Evidence map›Paper›PMID 28670666›Full record

ArticleInjury epidemiology2017

Epidemiologic comparisons of soccer-related injuries presenting to emergency departments and reported within high school and collegiate settings.

Zachary Y Kerr, Lauren A Pierpoint, Dustin W Currie, Erin B Wasserman, R Dawn Comstock

Abstract read
In one paragraph

Article in Injury epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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  10. Adolescent knee pain: fracture or normal? A case report.The Journal of the Canadian Chiropractic Association · 2018
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zachary Y KerrDepartment of Exercise and Sport Science, University of North Carolina, 313 Woollen Gym CB#8700, Chapel Hill, NC, 27599-8700, USA. zkerr@email.unc.edu.
Lauren A PierpointDepartment of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz, Mail Stop B119, 13001 E 17th Pl, Aurora, CO, 80045, USA.
Dustin W CurrieDepartment of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz, Mail Stop B119, 13001 E 17th Pl, Aurora, CO, 80045, USA.
Erin B WassermanDatalys Center for Sports Injury Research and Prevention, 401 W Michigan St, Suite 500, Indianapolis, IN, 46202, USA.
R Dawn ComstockDepartment of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz, Mail Stop B119, 13001 E 17th Pl, Aurora, CO, 80045, USA.

Funding

The Ohio State University Center for Clinical and Translational Science (KL2)KL2RR025754 · NCRR · OHIO STATE UNIVERSITY · PI JACKSON, REBECCA D · 2008 to 2011
$3.5M
Center for Injury Research and PolicyR49CE001172 · CE · RESEARCH INST NATIONWIDE CHILDREN'S HOSP · PI SMITH, GARY A. · 2008 to 2011
$3.2M
High School Sports Injury Surveillance-Incidence/exposurR49CE000674 · CE · RESEARCH INST NATIONWIDE CHILDREN'S HOSP · PI COMSTOCK, R DAWN · 2005 to 2005
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NCIPC CDC HHS R49 CE000674NCIPC CDC HHS R49 CE001172NCRR NIH HHS KL2 RR025754
6 · The paper itself

Abstract

backgroundFew studies compare sports injury patterns in different settings. This study described the epidemiology of soccer injuries presenting to emergency departments (EDs) and compared injuries presenting to EDs to injuries presenting to collegiate and high school athletic trainers (ATs).

methodsSoccer-related injuries (product code 1267) in the National Electronic Injury Surveillance System (NEISS) that were sustained by individuals at least two years of age in 2004-2013 were included. High School Reporting Information Online (HS RIO) data for high school soccer injuries during the 2005/06-2013/14 academic years were compared to NEISS data for those aged 14-17 years in 2005-2013. National Collegiate Athletic Association Injury Surveillance Program (NCAA-ISP) data for collegiate soccer injuries during the 2009/10-2013/14 academic years were compared to NEISS data for those aged 18-22 years in 2009-2013. All datasets included weights to calculate national estimates. Injury proportion ratios (IPRs) with 95% confidence intervals (CIs) compared nationally estimated injury distributions between the HS RIO/NCAA-ISP and NEISS data subsets.

resultsDuring the study period, 63,258 soccer-related injuries were captured by NEISS, which translates to an estimated 2,039,250 injuries seen at US EDs nationwide. Commonly injured body parts included the head/face (19.1%), ankle (17.6%), hand/wrist (15.3%), and knee (12.2%). Common diagnoses included sprains/strains (34.0%), fractures (22.2%), and contusions (17.7%). Compared to their respective age ranges in NEISS, sprains/strains comprised a larger proportion of injuries in HS RIO (48.3% vs. 33.7%; IPR = 1.38; 95% CI: 1.33, 1.42) and NCAA-ISP (51.3% vs. 37.0%; IPR = 1.39; 95% CI: 1.31, 1.46). In contrast, fractures comprised a smaller proportion of injuries in HS RIO than in NEISS (7.5% vs. 18.6%; IPR = 0.43; 95% CI: 0.39, 0.47) and NCAA-ISP (2.8% vs. 15.7%; IPR = 0.18; 95% CI: 0.14, 0.22).

conclusionsATs more commonly reported injuries that are easily diagnosed and treated (e.g., sprains/strains); EDs more commonly reported injuries with longer recovery times and rehabilitation (e.g., fractures). Although ED surveillance data can identify the most severe sports-related injuries, high school and college sports surveillance may better describe the breadth of sports-related injuries. Our findings may provide further support for school-based sports medicine professionals, but further research is needed to comprehensively examine the potential economic and health-related benefits.

Indexed as

Athletic trainingEpidemiologyInjury surveillanceSoccer

Identifiers

PMID28670666
PMCPMC5494284

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.