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Detecting nested clusters of human alveolar echinococcosis
ZEINABA SAID-ALI
1,2
, FRÉDÉRIC GRENOUILLET
1,2,3
, JENNY KNAPP
1,2,3
,
SOLANGE BRESSON-HADNI
1,2,3
, DOMINIQUE ANGÈLE VUITTON
2
,
FRANCIS RAOUL
1,2
, CARINE RICHOU
2,3
,LAURENCEMILLON
1,2
,
PATRICK GIRAUDOUX
1,2,3,4
* and THE FRANCECHINO NETWORK†
1
Chrono-environment, University of Franche-Comté – CNRS, Besançon, France
2
WHO-Collaborating Centre on Prevention and Treatment of Human Echinococcosis, Besançon, France
3
Centre National de Référence Echinococcose alvéolaire, Centre Hospitalier Régional Universitaire, Besançon, France
4
Institut Universitaire de France, Paris, France
(Received 3 July 2013; accepted 14 July 2013)
SUMMARY
Recent changes in the epidemiology of alveolar echinococcosis (AE) in Eurasia have led to increasing concerns about the risk
of human AE and the need for a thorough evaluation of the epidemiological situation. The aim of this study was to explore
the use of a National Register to detect complex distribution patterns on several scales. The data were human AE cases from
the FrancEchino register, diagnosed in France from 1982 to 2011. We used the Kulldorff spatial scan analysis to detect non-
random locations of cases. We proposed an exploratory method that was based on the successive detection of nested clusters
inside each of the statistically significant larger clusters. This method revealed at least 4 levels of disease clusters during the
study period. The spatial variations of cluster location over time were also shown. We conclude that National Human AE
registers, although not exempted from epidemiological biases, are currently the best way to achieve an accurate
representation of human AE distribution on various scales. Finally, we confirm the multi-scale clustered distribution of
human AE, and we hypothesize that our study may be a reasonable starting point from which to conduct additional research
and explore the processes that underlie such distributions.
Key words: Echinococcus multilocularis, national registry, nested clusters, spatial scan analysis, epidemiosurveillance, early
warning systems.
INTRODUCTION
Human alveolar echinococcosis (AE), a highly patho-
genic parasitic disease, is caused by the tumour-like
growth of larval Echinococcus multilocularis in the
liver (Brunetti et al. 2010). Human infection by
E. multilocularis results from the accidental ingestion
of tapeworm eggs that are passed into the environ-
ment through the faeces of definitive hosts (generally
foxes or dogs). Until now, no curative treatment has
been available for human AE, except for radical
surgical resection of the entire lesion, which can only
be performed at the early stages of the disease, and
long-term treatment is needed for the majority of
cases (Brunetti et al. 2010).
E. multilocularis is restricted to temperate cold
regions of the northern hemisphere, including
Eurasia and North America, where it appears to be
the most serious parasitic zoonosis (Eckert et al. 2000;
Torgerson et al. 2010). An estimation of the world-
wide median incidence indicates that AE occurs in
more than 18000 new human cases per year
(Torgerson et al. 2010). The non-homogeneous
geographical distribution of E. multilocularis is well
known. Some of the highest historical incidences of
human AE are recorded from St. Lawrence Island
* Corresponding author: Chrono-environment, University of Franche-Comté-CNRS, Place Leclerc, F-25030 Besançon,
France. Tel: +33 381 665 745. Fax: +33 381 665 797. E-mail: patrick.giraudoux@univ-fcomte.fr
† List of participants in the FrancEchino Network: Annecy: VITRAT Virginie. Besançon: BARDONNET Karine;
BARTHOLOMOT Brigitte; BEURTON-CHATAIGNER Isabelle; BLAGOSKLONOV Oleg; BRESSON-HADNI
Solange; BRIENTINI Marie Pascale; CAPPELLE Sylvie; DELABROUSSE Eric; DI MARTINO Vincent;
EVRARD Philippe; FELIX Sophie; GIRAUDOUX Patrick; GRENOUILLET Frédéric; HEYD Bruno;
KANTELIP Bernadette; KNAPP Jenny; KOCH Stéphane; MANTION Georges; MILLON Laurence; RAOUL
Francis; RICHOU Carine; VANLEMMENS Claire; VUITTON Lucine; VUITTON Dominique Angèle. Bourg en
Bresse: PROST Patricia. Charleville Mezière: GODET Claire. Clermont Ferrand: ABERGEL Armand; BEYTOUT Jean;
CAMBON Monique. Dijon: BESANCENOT Jean François; CUSENIER Bernadette; HILLON Patrick; MINELLO
Anne. Grenoble: FAURE Odile; LETOUBLON Christian. Lyon: CHYDERIOTYS Georges; DUMORTIER Jérôme;
GUILLAUD Olivier; PARTENSKY Christian; RABODONIRINA Meja; WALLON Martine. Marseille: PIARROUX
Martine; PIARROUX Renaud. Metz: CHATELAIN Eric; JOHANN Marc; RAABE Jean-Jacques. Mulhouse: SONDAG
Daniel. Nancy: GERARD Alain; LETRANCHANT Lorraine; MACHOUART Marie; WATELET Jérôme. Paris:
FARGES Olivier; SAMUEL Didier. Reims: CHEMLA Cathy; DELATTRE Jean Francois. Rodez: GUERIN Bruno.
Saint Ouen l’Aumône: DEBRUYNE Monique. Strasbourg: ABOU-BACAR Ahmed; AUDET Maxime; HANSMANN
Yves; LEFEBVRE Nicolas. Thonon: LI Véronique. Vesoul: ALBY-LEPRESLE Blandine.
1
Parasitology, Page 1 of 8. © Cambridge University Press 2013
doi:10.1017/S0031182013001352
(SLI) and Western Alaska (Davidson et al. 2012);
only two autochthonous cases of human AE have
been reported in central Canada and the USA
(Yamasaki et al. 2008). AE is widespread across the
arctic, subarctic and temperate climate zones of Asia
(Eckert et al. 2001). Human AE cases have been
reported in Kazakhstan and central and eastern
Anatolia, Turkey. In Russia and adjacent coun tries,
there are few available recent data on the distribution
and frequency of human AE (Jenkins et al. 2005).
The Japanese island of Hokkaido remains an impor-
tant endemic focus of E. multilocularis (Davidson
et al. 2012). Human AE is highly endemic in
nine provinces and autonomous regions of China
(Xinjiang, Inner Mongolia, Heilongjiang, Qinghai,
Gansu, Ningxia, Tibet, Sichuan and Shaanxi), which
form three foci with the largest number of human
cases in the world (Craig and Echinococcosis
Working Group in China, 2006; Torgerson et al.
2010). The prevalence of human AE ranged from
0·2% in northwestern Xinjiang to 4% in Gansu and
northwestern Sichuan (Vuitton et al. 2003;Liet al.
2010; Giraudoux et al. 2013b), and presently there are
inexplicably large differences in the incidence of
human AE between neighbouring villages (Danson
et al. 2003, 2004; Giraudoux et al. 2013a in press -
this Special Issue of Parasitology).
In Switzerland, the annual incidence of human
AE has more than doubled, increasing from a mean
of 0·10 cases per 100 000 inhabitants/year during
1993–2000 to a mean of 0·26 cases per 100000
inhabitants/year during 2001–2005, paralleling an
increase in the fox population (Schweiger et al. 2007).
Identical increases likely occurred in other countries
of the core endemic region (Austria, France and
Germany) (Moro and Schantz, 2009). France rep-
resented 42% of human AE cases notified in Europe
from 1982 to 2000 (Kern et al. 2003; Vuitton et al.
2003). Combes et al. (2012) documented an increase
of E. multilocularis infections in foxes in previously
known endemic areas and its presence in 25 ad-
ditional departments (French administrative div-
isions; median area of 5880 km
2
) where it had not
been previously detected. Earlier studies on human
AE distribution in France showed a significantly
larger incidence of human AE in the eastern part of
the country and the Massif Central (Kern et al. 2003;
Grenouillet et al. 2010), with relative risks 52·8–117
times higher than in the rest of the country (Piarroux
et al. 2013). The majority of the analyses were
conducted at the resolution level of the French
arrondissements (French administrative division,
median area of 640 km
2
) (Kern et al. 2003)or
departments (Grenouillet et al. 2010; Piarroux et al.
2013). In an earlier study conducted in the region
of Franche-Comté (16 202 km
2
), at a finer resolution
(French canton, average 140 km
2
), from 1971 to
1987, Vuitton et al. (1990) reported clusters of
human AE in the Doubs department and within the
Doubs department on the Jura plateau (600 –900 m
of altitude), a location where human AE prevalence
was shown to correlate with the population densities
of the intermediate host Arvicola terrestris (Viel et al.
1999). Elsewhere in the region of Franche-Comté
(e.g. the departments of Jura, Haute-Saône and
Territoire de Belfort), human AE cases were ran-
domly distributed.
Giraudoux et al.(2002) highlighted the impor-
tance of clustering in E. multilocularis and human
AE case distribution, which continues to pose
unsolved epidemiological questions. Clustering may
have consequences in the efficiency of national
epidemiosurveillance systems. For instance, many
local foci of clustered higher Em prevalence in foxes
are likely to remain undetected until human cases are
observed. Furthermore, in countries where human
AE cases are under-reported (e.g. because of the lack
of systematic collection of hospital records or where
AE patients may not be systematically referred to
hospitals), a number of human AE clusters are likely
to be concealed. In those cases, the incidence rates
that are averaged countrywide under the assumption
of a random distribution do not represent the actual
distribution and are barely relevant for the preven-
tion and control of AE. Detecting spatial and temp-
oral clusters has been facilitated by the Kulldorff ’s
spatial scan statistics (Kulldorff and Nagarwalla,
1995) and its software implementation. However,
using the method and interpreting the results are
not trivial issues (Chen et al. 2008), especially in cases
when complex structures are at stake, such as nested
hierarchies of clusters. A better understanding of the
transmission processes and epidemiosurveillance
efficiency should be facilitated by multi-scale ap-
proaches, which combine human and host animal epi-
demiology studies and landscape analysis (Giraudoux
et al. 2002).
A national population-based registry of human
AE is an essential tool for understanding the spatio-
temporal variation of the pattern of disease incidence
on relevant temporal and spatial scales. In China,
human case data are primarily derived from mass
screenings in the local community, clinical case re-
ports orhospital data that lack epidemiological details;
thus, the prevalence of cases may be largely under-
estimated, and local foci of human AE may be missed
(Zhou et al. 2000; Torgerson et al. 2010). France,
Germany and Switzerland have population-based
data registries for human AE, but they have not been
used to explore the detailed structure of human AE
distribution on several spatial and temporal scales.
The present study is based on data from the
French FrancEchino registry from 1982 to 2011. We
explored the multi-scale space-time distribution of
AE in France. We aimed to detect the spatial limits of
large clusters in France and the presence of a nested
hierarchy of clusters on the country scale. This study
may pave the way toward a better analysis of the
2Zeinaba Said-Ali and others
environmental and human factors that are
responsible for the distribution of the disease and
can also contribute to the development of predictive
models that better target information and preventa-
tive action.
MATERIALS AND METHODS
Human AE register
The French population-based registry, FrancEchino,
has recorded data on human AE cases since 1982.
Created in 1997, this registry included AE cases
retrospectively from 1982 to 1997 and prospectively
since 1998, using a previously described method-
ology (Piarroux et al. 2011). The registry has been
supported by the Institut de Veille Sanitaire (Institute
of Public Health Surveillance) since 2003. The
database includes only patients who present with
the criteria of possible, probable or proven human
AE (i.e. epidemiology, clinical history and a typical
liver lesion morphologically identified by imaging
techniques and positive serology, or lesions
confirmed by positive histopathology and molecular
techniques) (Brunetti et al. 2010; Piarroux et al.
2011). In the majority of cases, information about
the commune (French administrative division of some
tens of square kilometres) of the patient’s residence
at the time of diagnosis, previous residences, age,
sex and occupation are obtained from epidemiologi-
cal questionnaires. The data collection and recording
for the FrancEchino registry received the ethical
approval from the Protection of Human Subjects in
Biomedical Research Committee (CCPPRB) and the
National Commission on Informatics and Liberty
(CNIL) for the use of nominative data. At the time of
their diagnosis, all of the patients provided their
informed consent regarding the use of their data for
research purposes.
Reference population
Census population data were available at the
National Institute for Statistics and Economic
Studies (http://www.insee.fr) for 1982, 1990, 1999
and 2008. For each commune, we obtained the total
population subdivided by sex and age-group in
5-year intervals.
To increase the sample size of each spatial unit, we
used the canton as the statistical unit. The canton is an
administrative unit that pools communes, with a mean
area of 140 km
2
and a mean population of approxi-
mately 16 000 inhabitants (Ozouf-Marignier and
Verdier, 2009).
Statistics and graphical display
To visualize the spatial distribution of AE cases, we
performed classical disease mapping of the number
of cases per canton and of AE incidence rates using
R 2.12.2 (R Development Core Team, 2012) and the
package sp 1·0–5 (Bivand et al. 2008). We calculated
the incidence rates as the number of cases per
inhabitants/year for each canton. We estimated the
number of inhabitants/year in the present study as
the annual population count for each canton, from
1982 to 2011. The population data were available
for census times only. For times between censuses,
a linear interpolation was computed based on the
population at the bracketing census times. We also
studied the annual variation of AE cases.
Possible cluster location was analysed using the
spatial scan statistic developed by Kulldorff and
implemented in the SaT Scan v9.1.1 software
(Kulldorff and Nagarwalla, 1995). The scan statistic
is a method for detecting non-random distributions
in multidimensional point datasets. We tested the
null hypothesis that the number of cases in each
canton followed a Poisson distribution. Kulldorff’s
method imposes a circular window on the map and
moves the circle centre over each point location (the
centroid of each canton) so that the window includes
different sets of neighbouring points at different
positions. At each point location, the radius of the
circle is increased continuously from 0 to a user-
defined maximum radius. We adopted the default
setting of a maximum containing at most 50% of the
total population. SaTScan detects potential clusters
by calculating a likelihood ratio for each circle com-
paring the relative risks in and outside the window.
The circle with the maximum likelihood ratio among
all radius circles at all possible point locations is
considered to be the most likely cluster (called the
primary cluster). SaTScan also identifies secondary
clusters that have a significantly large likelihood ratio
but are not the primary cluster. The numb er of
expected cases was adjusted with age and sex as
covariates, using indirect standardization (Naing,
2000). For hypothesis testing, a Monte Carlo pro-
cedure was used to generate 999 random replications
of the dataset under the null hypothesis. Chen et al.
(2008) have noted and discussed a number of
limitations of the Kulldorff’s spatial scan statistic.
The primary limitation of this type of statistic is that
it is difficult to determine optimal settings for scaling
parameters and that SaTScan may subsequently
report statistically significant large clusters that
contain a high proportion of low-risk areas. Chen
et al. described those large areas as heterogeneous
clusters. Smaller homogeneous subsets within the
larger heterogeneous clusters may exhibit cumulative
incidence values that are high enough to reject
the null hypothesis on their own strength. Chen
et al. (2008) described these subsets as core clusters.
To account for the possible existence of a nested
hierarchy of clusters, we adopted the following ex-
ploratory approach. First, we searched for statistically
significant spatial clusters on the entire dataset using
3Nested clusters of human alveolar echinococcosis
SaTScan; subsequently, we performed spatial scans
within the subsets corresponding to each cluster
detected. This search was repeated iteratively for each
core cluster detected until we discerned no new
clusters.
For each cluster, the relative risk (RR) and the
standardized incidence rates (SIR) were generated
using the SaTscan software. The RR corresponded
to the ratio of the observed to expected cases inside
the scanned area divided by the ratio of observed
to expected cases outside the scanned area. The SIR
was the ratio of observed to expected cases within
the scann ed area. We also calculated the relative risk
(RRf) and the standardized incidence rates (SIRf)
using the totality of the French population as the
reference.
The low number of new AE cases each year did not
permit us to perform the spatio-temporal scan
analysis once with acceptable statistical robustness.
The data were collapsed by 10-year time-spans for
the spatial analysis, and temporal trends by year
were analysed separately with no spatial component.
We detected temporal clusters using SaTScan
and a window moving over time. The maximum
temporal window size was set to 50% of the total
population.
RESULTS
The July 2012 update of the FrancEchino registry
reported a total of 509 diagnosed AE cases between
1982 and December 2011. This finding corresponded
to an overall incidence rate of 0·027 cases per 100 000
inhabitants/year. Over the 30-year time-span of this
study, the number of cases per canton varied from 0 to
10 cases (Fig. 1). This maximum number occurred in
the canton of Pierrefontaine-les-Varans (located in
the Doubs department) (Fig. 1A), which corresponds
to an average incidence rate of 4·7 cases per 100000
inhabitants/year (Fig. 1B). The maximum incidence
rate was found in the canton of Amancey (also located
in the Doubs department), with an average incidence
rate of 8·1 cases per 100000 inhabitants/year (Fig. 1A
and B) with 8 cases diagnosed in 30 years.
Information on the locality of diagnosis, age and
gender was available for 489 cases only, which
were included in the cluster analysis. The results of
the spatial scan analysis for the entire study period
are summarized in Fig. 1C and Table 1. On the
country scale, two significant spatial clusters were
detected, which corresponded to Eastern France
(cluster a, P =10
− 17
, SIR = 7·06, RR = 34·7) and
the Massif Central region (cluster g, P = 1·6 × 10
− 12
,
Fig. 1. Human AE distribution in France, 1982–2011. Number of cases per canton (A); Incidence per 100000
inhabitants/year (B); Cluster levels (C).
4Zeinaba Said-Ali and others
SIR = 17·0, RR = 17·7). Within the Eastern France
cluster, 4 levels of nested clusters were detected. The
core cluster (level 4) with the highest incidence was
the canton of Amancey (P = 0·014, SIR
f
= 264·14,
RR
f
= 268·5).
Fig. 2 shows that cluster locations varied over
time, to some extent, with a similarly nested struc-
ture. Up to 3 cluster levels were detected in Eastern
France for each 10-year time span. The Eastern
cluster shrank during the 1991–2001 period and
subsequently expanded during the 2002–2011
period. Furthermore, the Massif Central cluster
faded during the last decade. The number of incident
cases showed a temporal variation from 8 to 34 cases
per year with a median of 16 cases per year (Fig. 3),
with a minimum incidence rate of 0·014/100 000
and a maximum of 0·059/100 000. Purely temporal
scan statistics detected a statistically significant
lower incidence rate cluster from 1991 to 2003
(RR = 0·59, P = 0·001) and a higher incidence rate
cluster from 2007 to 2011 (RR = 1·47, P = 0·011).
DISCUSSION
The incidence of patterns of disease and the mor-
tality rate over time and space can provide clues
to detect the processes and the causes of diseases
(Whittemore et al. 1987). Those patterns can be
complex, especially when the factors responsible for
disease transmission di ffer at various spatial scales.
Ecological systems are nested within one another,
which also applies to disease transmission systems;
this well-known fundamental hierarchical organiz-
ation is easy to detect in nature (Allen and Star, 1982).
Nevertheless, this hierarchical organization has been
generally undervalued as a source of influence on the
structure and development of pathogen transmission
patterns and also as a means for understanding the
crucial connections between the local processes and
the large-scale distribution patterns (Giraudoux et al.
2013b).
Nested spatial and temporal structures are neither
easily nor safely detected by simple examination of
choropleth (value-by-area) maps. Epidemiological
studies that investigate the nested spatial structure
of disease distribution specifically are still rare. For
instance, in earlier studies conducted in the United
States, several researchers detected nested clustered
distributions relative to cervical (Chen et al. 2008)
and prostate (Boscoe et al. 2003) cancer mortality.
The authors identified well-defined core clusters that
were included in less-certain periphery clusters on
two hierarchical levels.
In the present study, on the scale of a 550 000 km
2
country, we demonstrated that the distribution of
human AE, a rare zoonotic parasitic disease, can be
described as a nested hierarchy of clusters on 4 levels.
Furthermore, although AE endemic areas were
thought to be extremely stable spatially over time,
our results indicated unexpected variations in cluster
locations and limits over a 30-year time-span. This
approach is one step closer to disclosing the processes
Table 1. Cluster characteristics, for 1982–2011 (A) and 1982–1991, 1992–2001, 2002–2011 (B)
Time-span cluster Level Scanned area P SIR RR SIRf RRf
(A)
1982–2011
a 1 France 1·0×10
− 17
7·06 34·65 7·06 34·65
b 2 Cluster a 1·0×10
− 17
4·19 5·97 29·25 41·04
c 3 Cluster b 1·4×10
− 08
2·01 3·04 59·57 69·84
d 4 Cluster c 1·4×10
− 02
4·57 5·01 264·14 268·52
e 2 Cluster a 1·9×10
− 10
2·88 3·26 20·52 23·61
f 3 Cluster e 1·1×10
− 02
1·57 2·53 32·05 34·96
g 1 France 1·6×10
− 12
17·00 17·68 17·00 17·68
h 2 Cluster g 1·0×10
− 02
3·35 5·69 55·26 56·40
(B)
1982–1991
a 1 France 1·0×10
− 17
10·62 51·59 10·62 51·59
b 2 Cluster a 1·0×10
− 17
9·20 12·64 97·66 128·06
c 3 Cluster b 4·1×10
− 02
2·16 2·89 213·67 235·23
d 1 France 1·1×10
− 03
20·50 21·37 20·50 21·37
1992–2001
a 1 France 1·0×10
− 17
16·08 37·26 16·08 37·26
b 2 Cluster a 2·1×10
− 04
6·91 8·22 111·48 124·61
c 1 France 1·5×10
− 08
20·03 22·50 20·03 22·50
2002–2011
a 1 France 1·0×10
− 17
6·80 42·17 6·80 42·17
b 2 Cluster a 4·4×10
− 16
1·84 4·28 12·56 32·97
c 3 Cluster b 1·1×10
− 05
2·90 3·55 36·57 43·56
d 3 Cluster c 1·0×10
− 03
3·86 4·30 48·46 52·85
5Nested clusters of human alveolar echinococcosis
that support such patterns. However, the source of
variations may mix real variations in the transmission
processes and exposure with epidemiological arte-
facts because of variations in recruitment biases over
time and the choice of model parameters. Chen et al.
(2008) have shown in their study on cervical cancer
mortality how Kulldorff’s statistics are sensitive to
parameter choices related to cluster scaling (e.g. how
SaTScan clusters tend to contain heterogeneous
contents). In the present study, this difficulty has
been overcome by iteratively scanning each cluster
detected, top down, along a spatially nested hierarchy
until the scan statistics could not detect a new cluster.
Temporal clusters were also found as follows: a
significantly lower incidence from 1991 to 2003 and a
higher inci dence from 2007 to 2011. Those differ-
ences are attributable to selection biases. Those cases
that were diagnosed before 1997 were recorded re-
trospectively. Since 1998, the official creation of the
French registry permitted a prospective notification
of cases. Since 2003, the support of national health
authorities allowed for more active prospective
surveys. They were and still are implemented by
the FrancEchino coordination team and the devel-
opment of the FrancEchino Network. This new
combination of sources of information (microbiolo-
gists, pathologists and clinicians) presents a key
point for more exhaustive surveillance (Jorgensen
et al. 2008). Moreover, a significant increase in the
proportion of fortuitous cases diagnosed since 1982
has been reported. In 2011, more than 50% of the
patients were asymptomatic at the time of diagnosis;
for the majority of the cases, these patients were
diagnosed using imaging techniques implemented
for other reasons (Piarroux et al. 2011). This evol-
ution may indicate that medical teams have enhanced
awareness of this disease. From the beginning to the
end of the study period, an increased efficacy in case
detection has likely occurred. Another source of
selection bias is that a large sero-epidemiologic
Fig. 2. Clusters for 1982 –1991 (A), 1992–2001 (B) and 2001–2011 (C).
Fig. 3. Annual variation of AE distribution in France, 1982–2011.
6Zeinaba Said-Ali and others
screening was performed between 1987 and 1996 in
the Doubs department, the most prevalent area for
human AE in France at that time (Bresson-Hadni
et al. 1994). This study led to the diagnosis of AE
cases before 1991, which explains the high number of
AE cases diagnosed early in 1988. This study may
have led to a lower rate of AE diagnosis in the years
after this study (although the number of cases
detected during this screening does not totally
compensate for the observed decrease in the follow-
ing years).
Those biases alone can neither justify the patterns
observed nor their variations. For instance, the
shrinking of the eastern cluster during the period
1991–2003 does not parallel the putative increased
detection efficiency of human AE over the study
period. Furthermore, the spatial variations of the
location of the low-level clusters over time in
Franche-Comté cannot be attributed to the variations
of public health awareness in this area where AE
has been studied intensively from the 1980s (Vuitton
et al. 1990). Schweiger et al. (2007) have shown in
a 50-year survey that the increased prevalence of
human AE paralleled the increased density of the fox
population in Switzerland. Comparable studies are
unavailable in France but, in a study conducted from
2005 to 2010, Combes et al. (2012) have shown an
increase of E. multilocularis prevalence in foxes and
the extension of the parasite’s distribution range
toward western France. The discovery of sporadic
AE cases in these western areas from a decade ago
may indicate that parasite transmission in wildlife
and human exposure may have occurred and re-
mained undetected long ago (Vuitton et al. 2011).
In conclusion, our study demonstrates the intrin-
sically clustered distribution of AE and the fact that
large clusters may hide core-clusters up to 4 levels, of
which several vary in space and time. The processes
that support those nested-clustered patterns are far
from being understood, although the pattern itself
may be general in Asia (see Giraudoux et al. 2013a,
in press in this Special Issue of Parasitology). The
validity of any epidemiosurveillance system depends
on the data quality and the sampling strategy by
which those data are acquired (Jorgensen et al. 2008).
Furthermore, human AE surveillance can hardly be
considered an early warning system. Actually, the
detection of human cases reveals the existence of
intensive transmission years ago in animal hosts and
subsequent human exposure, when the risk could
have been disclosed by monitoring the definitive
host (fox, dog) infection. The National Human AE
registers are not exempted from epidemiological
biases; contrary to the mass-screening of self-selected
populations in areas generally known to be endemic,
the registers are likely the best way to achieve a valid
picture of human AE distribution on various scales.
They can help to optimize the designs of epi-
demiological surveillance systems and cost-effective
preventative strategies by considering the spatial
and temporal structure of this helminthic zoonosis.
The correlations between E. multilocularis or AE
distribution and climate, land use, host population
dynamics have been demonstrated for a long time
(Giraudoux et al. 2002, 2003, 2013b; Atkinson et al.
2013), but the details of the processes that explain
the observed multi-scale patterns have not been
established. The nested hierarchy of AE clusters
disclosed in the present study has not been explored
specifically from this perspective. Therefore, we
anticipate that our study may be a reasonable starting
point to pursue additional research in which environ-
mental and social factors could be considered on
several spatial and temporal scales to predict the risk
of human disease and guide pre-emptive public
health actions against human AE disease.
ACKNOWLEDGEMENTS
We thank all of the members of the FrancEchino Network
and the clinicians, biologists and pharmacists who con-
tributed to the reporting of cases and data collection in the
present study. We appreciate the endeavours of everyone
who worked diligently to conduct research and allow this
manuscript to come to fruition.
FINANCIAL SUPPORT
The FrancEchino Registry is supported, in part, by
the Institute of Public Health Surveillance (InVS). This
research has been conducted within the context of the
GDRI (International Research Network) ‘Ecosystem
Health and Environmental Disease Ecology’.
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8Zeinaba Said-Ali and others
