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From: TSS (
Subject: Epidemiological evidence of higher susceptibility to vCJD in the young [FULL TEXT]
Date: August 10, 2004 at 7:12 am PST

-------- Original Message --------
Subject: Epidemiological evidence of higher susceptibility to vCJD in the young
Date: Tue, 10 Aug 2004 08:55:56 -0500
From: "Terry S. Singeltary Sr."
To: Bovine Spongiform Encephalopathy

Epidemiological evidence of higher susceptibility to
vCJD in the young
Pierre-Yves Boëlle1,*, Jean-Yves Cesbron2, Alain-Jacques Valleron1
1 INSERM U444, Assistance Publique Hôpitaux de Paris, Université Pierre et
Marie Curie, Paris, France
2 Immunité Anti-Infectieuse JE 2236, UFR de Médecine de Grenoble,
Université Joseph Fourier, Grenoble, France
* Corresponding author
Address :
Faculté de Médecine Saint-Antoine,
27 Rue Chaligny,
75571 PARIS CEDEX 12
e-mail adresses :
Pierre-Yves Boëlle :
Jean-Yves Cesbron :
Alain-Jacques Valleron :
The strikingly young age of new variant Creutzfeldt-Jacob disease (vCJD)
remains unexplained. Age dependent susceptibility to infection has been
put forward,
but differential dietary exposure to contaminated food products in the
UK population
according to age and sex during the bovine spongiform encephalopathy (BSE)
epidemic may provide a simpler explanation.
Using recently published estimates of dietary exposure in mathematical
models of the
epidemiology of the new variant Creutzfeldt Jacob disease (vCJD), we examine
whether the age characteristics of vCJD cases may be reproduced.
The susceptibility/exposure risk function has likely peaked in
adolescents and was
followed by a sharp decrease with age, evocative of the profile of
exposure to bovine
material consumption according to age. However, assuming that the risk of
contamination was proportional to exposure, with no age dependent
susceptibility, the
model failed to reproduce the observed age characteristics of the vCJD
cases: The
predicted cumulated proportion of cases over 40 years was 48%, in strong
disagreement with the observed 10%. Incorporating age dependent
susceptibility led
to a cumulated proportion of cases over 40 years old of 12%.
This analysis provides evidence that differential dietary exposure alone
fails to
explain the pattern of age in vCJD cases. Decreasing age related
susceptibility is
required to reproduce the characteristics of the age distribution of
vCJD cases.
Owing to the lack of data concerning age dependent dietary exposure to
material in food in the UK, mathematical models used in the study of new
Creutzfeldt-Jacob disease (vCJD) have postulated an age risk function for
contamination where the influence of age dependent dietary exposure to
material could not be separated from intrinsic age related
susceptibility [1-3]. For
example, we modelled exposure/susceptibility by a plateau during the
first 15 years of
age followed by an exponential decrease afterwards [2]. Using this
approach we
estimated the duration of the incubation period for vCJD to circa 15
years; and
predicted that the epidemic had likely peaked in 2001-2002 which is now
with the observed data [2, 3]. The simple representation of the age risk
implied that few parameters were necessary to describe it, a desirable
property when
estimation is based on limited data.
By June 2003, 139 cases of vCJD had been reported to the vCJD
surveillance unit in
the UK, as compared to the 79 cases used in our original paper [2]. This
warrants the
inclusion of more detail in the age risk function, especially to
investigate whether the
constant risk assumption in children holds, as it led to predict a
bimodal age
distribution for cases in the coming years [3]. At the same time,
detailed estimates of
the dietary bovine material consumption in the UK population according
to age and
sex have been made available [4]. This offers the opportunity to try to
disentangle the
role of exposure from that of age-specific susceptibility. Indeed, the
estimates for
dietary exposure show that it peaked during adolescence and decreased
with age
afterwards, a pattern which is consistent with the finding that most
vCJD cases are
young and were therefore at most teenagers during the years when the bovine
spongiform encephalopathy (BSE) epidemic was at its maximum.
We therefore set out to estimate the age risk function with a versatile
based on step functions, and to investigate whether dietary exposure
alone could
explain the age distribution of cases.
We obtained the age, sex and date of onset for the 137 vCJD cases
reported to the UK
vCJD unit as of June 2003, all of which had onset before October 2002.
Delays in
reporting may reach 18 months (R Will, personal communication) and bias
the incidence curve. Therefore, we included in our analysis only the 129
cases with
onset before November 2001, consisting in 71 men and 58 women. Mortality
by age
and sex for the UK was obtained from the Office for National Statistics [5].
The model extends our previously described method [3]. The instantaneous
risk of
infection for vCJD, l(a,t), was assumed to depend on date t and age a in a
multiplicative manner, with l(a,t) = f(a) g(t), where
( ) ( )
( ) ( ) ? ? ?
< < - -
< < -
1996 1989 1989 596 . 0 exp
1989 1980 1989 596 . 0 exp
) (
t t r
t t
t g parallels the entry of infected cows
in the human food chain estimated from back-calculation results in the
UK [6] and r
corresponds to the impact of the specified risk material ban in 1989;
f(a) is a function
varying with age only (see following paragraph). Occurrence of cases of
vCJD of sex
i (i=Male or Female) is then modelled by a Poisson process in the (age,
time) plane,
with intensity
( ) ( ) ( ) ( ) ( ) ? ? ? ? ?
? ? ?
- + - ??
? + - - =
i i i i dv v a h v a t v du u a t u t a S t a
0 0
, , exp , , l l b p , so that the
expected number of onsets of sex i in the period [t,t+dt) and age
[a,a+da) is pi
(a,t) da dt
[2, 7]. In the equation above, h is the distribution of the incubation
time, Si(a,t) is the
probability of survival at time t for individuals of sex i aged a
calculated from census
information, and bi is the intensity of a homogeneous Poisson process.
The incubation
period distribution h was taken to be lognormal, as the choice of a
particular shape is
not critical for the estimates [3]. This distribution was also assumed
to be independent
of sex, and of age at contamination.
Estimates of the parameters were obtained by numerical maximization of
the loglikelihood,
defined as ( ) [ ] ( ) ??? ? -
= s D
i i s dt da t a t a
, , log
p p , where the first sum is on
all observed cases where si is the sex of individual i, and the second
is over both sexes
(s) and requires integration of the intensity over the domain (in time
and age) D =
[0,100]×[1 Jan 1980,31 Nov 2001]. Custom FORTRAN code, using SLATEC and
TOMS library for numerical routines, was used in this step. In all
instances, our
model allowed estimation of the mean and standard deviation of the
period, the shape of f(a), and b.
The model predicted distribution of age for cases with onset before
November 2001
was determined by integrating ( ) t a, Æp over the period [1/1/1980,
1/11/2001] for
consecutive age class of width 10 years. The c2 distance was computed to
agreement between the observed and the model calculated age
distributions, but no
formal test was carried out.
Age Risk function
Model Susceptibility & Diet: To allow a versatile shape in the age
risk function,
we chose a non parametric description based on step functions rather than a
mathematical function with few parameters.
We wrote f(a) =
( )
( ) ( ) ? ? ?
³ - -
= < £ -
25 , 25 exp
5 , , 1 , 5 1 5 ,
6 a a f
i i a i fi
so that the shape of
f(a) was unconstrained for a<25 and decreased exponentially after age
25. We then
estimated fi , i=1,&,6 and ? at maximum likelihood. With this particular
choice, it is
possible to visually check the progression in risk with age during
infancy (f1, f2, f3),
the presence of a peak in risk in teenagers (f 2, f 3,f4), and whether
the drop in risk after
20 is strong enough to be exponential (f4, f 5, f 6).
Model Diet Alone: To investigate whether dietary exposure to meat was
to explain the young age of vCJD cases, we first determined the exposure
to meat
according to age and sex from UK data, using consumption of carcass meat
[4]. In
these data, it is seen that the quantity consumed by week, as well as
the percentage of
consumers, changes with age. We therefore defined e(a) as the product of
consumption by the corresponding percentage of consumers in age class a,
and wrote
f(a) = f0 e(a), where f0 was estimated at maximum likelihood.
Model Susceptibility | Diet: Finally, to estimate the influence of age
on dietary exposure, we wrote f(a) =
( ) ( )
( ) ( ) ( ) ? ? ?
³ - -
= < £ -
25 , 25 exp
5 , , 1 , 5 1 5 ,
6 a a e a f
i i a i a e fi
and likewise estimated fi from
the data.
In model Susceptibility & Diet and Susceptibility | Diet, we
normalised the fi by
the largest of the values in the graphical presentation of results,
yielding risks relative
to the highest risk age class.
Confidence intervals
Maximum likelihood estimators have a limiting normal distribution around
the true
parameters, with limiting variance/covariance matrix given by the
inverse of the
Fisher information [8], therefore the quadratic form ( ) ( ) ( )( ) q q
q q q q Æ Æ -
- = i Q , where q
stands for a vector of k parameters, qÆ
for the estimates , and i( q) for the Fisher
information matrix, has a limiting chi-squared distribution with k
degrees of freedom.
We therefore determined 95% confidence intervals by finding the values of
parameters so that Q(q) was less than the 95th quantile of the
corresponding chisquared
The estimated age risk function in model Susceptibility & Diet is
presented in
Figure 1, and shows that an increase during childhood, peak during
adolescence and
sharp decrease afterwards provided the best fit. In this model, the
average incubation
period was estimated at 13.2 years (CI95% [11.2, 15.8]), with standard
deviation 2.0
years (CI95% [1.1,3.7]). The model predicted age for the cases showed good
agreement with the observed distribution ( 2 c =2.45; Figure 2).
The shape of the estimated age risk function in Figure 1 is evocative of
the age profile
of dietary exposure to bovine carcass meat in the 1980s in the UK, as
shown in Figure
3, where an increase in consumption was noted during childhood and
and decreased afterwards. However, in model Diet Alone, while the average
incubation period was estimated at 12.1 (CI 95% [10.2, 14.2]) years with
a standard
deviation of 2.4 (CI95% [1.2, 4.1])years, close to that of the first
model, the predicted
age distribution of the cases was at odds with the observed distribution
( 2 c =58.4), as
apparent in Figure 2. More precisely, the predicted percentage of cases
aged over 40
was 48% with the Diet Alone assumption, when the observed percentage
was only
10%; it was 12% with the Susceptibility & Diet model.
When estimating the residual influence of age, once exposure has been
taken into
account, model Susceptibility | Diet led to a profile for the fi as
shown in Figure 4.
This profile retained the major characteristics of the age risk function
obtained in the
Susceptibility & Diet model, although with an earlier maximum
With this model, the average incubation period established at 12.6 years
(CI 95%
[10.5, 14.7]) with standard error 1.8 years (CI95% [1.2, 3.5]), and the
predicted age
distribution of cases showed agreement similar to that of the
Susceptibility & Diet
model ( 2 c =2.36).
Incorporating differential dietary exposure to BSE infected products
according to age
and sex, in a flexible age risk function for vCJD contamination, we
found that
exposure alone could not explain the young age of vCJD cases seen in the UK.
Decreasing age related susceptibility had to be assumed to reproduce the
characteristics of the age distribution of these cases.
In all instances, the estimates for the epidemiological characteristics
of vCJD were in
line with those previously reported from comparable number of cases [3,
9, 10], and
pointed to an epidemic of moderate size. We obtained a smaller point
estimate for the
mean incubation period when the age risk was allowed to change during
rather than assumed constant (13.2 yrs CI95% [11.2, 15.8] vs. 16.4 yrs
CI95%[11, 24]
[3]). All models considered here predicted that, by 2010, the epidemic
should have
ended, provided it is limited to individuals with the observed
susceptible genotype (as
of today, all vCJD cases are methionine homozygous at codon 129 of the PrP
gene[11]). The average number of cases to come is predicted at 43,
leading to a total
size of 172 cases.
From the model Susceptibility & Diet, it appears that a previous
assumption of a
constant age risk in children and adolescents [2] has likely led to
overestimate the risk
of infection in young children. The age-risk profile estimated here
leads to a smaller
risk than previously found in children born after 1980, to fewer cases
among the
young and an overall smaller total size for the epidemic than reported
Furthermore, since few young cases are expected in the future, the
bimodality of the
predicted age distribution of cases is not found anymore. The estimated
profile of the
susceptibility/exposure age risk function agreed with that selected in
scenario analysis
[9], although our maximal risk is among the 1520 years old rather than
in the 1015
years old.
However, even after adjustment for dietary exposure, susceptibility
remains rapidly
decreasing in adults, as found in the Susceptibility | Diet model.
This finding
confirms that obtained in a recent analysis based on scenario analysis,
where it was
found that exponential decrease in susceptibility in the oldest cohorts
was desirable
[10]. This is also found here by direct estimation, and moreover, the
age susceptibility
appears to increase among children up to age 5-10, be almost constant among
teenagers and rapidly decreasing afterwards.
Three hypotheses have been put forward to explain the young age of the
vCJD cases:
age dependent incubation period, age dependent exposure, and age dependent
susceptibility. Age dependent incubation period has recently been
revived by Cooper
et al., because scenarios including longer incubation periods in old
cohorts provided a
better fit of the data[10]. However, the increase of the mean age of
cases with time
which should occur with age dependent incubation periods [2], is not
supported by the
data. The correlation between age at onset and calendar time remains indeed
extremely small (Spearman correlation coefficient r = 0.025, n=129).
The potential role of differential dietary exposure was proposed early
in the epidemic
[12], because substantial differences in exposure existed in adolescents
and adults. A
disequilibrium in sex ratio towards males was for example predicted on
this basis
[13], but is not statistically established today (c2 test, P=0.49)
although the proportion
of males is 55%. Our analysis shows that, while the estimated age risk
function for
infection with vCJD exhibits a peak in the young that was also found in
exposure to potentially BSE infected products, differential dietary
exposure alone
does not account for the observed pattern in the age of the vCJD cases.
Indeed, the
relative exposure does not decrease rapidly enough with age to reduce
the risk in older
adults. Therefore, an additional effect of age is required to fully
account for the age of
the vCJD cases. Once exposure is taken into account, this effect appears
to be peaking
in children less than 10 years old, and decreasing afterwards.
Contamination through BSE infected food is today the most plausible
explanation for
the occurrence of vCJD; this is the rationale for correcting the risk of
infection by the
level of dietary exposure. However, this explanation remains today
because even if epidemiology and biochemistry favour a link between BSE
and vCJD,
this is not regarded as ultimately conclusive [14]. In this work, we
examined the
further possibility that, provided dietary exposure was the culprit,
differences in agerelated
exposure could explain the age distribution of cases. This assumes that
the risk
of vCJD infection may have been larger in those consuming more bovine
however population based models linking bovine products consumption to vCJD
incidence were not conclusive in this respect [15].
The reasons for an increased susceptibility in teenagers compared to
young children
and adults are today speculative. In many infectious diseases, a decrease in
susceptibility with age is mediated through the immunological responses
to repeated
exposure since birth. However, for vCJD, most cohorts in the population
have had the
same duration of exposure to BSE infected material. Due to the age range
biological processes involved in the maturation of the immune system; in
response to
hormonal changes may be incriminated. For example, changes in the
permeability of
the intestinal barrier occurs with age with decreasing Peyers patches
[16]. Further
experimental research is required to provide explanations for what
remains a very
unusual characteristic in an infectious disease.
List of abbreviations
vCJD : variant Creutzfeldt-Jacob Disease
BSE : Bovine Spongiform Encephalopathy
Competing interests
Authors' contributions
PYB designed the study, analysed the results and drafted the manuscript. JYC
participated in the design and writing. AJV participated in the design
and writing. All
authors read and approved the final manuscript.
Robert Will for allowing access to vCJD data.
Jean Gagnon for helpful discussions.
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Figure 1 - Age dependent exposure/susceptibility risk function for vCJD
Risks are relative to the [15, 20] years old. Box and whiskers plots
show the 50% and
95% confidence intervals.
Figure 2 - Observed age distribution of the 129 vCJD cases with onset before
November 2001 and model predicted cumulated age distribution of cases for
susceptibility proportional to meat consumption only (dashed) or with
estimated age risk function (plain).
Bars limit 95% confidence intervals.
Figure 3 - Average dietary exposure per person per week in the UK according
to sex (plain : Male, dashed : Female).
Data from Cooper & Bird [4].
Figure 4 - Age dependent susceptibility risk function for vCJD infection
adjusted on dietary exposure to bovine material.
Risks are relative to the [5, 10] years old. Box and whiskers plots show
the 50% and
95% confidence intervals.
Age (yrs.)
Relative risk
0 10 20 30 40
age class (yrs)
0 10 30 50 70
0 20 40 60 80 100
0 50 100 150 200 250
age (yrs.)
meat consumption
Age (yrs.)
Relative susceptibility
0 5 10 15 20 25 30 35 0 5 10 15 20 25 30 35


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