,
so this can probably be considered an example of
mass significance. There are five transitions that did
not proceed in a logistic-linear way. These could also
be examples of mass significance, but they all show
the same pattern of logistic nonlinearity. Figure 2
illustrates this effect by showing all the logistic nonlinear transition curves in the same plot. It is clear
that the transition proceeded quickly in ages be-
Fig. 1. Transition among stages of attrition of first upper
molar. This is an example of an uncomplicated logistic-linear
progression of dental attrition.
tween 20 and around 40, followed by an age interval
with apparently no attrition, from around 40 to
around 60 years, after which age, attrition appears
to have started again.
Whole dentition analysis
In Table 4, the effects of the presence of the opposing molar and the number of functional chewing
DENTAL ATTRITION AND MORTALITY IN MIDDLE AGES
Fig. 2. Examples of logistic-nonlinear transition curves. Flat
and even declining parts of these curves can only be interpreted
as evidence that selective mortality as dental attrition on an
individual level is an absolutely irreversible process, so lack of
increasing attrition with increasing age on population level can
only be brought about by a positive association between rate of
wear and risk of dying. Curve 1, transition into attrition stage 5
in lower third molar; curve 2, transition into attrition stage 3 in
lower third molar; curve 3, transition into attrition stage 4 in
upper second molar; curve 4, transition into attrition stage 5 in
lower second molar.
positions between permanent molars are analyzed.
The picture created by Table 4 is much more confusing than the one created by Table 3. In general,
there are fewer significant associations—probably
at least partly a consequence of having fewer teeth
to analyze, but the pattern of associations is also less
consistent among teeth. The clearest image is created for the higher attrition scores of the first upper
molar. As generally expected, this tooth shows
higher levels of wear when the opposing tooth was in
position in the mouth. Further, this tooth clearly
shows the attrition effect of the number of functional
chewing positions in the mouth. The first lower molar shows a very similar pattern of associations, but
with fewer statistically significant associations. Figure 3 illustrates the effect of the number of chewing
positions on the transition into stage 8 for the first
lower molar.
For the second and third molars, the effect of the
presence of the opposing tooth seems to be reverse of
the pattern seen for the first molars. This might be
a reflection of a higher level of mobility of the teeth
further back in the mouth, so that attrition becomes
less dependent on the immediately opposing tooth
and more a reflection of the generally increased load
brought about by missing at least one functional
chewing position.
Temporal aspects of dental attrition
Chronology is the only potential source of heterogeneity that is possible to analyze with the present
173
level of registration of the skeletons. The first step in
this analysis is to estimate the effect of late vs. early
dating of the graves on the transition probabilities
for the different stages in all six molars on the level
of the individual tooth. The results of this analysis
are summarized in Table 5. The majority of odds
ratios are over 1 (20 out of 25 estimates, sign test
␹2 ⫽ 17.64, df ⫽ 1, P ⬍ 0.001). This indicates that
attrition for most teeth and into most stages proceeded more quickly late (ca. AD 1250 –1350) than
early (ca. AD 1150 –1300) in the history of the Tirup
population. Nearly half of the odds ratios (11 out of
25) are statistically significant (P ⬍ 0.05), and all
significant odds ratios are larger than 1. However,
this might partly be due to intercorrelation among
the odds ratios, as the analysis of six different types
of molars (upper and lower M1, M2, and M3) does
not provide six times as much information as a analysis of a single tooth. In spite of this, the results
reveal a temporal heterogeneity in dental attrition
in the Tirup population.
Five transition curves show evidence for significant deviations from logistic linear (Table 3) progression with age, and thus for an association between individual-level risk of dying (frailty) and
dental attrition. Four of these five transitions show
a statistically significant dating effect (Table 5). The
models describing the relative importance of heterogeneity independent of a dating effect and a dating
effect independent of heterogeneity (logistic nonlinearity of the transition curves) are not nested in
each other. This means that it is not possible directly to test them against each other. However, it is
possible to test them against a comprehensive model
containing both effects at the same time. This comprehensive model describes the transition probability (or rather, logistic transformation of this probability) as a function of the following factors: age,
age2, age3, dating, age * dating, age2 * dating, and
age3 * dating. In this list, “*” indicates interaction
effects. The two models nested in this comprehensive model can be denoted the heterogeneity model
(with the factors age, age2, and age3) and the dating
model (with the factors age, dating, and age * dating). Table 6 summarizes the analysis of the two last
models in relationship to the first, comprehensive
model. Dating of the burial is clearly an aspect of the
heterogeneity that can be seen in the rate of attrition of the permanent molars in the Tirup population. However, it is also clear that dating is not the
only factor affecting interpersonal differences in the
rate of attrition. Figure 4 illustrates the transition
curve for entry to stage 4 in the second upper molar.
Figure 4 illustrates the quicker transition in the late
than in the early period, and it shows how substantial the heterogeneity of individual transition rates
remains after removing the dating effect. It appears
that heterogeneity played a much bigger part in this
transition in the early than in the late period.
174
J.L. BOLDSEN
TABLE 4. Estimates of effect (odds ratio) of opposing tooth being present and of having one additional functional
chewing position along with ␹2 test statistics for hypothesis that these odds ratios are 11
Whole mouth effects
Opposing tooth
present
Stage
First upper molar
5
6
7
8
9
First lower molar
6
7
8
9
Second upper molar
3
4
7
8
Second lower molar
3
4
5
6
7
8
Third upper molar
3
4
Third lower molar
3
5
7
8
1
One extra chewing
position
␹2 test
Odds ratio
␹2 test
0.00
1.23
36.59
43.02
4.12
0.03
0.03
7.78
8.60
0.84
0.97
0.75
0.53
0.50
0.56
0.01
1.29
5.50
11.98
4.40
Age
Age
Age ⫹ OP ⫹ PO
Age ⫹ OP ⫹ PO
Age ⫹ PO
Age
Age
Age
None
0.21
3.40
3.21
1.28
0.96
1.20
1.46
0.06
0.76
0.70
0.49
0.58
0.81
2.98
13.91
6.77
Age
Age
Age ⫹ PO
PO
Age ⫹ sex
Age ⫹ age2 ⫹ age3
Age
Age
0.00
0.37
0.44
8.95
0.09
0.54
0.75
2.04
1.04
0.89
0.78
0.48
0.04
0.45
2.66
6.77
Age ⫹ sex
Age ⫹ age2
Age
Age ⫹ PO
Age
Age ⫹ age2 ⫹ age3
Age ⫹ age2 ⫹ age3
Age
Age
Age
0.57
0.12
0.25
0.19
0.54
0.15
0.42
4.06
2.22
4.03
0.53
1.22
0.85
0.98
0.85
0.85
0.65
0.95
0.67
0.01
1.05
1.14
14.43
Age
Age
0.98
0.62
0.00
0.11
1.23
1.18
1.60
0.92
0–36
0.30
0.49
0.27
1.67
2.90
1.06
2.58
0.66
0.69
0.58
0.56
7.09
8.68
17.63
17.93
Single tooth model
Age
Age
Age
Age
Age
Age ⫹ age2 ⫹ age3
Age ⫹ age2 ⫹ age3
Age
Odds ratio
Whole mouth model
Age
Age
Age ⫹ age2 ⫹ age3 ⫹ OP
Age
Age ⫹ OP
Age ⫹ PO
Age
Age
Age ⫹ PO
Age ⫹ PO
PO
PO
df ⫽ 1 ⫺ significant values, p ⬍ 0.05, in bold. OP, opposing tooth present; PO, one additional functional chewing position.
DISCUSSION
Fig. 3. Estimated transition probabilities for entry to stage 8
in first lower molar, shown as function of age at death and
number of functional chewing positions in mouth. Lowest curve is
one for dentitions with six surviving functional chewing positions,
and others are ordered in sequence, down to zero functional
chewing positions (top curve). For stage 8 of first lower molar,
number of chewing positions is a stronger determinant of transition probability than age, but both are highly significant.
Dental attrition is a nonpathological outcome of
wear and tear. Teeth are the only hard parts of the
body that do not react to external forces by both
breaking down and building up of tissue. Therefore,
dental attrition is a truly unidirectional process. On
an individual level, dental attrition can only proceed
from one stage to the next at whatever pace is set by
the strength of the individual teeth and by the hardness and quantity of the material chewed. It is obvious that the time a tooth has been exposed to
attrition is a generally important aspect of the
amount of wear the tooth has experienced. This fact
is reflected in Figure 1, showing the smooth succession of transition curves for the first molar lower.
However, the relationship between time of use of a
tooth (i.e., age) and level of attrition is confounded
by several factors that make it impossible to use
attrition scores for age estimation, even within a
relatively homogeneous population such as Tirup.
Clearly, some of the heterogeneity might be a consequence of the inaccuracy of the age estimates used
in the analyses. However, inaccuracy is not enough
to create the image of diverse attrition rates; a direct
association between dental attrition and other aspects of skeletal aging would be required to create
the image illustrated in Figure 2.
DENTAL ATTRITION AND MORTALITY IN MIDDLE AGES
175
TABLE 5. Estimates of the effect (odds ratio) of late vs. early
dating of grave on transition probability along with ␹2 test
statistics for hypothesis that these odds ratios are 11
Stage
First upper molar
5
6
7
8
9
First lower molar
6
7
8
9
Second upper molar
3
4
7
8
Second lower molar
3
4
5
6
7
8
Third upper molar
3
4
Third lower molar
3
5
7
8
1
Odds ratio
␹2
Age
Age
Age
Age
Age
4433.5
5.1
2.2
1.4
0.3
9.51
7.92
1.60
0.77
2.75
Age
Age
Age
5.4
3.4
1.4
0.8
3.68
5.45
0.61
0.00
Age ⫹ sex
Age ⫹ age2 ⫹ age3
Age
Age
0.4
1.5
1.2
1.2
0.00
3.97
2.75
1.11
Age
Age ⫹ age2 ⫹ age3
Age ⫹ age2 ⫹ age3
Age
Age
Age
2.8
1.2
1.3
1.7
1.4
0.7
2.57
2.46
3.92
8.31
6.21
1.81
Age
Age
1.7
1.8
5.08
3.94
Age ⫹ age2 ⫹ age3
Age ⫹ age2 ⫹ age3
Age
1.6
1.8
0.7
0.8
9.56
5.43
1.75
0.16
Single tooth model
df ⫽ 1 ⫺ significant values, p ⬍ 0.05, in bold.
TABLE 6. Tests for reduction of model containing both dating
and heterogeneity effects to nested, simpler models
containing only one of two factors1
Model ␹2 test for
Tooth and stage
Second upper molar, 4
Second lower molar, 4
Second lower molar, 5
Third lower molar, 3
Third lower molar, 5
Dating Heterogeneity
15.22
4.38
6.77
8.34
5.43
10.06
2.59
4.20
15.11
6.94
Model accepted/
best model
Comprehensive
Heterogeneity
Heterogeneity
Dating
Dating
1
A significant test indicates a significant loss of fit when moving
from comprehensive to reduced model. Significant test statistics
are given in bold.
The Tirup skeletal sample is (like all skeletal samples) a cross section of the population at death. It is
only the oral condition at time of death that it is
possible to examine. The lack of time depth in the
registrations is probably the reason why the presence of the opposing tooth plays so minor a part for
the level of attrition. The total number of functional
chewing positions is more stable, as it is like an
average that contains information from 12 different
teeth. This is probably the reason why this measure
is more closely correlated to the level of attrition
than is the presence of the opposing tooth. It is,
under all circumstances, important to evaluate the
whole mouth when analyzing attrition. In the
present study, only the molars were analyzed. This
means that it was not possible to analyze the poten-
Fig. 4. Estimated transition probabilities for entry into attrition stage 4 of second upper molar, shown separately for early
(AD 1150 –1310) and late (AD 1290 –1350) graves.
tial consequences of tooth loss/compensatory mastication area in the dental arcs in front of the molars.
In future studies of dental attrition and oral function of past populations, it will be important to include whole mouth information in the registrations.
Probably the distribution of strength of teeth has
remained more or less unchanged over the centuries, so the primary difference between patterns of
attrition is brought about by the hardness and quantity of the consumed food, chewed by different individual human beings and in general by people in
different historical periods. Most skeletal samples
are (like the Tirup sample) accumulated over prolonged periods of time, comprising both relatively
affluent and relatively poor phases. Such fluctuations will inevitably create heterogeneity among individuals, both in the rate of dental attrition and in
the risk of dying. But this is not the only source of
systematic differences among the people forming a
skeletal sample. In virtually all societies, social status differences are reflected in access to and quality
of food; such differences will usually also be reflected
by the risk of dying at different ages. On top of
systematic temporal and social differences among
individual human beings, more random differences
of the genetically determined strength of teeth create heterogeneity. The random component of attritional heterogeneity is unlikely to be associated with
the risk of dying; but the systematic, temporally, or
socially associated heterogeneity is likely to be reflected in mortality.
The general uniformity of Christian Medieval
burials and the lack of strong and uniform indicators
of social differences in the pattern of burials (as seen
in Löddeköpinge; see Cinthio and Boldsen, 1984)
make it virtually impossible to analyze the effect of
social inequality on the rate of dental attrition. This
makes temporal differences the only source of sys-
176
J.L. BOLDSEN
tematic interpersonal heterogeneity in the rate of
dental attrition that can be analyzed in the present
context. The period from around 1150 –1350 saw
some dramatic changes in living conditions in
Northern Europe. It appears that the population
had a maximum size by the end of the 13th century.
Prior to 1300, it appears that the population was
generally and slowly growing, leading to the formation of many rural village communities such as
Tirup. This came to an end in the 14th century. In
the second and third decades of that century, most of
Northern Europe was affected by several consecutive years of famine (e.g., Campbell, 1991; Dyer,
1989; Hybel, 1997, 2002). In the middle of the century, the Black Death took away a large portion of
the population. It is possible that a downturn of
living conditions in the first half of the 14th century
is reflected in the higher rate of dental attrition
among people buried in late arm position B (forearms placed over rather than along the body) graves.
However, it is more likely that a change in subsistence pattern with increasing reliance on grain for
food from the 12th to the 14th century is the main
reason for the increased level of dental attrition. On
the other hand, it is clear that this temporal shift in
the rate of attrition cannot account for all the heterogeneity in the rate of dental attrition observed in
the Tirup skeletal sample.
The analyses in this paper clearly show that there
is no such thing as a preindustrial pattern of dental
attrition that can be used for age estimation. In
general, teeth of older individuals were more worn
than teeth of younger people, as expected. However,
heterogeneity of the rate of attrition (on an individual level, on a social level, and on a temporal level)
changed attrition scores from indicators of age to
indicators of health and well-being. Other aspects of
oral health and general survival were tested and
found negative for a dating/arm position effect
(Boldsen, 1988, 1997, 2002). The fact that teeth preserve well and that they, through chewing, interact
directly with the environment is probably the reason
it has been possible to find a dating effect on dental
attrition and not on other indicators of public health
in the Tirup population. Probably the lack of significant dating effects for other variables in a statistical sense is a type 2 error. More information may be
extracted from the Tirup skeletons by the application of more elaborate registration schemes. However, it is likely that the lower number of complete
observations, which such schemes would lead to, are
sufficient to remove statistical significance from all
or most of the test. The planning of osteological
research into the health of past populations is a
delicate balance between quantity and quality of
data.
ACKNOWLEDGMENTS
This research would not have been possible without the highly skilled technical assistance from lab-
leader Ulla H. Freund. Bethany M. Usher is gratefully acknowledged for giving access to her scorings
for transitions analysis age estimation. Historian
Nils Hybel of the University of Copenhagen read the
manuscript, which benefited considerably from his
comments.
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