Spatial Autocorrelation of Cancer in Western Europe
We applied the techniques of spatial autocorrelation (SA) analysis to 40 cancer mortality distributions in Western Europe. One of the aims of these methods is to describe the scale over which spatial patterns of mortalities occur, which may provide suggestions concerning the agents bringing about th...
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| Veröffentlicht in: | European journal of epidemiology 1999-01, Vol.15 (1), p.15-22 |
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| container_title | European journal of epidemiology |
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| creator | Rosenberg, Michael S. Sokal, Robert R. Oden, Neal L. DiGiovanni, Donna |
| description | We applied the techniques of spatial autocorrelation (SA) analysis to 40 cancer mortality distributions in Western Europe. One of the aims of these methods is to describe the scale over which spatial patterns of mortalities occur, which may provide suggestions concerning the agents bringing about the patterns. We analyzed 355 registration areas, applying one- and two-dimensional SA as well as local SA techniques. We find that cancer mortalities are unusually strongly spatially structured, implying similar spatial structuring of the responsible agents. The small number of spatial patterns (4 or 5) in the 40 cancer mortalities suggests there are fewer spatially patterned agents than the number of cancers studied. SA present in variables will bias the results of conventional statistical tests applied to them. After correcting for such bias, some pairwise correlations of cancer mortality distributions remain significant, suggesting inherent, epidemiologically meaningful correlations. Local SA is a useful technique for exploring epidemiological maps. It found homogeneous high overall cancer mortalities in Denmark and homogeneous low mortalities in southern Italy, as well as a very heterogeneous pattern for ovarian cancer in Ireland. |
| doi_str_mv | 10.1023/A:1007559728848 |
| format | Article |
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One of the aims of these methods is to describe the scale over which spatial patterns of mortalities occur, which may provide suggestions concerning the agents bringing about the patterns. We analyzed 355 registration areas, applying one- and two-dimensional SA as well as local SA techniques. We find that cancer mortalities are unusually strongly spatially structured, implying similar spatial structuring of the responsible agents. The small number of spatial patterns (4 or 5) in the 40 cancer mortalities suggests there are fewer spatially patterned agents than the number of cancers studied. SA present in variables will bias the results of conventional statistical tests applied to them. After correcting for such bias, some pairwise correlations of cancer mortality distributions remain significant, suggesting inherent, epidemiologically meaningful correlations. Local SA is a useful technique for exploring epidemiological maps. It found homogeneous high overall cancer mortalities in Denmark and homogeneous low mortalities in southern Italy, as well as a very heterogeneous pattern for ovarian cancer in Ireland.</description><identifier>ISSN: 0393-2990</identifier><identifier>EISSN: 1573-7284</identifier><identifier>DOI: 10.1023/A:1007559728848</identifier><identifier>PMID: 10098991</identifier><identifier>CODEN: EJEPE8</identifier><language>eng</language><publisher>Dordrecht: Kluwer Academic Publishers</publisher><subject>Autocorrelation ; Biological and medical sciences ; Cancer ; Clines ; Cluster Analysis ; Coefficients ; Correlations ; Ecological genetics ; Epidemiology ; Europe - epidemiology ; Female ; Humans ; Male ; Medical sciences ; Mortality ; Neoplasms - mortality ; Ovarian cancer ; Ovarian Neoplasms - mortality ; Population genetics ; Spatial analysis ; Tumors</subject><ispartof>European journal of epidemiology, 1999-01, Vol.15 (1), p.15-22</ispartof><rights>Copyright 1999 Kluwer Academic Publishers</rights><rights>1999 INIST-CNRS</rights><rights>Copyright (c) 1999 Kluwer Academic Publishers</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c330t-747186f5458ae556168c5e5419a76a42664debb9ff261d6f73426c26b3e00ab83</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/3581795$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/3581795$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,799,4010,27900,27901,27902,57992,58225</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=1708196$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/10098991$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Rosenberg, Michael S.</creatorcontrib><creatorcontrib>Sokal, Robert R.</creatorcontrib><creatorcontrib>Oden, Neal L.</creatorcontrib><creatorcontrib>DiGiovanni, Donna</creatorcontrib><title>Spatial Autocorrelation of Cancer in Western Europe</title><title>European journal of epidemiology</title><addtitle>Eur J Epidemiol</addtitle><description>We applied the techniques of spatial autocorrelation (SA) analysis to 40 cancer mortality distributions in Western Europe. One of the aims of these methods is to describe the scale over which spatial patterns of mortalities occur, which may provide suggestions concerning the agents bringing about the patterns. We analyzed 355 registration areas, applying one- and two-dimensional SA as well as local SA techniques. We find that cancer mortalities are unusually strongly spatially structured, implying similar spatial structuring of the responsible agents. The small number of spatial patterns (4 or 5) in the 40 cancer mortalities suggests there are fewer spatially patterned agents than the number of cancers studied. SA present in variables will bias the results of conventional statistical tests applied to them. After correcting for such bias, some pairwise correlations of cancer mortality distributions remain significant, suggesting inherent, epidemiologically meaningful correlations. Local SA is a useful technique for exploring epidemiological maps. It found homogeneous high overall cancer mortalities in Denmark and homogeneous low mortalities in southern Italy, as well as a very heterogeneous pattern for ovarian cancer in Ireland.</description><subject>Autocorrelation</subject><subject>Biological and medical sciences</subject><subject>Cancer</subject><subject>Clines</subject><subject>Cluster Analysis</subject><subject>Coefficients</subject><subject>Correlations</subject><subject>Ecological genetics</subject><subject>Epidemiology</subject><subject>Europe - epidemiology</subject><subject>Female</subject><subject>Humans</subject><subject>Male</subject><subject>Medical sciences</subject><subject>Mortality</subject><subject>Neoplasms - mortality</subject><subject>Ovarian cancer</subject><subject>Ovarian Neoplasms - mortality</subject><subject>Population genetics</subject><subject>Spatial analysis</subject><subject>Tumors</subject><issn>0393-2990</issn><issn>1573-7284</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1999</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNpd0EtLAzEQAOAgiq3VsxeRRcTbal6Th7dS6gMKHlQ8Ltk0gS3bzZrsHvz3BloVPIXJfDPMDELnBN8STNnd_J5gLAG0pEpxdYCmBCQrc8QP0RQzzUqqNZ6gk5Q2GGOFNRyjSS7SSmsyRey1N0Nj2mI-DsGGGF2b49AVwRcL01kXi6YrPlwaXOyK5RhD707RkTdtcmf7d4beH5Zvi6dy9fL4vJivSssYHkrJJVHCAwdlHIAgQllwwIk2UhhOheBrV9faeyrIWnjJ8p-lomYOY1MrNkM3u759DJ9jHqHaNsm6tjWdC2OqhBbA8u4ZXv2DmzDGLs9WUcKVAC5ZRpd7NNZbt6762GxN_Kp-bpHB9R6YZE3rY16_SX9OYkW0yOxixzZpCPE3zUARqYF9A1f7c-Q</recordid><startdate>19990101</startdate><enddate>19990101</enddate><creator>Rosenberg, Michael S.</creator><creator>Sokal, Robert R.</creator><creator>Oden, Neal L.</creator><creator>DiGiovanni, Donna</creator><general>Kluwer Academic Publishers</general><general>Springer</general><general>Springer Nature B.V</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>3V.</scope><scope>7QL</scope><scope>7T2</scope><scope>7TK</scope><scope>7TS</scope><scope>7U7</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88C</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>K9.</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>M2O</scope><scope>MBDVC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>19990101</creationdate><title>Spatial Autocorrelation of Cancer in Western Europe</title><author>Rosenberg, Michael S. ; 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One of the aims of these methods is to describe the scale over which spatial patterns of mortalities occur, which may provide suggestions concerning the agents bringing about the patterns. We analyzed 355 registration areas, applying one- and two-dimensional SA as well as local SA techniques. We find that cancer mortalities are unusually strongly spatially structured, implying similar spatial structuring of the responsible agents. The small number of spatial patterns (4 or 5) in the 40 cancer mortalities suggests there are fewer spatially patterned agents than the number of cancers studied. SA present in variables will bias the results of conventional statistical tests applied to them. After correcting for such bias, some pairwise correlations of cancer mortality distributions remain significant, suggesting inherent, epidemiologically meaningful correlations. Local SA is a useful technique for exploring epidemiological maps. 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| ispartof | European journal of epidemiology, 1999-01, Vol.15 (1), p.15-22 |
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| source | Jstor Complete Legacy; MEDLINE; SpringerLink Journals - AutoHoldings |
| subjects | Autocorrelation Biological and medical sciences Cancer Clines Cluster Analysis Coefficients Correlations Ecological genetics Epidemiology Europe - epidemiology Female Humans Male Medical sciences Mortality Neoplasms - mortality Ovarian cancer Ovarian Neoplasms - mortality Population genetics Spatial analysis Tumors |
| title | Spatial Autocorrelation of Cancer in Western Europe |
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