Abstract
Objective: To identify individuals at risk of asthma by assessing the prevalence of asthma in an urban, athletic adolescent population using preparticipation physical evaluation (PPE) data. Study Design: Using the Athlete Health Organization (AHO) PPE data from 2016 to 2019, asthma prevalence was collected by reported diagnosis in the history or physical. Chi-square tests and logistic regression were performed to characterize the relationship between asthma and social factors such as race, ethnicity, and income. Control variables such as age, body mass index, blood pressure, sex, and family history were also collected. Results: Over 2016–2019, 1,400 athletes ranging from 9 to 19 years of age had completed PPEs (Table 1). A large percentage of student-athletes were found to have asthma (23.4%), of whom a majority 86.3% resided in low-income zip-codes. Additionally, 65.5% of athletes with asthma identified as Black, with race being associated with asthma prevalence (p < 0.05). Demographic factors like income, age, and gender were not significantly associated with asthma prevalence. Conclusions: Self-identified Black individuals reported higher prevalence of asthma when compared to the general population. Identifying factors like race and income that place adolescent athletes at risk of asthma is a key step to understanding the complex relationship between asthma and social determinants of health. This work advances the conversation for establishing best practices for serving vulnerable populations, as seen in this urban population of children with asthma.
Similar content being viewed by others
Data Availability
The data that support the findings of this study are available from the corresponding author upon request.
References
Niimi, A. (2011). Cough and asthma. Curr Respir Med Rev, 7(1), 47–54. https://doi.org/10.2174/157339811794109327.
National Asthma Education and Prevention Program TEP on the D and M of A. Expert Panel Report 3: Guidelines for the Diagnosis and Management of Asthma (2007). https://www.ncbi.nlm.nih.gov/books/NBK7232/. Accessed February 28, 2021.
Most Recent National Asthma Data | CDC. National Health Interview Survey, National Center for Health Statistics, CDC (Published 2018). https://www.cdc.gov/asthma/most_recent_national_asthma_data.htm. Accessed February 28, 2021. CDC National Center for Health Statistics, National Health Interview Survey (NHIS). National Surveillance of Asthma: United States, 2001–2017.
Moorman, J., Akinbami, L., & Bailey, C. (2012). National Surveillance of Asthma: United States, 2001–2010. Natl Cent Heal Stat. ;3(35).
Measures to Identify and Track Racial Disparities in Childhood Asthma (Published 2016). Center for Disease Control, Workgroup, Standards Subcommittee of the Asthma Disparities. https://www.cdc.gov/asthma/asthma_disparities/default.htm. Accessed February 28, 2021.
Kornblit, A., Cain, A., Bauman, L. J., Brown, N. M., & Reznik, M. (2018). Parental perspectives of barriers to physical activity in Urban Schoolchildren with Asthma. Academic Pediatric, 18(3), 310–316. https://doi.org/10.1016/j.acap.2017.12.011.
Coordinated Federal Action (2012). Plan to Reduce Racial and Ethnic Asthma Disparities. Washington, DC;
Welsh, L., Roberts, R. G. D., & Kemp, J. G. (2004). Fitness and physical activity in children with asthma. Sport Med, 34(13), 861–870. https://doi.org/10.2165/00007256-200434130-00001.
Fitch, K. D., & Morton, A. R. (1971). Specificity of exercise in exercise-induced asthma. Br Med J, 4(5787), 577–581. https://doi.org/10.1136/bmj.4.5787.577.
Participation in sports teams or sports lessons after school or on weekends, Nationwide: Child and Adolescent Health Measurement Initiative. National Survey of Children’s Health (NSCH) data query. Data Resource Center for Child and Adolescent Health supported by the U.S. Department of Health and Human Services, Health Resources and Services Administration (HRSA), Maternal and Child Health Bure. https://www.childhealthdata.org/browse/survey/results?q=7042&r=1. Published 2018. Accessed February 27, 2021.
Noel-London, K., Breitbach, A., & Belue, R. (2018). Filling the gaps in adolescent care and School Health Policy-Tackling Health Disparities through Sports Medicine Integration. Healthcare, 6(4), 132. https://doi.org/10.3390/healthcare6040132.
Mirabelli, M. H., Devine, M. J., Singh, J., & Mendoza, O. M. (2015). The Preparticipation Sports Evaluation. Vol 92.; www.aafp.org/afpAmericanFamilyPhysician371. Accessed February 27, 2021.
Athlete Health Organization (2020). http://athletehealth.org/#our-commitment. Accessed March 1, 2021.
Kropa, J., Close, J., Shipon, D., Hufnagel, E., Terry, C., Oliver, J., & Johnson, B. (2016). High prevalence of obesity and high blood pressure in Urban Student-Athletes. The Journal of Pediatrics, 178, 194–199. https://doi.org/10.1016/j.jpeds.2016.07.006.
Harris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. G., Research electronic data capture (REDCap) – A metadata-driven methodology and workflow process for providing translational research informatics support, J Biomed Inform. 2009 Apr;42(2):377 – 81
Harris, P. A., Taylor, R., Minor, B. L., Elliott, V., Fernandez, M., O’Neal, L., McLeod, L., Delacqua, G., Delacqua, F., Kirby, J., Duda, S. N., & REDCap Consortium. (2019)., The REDCap consortium: Building an international community of software partners, J Biomed Inform. 2019 May 9 [doi: 10.1016/j.jbi.103208]
U.S. Census Bureau (2016). Selected housing characteristics, 2012–2016 American Community Survey 5-year estimates. Retrieved from http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_11_5YR_DP04.
JASP Team (2021). “JASP (Version 0.11.1)[Computer Software].” https://jasp-stats.org/.
GraphPad Prism for macOS Version 9.4.1. GraphPad Software, San Diego, California USA, www.graphpad.com.
Assari, S., & Moghani Lankarani, M. (2018). Poverty status and Childhood Asthma in White and black families: National Survey of Children’s Health. Healthcare, 6(2), 62. https://doi.org/10.3390/healthcare6020062.
Fletcher, J. M., Green, J. C., & Neidell, M. J. (2010). Long term effects of childhood asthma on adult health. Journal of health economics, 29(3), 377–387. https://doi.org/10.1016/j.jhealeco.2010.03.007.
MacCallum, R. C., Zhang, S., Preacher, K. J., & Rucker, D. D. (2002). On the practice of dichotomization of quantitative variables. Psychol Meth, 7, 19–40.
Cohen, J. (1983). The cost of dichotomization. Applied Psychological Measurement, 7, 249–253.
Funding
No funds, grants, or other support was received.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing Interests
Jeremy Close serves as an unpaid co-director of the Athlete Health Organization. The other authors have no relevant financial or non-financial interests to disclose.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic Supplementary Material
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Siegel, C., Tecce, E., Vaile, J.R. et al. Asthma Prevalence among Athletes in an Urban Adolescent Population. J Community Health 48, 898–902 (2023). https://doi.org/10.1007/s10900-023-01239-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10900-023-01239-z