DOT HS 812 180 August 2015

Assessment of 

Characteristics of 

State Data Linkage 

Systems
DISCLAIMER

This publication is distributed by the U.S. Department of Health and Human Services, Centers 

for Disease Control and Prevention and the U.S. Department of Transportation, National 

Highway Traffic Safety Administration, in the interest of information exchange. The opinions, 

findings, and conclusions expressed in this publication are those of the authors and not 

necessarily those of the Department of Health and Human Services or the Centers for Disease 

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Suggested APA Format Citation:

Milani, J., Kindelberger, J., Bergen, G. Novicki, E. J., Burch, C., Ho, S. M., & West, B. A. 

(2015, August). Assessment of characteristics of state data linkage systems. (Report No. 

DOT HS 812 180). Washington, DC: National Highway Traffic Safety Administration, 

and Atlanta: Centers for Disease Control and Prevention.

. 
ii

Technical Report Documentation Page

1. Report No. 2. Government Accession No. 3. Recipient’s Catalog No.

DOT HS 812 180

4. Title and Subtitle 5. Report Date

Assessment of Characteristics of State Data Linkage Systems August 2015

6. Performing Organization Code

7. Author(s) 8. Performing Organization Report No.

Milani, J., Kindelberger, J., Bergen, G., Novicki, E. J., Burch, C., Ho, 

S. M., and West, B. A.

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS)

National Center for Statistics and Analysis

National Highway Traffic Safety Administration

Washington, DC 20590

11. Contract or Grant No.

12. Sponsoring Agency Name and Address 13. Type of Report and Period 

National Highway Traffic Safety Administration

1200 New Jersey Avenue SE. 

Washington, DC 20590 

Centers for Disease Control and Prevention 

4770 Buford Highway NE 

Atlanta, GA 30341 

NHTSA Report

14. Sponsoring Agency Code

15. Supplementary Notes

J. Milani, C. Burch, and S. Ho are with the University of Maryland National Study Center. J. 

Kindelberger is with the National Highway Traffic Safety Administration. G. Bergen, E. Novicki, and B. 

West are with the Centers for Disease Control and Prevention. 

16. Abstract

State systems that integrate data by linking police crash reports to medical records provide important 

information that can be used to better understand motor vehicle crash injuries. The objective of this study 

was to better understand the facilitators and barriers to successful data linkage systems. Data were 

collected by questionnaire, focus group, and a literature search. Twenty-five States completed the survey 

and 19 participated in focus groups. Facilitators of these systems included a community context that 

supports linkage, one lead agency to oversee the system, coalition of data owners, memorandums of 

understanding to obtain data, high quality data, staff with the right mix of technical and non-technical 

skills, technical assistance and training, and stable funding. Barriers included lack of funding, staff 

turnover, lack of documented procedures, lags in obtaining data, statutory requirements for data use, 

complex linkage techniques, and failure to adequately market information available from linked 

databases.

17. Key Words 18. Distribution Statement

NHTSA; Crash Outcome Data Evaluation System; 

CODES; CODES Data Network; data linkage; 

linked data applications 

Document is available to the public from the 

National Technical Information Service 

www.ntis.gov.

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22

Unclassified Unclassified 77
ii

EXECUTIVE SUMMARY

Motor vehicle crashes are a leading cause of death and injury in the United States, accounting for 

33,561 fatalities and 2.4 million injuries in 2012. Although risk factors for fatal crashes are welldocumented in transportation safety research, fewer studies exist regarding risk factors for nonfatal injury crashes. Several States have developed systems that link crash reports to medical data 

to create a source of nonfatal injury data; however, these systems vary from State to State. 

One example of a State linkage system is the Crash Outcome Data Evaluation System (CODES), 

which was originated by the National Highway Traffic Safety Administration in 1992. In 

CODES, NHTSA provided partial funding and technical assistance to State linkage programs 

through cooperative agreements in participating States. In 2009, NHTSA advised funded 

CODES programs to plan for transition to independent State systems—without NHTSA funding 

or technical assistance. NHTSA finalized that transition in 2013. Outside of CODES, some 

States have conducted linkage projects, and other motor vehicle safety organizations also have 

expressed interest in linkage programs. 

From 2010 to 2012 the Centers for Disease Control and Prevention and NHTSA formed a 

partnership to explore the feasibility and benefits of using linked data in CODES to determine 

risk factors for motor vehicle injury and to design and evaluate interventions to address these risk 

factors. This report describes a program appraisal of data linkage programs that was conducted 

during the final year of the partnership. The purpose was to examine State CODES and other 

State linkage programs to better understand the facilitators and barriers to linking and analyzing 

data to produce useful products. This information can be used to guide future improvements to a 

State’s data linkage system and to implement a new State data linkage system. 

The program appraisal was supported by the University of Maryland National Study Center for 

Trauma and EMS (NSC) under its CODES cooperative agreement as a resource center. The 

study team was composed of CDC, NHTSA, and NSC specialists who jointly designed and 

conducted the study to document information that could improve current and future programs.

 

The study team conducted a literature review regarding organizational theory to determine what

theory would provide a framework for studying State data linkage systems. Because such

programs involve a coalition of organizations and an information technology system, the CCAT

and the ITIM were selected and used to develop a framework for the study of the following nine 

theory constructs. 

• Community context

• Lead agency/convener group

• Coalition membership

• Leadership and staffing

• Pooled member and external resources

• Member engagement

• Implementation of strategies/information technology infrastructure

• Outcomes

• Community capacity 
iii

Data collection consisted of collecting primary data (a survey of States conducting linkage 

programs, followed by focus groups with the surveyed States to further explore the survey 

results) and obtaining secondary data on program operations and products (available State 

Traffic Records Assessments and bibliographies of publications using linked data). CDC 

obtained approval for primary data collection from the Office of Management and Budget 

(OMB) under OMB clearance number 0920-0879. The survey was administered to 16 CODES 

States and 14 States that were known to have State data linkage systems outside of CODES 

during December 2012. Five focus groups were also conducted with these same States during 

May 2013 to explore the survey results in more detail. Twenty-five States (response rate 86%) 

completed the survey, and 19 of the responding States participated in follow-up focus groups

(response rate 76%). 

 

Results of the survey indicated that the majority of responding States identified motor vehicle

injury as a high priority health problem for their State. States consistently reported having crash, 

hospital inpatient, and emergency department data available to link, and most States were 

successful in linking those datasets. Through data results and the construct applications, the team 

noted key facilitators and barriers to collecting, linking, analyzing, and reporting data . 

Key facilitators included:

• Characteristics of the community, such as the importance placed on motor vehicle safety,

that either facilitate or inhibit the linkage of data and the use of the resulting database; 

• Housing overall responsibility for the data linkage program in one organization; 

• Establishing a coalition that includes all data owners; 

• Establishing memorandums of understanding and/or undergoing institutional review 

board review to obtain data; 

• Establishing matching variables in the data; 

• Interacting with other State and local organizations interested in motor vehicle safety; 

• Staffing with personnel that have the appropriate skills, including a high level of 

expertise with the linkage software packages, epidemiology, statistics, knowledge of 

traffic safety, data sources being linked, and presentation and marketing skills to ensure 

data are used; 

• Providing technical assistance and training in linking and analyzing linked data; 

• Providing stable funding; and

• Participating in a community of practice of data linkage practitioners. 

 

Barriers noted included: 

• Insufficient funding, 

• Staffing turnover, 

• Lack of process documentation, 

• Long lag times in obtaining source data for linkage, 

• Statutory requirements for obtaining and reporting data, 

• Complex linkage techniques such as probabilistic linkage, and

• Marketing linked data so that others understand how they can be used to increase traffic 

safety. 
iv

State data linkage systems that link police crash reports to medical records are important sources 

of data on non-fatal motor vehicle crashes and injury severity from those crashes. The 

information obtained in this study on facilitators and barriers to effective State linkage systems

can be used by States to modify existing data-linkage systems to improve the efficiency and 

success rate of data linkage, conduct more analysis of linked data, and increase dissemination of 

the analysis. This information can also be used by States, when starting a new data linkage 

program, to design a program based on the experiences of other States. 
v 

Table of Contents

EXECUTIVE SUMMARY ........................................................................................................................... ii

INTRODUCTION ......................................................................................................................................... 1

Background ................................................................................................................................................ 1

History of CODES ..................................................................................................................................... 2

Interest in Data Linkage in the Transportation Safety Field ..................................................................... 3

Purpose ...................................................................................................................................................... 4

METHODS .................................................................................................................................................... 5

Theoretical Framework.............................................................................................................................. 5

Data Collection .......................................................................................................................................... 7

Primary Data Collection ........................................................................................................................ 7

Secondary Data Collection .................................................................................................................... 8

Data Analysis ............................................................................................................................................. 8

Survey .................................................................................................................................................... 8

Focus Groups ......................................................................................................................................... 9

Traffic Records Assessments .............................................................................................................. 10

Bibliography ........................................................................................................................................ 10

Synthesis of Data Sources ................................................................................................................... 10

RESULTS .................................................................................................................................................... 11

Community Context ................................................................................................................................ 11

Lead Agency/Convener Group ................................................................................................................ 11

Coalition Membership ............................................................................................................................. 12

Leadership and Staffing ........................................................................................................................... 14

Pooled Member and External Resources ................................................................................................. 15

Technical Assistance and Training ...................................................................................................... 15

Funding ................................................................................................................................................ 16

Member Engagement ............................................................................................................................... 18

Implementation of Strategies/Information Technology Infrastructure ................................................... 21

Databases/Data Sources ...................................................................................................................... 21

Linking Techniques ............................................................................................................................. 23

Staff Capabilities ................................................................................................................................. 25

Outcomes ................................................................................................................................................. 26

Linkage Success .................................................................................................................................. 26

Products from Analyses of Linked Datasets ....................................................................................... 26

Community Capacity ............................................................................................................................... 28

DISCUSSION .............................................................................................................................................. 30

General Findings ...................................................................................................................................... 30

Common Themes ..................................................................................................................................... 30

Facilitators ........................................................................................................................................... 30

Barriers ................................................................................................................................................ 32

Use of Theory .......................................................................................................................................... 33

Limitations ............................................................................................................................................... 33

Conclusions ............................................................................................................................................. 34

Acknowledgments ....................................................................................................................................... 35

References ................................................................................................................................................... 37

ATTACHMENTS ....................................................................................................................................... 40

Attachment A. State participation in study.............................................................................................. 40

Attachment B. Questionnaire for Study .................................................................................................. 41

Attachment C. Focus Group Script ......................................................................................................... 51

Attachment D. Bibliography.................................................................................................................... 57
vi

Table 1: Measurement of Relevant Constructs ............................................................................................. 6

Table 2: Time period when State started linking data ................................................................................. 11

Table 3: Agency type by responsibility for function in linkage program ................................................... 12

Table 4: Percentage of States by CODES status and training areas received ............................................. 15

Table 5: Agency or other coalition member by frequency of interaction ................................................... 19

Table 6: Frequency of data requests from outside organizations ................................................................ 19

Table 7: Data request frequency by length of time linkage project has been in place ................................ 20

Table 8: Frequency of sharing data with other organizations ..................................................................... 20

Table 9: Proficiency in linking by software type ........................................................................................ 25

Table 10: Proficiency in analysis by relevant software type ....................................................................... 26

Table 11: Places where linked data have been presented or published ....................................................... 27

Figure 1: Percentage of States listing organization as a member of board of advisors or directors ........... 13

Figure 2: Funding sources for State linkage systems .................................................................................. 17

Figure 3: Challenges in obtaining data for linkage ..................................................................................... 22

Figure 4: Lag time estimates in months to receive data from your State data partners .............................. 23

Figure 5: Reported challenges in linking data by number of States reporting ............................................ 24

Figure 6: Reported challenges in analyzing linked data by number of States reporting ............................. 25
1 

INTRODUCTION

Background

Motor vehicle crashes are a leading cause of death in the United States, accounting for 33,561 

fatalities and 2.4 million injuries in 2012 (NCSA, 2013). Research exists regarding factors that 

contribute to motor vehicle crashes (e.g., speeding, use of alcohol) and on protective measures, 

such as seat belts, that effectively reduce injuries and deaths when a crash occurs (NCSA, 2013). 

However, the majority of this research on motor vehicle crashes has been based on fatal injuries 

because less data are available for non-fatal injuries. Because of this lack of data, not as much is 

known about circumstances leading to nonfatal injury crashes (i.e., contributing factors and other 

details noted in police reports) and their effects on injury severity. 

Large, nationally representative data sources for nonfatal motor vehicle injuries do exist. One 

such data source is NHTSA’s National Automotive Sampling System General Estimates System 

(NASS GES), a nationally representative probability sample of police-reported crashes that 

provides information on State-reported traffic crashes, including non-fatal and property-damage–

only crashes and allows identification of nationwide trends in occurrences and characteristics of 

motor vehicle crashes. NASS GES uses the police officer’s assessment to define injury severity

sustained in a crash on a 5-point scale originally designed by the National Safety Council and 

known as KABCO: killed (K), incapacitating injury (A), non-incapacitating injury (B), possible 

injury (C), and no injury (O). Individual State crash databases also use this scale in some form. 

However, previous research has determined that the KABCO scale does not effectively capture 

injury severity and/or the actual injury outcome as measured by the Maximum Abbreviated 

Injury Score (MAIS) (Burch, Cook, & Dischinger, 2013; Compton, 2005; Farmer, 2003; Miller, 

Viner, Rossman, & Pindus,1991; Popkin, Campbell, Hansen, & Stewart, 1991). 

A second source of nationally representative data for nonfatal motor vehicle–related injuries is 

the National Electronic Injury Surveillance System-All Injury Program (NEISS-AIP), an 

emergency department surveillance system operated by the United States Consumer Product 

Safety Commission, which provides ongoing national estimates of nonfatal injuries. The Centers 

for Disease Control and Prevention’s Web-based Injury Statistics Query and Reporting System 

(WISQARS) online database contains the NEISS-AIP data, which collects detailed data 

abstracted from medical records of initial emergency department visits for all types and causes of 

nonfatal injuries (including motor vehicle–related injuries) and poisonings in the United States. 

These data do not provide information on crash-related factors such as speeding or alcohol use or 

on protective factors such as seat belt use. Neither of these systems (NASS GES nor NEISS-AIP) 

can provide data at the State level. Thus, the need exists for a single source of data that provides 

motor vehicle crash data linked to medical records data to better understand motor vehicle

crashes. Certain States have developed systems to link crash reports to medical data to create a 

single source of State data. One example of a State linkage system is the Crash Outcome Data 

Evaluation System (CODES)—a sizeable and well-known motor vehicle linkage system

originally created by NHTSA, with 15 States participating in 2012.
2 

History of CODES

NHTSA initially created CODES to quantify and report the benefits of safety equipment and 

legislation relative to mortality, morbidity, injury severity and health-care costs. In 1992, 

NHTSA sought grant applications from entities with existing statewide crash and injury data 

systems that were capable of generating crash, medical, and financial-outcome information if 

linked together. Any State agency, non-profit organization, or educational institution was eligible 

to develop and coordinate a coalition of data owners and users to perform the desired linkages. 

Applicants were required to (1) demonstrate the existence and accessibility of appropriate source 

data systems, (2) work with NHTSA to implement the probabilistic linkage algorithm, and (3) 

ensure transfer of their linked data to NHTSA for use in preparing a report to Congress. Seven 

States were awarded grants to establish CODES, effective October 1, 1992. A NHTSA report to 

Congress on seat belts and motorcycle helmets was created using linked data (NCSA, 1996). 

CODES became institutionalized in the awarded States based on a series of partnerships among 

State traffic safety and public health agencies and NHTSA. Since then, States that have 

successfully linked at least 2 years of crash and injury-outcome data have had the opportunity to 

apply to join the CODES Data Network. State programs in the CODES Data Network received 

NHTSA-provided partial funding and technical and program assistance through cooperative 

agreements. Lead organizations have usually been State agencies, universities or affiliates, or

non-profit institutions. In some cases, lead organizations entered into agreements with support 

entities, such as universities, to conduct the actual data linkage and/or analyses. CODES 

cooperative agreements were administered through NHTSA’s National Center for Statistics and 

Analysis. Among the grantee requirements were (1) assembling and convening a CODES board

of directors composed of State data owners, (2) reporting to NHTSA on the progress of linkage 

and analysis activities, and (3) participating in NHTSA-initiated data requests and annual 

training meetings. 

As CODES developed, NHTSA encouraged grantees to seek and secure other supplemental 

funding for their CODES program and to move towards program institutionalization to ensure

sustainability. In 2009, NHTSA advised the 16 funded CODES States to prepare for cessation of 

NHTSA funding and technical assistance and to plan for the transition to independent projects. 

In 2013, NHTSA finalized the transition and encouraged States to continue to seek other funding 

sources, including possible NHTSA grants through State Highway Safety Offices, so that data 

linkage could continue. As of 2013, NHTSA had CODES cooperative agreements with grantees

in 15 States: Connecticut, Delaware, Georgia, Illinois, Kentucky, Maine, Maryland, Minnesota, 

Missouri, Nebraska, New York, Ohio, South Carolina, Utah, and Virginia. 

During the course of CODES, some States ended their participation in the CODES Data Network 

but continued to conduct linkage independently. In other cases, States left the network and 

retired their program. Other States initiated linkage projects on their own that were not part of 

the CODES Data Network. Some of these States have projects linking crash and hospital data 

using matching identifiers and deterministic linkage. Others have initiated projects linking 

Emergency Medical Services and trauma registry data with the goal of adding crash and other 

data sets. Still others have used commercial software that replicated the CODES methodology 

and set up or piloted State projects similar to those seen in CODES. 
3 

Interest in Data Linkage in the Transportation Safety Field

In addition to State-based data linkage activities, the topic of crash to medical data linkage has 

interested other motor vehicle safety focused organizations, including the following: 

• CDC: CDC has identified motor vehicle–related injuries as one of six “Winnable 

Battles,” and CDC’s National Center for Injury Prevention and Control has a team to 

address transportation safety (www.cdc.gov/motorvehiclesafety/) (CDC, 2013). In 2010, 

CDC and NHTSA administrators signed a memorandum of understanding for 

collaborative strategies. During 2010 to 2012, CDC and NHTSA agreed to explore the 

feasibility and benefits of an ongoing partnership in the CODES program. CDC 

determined that linked data, such as that produced by CODES, was valuable to

determining risk factors for motor vehicle injury and in designing and evaluating 

interventions to address these risk factors. CDC’s continuing interest in crash-medical 

data linkage is a primary motivator of this study. 

• National Transportation Safety Board (NTSB): In a study of single-unit truck crashes and 

injuries, the NTSB recommended that data linkage systems such as CODES be continued

and issued the following conclusion (NTSB, 2013): 

“Data from the Crash Outcome Data Evaluation System provide detailed 

information on injury diagnoses and severities in relation to crash 

characteristics, cover a large proportion of the population of the participating 

States, are not available elsewhere, and provide useful insight into traffic safety 

problems.”

• Federal Highway Administration (FHWA): The “Moving Ahead for Progress in the 21st

Century” Act (MAP-21), signed into law on July 6, 2012, requires the FHWA to establish 

measures for State departments of transportation (State DOTs) to assess and 

report numbers and rates per vehicle mile traveled, including roadway fatalities and 

serious injuries. (§1203; 23 USC 150(c)). In a Notice of Proposed Rulemaking published 

on March 11, 2014, FHWA included a proposed recommendation that States prepare

themselves so that no later than January 1, 2020, all States use a medical record injury 

outcome reporting system that links injury outcomes from medical records to crash 

reports. FHWA issued a Federal Register Notice to elicit comments on this Notice of 

Proposed Rulemaking and will be issuing a final rule (Fed. Reg. 79). 

• Transportation Research Board (TRB): FHWA, NHTSA, and CDC are liaisons to TRB’s

National Cooperative Highway Research Program (NCHRP) project 17-57, Development 

of a Comprehensive Approach for Serious Traffic Crash Injury Measurement and 

Reporting Systems. The project’s goals are to identify an injury scoring system for further 

consideration, develop a roadmap to assist States in developing and implementing an 

interim system, and develop a State-based framework to perform comprehensive linkage 

of records related to motor vehicle crashes that resulted in serious injuries, and

incremental steps and priorities for achieving the linkage 

(http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=3179). A conclusion 
4 

of the project, as of January 2013, is that linking police crash reports to medical data is 

the best way to obtain a serious injury measure (Flannagan, Mann, & Rupp, 2013).

• International Traffic Safety Data and Analysis Group (known as the IRTAD Group): The 

IRTAD group, an on-going working group of the Joint Transport Research of the 

Organisation for Economic Co-operation and Development (OECD) and the International 

Transport Forum, issued recommendations for reporting on serious road traffic casualties 

including that the assessment of the severity of injuries should preferably be done by 

medical professionals, that police data should be complemented by hospital data, and that 

a ‘seriously injured road casualty’ be defined as a person with injuries assessed at level 3 

or more on the Maximum Abbreviated Injury Scale, i.e., "MAIS3+" (International 

Transport Forum, n.d.) 

Purpose

CDC and NHTSA developed this joint project to capture and preserve information on CODES 

and other State linkage systems to better inform future linkage pursuits. The study was supported 

by the University of Maryland National Study Center for Trauma and EMS under its CODES 

cooperative agreement with NHTSA as a CODES resource center. The study team was 

composed of CDC, NHTSA, and NSC specialists. 

The purpose of this report is to study the characteristics of State-level programs that link crash 

data to medical outcome data to determine the facilitators and barriers of these programs. 

Information on facilitators and barriers in linkage systems can be used to guide future 

improvements to a State’s data linkage system and to inform implementation of new State data 

linkage systems. Effective and useful data linkage systems optimize the utility of these systems 

for addressing public health and traffic safety issues.
5 

METHODS 

Theoretical Framework

Theory provides a framework of constructs and relations that lead to a desired outcome (Glanz,

Rimer, & Lewis, 2002). This framework can be used to identify needed data, and to categorize 

the collected data to obtain a better understanding of the system. The study team conducted a

literature review regarding organizational theory to determine what theory would provide a 

suitable framework for studying State data linkage systems. Because linkage programs involve a 

coalition of organizations working together to achieve a common goal, and as coalitions are 

complex organizations, the Community Coalition Action Theory (CCAT) was selected as the 

best theory to describe and understand State data linkage programs. The product of a State data 

linkage program is an information technology product, a data base of motor vehicle crash 

information; therefore, one construct—Information Technology Infrastructure—from the ITIM 

was selected to supplement the Implementation of Strategies construct from CCAT. 

CCAT presents a framework for building and evaluating effective State coalitions (Butterfoss & 

Kegler, 2002). In this theory, coalition membership is created by a lead agency or convener 

group. The resulting coalition sets up leadership and staffing, operations, and processes along 

with structures that lead to pooled resources, member engagement, assessment, and planning. 

This facilitates coalition’s implementation of strategies that address health problems, and lead to 

improved health outcomes. 

A coalition typically defines the strategies that will be implemented. For State linkage programs, 

the strategy to be implemented is defined ahead of time as a data linkage system. As a data 

linkage system is an information system, information-system development theories were 

searched to determine the characteristics that should be studied as part of implementation

strategies. The Information Technology Interaction Model (ITIM) has a construct of information 

technology infrastructure that lists the following components that are relevant to data linkage 

systems as being critical to developing a computer system (Silver, Markus, & Beath, 1995): 

1. Software development tools, 

2. Databases (or data sources), and

3. Capabilities of information technology personnel (i.e., people developing the system). 

The study team developed a framework for the study, which used eight applicable constructs 

from CCAT and combined one construct from CCAT with one from ITIM to form a ninth 

construct. Constructs were selected by considering their applicability to State data linkage 

systems. Outcomes were an important aspect: attributing improved health outcomes specifically 

to State linkage systems was not possible, but studying the immediate products was possible— a 

linked dataset and analyses of the linked dataset that can be used to improve health outcomes. 

Selected constructs, measures used for each construct, and the data source used to obtain each 

measure are shown in Table 1. 
6 

Table 1: Measurement of Relevant Constructs 

Construct Definition Measure

Community context Characteristics of State traffic safety 

community that affect coalition function 

and development. 

Experience (number years 

linking data), access to 

resources (CODES system

member), priority of motor 

vehicle crashes as a problem

Lead agency/

convener group

Agency responsible for primary role of 

CODES; data linkage, serving as 

gatekeeper to data.

Lead agency, 

advantages/disadvantages of 

type of agency serving as the 

lead

Coalition membership Organizations that make up coalition . Organizations involved

Leadership and staffing Staff needed to facilitate the coalition 

team. 

Number and type of staffing 

needed, staffing challenges

Pooled member and 

external resources 

Resources (funding, expertise, 

equipment, etc.) and collaborations, both 

internal and external.

Technical assistance, 

training, funding 

Member engagement Engagement of coalition with the 

community.

Interaction with community 

partners

Implementation of 

strategies/ Information 

technology infrastructure

Organizational resources that provide

capacity to generate the information 

technology application.

Databases/ data sources, 

linking techniques, staff 

capabilities 

Outcomes Community change outcomes that are 

likely to improve health and to increase 

capacity.

Linkage success, products 

from analyses of linked 

datasets 

Community capacity Increased capacity that can be applied to 

other health and social issues.

Positive outcomes of being 

involved in data linkage

All employed constructs are from CCAT (Butterfoss & Kegler, 2002) with the exception of 

Information Technology Infrastructure, which is from ITIM (Silver et al., 1995). 
7 

Data Collection

A study team consisting of traffic safety and public health specialists from NHTSA, CDC, and 

NSC collaborated on data collection for this project. The team collected both primary data (data 

collected specifically for the study) and secondary data (data that already exists or can be 

compiled from existing materials). Data sources and types of data used for this study included:

1) A survey of States conducting linkage programs (primary data collection), 

2) Focus groups with the surveyed States (primary data collection), 

3) Analysis of traffic records assessment (secondary data collection), and

4) Bibliographies of publications that used linked data (secondary data collection). 

Primary Data Collection

States participating in CODES as of 2012 (hereafter CODES States) and also States that had

data-linkage systems outside of CODES (hereafter referred to as other State data linkage 

systems) were included in the sample for the primary data collection. Other State data linkage 

systems are State data linkage systems that either linked or were actively trying to link police 

crash data to some form of medical data (e.g., hospital inpatient, emergency department, EMS) 

and had been active in the past 2 years. A subset of the other State data linkage systems were in 

former CODES States, and some were still independently conducting data linkages using the

CODES methods; however, these systems remained outside of the NHTSA CODES program. 

The final sample of 29 States included 15 CODES States and 14 other data linkage system

States. A list of these States and the parts of the study that they participated in is illustrated in 

Attachment A. CDC obtained approval to collect primary data for this study under OMB 

clearance number 0920-0879. 

Survey: The study team developed, reviewed, and finalized a questionnaire designed to obtain 

information regarding each of the constructs identified in Table 1. State-level CODES staff 

members also reviewed and tested the questionnaire for wording and timing. Testing was

conducted via an online survey software SurveyMonkey. CDC submitted the study protocol to 

the Office of Management and Budget (OMB) for approval under the CDC Office for State, 

Tribal, Local and Territorial Support (OSTLTS) Survey Center generic clearance package to 

obtain expedited approval. The Microsoft Word version of the final questionnaire is shown in 

Attachment B. Upon OMB approval, the survey was administered via SurveyMonkey to 15 

CODES States and 14 other data linkage system States (Attachment A) during December 2012. 

Twenty-five States responded for a response rate of 86 percent. 

Focus Groups: Based on initial analysis of the survey data, the team conducted focus groups to 

further explore the survey results. They developed an interview guide for the focus groups by 

reviewing survey results and creating open-ended follow-up questions to obtain further 

understanding of the constructs in Table 1. The team reviewed and revised the focus group 

questions to ensure that a group of five to seven people could fully discuss the topics within the 

2-hour time limit. Upon submission, OMB granted approval to the focus group addendum to the 

original application. Each of the 25 States that were asked to participate in the survey was invited 

to identify one person to participate in the focus groups. One trial focus group was conducted 
8 

with members of State CODES programs to verify the timing. Results of this focus group were 

included in this study because minimal changes to the interview guide were necessary as a result

of the trial. Four additional focus groups were conducted. The final focus group guide is in 

Attachment C. In addition to the questions in the guide, follow-up questions were asked to elicit 

further details. All focus groups were conducted remotely using GoToMeeting software, with 19

of the 25 States participating (Attachment A) for a response rate of 76 percent. Audio recordings 

were made of all five sessions, including one initial trial GoToMeeting session and four other

GoToMeeting sessions. 

Secondary Data Collection

Traffic Records Assessments: A State traffic records system usually comprises six core areas: 

crash, vehicle, driver, roadway, citation and adjudication, and injury surveillance. To qualify for 

NHTSA traffic records grant funds, States must have a Traffic Records Assessment completed 

every 5 years. NHTSA’s Traffic Records team oversees the assessment process, in which a panel 

of five independent subject matter experts (SMEs) interview representatives of the six traffic

records system components. The SMEs develop a final narrative report that includes information 

from the interviewees and other research derived from an internet search. States often post their 

completed assessment on their State Web site. 

For this study, traffic records assessments were collected through an internet search or by direct 

contact to the participating States; assessments for 19 States were obtained (Attachment A). 

From the assessment, the study team created an abstraction form to gather data regarding the 

quality and coverage of State motor vehicle and medical data systems. One form was completed 

for each traffic record assessment. 

Bibliographies: State linkage systems produced varied products. However, because a central 

repository for linkage products (including annual State fact sheets, Web pages, conference 

presentations) was not available, this study used State Web sites, PUBMED, and compilations

from CODES programs reported to NHTSA to identify products produced during the past decade 

(2004–2013). The Bibliography in Attachment D provides a representation of the various types 

of products/outcomes identified that can be produced from data linkage; it is not meant to be a 

reference list or an inclusive list of all products produced by State data linkage systems.

Data Analysis

The study team examined data from four data sources—survey, focus groups, traffic records 

assessments, and bibliography. Findings were mapped to the appropriate theory construct (Table 

1) and are reported by the applicable construct. 

Survey

The team created frequencies for each survey question; doing so involved collapsing some 

categories and, when appropriate, moving write-in answers for “Other” that actually belonged in 

an existing category. Some questions were cross-tabulated by whether the State was a CODES 

State. Results were analyzed using Microsoft Excel and SAS 9.3 (SAS Institute, Cary, NC). 
9 

Results are descriptive of the responding State programs and do not make extrapolations or tests 

of significance for inference. 

To relate certain conditions to the success of linkage in each State, a linkage outcome indicator 

was developed by dividing the number of years that the State successfully linked police crash 

records to medical records (hospital inpatient, emergency department, or emergency medical 

service) by the number of years that the State had these sources of data available. Analysis was 

limited to the 10 years before the survey. If a State had been performing linkage for only a subset 

of those 10 years, only that subset of years is in the analysis. For the purposes of analysis, the 

linkage outcome indicator is dichotomized with values ≥0.75 defined as high linkage success and 

values below 0.75 defined as low linkage success. The value of 0.75 was obtained by graphing 

the resulting linkage outcomes and examining them for a natural breakpoint, which was based on 

the way the data were clustered.

 

The number of full time equivalents (FTEs) for each program was estimated by multiplying the 

number of hours a person worked per week (e.g., 20, 40) by the percentage of time that person 

was assigned to work on the data linkage program. The resulting numbers were first summed and 

then divided by 40 hours to obtain the total FTEs required to operate the State data linkage 

program. Microsoft Excel® was used to explore the associations between the linkage outcome 

indicator and the number of FTEs. 

Focus Groups

A single member of the study team analyzed the five focus group sessions to identify general 

themes. The team then compiled notes from three different transcribers and compared them to 

create one set of response outputs by question. Notes from the different scribes were consistent, 

finding the exact or similar wording themes between scribes for individual respondent 

statements. Word processing software was used to find common words, and text was reviewed 

for key points and common themes. Words and themes repeated by more than one respondent 

were given higher priority. Team members also reviewed notes for key points that might have 

only been stated once. These tools were used to prepare a final set of prioritized outputs by

question. In addition, the three most data-rich focus group sessions were independently analyzed 

by a second member of the study team. During this second review, the study team member 

listened to the audio recordings and created an abridged transcript (Kruger & Casey, 2009). The 

abridged transcript transcribed the participants’ responses, but not directions and/or questions 

from the moderator. The transcripts were then analyzed in an iterative manner using word 

processing software. In the first round, the data were organized using broad codes based on the 

focus group questions and topics that spontaneously emerged, such as the best type of 

organization to lead linkage programs. In the second round, team members analyzed data within 

each code and further refined common themes, giving consideration to extensiveness (how many 

different people stated the same theme while keeping in mind that key insights might only be 

made once) and specificity (comments that were detailed rather than general or vague) (Krueger 

& Casey, 2009). After the team had fully developed themes, exemplar quotes were selected to 

help illustrate key points made by participants. 
10

Traffic Records Assessments

Members of the research team developed a template abstraction form to gather data from the 

participating State’s traffic records assessments on the quality and coverage of State motor 

vehicle and medical data systems. The template included: 

• A review of the core data sets often used in relevant linkage (i.e., crash, citation, EMS, 

hospital, trauma registry, mortality); 

• The data files a State had available; 

• Whether data followed a uniform State template; 

• How data were transmitted; 

• Whether others in the State participated in the use of the collection; and

• Whether the data had been linked, and if so, for how many years. 

 

To create a standard review, one member of the research team conducted information extraction 

from the traffic records assessment by reading the assessments and documenting standardized 

information on the research template.

 

Bibliography

The team reviewed the bibliography (Attachment D) to determine the general topic areas covered 

by the products and to determine how and where linked data were disseminated.

Synthesis of Data Sources

Information from each of the data sources was grouped by applicable construct. Each of the five 

team members independently reviewed the results and drafted a list of key points. The five lists 

were consolidated and factors mentioned by more than one team member retained to produce the 

final facilitators and barriers. Information from the Traffic Records Assessment data were used 

to check results from the survey and focus group for consistency. 
11

RESULTS 

Community Context

The CCAT construct of “Community Context” refers to the characteristics of the community that 

impact the coalition function and development (Table 1). 

In data results mapped to this construct, the majority of States (72%) identified motor vehicle 

injury as either the highest or a high priority compared with other health problems. 

Fifteen of the 25 States responding to the survey were part of the CODES linkage program. 

States who identified themselves as CODES States indicated that they had been linking data 

longer than other State data linkage systems with 67 percent of CODES States having started 

before 2000, as opposed to 20 percent of other linkage States (Table 2). 

Table 2: Time period when State started linking data (n=25)

Year data

linkage 

began

CODES States 

(n=15)

Other State data linkage

systems (n=10) Total (n=25)

% (n) % (n) % (n)

Before 2000 67% (10) 20% (2) 48% (12)

2000–2005 13% (2) 10% (1) 12% (3)

After 2005 20% (3) 70% (7) 40% (10)

TOTAL 100% (15) 100% (10) 100% (25)

Lead Agency/Convener Group 

The Lead Agency/Convener Group construct refers to the agency responsible for the data linkage 

and serves as the gatekeeper to the data (Table 1).

In data results mapped to this construct, the State department of public health was most likely to 

be the department responsible for coordination of the overall State linkage program, data linkage, 

and data analysis with at least half of the States reporting public health as the lead agency (Table 

3). The next agency most likely to be responsible was an academic institution followed by a 

State DOT. Only one State had a linkage program that was not run by one of these three types of 

agencies. Furthermore, this information was validated through Traffic Records Assessments. 

These 19 assessments (data not shown) revealed that multiple agencies perform the data linkage, 

including departments of health, highway safety, and academic institutions; however, more 

States used State agencies rather than academic institutions as the lead agency/convener group. 
12

Table 3: Agency type by responsibility for function in linkage program (n=25)

Agency State Dept. of 

Public Health

Academic 

Institutions

State Dept. of 

Transportation/

Highways

Other State 

Department

Function % (n) % (n) % (n) % (n)

The coordination of 

your overall 

linkage/CODES 

project? 

52% (13) 24% (6) 20% (5) 4% (1)

Your primary 

linkage/CODES project 

linkage? 

60% (15) 20% (5) 16% (4) 4% (1)

Your primary 

linkage/CODES project 

data analysis? 

56% (14) 24% (6) 16% (4) 4% (1)

In the focus groups, several participants commented on the best organizational home for linkage 

programs. Academic settings have the advantage of being a neutral third party and enjoy more 

freedom when selecting methodology and topics of study. The major disadvantage is that it can 

take longer to obtain data because they do not own any of the source data sets and are dependent 

upon other organizations to provide it. Of possible homes among State agencies, consensus was 

that the best home varies from State to State, depending on data ownership and rules/regulations 

concerning data sharing. As one person put it, 

“...we could have made it work, I think, were it housed in any of the other participating 

agencies as well...there’s not necessarily a right home or a best home. It kind of depends 

on the State’s situation.”

Several participants agreed that placing linkage programs in an agency that already owned one or 

more data sets was advantageous. Having a single agency serve as the data hub also was reported 

as beneficial. In such a plan, data collection activities are streamlined with one entity collecting 

the majority of the data and serving as a data warehouse for the State. Another benefit is 

minimizing service duplication with one single entity collecting, cleaning, linking, and analyzing 

data.

Coalition Membership 

The CCAT construct of “Coalition Membership” refers to organizations that make up the 

membership of the coalition (Table 1).

In data results mapped to this construct, 80 percent of those surveyed had a board of advisors or 

directors for their State linkage program. However, fewer than half (44%) had a small group or 

subcommittee that served as an advisory board for identifying linkage or research priorities. The 

majority (96%) of States indicated that formal and informal partnerships with agencies were 

essential to linking and analyzing data. 
13

In focus groups, the board of advisors or directors was described as the key mechanism through 

which data are shared among multiple data owners. Coalitions and boards of directors also were

described as essential facilitators of interagency partnerships. Although most spoke positively of 

this type of group and its role in linkage programs, one person did note that agencies still have 

different needs and interests. In addition to a board of advisors or directors, focus group 

participants also spoke about the value of a subcommittee that had a shared vision in obtaining a 

linked data set. Participants explained that if all data owners participate, the subcommittee can be 

a useful vehicle for making decisions about research topics. The arrangement also can promote 

increased accountability because the subcommittee must report to the larger board. Finally, one 

participant mentioned using her subcommittee’s relationship with the board to address policy 

roadblocks because the board is composed of higher-level agency management.

Of programs with a board of directors or advisors, a majority (64%) reported quarterly board

meetings. State Departments of Public Health were the organizations most likely to be on the 

board of advisors, followed by State motor vehicle agencies (Figure 1). The number of 

organizations represented on the board ranged from 1 to 12 with a median of 6 for States that had 

boards of directors.

Figure 1: Percentage of States listing organization as a member of board of advisors or directors 

(n=25)
14

Other identified members (not shown) listed by at least one State included EMS regulatory 

boards, AAA, Vital Records, other data users, Safe Kids, Council of Governments, public safety, 

other State agencies that have legislative needs/inquiries, and highway patrol/law enforcement 

members.

Leadership and Staffing

The CCAT construct of “Leadership and Staffing” refers to the staffing needed to facilitate the 

coalition (Table 1). 

In data results mapped to this construct, the majority of States (75%) had at least one position for

which the job description included planning, implementing, and/or evaluating data linkage and 

analysis activities. Focus group participants most frequently identified epidemiologist, SAS 

programmer, statistician, and administrator as key positions for a State linkage program. Other 

positions mentioned include health educator, GIS analyst, and marketer. Further probing 

identified the following as important attributes for proficiency in linking and analyzing data: 

analytical/statistical training; experience in programming; some understanding of traffic safety; 

familiarity with data variables and data sets; some understanding of linking and analysis 

software; comfort with SAS, MS Excel, or MS Access; and working with large data sets. Other 

attributes mentioned less frequently included understanding hospital coding (AIS, ICD-9 and 

ICD-10), knowledge of the Health Insurance Portability and Accountability Act (HIPAA) and 

other confidentiality considerations, and experience doing theory-guided analysis. Personality 

attributes identified for linkage staff included being focused, curious, analytical, and detailoriented.

The number of FTEs per State that were devoted to linkage ranged from .13 to 2.75 employees. 

Participants in the focus groups emphasized the need for full-time staff. As one person explained,

 

“Yeah, I would advocate for a full-time – go ahead and say it takes a full-time employee

to make a successful linkage system... You can’t have them come in and out, like a 

student come in over the summer and do it, just because they’re a Ph.D. statistical 

candidate.”

Staffing was identified as a key challenge to linkage and analysis. Turnover, both among core 

project staff and partner agencies, also was identified as a barrier. A single person working in 

isolation was mentioned as another barrier. As a focus group participant put it,

“I just have to say, to have another scientist working on it who is of equivalent 

background that you can bounce things off back and forth, I think it goes a long way to 

just being able to move it ahead in a high-quality data way that, you know, you’re going 

to be proud of that data and the output from it.” 
15

Pooled Member and External Resources

The CCAT construct of “Pooled Member and External Resources” refers to resources and 

collaborations, both internal and external, needed for linkage programs to function (Table 1).

Resources can include funding, expertise, equipment, and more. 

Data results mapped to this construct covered technical assistance and funding.

Technical Assistance and Training

For all topic areas, CODES States had either equal or higher proportions of training compared 

with other State data linkage systems (Table 4). 

Table 4: Percentage of States by CODES status and training areas received (n=24)

Training area

CODES 

State

% (n=15)

Other State

% (n=10) Total

% (n=24)

Data 

preparation

Understanding the structure and content 

of crash data 53% (8) 50% (5) 52% (13)

Creating clean useable datasets for 

analysis: using linkage results 67% (10) 20% (2) 48% (12)

Linkage Linkage using CODES2000 93% (14) 30% (3) 68% (17)

Linkage using Linksolv 40% (6) 40% (4) 40% (10)

Linkage using other linkage software 13% (2) 10% (1) 12% (3)

Analysis Missing data imputation 93% (14) 30% (3) 68% (17)

Analyzing linked, imputed data 80% (12) 30% (3) 60% (15)

Understanding the structure and content 

of crash data 53% (8) 50% (5) 52% (13)

Discussion in the focus groups confirmed that other State data linkage systems had less access to 

training and technical assistance on linkage than CODES States. These programs relied on the 

expertise possessed by available staff and resource learning materials/books.

 

If additional training could be provided for data linkage and analysis, States would be interested 

in the following topics: 

• Missing data imputation; 

• Analyzing linked and imputed data; 

• Creating public use data sets; 

• Creating an online query system, including mapping; 

• Probabilistic matching; 

• Evaluating the validity of using deterministic linkage methods; and 

• Increased understanding about the role and limitations of imputed data. 
16

Training that is hands-on, meaning either interactive in-person sessions or webinars with

homework afterward, was seen as more helpful than lecture-style training. Focus group 

participants indicated that practice and repetition were necessary for attaining proficiency in 

linkage and analysis. 

Most programs surveyed agreed or strongly agreed they had adequate access to technical 

assistance on linking (75%) and analyzing (74%) data. Further exploration in the focus groups

found that one-on-one technical assistance was viewed as helpful, but linkage projects were often 

stalled while States waited their turn for one-on-one assistance. This occurred among both 

CODES and other State data linkage systems. As one participant explained, 

“...when we needed the technical assistance...we had to wait for the next time technical 

assistance was available. So on our end, we had, I had a data person sitting there, 

waiting, not able to move the project forward.”

The focus groups also found that States desired a best practice, self-help manual or some other 

nationally outlined plan of what is considered acceptable standards for probabilistic linkage. 

Having access to such a tool would reduce some of the roadblocks to training, especially for new 

employees; such a plan also would serve as a roadmap for current projects. States discussed the 

need for training on techniques for those collecting the inputdata sources on techniques for 

improving data quality, since 

“...outputs are only as good as the data collected.” 

Funding 

NHTSA was the major source of funding reported (Figure 2). Further probing through the focus 

group discussions revealed that States received NHTSA funding directly from NHTSA and from 

other State organizations (i.e., highway safety offices or DOTs) via pass-through funding. In 

addition to NHTSA-specific CODES funding, respondents discussed that State data linkage

system funding was originally derived through section 408: Traffic Safety Information System 

funds. However, that funding had been replaced in 2013 with MAP-21 NHTSA section 405 and 

402 funds and could alter their funding application with the State. 
17

Figure 2: Funding sources for State linkage systems (n=25)

More than half (54%) of States indicated they had inadequate financial resources to sustain data 

linkage and analysis programs. 

Several States had not considered where they might seek new funding as NHTSA CODESspecific funding was ending. Others indicated that they were planning to connect with their State 

Traffic Records Coordinating Committee to pursue NHTSA Section 405 funding, and others 

indicated that they were beginning to make connections with other public health or injury 

prevention interest groups/agencies or with the CDC. Respondents indicated that they thought 

their data would have some utility outside of the traffic safety realm; therefore, other non-traffic 

sources of funding might be possible. The majority of States indicated that future funding was 

uncertain. Although several anticipated receiving funds through the State Highway Safety 

Office, the future of MAP-21 introduced an additional element of uncertainty. One State that is 

funded on a year-to-year basis addressed the time that is diverted from linkage and analysis 

towards grant writing.

“And so when you’re funded year to year, that’s one of our biggest challenges is we 

spend a lot of administrative time just trying to write the next year’s grant, and you know, 

doing all of the things that we need to do administratively that takes away from the 

science of it.”
18

Two States indicated that they had decided to discontinue their linkage programs, but that they 

would make every effort to obtain the data so it would be available when funding might be 

secured. 

A majority of States (96%) did not charge other agencies for data requests. In fact, some 

participants actively avoided charging fees, because they believed that as public dollars funded

the linkage program, the data and analysis should be available to others free of charge.

“To the degree, when there were prodigious requests involving a lot of hours or 

potentially, we tried make it something the health department or CODES board was 

interested in anyway, so we could dedicate the time necessary to do it ... even though we 

have permission to [charge for data requests] we try not to get in the position where we’re 

charging. I think we pretty much tried to avoid that.”

States that do charge for data and data requests took different approaches to pricing. Two States, 

both of which are housed in universities, charge for the time required to fulfill a large data 

request, but not the data itself. They determine the cost based on the salaries of staff involved 

and the number of hours estimated to complete the request. If it is a simple request like a fact 

sheet, they do not charge. One State charges a flat rate per 1,000 records for the data itself, and if 

additional programming were needed, charges are based on an hourly rate. Their customers are 

typically researchers, other State agencies, and hospitals.

Member Engagement 

The CCAT construct of “Member Engagement” refers to engagement of the coalition with the 

community (Table 1). 

 

In data results mapped to this construct, data-linkage programs reported the highest engagement 

with their State Traffic Records Coordinating Committees (TRCC), entities set up as a precondition for NHTSA grants under Section 411, CFR 23 - State Highway Safety Data 

Improvements (Table 5). They showed a lesser amount of engagement with other agencies and 

the lowest with NHTSA’s Regional Offices. (The survey did not ask about NHTSA’s National 

Center for Statistics and Analysis because that interaction was required for all CODES 

cooperative agreements.) In the focus groups, several participants noted that a history of 

collaboration with other agencies is a valuable facilitator of linkage projects. 
19

Table 5: Agency or other coalition member by frequency of interaction (n=25)

Agency or other Coalition 

interaction (N=25)

We 

interact 

frequently

We interact 

occasionally

We rarely 

interact

We never 

interact

% (n) % (n) % (n) % (n)

State Traffic Records Coordinating 

Committee 84% (21) 16% (4) 0 0 

Department of Public Health 60% (15) 32% (8) 4% (1) 4% (1)

Strategic Highway Safety Plan 

Coalition* 48% (12) 28% (7) 12% (3) 8% (2)

Core Violence and Injury Prevention 

Program 48% (12) 16% (4) 16% (4) 16% (4)

NHTSA Regional Office* 16% (4) 38% (9) 38% (9) 8% (2)

*Some respondents did not answer – missing not shown

One measure of engagement was the frequency with which data requests are received from 

various entities (Table 6). 

Table 6: Frequency of data requests from outside organizations (n=25)*

Indicate how often you get data 

requests from each of the following:

Frequently Occasionally Rarely Never

% (n) % (n) % (n) % (n)

Traffic Records Coordinating 

Committee 16% (4) 28% (7) 20% (5) 28% (7)

Department of Public Health – Injury 

Surveillance 16% (4) 40% (10) 20% (5) 12% (3)

Strategic Highway Safety Plan 16% (4) 24% (6) 20% (5) 32% (8)

NHTSA Regional Office 0% (0) 12% (3) 36% (9) 44% (11)

State Core Violence and Injury 

Prevention Program 20% (5) 12% (3) 20% (5) 28% (7)

**Other 16% (4) 4% (1) 0% (0) 12% (3)

* Some respondents did not answer – missing not shown

** Other requests include Highway Safety Offices and research entities such as universities.

Linkage projects that had been in place longer (i.e., began linkage before 2000) received more 

data requests per year than linkage projects that began after 2005 ( Table 7). 
20

Table 7: Data request frequency by length of time linkage project has been in place (n=22)* 

Frequency data requested for the 

following reasons:

Ongoing/Several per year Rarely/never

Prior 2000

(n=12)

After 2005 

(n=10)

Prior 2000

(n=12)

After 2005 

(n=10)

% (n) % (n) % (n) % (n)

Identifying traffic safety problems 67% (8) 40%(4) 0% (0) 20% (2)

Supporting traffic safety decision 

makers 50% (6) 20% (2) 8% (1) 30% (3)

Educating the public 58% (7) 20% (2) 16% (2) 20% (2)

Legislative decision making 58% (7) 10% (1) 8% (1) 30% (3)

* Some respondents did not answer – missing not shown

Years 2000 to 2005 were excluded because of small numbers

Linked data were shared most frequently with TRCC groups and Department of Public Health 

Injury surveillance divisions (Table 8). 

Table 8: Frequency of sharing data with other organizations (n=25)* 

Indicate how often you share or provide 

linked/CODES data to each of the following:

Frequently Occasionally Rarely Never

% (n) % (n) % (n) % (n)

Traffic Records Coordinating Committee 20% (5) 40% (10) 20% (5) 12% (3)

Department of Public Health – Injury 

Surveillance 16% (4) 44% (11) 16% (4) 12% (3)

Strategic Highway Safety Plan 16% (4) 20% (5) 24% (6) 32% (8)

NHTSA Regional Office 8% (2) 12% (3) 32% (8) 40% (10)

State Core Violence and Injury Prevention 

Program 16% (4) 16% (4) 12% (3) 36% (9)

Other ** 16% (4) 16% (4) 12% (3) 36% (9)

*Some respondents did not answer – missing not shown

State linkage programs received data requests from a list of partners; however, they did not

believe that their data were being used to the fullest capacity. The focus groups confirmed this 

finding. 

Focus group discussion revealed that States don’t know how to market their data to other 

potential users. Also, as linkage programs become more established, marketing might become a 

lower priority. As one focus group participant explained, 
21

“I think I kind of fall into the trap that we’ve been around for 20 years and people should 

know us.” 

Misperceptions among potential users regarding linked data and its capabilities also were noted 

as a challenge. As one person explained, 

“It’s still not well known what’s available, and more importantly, what you can do with 

the data that is available.”

When data were released to potential users, the recipients used it in several ways, such as to 

support or oppose legislation on seat belts and motorcycle helmets. Participants indicated those 

who did use their products were pleased and regularly requested additional analysis. The most 

frequently reported requesters were traffic safety decision makers/State executives, legislative 

lobbyists, health professionals, and the media. Several States also discussed the option of making

certain data accessible through their Web sites. States who believed they were successful in 

marketing their data indicated that they promoted the use of their data by attending traffic safety 

coalition meetings, joining the TRCC, presenting at other task forces committed to injury 

prevention, presenting at conferences, issuing press releases, and collaborating with law 

enforcement.

Implementation of Strategies/Information Technology Infrastructure

The ITIM construct of “Implementation of Strategies/IT Infrastructure” refers to organizational 

resources that give the organization the capacity to generate the information technology 

application (Table 1). 

Data results that apply to this construct covered databases, linking data, and staff capabilities.

Databases/Data Sources

Traffic-record assessment analysis documented that a majority of the States had access to 

variables needed for linkage in the three datasets that are most commonly linked (Table 9). All of 

the States had a uniform crash report, but several had no reports submitted to the statewide file 

electronically. Similarly, the majority of States (15 of 19) had a uniform traffic citation but not 

all had electronic transfer of those citations, and fewer than one-third had the ability to receive 

electronic transmissions at the court level. Medical records are almost exclusively submitted 

electronically to their respective reporting agencies (i.e., hospital association or State health 

department). The records might be sent on a monthly, quarterly, or annual basis.

Information regarding variables typically used for linkage in each dataset was available. In the 

crash database, all States had access to driver date of birth, license number, date, and location 

(jurisdiction) of crash. In the citation database, nearly all States had access to driver date of birth, 

license number, and vehicle tag number. In the EMS database, all States had access to the 

patient’s date of birth, date of incident, and mechanism of injury whereas most had access to the 

location (jurisdiction) of the receiving hospital. In the hospital databases (emergency department 

and hospital inpatient), all States had access to the patient’s date of birth, date of admission, and 
22

e-code, which identifies cases by the mechanism of injury. Of the States with a statewide trauma 

registry file, all had access to the patient’s date of birth, date of admission, e-code, and 

Abbreviated Injury Scale (AIS) scores. Finally, in the mortality database, all States had access to

the victim’s date of birth, date of death, location (jurisdiction) of death, and International 

Classification of Disease (ICD)-10 cause of death code. Little information was contained in the 

assessments regarding the specific variables used for linkage. However, the information above 

indicates that date of birth, date of incident, and locations of incident are available and most 

likely are used for probabilistic linkage. 

Data lag time was identified as a main barrier to obtaining data for linkage (Figure 3).

Figure 3: Challenges in obtaining data for linkage

Data lag times varied from less than 1 year to more than 2 years, depending on the State and the 

data source, with the majority of States reporting lag times of 1 year or less (Figure 4). 

 
23

Figure 4: Lag time estimates in months to receive data from your State data partners

Focus group sessions also found that lag time was a primary barrier to receiving data.

“...by the time we get them linked and analyzed they’re 2-3 years old”

No single type of data was found to be the reason for the lag; several States reported the crash 

data set took longer to obtain, whereas others reported the hospital, mortality, or emergency 

department data as having the longest lag time. When States were asked why lag times occurred, 

they indicated that lengthy lag times are sometimes unavoidable because of statutory 

requirements or legal concerns. A participant explained,

“The inpatient data by law has to be embargoed for a year after the end of the year, 

so we usually get, we’d get the 2012 data set in December [2013] or January [2014]. 

And that’s just, you know, the statutes that govern the use of that data set.”

However, other States reported that statutes helped facilitate speedy linkage, such as including 

vehicle-crash injuries as a reportable condition or requiring that hospital and emergency 

department data be submitted within 30 days of the end of a month.

The focus group sessions reported Institutional Review Board (IRB) requirements were a barrier 

or an addition to lag time. A majority believed that the requirements to protect confidentiality in 

their State were reasonable, but a few perceived their State’s requirements as overly strict and a 

barrier to data use. Other reasons for lag times included major changes to computer systems or

transferring from paper data collection to electronic data collection. 

Linking Techniques 

Fewer than half (n=10) of the States answered the question regarding the type of linkage they 

used. Of the States who answered, two-thirds of these States reported using probabilistic 

methods with the remaining one-third reporting using deterministic methods. Focus group 
24

participants indicated that the linkage technique can be complicated and the linking difficult. For 

this reason, staff involved with data linkage need a high level of understanding and technical 

expertise. The primary challenge to linking data was found to be staffing and technical assistance

problems followed by poor data quality and/or lack of unique identifiers (Figure 5). 

Figure 5: Reported challenges in linking data by number of States reporting

The primary challenges to analyzing data were lack of variables for matching, staffing, and 

insufficient training (Figure 6). 
25

Figure 6: Reported challenges in analyzing linked data by number of States reporting

Staff Capabilities

Approximately half of the States had either expert or intermediate proficiency in using two 

common linkage software packages: Linksolv and CODES2000 (Table 9). 

Table 9: Proficiency in linking by software type

Software

Proficiency in Linking

Expert1

% (n)

Intermediate2

% (n)

Beginner3

% (n)

Not proficient4

% (n)

Linksolv (n=13) 38% (5) 31% (4) 23% (3) 8% (1)

CODES2000 (n=18) 39% (7) 44% (8) 6% (1) 11% (2)

SAS (n=19) 74% (14) 26% (5) 0% (0) 0% (0)

ACCESS (n=19) 67% (10) 33% (5) 0% (0) 0% (0)

EXCEL (n=15) 42% (8) 58% (11) 0% (0) 0% (0) 1

Expert: Assists or able to teach others in using software

2

Intermediate: Can generally develop linkage specifications/program or run software without assistance 3

Beginner: Needs assistance in developing linkage specifications/programs or running software 

4

Not proficient at all

Expertise in using the software packages typically used to analyze linked data was higher. States 

had high expertise in SAS, a software used for analyzing linked data (Table 10).
26

Table 10: Proficiency in analysis by relevant software type

Software

Proficiency in analysis

Expert

% (n)

Intermediate

% (n)

Beginner

% (n)

Not proficient

% (n)

SAS (n=19) 74% (14) 26% (5) 0% (0) 0% (0)

EXCEL (n=13) 69% (9) 31% (4) 0% (0) 0% (0)

ACCESS (n=12) 42% (5) 58% (7) 0% (0) 0% (0)

IVEWARE (n=9) 22% (2) 44% (4) 22% (2) 11% (1)

Outcomes

The theory construct of “outcomes” refers to community change outcomes that are likely to 

improve health and increase capacity (Table 1). 

Linkage Success

When considering linking police crash reports to hospital, emergency department, or EMS, the 

majority of States (80%) were able to link 75 percent to 100 percent of the years of available 

data. For those States with lower linkage success (linking <75% of available years of data), two

of the five had just started their linkage effort. The Traffic Records Assessments validated these 

findings. Certain States reported successful linkages with and without a CODES program. 

Twenty-three States linked hospital records (emergency department and hospital inpatient) with 

crash data for at least 1 year. Only 1 State integrated citation data with other traffic records 

systems. All but 2 States linked hospital records (emergency department and hospital inpatient) 

with crash data. Fewer than half of States reported linking trauma registry or mortality data to 

crash reports, perhaps because of accessibility or completeness. 

States that had high linkage success (linking ≥ 75% of available years of data) had higher median 

FTEs (median: .94 FTE, range: .13–2.75 FTE) than States with lower success (median: .75 FTE, 

range: .63–1 FTE). However, some States achieved high linkage success with fewer than one 

FTE. Forty percent of the States with high linkage success had more than one FTE and none of 

the States with lower linkage success had more than one FTE. 

Products from Analyses of Linked Datasets

Products included presentations, reports, peer-reviewed publications, and fact sheets. Linked data 

were also used to support legislative activities and to provide information on specific motor

vehicle concerns. One other linkage State provided access to their linked data via an online 

database. The topic areas of these products included but were not limited to motor vehicles 

overall, analyses by age of driver/passenger, restraint use, motorcycles and traumatic brain injury 

(See Attachment D for a sample bibliography). Linked data also were used for several methods 

studies that resulted in suggestions for improving data quality. Areas that were unique to linked 

data included exploring motor vehicle crash data for morbidity, and by injury severity, nature of 

injury, and body region. Because of the linkage of crash reports with medical records, analyses 
27

were able to explore relationships such as seat belt use and injury outcome and helmet use and 

cost of injury.

Linked data were presented at various conferences and in numerous publications (Table 11). The 

conferences where linked data were presented ranged from regional to State, national, and even 

one international conference. Publications appeared in several specialized journals, including 

those that focused on medical, injury prevention, and motor vehicle injury topics. 

Table 11: Places where linked data have been presented or published

Conferences

Annual Meeting of National Association of Emergency Medical Services Physicians 

Annual Michigan Traffic Safety Summit

California Child Passenger Safety Community Teleconference

CODES Network Technical Assistance Meeting

Council State and Territorial Epidemiologists

Delaware Trauma System Committee

Emergency Medical Services for Children Annual Program Meeting

Illinois Traffic Engineering and Safety Conference

International Forum on Traffic Records & Highway Safety Information System

International Traffic Forum

Joint Annual Meeting of the Safe States Alliance, SAVIR and CDC Core I & II State Injury Grantees

Midwest Region/Iowa Safety Summit

Motorcycle Winter Conference

New York Highway Safety Symposium

New York State Association of Traffic Safety Boards

New York State Partnership for Teen Driving Safety

NHTSA Region 7 Safety Data Conference

Safe Communities Best Practices Conference

Safe Communities Workshop

State CODES Advisory Groups

State Lifesavers Conference

State Traffic Records Coordinating Committee Meeting

Traffic Records Forum 

Western Trauma Association Annual Meeting

Journals

Accident Analysis and Prevention

The American Surgeon

Annals of Advances in Automotive Medicine

Annals of Emergency Medicine

Injury Prevention

Journal of Neurosurgery of the Spine

Journal of Nursing Scholarship

Journal of Public Health Management Practice

Journal of Safety Research

Journal of Safety Research-Traffic Records Forum Proceedings
28

The Journal of Trauma

Kentucky Epidemiologic Notes & Reports

Missouri Medicine

Missouri Monthly Vital Statistics

MMWR

Pediatrics

Prehospital Emergency Care

Topics in Health Records management

Traffic Injury Prevention

Wisconsin Medical Journal

Other Publications

NHTSA Reports

State Reports

Examples of the types of requests linked data were used for include the following: 

• The effects of child safety seats on motor vehicle crash injury that were shared relative to 

a bill requiring safety seats for children 4 to 8 years old. 

• Data on injuries to children in bus crashes given to State governor’s school bus safety 

task force.

• Safety belt use and degree of injury data given to State Department of Transportation to 

present to district engineers.

• Crash types and costs given to Transportation Safety Board to develop costs of different 

types of crashes.

Community Capacity

The CCAT construct of Community Capacity refers to increased capacity that can be applied to 

other health and social problems (Table 1). 

Results mapped to this construct covered unexpected benefits of State data linkage systems that 

increased the Community Context. These following benefits were not covered in our survey but 

were discussed during the focus groups:

Linking data improved data quality. Because a single agency collects and monitors all the data 

sets, a certain level of quality control and monitoring is possible. Because of familiarity with the 

data systems, linkage staff have helped State agencies rewrite data collection forms such as EMS 

and police/citation. Linking data had a legislative impact as findings related to booster seats, seat 

belts, graduated driver’s licenses, motorcycle helmets, and cell phone laws were used to help 

explain the traffic safety concerns more clearly. In a focus group, a participant spoke about their 

legislative impact, saying, 

“But it’s always really exciting to me whenever we can put together a fact sheet and 

provide it to people who, you know, are at the legislature that are actually interested in 

seeing our results and interested in it. We’ve had the opportunity to, you know, testify 
29

during various committees about our data and to educate people that would help support 

or not support laws. So we’ve been able to be involved in, you know, motorcycle helmets 

or safety belts. We helped with the GDL that’s currently in [State], booster seats. We’ve 

helped with cell phone laws. Different things like that, so it’s been a really fun thing to 

see, and a really good benefit of linkage.”

Interagency collaboration improved. Because agencies were naturally collaborating when they 

shared data, they also had begun to collaborate in initiating and developing agency ideas. States 

have also connected with new partners. One participant put it, 

“I think an unexpected benefit is we thought prior to this that we were pretty well 

connected to the injury community in the State. Through TRCC and our CODES project 

we found a number of additional partners that we had not engaged before that have really 

informed our work in a number of areas.”
30

DISCUSSION

General Findings 

State data linkage programs were successful in linking crash data to medical data with the 

majority linking three quarters or more of the years of available data. The resulting databases 

provided diverse data findings that enabled States to create numerous products (e.g., fact sheets, 

presentations and peer reviewed publications on various motor-vehicle– related topics). These 

products were disseminated through the internet, journals, and scientific meetings. Linkage also 

led to positive outcomes beyond data and data products, most notably increased community 

capacity through greater collaboration with partner agencies, improved data quality, and more 

efficient data collection and processing. Finally, States were able to apply their linkage skills to 

other data sets outside of the typical crash, roadway, vehicle, driver, enforcement, and injury

surveillance data systems used in traffic safety analysis. 

 

NHTSA CODES provided essential resources to the States that participated. It enhanced 

sustainability of linkage programs by providing States with a funding source for several years, 

which allowed States to focus on program implementation rather than on obtaining funding. One 

possible result of this is that CODES programs, overall, had been in place longer than other State 

data linkage programs. Another crucial resource provided by CODES was training and technical 

assistance that were important to successful data linkage. As a result, CODES programs received 

more technical assistance and training compared with other State data linkage programs. In the 

absence of the overarching NHTSA CODES infrastructure, States might consider implementing 

a community of practice to exchange information and ideas as found beneficial through other 

research efforts (Snyder, 2003).

Common Themes

In synthesizing the results of the study, the team noted common themes and findings from the 

four data collection modes. In particular, the survey and focus groups provided insight into key 

themes and points for developing or sustaining a linkage program. Facilitators and barriers were 

identified as follows:

Facilitators

Facilitators are positive factors that influence a program (Robinson, 2006). Certain facilitators 

stood out as contributing to success in linkage and in analysis of linked data. Those the study 

team identified include the following: 

• Community Context 

A community context that facilitates linkage includes establishing traffic safety as a high 

priority and having access to data that are suitable for linkage.

• Housing the overall responsibility for the data linkage program in one organization

When establishing a linkage program, strategically selecting the right organization to run the 

linkage program facilitates success. One factor to consider is which organizations own the 
31

source datasets to be linked; choosing one of these as the lead can facilitate obtaining the 

data. In contrast, if a State linkage project wants greater neutrality and freedom to select 

topics of study, a non-State organization such as an academic institution might be a better 

choice.

• Establishing a coalition that includes all data owners

In addition to choosing an organizational home, setting up a coalition that includes all data 

owners and any other relevant parties that meets regularly is a key facilitator. This provides a 

forum for agencies to meet on a regular basis and provides leadership and direction.

• Establishing memorandums of understanding and/or undergoing institutional review board 

review to obtain data

Formal memorandums of understanding and institutional board review set parameters for 

obtaining data and releasing results.

• Good identifiers/matching variables in the data

The majority of the States had at least a minimal set of variables in the police and medical 

datasets that could be used to link.

• Interactions with other organizations

Interacting with other organizations such as Traffic Record Coordinating Committees and the 

Core Violence and Injury Prevention Programs gives linkage programs a way to understand 

data needs and to market linked data and products.

• Having the right staff with the right skills 

The right skills include not only a high level of expertise with software packages and 

epidemiology/statistics but also knowledge of traffic safety, the data sources being linked, 

and presentation and marketing skills to ensure data are used. Permanent staff who are able to 

carry over past years linkage knowledge are preferable to temporary staff brought in to 

perform linkage. Multiple persons (either staff or members of the coalition) working on 

linkage facilitates progress as they can provide the skills and knowledge needed for success.

• Technical assistance and training

Successful linkage is facilitated by staff that has access to training and technical assistance on 

the software and methods used in preparing data for linkage, linking data, and analyzing the 

linked data. Repetitive training and technical assistance that allow the recipient to learn 

something, work on it, then return for the next step is preferable to one-time offerings.

• Stable funding

Stable funding (for a period of more than a year) for the data linkage program relieves the

program of constantly writing grants and applications for funding.

• Organized data-linkage system beyond the State level.

Being part of an organized data linkage system such as CODES can facilitate data linkage 

and analysis as members have more access to technical assistance and training and also had 

funding guaranteed for more than 1 year.
32

Barriers

Barriers are negative factors that can impede or reduce the success of a State linkage program. 

As with facilitators, the study team looked through common responses in the survey and focus 

groups to identify common themes of barriers. Noted barriers included the following: 

• Lack of funding and other resources

Most States already felt they did not have adequate financial resources for linkage and 

analysis. These barriers will likely increase as States transition from CODES-specific 

NHTSA funding. In many cases, States currently lack a concrete plan for seeking other 

sources of funding. Additionally, when sufficient resources are not available for 

training/technical assistance, that can present a barrier.

• Staffing turnover

Finding and retaining staff with appropriate skills can be difficult. Turnover can slow a 

project substantially because departing staff take with them key institutional knowledge such 

as data quality and State data characteristics Considerable time and resource investment are 

required to bring a new employee up to speed on intricacies and nuances of data sets and 

linkage methodology.

• Documentation of processes

A lack of documented procedures related to cleaning and linking data delayed linkage in 

States that either did not have or had to wait for technical assistance or training.

• Data lags

Data were sometimes not available for as long as 2 years after the end of the data year and 

often one data set had a substantially longer lag time than the others, limiting linkage. A long 

data lag can diminish stakeholder enthusiasm for linkage, because it is considered “old data” 

and presumed to be no longer relevant.

• Statutory requirements for data use

Statutory requirements, legal concerns, and institutional review board requirements could be 

overly burdensome. They contributed to data lag times and posed substantial roadblocks to 

obtaining data in some States. 

• Linkage techniques

Probabilistic linkage and imputation are advanced statistical techniques that require training 

or assistance to learn. In combination with the barrier of staffing turnover, linkage techniques 

can be a barrier to successfully completing linkages. Furthermore, results can be difficult to 

explain to those using the data.

• Marketing linked data

Failure to market linked data, network to expand the audience that linkage projects reach, or 

to cultivate new partnerships were barriers for continued demand of the linked data. 
33

The facilitators and barriers to successful data linkage that this study identified were supported 

by other studies. A case study of five Canadian provinces involved in the dissemination of a 

Heart Health Initiative found that the major facilitators were appropriately skilled and/or 

committed persons, funds/resources, and strong partnerships (Robinson, 2006). Major barriers 

were competing priorities, lack of funds/resources, and lack of skilled/committed persons. 

Leadership competence, effective collaboration, demonstrating program results, and strategic 

funding are components of community-based program sustainability (National Opinion Research 

Center, 2010). This study confirmed that resources, including funding and skilled staff, 

leadership, and collaboration and partnerships, were facilitators of linkage programs. A concern 

for existing State data-linkage programs is that two keys for sustainability are demonstrating 

program needs and strategic funding. The current study found that data-linkage results were not 

used or recognized as much as they could be and that most States did not have a plan for future 

funding. 

Use of Theory

Using theory to design the research instruments in this study was essential to suggesting vital 

characteristics of systems that should be studied. Theory is useful not only in studying data

linkage systems but could also be used to design and improve such systems by specifying 

essential characteristics and the relationships between characteristics that lead to a desired 

outcome (Glanz, 2002 ). Data linkage systems are public health programs, often overseen by a 

coalition, and involve an information system. As a result, theories from these three areas can be 

used to inform how to design and implement better data linkage systems. Finally, given the lack 

of a system such as CODES to coordinate and fund linkage programs, theories of program 

sustainability can help States who are considering maintaining their data linkage system or 

starting a new one to determine factors needed to sustain these programs (Schell, 2013; Benz, 

Infante, Oppenheimer, Scheer, & Tilson, , 2010). 

Limitations

This study had several limitations. It was initially planned as a more quantitative analysis of the 

factors related to successful linkage, but the number of States who were considered not 

successful was so low that quantitative analysis was not appropriate. A second limitation is that 

the majority of the States in this study made traffic safety a priority and two-thirds of the States 

were in the CODES program. Participation in this program required States to demonstrate that 

they had access to data sets and the ability to successfully link them. As a result, these findings

might not be applicable to all States. Another limitation is that the information taken from traffic 

records assessments is accurate only to the State’s last assessment (within 5 years). Finally, not

all constructs of the theories used to structure this study were included. Because of a desire to 

limit the time required of subjects to respond to the questionnaire and to participate in the focus 

groups, constructs selected were the ones most relevant to data linkage systems. Additionally, the 

measures for the constructs selected were the ones considered to be most relevant and serve as 

proxies for the constructs.
34

Conclusions

Given the interest in and need for linked police crash with medical record data and the success of 

many states States in linking data and producing products, this assessment can be useful in 

identifying the facilitators of and barriers to an effective data linkage program. In addition to 

considering these facilitators and barriers in implementing and maintaining data linkage 

programs the following are guidelines that States should consider for current and future linkage 

programs: 

• Set up a coalition or create a subcommittee of an existing motor vehicle safety coalition.

• Select an organizational home.

• Hire full time, permanent staff, rather than part-time or temporary staff. Select staff with

appropriate skills and abilities.

• Provide, on an annual basis, funds for training and technical assistance. Data linkage is a 

complex process that requires highly skilled staff. Choose hands-on training and technical 

assistance that can be available on an as-needed, ongoing basis to maximize value.

• Create/join a community of practice of linkage practitioners to avoid working in isolation.

• Use Memorandums of Understanding and Institutional Review Boards to establish 

parameters around data use and data sharing, but work to overcome rules that create 

substantial data lag.

• Prioritize training and marketing to others on the use of linked data outputs and data 

products.

• Plan for longevity and sustainability rather than relying on year-to-year grant writing.

State data linkage systems that link police crash reports to medical records are a valuable source 

of data on non-fatal motor vehicle crashes and on the severity of injuries from these crashes. 

These data might be used to identify risk factors, design strategies to address these risk factors, 

and evaluate these strategies once implemented. In addition to directly producing important 

public health data, linkage systems increase the State’s capacity to understand and analyze both 

motor vehicle crash and other data. Understanding the facilitators and barriers to an effective and 

useful linkage system can optimize the system’s utility for public health and transportation 

safety. 
35

Acknowledgments

The authors acknowledge the following individual people and organizations:

Angela Salazar, Public Health Adviser, National Center for Environmental Health and Bobby 

Rasulnia, Acting Deputy Director, Office for State, Territorial, Local and Tribal Support at the 

Centers for Disease Control and Prevention for their invaluable facilitation of the OMB process.

Tim Kerns, Database Engineer, University of Maryland National Study Center for Trauma and 

EMS, Patricia Dischinger, Professor and Principle Investigator for Maryland CODES, University 

of Maryland National Study Center for Trauma and EMS for their guidance, knowledge, and 

support on this project, and Suzanne Hewitt, medical writer/editor, for her careful editing of the 

final report.

All the participants in State data-linkage programs who participated in the survey and focus 

groups to provide the data for this study:

Alaska Alaska Department of Health and Social 

Services Division of Public Health

Alice Rarig

California California Department of Public Health Lynn Walton-Haynes

Colorado Colorado EMS and Trauma Data Program Jesse Hawke

Connecticut Connecticut Department of Public Health Justin Peng

Delaware Delaware Office of Emergency Medical 

Services

Laurie Lin

Florida Florida Department of Health Karen Card

Illinois Illinois Department of Transportation, 

Division of Traffic Safety

Mehdi Nassirpour

Indiana Perdue University, Center for Road Safety Jose Thomaz

Iowa Iowa Department of Public Health Donald Shepard

Kentucky University of Kentucky, Kentucky Injury 

Prevention and Research Center

Mike Singleton

Maine Maine Department of Transportation Joseph Riddick

Maryland University of Maryland School of Medicine, 

National Study Center for Trauma and EMS

Tim Kerns

Minnesota Minnesota Department of Public Health, 

Injury and Violence Prevention Unit 

Mark Kinde

Missouri Missouri Departmet of Health and Senior 

Services

Mark Vantuinen

Nebraska Nebraska Department of Health and Human 

Services

Ying Zhang

New York New York State Department of Health Michael Bauer

North Carolina EMS Performance Improvement Center Chad Lohmeier

Nevada Nevada Department of Transportation Kim Edwards
36

Ohio The Research Institute at Nationwide 

Children's Hospital

Thiphalak Chounthirath & 

Gary Smith

Oklahoma Oklahoma Health Department Tracy Wendeling

South Carolina South Carolina State Budget and Control 

Board

Tracy Joyce Smith

Utah University of Utah, Intermountain Injury 

Control Research Center

Larry Cook and

Andrea Thomas 

Virginia Virginia Department of Motor Vehicles, 

Virginia Highway Safety Office

Angelisa Jennings

Washington Washington Department of Health Zeynep Shorter

Wisconsin Wisconsin Department of Health Services Richard Miller

This study was funded by CDC through interagency agreement 10FED1011909 with NHTSA.
37

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40

ATTACHMENTS 

Attachment A. State participation in study
41

Attachment B. Questionnaire for Study
42
43
44
45
46
47
48
49
50
51

Attachment C. Focus Group Script

<Display slide 1 – Attachment F>

Welcome, thank you for joining us today for our focus group. As described in our email 

invitation, we are here to discuss the characteristics of the Crash Outcomes Data Evaluation 

System (CODES) and similar State Linkage Systems that are related to successful data linkage 

and analysis. We conducted an assessment of States who had CODES/linkage programs during 

December and January. During this session, we will present initial results from that assessment, 

ask for feedback on some of them, and ask new questions based on the results. Today’s session 

will be recorded so that members of the research team can listen to the recordings to identify 

general themes and conduct analysis. The data will be kept anonymous and no individual results 

will be released with either your name or State attached to them.

We’d first like to introduce the members of the team that are joining for today’s call. I am Jackie 

Milani and I will be serving as the facilitator. I am a research project coordinator with the 

University of Maryland Baltimore, National Study Center for Trauma and EMS. Also joining are 

(CDC Representatives, NHTSA Representatives, and NSC Representatives). 

<Display slide 2-Attachment F >

I’d like to ask everyone joining us today to introduce his- or herself by giving his/her name, job 

title, organization, and verbally let us know that you agree to be recorded. Once we finish with 

our introductions we will formally begin our session. 

<Display slide 3-Attachment F>

Thank you for those great introductions. We are going to now move on to talk with you about the 

flow of our session today. We will begin by sharing the objectives, move on to establish working 

ground rules and then start the actual question and answer session.

<Display slide 4-Attachment F>

So our Objectives for today are:

To gain a better understanding of the characteristics of successful linkage and analysis 

programs in your State

To identify barriers and facilitators of current linkage and analysis programs in your State

and

To learn more about the sustainability of linkage and analysis programs for the future.

Does anyone have any questions or feedback for us to this point?

<Display slide 5-Attachment F >

OK great then we will move on as a group to establish our ground rules. 

We’re going to share some of the rules that we have established and then ask you to contribute 

some additional ideas. Here are some base ground rules:
52

• The focus group session will run for 2 hours and no longer. 

• There are six areas that will need to be covered in that 2 hour time period. We will have a 

separate person serve as time keeper for the session to help us stay on task.

• No answer is a bad answer.

• All participants will be asked to give some information. We will try to go around the 

room encouraging everyone to respond to each question in some manner.

• And as a reminder again, all information and ideas will be recorded.

Does anyone have any additional ideas to contribute to our ground rules?

At this point discuss ideas for ground rules and add new ones.

Now we will start with the question and answer session. For some of the questions we will be 

showing you relevant results from the assessment that was recently conducted on 

CODES/linkage programs. These will be displayed on your computer screen.

<Display slide 6-Attachment F>

We’ll start the first section with questions about the agencies involved in your linkage project.

• For our first question, please look at this slide that shows the assessment responses to the 

question of which agency is responsible for coordinating the linkage project. 

o In thinking about these results, can you share some of the advantages of each of 

these types of agencies being involved with the coordination of a linkage project? 

o Can you share some of the disadvantages of each of these types of agencies being 

involved with the coordination of a linkage project? 

• If while working on your linkage project you have worked on small-groups or subcommittees outside of your board of directors or advisors 

o Share with us the reasons why these small-groups or sub-committees may have 

been formed.

o Share with us some of the successes that your group encountered while working 

in this type of setting?

o Share with us some of the barriers that your group encountered while working in 

this type of setting?

• If while working on your linkage project you have never worked on small-groups or subcommittees outside of your board of directors or advisors, can you share with us the 

reasons why small-groups or sub-committees may have never been formed?

(Display slide 7-Attachment F)

• Shown on the screen is a list of agencies that we found from our assessment that were 

most frequently mentioned as being represented on your linkage/CODES Board of 

Directors or Advisors. 

o Which agencies do you believe are needed to perform 

 the linkage

 to analyze the results
53

o How do these agencies uniquely contribute to your efforts in linking the three data 

sets?

Discussion of question along with probes from the facilitator. 

Next we’ll talk about funding linkage programs.

<Display slide 8-Attachment F >

This slideshows the various sources of funding identified from the assessment. 

• Which of these is the primary funding source for your linkage program?

Discussion of question along with probes from the facilitator.

• For each of these agencies, which funding streams, grant titles, or program areas are used 

to provide funding to your linkage program?

Discussion of question along with probes from the facilitator.

• Where do you plan to obtain funding for your linkage program in the future? (brainstorm 

ideas) 

Discussion of question along with probes from the facilitator.

• If you perform analysis of linked data do you establish a means to charge for those 

products or deliverables?

o If so, please describe and explain how charges are calculated.

o Are there different charges based on the nature and type of the requesting agency?

Discussion of question along with probes from the facilitator.

Now we’d like to move on and discuss the organizations who have used your linked data along 

with the way in which these data were used.

(Show slide 9-Attachment F) 

This slide shows the percentage of States that interact with various State and regional 

organizations along with the frequency of data interactions.

• Please describe the types of interactions you have with these organizations .

• The next question is going to be displayed to you on the screen. We would like to ask you 

to give us some immediate feedback about the types of products that you have created for 

each of these organizations using your linked data:

(Show slide 10-Attachment F) 
54

This slide shows the ways in which States have reported using their linked data.

• Were these linked data used as much as you would have liked for them to have been 

used? If not, why not?

• How have you promoted the use of your linked data with other potential partners?

Discussion of question along with probes from the facilitator.

(Show slide 11-Attachment F)

This slide shows the proficiency in linking and analyzing data using various software packages 

that the States reported. 

• How were these software packages used in linkage and analysis?

Discussion of question along with probes from the facilitator.

• We’d like to know what is needed to make staff proficient at linkage and analysis.

• What types of experience do you think are needed for an individual to be proficient in 

linking the data (for example work or training experiences that might contribute)

Discussion of question along with probes from the facilitator.

• What types of experience do you think are needed for an individual to be proficient using 

the linked data for data analysis (again reflecting on work or training experiences)

Discussion of question along with probes from the facilitator.

 (show slide 12-Attachment F) 

Slide 12 shows the areas of training and technical assistance that States reported receiving.

• If you had training or technical assistance support what aspects did you find were of 

particular benefit in supporting your linkage or analysis efforts?

Discussion of question along with probes from the facilitator.

• Thinking back over the training and technical assistance support you have received what 

do you think could have been improved?

Discussion of question along with probes from the facilitator.

 

Another aspect of linkage systems is obtaining the source data to be used in linkages. We’d like 

to discuss how that process worked in your State. 
55

(show slide 13-Attachment F) 

This slide graphically shows the lag times for receiving the top three sources of data – crash, 

inpatient hospital, emergency department. 

• Did you have barriers obtaining source data in your State? If so describe some of the 

barriers in obtaining 

Discussion of question along with probes from the facilitator.

*Probe more if this is not shared to examine potential responses:

What barriers did you encounter due to data confidentiality or data release policies?

Discussion of question along with probes from the facilitator.

Finally we’d like to finish with a discussion of overall challenges and successes in conducting 

linkage. 

(show slide 14-Attachment F) 

This slide shows some of the challenges with linkage identified in the assessment,

• Please share more about these challenges and 

• Are there any other challenges that we may not have discussed that you think we should 

know about?

Discussion of question along with probes from the facilitator.

(show slide 15-Attachment F)

This slide shows some of the challenges with analysis identified in the assessment.

• Please share more about these challenges and 

• Are there any other challenges that we may not have discussed that you think we should 

know about?

Discussion of question along with probes from the facilitator.

• For this next question we would like learn how you have managed questions about the 

linkage process and data outputs that come from customers who have requested the data. 

Could you share specific experiences and how you responded?

Discussion of question along with probes from the facilitator.

• Have you found any unexpected benefits from working with the linkage project in your 

State? 
56

Probe further to explore: data quality, additional variables that could be added, feedback to law 

enforcement for additional training, enhancing other reporting forms to connect the data more 

easily

Discussion of question along with probes from the facilitator.

• Does anyone have anything else that they would like to say on any areas that we’ve 

covered or any new areas?

Thank you for participating in this focus group. 
57

Attachment D. Bibliography

This bibliography provides a representation of the various types of identified products/outcomes 

that can be produced from data linkage; it is not meant to be a reference list or an inclusive list of 

all products produced by State data linkage systems.

Presentations (Author (if given), Date of conference (if given), Title, Conference Name, 

Conference Location (if given), (State that data was from))

2003 

• Omowale M., Lin, L., Megargel, R., O’Connor R. E. (2003, January). Reduction in Injury 

Severity and Cost of Treatment with Motorcycle Helmet Use, Annual Meeting of National 

Association of Emergency Medical Services Physicians, Panama Beach, FL. (Delaware)

• Omowale M., Lin, L., Megargel, R., & O’Connor, R. E. (2003, January). Reduction in Injury 

Severity and Cost of Treatment with the Use of Occupant Restraint (poster), Meeting of National 

Association of Emergency Medical Services Physicians, Panama Beach, FL. (Delaware)

• Lin, L. (2003, July). Crash and Injury Analysis of Young Drivers in State of Delaware, 

International Traffic Forum,. Nashville, TN. (Delaware)

• Qu, M., & Dhungana, P. (2003, July). Hospital Charges for Patients Involved in Motor Vehicle 

Crashes in Nebraska, 1999-2000, International Traffic Forum. Nashville, TN. (Nebraska) 

• Cook, L. J., Hyde L. K., Olson L. M., Weis H. B., & Dean J. M. Pregnancy-Associated Crash and 

Birth Outcomes: Linking Birth/Fetal Death to Motor Vehicle Data. Ninth Annual Maternal and 

Child Health Epidemiology Conference. (Utah) 

• Cook, L. J., Knight S., & & Olson L. M. Differences in crash characteristics and outcomes 

between DUI and aggressive driving crashes. American Public Health Association annual 

meeting, San Francisco, CA, 2003. (Utah) 

• Donaldson, A. E., Cook, L. J., Hutchings, C. A., & Dean, J. M. Crossing County Lines: The 

Impact of Crash Location and Driver’s Residence on Motor Vehicle Crash Fatality. American 

Public Health Association annual meeting, San Francisco, CA, 2003. (Utah) 

• Hyde, L. K., Cook, L. J., Lightfoot A. C., & Olson L. M. Graduated Driver Licensing in Utah: Is 

It Working? American Public Health Association annual meeting, San Francisco, CA, 2003. 

(Utah)

• Cook, L. J. Probabilistic Linkage Basics. Data Management, Integration, and Dissemination for 

Public Health Action Meeting, Atlanta, GA, 2003. (Utah) 

• Cook, L. J., Knight S., & Olson L. M. A Comparison of Aggressive and DUI Crashes. 6th Annual 

CODES/Network Technical Assistance Meeting, 2003. (Utah)

2004 

• Crash and Injury Analysis of Young Drivers in State of Delaware, Traffic Records Forum. 

(Delaware)

• Knight S., Cook, L. J., & Hyde L. K. (2004, November). Street Racers in Utah. American Public 

Health Association annual meeting, Washington DC. (Utah) 

• Cook, L. J., Olson L. M., & Dean J. M. (June, 2004). Using Probabilistic Linkage to Study Injury 

Outcomes. International Conference on Statistics in Health Sciences, Nantes, France. (Utah)

2005 

• O’Connor. R., Tinkoff G., & Lin L. (2005, February). Effect of a Graduated Driver Licensing 

System on the Proportion of Crashes and Injuries Involving Drivers Under Age 18, Western 

Trauma Association 35th Annual Meeting. Jackson Hole, WY. (Delaware)

• Prubhaker, D., & Qu, M. (2005, August) Why is it so Risky to drive on Roads where the Speed 

Limit is posted at 50 mph? International Traffic Records Forum. Buffalo, NY. (Nebraska)

• Joyce T. (2005) Using CODES Data to Support Primary Enforcement Legislation, CODES 

Annual Meeting. Washington, DC. (South Carolina) 
58

• Cook, L. J., Knight S., & Olson L. M. (2005, August). A Comparison of Aggressive and DUI 

Crashes. Traffic Records Forum, Buffalo, NY. (Utah)

2006 

• Support for Passenger Limitation on 16-and 17-year-old Drivers, CODES Network Technical 

Assistant Meeting. (Delaware)

• CODES and 408 Fund in Illinois, Illinois Traffic Records Coordinating Committee Meeting. 

(Illinois)

• Estimating long-term direct medical costs of low safety belt usage in Kentucky, CODES 

Technical Assistance Meeting. (Kentucky)

• Cook, L. J., Nelson, D., &Olson, L. M. (November, 2006). Are Adults Who Use Child Restraints 

For Children Using Seatbelts Themselves? American Public Health Association annual meeting, 

Boston, MA. (Utah) 

2007 

• Delaware Aggressive Driving Study, CODES Network Technical Assistance Meeting. 

(Delaware)

• Illinois Crash Outcome Data Evaluation Systems (CODES) Project, CODES Technical Advisory 

Group. (Illinois) 

• Inpatient Hospital Charges and Utilization Patterns of Crash Victims in Illinois, CODES 

Technical Advisory Group. (Illinois) 

• Kentucky’s Crash Outcome Data Evaluation System. Kentucky Lifesavers Conference. 

(Kentucky)

• Rothenberg, H., Benavente, M., Knodler, M. A., & Kennedy, J. (2007). An Application of 

CODES Data Linkages for Crashworthiness Computations, Society of Automotive Engineers 

World Congress. Detroit, MI. (Massachusetts) 

• Cook, L. J., & Olson L. M. (2007, November). Motorcycle Helmets: Using Linked Data to 

Assess Outcomes in Motorcycle Crashes. American Public Health Association annual meeting, 

San Diego, CA. (Utah) 

• Cook, L. J., Knight S., & Olson L. M. (2007, November). Impact of Including Low Probability 

Matches on Probabilistic Linkage Results: A Comparison of High Probability vs. Imputed Match 

Sets. American Public Health Association annual meeting, San Diego, CA. (Utah)

• Cook, L. J. Probabilistic Linkage and the CODES Data Network. (2007). Annual Conference on 

Assessment Initiative, Atlanta, GA. (Utah)

2008 

• Analysis of Motor Vehicle Related Injuries and Fatalities Among Occupants of Passenger Cars in 

Illinois, International Forum on Information Systems, Orlando, FL & Illinois Traffic Engineering 

and Safety Conference. (Illinois) 

• Inpatient Hospital Charges and Utilization Patterns of Crash Victims in Illinois, Traffic Records 

Coordinating Committee meeting. (Illinois)

• Analysis of Motor Vehicle Related Injuries and Fatalities Among Occupants of Passenger Cars in 

Illinois, International Forum on Traffic Records & Highway Safety Information System in 

Orlando, FL. (Illinois) 

• Analysis of Motor Vehicle Related Injuries and Fatalities Among Occupants of Passenger Cars in 

Illinois. Illinois Traffic Engineering and Safety Conference. (Illinois) 

• Burch, C., A Comparison of KABCO and MAIS, Traffic Records Forum (Maryland) 

• Rothenberg, H., Crash Outcomes for Older Occupants of Motor Vehicles in Crashes, Traffic 

Records Forum (Massachusetts)

• Seymour, L., Gaichas, A., Roesler, J., & Kinde, M., ABC: Alcohol, Belts, and Crashes, Traffic 

Records Forum (Minnesota) 
59

• Van Tuinen, M., The Nature and Severity of Injuries to Children in School Bus Crashes. Traffic 

Records Forum, Orlando Florida. (Missouri)

• Introduction to CODES, Midwest Region/Iowa Highway Safety Summit. (Nebraska) 

• Joyce, T., Teen Drivers in South Carolina, Traffic Records Forum. (South Carolina) 

• Olsen, C. S., Cook, L. J., Keenan, H. T., & Olson, L. M. Impact of Adult Restraint Non-Use on 

Child Injuries in Motor Vehicle Crashes. American Public Health Association annual meeting, 

San Diego, CA, October 2008, and Forum on Traffic Records & Highway Safety Information 

Systems, Orlando, FL. (Utah) 

• Cook, L. J. Teen Drivers and Graduated Driver Licensing. University of Utah Department of 

Pediatrics Grand Rounds, Salt Lake City, UT, 2008. (Utah) 

• Cook, L. J. Using CODES to Assess Motorcycle Crash Outcomes. 34th International Forum on 

Traffic Records and Highway Information Systems, Orlando, FL, 2008. (Utah) 

2009 

• Injury Comparisons in Motorcycle Crashes, CODES Network Technical Assistance Meeting,

(Delaware)

• Illinois Crash Outcome Data Evaluation Systems (CODES) Project, CODES Technical Advisory 

Group, (Illinois) 

• CODES in Kentucky, CODES Technical Assistance Meeting, (Kentucky)

• Addressing the Teen Driver Problem in New York State, 35th International Forum on Traffic 

Records & Highway Safety Information Systems. (New York) 

• Effects of Upgraded Child Restraint Law in New York State, 35th International Forum on Traffic 

Records & Highway Safety Information Systems. (New York) 

• Motor Vehicle Crash-Related Traumatic Brain Injury in New York State, 35th International 

Forum on Traffic Records & Highway Safety Information Systems. (New York) 

• Effects of Upgraded Child Restraint Law in New York State, Crash Outcome Data Evaluation 

System Program and Technical Assistance Meeting. (New York)

• Crash Outcome Data Evaluation System Program (CODES) in South Carolina, South Carolina 

Traffic Records Coordinating Committee. (South Carolina) 

• Growing CODES in South Carolina, 2009 CODES Annual Meeting. (South Carolina) 

• Inpatient Hospital Charges and Utilization Patterns of Crash Victims in Illinois, Illinois Traffic 

Records Coordinating Committee meeting. (Illinois)

• Illinois Crash Outcome Data Evaluation Systems (CODES) Project in Illinois, IL TRCC meeting. 

(Illinois)

• Illinois Crash Outcome Data Evaluation Systems (CODES) Project in Illinois. Annual CODES 

meeting. (Illinois)

• CODES Project and Highway Safety Programs in Illinois, Illinois Traffic Safety Staying Alive 

Conference. (Illinois) 

• Thomas, A. M., Cook, L. J., & Olson L. M. Evaluation of National Safety Belt Campaign. 

American Public Health Association annual meeting, Philadelphia, PA. (Utah) 

• Olsen, C. S., Donaldson, A. E., & Cook, L. J. Analyzing Continuous Injury Outcomes with 

Quantile Regression Methods. American Public Health Association annual meeting, Philadelphia, 

PA. (Utah)

• Cook, L. J., & Olson, L. M. Motorcycle Helmets: Using Linked Data to Assess Outcomes in 

Motorcycle Crashes. American Public Health Association annual meeting, Philadelphia, PA. 

(Utah)

• Cook, L. J. Probabilistic Linkage Theory and Applications. Center for Epidemiology Studies 

Seminar, Logan, UT. (Utah) 
60

2010 

• Using CODES Data to Support Back Seat Safety Belt Legislation in New York State , National 

Highway Traffic Safety Administration, Crash Outcome Data Evaluation System Grand Rounds 

Electronic Webinar. (New York)

• Distracted Driving Crashes in New York State, 36th International Forum on Traffic Records & 

Highway Safety Information Systems. (New York)

• Effects of Booster Seats as Compared to Safety Belts in Reducing Injury Risk to Children Aged 

4-8 in Motor Vehicle Crashes in New York State, 36th International Forum on Traffic Records & 

Highway Safety Information Systems. (New York)

• Using CODES Data to Support Back Seat Safety Belt Legislation in New York State, Crash 

Outcome Data Evaluation System Program and Technical Assistance Meeting. (New York)

• Effects of Booster Seats as Compared to Safety Belts in Reducing Injury Risk to Children Aged 

4-8 in Motor Vehicle Crashes in New York State, California Child Passenger Safety Community 

Teleconference. (New York)

• Your Partner in Traffic Safety, New York State Association of Traffic Safety Boards. (New 

York)

• The New York State Crash Outcome Data Evaluation System: How You Can Use the Data in 

Your County, New York Highway Safety Symposium. (New York) 

• Defining the Teen Driver Problem, New York State Partnership for Teen Driving Safety. (New 

York)

• Data Resources at the Bureau of Injury Prevention, New York State Association of Traffic Safety 

Boards. (New York) 

• CODES Project and Highway Safety Programs in Illinois, NHTSA Region V Leadership 

meeting. (Illinois)

• Safety Belt Use and Head Injuries Among Crash Victims. Data Committee of Illinois Department 

of Public Heath. (Illinois)

• Health Care Cost and Utilization Patterns of Crash Victims In Illinois, CODES Advisory Group 

Meeting. (Illinois)

• The Effect of Seat Belt Use on Head Injuries Among Crash Victims, CODES Annual Training 

Meeting, (Illinois)

• Cook, L. J. The Use of Nontraditional Outcomes to Measure Crash Severity. Traffic Records 

Forum, New Orleans LA, 2010. (Utah) 

• Thomas, A. M., Cook, L. J., & Olson L. M. Injury Severity and Citations in Commercial Vehicle 

Crashes. American Public Health Association annual meeting, Denver, CO. (Utah)

• Cook, L. J., Thomas, A. M., Olsen, C. S., & Olson L. M. Using CODES data to determine the 

association between universal helmet laws and motorcycle crash outcomes. American Public 

Health Association annual meeting, Denver CO. (Utah)

• Olsen, C. S., Donaldson, A. E., Thomas, A., Cook, L. J. Estimating Median Hospital Charges and 

Confidence Intervals for Multiple Imputed Data using Quantile Regression. American Public 

Health Association annual meeting, Denver, CO. (Utah)

2011 

• Injury Analysis of Alcohol Impaired Driving Crashes, CODES Network Technical Assistance 

Meeting, (Delaware)

• Factors associated with the decrease in nonfatal motor vehicle crash-related injuries in Kentucky 

from 2000 to 2009. Traffic Records Forum, (Kentucky) 

• Using CODES data to estimate the burden of neck injuries resulting from traffic crashes in 

Kentucky, 2008-2009. CODES Annual Meeting, (Kentucky) 
61

• Magic Behind Motor Vehicle Crash Fatality Trends: A Closer look at Nebraska Crash Data, 

CSTE Annual Meeting. (Nebraska)

• Lessons Learned for Data Linking Project: Cardiovascular Disease Related EMS and Hospital 

Discharge Data, CSTE Annual Meeting. (Nebraska)

• The New York State Department of Health, Injury Prevention Program: A Model for Data to 

Program Use, 37th International Forum on Traffic Records & Highway Safety Information 

Systems. (New York)

• A Comparison Study of Injury Outcomes by Seating Position for 0-12 Year Olds Involved in 

Motor Vehicle Crashes in New York State, 37th International Forum on Traffic Records & 

Highway Safety Information Systems. (New York)

• The Epidemiology of Traumatic Brain Injuries in New York State, Council of State and 

Territorial Epidemiologist Annual Conference. (New York) 

• New York State Booster Seat Laws: Saving Lives and Reducing Injuries, Joint Annual Meeting 

of the Safe States Alliance, SAVIR and CDC core I & II State Injury Grantees. (New York)

• Cracking CODES(motorcycle-related injury in Ohio), 16th Annual Michigan Traffic Safety 

Summit. (Ohio) 

• Motorcycle Safety Awareness Survey (Preliminary Results), TRCC meeting. (Illinois)

• Thomas, A. M., Thygerson, S. M., Merrill, R. M., Cook, L. J. Identifying Work-Related Motor 

Vehicle Crashes in Multiple Databases. American Public Health Association annual meeting, 

Washington DC. (Utah) 

• Thomas, A. M. Using Linked Data for State Legislation: Defending the Utah Booster Seat Law. 

Zero Fatalities Safety Summit, Salt Lake City, UT. (Utah)

• Cook, L. J., Thomas, A. M., Olsen, C. S. Using data to support child passenger safety. Zero 

Fatalities Safety Summit, Salt Lake City, UT. (Utah)

• Thomas, A. M. Using Linked Data for State Legislation: Defending the Utah Booster Seat Law. 

Joint Annual Meeting of the Safe States Alliance, Society for Advancement of Violence and 

Injury Research, and Centers for Disease Control Core I and II State Injury Grantees, Coralville, 

IA. (Utah)

• Cook, L. J. Injury control and probabilistic linkage. Joint Annual Meeting of the Safe States 

Alliance, Society for Advancement of Violence and Injury Research, and Centers for Disease 

Control Core I and II State Injury Grantees, Coralville, IA, April 2011. (Utah) 

• Cook, L. J., Thomas, A. M., Olsen, C. S., & Olson L. M. A comparison of motorcycle crash 

outcomes between universal and partial helmet law States. Joint Annual Meeting of the Safe 

States Alliance, Society for Advancement of Violence and Injury Research, and Centers for 

Disease Control Core I and II State Injury Grantees, Coralville, IA, April 2011. (Utah) 

• Edelman, L.S., Cook, L. J., & Olson L. M. Injuries occurring to rural older adults. Gerontological 

Society of America. San Diego, CA, 2011. (Utah) 

2012 

• Injury Analysis of 16 - and 17-Year – Old Drivers in Delaware, CODES Network Technical 

Assistance Meeting, (Delaware)

• Differences in protective effect of motorcycle helmets by nature of traumatic brain injury, 

CODES Annual Meeting, (Kentucky)

• Do Not Let Distraction Take Your Life Away: Distracted Driving in Nebraska 2006-2010, CSTE 

Annual Meeting, (Nebraska) 

• Racial Disparities in Non-fatal Motor Vehicle Crash Injuries in Nebraska, CSTE Annual Meeting, 

(Nebraska)

• Using Seatbelts in the Backseat Saves Lives, Reduces Injuries and Saves Money, Joint Annual 

Meeting of the Safe States Alliance and CDC Core State Injury Grantees. (New York)

• Teen Drivers in South Carolina, CODES Annual Meeting, (South Carolina) 
62

• Motorcycle Related Injuries and Costs (Based on CODES Data), TRCC meeting, Illinois

• Motorcycle Related Crash Victims and Their Associates Hospital Charges in Illinois, CODES 

Annual Training (Webinar) (Illinois) 

• Motorcycle Related Crash Victims and Their Associates Hospital Charges in Motorcycle, Winter 

Conference in Springfield, IL (Illinois) 

• Thomas, A. M., Olson, L. M., & Cook, L. J. Implementation and evaluation of the Ticketing 

Aggressive Cars and Trucks program. American Public Health Association annual meeting, San 

Francisco, CA. (Utah) 

2013 

• Cook, L. J. Linking motor vehicle crash and hospital databases to analyze injury outcomes. 

International Forum on Traffic Records and Highway Safety Information Systems. Special 

Session: Data Integration: Linking Data to Help Save Lives. Edmonton, Alberta. (Utah)

Reports (Author (if one given), Title, Organization, (State that data came from)) 

2003 

• Qin, H., & Singleton, M. Motor Vehicle Crash Outcomes in Kentucky: 2000 Management Reports. 

University of Kentucky: KIPRC; 2003. (Kentucky) 

• Singleton M., Christian W., Struttman T., Sublett J. Using Linked Data to Evaluate Risk Factors for 

Traumatic Brain Injury to Drivers in Passenger Vehicle Crashes in Kentucky. University of 

Kentucky: KIPRC; 2003. (Kentucky) 

• Singleton, M., & Qin, H. Kentucky motor vehicle crash facts: safety belt use and primary 

enforcement in Kentucky. University of Kentucky: KIPRC; 2003. (Kentucky) 

• Nebraska CODES Management report 2001-2003. (Nebraska) 

2005

• Singleton, M., Xiao, Q., & Agent, K. Economic Costs of Low Safety Belt Usage in Motor 

Vehicle Crashes in Kentucky. University of Kentucky College of Engineering. Kentucky 

transportation Center; 2005. (Kentucky) 

• Dischinger, P. C., Read, K. M., Kufera, J. A., Kerns, T. J., Ho, S. M., Burch, C. A., Jawed, N., & 

Burgess, A. R. Consequences and Costs of Lower Extremity Injuries. Technical Report, United 

States Department of Transportation, Office of Vehicle Safety Research, DOT HS 809 871. June 

2005. ( Maryland) 

2006 

• Singleton, M. Collisions with Fixed Objects in Kentucky, 2000-2004. University of Kentucky: 

KIPRC; 2006. (Kentucky)

2007 

• Singleton, M. Nonfatal traffic injuries in booster-age child passengers in Kentucky from 2000-

2004. University of Kentucky: KIPRC; 2007. (Kentucky) 

• Rothenberg, H., Benavente, M., Knodler, Jr., M. & Kennedy, J. An Application of CODES Data 

Linkages for Crashworthiness Computations. (Massachusetts)

• Rothenberg, H., Benavente, M., & Knodler, Jr., M. CODES Statewide Application: Older 

Occupants of Motor Vehicles. (Massachusetts)

• Connor, K. A., Xiang, H., & Smith, G. A. The Impact of a Standard Enforcement Safety Belt 

Law on Fatalities and Hospital Charges in Ohio: An Analysis using 2003 Ohio CODES Data 

(2007 Report). (Ohio) 

2008 

• Singleton, M., & Yu, L. Motorcycles in Kentucky, 2000-2004. University of Kentucky: KIPRC; 

2008. (Kentucky)

• New York State Traffic Data Report for 2007 and 2008. Available at 

(www.health.ny.gov/statistics/prevention/injury_prevention/traffic/index.htm). (New York)
63

• Center for Injury Research and Policy, the Research Institute at Nationwide Children’s Hospital, 

The Medical and Economic Impact of Motorized Recreational Vehicle-Related Traumatic Brain 

Injury in Ohio (2008 Report). (Ohio)

2009 

• Singleton, M., & Yu, L. Preliminary nonfatal injury analysis for selected emphasis areas of the 

Governor’s Executive Committee on Highway Safety. University of Kentucky: KIPRC; 2009. 

(Kentucky)

• Cook, L. J., Kerns, T. , Burch, C., Thomas, A. M., & Bell, E. (2009) Associations between helmet 

use and motorcyclist head and facial crash outcomes in CODES linked data. National Highway 

Traffic Safety Administration, DOT HS 811 208. Multiple States.

2010 

• Singleton, M. Factors associated with the decrease in nonfatal motor vehicle crash-related injuries 

in Kentucky from 2000 to 2009. (Kentucky) 

• Singleton, M. CODES nonfatal injury indicator reports for 2008. (Kentucky)

• Center for Injury Research and Policy, the Research Institute at Nationwide Children’s Hospital, 

Injury in Ohio. (Ohio) 

• Shepherd, D. A Comparison of Iowas Crashes Involving Older Drivers Using Linked Data. 

(Iowa)

2011 

• Singleton, M. Using CODES data to estimate the burden of neck injuries resulting from motor 

vehicle crashes in Kentucky in 2008 and 2009. (Kentucky) 

• Singleton, M. CODES nonfatal injury indicator reports for 2009. (Kentucky) 

• Nebraska CODES Management report 2005-2011. (Nebraska)

• Center for Injury Research and Policy, the Research Institute at Nationwide Children’s Hospital, 

Motorcycle Injuries in Ohio. (Ohio) 

2012 

• Center for Injury Research and Policy, the Research Institute at Nationwide Children’s Hospital, 

Injuries Among Older Adults in Ohio. (Ohio) 

• The Community Crash Reports have been produced since 1995 using the CODES linked data. 

The Reports have been used in a variety of Community Education Programs throughout the State, 

including communities involved in Wisconsin's 'Safe Communities Program'. The reports are 

produced using the zip code of residence of the occupants of vehicles involved in crashes in as 

requested by the Wisconsin Department of Transportation. (Wisconsin) 

2013 

• National Transportation Safety Board. (2013). Crashes involving single-unit trucks that resulted 

in injuries and deaths. (Publication number NTSH/SS-13/01, PB2013-13-106637). Washington, 

DC:NTSB. (Multiple States)

Fact Sheets (Author (if given), Title, url (if given), (State that data came from))

2005 

• Rothenberg, H. Massachusetts Lane Departure Crash and CODES Data Analysis, 

http://www.ecs.umass.edu/masssafe/CODES%20WEBSITE/Lane_Departure_FactSheet.pdf. 

(Massachusetts)

• Motorcycle Helmets and Traffic Safety- Nebraska 1974-2004, 2005. (Nebraska) 

• Seatbelts and Teens- Nebraska. (Nebraska)

• Death and Injury Rates and Posted Speed Limit- 1999-2000, 2005. (Nebraska) 

• Alcohol Related Crash Costs and Morbidity, Wisconsin. (Wisconsin)

• Seatbelt Use Outcomes, Wisconsin. (Wisconsin)

• Motorcycle Helmet Use and Crash Outcomes, 2003-2005. (Wisconsin) 
64

2006 

• Non-Nebraska Drivers Involved in Motor Vehicle Crashes Occurring in Nebraska. 1999-2003, 

2006. (Nebraska) 

• Motor Vehicle Crashes (MVC): The Leading Cause of Injury Death in Nebraska. 1999-2003, 

2006. (Nebraska) 

• Alcohol Related Crash Costs and Morbidity. Wisconsin. (Wisconsin)

• Seatbelt Use Outcomes. Wisconsin. (Wisconsin)

• Motorcycle Helmet Use and Crash Outcomes. Wisconsin. (Wisconsin)

• Utah Teenage Distracted Driving Crashes, 2006. (Utah)

2007 

• Restraint Use of Pickup Truck Occupants in Nebraska. 2002-2005, 2007. (Nebraska) 

• Alcohol Related Crash Costs and Morbidity , Wisconsin, 2007. (Wisconsin) 

• Seatbelt Use Outcomes, Wisconsin, 2007. (Wisconsin)

• Motorcycle Helmet Use and Crash Outcomes, Wisconsin, 2007. (Wisconsin)

• Children Involved in Crashes. 2006-2007. (Utah)

2008 

• Motor Vehicle Crashes: The Leading Cause of Injury Death in Nebraska. 2002-2006, 2008. 

(Nebraska)

• Alcohol Impaired Driving in Nebraska. 2005-2007, 2008. (Nebraska) 

• Restraint Use in Nebraska. 2007, 2008. (Nebraska) 

• Increased risk of injuries for riding in the back seat unrestrained. (New York) 

• Alcohol Related Crash Costs and Morbidity , Wisconsin, 2008. (Wisconsin) 

• Seatbelt Use Outcomes, Wisconsin, 2008. (Wisconsin)

• Motorcycle Helmet Use and Crash Outcomes, Wisconsin, 2008. (Wisconsin)

2009 

• Underage Drinking and Driving Fact Sheet- Nebraska. 2004-2008, 2009. (Nebraska) 

• Motorcycle Helmets and Head Injury- Nebraska. 2004-2008, 2009. (Nebraska) 

• Illinois Department of Transportation. Car Crash Factsheet. (Illinois)

• Illinois Department of Transportation Teenage Driver Factsheet. (Illinois)

• Illinois Department of Transportation Factsheet –Impaired. (Illinois)

• Illinois Department of Transportation Motorcycle Crash Victims and their Health Care Charges in 

Illinois, Fact Sheet. (Illinois)

• Injuries and costs of unbuckled occupants during a crash, with a comparison to those buckled up, 

New York. Targeted toward police and not available for the general public. (New York) 

• Increased risk of injuries for riding in the back seat unrestrained, New York. (New York)

• Alcohol Related Crash Costs and Morbidity, Wisconsin, 2009. (Wisconsin) 

• Motorcycle Helmet Use and Crash Outcomes, Wisconsin, 2009. (Wisconsin)

• Medical Outcomes of Safety Restraint Compliance on High-Speed Roadways, 2009. (Utah)

• Hospital Characteristics by Safety Restraint Compliance on High-Speed Roadways. 2000-2009. 

(Utah)

2010 

• Motor Vehicle Crash Deaths in Nebraska, 2010. (Nebraska)

• Restraint Use and Crash Outcomes in Nebraska, 2010. (Nebraska),

• Alcohol and Crash Outcomes in Nebraska, 2010. (Nebraska)

• Injury burden for unrestrained passengers for each county in New York and for the whole State. 

Targeted toward police and not available for the general public. (New York) 

• Injury burden for unrestrained children (15 and younger) in the back seat, New York. Targeted 

toward police and not available for the general public. (New York) 
65

• South Carolina Motorcycle Fact Sheet, 2010. (South Carolina) 

• Alcohol Related Crash Costs and Morbidity , Wisconsin, 2010. (Wisconsin) 

• Motorcycle Helmet Use and Crash Outcomes, Wisconsin, 2010. (Wisconsin) 

2011 

• Glare-related Crashes in Nebraska, 2011. (Nebraska)

• Adverse Weather Related Crashes in Nebraska, 2011. (Nebraska)

• Do Not Let Distraction Take Your Life Away, 2011. (Nebraska) 

2013 

• South Carolina Teen Driving Fact Sheet, 2013. (South Carolina) 

Journal Publications (Author Title, Journal, Volume/Issue, Pages, (State that data came from))

2003 

• Singleton, M., & Qin, H. Improving surveillance of injury deaths using probabilistic 

dataLlinkage. Kentucky Epidemiologic Notes & Reports. Fall–Winter 2003. (Kentucky) 

• Hyde, L. K,, Cook, L.J., Olson, L. M., Weiss, H. B., & Dean, J. M. (2003). Effect of motor 

vehicle crashes on adverse fetal outcomes. Obstet and Gynecol, 102(2):279–286. (Utah)

• Hutchings, C.B., Knight, S., & Reading, J. C. (2003). The use of generalized estimating equations 

in the analysis of motor vehicle crash data. Accident Anal Prev, 35(1):3−8. (Utah)

2004 

• Singleton, M., Qin, H., & Luan, J. (2003). Factors associated with higher levels of injury severity 

in occupants of motor vehicles that were severely damaged in traffic crashes in Kentucky, 2000-

2001. Traffic InjPrev (June 2004) (Kentucky) 

• Smith, R., Cook, L. J., Olson, L. M., Reading, J. C., & Dean, J. M. (2004). Trends of behavioral 

risk factors in motor vehicle crashes in Utah, 1992-1997. Accident Anal Prev, 36(2):249–255. 

(Utah)

• Vernon, D. D., Cook, L. J., Peterson, K. J., & Dean, J. M. (2004) Effect of repeal of the national 

maximum speed limit law on occurrence of crashes, injury crashes, and fatal crashes on Utah 

highways. Accident Anal Prev, 36(2):223–229. (Utah) 

• Knight, S., Cook, L. J., & Olson, L. M. (2004) The fast and the fatal: street racing fatal crashes in 

the United States. Inj Prev, 10(1):53–55. (Utah) 

2005 

• Dhungana, P., & Qu, M. (2005). The risks of driving on roadways with 50 miles per hour posted 

speed limit. J Safety Res-Traffic Records Forum proceedings. 36 501–504. (Nebraska) 

• Zhu, M., Hardman, S., & Cook, L. (2005). Backseat safety belt use and crash otcome.” J Safety 

Res – Traffic Records Forum Proceedings. (New York) 

• Durkin, M., McElroy, J., Guan, H., Bigelow, W., & Brazelton, T. ( 2005). Geographic analysis of 

traffic injury in Wisconsin: Impact on case fatality of distance to level I/II trauma care. Wisc Med

J, 104:( 2). (Wisconsin) 

• Sauter, C., Zhu, S., Allen, S., Hargarten, S., & Layde, P. M. (2005) Increased risk of death or 

disability in unhelmeted Wisconsin motorcyclists. Wisc Med J, 104(2):39–44., 2005. (Wisconsin)

Hyde, L. K., Cook, L. J., Knight, S., & Olson, L. M. (2005). Graduated driver licensing in Utah: 

is it effective? Ann Emerg Med, 45(2):147–154. (Utah) 

• Mann, N. C., Knight, S., Olson, L. M., & Cook, L. J. (2005). Underestimating injury mortality 

using statewide databases. J Trauma, 58(1):162–167. (Utah)

• Zhu, M., Hardman, S. B., & Cook, .L. J. (2005). Backseat safety belt use and crash outcome. J 

Safety Res, 36(5):505–507. (Utah)

• Cook, L. J., Knight, S., & Olson, L. M. (2005). A comparison of aggressive and DUI crashes J 

Safety Res, 36(5):491–493. (Utah) 
66

• Soderstrom, C.A., Dischinger, P. C., Kufera, J. A., Ho, S. M., Shepard, A. (2005). Crash 

culpability relative to age and sex for injured drivers using alcohol, marijuana or cocaine. Annual 

Proceedings/Association for the Advancement of Automotive Medicine. pp. 327–341, 2005. 

(Maryland)

2006 

• Allen, S., Zhu, S., Sauter, C., Layde, P., Hargarten, S. A comprehensive statewide analysis of 

seatbelt non-use with injury and hospital admissions: new data, old problem. Acad Emerg Med. 

2006 Apr;13(4):427–34. Epub 2006 Mar 10. (Wisconsin)

• Donaldson, A. E., Cook, . L. J., Hutchings, C. B., & Dean, J. M. (2006). Crossing county lines: 

the impact of crash location and driver's residence on motor vehicle crash fatality. Accident Anal 

Prev. 38(4):723–727. (Utah) 

• Dischinger, P. C., Ryb, G. E., Ho, S. M., & Braver, E. R, (2006). Injury patterns and severity 

among hospitalized motorcyclists: a comparison of younger and older riders. Annual 

Proceedings/Association for the Advancement of Automotive Medicine. 50:237-49,. (Maryland). 

• Kufera, J. A, Soderstrom, C.A., Dischinger, P. C., Ho, S. M., & Shepard, A. 2006. Crash 

culpability and the role of driver blood alcohol levels. Annual Proceedings/Association for the 

Advancement of Automotive Medicine, pp. 91-106,. (Maryland). 

2007 

• O’Connor, R., Tinkoff, G. H., Ellis, H., , & Lin, L. Effect of a graduated licensing system on 

motor vehicle crashes and associated injuries involving drivers less than 18 years-of-age. 

Prehospital emergency care.

• Guo, H., Eskridge, K., Christensen, D., Qu, M., & Safranek, T. (2007). Statistical adjustment for 

misclassification of seat belt and alcohol use in the analysis of motor vehicle accident data. 

Accident AnalPrev,39(1):,117–124 

2008 

• Conner, K. A., Xiang, H., Gronger, J. I., & Smith, G. A. (2008). Using data linkage to assess the 

impact of motorized recreational vehicle-related injuries in Ohio. J Safety Res,;39:469–475. 

• Cook, L. J., Hoggins, J. L., & Olson, L. M. (2008). Observed seatbelt usage among drivers of 

heavy commercial vehicles in Utah. Accident Anal Prev 40(4):1300–1304. (Utah) 

• Bissonette, J. A., Kassar, C., & Cook, . LJ. (2008). Assessment of costs associated with deervehicle collisions: human death and injury, vehicle damage, and deer loss. Human-Wildlife 

Conflicts, 2(1):17–27. (Utah) 

• Dischinger, P. C., Ryb, G. E., Ho, S. M., & Burch, C. A. The association between age, injury, and 

survival to hospital among a cohort of injured motorcyclists. Annual Proceedings/Association for 

the Advancement of Automotive Medicine. 2008; 51: 97–110. (Maryland)

2009 

• Zhu, M., Chu, H., & Li, G. (2009). Effects of Graduated Driver Licensing on Licensure and 

Traffic Injury Rates in Upstate New York. Accident Anal Prev 41(3):531-5. (New York)

• Rochette, L. M., Conner, K.A., & Smith, G. A. (2009). The contribution of traumatic brain injury 

to the medical and economic outcomes of motor vehicle-related injuries in Ohio. J Safety 

Res,40(5):353–8. (Ohio) 

• Hanrahan, R. B., Layde, P. M., Zhu, S., Guse, C. E., & Hargarten S. W. (2009). The association 

of driver age with traffic injury severity in Wisconsin. Traffic Inj Prev, (4):361–7. (Wisconsin) 

• Wang, M. C., Pintar, F., Yoganandan, N., & Maiman, D. J. (2009). The continued burden of spine 

fractures after motor vehicle crashes. J Neuro. 10(2):86–92. (Wisconsin) 

• Ryb, G. E., Dischinger, P. C., Braver, E. R., Burch, CA., Ho, S. M., & Kufera, J. A. (2009). 

Expected differences and unexpected commonalities in mortality, injury severity and injury 

patterns between near versus far occupants of side impact crashes. J,.66(2):499–503. (Maryland) 
67

2010 

• Sun, K., Bauer, M., & Hardman, S. (2010). Effects of upgraded child restraint law designed 

toIincrease booster seat use in New York. Pediatrics 126(3):484-9. (New York)

• Conner, K. A., Xiang, H., & Smith, G. A. (2010). The impact of a standard enforcement safety 

belt law on fatalities and hospital charges in Ohio. J Safety Res, 41(1):17–23. (Ohio) 

• Olsen, C. S., Cook, L. J., Keenan, H. T., & Olson, L. M. (2010). Driver seat belt use indicates 

decreased risk for child passengers in a motor vehicle crash. Accident Anal Prev, 42(2):771–777. 

(Utah)

2011 

• Thygerson, S. M., Merrill, R. M., Cook, L. J., Thomas, A. M., & Wu, A. C. (2011a). 

Epidemiology of motor vehicle crashes in Utah. Traffic Inj Prev, 12(1):39–47. (Utah)

• Thomas, A. M., Cook, L. J., & Olson, L. M. (2011). Evaluation of the Click It or Ticket 

intervention in Utah. Accident AnalPrev, 43(1):272–275. (Utah) 

• Thygerson, S. M., Merrill, R. M., Cook, L. J., & Thomas, A. M. (2011b). Comparison of factors 

influencing emergency department visits and hospitalization among drivers in work and 

nonwork-related motor vehicle crashes in Utah, 1999-2005. Accident AnalPrev, 43(1):209-213. 

(Utah)

• Dischinger, P., Li, J., Smith, G. S., Ho, S., Auman, K., & Shojai, D. Prescription medication 

usage and crash culpability in a population of injured drivers. Annual Proceedings/Association 

for the Advancement of Automotive Medicine. Oct; 55: 207-216, 2011. (Maryland)

2012 

• Miller, T., Gibson, R., Zaloshnja, E., Blincoe, L., Kindelberger, J., Strashny, A., ... & Zhang, Y. 

“Underreporting of Driver Alcohol Involvement in United States Police and Hospital Records: 

Capture-Recapture Estimates.” Annual Proceedings/Association for the Advancement of 

Automotive Medicine. 2012 October; 56:87-96. (Multiple States)

• Nordstrom, D. L., Yokoi-Shelton, M. .L, & Zosel, A. (2012) . Using Multiple Cause-of-Death 

Data to Improve Surveillance of Drug-Related Mortality. J Public Health Management and 

Practice. 19(5):402-11. (Wisconsin) 

• Thomas, A. M., Thygerson, S. M., Merrill, R. M., & Cook, L. J. (2012). Identifying work-related 

motor vehicle crashes in multiple databases. Traffic InjPrev, 13(4):348–354. (Utah)

• Olsen, C. S., Clark, A. E, Thomas, A.M., & Cook, L. J. (2012). Comparing least-squares and 

quantile regression approaches to analyzing median hospital charges. Acad Emerg Med, 

19(7):866–875. (Utah) 

2013 

• Bunn, T, Singleton, M., Slavova, S., Nicholson, V. (2013). Concordance of motor vehicle crash, 

emergency department, and Inpatient hospitalization datasets in the identification of drugs in 

injured drivers. Traffic Inj Prev, 14(7):680–9. (Kentucky) 

• Burch, C., Cook, L., & Dischinger, P. (2014). A comparison of KABCO and AIS Injury severity 

metrics using CODES linked data. Traffic Inj Prev 15(6):627-30. (Maryland and Utah)

• Zhang, Y., & Lin, G. (2013). Disparity surveillance of nonfatal motor vehicle crash injuries. 

Traffic Inj Prev14(7):697–702. (Nebraska) 

Data supplied to answer requests

2008 

• Booster seat required for children aged 5-7 years. Provided statistics to traffic safety groups and 

legislators. Spoke to legislators about statistics. Bill passed. (2002-2007 activity) (Utah)

• Graduated Driver Licensing law repeal. Provided statistics to traffic safety groups and legislators. 

Presented to legislators. Bill defeated. (Utah)
68

2009 

• Texting while driving. Provided statistics to traffic safety groups and legislators. Spoke to 

legislators about statistics. Bill passed. (Utah)

• Safety belt enforcement of commercial vehicle occupants. Provided statistics to traffic safety 

groups and legislators. Bill amended to include all work-related occupants. Bill defeated. (Utah)

2010 

• Cell phone ban for teen drivers. Provided factsheets to traffic safety groups, legislators, and 

American Academy of Pediatrics. Spoke to legislators about factsheets at educational session. 

Bill passed. (Utah) 

• Booster seat law repeal. Provided factsheets showing protective impact of booster seats to traffic 

safety groups and legislators. Bill defeated. (Utah)

2011 

• Booster seat law repeal (second attempt) Provided factsheets showing protective impact of 

booster seats to traffic safety groups and legislators. Bill defeated. (Utah)

2012 

• Safety belt enforcement on high-speed roadways. Provided factsheets to Utah Highway Patrol 

and Utah legislators. Spoke at legislative interim session. Bill brought to 2013 legislative session. 

(Utah)

2013 

• Safety belt enforcement on high-speed roadways. Provided factsheets to Utah Highway Patrol 

and (Utah) legislators. Invited to speak at legislative committee session. Bill defeated. (Utah)

 No Year Available 

• Data on the effects of child safety seats were provided to a hospital administrator for sharing with 

a State legislator pursuant to a bill requiring safety seats for children 4-8. (Missouri) 

• An overview of CODES data was given to MODOT analysts. As a result, they requested a 

crosstab table comparing the KABCO scale to CODES injury data. A similar table was sent to 

Robert Pollack at DOT for a presentation on the value of CODES data. (Missouri)

• Data on injuries to children involved in bus crashes was summarized to the Missouri Governors 

School Bus Safety Task Force. (Missouri)

• An overview of CODES data was presented at a planning meeting of the Missouri Trauma 

System Evaluation and Planning Committee. (Missouri)

• Data on safety belt use and degree of injury were provided to MODOT for presentation to district 

engineers. (Missouri)

• Data was supplied for: “The New York City (NYC) Pedestrian Safety and Action Plan: Technical 

Supplement”, published on the NYC Department of Transportation Website, August 2010. (New 

York)

• Motorcycle injury cost data and injury prevention program information were provided to the 

GTSC in response to a request from the US Government Accountability Office (GAO) (New 

York)

• 2012 Charleston county charges and cost data request. (South Carolina) 

• Request on Children in South Carolina in motor vehicle injuries using 2006-2008 CODES data. 

(South Carolina) 
DOT HS 812 180

August 2015

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