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August 20, 2026Injury Prevention

Population estimation in injury surveillance using a quasi-bootstrapping approach with convenience sample data

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Authors

ACAvinash ChandranFlorida State UniversityDBDerek W. BrownNational Institutes of Health

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Implication

Simulation and validation study demonstrates accurate population injury estimation in collegiate athletics, indicating a robust framework for convenience sample surveillance.

Key Points

  • To develop and evaluate a quasi-bootstrapping algorithm leveraging historical data to improve population-level injury and exposure estimates from convenience samples in sports injury surveillance.
  • Designed a phased evaluation comprising a simulation study and practical application grounded in operational protocols from the NCAA Injury Surveillance Program (NCAA ISP).
  • Modeled a quasi-bootstrapping algorithm that capitalizes on archived surveillance data to adjust for school-reporting fractions, under-reporting, and real-world variability.
  • The quasi-bootstrapping method approximated true population values with near-zero bias across all simulated conditions and demonstrated greater stability than the current inverse-scaling method.
  • The model generated valid uncertainty intervals for both injury counts and athlete exposure metrics while delivering plausible, credible estimates when applied to real-world surveillance data.

Cite This Study

Chandran et al. (2026) studied this question.

synapsesocial.com/papers/6a86b5978a91293e6a1cd00fhttps://doi.org/10.1136/ip-2025-046042
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