Interaction of Surface Type, Temperature, and Week of Season on Concussion Risk in the National Football League: A Bayesian Analysis

Abstract

Background: Artificial turf fields and environmental conditions may influence sports concussion risk, but existing research is limited by uncontrolled confounding factors, limited sample size, and the assumption that risk factors are independent of one another. The purpose of this study was to examine how playing surface, time of season, and game temperature relate to diagnosed concussion risk in the National Football League (NFL). Methods: This retrospective cohort study examined data from the 2012 to the 2019 NFL regular season. We fit Bayesian negative binomial regression models to relate how playing surface, game temperature, and week of the season independently related to diagnosed concussion risk and any interactions among these factors. Results: We identified 1096 diagnosed concussions in 1830 games. There was a >99% probability that concussion risk was reduced on grass surface (median incidence rate ratio [IRR] = 0.78 [95% credible interval: 0.68, 0.89]), >99% probability that concussion risk was lower at higher temperatures (IRR = 0.85 [0.76,0.95] for each 7.9 °C), and >91% probability that concussion risk increased with each week of the season (IRR = 1.02 [1.00,1.04]). There was an >84% probability for a surface × temperature interaction (IRR = 1.01 [0.96, 1.28]) and >75% probability for a surface × week interaction (IRR = 1.02 [0.99, 1.05]). Conclusions: Diagnosed concussion risk is increased on artificial turf compared with natural grass, and this is exacerbated in cold weather and, independently, later in the season. The complex interplay between these factors necessitates accounting for multiple factors and their interactions when investigating sports injury risk factors and devising mitigation methods.