IF: 0.644

Developing a Minimum Data Set for an Information Management System to Study Traffic Accidents in Iran


Ali Mohammadi 1 , Maryam Ahmadi 2 , 3 , * , Alireza Gharagozlu 4


1 Department of Health Information Technology, Paramedical School, Kermanshah University of Medical Sciences, Kermanshah, IR Iran

2 Department of Health Information Management, School of Management and Medical Information Sciences, Iran University of Medical Sciences, Tehran, IR Iran

3 Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, IR Iran

4 Geomatics College of National Cartographic Center of Iran, Tehran, IR Iran

How to Cite: Mohammadi A, Ahmadi M, Gharagozlu A. Developing a Minimum Data Set for an Information Management System to Study Traffic Accidents in Iran, Iran Red Crescent Med J. 2016 ; 18(3):e23677. doi: 10.5812/ircmj.23677.


Iranian Red Crescent Medical Journal: 18 (3); e23677
Published Online: March 3, 2016
Article Type: Research Article
Received: September 15, 2014
Revised: October 3, 2014
Accepted: October 22, 2014




Background: Each year, around 1.2 million people die in the road traffic incidents. Reducing traffic accidents requires an exact understanding of the risk factors associated with traffic patterns and behaviors. Properly analyzing these factors calls for a comprehensive system for collecting and processing accident data.

Objectives: The aim of this study was to develop a minimum data set (MDS) for an information management system to study traffic accidents in Iran.

Materials and Methods: This descriptive, cross-sectional study was performed in 2014. Data were collected from the traffic police, trauma centers, medical emergency centers, and via the internet. The investigated resources for this study were forms, databases, and documents retrieved from the internet. Forms and databases were identical, and one sample of each was evaluated. The related internet-sourced data were evaluated in their entirety. Data were collected using three checklists. In order to arrive at a consensus about the data elements, the decision Delphi technique was applied using questionnaires. The content validity and reliability of the questionnaires were assessed by experts’ opinions and the test-retest method, respectively.

Results: An (MDS) of a traffic accident information management system was assigned to three sections: a minimum data set for traffic police with six classes, including 118 data elements; a trauma center with five data classes, including 57 data elements; and a medical emergency center, with 11 classes, including 64 data elements.

Conclusions: Planning for the prevention of traffic accidents requires standardized data. As the foundation for crash prevention efforts, existing standard data infrastructures present policymakers and government officials with a great opportunity to strengthen and integrate existing accident information systems to better track road traffic injuries and fatalities.


Accidents Traffic Trauma Centers Emergencies

Copyright © 2016, Iranian Red Crescent Medical Journal. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.

1. Background

Modern industrialization has exposed humans to environmental hazards that can be threatening to their overall health. Road crashes, which emerged as consequence of industrialization, have also contributed to threatening humans’ lives (1, 2). Annually, road traffic systems contribute to around 1.2 million deaths and more than 50 million injuries worldwide (3, 4). There are many reasons behind these road crashes. Having a standard data set can improve our understanding of these events, which is essential and of importance for better planning to save lives and avoid the wasting of resources (5).

Emergency and health care systems also play a decisive role in the clinical and financial consequences of traffic accidents. Determining the factors related to crashes is important for the performance of care systems, but analyzing these factors requires a comprehensive system for collecting and processing accident data. In this way, the large volume of data created renders traffic accident information management an integral part of these systems (6). Furthermore, having data sets that potentially provide detailed information about all crashes can be useful for various beneficiaries, but this is not possible without the creation of standard tools to gather uniform and accurate data (7, 8). In line with the documented benefits of the crash data-bases, some developed countries have developed specialized ones for their region; New Zealand has the Crash Analysis System (CAS); at the European level, the Community Road Accident Database (CARE) has been developed; and in the United States, there are specialized safety databases at the national level while each state has its own safety database that is supposed to follow the Minimum Uniform Crash Criteria Model (MMUCC) (9). Primarily evidence suggests that the crash registration system in Iran suffers from an insufficiency of accurate and up-to-date data (10).

Data collection is the most important part of information management, and MDS is a standard tool for collecting data. The first step in controlling incidents is analyzing them, to identify the underlying causes; therefore, development of an MDS to collect data in a standard and integrated manner at the national level is of great importance (11).

2. Objectives

The aim of this study was to develop a national minimum data set for an information management system to study traffic accidents in Iran.

3. Materials and Methods

This qualitative and descriptive study was performed in 2014. The data were collected from governmental centers, traffic police, trauma, and medical emergency centers in Iran. Data were collected using both forms and databases from traffic accident injures in trauma centers, traffic police, and forms completed in medical emergency centers. Since the forms and databases in all centers were identical, one sample of each form and database was selected for analysis. Thereafter, the included data elements were assessed. A checklist was used in each center to extract data elements.

In the next stage, a literature review was performed to retrieve relevant resources. Data sources for this stage were papers, reports, and forms found on the internet. In this stage, a checklist was used to extract the data elements. Materials relevant to the subject were found using a search strategy (Table 1).

Table 1. Search Strategy for Retrieving Data Elements of Information Management System for Traffic Accidents
Sites, Criteria, StrategyDescriptions, Characteristics
Websitesworld health organization, texas department of transportation; www.miros.gov.my; www.jmwengineering.com/aims00/new.html; http://roadsafety.transport.nsw.gov.au, http://internationaltransportforum.org/irtadpublic; website: www.sgi.sk.ca
Search enginesYahoo; Google
DatabasesGoogle Scholar; PubMed; ISI Web of Science; Scopus; EMBASE, IEEE; Cochrane; SID; Mag Iran; IranMedex, (through July 30, 2014)
Inclusion and Exclusion criteriaInclusion criteria: Literature in the English language; papers; annual reports; reports; guidelines, and forms of research published from 2000 through July 2014; in full text form, from valid sources, with a clearly stated purpose. Exclusion criteria: Non peer-reviewed papers; reports; and forms retrieved from personal weblogs and abstracts without accessible full text.
#1“accident or crash data element”; “traffic accident data element”; “accident information management System”; “accident information management”; “traffic accident information management system”; “minimum data set” and crash or accident; “road traffic accident” and “minimum data set”

Sampling was not performed at this stage, and all the relevant literature were retrieved and evaluated based on the inclusion criteria. The data elements were entered into the checklist. A literature review was performed until data saturation was reached.

A separate checklist was applied for data collection in each center. Then, the content of the final checklist was constructed by combining the data elements extracted from reviewed forms, databases in Iran, and data elements obtained from the literature review. The data elements of the checklists were used to develop questionnaires. Two columns, “needed” and “not needed,” were added in front of each data element. At the end of each section, an empty box was provided to allow experts add any additional data elements they thought were necessary to register.

The content validity of the questionnaires was evaluated using the comments from experts in the fields of health information management, traffic police, computer engineering, and clinical staff (physicians and nurses). A total of eight persons, two experts from each field, consulted.

To ensure their reliability, the questionnaires were completed by 20 of the aforementioned experts; they were asked to complete the questionnaires for a second time after two weeks. The collected data were analyzed using SPSS 16. The Spearman's rank correlation coefficient was used to evaluate the reliability of the questionnaires, which showed coefficients of 77%, 74%, and 76% for the traffic police, trauma center, and medical emergency center questionnaires, respectively.

To determine the MDS of the traffic accident information management system, the final data elements were chosen from 220 samples of attended experts in the aforementioned centers (Table 2), applying the Decision Delphi technique in two rounds. Decisions about the included data elements were based on the agreement level. In this way, data elements with less than 50% agreement were excluded in the first round, and those with more than 75% agreement were included in the first round. Those with 50% to 75% agreement were surveyed in the second round and, if there was 75% consensus over a subject, it was regarded as a final data element.

Table 2. Characteristics of the Samplesa
Institute/OrganizationSamplesGenderAcademic FieldEducation LevelNumberTotal Number of Samples From Each Institute
Traffic police66
Accident officer22NAPolice = 22Expert accident = 2222
IT expert175Computer sciences = 22Bachelor of sciences = 15; master of sciences = 722
Statistician193Statistics = 22Bachelor of sciences = 19; master of sciences = 322
Trauma center88
Staff for registration accident records517Medical record = 22Bachelor of sciences = 2222
Staff for registration accident costs913Accounting = 22Bachelor of sciences = 2222
Director of information management department1012Medical record = 22Bachelor of sciences = 18; master of sciences = 422
Hospital manager193Physician = 14; health services management = 8General physician = 12; specialist = 4; health services management = 622
Emergency center66
Chef of emergency centre22NAPhysician = 22Specialist = 13; general hysician = 922
Statistician1210Statistics = 22Bachelor of sciences = 18; master of sciences = 422
IT expert166Computer sciences = 22Bachelor of sciences = 19; master of sciences = 322

aTotal number of samples = 220.

4. Results

The MDS of the traffic accident information management system was assigned to three sections: traffic police with six, trauma centers with five, and medical emergency centers with 11 classes. Total number of data elements collected from the traffic police offices, trauma centers, and medical emergency center sections were 138, 75 and 91, respectively. After applying the two rounds of the decision Delphi technique, the final set of data elements was determined for traffic police with 118, trauma centers with 57, and medical emergency centers with 64 (Tables 3 - 5).

Table 3. Traffic Police Data Classes for a Minimum Data Set for an Information Management System for Traffic Accidents
InstituteData ClassesNumber of Data ElementsFirst Round of DelphiSecond Round of DelphiFinal
< 50%50 - 75%75% << 50%50 - 75%75% <
Traffic police
Crash location and date/time17121410115
Crash Information21431420115
Road and weather conditions21251420317
Vehicle information35382420630
Driver information22081410721
Pedestrian and passenger information22022020020
Table 4. Trauma Center Data Classes for a Minimum Data Set for an Information Management System for Traffic Accidents
InstituteData ClassesNumber of Data ElementsFirst Round of DelphiSecond Round of DelphiFinal
< 50%50 - 75%75% << 50%50 - 75%75% <
Trauma center
Trauma center profile130013NANANA13
Injured information150015NANANA15
Accident descriptions93151005
Injury descriptions143654027
Services cost24451530217
Table 5. Emergency Center Data Classes for a Minimum Data Set for an Information Management System for Traffic Accidents
InstituteData classesNumber of data elementsFirst round of DelphiSecond round of DelphiFinal
< 50%50 - 75%75% << 50%50 - 75%75% <
Emergency center
Emergency center profile26281670117
Ambulance information40222002
Network connection details6501NANANA1
Personnel information100464006
Injured information5005NANANA5
Date/Time of emergency mission8008NANANA8
Transferor information5104NANANA4
Injury location60333003
Injured status8008NANANA8
Mission results3003NANANA3

The traffic police data classes consisted of many elements. First, a data class was related to the date, location, and time when crash occurred, including its location, road name, coordinate system, date and time when the traffic police were informed about the crash, when police appeared on the scene, and when police surveyed the crash. Second, a crash information data class included data elements about the type of crash and the identifying factors that related to law or legal factors (human, vehicle, justice, and total). Third was a road and weather conditions data class related to road failures, lighting, the road surface, and weather conditions (cloudy, rainy, dusty). The fourth class was related to vehicle information and included data elements related to culpable and non-culpable vehicles involved in the crash. Fifth, the driver information data class included drivers’ demographic information, health condition (physical and mental), drug/alcohol abuse, and license status. Sixth, the passengers and pedestrian information data class included elements about the number of injured persons, their demographic information, injury severity, and set position.

The trauma center data classes consist of, first, a trauma center profile data class that included elements related to contact information, equipment, in-patient and para-clinical wards. A second class, injured information, showed data about the date and time of admission, as well as patients’ record numbers. The third and fourth classes, accident and injury description, included data elements that describe the accidents according to V00-V85 and injuries according to S00-T07, the subcategories established by the International Statistical Classification of Diseases and Related Health Problems: Tenth Revision (ICD-10) and procedures according to volume three of the International Classification of Diseases-Ninth Revision-Clinical Modification (ICD-9-CM). Fifth, the service cost data class, included elements pertaining to cost of each service, both separately and as a whole.

The emergency center data classes include first, the emergency center profile data class, which consists of the name, type, and coordinate system and emergency center distance from/to police/ fire station/another emergency center. Second and third, the ambulance and network connection classes included the number of ambulance and type of communication device that was used. Fourth, the personnel data class was related to the number of staff on hand and their type of expertise. Fifth data class, injured information, related to data elements about the demographic information of injured persons. Sixth, the date/time of emergency mission data class included: inform time, arriving at the scene, dispatch time, and arriving at the hospital.

Seventh, the transferor data class showed the name and code of the transferor. Eighth, the injury location data class indicated the coordinate system and mission environment (e.g., residential, educational, sports, nature). Ninth, the injured status data class included data elements on vital signs and drug prescriptions. Tenth, the diagnoses data class included data elements about patient status such as their delivery time to the hospital, probable diagnosis, pain score, and procedures that were performed. The final data class was mission results by emergency, which included the hospital name, if the injured person was transferred, outpatient treatment on the scene, and death (Table 6).

Table 6. Examples of Data Elements in the Information Management System for Traffic Accidents
Data classesData elements
Police Office
Crash location and date/timeProvince name; street name; accident location; coordinates
Crash informationType of crash (property, injury, death); definite cause of crash; crash with (Motorcycles, bicycles, single or multi-vehicle)
Road and weather conditionsLighting; barriers to see; weather; road repairs
Vehicle informationType of vehicle; vehicle registration number; technical examination
Driver informationNationality; driver name and family; educational level; license status
Pedestrian and passenger informationTotal occupants; number of injured persons; name and family; education; Job
Trauma Center
Trauma center profileHospital name; coordination X,Y; number of active beds
Injured informationAdmission date; admission time; hospital record number
Accident descriptionInjury description (V00-V99, ICD-10); injured set position
Injury descriptionType of injury; type of operation; length of stay
Services costVisit cost; operation cost; bed cost; cost as a whole
Emergency Center
Emergency center profileEmergency center name; type of emergency center; distance to road; distance to the next emergency
Ambulance informationThe total number of ambulances; the total number of active ambulances
Network connection detailsMobile; masts; fixed; manual
Personnel informationThe total number of personnel; number of physicians; number of experts
Injured informationInjured name and family; age; gender
Date/Time of emergency missionCrash report to emergency; dispatch from emergency; arrival time of EMS to the scene; move from the scene ; arrival time to hospital
Transferor informationTransmitter name; transmitter code
Injury locationType of trauma; mission environment (residential, educational, nature); coordination X,Y
Injured StatusPulse rate; respiratory rate; GCS
DiagnosesPatient status in delivered time to hospital (conscious, semiconscious, coma); type of trauma; pain score
Mission resultsTransfer to hospital; outpatient treatment; died before reaching the technician, during transfer

5. Discussion

The quality of decision making in road safety and death prevention is dependent on the quality of the data on which decisions are based (9). When addressing this issue, collecting accurate and standard data is of great importance. The WHO has urged countries to design and develop traffic accident information systems (12).

The high rate of accidents in Iran highlights need to take measures to improve the underlying infrastructure (13, 14). The results of this study showed that, in Iran, there are gaps in traffic accident data coverage, in terms of sufficient data elements, standardized tools, and integrated information systems that may be used by police, trauma, and medical emergency centers.

In the data classes related to traffic police, the lack of road numbers, work zones, and week days were highlighted, and in the recording of crash information, the different resources revealed that sketches are drawn in various ways (digitally, manually, or photos are taken) (9). In contrast, in Iran, sketches are drawn manually.

For crash fatality information, the absence of the internationally recommended 30-day follow-up of a crash fatality for most resources was obvious (12). This leads to an inconsistency in the comparison of data across countries and within different sectors of a country (15). Injury severity was only included in some resources. There was a large variation in property damage reports for different contexts; in Iran, this report included a notation if the value of damage exceeded 30,000,000 Rials in 2014 (16, 17).

Despite the fact that climate factors largely contribute to road accidents throughout the world (18), data for traffic volumes and road classification have not been recorded in Iranian databases, even though data about vision obstacles and road repairs were uniquely included. Data about vehicles, drivers, and passengers/pedestrians, including the availability of safety equipment, safety equipment performance, type of insurance and insurance expiration dates, drug and alcohol tests, and physical status, injury severity, and cause of injury for passengers or pedestrians were not recorded for Iran. Though establishing standard data elements for crashes allows for a comparison across countries (19, 20), there is a large deficiency in Iran’s data elements.

Health Care system is the main responsible body for determining the medical and financial consequences of a traffic accident (6). In this regard, the Iranian Ministry of Health established a road traffic management system in 2010 to oversee data collection in trauma centers related to traffic injuries (21). The most important weaknesses of this data, in comparison with other countries, were the lack of a full, documented diagnosis of traumatic injuries based on the ICD, evaluating the severity of the accidents, and rehabilitation time.

Moreover, this system was established only in trauma centers, and there are no interactions in terms of data exchanges with other beneficiaries. This was consistent with findings from some other studies (15). To solve this problem, we constructed an MDS for trauma centers in five classes, which makes data exchangeable across different beneficiaries. As illustrated in Table 6, these classes describe data about the equipment in trauma centers, injured parties’ information, the accidents, injury descriptions based on ICD, and the cost of services.

From the results of this study, it is obvious that there were considerable gaps in terms of required minimum data sets, and these needed to be addressed. It is suggested that rather disparate sources from various sectors should be integrated uniformly, in order to maximally capture the true burden of crashes (22, 23) and increase their comparability using international guidelines.

Crashes are one of the main causes for Traffic congestion. This is more noticeable during peak hours, and in places that are far from emergency centers or where the population density is high. In today’s traffic world, ambulances play a major role in saving the lives of accident victims (23, 24).

Transportation of an injured person to an emergency hospital is a complicated mission (25). Hence, developing an MSD to identify the required data, in order to help decision makers to improve and equip the organization of pre-hospital emergency efforts, is critical (26).

To complete an MDS of traffic accident information, data elements required for emergency centers were built on 11 classes. These classes, as shown in Table 6, included data on the profiles and equipment of emergency centers, status and numbers of ambulances, technologies that are used to connect and inform responders about crashes, and the number of personnel on hand and their specialties. Data elements associated with the times related to dispatch, transferor and injured information, performed procedures, and mission results were also determined to comprise an emergency minimum data set.

Road accidents are the main cause of the injuries in the world (22). On the other hand, precise and accurate data are required for the purpose of ensuring the continuity of care and clarifying legal statements. Therefore, standard collection techniques and definitions should be used for data with minimal free text (27). As Laing states, the MDS can provide a construction to facilitate a comprehensive documentation of the records (28). As the base for crash prevention programs, the existence of standardized data infrastructures is essential. Using these data, policymakers and decision makers will be better able to track road traffic injuries and fatalities.




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