Seasonal variability in public searches of keratosis pilaris: How the internet can illuminate a pattern in public interest.

Abstract

In this study, we examine keratosis pilaris search patterns using Google Trends to determine any seasonality. Monthly searches were collected from January 2004 to January 2020 using "keratosis pilaris" as the search term in the Google Trends database. The US search data were compared to monthly temperatures and tested for correlation. Worldwide search interest was also acquired and, along with the US data, a two-model analysis was performed to determine any seasonal patterns. Peaks in search interest closely overlapped with higher temperatures in the United States and showed correlation (.44; P < .0001). The US and worldwide search interest also exhibited seasonality, which was confirmed with a sinusoidal regression being the best-fit model (R2 = .867 and .895). These results show higher search volume during warmer months in the United States and a clear cyclical pattern in searches worldwide and in the United States. Examination of these trends could elucidate peaks that health care providers may not have been aware of yielding improved resource allocation and preparedness for larger volume periods. This information in conjunction with clinical data could also shed more light in the future on potential peak seasons of incidence and prevalence.