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American Journal of Epidemiology
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Vol. 183, No. 5
DOI: 10.1093/aje/kwv348
Advance Access publication:
February 12, 2016
Practice of Epidemiology
Developing and Implementing Major League Baseball’s Health and Injury Tracking
System
Keshia M. Pollack*, John D’Angelo, Gary Green, Stan Conte, Stephen Fealy, Chris Marinak,
Edward McFarland, and Frank C. Curriero
* Correspondence to Dr. Keshia M. Pollack, Johns Hopkins Center for Injury Research and Policy, Johns Hopkins Bloomberg School of Public
Health, 624 N. Broadway, Room 555, Baltimore, MD (e-mail: [email protected]).
Initially submitted August 1, 2015; accepted for publication December 9, 2015.
In 2010, Major League Baseball and the Major League Baseball Players Association reached an agreement
regarding the development and implementation of an electronic medical record system and a new league-wide injury surveillance system. The systems were developed to create a more efficient method to track medical histories of
players longitudinally as they move across Major and Minor league affiliates, as well as to identify and monitor injury
trends in the sport, identify areas of specific concern, and conduct epidemiologic research to better optimize player
health and safety. The resulting injury surveillance system, the Health and Injury Tracking System (HITS), is a robust
system that includes all players from the both the Major and Minor Leagues. HITS also allows for data linkage with
other player- and game-level data to inform the development of injury prevention policies and programs. In the
present article, we document the development and implementation of HITS; describe its utility for epidemiologic
research; illustrate the potential analytic strength of the surveillance system and its ability to inform policy change;
and note the potential for this new surveillance system to advance the field of sports injury epidemiology.
baseball; injury epidemiology; sports injury; surveillance
Abbreviations: AE, athlete exposures; AT, athletic trainer; EMR, electronic medical record; HITS, Health and Injury Tracking
System; MLB, Major League Baseball; MTBI, mild traumatic brain injury.
Surveillance is “the ongoing systematic collection, analysis, and interpretation of health data, essential to the planning,
implementation, and evaluation of health practice, closely integrated with the timely dissemination of these data to those
who need to know” (1, p. 164). In the United States, surveillance of injuries has allowed the detection of important
trends, identification of leading correlates of injuries, and development of prevention policies and programs (2). Injury
surveillance is also critical for conducting descriptive epidemiologic studies, which help illuminate the patterns, affected
populations, and distribution of disease and illness.
Sports and recreation are highly popular activities in the
United States, and several sports injury surveillance systems
exist to document trends and inform prevention efforts. For
example, estimates of injuries resulting from sports and recreation for the general US population are captured by the National Electronic Injury Surveillance System, which includes
injury data from a sample of emergency departments around
the United States (3, 4). For college injury surveillance, data
are collected using the National Collegiate Athletic Association’s Injury Surveillance Program (5–7). The National Collegiate Athletic Association’s Injury Surveillance Program
collects injury and athlete exposure (AE) data from a representative sample of collegiate institutions and sports (7). Injuries among high school athletes are captured using the High
School Reporting Information Online, which collects data on
time-loss injuries among a national sample of specific US
high school athletic teams (8–10). These surveillance systems have documented important injury trends in specific
sports and helped to inform the development of prevention
strategies among student athletes (11, 12). For example, analysis of data from the National Collegiate Athletic Association’s Injury Surveillance Program led to policy and rule
changes in ice hockey to reduce the number of concussions,
490
Am J Epidemiol. 2016;183(5):490–496
Professional Baseball Injury Surveillance System 491
in women’s lacrosse to reduce the number of catastrophic eye
injuries, and in football to reduce injuries during kickoff (7).
The ability to identify leading injuries and monitor trends,
especially to determine the impact of policy and rules changes, is an added benefit of these sports injury surveillance
systems.
For professional baseball players, injury surveillance was
primarily conducted using the Major League Baseball (MLB)
Disabled List. The Disabled List has been analyzed to document injuries overall (13, 14), musculoskeletal injuries (15),
and injuries resulting from collisions (16), among others.
Limitations of the Disabled List have been noted, specifically
that the Disabled List does not include all injuries and that
decisions to place a player on the Disabled List might be
based on nonmedical factors in order to maximize the chances
of having the best available players on the active roster at any
given time (13). The lack of a formal injury surveillance system for professional baseball has been noted as one reason
little is known about the injury rates in MLB players (13).
In 2010, MLB and the Major League Baseball Players Association reached an agreement to develop and implement a
new electronic medical record (EMR) and injury tracking
system. The agreement included discussions about the types
of data that would be collected, data storage, and research,
among other legal and logistical topics. The primary goal
of the new system was to create a more efficient method to
track medical histories of players longitudinally as they move
across Major and Minor league affiliates. A second goal was to
identify and monitor injury trends in the sport, identify areas
of specific concern, and conduct epidemiologic research to
better understand injuries and optimize player health and
safety through possible rules changes, equipment modifications, or medical education. The unique aspect of this surveillance system is that it includes all players from the Major and
Minor Leagues, rather than just a sample of players from certain teams. Our purpose in the present article was to document
the development of this new baseball injury surveillance system, describe the utility of a league-wide injury surveillance
system for research, illustrate the potential analytic strength
of the database and its ability to inform policy change, and
note the potential for this surveillance system to advance the
field of sports injury epidemiology.
METHODS
Athlete population
In the United States, professional baseball consists of the
Major and Minor Leagues, which are administered by the
MLB. The Major Leagues are divided into 30 clubs that play
162 games in 183 calendar days and have 25 active players on
their rosters at any one time, for a total of 750 active players.
Clubs can also have up to 15 additional players on their Major
League rosters who are either not active or are optioned to the
Minor Leagues; these players, combined with those on the
25-man active rosters, comprise the Major League 40-man
rosters.
The Minor Leagues are a network of more than 200 clubs
throughout the United States that are each affiliated with a
Major League club. The Minor League clubs are organized
Am J Epidemiol. 2016;183(5):490–496
into a number of different leagues based on both geography
and level of play. Season length in the Minor Leagues differs
depending on the league, with a range of 80 days for the lowlevel short seasons to approximately 150 days for the highlevel AAA leagues. There are approximately 7,500 players
assigned to the Minor Leagues each year, with nearly 6,500
actively playing at any particular time.
MLB’s EMR system
The MLB’s EMR system was built to provide a more standardized and streamlined way to enter, track, and transfer
player medical records across professional baseball. All professional baseball players in both the Major and Minor
Leagues provide their consent for their medical records to
be included in the EMR system. The EMR system was released across baseball at the beginning of the 2010 season
and is available on the MLB intranet. The system has a number of levels of security, with users needing to enter both a
password and an RSA SecurID (EMC Corporation, Hopkinton, Massachusetts) token code in order to gain access.
The EMR system is linked to MLB’s electronic Baseball
Information System. The electronic Baseball Information
System is a web-based program that facilitates many functions of baseball operations for the 30 Major League clubs
and the Office of the Commissioner. The system serves as
the core method through which clubs carry out transactions,
such as inputting information about contract terms. Linking
the EMR system to the electronic Baseball Information System allows roster changes to be made automatically, which in
turn facilitates the transfer of medical records as players move
between and within organizations.
The core component of the EMR system is the entry of all
injury, medical, treatment, and prevention data for each player
on a club’s roster by the athletic trainer (AT). Once one of
these occurrences (i.e., any injury, medical, treatment or
prevention-related occurrence) has been entered into a player’s record, treatment notes, doctor’s notes, and diagnostics
can also be entered into the system and linked to the applicable event. Files such as videos and magnetic resonance images can also be uploaded and attached to each event. The
EMR system also allows the creation and input of standard
physical examination forms, medical history forms, allergy
and immunization information, exit questionnaire forms,
and Health Insurance Portability and Accountability Act authorization forms for each player. Players complete a disclosure of health information form when signing their contracts
that allows these approved system users access to the players’
medical records.
Health and Injury Tracking System: overview and
measures
Injury data are included in Health and Injury Tracking
System (HITS), a centralized database containing the deidentified medical data from the EMR system. All records
were assigned a computer-generated unique identifier to preserve confidentiality. In addition, all individually identifiable
information was stripped based on Health Insurance Portability and Accountability Act guidelines.
492 Pollack et al.
Table 1. Sample Subset of Variables in the Health and Injury Tracking System, Their Definitions, and 2 Sample Entries for Each Variable
Variable
Definition
Sample Entry
Sample Entry
Level of play
The level of play (Major League or Minor League) for the player
when the injury occurred
Major League
Minor League
Body region
The region of the body on which the injury occurred
Elbow
Knee
Body side
The side of the body on which the injury occurred
Right
Left
Event position
detail
The position the player was performing when the injury occurred
Pitcher
Base runner
Injury mechanism
Mechanism of the injury, such as contact with ball, contact with
ground, or noncontact
Noncontact
Contact with
ground
Injury activity
Activity at the time of injury (batting, running, pitching, etc.)
Pitching
Base running
Injury location
Location where the injury occurred (home plate, outfield, etc.)
Pitcher’s mound
Home plate
SMDCSa
description
A method of categorizing sports injuries
Ulnar collateral ligament
sprain 1°
Knee contusion
ICD-9 code
Diagnostic code for epidemiologic, health management, and clinical use Code 138.6
Re-injury
Indicator (yes vs. no) of whether the injury has occurred before
Yes
No
Injury date
Date the injury occurred
May 3, 2012
August 15, 2014
Medical clearance
date
Date the player was medically cleared to return to play
May 15, 2012
February 12,
2015
Time lost
Calculated as the difference between the injury date and the
medical clearance date
84 days
3 days
Code 179.0
Surgery required
Whether the injury ultimately required surgery
Yes
No
Session
Whether the injury occurred before, during, or after a game
Game
Batting practice
Inning
The inning of the game during which the injury occurred, if the
injury occurred during a game
8
4
Field surface
The surface of the field on which the injury occurred
Natural grass
Turf
Abbreviations: ICD-9, International Classification of Diseases, Ninth Revision; SMDCS, Sports Medicine Diagnostic Coding System.
a
The SMCDS is one way of categorizing sports injuries. These codes allow for comparison across studies. It is included in the Health and Injury
Tracking System to provide another way to classify injuries.
A research team from the Johns Hopkins University Bloomberg School of Public Health worked with the MLB to develop
the specifics of the injury-tracking system and subsequently
has provided epidemiologic and statistical support for the research. Development of the database involved reviewing existing sports injury surveillance systems and consulting with the
ATs, as well as with experts who had experience with existing
sports databases and team physicians. HITS was created in
2009, pilot tested during the 2010 season, and fully implemented for injury surveillance during the 2011 season.
HITS includes any injury or physical complaint sustained
by a player that affects or limits participation in any aspect of
baseball-related activity (e.g., playing in a game, practice,
warm up, conditioning, weight training). For research studies, injuries are operationally defined as those that are workrelated, did not occur in the offseason (i.e., occurred onlyspring
training, the regular season, or the postseason), were a primary diagnosis, and resulted in at least 1 day out of play. Because of the multiple pieces of data collected for each injury,
injuries can be investigated by nature, body part injured,
mechanism, location, or activity, as well as by demographic
fields such as player position or age. Data are inputted via
dropdown menu choices for each variable. A summary of the
variables included in HITS is presented in Table 1.
For sports injury research, the use of AEs is a wellestablished procedure for calculating rates (5, 7, 17). Injury
research using HITS defines AE as the average number of
players per team per game calculated based on analysis of
regular season game participation via ( publically available)
box scores. This average number over a season multiplied by
the number of team games at each professional level of baseball yields an estimate of AE.
HITS has a unique identifying number for each player;
thus, data can be deterministically linked across various databases. Linking the data allows for investigation of injuries
with regard to other key measures of exposure, such as pitch
count and number of plate appearances, surgical outcomes data,
personnel records to calculate cost data, and demographic
data. Also, we can link injury occurrences to cost data and
quantify the relative impact that different injuries have on
player health to help us focus our efforts on research that
has the greatest potential to address those areas. In addition,
player data can be linked to previous injuries of the same or
related type to better understand the influence of injury history on current or future injuries.
RESULTS
Implementation of HITS
The primary research team from Johns Hopkins University
Bloomberg School of Public Health oversees most of the
Am J Epidemiol. 2016;183(5):490–496
Professional Baseball Injury Surveillance System 493
epidemiologic research involving HITS, as well as other primary data collection efforts to supplement the HITS data
(e.g., primary data collection using an online survey). The
Bloomberg School of Public Health Institutional Review
Board approves all study procedures, which include analysis
of the de-identified HITS data. Centralizing the institutional
review board approvals across the studies has ensured consistency with regard to the protection of confidential player data.
A system audit was initiated in 2010 to explore the initial
HITS data. The audit focused on evaluating the consistency
of data entry across teams and leagues, evaluating the consistency between variable definitions and variable completeness (i.e., missing data), and identifying anomalous entries in
the database. All data from 2010 comprising approximately
30,000 records from both the Major and Minor Leagues
were downloaded from HITS and analyzed using descriptive
statistics. Summary tables describing distributional characteristics were generated for more than 30 variables. Results
were also stratified by league and by team/club affiliate. In
the audit, the type of data and level of detail required to
support research initiatives were evaluated. For example,
for injuries occurred while running a variable was added to
distinguish whether the running activity was while on offense
or defense. As of 2011, an annual audit of the HITS data was
being performed to ensure continued consistency. Table 2
shows the total number of injuries per season, along with
the average percent of missing values. Variables such as injury mechanism, which describes how the injury occurred
(e.g., contact with ball, contact with ground, noncontact), injury activity, which describes what the player was doing
when injured (e.g., batting, running, pitching), and injury location, which describes where on the field the injury occurred
(e.g., home plate, specific base, outfield) are key to providing
an overall injury summary. Because of increased efforts to reduce missing data, the percent of records with missing values
for these important variables in at least 1 entry has substantially declined over the years.
Since the inception of HITS, several improvements have
been made, including reducing the variable definitions (e.g.,
collapsing categories in the mechanism of injury variable to
Table 2. Frequency of Injury Eventsa by Level of Play for the 2010–
2014 Seasons
No. of Injuries by Year
Level of Play
2010
Major League
2011
2012
2013
2014
2,076 1,641 1,347 1,270 1,249
Total
7,583
Minor League
7,828 7,234 6,704 6,909 7,269 35,944
Total
9,904 8,875 8,051 8,179 8,518 43,527
Missing
informationb
18.0
17.0
<1.0
<1.0
<1.0
a
Injury events were defined to include only events that were work
related, did not occur in the offseason (e.g., events that occurred
during spring training, the regular season, or the postseason), were
a primary diagnosis, and resulted in at least 1 day out of play.
b
Values are expressed as percentages. Values were based on the
number of records with missing data for at least 1 of the entries for the
variables injury mechanism, activity, or location.
Am J Epidemiol. 2016;183(5):490–496
be focused around the type of contact) and enabling the system to automatically populate data entries of variables given
data inputted to other variables, which dramatically improved
completeness. For example, if a player sustains a sprain, a
dropdown box will appear to allow the person entering the
data to select the degree of the sprain as a measure of severity.
We also learned from the ATs that a simplified user interface
was needed; after this change was made, the percent of missing data declined.
Implementation of HITS also involved creating guidance
for all data analysis. The initial data analysis approach for
all research studies has been to complete a descriptive epidemiologic analysis. The benefits of this approach include:
1) conducting exploratory data analysis to describe the injury
of interest as it relates to variables such as level of play, position, mechanism, activity, location, and time lost; 2) highlighting trends worthy of further investigation; and 3) providing
a common starting point for all research studies to ensure a
level of consistency across all ongoing research, as well as future planned studies. These descriptive epidemiologic analyses
also included injuries reported as rates per 1,000 AEs, allowing
our work to be based within the context of previous works on
related injuries (baseball and otherwise) that used other databases and also reported injury rates (5–7, 17). Finally, data
are aggregated across the entire league beforeto dissemination
of any analysis results.
Results from selected completed and current projects
using HITS
After a review of the data to identity the most frequent factors
correlated with injury and the body parts that were most frequently injured (Tables 3 and 4), a series of research projects
was initiated to investigate injury patterns and identify datainformed solutions to prevent injury and disability to players.
Decisions about which injury topics to explore were made
jointly by the MLB and Major League Baseball Players Association, with input from the Bloomberg School of Public Health
research team. Specific detail on methods and findings can be
found in each of the articles referenced in the subsequent section.
Hamstring strains. The first publication using the HITS
data was by Ahmad et al. (18); in that study, the authors explored hamstring strains during the 2011 season. They found
that base running, specifically running to first base, was the
activity that most frequently resulted in a hamstring strain.
History of a previous hamstring strain in the prior year (2010)
was found in 20% of the Major League players and 8% of the
Minor League players. Based on this analysis, the authors
concluded that hamstring strains are affected by prior history
of hamstring strain, seasonal timing, and running to first base
(18). Ahmad et al. reported a hamstring strain injury rate of
0.7 per 1,000 AEs for both Major and Minor League players,
although there were more hamstring strains in players in the
Minor League.
Mild traumatic brain injury. The second publication using
HITS data was by Green et al. (19), who explored mild traumatic brain injury (MTBI). Although there has been extensive literature about MTBI in sports, their study was the
first in which an entire population of professional baseball
players was examined to determine the distribution and
494 Pollack et al.
Table 3. The Top 10 Most Frequent Injury Eventsa in the Major and
Minor Leagues, by the Body Region Variable in the Health and Injury
Tracking System, in the 2011–2014 Seasons
Major League
Body Region
No.
%
Minor League
Rank
No.
%
Rank
Table 4. Most Frequent Factors Leading to an Injury Eventa for a
Subset of Health and Injury Tracking System Variables for the
2011–2014 Major and Minor Leagues Seasons Combined
Factor
No. of Injuries
Percentb
Event position detail
Upper leg (thigh)
724 13.1
1
3,218 11.4
2
Pitcher
6,330
32.4
Shoulder/clavicle
672 12.2
2
4,280 15.2
1
Batter
4,936
25.2
Hand/finger/thumb
501
3
2,908 10.3
3
Base runner
3,332
17.0
Injury mechanism
9.1
Elbow
430
7.8
4
2,755
9.8
4
Knee
410
7.4
5
1,761
6.3
5
Noncontact
17,873
55.3
Lower back/sacrum/
pelvis
385
7.0
6
1,510
5.4
6
Contact with ball
4,456
14.1
Contact with ground
3,183
9.8
Chest/sternum/ribs/
upper back
322
5.8
7
983
Lower leg/Achilles
tendon
303
5.5
8
1,247
Batting/hitting
6,290
19.2
Pitching
6,273
19.1
Abdomen
268
4.9
9
839
Fielding
6,070
18.5
Foot/toes
254
4.6
10
1,175
All other injuries
1,238 22.5
NA
7,440 26.5
Total
5,507
3.5 13
4.4
9
3.0 15
4.2 10.5b
NA
28,116
Abbreviation: NA, not applicable.
a
Injury events were defined to include only events that were work
related, did not occur in the offseason (e.g., events that occurred
during spring training, the regular season, or the postseason), were
a primary diagnosis, and resulted in at least 1 day out of play. They
are ranked in descending order by the total number of events in the
Major League.
b
Foot/toes and wrist were tied for 10th in the Minor League, with
1,175 injuries each.
correlates of MTBI in baseball. Green et al. concluded that
MTBI is an important and understudied injury issue in professional baseball players. The overall game rate of MTBIs
across both Major and Minor League clubs was 0.42 per
1,000 AEs, although the MTBI rate was 1.8 times higher
among Minor League players (0.46 per 1,000 AEs) than
among Major League players (0.26 per 1,000 AEs) (19).
The study revealed that catchers were significantly overrepresented among players who sustained a MTBI relative to other
positions and that concussions most often occurred from contact with the ball (i.e., foul tips) (19). This has resulted in
follow-up studies to better understand the risk factors for concussions among catchers and whether these injuries can be
prevented through changes to technique or equipment.
Otherongoing research. These studies by Ahmad et al. (18)
and Green et al. (19) both built on previous studies in their respective areas by using data from an entire athlete population,
unlike prior studies of smaller samples that relied on sampling.
The HITS data also advanced thinking on the areas under study
by providing in-depth investigation into injuries that have not
been comprehensively examined. Results from studies in which
injuries to the knee, shoulder, elbow, and hip/groin were examined are anticipated in the coming year. Findings from research
exploring sliding injuries and a primary data collection effort to
understand catcher injuries from foul tips are forthcoming.
Furthermore, these initial investigations have led to followup investigations conducted in an effort to modify the trends
Injury activity
Injury location
Home plate
7,775
24.2
Pitcher’s mound
6,477
20.1
Otherc
5,084
15.8
a
Injury events were defined to include only events that were work
related, did not occur in the offseason (e.g., events that occurred
during spring training, the regular season, or the postseason), were
a primary diagnosis, and resulted in at least 1 day out of play.
b
Percent calculated over the total number of events for which
corresponding variable entries were nonmissing.
c
Other includes all other locations on the baseball field: outfield,
infield, and foul/bullpen territory.
portrayed by the surveillance data. For example, an intervention
study of hamstring injuries is currently being pilot tested. This
intervention was implemented based on results from Ahmad
et al. that identified factors correlated with hamstring strain (18).
Future analyses will determine whether the trends in these injuries have been altered. Another example is the current research
in which risk factors for elbow injury are being explored. A
prospective epidemiologic study is underway to determine risk
factors for an ulnar collateral ligament injury. In that study,
investigators enrolled Minor League pitchers and collected
baseline data, including biomechanical data, to determine risk
factors for the development of an ulnar collateral ligament injury
over time (Major League Baseball, unpublished data, 2015).
DISCUSSION
HITS is the first comprehensive injury surveillance system
developed to explore injuries among professional baseball
players. There are several analytic strengths of HITS, including the ability to link data to ascertain individual-level variables; the ability to develop better measures of exposure; the
creation of a record of all player illnesses and injuries in the
EMR; and the creation of a record of injuries that result in lost
time, which allows investigations of both medical and injury
diagnoses. The system also provides an opportunity to conduct
prospective research in a way that was impossible before. For
example, for the current prospective study of elbow injuries,
Am J Epidemiol. 2016;183(5):490–496
Professional Baseball Injury Surveillance System 495
once the initial assessments were conducted, it was possible to
continue to assess player’s injuries for the rest of their careers.
This passive surveillance system reduces the burden on the
trainers who do not have to actively search for cases.
One benefit of a robust data system is the ability to measure
exposures. Identifying the best denominator for future analyses
will propel the field beyond using crude measures of AEs for
rates. Also, data on medical conditions will fill an important
gap in knowledge and allow us to understand the occurrence
of health conditions beyond injury. This is important because
noninjury health issues and conditions can affect injury rates
and risk, and vice versa. HITS will also allow us to advance
research that validates sport injury surveillance systems. For instance, with access to the medical records and video recordings,
investigators can conduct future studies that validate the reporting of injuries by analyzing video recordings from games.
Findings from these analyses have been used to create policies that inform play. For example, analysis of the HITS data
revealed that players recovered from concussions in fewer than
15 days, which created a difficult situation for the player and
the club because of the difference between recovery time and
required Disabled List stint. This led to the implementation of
a 7-day Disabled List for concussions that has promoted more
players being placed on the Disabled List and having the appropriate time to recover from those injuries. HITS data have
also been used to inform discussions about potential rule
changes, including those related to collisions between base
runners and fielders at home plate and other bases.
As previously noted, in addition to the HITS data, with the
EMR there is an opportunity to identify leading illnesses and
appropriate prevention strategies. There is a dearth of published research exploring medical issues in MLB. With the
EMR data, studies in which leading medical conditions among
players and treatment options are explored can advance
knowledge about player health. Several research questions
remain in this area, and MLB and the Major League Baseball
Players Association in concert with the research team are prioritizing health conditions for future investigation, for example, the injury rate associated with sliding into bases, foul ball
impacts on catchers, and the epidemiology of various throwing injuries. In 2011, Posner et al. noted that, “Surprisingly,
few published articles are available that pertain to the epidemiology of MLB injuries” (13, p. 1679). With the development of
HITS, this is changing; in recent publications, investigators
have explored the epidemiology of hamstring strains and MTBI,
and several other studies are anticipated in the near year.
It is important to also note that the creation of HITS has
resulted in significant growth in the number of research initiatives jointly sponsored by MLB and the Major League Baseball Players Association. This growth has also resulted in a
need for new standards and procedures for research and dissemination of data via publications and presentations.
Challenges in implementing HITS
The HITS database has in part revolutionized research on
baseball injuries, but there have been some challenges. First,
since the inception of the new system, data quality was a primary concern. Ensuring that the database is user-friendly for
the ATs who are entering the data is critical for valid and
Am J Epidemiol. 2016;183(5):490–496
reliable outputs. Part of ensuring quality has involved collaborating with the ATs to obtain their feedback on aspects that are
not working and modifying the database accordingly. Because
analysis of HITS data can only include data that are entered in
the system, collaborative efforts have sought to minimize missing data and improve data reliability and validity.
Second, the AE metric previously defined is at best an
approximate estimate, because in baseball, a player who only
appears for 1 at bat (e.g., a pinch hitter) is counted as the equivalent of player who has played the whole game. This is a limitation, and it affects the measurement of risk (17). Moreover,
there is currently no way to measure exposures during practice;
the number of pitches thrown, number of at bats, etc., are not
recorded during practice. Although our studies to date have
shown that very few injuries occur in any given practice session,
there could be a cumulative effect of exposures during practice,
which could be associated with overuse injuries.
The next step currently under consideration is a refined exposure assessment to develop methods to establish more representative (sport- and injury-specific) exposure metrics for
risk assessment and rate calculations for various injuries.
For example, more appropriate denominators for use in studies
of pitcher injuries could include number of games pitched,
number of innings pitched, total pitches per inning or overall
within a specific time period, pitch selection, and pitch velocity. HITS provides a level of information detail that supports
these more refined exposure metrics. These results are also
needed to support more advanced analytical methods based
on statistical regression techniques to identify and quantify
risk factors and risk factor interactions for injury research.
Finally, having the team from the Johns Hopkins Bloomberg School of Public Health lead the analysis of all research
has helped to ensure consistency across research studies.
Practically, this means that because of the desire to ensure
consistency using the HITS data, there is a queue for research
projects, which does not always align with the desire timeline
of the investigators involved with a particular project.
Conclusions
HITS is the first comprehensive injury surveillance system
developed to explore injuries among professional baseball
players. The richness of the data is unprecedented and creates
an opportunity to identify and monitor injury trends in baseball,
identify areas of specific concern, and conduct epidemiologic
research to better understand player risk and optimize player
health and safety through possible rule changes, equipment
modifications, or medical education.
The implementation of HITS has advanced sports injury
research overall, and professional baseball research in particular. Future research involving HITS data will continue to optimize player health and safety for one of America’s greatest
pastimes, as well as generate important overall lessons for injury surveillance.
ACKNOWLEDGMENTS
Author affiliations: Johns Hopkins Center for Injury Research and Policy, Bloomberg School of Public Health,
496 Pollack et al.
Johns Hopkins University, Baltimore, Maryland (Keshia
M. Pollack); Office of the Commissioner, Major League
Baseball, New York, New York (John D’Angelo, Gary
Green, Chris Marinak); Division of Sports Medicine, David
Geffen School of Medicine, University of California, Los
Angeles, Los Angeles, California (Gary Green); Conte Injury
Analytics, Los Angeles, California (Stan Conte); Department
of Sports Medicine & Shoulder Service, Hospital for Special
Surgery, New York, New York (Stephen Fealy); Department
of Orthopaedic Surgery, School of Medicine, Johns Hopkins
University, Baltimore, Maryland (Edward McFarland); and
Department of Epidemiology, Bloomberg School of Public
Health, Johns Hopkins University, Baltimore, Maryland
(Frank C. Curriero).
This research was supported by a contract from the Office
of the Commissioner, Major League Baseball to Johns Hopkins Bloomberg School of Public Health for epidemiologic
design, analysis, and statistical support.
We thank the Office of the Commissioner of Major League
Baseball, New York, New York, especially Jon Coyles, and
the Major League Baseball Players Association for their support and Dr. Bert Mandelbaum and Randy Dick, who provided input into developing and implementing Health and
Injury Tracking System. Finally, we thank the athletic trainers for their dedication in collecting and entering data.
Conflict of interest: none declared.
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