IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0229978.html
   My bibliography  Save this article

The association between adolescent football participation and early adulthood depression

Author

Listed:
  • Sameer K Deshpande
  • Raiden B Hasegawa
  • Jordan Weiss
  • Dylan S Small

Abstract

Concerned about potentially increased risk of neurodegenerative disease, several health professionals and policy makers have proposed limiting or banning youth participation in American-style tackle football. Given the large affected population (over 1 million boys play high school football annually), careful estimation of the long-term health effects of playing football is necessary for developing effective public health policy. Unfortunately, existing attempts to estimate these effects tend not to generalize to current participants because they either studied a much older cohort or, more seriously, failed to account for potential confounding. We leverage data from a nationally representative cohort of American men who were in grades 7–12 in the 1994–95 school year to estimate the effect of playing football in adolescent on depression in early adulthood. We control for several potential confounders related to subjects’ health, behavior, educational experience, family background, and family health history through matching and regression adjustment. We found no evidence of even a small harmful effect of football participation on scores on a version of the Center for Epidemiological Studies Depression scale (CES-D) nor did we find evidence of adverse associations with several secondary outcomes including anxiety disorder diagnosis or alcohol dependence in early adulthood. For men who were in grades 7–12 in the 1994–95 school year, participating or intending to participate in school football does not appear to be a major risk factor for early adulthood depression.

Suggested Citation

  • Sameer K Deshpande & Raiden B Hasegawa & Jordan Weiss & Dylan S Small, 2020. "The association between adolescent football participation and early adulthood depression," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-14, March.
  • Handle: RePEc:plo:pone00:0229978
    DOI: 10.1371/journal.pone.0229978
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0229978
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0229978&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0229978?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kewei Ming & Paul R. Rosenbaum, 2000. "Substantial Gains in Bias Reduction from Matching with a Variable Number of Controls," Biometrics, The International Biometric Society, vol. 56(1), pages 118-124, March.
    2. Devon Gorry, 2016. "Heterogenous effects of sports participation on education and labor market outcomes," Education Economics, Taylor & Francis Journals, vol. 24(6), pages 622-638, November.
    3. Ransom, Michael R & Ransom, Tyler, 2018. "Do high school sports build or reveal character? Bounding causal estimates of sports participation," Economics of Education Review, Elsevier, vol. 64(C), pages 75-89.
    4. Samuel D. Pimentel & Frank Yoon & Luke Keele, 2015. "Variable-Ratio Matching with Fine Balance in a Study of the Peer Health Exchange," Mathematica Policy Research Reports 48f136c594194d14b494b8358, Mathematica Policy Research.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dettmann, E. & Becker, C. & Schmeißer, C., 2011. "Distance functions for matching in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1942-1960, May.
    2. Augurzky, Boris & Kluve, Jochen, 2004. "Assessing the performance of matching algorithms when selection into treatment is strong," RWI Discussion Papers 21, RWI - Leibniz-Institut für Wirtschaftsforschung.
    3. Zhenzhen Xu & John D. Kalbfleisch, 2013. "Repeated Randomization and Matching in Multi-Arm Trials," Biometrics, The International Biometric Society, vol. 69(4), pages 949-959, December.
    4. Sylvia Brandt & Sara Gale & Ira Tager, 2012. "The value of health interventions: evaluating asthma case management using matching," Applied Economics, Taylor & Francis Journals, vol. 44(17), pages 2245-2263, June.
    5. Dimitrios Nikolaou & Laura M. Crispin, 2022. "Estimating the effects of sports and physical exercise on bullying," Contemporary Economic Policy, Western Economic Association International, vol. 40(2), pages 283-303, April.
    6. Lesner, Rune Vammen & Damm, Anna Piil & Bertelsen, Preben & Pedersen, Mads Uffe, 2022. "The Effect of School-Year Employment on Cognitive Skills, Risky Behavior, and Educational Achievement," Economics of Education Review, Elsevier, vol. 88(C).
    7. Dettmann, Eva & Becker, Claudia & Schmeißer, Christian, 2010. "Is there a Superior Distance Function for Matching in Small Samples?," IWH Discussion Papers 3/2010, Halle Institute for Economic Research (IWH).
    8. Jochen Kluve & Boris Augurzky, 2007. "Assessing the performance of matching algorithms when selection into treatment is strong," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 533-557.
    9. Groen, Jeffrey A. & Pabilonia, Sabrina Wulff, 2019. "Snooze or lose: High school start times and academic achievement," Economics of Education Review, Elsevier, vol. 72(C), pages 204-218.
    10. Laura M. Crispin & Michael Kofoed, 2019. "Does Time To Work Limit Time To Play?: Estimating A Time Allocation Model For High School Students By Household Socioeconomic Status," Contemporary Economic Policy, Western Economic Association International, vol. 37(3), pages 524-544, July.
    11. Jochen Kluve & Boris Augurzky, 2005. "Assessing the performance of matching algorithms when selection into treatment is strong," RWI Discussion Papers 0021, Rheinisch-Westfälisches Institut für Wirtschaftsforschung.
    12. Richard W. DiSalvo & Jing Che, 2022. "Causal inference on the engagement effects of athletic participation from within‐student variation," Economic Inquiry, Western Economic Association International, vol. 60(4), pages 1911-1928, October.
    13. Samuel D. Pimentel & Lauren Vollmer Forrow & Jonathan Gellar & Jiaqi Li, 2020. "Optimal matching approaches in health policy evaluations under rolling enrolment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1411-1435, October.
    14. Ruoqi Yu, 2023. "How well can fine balance work for covariate balancing," Biometrics, The International Biometric Society, vol. 79(3), pages 2346-2356, September.
    15. Elena Claudia Meroni & Daniela Piazzalunga & Chiara Pronzato, 2022. "Allocation of time and child socio-emotional skills," Review of Economics of the Household, Springer, vol. 20(4), pages 1155-1192, December.
    16. repec:zbw:rwidps:0021 is not listed on IDEAS
    17. Colin B. Fogarty & Pixu Shi & Mark E. Mikkelsen & Dylan S. Small, 2017. "Randomization Inference and Sensitivity Analysis for Composite Null Hypotheses With Binary Outcomes in Matched Observational Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 321-331, January.
    18. Michela Bia & Roberto Leombruni & Pierre-Jean Messe, 2009. "Young in-Old out: a new evaluation based on Generalized Propensity Score," LABORatorio R. Revelli Working Papers Series 93, LABORatorio R. Revelli, Centre for Employment Studies.
    19. Fernando Muñoz-Bullón & Maria J. Sanchez-Bueno & Antonio Vos-Saz, 2017. "The influence of sports participation on academic performance among students in higher education," Sport Management Review, Taylor & Francis Journals, vol. 20(4), pages 365-378, October.
    20. John Cullinan & Kevin Denny & Darragh Flannery, 2021. "A distributional analysis of upper secondary school performance," Empirical Economics, Springer, vol. 60(2), pages 1085-1113, February.
    21. Howard Birnbaum & Crystal Pike & Ritesh Banerjee & Tracy Waldman & Mary Cifaldi, 2012. "Changes in Utilization and Costs for Patients with Rheumatoid Arthritis, 1997 to 2006," PharmacoEconomics, Springer, vol. 30(4), pages 323-336, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0229978. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.