Year: 2025
Type: article
Abstract: BACKGROUND: The injury rate is a common measure of injury occurrence in epidemiological surveillance and is used to express the incidence of injuries as a function of both the population at risk as well as at-risk exposure time. Traditional approaches to surveillance-based injury rates use a frequentist perspective; here, we discuss the Bayesian perspective and present a practical framework on how to apply a Bayesian analysis to estimate injury rates. We estimated finescale injury rates across a broad range of categories for men's and women's soccer, applying a Bayesian methodology and using injury surveillance data captured within the National Collegiate Athletic Association Injury Surveillance Program from 2014/15-2018/19. RESULTS: Through an iterative process of assessing model fidelity, we found that a negative binomial model was an effective choice for modeling surveillance-based injury rates. We also found differences between schools to be a key driver of variation in injury rates. CONCLUSIONS: Our findings indicate that the Bayesian framework naturally characterizes injury rates by modeling injury counts as outcomes of an underlying data-generation process that explicitly incorporates inherent uncertainty, complementing traditional frequentist approaches. Key benefits of the Bayesian approach in this context are the ability to test model suitability in a variety of methods, and to be able to generate plausible estimates with sparse data.
Source: Injury Epidemiology
Authors: Avinash Chandran, Ben Lambert
Language: English
Subfield: Orthopedics and Sports Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,290
OpenAlex ID: W4411102703