Victoria Mar1, Rory Wolfe, John W Kelly. 1. Victorian Melanoma Service, The Alfred Hospital, Melbourne, Victoria, Australia. torimar@ymail.com
Abstract
BACKGROUND: As melanoma incidence in Australia continues to rise, targeting high-risk individuals for early detection is of paramount importance. OBJECTIVES: We aimed to design a population-specific risk assessment tool to improve on the use of intuition alone for assignment of surveillance strategies for high-risk individuals and help communicate risk more accurately and effectively to patients. METHODS: Methods used in the development of breast cancer risk models were adopted. Data from a large meta-analysis was used to determine risk estimates. Attributable risk was calculated for each risk factor using data from the Victorian Melanoma Service. Local prevalence data from state cancer registries was incorporated to estimate 5-year risk of melanoma. RESULTS: Independent risk factors identified were common naevi, atypical naevi, hair colour, freckles, family history of melanoma and personal history of non-melanoma skin cancer. Personal history of melanoma was the strongest risk factor for developing another (relative risk 7.28, 7.24). Absolute risk for individuals varies greatly with age, risk factor profiles and proximity to the equator. CONCLUSION: We have developed a melanoma risk assessment tool based on the best available information (http://www.victorianmelanomaservice.org/calculator). The tool is easily modified as new information becomes available.
BACKGROUND: As melanoma incidence in Australia continues to rise, targeting high-risk individuals for early detection is of paramount importance. OBJECTIVES: We aimed to design a population-specific risk assessment tool to improve on the use of intuition alone for assignment of surveillance strategies for high-risk individuals and help communicate risk more accurately and effectively to patients. METHODS: Methods used in the development of breast cancer risk models were adopted. Data from a large meta-analysis was used to determine risk estimates. Attributable risk was calculated for each risk factor using data from the Victorian Melanoma Service. Local prevalence data from state cancer registries was incorporated to estimate 5-year risk of melanoma. RESULTS: Independent risk factors identified were common naevi, atypical naevi, hair colour, freckles, family history of melanoma and personal history of non-melanoma skin cancer. Personal history of melanoma was the strongest risk factor for developing another (relative risk 7.28, 7.24). Absolute risk for individuals varies greatly with age, risk factor profiles and proximity to the equator. CONCLUSION: We have developed a melanoma risk assessment tool based on the best available information (http://www.victorianmelanomaservice.org/calculator). The tool is easily modified as new information becomes available.
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