Individual risk of cutaneous melanoma in New Zealand: developing a clinical prediction aid
New Zealand and Australia have the highest melanoma incidence rates worldwide. In New Zealand, both the incidence and thickness have been increasing. Clinical decisions require accurate risk prediction but a simple list of genetic, phenotypic and behavioural risk factors is inadequate to estimate in...
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Veröffentlicht in: | BMC cancer 2014-05, Vol.14 (1), p.359-359, Article 359 |
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description | New Zealand and Australia have the highest melanoma incidence rates worldwide. In New Zealand, both the incidence and thickness have been increasing. Clinical decisions require accurate risk prediction but a simple list of genetic, phenotypic and behavioural risk factors is inadequate to estimate individual risk as the risk factors for melanoma have complex interactions. In order to offer tailored clinical management strategies, we developed a New Zealand prediction model to estimate individual 5-year absolute risk of melanoma.
A population-based case-control study (368 cases and 270 controls) of melanoma risk factors provided estimates of relative risks for fair-skinned New Zealanders aged 20-79 years. Model selection techniques and multivariate logistic regression were used to determine the important predictors. The relative risks for predictors were combined with baseline melanoma incidence rates and non-melanoma mortality rates to calculate individual probabilities of developing melanoma within 5 years.
For women, the best model included skin colour, number of moles > =5 mm on the right arm, having a 1st degree relative with large moles, and a personal history of non-melanoma skin cancer (NMSC). The model correctly classified 68% of participants; the C-statistic was 0.74. For men, the best model included age, place of occupation up to age 18 years, number of moles > =5 mm on the right arm, birthplace, and a history of NMSC. The model correctly classified 67% of cases; the C-statistic was 0.71.
We have developed the first New Zealand risk prediction model that calculates individual absolute 5-year risk of melanoma. This model will aid physicians to identify individuals at high risk, allowing them to individually target surveillance and other management strategies, and thereby reduce the high melanoma burden in New Zealand. |
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A population-based case-control study (368 cases and 270 controls) of melanoma risk factors provided estimates of relative risks for fair-skinned New Zealanders aged 20-79 years. Model selection techniques and multivariate logistic regression were used to determine the important predictors. The relative risks for predictors were combined with baseline melanoma incidence rates and non-melanoma mortality rates to calculate individual probabilities of developing melanoma within 5 years.
For women, the best model included skin colour, number of moles > =5 mm on the right arm, having a 1st degree relative with large moles, and a personal history of non-melanoma skin cancer (NMSC). The model correctly classified 68% of participants; the C-statistic was 0.74. For men, the best model included age, place of occupation up to age 18 years, number of moles > =5 mm on the right arm, birthplace, and a history of NMSC. The model correctly classified 67% of cases; the C-statistic was 0.71.
We have developed the first New Zealand risk prediction model that calculates individual absolute 5-year risk of melanoma. This model will aid physicians to identify individuals at high risk, allowing them to individually target surveillance and other management strategies, and thereby reduce the high melanoma burden in New Zealand.</description><identifier>ISSN: 1471-2407</identifier><identifier>EISSN: 1471-2407</identifier><identifier>DOI: 10.1186/1471-2407-14-359</identifier><identifier>PMID: 24884419</identifier><language>eng</language><publisher>England: BioMed Central</publisher><subject>Adult ; Aged ; Case-Control Studies ; Chi-Square Distribution ; Decision Support Techniques ; Epidemiology ; Ethics ; Female ; Genetic Predisposition to Disease ; Genotype & phenotype ; Humans ; Incidence ; Interviews ; Laboratories ; Logistic Models ; Male ; Medicine ; Melanoma ; Melanoma - diagnosis ; Melanoma - epidemiology ; Melanoma - genetics ; Middle Aged ; Missing data ; Mortality ; Multivariate Analysis ; Nevus - diagnosis ; Nevus - epidemiology ; Nevus - genetics ; New Zealand - epidemiology ; Population ; Predictive Value of Tests ; Prevention ; Residence Characteristics ; Risk Assessment ; Risk Factors ; Scandals ; Sex Factors ; Skin cancer ; Skin Neoplasms - diagnosis ; Skin Neoplasms - epidemiology ; Skin Neoplasms - genetics ; Skin Pigmentation ; Time Factors ; Variables ; Women ; Workplace ; Young Adult</subject><ispartof>BMC cancer, 2014-05, Vol.14 (1), p.359-359, Article 359</ispartof><rights>2014 Sneyd et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.</rights><rights>Copyright © 2014 Sneyd et al.; licensee BioMed Central Ltd. 2014 Sneyd et al.; licensee BioMed Central Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b484t-ad6ae472233d0c870c000f301c0148c3445f213da18fe6a26cc1b0ffe6fe0ee93</citedby><cites>FETCH-LOGICAL-b484t-ad6ae472233d0c870c000f301c0148c3445f213da18fe6a26cc1b0ffe6fe0ee93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4038363/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4038363/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24884419$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sneyd, Mary Jane</creatorcontrib><creatorcontrib>Cameron, Claire</creatorcontrib><creatorcontrib>Cox, Brian</creatorcontrib><title>Individual risk of cutaneous melanoma in New Zealand: developing a clinical prediction aid</title><title>BMC cancer</title><addtitle>BMC Cancer</addtitle><description>New Zealand and Australia have the highest melanoma incidence rates worldwide. In New Zealand, both the incidence and thickness have been increasing. Clinical decisions require accurate risk prediction but a simple list of genetic, phenotypic and behavioural risk factors is inadequate to estimate individual risk as the risk factors for melanoma have complex interactions. In order to offer tailored clinical management strategies, we developed a New Zealand prediction model to estimate individual 5-year absolute risk of melanoma.
A population-based case-control study (368 cases and 270 controls) of melanoma risk factors provided estimates of relative risks for fair-skinned New Zealanders aged 20-79 years. Model selection techniques and multivariate logistic regression were used to determine the important predictors. The relative risks for predictors were combined with baseline melanoma incidence rates and non-melanoma mortality rates to calculate individual probabilities of developing melanoma within 5 years.
For women, the best model included skin colour, number of moles > =5 mm on the right arm, having a 1st degree relative with large moles, and a personal history of non-melanoma skin cancer (NMSC). The model correctly classified 68% of participants; the C-statistic was 0.74. For men, the best model included age, place of occupation up to age 18 years, number of moles > =5 mm on the right arm, birthplace, and a history of NMSC. The model correctly classified 67% of cases; the C-statistic was 0.71.
We have developed the first New Zealand risk prediction model that calculates individual absolute 5-year risk of melanoma. This model will aid physicians to identify individuals at high risk, allowing them to individually target surveillance and other management strategies, and thereby reduce the high melanoma burden in New Zealand.</description><subject>Adult</subject><subject>Aged</subject><subject>Case-Control Studies</subject><subject>Chi-Square Distribution</subject><subject>Decision Support Techniques</subject><subject>Epidemiology</subject><subject>Ethics</subject><subject>Female</subject><subject>Genetic Predisposition to Disease</subject><subject>Genotype & phenotype</subject><subject>Humans</subject><subject>Incidence</subject><subject>Interviews</subject><subject>Laboratories</subject><subject>Logistic Models</subject><subject>Male</subject><subject>Medicine</subject><subject>Melanoma</subject><subject>Melanoma - diagnosis</subject><subject>Melanoma - epidemiology</subject><subject>Melanoma - genetics</subject><subject>Middle Aged</subject><subject>Missing data</subject><subject>Mortality</subject><subject>Multivariate Analysis</subject><subject>Nevus - diagnosis</subject><subject>Nevus - epidemiology</subject><subject>Nevus - genetics</subject><subject>New Zealand - epidemiology</subject><subject>Population</subject><subject>Predictive Value of Tests</subject><subject>Prevention</subject><subject>Residence Characteristics</subject><subject>Risk Assessment</subject><subject>Risk Factors</subject><subject>Scandals</subject><subject>Sex Factors</subject><subject>Skin cancer</subject><subject>Skin Neoplasms - diagnosis</subject><subject>Skin Neoplasms - epidemiology</subject><subject>Skin Neoplasms - genetics</subject><subject>Skin Pigmentation</subject><subject>Time Factors</subject><subject>Variables</subject><subject>Women</subject><subject>Workplace</subject><subject>Young Adult</subject><issn>1471-2407</issn><issn>1471-2407</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp1kUFv1DAQhS0EomXhzglZ4sIlMBM7icMBCa0oVKrgApdeLK89KS6JvdjJIv59vdqyahGc_DTz_PT0DWPPEV4jqvYNyg6rWkJXoaxE0z9gp8fRwzv6hD3J-RoAOwXqMTuppVJSYn_KLs-D8zvvFjPy5PMPHgdul9kEikvmE40mxMlwH_hn-sUvyZSBe8sd7WiMWx-uuOF29MHbErBN5LydfQzcePeUPRrMmOnZ7bti384-fF1_qi6-fDxfv7-oNlLJuTKuNSS7uhbCgVUdWAAYBKAFlMoKKZuhRuEMqoFaU7fW4gaGogcCol6s2LtD7nbZTOQshTmZUW-Tn0z6raPx-v4m-O_6Ku60BKFEK0rA-hCw8fE_Afc3Nk56z1bv2RalC_qS8uq2Roo_F8qznny2NI4HlhobUfei7zpZrC__sl7HJYUCae_qEJum4FgxOLhsijknGo6NEPT-_P_q8OIuiuOHP_cWN24Lq-s</recordid><startdate>20140522</startdate><enddate>20140522</enddate><creator>Sneyd, Mary Jane</creator><creator>Cameron, Claire</creator><creator>Cox, Brian</creator><general>BioMed Central</general><general>BioMed Central Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TO</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>H94</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20140522</creationdate><title>Individual risk of cutaneous melanoma in New Zealand: developing a clinical prediction aid</title><author>Sneyd, Mary Jane ; Cameron, Claire ; Cox, Brian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b484t-ad6ae472233d0c870c000f301c0148c3445f213da18fe6a26cc1b0ffe6fe0ee93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Case-Control Studies</topic><topic>Chi-Square Distribution</topic><topic>Decision Support Techniques</topic><topic>Epidemiology</topic><topic>Ethics</topic><topic>Female</topic><topic>Genetic Predisposition to Disease</topic><topic>Genotype & phenotype</topic><topic>Humans</topic><topic>Incidence</topic><topic>Interviews</topic><topic>Laboratories</topic><topic>Logistic Models</topic><topic>Male</topic><topic>Medicine</topic><topic>Melanoma</topic><topic>Melanoma - diagnosis</topic><topic>Melanoma - epidemiology</topic><topic>Melanoma - genetics</topic><topic>Middle Aged</topic><topic>Missing data</topic><topic>Mortality</topic><topic>Multivariate Analysis</topic><topic>Nevus - diagnosis</topic><topic>Nevus - epidemiology</topic><topic>Nevus - genetics</topic><topic>New Zealand - epidemiology</topic><topic>Population</topic><topic>Predictive Value of Tests</topic><topic>Prevention</topic><topic>Residence Characteristics</topic><topic>Risk Assessment</topic><topic>Risk Factors</topic><topic>Scandals</topic><topic>Sex Factors</topic><topic>Skin cancer</topic><topic>Skin Neoplasms - diagnosis</topic><topic>Skin Neoplasms - epidemiology</topic><topic>Skin Neoplasms - genetics</topic><topic>Skin Pigmentation</topic><topic>Time Factors</topic><topic>Variables</topic><topic>Women</topic><topic>Workplace</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sneyd, Mary Jane</creatorcontrib><creatorcontrib>Cameron, Claire</creatorcontrib><creatorcontrib>Cox, Brian</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>BMC cancer</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sneyd, Mary Jane</au><au>Cameron, Claire</au><au>Cox, Brian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Individual risk of cutaneous melanoma in New Zealand: developing a clinical prediction aid</atitle><jtitle>BMC cancer</jtitle><addtitle>BMC Cancer</addtitle><date>2014-05-22</date><risdate>2014</risdate><volume>14</volume><issue>1</issue><spage>359</spage><epage>359</epage><pages>359-359</pages><artnum>359</artnum><issn>1471-2407</issn><eissn>1471-2407</eissn><abstract>New Zealand and Australia have the highest melanoma incidence rates worldwide. In New Zealand, both the incidence and thickness have been increasing. Clinical decisions require accurate risk prediction but a simple list of genetic, phenotypic and behavioural risk factors is inadequate to estimate individual risk as the risk factors for melanoma have complex interactions. In order to offer tailored clinical management strategies, we developed a New Zealand prediction model to estimate individual 5-year absolute risk of melanoma.
A population-based case-control study (368 cases and 270 controls) of melanoma risk factors provided estimates of relative risks for fair-skinned New Zealanders aged 20-79 years. Model selection techniques and multivariate logistic regression were used to determine the important predictors. The relative risks for predictors were combined with baseline melanoma incidence rates and non-melanoma mortality rates to calculate individual probabilities of developing melanoma within 5 years.
For women, the best model included skin colour, number of moles > =5 mm on the right arm, having a 1st degree relative with large moles, and a personal history of non-melanoma skin cancer (NMSC). The model correctly classified 68% of participants; the C-statistic was 0.74. For men, the best model included age, place of occupation up to age 18 years, number of moles > =5 mm on the right arm, birthplace, and a history of NMSC. The model correctly classified 67% of cases; the C-statistic was 0.71.
We have developed the first New Zealand risk prediction model that calculates individual absolute 5-year risk of melanoma. This model will aid physicians to identify individuals at high risk, allowing them to individually target surveillance and other management strategies, and thereby reduce the high melanoma burden in New Zealand.</abstract><cop>England</cop><pub>BioMed Central</pub><pmid>24884419</pmid><doi>10.1186/1471-2407-14-359</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Adult Aged Case-Control Studies Chi-Square Distribution Decision Support Techniques Epidemiology Ethics Female Genetic Predisposition to Disease Genotype & phenotype Humans Incidence Interviews Laboratories Logistic Models Male Medicine Melanoma Melanoma - diagnosis Melanoma - epidemiology Melanoma - genetics Middle Aged Missing data Mortality Multivariate Analysis Nevus - diagnosis Nevus - epidemiology Nevus - genetics New Zealand - epidemiology Population Predictive Value of Tests Prevention Residence Characteristics Risk Assessment Risk Factors Scandals Sex Factors Skin cancer Skin Neoplasms - diagnosis Skin Neoplasms - epidemiology Skin Neoplasms - genetics Skin Pigmentation Time Factors Variables Women Workplace Young Adult |
title | Individual risk of cutaneous melanoma in New Zealand: developing a clinical prediction aid |
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