Does the Prevalence of Skin Cancer Differ by Metropolitan Status for Males and Females in the United States?

Abstract

Purpose: Skin cancer is a major health concern in the general population, but there are conflicting findings regarding its relationship to where people live. The purpose of this study was to determine whether skin cancer differs by metropolitan status in the general population of U.S. adult males and females aged 18 and older. Methods:This cross sectional analysis used 2015 data from the Behavioral Risk Factor Surveillance System (BRFSS) for males and females aged 18 years and older. Multiple logistic regression analysis was used to assess the relationship between skin cancer and metropolitan status separately by gender while controlling for other cancers, general health, educational level, employment status, income level, ethnicity, and age.Results: Across states, 20% of males and 15% of females reported skin cancer and roughly 20% of both males and females lived in rural areas, 40% in suburban areas, and 40% in urban areas. After controlling for health, socioeconomic and demographic factors, skin cancer and metropolitan status were significantly related for both males and females. Skin cancer was also related to other cancers, income level, ethnicity/race, and age for both genders. Conclusion:This study found that skin cancer significantly differed by metropolitan status with both males and females living in suburban areas reporting more cancer than those living in urban or rural areas. Limitations to this study include a broad definition of skin cancer and no lifestyle variables specific to sun exposure. It is recommended that general practitioners screen, educate, and provide referral services as necessary for both males and females, especially those who live in suburban areas, have a history of cancer, are 65 or older, and are white, non-Hispanic.

Comparison of melanoma incidence in metropolitan areas versus nonmetropolitan areas in the state of Texas stratified by poverty classification

“…Within this data set, the patients residing in the nonmetropolitan group labeled urban area with 20,000 adjacent to a metropolitan area had the highest incidence of melanoma. This is consistent with the findings of Bram et al, 7 who showed that skin cancer incidence (type unspecified) significantly differed by metropolitan status, with more reports among people living in suburban areas (defined as areas with a population of 20,000-50,000) than in urban or rural areas. The Bram et al study classification of "suburban" is equivalent to the group "urban area with 20,000 adjacent to a metropolitan area" in this study.…”

Deep Learning Approaches for Prognosis of Automated Skin Disease

“…The matching algorithm unit tests the characteristics needed to categorize skin disorders. The process of extracting features from testing pictures is linked with extracting features from previously learned images [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ]. The matching algorithm is designed so that if illnesses are identified, they are categorized, and, ultimately, patients receive an e-prescription; if no diseases are found, the system classifies the skin as healthy.…”