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Spatial clustering of childhood leukaemia : summary results from the EUROCLUS project

Identifieur interne : 00CA93 ( Main/Exploration ); précédent : 00CA92; suivant : 00CA94

Spatial clustering of childhood leukaemia : summary results from the EUROCLUS project

Auteurs : F. E. Alexander [Royaume-Uni] ; P. Boyle [Italie] ; P.-M. Carli [France] ; J. W. Coebergh [Pays-Bas] ; G. J. Draper [Royaume-Uni] ; A. Ekbom [Suède] ; F. Levi [Suisse] ; P. A. Mckinney [Royaume-Uni] ; W. Mcwhirter [Australie] ; J. Michaelis [Allemagne] ; R. Peris-Bonet [Espagne] ; E. Petridou [Grèce] ; V. Pompe-Kirn [Slovénie] ; I. Plisko [Slovaquie] ; E. Pukkala [Finlande] ; M. Rahu [Estonie] ; H. Storm [Danemark] ; B. Terracini [Italie] ; L. Vatten [Norvège] ; N. Wray [Royaume-Uni]

Source :

RBID : Pascal:98-0175294

Descripteurs français

English descriptors

Abstract

The interpretation of reports of clusters of childhood leukaemia is difficult, first because little is known about the causes of the disease, and second because there is insufficient information on whether cases show a generalized tendency to cluster geographically. The EUROCLUS project is a European collaborative study whose primary objective is to determine whether the residence locations of cases at diagnosis show a general tendency towards spatial clustering. The second objective is to interpret any patterns observed and, in particular, to see if clustering can be explained in terms of either infectious agents or environmental hazards as aetiological agents. The spatial distribution of 13 351 cases of childhood leukaemia diagnosed in 17 countries between 1980 and 1989 has been analysed using the Potthoff-Whittinghill method. The overall results show statistically significant evidence of clustering of total childhood leukaemia within small census areas (P= 0.03) but the magnitude of the clustering is small (extra-Poisson component of variance (%) = 1.7 with 90% confidence interval 0.2-3.1). The clustering is most marked in areas that have intermediate population density (150-499 persons km-2). It cannot be attributed to any specific age group at diagnosis or cell type and involves spatial aggregation of cases of different ages and cell types. The results indicate that intense clusters are a rare phenomenon that merit careful investigation, although aetiological insights are more likely to come from investigation of large numbers of cases. We present a method for detecting clustering that is simple and readily available to cancer registries and similar groups.


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Le document en format XML

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<sZ>9 aut.</sZ>
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<name sortKey="Michaelis, J" sort="Michaelis, J" uniqKey="Michaelis J" first="J." last="Michaelis">J. Michaelis</name>
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<s1>Johannes Gutenberg-Universitat Mainz Klinikum, Institut fur Med Statistik und Dokumentation, Langenbeckstr. 1, Postfach 3960</s1>
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<s3>DEU</s3>
<sZ>10 aut.</sZ>
</inist:fA14>
<country>Allemagne</country>
<wicri:noRegion>55101 Mainz</wicri:noRegion>
<wicri:noRegion>Postfach 3960</wicri:noRegion>
<wicri:noRegion>55101 Mainz</wicri:noRegion>
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<author>
<name sortKey="Peris Bonet, R" sort="Peris Bonet, R" uniqKey="Peris Bonet R" first="R." last="Peris-Bonet">R. Peris-Bonet</name>
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<s1>Instituto de Estudios Documentales e Historicos sobre la Ciencia (IEDHC) (Universided de Valencia - CSIC), Av. Blasco Ibanez, 15</s1>
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<s3>ESP</s3>
<sZ>11 aut.</sZ>
</inist:fA14>
<country>Espagne</country>
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<region nuts="2" type="communauté">Communauté valencienne</region>
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<author>
<name sortKey="Petridou, E" sort="Petridou, E" uniqKey="Petridou E" first="E." last="Petridou">E. Petridou</name>
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<s1>Department of Hygiene & Epidemiology, University of Athens, School of Medicine</s1>
<s2>11527 Athens (GUDI)</s2>
<s3>GRC</s3>
<sZ>12 aut.</sZ>
</inist:fA14>
<country>Grèce</country>
<wicri:noRegion>11527 Athens (GUDI)</wicri:noRegion>
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<name sortKey="Pompe Kirn, V" sort="Pompe Kirn, V" uniqKey="Pompe Kirn V" first="V." last="Pompe-Kirn">V. Pompe-Kirn</name>
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<name sortKey="Pukkala, E" sort="Pukkala, E" uniqKey="Pukkala E" first="E." last="Pukkala">E. Pukkala</name>
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<country>Finlande</country>
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</inist:fA14>
<country>Estonie</country>
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<name sortKey="Storm, H" sort="Storm, H" uniqKey="Storm H" first="H." last="Storm">H. Storm</name>
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<s1>Danish Cancer Registry, Division for Cancer Epidemiology, Strandboulevarden 49, Box 839</s1>
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<sZ>17 aut.</sZ>
</inist:fA14>
<country>Danemark</country>
<placeName>
<settlement type="city">Copenhague</settlement>
<region type="région" nuts="2">Hovedstaden</region>
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</author>
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<name sortKey="Terracini, B" sort="Terracini, B" uniqKey="Terracini B" first="B." last="Terracini">B. Terracini</name>
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<s1>Department of Biological Sciences & Human Oncology, University of Turin, Via Santena 7</s1>
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</inist:fA14>
<country>Italie</country>
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<name sortKey="Vatten, L" sort="Vatten, L" uniqKey="Vatten L" first="L." last="Vatten">L. Vatten</name>
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<s1>Institute of Community Medicine, University Medical Centre</s1>
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<s3>NOR</s3>
<sZ>19 aut.</sZ>
</inist:fA14>
<country>Norvège</country>
<placeName>
<settlement type="city">Trondheim</settlement>
<region type="région" nuts="2">Trøndelag</region>
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<s1>Department of Public Health Sciences, The University of Edinburgh, Medical School, Teviot Place</s1>
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<s3>GBR</s3>
<sZ>1 aut.</sZ>
<sZ>20 aut.</sZ>
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<country>Royaume-Uni</country>
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<orgName type="university">Université d'Édimbourg</orgName>
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<settlement type="city">Édimbourg</settlement>
<region type="country">Écosse</region>
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</analytic>
<series>
<title level="j" type="main">British journal of cancer</title>
<title level="j" type="abbreviated">Br. J. cancer</title>
<idno type="ISSN">0007-0920</idno>
<imprint>
<date when="1998">1998</date>
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<title level="j" type="main">British journal of cancer</title>
<title level="j" type="abbreviated">Br. J. cancer</title>
<idno type="ISSN">0007-0920</idno>
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</fileDesc>
<profileDesc>
<textClass>
<keywords scheme="KwdEn" xml:lang="en">
<term>Child</term>
<term>Epidemiology</term>
<term>Etiology</term>
<term>Geographic distribution</term>
<term>Infection</term>
<term>Leukemia</term>
<term>Register</term>
<term>Spatial distribution</term>
<term>Statistical method</term>
</keywords>
<keywords scheme="Pascal" xml:lang="fr">
<term>Leucémie</term>
<term>Répartition spatiale</term>
<term>Répartition géographique</term>
<term>Méthode statistique</term>
<term>Etiologie</term>
<term>Infection</term>
<term>Registre</term>
<term>Epidémiologie</term>
<term>Enfant</term>
</keywords>
<keywords scheme="Wicri" type="topic" xml:lang="fr">
<term>Répartition géographique</term>
<term>Méthode statistique</term>
<term>Enfant</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front>
<div type="abstract" xml:lang="en">The interpretation of reports of clusters of childhood leukaemia is difficult, first because little is known about the causes of the disease, and second because there is insufficient information on whether cases show a generalized tendency to cluster geographically. The EUROCLUS project is a European collaborative study whose primary objective is to determine whether the residence locations of cases at diagnosis show a general tendency towards spatial clustering. The second objective is to interpret any patterns observed and, in particular, to see if clustering can be explained in terms of either infectious agents or environmental hazards as aetiological agents. The spatial distribution of 13 351 cases of childhood leukaemia diagnosed in 17 countries between 1980 and 1989 has been analysed using the Potthoff-Whittinghill method. The overall results show statistically significant evidence of clustering of total childhood leukaemia within small census areas (P= 0.03) but the magnitude of the clustering is small (extra-Poisson component of variance (%) = 1.7 with 90% confidence interval 0.2-3.1). The clustering is most marked in areas that have intermediate population density (150-499 persons km
<sup>-2</sup>
). It cannot be attributed to any specific age group at diagnosis or cell type and involves spatial aggregation of cases of different ages and cell types. The results indicate that intense clusters are a rare phenomenon that merit careful investigation, although aetiological insights are more likely to come from investigation of large numbers of cases. We present a method for detecting clustering that is simple and readily available to cancer registries and similar groups.</div>
</front>
</TEI>
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<li>Allemagne</li>
<li>Australie</li>
<li>Danemark</li>
<li>Espagne</li>
<li>Estonie</li>
<li>Finlande</li>
<li>France</li>
<li>Grèce</li>
<li>Italie</li>
<li>Norvège</li>
<li>Pays-Bas</li>
<li>Royaume-Uni</li>
<li>Slovaquie</li>
<li>Slovénie</li>
<li>Suisse</li>
<li>Suède</li>
</country>
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<li>Canton de Vaud</li>
<li>Communauté valencienne</li>
<li>Hollande-Méridionale</li>
<li>Hovedstaden</li>
<li>Lombardie</li>
<li>Oxfordshire</li>
<li>Piémont</li>
<li>Trøndelag</li>
<li>Écosse</li>
</region>
<settlement>
<li>Copenhague</li>
<li>Lausanne</li>
<li>Milan</li>
<li>Oxford</li>
<li>Rotterdam</li>
<li>Trondheim</li>
<li>Turin</li>
<li>Édimbourg</li>
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<li>Université d'Oxford</li>
<li>Université d'Édimbourg</li>
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<name sortKey="Storm, H" sort="Storm, H" uniqKey="Storm H" first="H." last="Storm">H. Storm</name>
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<name sortKey="Vatten, L" sort="Vatten, L" uniqKey="Vatten L" first="L." last="Vatten">L. Vatten</name>
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