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

为伊朗交通事故研究构建信息管理系统的最小数据集

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Abstract

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.

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