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A Review of Analytical Methods Used for Evaluating Clustering in Concussion-Related Symptoms

Current Epidemiology Reports volume 7, pages 315–326 (2020)Cite this article

Abstract

Purpose of Review

Clinicians often use symptom cluster presentations to inform concussion diagnosis and provision of care. The current review appraises the analytical methods used for the identification of clinically meaningful clusters based on symptom assessments.

Recent Findings

Symptom clustering was commonly examined in relation to scores calculated using established assessment instruments. The majority of studies utilized Factor Analysis techniques for examining clustering, although statistical analyses were heterogeneously described by authors. Other techniques employed included time series network models, and cluster analysis using the joining tree method.

Summary

While there exists strong evidence to suggest multidimensionality in symptom presentations, the analytical foundations of these conclusions warrant further consideration. Future work in reconciling the underlying structure of symptom presentations may consider the nuances of the data captured using symptom assessment instruments, and the applicational limitations of commonly utilized data reduction techniques. Techniques that accommodate temporal dynamics of symptom presentations also warrant exploration in conducting this work.

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Authors and Affiliations

  1. NCAA Injury Surveillance Program, Datalys Center for Sports Injury Research and Prevention, 401 W. Michigan Street, Suite 500, Indianapolis, IN, 46202, USA

    Avinash Chandran & Sarah N. Morris

  2. Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Avinash Chandran, Aliza K. Nedimyer, Zachary Y. Kerr & Johna K. Register-Mihalik

  3. School of Health Professions, University of Southern Mississippi, Hattiesburg, MS, USA

    Melissa C. Kay

  4. Human Movement Science Curriculum, University of North Carolina, Chapel Hill, NC, USA

    Aliza K. Nedimyer

  5. Matthew Gfeller Sport-Related Traumatic Brain Injury Research Center, University of North Carolina, Chapel Hill, NC, USA

    Aliza K. Nedimyer, Zachary Y. Kerr & Johna K. Register-Mihalik

  6. Injury Prevention Research Center, University of North Carolina, Chapel Hill, NC, USA

    Zachary Y. Kerr & Johna K. Register-Mihalik

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  1. Avinash Chandran
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Correspondence to Avinash Chandran.

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Conflict of Interest

Dr. Chandran has nothing to disclose.

Mrs. Nedimyer has nothing to disclose.

Dr. Kay has nothing to disclose.

Dr. Morris has nothing to disclose.

Dr. Kerr reports grants from Centers for Disease Control and Prevention, grants from National Football League, grants from National Institutes of Health, outside the submitted work.

Dr. Register-Mihalik reports grants from CDC/NCIPC, grants from NOCSAE, grants from NATA Foundation, grants from NFL, grants from NCAA-DOD Mind Matters Award, non-financial support and other from USA Football Football Development Council, other from Allied Health Education, outside the submitted work.

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Chandran, A., Kay, M.C., Nedimyer, A.K. et al. A Review of Analytical Methods Used for Evaluating Clustering in Concussion-Related Symptoms. Curr Epidemiol Rep 7, 315–326 (2020). https://doi.org/10.1007/s40471-020-00254-1

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  • DOI: https://doi.org/10.1007/s40471-020-00254-1

Keywords

  • Concussions
  • Symptom clustering
  • Symptom assessment