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Differences in skin cancer risk factors among Asian Americans identified

A cross-sectional survey study in the U.S. has identified differences in skin cancer risk factors among Asian American individuals. The study’s authors write that these differences may be used to identify high-risk subgroups and to help inform culturally aware counselling.

“Asian American individuals are the fastest growing racial group in the U.S. but remain underrepresented in health disparities research, including research on skin cancer risk factors and screening,” the authors write. “Improved understanding of preventable skin cancer risk factors and screening may demonstrate unmet needs among Asian American individuals.”

Published in JAMA Dermatology (Jan. 26, 2022), the study used data from the U.S. National Health Interview Survey, a nationally representative cross-sectional survey in the U.S. that assesses health behaviours. The authors included survey respondents from survey years 2000, 2005, 2010, and 2015 who self-identified as Asian Indian, Chinese, Filipino, non-Hispanic White, or other Asian.

Of 84,030 participants, 5,694 were Asian American (6.8%) and 78,336 (93.2%) were non-Hispanic White. This included 1,073 Asian Indian, 1,165 Chinese, 1,312 Filipino, and 2,144 Other Asian.

The investigators found that all of the Asian American subgroups were more likely to seek shade, wear long clothing to the ankles, and wear long-sleeved shirts than the non-Hispanic White respondents. They were also less likely to sunburn or tan indoors.

However, Asian Americans were less likely to apply sunscreen or receive total-body skin examinations than non-Hispanic White individuals.

When they looked at the sub-group data, the researchers found that Asian Indian individuals were less likely than Chinese participants to apply sunscreen or wear a hat but were more likely to wear long-sleeved shirts or long clothing to the ankles.

“Future studies should further sample Asian American individuals to evaluate for potential masked health disparities through disaggregated analysis,” the authors write.

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