Utilization of indoor tanning: A cross-sectional study using mobile device data

Indoor tanning using ultraviolet (UV) radiation increases skin cancer risk. However, there is little objective information on when or where indoor tanning is used. We aimed to evaluate tanning salon geography and patterns of use using objective data rather than self-report. We used data from SafeGraph, a company that combines smartphone location data and proprietary geographic data. Our dataset included aggregate, anonymous data from January 1, 2018-December 31, 2020. We developed and validated an algorithm (positive predictive value 92.6%) to identify businesses offering UV indoor tanning. We evaluated tanning salon locations, number of tanning salons per state population, and foot traffic patterns by visits per month, per day of the week, and per hour of the day. Our algorithm identified 7412 businesses as tanning salons. Of those, 2795 (37.7%) had foot traffic data available. The highest concentrations of tanning salons were in Midwestern states. We found peaks in the spring (April) of 2018 and 2019, a slightly later peak (June) in 2020, and a short-term decrease in tanning salon visits during the early phases of the COVID-19 pandemic (March-May 2020). Visits were most frequent during weekdays (Monday-Friday). Peak times of day were 12pm-3pm. Our study has limitations: it includes only a small portion of the US population (approximately 10% of mobile devices) and we could not account for indoor tanning outside of tanning salons. Indoor tanning is a known carcinogen, but the majority of information on use is based on cross-sectional surveys. Our study represents new information for public health strategies to decrease exposure to this carcinogen.

Influence of COVID-19-related measures on tourist booking behavior using the example of flight packages. (Special Issue: Covid-19 - eine Zeitenwende im Tourismus.) [German]

The COVID-19 pandemic has impacted the global tourism industry. However, when comparing the bookings made for different popular destinations in 2020 and 2019, the differences are very heterogeneous. Given this context, it is unclear whether and which COVID-19 countermeasures taken explain the heterogeneous booking behavior. In this paper, tourism-related measures are identified and analyzed with regard to flight package tour bookings from Germany, travel dates being between May and October 2020. The analysis of a comprehensive primary data set by using Fixed Effects Model methodology shows that several measures in both source and destination countries have had a significant influence on booking behavior. These include, amongst others, governmental travel warnings and quarantine regulations on return.