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Background: Skin cancer is strongly associated with photodamaged skin, but body sites are often referred to as 'exposed' or 'unexposed' to sun without recognizing extent of site‐specific variation. Objectives: To assess whole‐body patterns of photodamage in an Australian population. Methods: A random sample of adult residents of Queensland underwent imaging across 10 body sites. Photodamage was graded from images using an ordinal photonumeric scale. We used cluster analysis to identify whole‐body photodamage patterns and prevalence proportion ratios (PPRs) to assess associated factors. Results: Of 190 adults (median age 52; 58% males), 58% showed severe or moderate‐to‐severe photodamage on most body sites. A higher proportion of woman had severe photodamage on the arms (upper: P = 0.002, lower: P = 0.034). A higher proportion of men had moderate or severe photodamage on the lower back (P = 0.004). We identified four photodamage patterns: 'severe general' (n = 24, 13%), 'moderate‐severe general' (n = 86, 45%), 'moderate‐severe v‐neck' (n = 40, 21%) and 'mild‐moderate upper body' (n = 12, 6%). All participants with 'severe‐general' photodamage were >50 years and more likely to have past skin cancer (PPR: 2.54, 95% CI: 1.44–4.49) than those with 'moderate‐severe v‐neck' photodamage. Those with 'moderate‐severe general' photodamage showed similar associations and were more likely female (PPR: 1.33, 95% CI: 1.04–1.69). Past or current smoking was associated with having higher levels of photodamage, with no smokers in those with 'mild‐moderate upper body' photodamage. Conclusions: Moderate‐to‐severe photodamage across much of the body is common in Queensland adults and associated with age, sex, past skin cancer and smoking. Assuming a universal pattern of site‐specific sun exposure could lead to spurious correlations, while accurate and objective assessment of site‐specific photodamage can add to understanding of the development of sun‐associated skin cancers, in particular site‐specific skin carcinogenesis. Additionally, degree of site‐specific photodamage has the potential to assist skin cancer screening. [ABSTRACT FROM AUTHOR]

Dear Editor, Risk-prediction models, which include a combination of risk factors, have been developed as a more accurate way of estimating risk and personalizing preventive interventions. The Sneyd model for men, its predictors, the comparable variable in the AMFS dataset, the relative risk estimates and participants included in the external validation analyses. The Cho model, its predictors, the comparable variable in the AMFS dataset, the relative risk estimates and participants included in the external validation analyses..Table S2. [Extracted from the article]

Summary: Melanoma is a cancer that develops in the melanocytes, the pigment cells of the skin. Melanoma incidence rates, which show how common it is, have been increasing in people with fair skin, with the highest rates in Australia, New Zealand, North America and Europe. In Australia, one in 14 men and one in 24 women will be diagnosed with melanoma during their lifetime. Risk factors for melanoma include sun exposure, sunbed use, the number of moles on the skin, the skin's sensitivity to the sun, the number of freckles, skin colour, eye colour, hair colour, family history and a number of susceptibility genes (genes that place you at higher risk). Melanoma risk prediction models, which combine many individual melanoma risk factors into an overall risk, may be useful in the prevention of melanoma by matching prevention strategies (interventions) to the patient's melanoma risk levels. Many melanoma risk prediction models use self‐assessed risk factors because it is quicker and less costly compared to the clinical (medical) assessment of risk factors, but many people underestimate the number of moles they have. This study aims to derive a melanoma risk prediction model, which includes the number of moles on the body and solar lentigines (darker skin patches caused by the sun) as clinically‐assessed risk factors, and pigmentation (skin colour) characteristics, sun exposure, family history and history of skin cancer as self‐assessed risk factors. The number of moles is the strongest risk factor in the model. The model performs well on discrimination, the model's ability to distinguish between individuals with and without melanoma. In a clinical setting, this model may help clinicians identify patients for targeted prevention strategies based on the patient's melanoma risk. This is a summary of the study: Development and external validation study of a melanoma risk prediction model incorporating clinically assessed naevi and solar lentigines Linked Article: Vuong et al. Br J Dermatol 2020; 182:1262–1268 [ABSTRACT FROM AUTHOR]

Abstract: Background: Incidence and mortality from skin cancers including melanoma are highest among men 50years or older. Thorough skin self-examination may be beneficial to improve skin cancer outcomes. Objectives: To develop and conduct a randomized-controlled trial of a video-based intervention to improve skin self-examination behavior among men 50years or older. Methods: Pilot work ascertained appropriate targeting of the 12-minute intervention video towards men 50years or older. Overall, 968 men were recruited and 929 completed baseline telephone assessment. Baseline analysis assessed randomization balance and demographic, skin cancer risk and attitudinal factors associated with conducting a whole-body skin self-examination or receiving a whole-body clinical skin examination by a doctor during the past 12months. Results: Randomization resulted in well-balanced intervention and control groups. Overall 13% of men reported conducting a thorough skin self-examination using a mirror or the help of another person to check difficult to see areas, while 39% reported having received a whole-body skin examination by a doctor within the past 12months. Confidence in finding time for and receiving advice or instructions by a doctor to perform a skin self-examination were among the factors associated with thorough skin self-examination at baseline. Conclusions: Men 50years or older can successfully be recruited to a video-based intervention trial with the aim to reduce their burden through skin cancer. Randomization by computer generated randomization list resulted in good balance between control and intervention group and baseline analysis determined factors associated with skin cancer early detection behavior. [Copyright &y& Elsevier]