Bayesian methods for estimating injury rates in sport injury epidemiology
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...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-06-01
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| Series: | Injury Epidemiology |
| Online Access: | https://doi.org/10.1186/s40621-025-00583-z |
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| Summary: | 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. |
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| ISSN: | 2197-1714 |