Seasonal Variation in the Diagnosis of Skin Cancers From 1983 to 2017 in Greenville, North Carolina

Abstract

Background Seasonality of diagnosis occurs in many types of cancer and is well-established in non-melanoma (NMSC) and melanoma (MSC) skin cancers. Benign skin conditions have also been shown to demonstrate a similar seasonality pattern. Investigations into the seasonality of NMSC and MSC diagnoses are less common than benign skin conditions despite the high healthcare burden of the disease. In this study, we investigated if seasonality and monthly patterns of NMSC and MSC diagnoses are present in Eastern North Carolina. Methodology We observed and analyzed incident cancer diagnoses for patients visiting the Physicians East Dermatology clinic in Greenville, North Carolina, from 1983 to 2017 (n = 8,021 basal cell carcinomas (BCCs), n = 5,660 squamous cell carcinomas (SCCs), n = 451 MSCs, n = 14,132 total). Results Chi-square tests showed the highest rates of diagnosis for BCCs in August (9.85%), September (9.62%), and October (10.0%). For SCCs, the diagnosis rates were the highest in July (8.62%), August (9.63%), and October (9.58%). For MSCs, the diagnosis rates were the highest in May (9.98%), June (10.2%), and July (10.4%). Analysis of the differences between observed skin cancer diagnoses by month and equal distribution across all months in the event of no seasonality revealed peaks of skin cancer diagnoses corresponding to July through October for BCCs; July, August, and October for SCCs; and May through September for MSCs. Analysis of the patterns of diagnosis of this data over 34 years illustrated a continuously increasing pattern of diagnosis for all three cancer subtypes from 1983 to 2017. Conclusions This study identified a statistically significant pattern of seasonality in both NMSCs and MSCs, which was consistent with the findings of previous studies. Moving forward, further research should investigate the roles of temperature, quantified ultraviolet exposure, and geographic location and their relationships to seasonality.