Report to the

World Health Organization Collaborating Centers

on the Classification of Disease

September 2000

CDC's Short Version of the ICECI

International Classification of

External Causes of Injury

A Pilot Test

Department of Health & Human Services 

Centers for Disease Control and Prevention

National Center for Disease Prevention and Control
Prepared by:

J. Lee Annest, PhD, Director

Chester L. Pogostin, DVM, MPA, Deputy Director

Office of Statistics and Programming

National Center for Injury Prevention and Control (NCIPC)

Centers for Disease Control and Prevention (CDC)

Atlanta, GA, USA

Pilot Study Collaborators:

Judy Conn, MS, Patricia Holmgreen, MS, and Marcie-jo Kresnow, MS, NCIPC, CDC;

Torie Ozonoff, PhD, Jennifer Taylor, MPH, Laurie Jannelli, Elizebeth Castagneto, and

Stacy Klingler, Massachusetts Department of Public Health, Boston, MA; Art McDonald, MA,

Joel Friedman, Stephanie Drury, Linda Swenson, Verlene Swenson, Storm Randall,

Carol Strunk, Kirby Riffey, Karen Wozniak, Robert Hayden, and Bea Belton,

U.S. Consumer Product Safety Commission, Bethesda, MD.

We would also like to acknowledge our colleagues who participated in the development

of CDC’s short version of the ICECI:

Dan Pollock, MD, NCIPC, CDC, Atlanta, GA;

Lois Fingerhut, MA, and Donna Pickett, RRA, MPH,

National Center for Health Statistics, CDC, Hyattsville, MD
Executive Summary

This report summarizes the findings of a pilot study to assess the usefulness of the Centers for

Disease Control and Prevention’s (CDC) short version of the International Classification of

External Causes of Injury (ICECI) in capturing external-cause-of-injury data from hospital

emergency department (ED) records. CDC’s short ICECI was designed to capture major

mechanisms of injury (e.g., fall, motor vehicle traffic crash, struck by/against, cut/pierce,

fire/burn, poisoning, firearm, and others), intent of injury, locale of the injury incident,

activity at the time of the injury, work-relatedness, safety equipment use, consumer products

involved, and a narrative describing the circumstances of the injury incident. For this pilot

study, coders used the short ICECI data collection form to code external-cause-of-injury data

from a standard set of case scenarios and from hospital ED records. Coded data based on the

case scenarios were then analyzed for percent agreement with a gold standard code set

(i.e., test of validity), percent agreement among coders (i.e., test of inter-coder reliability), and

percent agreement for repeated cases for individual coders (i.e., test of intra-coder reliability).

Coded data based on hospital ED records were used to assess inter-coder reliability, timeliness,

and completeness of coded data. The study used two independent ED surveillance systems:

the Massachusetts (MA) Department of Public Health’s Emergency Department Surveillance

and Coordinated Injury Prevention Program (ED-SCIP) and the U.S. Consumer Product

Safety Commission’s National Electronic Injury Surveillance System (NEISS). The findings

from this study suggest that CDC’s short ICECI can be a useful and efficient injury surveillance tool for coding valid and reliable external-cause-of-injury data. The pilot study also

helped identify some limitations of the current version; therefore, we plan to revise and

develop a version 2.0 of CDC’s short ICECI for further testing and refinement.

We identified some important aspects of this classification system that have implications for

the further development and implementation of the full ICECI and various short versions of

the ICECI for use in injury morbidity data systems. These aspects, which are discussed later in

the report, are listed below:

1. Protocols and instruction manuals for orientation and training of coders and for

ongoing quality assurance activities are essential for ensuring complete and valid

coding of external-cause-of-injury data.

 2. A detailed, user-friendly coding manual is necessary for specifying the case definition,

coding rules and definitions, and guiding principles for coding.

 3. The data collection instrument should make collecting and processing short ICECI

data from medical records more efficient; electronic versions of the data collection

form are needed.

 4. Coding of mechanism of injury should be limited to two mechanisms: the precipitating

cause (i.e., the cause that initiated the chain of events leading to the injury) and the

immediate cause of injury (i.e. the direct cause of the most severe injury being treated).
 5. A narrative description of the injury incident should be included as an essential ICECI

data element. Narratives can be very helpful in conducting quality assurance reviews

of assigned codes and for capturing further details about injury-related circumstances.

 6. Training and orientation of hospital medical staff is critical to improve the quality

of information on intent of injury, mechanism of injury, and other injury-related

circumstances contained in the medical record.

 7. Proposed short ICECI data elements and code sets should be cross-walked to the

full ICECI, ICD-10, and ICD-10-CM code sets as a measure of compatibility and

cross-reference. The full ICECI should serve as a standard coding system for all

proposed short ICECIs.

Development of an injury surveillance tool like CDC’s short ICECI can provide a useful

instrument for routinely capturing and coding population-based data on external causes

of injury for the large number of injury-related cases treated in hospital emergency departments. However, if this tool is to be useful and appropriate, it must be consistent with existing

international classification standards for morbidity data systems. With careful planning and

development, pilot testing, and implementation efforts, this type of surveillance tool can

open new avenues for obtaining detailed data important for both clinical practice and injury

prevention efforts. Clearly, we need to step beyond the limits of data captured using ICD-10

external cause codes and derive compatible coding systems and software that will allow for

routine and timely capture of more detailed data about the injury incident. Timely access to

population-based, injury-related data is critical in designing and evaluating injury prevention

programs. Because of advances in information technology, now is the time for a concerted

effort to develop injury surveillance standards and tools.

Introduction

Background on ICECI and Short Versions of the ICECI

An international effort is underway to develop a new multi-axial classification system for

external causes of injury designed for use in mortality and morbidity data systems. This

system will provide a standard for coding more detailed information about injury circumstances than is possible using the current ICD-10 external-cause-of-injury coding system.

This new system, called the International Classification of External Causes of Injury (ICECI),1

is designed to capture details about the place of occurrence, activity at the time of injury,

alcohol and drug involvement, objects or substances involved, intent of injury, and mechanism of injury. Specific modules are being developed to capture more detailed information

about injuries related to violence, transportation, sports, and work (occupational). Several

short versions of the ICECI (subsequently referred to as short ICECIs) are being developed

for use as injury surveillance tools in different settings. For example, a set of short ICECIs has

been proposed by Dr. Yvette Holder, Dr. Etienne Krug, and colleagues for use in countries or

societies with limited resources for injury surveillance.2

 Also, CDC has developed a short

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ICECI for use in capturing external-cause-of-injury data from hospital emergency department

(ED) records in the United States.3

 This report will focus on a pilot study that was conducted

in the United States to test the feasibility of CDC’s short ICECI for abstracting data on external

causes of injury using hospital ED records. The remainder of this report will provide a

description of CDC’s short ICECI as well as the methods, results, discussion, and recommendations stemming from the pilot study.

Purpose of the Pilot Test of CDC’s Short ICECI

The purpose of the short ICECI pilot study was to test the reliability of capturing externalcause-of-injury data using the short ICECI data elements and code sets. The pilot study

had two parts: (1) a case scenario test to measure validity, timeliness, and inter-coder and

intra-coder reliability; and (2) a field test to measure inter-coder reliability and completeness

of external-cause-of-injury data obtained from hospital ED records.

CDC’s Short ICECI and Its Components

CDC’s short ICECI has five major components: (1) type of incident (work-related or not),

(2) locale of injury incident, (3) type of activity when the injury occurred, (4) intent of injury,

and (5) mechanism of injury.3

 Each of the major components or data elements consists of a

code set with specified categories pertinent to the injury incident. For intent of injury and

mechanism of injury, there are also subdata elements with code sets designed to capture

more information about specific types of injury-related incidents. For instance, if a patient

was treated for an injury resulting from a motor vehicle crash, the short ICECI captures

information on traffic-relatedness, type of motor vehicle involved, occupant status

(e.g., driver or passenger, boarding or alighting), and counterpart to the crash (e.g., another

vehicle, pedestrian, or fixed object). In addition, the short ICECI data collection form provides

space for recording safety equipment use, consumer products involved, and a narrative

description of the injury incident.

The short ICECI data elements and code sets were derived from the full ICECI data elements

and codes sets and the ICD-10 external-cause-of-injury code set. The short ICECI code sets

have been cross-walked to code sets for both of these classification systems. The short ICECI

consists of a hard copy check-box type data collection form, a manual with coding definitions

and rules, and a training module. The current or modified versions of CDC’s short ICECI are

under consideration or are being used for other injury surveillance activities in the United

States (e.g., voluntary reporting from hospital EDs in Michigan; data linkage study at the

Grady Hospital ED in Atlanta). The short ICECI data elements and code sets for intent of

injury and mechanism of injury components also have been modified to capture externalcause-of-injury data in the National Electronic Injury Surveillance System (NEISS), operated

by the U.S. Consumer Product Safety Commission (CPSC). CPSC, in collaboration with CDC

is expanding NEISS to capture nationally-representative data on all types and causes of

injuries treated in U.S. hospital EDs.

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Methods and Procedures

Massachusetts ED-SCIP and NEISS Substudies

The short ICECI Pilot Test consisted of two independent substudies involving hospital EDs in

two different injury surveillance systems: the Massachusetts (MA) Emergency Department

Surveillance and Coordination Project (ED-SCIP) and CPSC’s National Electronic Injury

Surveillance System (NEISS). The Massachusetts Department of Public Health is developing

ED-SCIP as a statewide surveillance system that is based on voluntary reporting from a

representative sample of hospitals. The reporting system is being implemented in a stratified

random sample of 20 of the state’s 79 hospital EDs. The NEISS is comprised of 100 hospital

EDs that are a stratified probability sample of hospitals in the United States that have at least

six beds and provide 24-hour emergency care.

The ED-SCIP substudy involved 4 public health professionals from the MA Department of

Public Health who coded medical records in 16 of the 20 ED-SCIP hospital emergency departments. The NEISS substudy involved 7 on-site data abstractors who routinely code data from

ED records for product-related injuries at 7 NEISS hospital EDs. Sample hospitals in both

substudies consisted of hospitals located in rural, urban, suburban, and inner-city areas.

Training of Coders and Quality Assurance Methods

The training of coders was conducted independently for the two substudies. For the ED-SCIP

substudy, coders were mailed a coding manual and the coding rules and definitions for their

review approximately 10 days in advance of the training session. The ED-SCIP coders

attended an 8-hour training session with a detailed presentation and discussion of the study

protocol, coding rules and definitions, and specific training exercises. For the NEISS substudy,

coders were not given training material prior to attending a 2-hour orientation to the study

protocol and coding rules and definitions.

For the ED-SCIP substudy, coders were asked to conduct a prepilot test at two study hospitals

using the short ICECI data collection form and to provide feedback to the CDC investigators.

A conference call was conducted to clarify how to interpret and apply coding rules and

definitions. For the NEISS substudy, coders were asked to proceed without prepilot testing.

Both ED-SCIP and NEISS substudy coders then followed similar study protocols for the case

scenario test and the field test. Completed data collection forms were reviewed for completeness and mailed to CDC for data processing and analysis. At CDC, data were key-entered,

checked electronically for valid codes, and visually reviewed for accuracy of data entry. In

some cases, coders did not follow the skip patterns correctly in filling out the data collection

form. All forms were key-entered as they were completed by the coder; therefore, these errors

were included in the analysis of percent agreement.

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Protocol for the Case Scenario Test

One hundred case scenarios and “gold standard” code sets were prepared for this test.

(Note: These same case scenarios were also used by Malinda Steenkamp and James Harrison,

Australia’s National Injury Surveillance Unit, to conduct a pilot study of inter-coder reliability

using the full ICECI.) Case scenarios represented a wide variety of injury-related circumstances including unintentional injuries, intentionally self-inflicted injuries, assaults, and legal

interventions. Seven of the case scenarios did not involve injuries at all. Gold standard code

sets were established by having three coinvestigators code each case independently and then

arrive at consensus on the appropriate codes based on the coding rules and definitions.

Coders participating in the short ICECI pilot study were asked to code 50 case scenarios prior

to the field test and 50 case scenarios at the end of the field test. About two weeks after the

coders completed the second set of 50 case scenarios, they were asked to code an additional

20 cases. Coders were not told that these additional cases represented recodes of cases

selected from the second set of 50 case scenarios. These data were then used to measure

validity and reliability by estimating percent agreement for (1) codes assigned by each

coder with the gold standard (to measure accuracy in applying coding rules and definitions),

(2) codes assigned by multiple coders (to measure inter-coder reliability), and (3) codes

assigned by the same coder (to measure intra-coder reliability).

Protocol for the Field Test

For the field test, coders were asked to review medical records of approximately 100 injuryrelated ED cases and to code all pertinent data elements using the short ICECI data collection

form. Adverse effects of therapeutic use of drugs and adverse effects of medical and surgical

care were excluded. For the ED-SCIP substudy, ED cases were randomly selected from cases

with a principal diagnosis of an injury or poisoning (ICD-9-CM diagnosis codes of 800–999),

which were in the preexisting ED-SCIP injury surveillance database of each sample hospital.

For one of the larger inner-city hospitals, assaults were oversampled to increase the number of

intentional injuries in our study. In addition, 5–10 medical records of injury-related cases at

each hospital were randomly chosen to be coded independently by at least two coders for use

in measuring inter-coder reliability. Because the ED-SCIP hospital ED data obtained in this

field study are not representative of all ED injury-related visits in the state, they cannot be

used to project statewide injury incidence by external cause of injury.

For the NEISS substudy, ED cases were selected to represent a broad spectrum of external

causes of injury. This was done to examine the use of the short ICECI for capturing information on a wide variety of injury-related circumstances for injured persons treated in

hospital EDs.

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Statistical Methods Used to Assess Gold Standard,

Inter-coder, and Intra-coder Comparisons

The kappa statistic, expressed as a percent, was used as a measure of agreement to test for

validity and reliability. This statistic provides a new dimension to the percent observed

agreement by assuming that, except in most extreme cases, some degree of agreement is to

be expected by chance alone. The estimated kappa was calculated as [(po – pe

)/(1–pe) x 100],

where po

 is the observed agreement and pe is the expected agreement based on chance.4

Their difference, (po – pe), represents the obtained excess agreement beyond chance, while

the maximum possible excess agreement beyond chance is represented by the quantity (1–pe

).

The ratio of these two, or the kappa statistic, can be interpreted as the percent agreement

among coders beyond that which is expected by chance. The kappa statistic was used to

compare (1) each individual coder’s ratings with the gold standard (validity), (2) all substudy

coders’ ratings simultaneously (inter-coder reliability) and (3) each coder’s rating with his or

her own ratings for repeated cases (intra-coder reliability). Landis and Koch5

 suggest that

values greater than 75% may be taken to represent excellent agreement beyond chance,

values below 40% to represent poor agreement beyond chance, and values between 40%

and 75% to represent fair to good agreement beyond chance. Standard errors of kappas

were calculated using a method, as described by Fleiss et. al.,6

 that accounts for different sets

of coders for different cases. Standard errors were used to compute 95% confidence intervals

(CIs) for all kappa statistics.

For the analysis of mechanism of injury, we created an analytic variable with 15 categories,

excluding adverse effects of drugs and adverse effects of surgical and medical care, with all

14 major mechanisms of injury and an “other specified” category. This variable was constructed to reflect the immediate or most direct cause of the most severe injury being treated.

The most severe injury was determined by the principal diagnosis of the physician at the

time of the ED visit. Only first-time visits for an injury were included in the study.

Pilot Study Participant Survey

Each coder was asked to complete a survey to provide coinvestigators with feedback on his

or her experience in using the short ICECI.

Results

Results of the Case Scenario Test

Ninety-three of the 100 case scenarios were injury-related cases; the remaining seven did not

meet the definition of an injury case. The latter case scenarios were purposely included to see

if coders would be able to distinguish injury-related cases from non-injury cases. The ability to

classify a case as an injury versus non injury varied substantially among coders. Among the

four Massachusetts coders, two recognized 5 of 7 non-injury cases and two recognized only

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1 of the 7 non-injury cases. Also, 1 or 2 of 93 injury-related case scenarios were classified as

non-injury cases by two of the four Massachusetts coders. Among the seven NEISS coders,

three recognized all 7 non-injury cases, and the others recognized between 1 and 5 of the

7 non-injury cases. From 1 to 3 of 93 injury-related case scenarios were classified as noninjury cases by 3 of the 7 NEISS coders.

For injury-related cases, we found no significant differences in percent observed agreement

or kappa statistics for case scenarios coded before or after the field test. Therefore, all

93 injury-related case scenarios were combined for further analysis.

For the 93 injury-related case scenarios, the percent observed agreement among MA ED-SCIP

coders and between MA ED-SCIP coders and the gold standard for five major data elements

(i.e., work-relatedness, locale of injury incident, activity at time of injury, intent of injury, and

mechanism of injury) were observed to be quite high (Table 1). The observed inter-coder

agreement ranged from 85.7% to 97.8%. The average percent observed agreement for coder

and gold standard code sets ranged from 80.6% to 94.1%. However, a better measure of

reliability can be obtained by using the kappa statistic, a measure of percent agreement

beyond what can be expected by chance alone.

The average kappa (in percent) between coder and gold standard code sets and among

coders for the five major short ICECI data elements were all in the good to excellent range for

both the Massachusetts ED-SCIP and NEISS substudies (Figure 1, Tables 2 and 3). Kappas for

MA coders were consistently higher than for NEISS coders for all five major short ICECI data

elements (Table 4). Overall, excellent agreement was observed among the MA coders and

between the MA coders and the gold standard.

Intra-coder reliability for the Massachusetts ED-SCIP substudy, as measured by average

kappas, was also found to be in the excellent range or borderline excellent range for all five

major data elements (Table 5).

Results of the Field Test

Inter-coder reliability, as measured by kappa, for randomly selected pairs of MA coders for a

set of 127 injury-related ED cases shows percent agreement in the good to excellent range for

most of the categories reported within four of the five major data elements (Table 6). Intent of

injury was not included in this Table because all but four of the 127 cases were unintentional

injuries; therefore the expected agreement was very high. For mechanism of injury, intercoder reliability achieved 100% agreement for motor vehicle occupant, pedestrian-vehicle

crash, poisoning, and foreign body. The lowest kappa was 83.5 for struck by/against.

The median coding time for abstracting data from ED records and completing the data collection form was 3 minutes for the Massachusetts ED-SCIP substudy and 4 minutes for

the NEISS substudy. Average coding time was significantly higher for NEISS coders

(mean= 4.9 minutes, 95% CI= [4.7%, 5.1%], range=1–15 minutes) than for Massachusetts

coders (mean=3.4 minutes, 95% CI= [3.3%, 3.5%], range=1–18 minutes).

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NEISS coders were much more likely to record multiple mechanisms of injury (i.e., 2 or

more mechanisms for the same case) than MA coders. For NEISS coders, 22.9% of

705 injury-related ED cases were coded with multiple mechanisms of injury. For MA coders,

2.6% of 1,710 injury-related ED cases were coded with multiple mechanisms of injury. Most

of the cases with multiple mechanisms involved combinations of “struck by/against” and

“fall” or of “struck by/against” and “cut/pierce.” This difference in recording multiple mechanisms of injury did not affect our calculations of percent observed agreement or kappa

statistics for mechanism of injury (presented in Tables 1–6), because we limited our analysis

to only the immediate, or most direct, cause of injury.

A description of short ICECI data for 1,841 injury-related ED patients obtained from review

of medical records at 16 Massachusetts ED-SCIP hospitals is presented in Table 7. (NOTE: This

number is higher than the 1,710 reviewed by MA coders; it includes an additional 131 cases

reviewed by CDC coinvestigators during a site visit to two of the 16 hospitals.) A majority of

data abstractions took between 2 and 5 minutes to complete. The sex and age of the patient

were readily available in the medical record. Almost 15% of the cases abstracted were determined to be work-related. However, about 20% of the time, no information was available to

determine work-relatedness. Locale of injury incident and activity at the time of injury could

not be classified in about 40% of cases. Intent of injury and mechanism of injury could be

classified in over 98% of cases. Of 200 assault cases, the relationship of perpetrator to victim

and the reason for assault could be classified for about 55% of injury incidents. For 218 motor

vehicle occupant-related cases, traffic-relatedness, type of motor vehicle in which the patient

was riding, and the occupant status of the patient could be determined for over 90% of the

injury incidents. Counterpart for the motor vehicle crash was classified in 60% of cases. The

number of pedestrian, motorcyclist, and pedal cyclist cases was relatively small in this sample.

However, counterpart for the injury incident could be classified in over 90% of these types of

transportation-related cases. Traffic-relatedness could not be determined for 55% of pedal

cyclist injuries. For cases where the mechanism of injury was struck by/against, the source of

force and type of force could be determined in over 95% of injury incidents. The type of

firearm used in gunshot injuries was recorded for only about half of the cases. Injuries where

the mechanism of injury was stab/cut/pierce, fire/burn, or poisoning could be further characterized by type in over 89% of cases. Other specified mechanisms of injury were predominantly bites and stings.

Use of safety equipment was indicated for 238 of the 1,841 Massachusetts ED-SCIP cases. Most

of these cases were associated with seat belt use and air bag deployment. Of the 218 motor

vehicle occupant-related cases, seat belt use was indicated for 127 cases (58.3%), and air bag

deployment was recorded for 19 cases (8.7%).

A listing of consumer products and a narrative description of the injury incident were provided, where applicable, on completed short ICECI data collection forms. NEISS and MA

coders, in general, provided good details about the injury incident that further characterized

the intent of injury, mechanisms of injury, and other circumstances of the injury incident.

NEISS coders routinely recorded the principal diagnosis of the injury in the narrative field,

as this is common practice in their injury surveillance activities.

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Discussion

This pilot study suggests that CDC’s short ICECI can be a useful surveillance tool for capturing external-cause-of-injury data on hospital emergency department visits through the use

of medical records. The current version needs to be revised and refined to improve its utility

and efficiency. However, this pilot study did demonstrate that with the use of this coding

system in the United States, valid and reliable data can be captured in a timely manner.

Further development of CDC’s short ICECI as an ED surveillance tool will need to consider

compatibility with the full ICECI, ICD-10, and ICD-10-CM coding systems. CDC’s short ICECI

has code sets for each of the fundamental components of these systems, including workrelatedness, locale of the injury incident, activity at the time of injury, intent of injury, and

mechanism of injury. It also has components to capture data on safety equipment use, consumer products involved, and provides a narrative description of the injury circumstances.

Information in the narrative could be used for more detailed coding that uses the full ICECI

and the ICD coding systems.

Based on data from a 1997 pilot study on expanding NEISS to capture all injuries, we estimate

that for every injury death in the United States, approximately 190 injured persons are treated

and released from hospital emergency departments.7

 Interest in having a surveillance tool for

timely capture of external-cause-of-injury data from hospital ED records to establish state and

local injury morbidity data systems is increasing. CDC’s short ICECI could be a useful tool for

obtaining population-based data on injury-related ED visits in state and local jurisdictions.

This pilot study of CDC’s short ICECI brought to light a number of aspects that are relevant

to future development and implementation of the full ICECI and various short versions of

the ICECI for use in injury morbidity data systems. Below is a discussion of each of these

aspects based on our findings:

1. Protocols and instruction manuals for orientation and training of coders and for

ongoing quality assurance activities are essential to ensuring valid and complete

coding of external-cause-of-injury data.

Kappa statistics for the Massachusetts ED-SCIP substudy were consistently higher than

those for the NEISS substudy. This difference in kappas could be attributed to a number

of factors: coders in the Massachusetts ED-SCIP substudy had the training material in

advance, took part in a substantially enhanced training session, and were subject to an

ongoing quality assurance effort. A concerted effort was made to provide extensive

up-front training and orientation and ongoing feedback to coders in the Massachusetts

ED-SCIP substudy. This effort, including an 8-hour training session, a prepilot test at two

hospitals, ongoing com-munication by conference call, and a site visit by CDC coinvestigators, resulted in excellent agreement for code sets among coders and between the gold

standard and each coder for the case scenarios and hospital ED cases.

A detailed training module with practice case scenarios was used to instruct coders to

properly apply coding rules and definitions. After the pilot study, coders commented that

the training module would have been more effective if it had included practice exercises

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using actual ED records followed by immediate feedback from coinvestigators. Prepilot

visits by coders to two Massachusetts ED-SCIP hospitals helped to reveal ambiguities

in the coding guidelines. Follow-up discussions between CDC coinvestigators and the

MA coders helped to clarify coding rules and definitions before proceeding to other

study hospitals.

2. A detailed, user-friendly coding manual is needed to provide the case definition,

coding rules and definitions, and guiding principles for coding.

A reference manual with clear definitions and detailed examples of inclusions and

exclusions for each data element and associated response categories is essential for coders.

This set of standard coding guidelines should be readily available to each coder to ensure

consistency and uniformity in how codes are assigned. In addition, coders must refer to

these documents often; otherwise, coders could have a tendency to base their coding

decisions on their own intuitions, opinions, or preconceived notions.

In the pilot study, coders were given a detailed manual with coding rules and definitions,

inclusions, and exclusions for use as a reference during the study. Whenever coders

questioned how to code a case, they were instructed to refer to the manual. Then, if the

manual was unclear, the CDC coinvestigators would clarify and interpret the coding

rules and definitions. To maintain consistency in coding, coders were then notified about

these coding decisions. Changes to the coding rules and definitions were then noted in

the manual.

3. The data collection instrument should facilitate efficiency in the collection and

processing of ICECI data from medical records; electronic versions are needed.

The check-box type format of the short ICECI data collection form worked well in recording and coding external-cause-of-injury data using medical records. The average time to

code all short ICECI data elements from a medical record was about 3 to 4 minutes. However, coders sometimes failed to code fields or to follow skip patterns. These problems

could be avoided by developing a computer data entry screen with drop-down windows

revealing code choices. Selections could then easily be made by the click of a computer

mouse. Automatic skip patterns and range checks could be programmed into this system

to minimize recording errors. The data could be handled entirely electronically to perform additional checks for completeness and consistency. These electronic data could

then be passed to a data base for further quality assurance analysis and feedback to the

coders. Final edited data could be prepared for timely analysis and reporting.

CDC is currently working with CPSC to expand NEISS to capture all types and causes

of injuries treated in hospital EDs. The new system is called the NEISS All Injury

Program. CPSC is developing such an automated system for capturing data on an

expected 600,000 injury-related ED cases annually from a nationally representative

sample of 100 U.S. hospital EDs. NEISS is capturing data on intent of injury and

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mechanism of injury in addition to the routine injury-related data elements historically

collected in NEISS (i.e., age, sex, race/ethnicity, diagnosis, primary body part affected,

locale of injury incident, work-relatedness, consumer products, disposition at

ED discharge, and a narrative description of the diagnosis and circumstances of the

injury incident). Intent of injury and mechanism of injury were derived as modified

data elements from CDC’s short ICECI.

4. Coding the mechanism of injury should be limited to two mechanisms: the precipitating cause (i.e., the cause that initiated the chain of events leading to the injury)

and the immediate cause of injury (i.e., the direct cause of the most severe injury

being treated).

Coding practices of NEISS and MA coders varied substantially in the number of ED cases

that were assigned more than one mechanism of injury. NEISS coders were much more

likely to use more than one mechanism. The data collection form was set up to capture

as many mechanisms as applicable. Sometimes coders assigned several mechanism codes

to account for multiple injuries noted in the hospital ED record, making it difficult to

interpret the data. This problem could be eliminated by specifying the following rules:

Code the intent and mechanism of injury for the most severe injury being treated.

This is usually the injury indicated as the principal diagnosis by the attending

physician or other health care provider.

Code only two mechanisms of injury: the precipitating cause (i.e., the mechanism

that initiated the chain of events that led to the injury) and the immediate cause

(i.e., the mechanism directly responsible for the most severe injury being treated).

We plan to modify the short ICECI data collection form to accommodate these rules. The

NEISS All Injury Program is using these rules to code intent and mechanism of injury.

5. A narrative description of the injury incident should be included as an essential ICECI

data element. Narratives can be very helpful in quality assurance reviews of the

assigned codes and for capturing further details about injury-related circumstances.

A narrative description of the injury incident should be an essential component of the

full version and all short versions of the ICECI. We found that these brief descriptions

of the injury circumstances were invaluable in reviewing how coding rules and definitions were applied by coders and allowed us to provide timely feedback to the coders.

The narratives can also be useful to researchers who need more specific details about

the causes and circumstances of the injury incident.

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6. Training and orientation of hospital medical staff is imperative to improve information

on intent of injury, mechanism of injury and other injury-related circumstances

provided in the medical record.

To maintain objectivity in coding for the pilot study, coders were instructed to record

information only if it was provided in the medical record. Therefore, coders were often

not able to capture data on locale of the injury incident, activity at the time of injury,

and other circumstances because the information was not provided in the medical record.

Also, infor-mation provided in EMS reports, nurses’ notes, and doctors’ notes were

sometimes in disagreement. Research needs to be conducted on methods to improve

these data sources. EMS technicians, nurses, and doctors need to be educated about their

important role in obtaining high-quality external-cause-of-injury data.

7. Proposed short ICECI data elements and code sets should be cross-walked to the full

ICECI, ICD-10, and ICD-10-CM code sets as a measure of compatibility and crossreference. The full ICECI should serve as a standard coding system for all proposed

short ICECIs.

All proposed short ICECI coding systems must map to the full ICECI data elements and

code sets. In this way, the full ICECI can serve as the standard coding system for all short

ICECIs. A process is underway for further development of the ICECI for use in the ICD

coding framework. Therefore, proposed short ICECIs also need to consider compatibility

with ICD-10. CDC’s short ICECI has been cross-walked to the current version of the full

ICECI and to the ICD-10. We also plan to cross-walk the short ICECI to ICD-10-CM once

this classification system has been finalized in the United States.

Next Steps for CDC’s Short ICECI

This pilot study suggests that CDC’s short ICECI, in its current form, can be useful as a

surveillance tool for obtaining external-cause-of-injury data from hospital ED records and

can serve as a basic instrument for coding external-cause-of-injury data in national, state,

and local ED surveillance systems. As mentioned previously, CDC’s short ICECI is being

adapted for use in capturing intent of injury and mechanisms of injury in the newly

expanded NEISS All Injury Program.

Modifications to CDC’s short ICECI will improve its usefulness and efficiency in capturing

and coding high-quality data. We plan to develop version 2.0 of CDC’s short ICECI based

on findings from this pilot study. Revisions will improve the data collection form; instruction

manual with case definition, coding rules and definitions, and guiding principles for coding;

and the training module with coding guidelines and practice exercises. After further pilot

testing and refinement of version 2.0, CDC’s short ICECI will be made available in electronic

and hard copy forms. Injury researchers and public health professionals should find

CDC’s short ICECI to be a useful tool for injury surveillance activities at the local, state,

and national levels in the United States, and internationally.

-12-
References

1. WHO-Working Group for Injury Surveillance Methodology Development and Its

Technical Group. International Classification of External Causes of Injuries (ICECI).

Amsterdam: Consumer Safety Institute, June 1999.

2. Holder Y, Krug E, et al. World Health Organization Injury Surveillance Guidelines

for Less-Resourced Environments. Draft proposal released for comment at the

5th World Conference of Injury Prevention and Control; 2000 Mar 5–8;

New Delhi (India). Atlanta, GA: National Center for Injury Prevention and Control,

CDC, February 16, 2000.

3. Annest JL, Pogostin CL, Conn J, Fingerhut LA, Pickett D. Proposed short version of

the International Classification of External Causes of Injuries (ICECI). Chapter 6.

In: Proceedings of the International Collaborative Effort on Injury Statistics, Vol III,

2nd Symposium; 1999 June; Washington (DC). Available at www.cdc.gov/nchs/about/

otheract/ice/pro-iii.htm. Accessed April 2000.

4. Fleiss JL. Statistical Methods for Rates and Proportions. 2nd ed. New York, NY:

John Wiley & Sons, 1981: 212–225.

5. Landis JR, Koch GG. The measurement of observer agreement for categorical data.

Biometrics 1977;33:159–174.

6. Fleiss JL, Nee JC, Landis JR. Large sample variance of kappa in the case of different

sets of raters. Psychological Bulletin 1979;86:974–977.

7. Quinlan KP, Thompson MP, Annest JL, Peddicord J, Ryan G, Kessler EP, McDonald AK.

Expanding the National Electronic Injury Surveillance System to monitor all nonfatal

injuries treated in U.S. hospital emergency departments. Annals of Emergency Medicine

1999;34:637–645.

-13-

Table 1. Percent Observed Agreement among Massachusetts ED-SCIP Coders and between

Coders and the Gold Standard for Five Major Data Elements, Overall and by Coder:

93 Injury-Related Case Scenarios, CDC’s Short ICECI Pilot Study

* All four Massachusetts coders coded these 93 injury-related case scenarios.

The inter-coder percent observed agreement was calculated as the percentage

of cases where at least 3 of the 4 coders assigned the same code with respect

to each of the five major data elements listed.

Table 2. Kappa Statistics* among Massachusetts ED-SCIP Coders and between Coders and the

Gold Standard for Five Major Data Elements, Overall and by Coder:

93 Injury-Related Case Scenarios, CDC’s Short ICECI Pilot Study

* The kappa statistic measures the percent agreement beyond what can be expected by chance alone.

Values of kappa >75% are considered to be excellent, and values between 40% and 75% represent

fair to good agreement.5

† The inter-coder kappa statistic measures agreement for all four coders simultaneously.

Data

Element

Inter-coder*

% Observed

Agreement

Gold/Coder A

% Observed

Agreement

Gold/Coder B

% Observed

Agreement

Gold/Coder C

% Observed

Agreement

Gold/Coder D

% Observed

Agreement

Gold/Overall

Average % Observed

Agreement

Work-Relatedness 97.8 94.6 94.6 95.7 91.4 94.1

Locale of Injury

Incident 91.9 81.9 80.8 81.9 79.9 81.1

Activity at Time

of Injury 85.7 80.7 86.2 77.5 79.7 81.0

Intent of Injury 96.9 89.3 91.4 89.2 90.3 90.1

Mechanism

of Injury

86.5 76.6 87.4 79.7 78.8 80.6

-15-

Data

Element

Inter-coder

Kappa

(95% CI)

Gold/Coder A

Kappa

(95% CI)

Gold/Coder B

Kappa

(95% CI)

Gold/Coder C

Kappa

(95% CI)

Gold/Coder D

Kappa

(95% CI)

Gold/Overall

Average

Kappa

Work-Relatedness

81.1

(74.2−88.1)

84.0

(65.9−102)

83.4

(64.5−102)

86.5

(67.6−105)

76.4

(58.8−93.9)

82.4

(73.2−91.5)

Locale of Injury

Incident

82.6

(79.2−85.9)

78.0

(69.7−86.3)

76.5

(68.2−84.9)

78.1

(69.9−86.4)

75.4

(67.2−83.7)

77.1

(72.9−81.2)

Activity at Time

of Injury

73.6

(70.4−76.9)

77.6

(69.7−85.5)

83.8

(75.8−91.8)

73.9

(65.9−81.8)

76.3

(68.3−84.3)

77.9

(74.0−81.9)

Intent of Injury

89.7

(84.2−95.2)

81.2

(68.8−93.7)

84.3

(71.3−97.3)

80.3

(67.1−93.4)

82.5

(69.7−95.4)

82.1

(75.6−88.5)

Mechanism

of Injury

79.7

(76.6−82.7)

72.8

(65.2−80.3)

85.1

(77.6−92.6)

76.3

(68.5−84.0)

75.3

(68.0−82.7)

77.4

(73.6−81.1)
Table 3. Kappa Statistics*

 among NEISS Coders and between Coders and the Gold Standard

for Five Major Data Elements, Overall and by Coder: 93 Injury-Related Case Scenarios,

CDC’s Short ICECI Pilot Study

* The kappa statistic measures the percent agreement beyond what can be expected by chance alone. Values of kappa >75%

are considered to be excellent, and values between 40% and 75% represent fair to good agreement.5

† The inter-coder kappa statistic measures agreement for all 7 coders simultaneously.

Table 4. Comparison of Kappa Statistics* between Massachusetts ED-SCIP Coders and NEISS Coders

for Five Major Data Elements: 93 Injury-Related Case Scenarios, CDC’s Short ICECI Pilot Study

* The kappa statistic measures the percent agreement beyond what can be expected by chance alone. Values of kappa >75%

are considered to be excellent, and values between 40% and 75% represent fair to good agreement.5

Data

Element

Inter-coder

for MA

Kappa

Inter-coder

for NEISS

Kappa

% Difference

in Kappas

(MA-NEISS)

NEISS

Gold/MA

Coder

Average

Kappa

Gold/NEISS

Coder

Average

Kappa

% Difference

in Kappas

(MA-NEISS)

NEISS

Work-Relatedness 81.1 76.8 +5.6 82.4 81.9 +0.6

Locale of Injury

Incident 82.6 71.4 +15.7 77.1 67.4 +14.4

Activity at Time

of Injury 73.6 59.3 +24.1 77.9 64.5 +20.8

Intent of Injury 89.7 74.2 +20.9 82.1 77.7 +5.7

Mechanism

of Injury 79.7 59.7 +33.5 77.4 59,1 +28.8

-16-

Data

Element

Inter-coder

Kappa

(95% CI)

Gold/Coder 1

Kappa

(95% CI)

Gold/Coder 2

Kappa

(95% CI)

Gold/Coder 3

Kappa

(95% CI)

Gold/Coder 4

Kappa

(95% CI)

Gold/Coder 5

Kappa

(95% CI)

Gold/Coder 6

Kappa

(95% CI)

Gold/Coder 7

Kappa

(95% CI)

Gold/Overall

Average

Kappa

WorkRelatedness

76.8

(72.9−80.6)

92.8

(72.5−113)

75.9

(58.1−93.6)

71.8

(55.0−88.6)

75.0

(56.0−94.0)

81.5

(63.3−99.6)

90.0

(69.6−110)

89.9

(70.1−110)

81.9

(74.8−88.9)

Locale of

Injury

Incident

71.4

(68.0−74.7)

69.8

(61.2−78.5)

70.4

(62.2−78.6)

74.2

(66.1−82.4)

66.4

(58.1−74.6)

64.2

(56.1−72.3)

68.6

(60.3−77.0)

58.0

(50.0−66.0)

67.4

(64.3−70.5)

Activity at

Time

of Injury

59.3

(54.4−64.2)

75.0

(67.0−83.0)

67.5

(59.4−75.5)

68.5

(60.2−76.7)

42.0

(33.8−50.2)

72.5

(64.4−80.6)

59.7

(51.5−67.9)

65.0

(56.9−73.0)

64.5

(61.4−67.5)

Intent of

Injury

74.2

(71.5−76.9)

85.0

(72.3−97.6)

83.1

(70.7−95.6)

75.6

(63.2−88.0)

72.1

(59.7−84.6)

76.5

(64.2−88.8)

77.2

(64.5−89.9)

74.4

(61.9−86.9)

77.7

(73.0−82.4)

Mechanism

of

Injury

59.7

(56.4−62.9)

67.4

(59.7−75.1)

65.9

(57.5−74.2)

65.2

(57.7−72.7)

54.9

(47.1−62.6)

53.2

(45.3−61.0)

58.1

(50.4−65.8)

55.7

(47.9−63.5)

60.1

(57.2−63.1)
Data

Element

Coder A

Kappa

(95% CI)

Coder B

Kappa

(95% CI)

Coder C

Kappa

(95% CI)

 Coder D

Kappa

(95% CI)

Intra-Coder Reliability

Average

Kappa

Work-Relatedness

87.7

(48.6−127)

100

(60.6−139)

 67.2

(29.8−105)

79.3

(43.3−115)

83.2

(64.3−102)

Locale of Injury

Incident

100

(77.7−122)

100

(77.7−122)

84.5

(60.7−108)

92.4

(70.4−114)

94.2

(83.1−105)

Activity at Time

of Injury

93.1

(70.9−115)

86.0

(63.1−109)

72.2

(49.4−94.9)

100

(75.2−125)

88.0

(77.0−98.9)

Intent of Injury

82.7

(53.8−112)

81.6

(50.7−113)

90.8

(58.9−123)

90.8

(58.9−123)

86.4

(71.1−102)

Mechanism of

Injury

70.8

(48.7−92.9)

77.7

(55.6−99.8)

78.6

(57.1−100)

70.3

(48.4−92.2)

74.4

(63.5−85.3)

Table 5. Kappa Statistics* for Massachusetts ED-SCIP Coders for Five Major Data Elements,

Overall and by Coder: 18 Injury-Related Case Scenarios Coded Twice by the Same

Coder at Different Times, CDC’s Short ICECI Pilot Study

* The kappa statistic measures the percent agreement beyond what can be expected by chance alone.

Values of kappa >75% are considered to be excellent, and values between 40% and 75% represent

fair to good agreement.5

-17-
Table 6. Kappa Statistics*

 among Randomly Selected Pairs of Massachusetts ED-SCIP Coders for

Categories for Four Major Data Elements: 127 Injury-Related Hospital ED visits,

CDC’s Short ICECI Pilot Study

-18-

 Inter-coder Reliability

Data Element and Categories† Kappa (95% CI)

Work-Relatedness

Overall 67.1 (54.3–80.0)

Work-related 91.4 (74.0–109)

Not work-related 67.6 (50.1–85.1)

Locale of Injury Incident

Overall 73.5 (65.2–81.8)

Home/mobile home 83.5 (65.9–101)

Residential institution 100.0 (82.5–118)

Farm/ranch -----‡

Street/highway 90.9 (73.4–108)

Trade and service area 66.0 (48.4–83.5)

Industrial/construction area 52.5 (34.9–70.0)

School/educational area 66.3 (48.7–83.8)

Other public building 100.0 (82.5–118)

Sports and athletic area 65.9 (48.3–83.4)

Activity at Time of Injury

Overall 67.5 (59.2–75.8)

Sports 92.9 (75.5–110)

Leisure 63.6 (46.2–81.0)

Traveling 85.7 (68.3–103)

Paid work 91.3 (73.9–109)

Unpaid work 48.4 (31.0–65.8)

Educational activity -----

Vital activity -----

Mechanism of Injury

Overall 91.4 (84.1–98.8)

Motor vehicle (occupant) 100.0 (82.5–117)

Pedestrian-vehicle crash 100.0 (82.5–117)

Motorcycle -----

Pedal cycle 85.3 (67.9–103)

Struck by/against or crushed 83.5 (66.0–101)

Fall 94.1 (76.7–112)

Gunshot, firearm-related -----

Stab/cut/pierce 89.5 (72.1–107)

Fire/burn 88.5 (71.0–106)

Smoke inhalation -----

Poisoning 100.0 (82.5–117)

Near-drowning/drowning/submersion -----

Foreign body 100.0 (82.5–117)

Overexertion 90.8 (73.3–108)

* The kappa statistic measures the percent agreement beyond what can be expected by chance alone. Values of kappa >75%

are considered to be excellent, and values between 40% and 75% represent fair to good agreement.5

† Intent of injury is not presented here because all but four cases were unintentional injuries; refer to Table 1 for percent

observed agreement on this data element.

‡ ----- denotes that no cases were reported for this category.
-19-

Table 7. Description of Short ICECI Data for Injury-Related ED Patients Obtained from

16 Massachusetts ED-SCIP Hospitals: CDC’s Short ICECI Pilot Study

Number of Percent

Data Element and Categories1 Patients Distribution

Coding Time (in minutes)

Total 1,841 100.0

 1 min 49 2.7

 2 mins 17 28.1

 3 mins 610 33.1

 4 mins 323 17.5

 5 mins 177 9.6

 6 mins 38 2.1

 7 mins 24 1.3

 8 mins 26 1.4

 9 mins 12 0.7

10 mins 15 0.8

11–18 mins 14 0.8

Not recorded/unknown 36 2.0

Sex of Patient

Total 1,841 100.0

Male 1,088 59.1

Female 748 40.6

Not stated/unknown 5 0.3

Age of Patient (in years)

Total 1,841 100.0

00–04 yrs 189 10.3

05–09 yrs 96 5.2

10–14 yrs 132 7.2

15–19 yrs 227 12.3

20–24 yrs 228 12.4

25–44 yrs 615 33.4

45–64 yrs 228 12.4

65+ yrs 115 6.2

Not stated/unknown 11 0.6

Work-Relatedness

Total 1,841 100.0

Work-related 272 14.8

Not work-related 1,210 65.7

Not recorded/unspecified 359 19.5
Table 7 (Continued). Description of Short ICECI Data for Injury-Related ED Patients Obtained

from 16 Massachusetts ED-SCIP Hospitals: CDC’s Short ICECI Pilot Study

Number of Percent

Data Element and Categories1 Patients Distribution

Locale of Injury Incident

Total 1,841 100.0

Home/mobile home 401 21.8

Residential institution 39 2.1

Farm/ranch 2 0.1

Street/highway 277 15.0

Trade and service area 115 6.2

Industrial/construction area 51 2.8

School/educational area 47 2.6

Other public building 20 1.1

Sports and athletic area 83 4.5

Other specified 82 4.5

Not recorded/unknown 724 39.3

Activity at Time of Injury

Total 1,841 100.0

Sports 150 8.1

Leisure 271 14.7

Traveling 233 12.7

Paid work 261 14.2

Unpaid work 66 3.6

Educational activity 4 0.2

Vital activity 29 1.6

Other specified 70 3.8

Not recorded/unspecified 757 41.1

Intent of Injury

Total 1,841 100.0

Unintentional 1,580 85.8

Intentionally self-inflicted 25 1.4

Assault, confirmed or suspected 200 10.9

Legal intervention 4 0.2

Operations of war and civil insurrection -----* -----

Not recorded/undetermined 2 1.7

* ----- denotes that no cases were reported for this category.

-20-
Table 7 (Continued). Description of Short ICECI Data for Injury-Related ED Patients Obtained

from 16 Massachusetts ED-SCIP Hospitals: CDC’s Short ICECI Pilot Study

Number of Percent

Data Element and Categories1 Patients Distribution

Relationship of Perpetrator

 to Victim for Assaults

Total 200 100.0

Spouse or partner 25 12.5

Parent 3 1.5

Other relative 11 5.5

Unrelated care giver -----* -----

Acquaintance or friend 17 8.5

Official/legal authorities 1 0.5

Multiple perpetrators 18 9.0

Stranger 25 12.5

Other specified person(s) 9 4.5

Not recorded/unknown 91 45.5

Reason for Assault

Total 200 100.0

Altercation 91 45.5

During illegal acquisition of 5 2.5

 money or property

Drug-related ----- -----

Sexual assault 7 3.5

Gang-related ----- -----

Other specified 8 4.0

Not recorded/unknown 89 44.5

Mechanism of Injury

Total 1,841 100.0

Motor vehicle (occupant) 218 11.8

Pedestrian-vehicle crash 12 0.7

Motorcycle 21 1.1

Pedal cycle 20 1.1

Struck by/against or crushed 389 21.1

Fall 416 22.6

Gunshot, firearm-related 17 0.9

Stab/cut/pierce 294 16.0

Fire/burn 33 1.8

Smoke inhalation 1 0.1

Poisoning 35 1.9

Near-drowning/drowning/submersion ----- -----

Foreign body 52 2.8

Overexertion 191 10.4

Other specified mechanism 132 7.2

Not recorded/undetermined 10 0.5

* ----- denotes that no cases were reported for this category.

-21-
Table 7 (Continued). Description of Short ICECI Data for Injury-Related ED Patients Obtained

from 16 Massachusetts ED-SCIP Hospitals: CDC’s Short ICECI Pilot Study

Number of Percent

Data Element and Categories1 Patients Distribution

Traffic-Relatedness for

 Motor Vehicle Occupants

Total 218 100.0

Traffic-related 197 90.4

Nontraffic-related 5 2.3

Not recorded/unknown 16 7.3

Type of Motor Vehicle that Patient

 Was Riding

Total 218 100.0

Automobile 183 83.9

Pickup truck or van 8 3.7

Heavy transport vehicle 1 0.5

Bus -----* -----

3-wheel motor vehicle 1 0.5

Other specified 5 2.3

Not recorded/unknown 20 9.2

Motor Vehicle Occupant Status

Total 218 100.0

Driver 141 64.7

Passenger 69 31.7

Person boarding or alighting 1 0.5

Person on outside of motor vehicle 1 0.5

Not recorded 6 2.8

Counterpart for Motor Vehicle Crash

 Involving Motor Vehicle Occupant

Total 218 100.0

Automobile 73 33.5

Pickup truck or van 3 1.4

Heavy transport vehicle 1 0.5

Bus ----- -----

3-wheel motor vehicle ----- -----

Motorcycle ----- -----

Railway train/vehicle ----- -----

Pedal cycle ----- -----

Pedestrian ----- -----

Animal or animal-drawn vehicle ----- -----

Fixed or stationary object 33 15.1

No counterpart 16 7.3

Other specified 5 2.3

Not recorded/unknown 87 39.9

* ----- denotes that no cases were reported for this category.

-22-
-23-

Table 7 (Continued). Description of Short ICECI Data for Injury-Related ED Patients Obtained

from 16 Massachusetts ED-SCIP Hospitals: CDC’s Short ICECI Pilot Study

Number of Percent

Data Element and Categories1 Patients Distribution

Traffic-Relatedness for

 Pedestrian Injuries

Total 12 100.0

Traffic-related 9 75.0

Nontraffic-related 2 16.7

Not recorded/unknown 1 8.3

Counterpart for

 Pedestrian-Vehicle Crash

Total 12 100.0

Automobile 8 66.7

Pickup truck or van -----* -----

Heavy transport vehicle ----- -----

Bus ----- -----

3-wheel motor vehicle 1 8.3

Motorcycle 2 16.7

Railway train/vehicle ----- -----

Pedal cycle ----- -----

Pedestrian ----- -----

Animal or animal-drawn vehicle ----- -----

Fixed or stationary object ----- -----

No counterpart ----- -----

Other specified 1 8.3

Not recorded/unknown ----- -----

Traffic-Relatedness for

 Motorcyclist Injuries

Total 21 100.0

Traffic-related 14 66.7

Nontraffic-related 2 9.5

Not recorded/unknown 5 23.8

Motorcyclist Status

Total 21 100.0

Driver 13 61.9

Passenger 8 38.1

Person boarding or alighting ----- -----

Person on outside of motor vehicle ----- -----

Not recorded/unknown ----- -----

* ----- denotes that no cases were reported for this category.
Table 7 (Continued). Description of Short ICECI Data for Injury-Related ED Patients Obtained

from 16 Massachusetts ED-SCIP Hospitals: CDC’s Short ICECI Pilot Study

Number of Percent

Data Element and Categories1 Patients Distribution

Counterpart for Motorcycle Crash

Total 21 100.0

Automobile 3 14.3

Pickup truck or van -----* -----

Heavy transport vehicle ----- -----

Bus ----- -----

3-wheel motor vehicle ----- -----

Motorcycle ----- -----

Railway train/vehicle ----- -----

Pedal cycle ----- -----

Pedestrian 1 4.8

Animal or animal-drawn vehicle 3 14.3

Fixed or stationary object 3 14.3

No counterpart 9 42.9

Other specified ----- -----

Not recorded/unknown 2 9.5

Traffic-Relatedness for

 Pedal Cyclist Injuries

Total 20 100.0

Traffic-related 8 40.0

Nontraffic-related 1 5.0

Not recorded/unknown 11 55.0

Counterpart for Pedal Cycle Crash

Total 20 100.0

Automobile 3 15.0

Pickup truck or van ----- -----

Heavy transport vehicle ----- -----

Bus ----- -----

3-wheel motor vehicle ----- -----

Motorcycle ----- -----

Railway train/vehicle ----- -----

Pedal cycle ----- -----

Pedestrian ----- -----

Animal or animal-drawn vehicle 1 5.0

Fixed or stationary object 2 10.0

No counterpart 13 65.0

Other specified ----- -----

Not recorded/unknown 1 5.0

* ----- denotes that no cases were reported for this category.

-24-
Table 7 (Continued). Description of Short ICECI Data for Injury-Related ED Patients Obtained

from 16 Massachusetts ED-SCIP Hospitals: CDC’s Short ICECI Pilot Study

Number of Percent

Data Element and Categories1 Patients Distribution

Source of Force Applied for

 Struck By/Against Injuries

Total 389 100.0

Human 111 28.5

Animal 3 0.8

Inanimate object or force 256 65.8

Not recorded/unknown 19 4.9

Type of Force Applied for

 Struck By/Against Injuries

Total 389 100.0

Struck by 219 56.3

Crushed by 63 16.2

Struck against 103 26.5

Not recorded/unknown 4 1.0

Type of Firearm Used in

 Gunshot Injuries

Total 17 100.0

Handgun 7 41.2

Rifle -----* -----

Shotgun 1 5.9

Larger firearm ----- -----

Not recorded/unknown 9 52.9

Type of Stabbing Weapon, Instrument,

 or Object in Cut/Pierce Injuries

Total 294 100.0

Knife 68 23.1

Sharp instrument/tool other than knife 45 15.3

Sharp glass 40 13.6

Other specified 109 37.1

Not recorded/unknown 32 10.9

Type of Burn in Fire/Burn Injuries

Total 33 100.0

Fire/flame 3 9.1

Hot object 14 42.4

Hot liquid 11 33.3

Steam 1 3.0

Chemical 1 3.0

Other specified 2 6.1

Not recorded/unknown 1 3.0

* ----- denotes that no cases were reported for this category.

-25-
-26-

Table 7 (Continued). Description of Short ICECI Data for Injury-Related ED Patients Obtained

from 16 Massachusetts ED-SCIP Hospitals: CDC’s Short ICECI Pilot Study

Number of Percent

Data Element and Categories1 Patients Distribution

Type of Poisoning

Total 35 100.0

Drug (excludes alcohol) 25 71.4

Alcohol 2 5.7

Chemical 3 8.6

Other specified 4 11.4

Not recorded/unknown 1 2.9

Other Specified Mechanisms

Total 132 100.0

Railway/streetcar -----* -----

Water transport 1 0.8

Air transport ----- -----

Thrown or fallen from animal or 1 0.8

 animal-drawn vehicle

Other transport ----- -----

Inhalation/ingestion of food 2 1.5

 (blocking airway)

Inhalation/ingestion of other objects 3 2.3

 (blocking airway)

Hanging or strangulation 3 2.3

Suffocation ----- -----

Entrapment in closed space ----- -----

Venomous bite or sting 11 8.3

Human bite 12 9.1

Dog bite 24 18.2

Bite by animal other than dog 31 23.5

Sting 3 2.3

Fireworks explosion ----- -----

Explosive blast ----- -----

BB or pellet gunshot 2 1.5

Other firearm ----- -----

Lightning 1 0.8

Electrical current 2 1.5

Radiation 1 0.8

Welding 1 0.8

Machinery 6 4.5

Exposure to excessive natural heat 2 1.5

Exposure to excessive natural cold 1 0.8

Sunlight 1 0.8

Natural disaster ----- -----

Other specified 24 18.2

* ----- denotes that no cases were reported for this category.
-27-

Figure 1. Average Kappa Statistic,* with 95% Confidence Interval, between Coders and the Gold

Standard, by Selected Data Elements and Substudy Groups: CDC’s Short ICECI Pilot Study

*The kappa statistic measures the percent agreement beyond what can be expected by chance alone.

Values of kappa >75% are considered to be excellent, and values between 40% and 75% represent

fair to good agreement.5

Kappa (in percent)

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

W ork-Relatedness LocaleofInjury Activity atTime ofInjury IntentofInjury M echanism ofInjury

MassachusettsED-SCIP substudy NEISS substudy

Work-Relatedness Locale of Injury Activity at Time

of Injury

Intent of Injury Mechanism of

Injury

Massachusetts ED-SCIP Substudy

NEISS Substudy