56 Catalysing advancements in traffic safety via data linkage: case example of the new jersey traffic safety outcomes program data warehouse

Author(s):  
Allison Curry ◽  
Melissa Pfeiffer ◽  
Kristina Metzger ◽  
Meghan Kirk
2019 ◽  
Vol 20 (sup2) ◽  
pp. S151-S155 ◽  
Author(s):  
Allison E. Curry ◽  
Melissa R. Pfeiffer ◽  
Meghan E. Carey ◽  
Lawrence J. Cook

2021 ◽  
pp. injuryprev-2020-044101
Author(s):  
Allison E Curry ◽  
Melissa R Pfeiffer ◽  
Kristina B Metzger ◽  
Meghan E Carey ◽  
Lawrence J Cook

ObjectiveOur objective was to describe the development of the New Jersey Safety and Health Outcomes (NJ-SHO) data warehouse—a unique and comprehensive data source that integrates state-wide administrative databases in NJ to enable the field of injury prevention to address critical, high-priority research questions.MethodsWe undertook an iterative process to link data from six state-wide administrative databases from NJ for the period of 2004 through 2018: (1) driver licensing histories, (2) traffic-related citations and suspensions, (3) police-reported crashes, (4) birth certificates, (5) death certificates and (6) hospital discharges (emergency department, inpatient and outpatient). We also linked to electronic health records of all NJ patients of the Children’s Hospital of Philadelphia network, census tract-level indicators (using geocoded residential addresses) and state-wide Medicaid/Medicare data. We used several metrics to evaluate the quality of the linkage process.ResultsAfter the linkage process was complete, the NJ-SHO data warehouse included linked records for 22.3 million distinct individuals. Our evaluation of this linkage suggests that the linkage was of high quality: (1) the median match probability—or likelihood of a match being true—among all accepted pairs was 0.9999 (IQR: 0.9999–1.0000); and (2) the false match rate—or proportion of accepted pairs that were false matches—was 0.0063.ConclusionsThe resulting NJ-SHO warehouse is one of the most comprehensive and rich longitudinal sources of injury data to date. The warehouse has already been used to support numerous studies and is primed to support a host of rigorous studies in the field of injury prevention.


2018 ◽  
Vol 73 ◽  
pp. 12007
Author(s):  
Budiawan Wiwik ◽  
Singgih Saptadi ◽  
Ary Arvianto

Traffic accidents are one of the major health problems that cause serious death in the world and ranks 9th in the world. Traffic accidents in Indonesia ranks 5th in the world. One effort to improve traffic safety is to design traffic accident prediction models. Prediction models will utilize accident-related data in traffic through data mining processing. The data warehouse offers benefits as a basis for data mining. Building an effective data warehouse requires knowledge and attention to key issues in database design, data acquisition and processing, as well as data access and security. This study is the first step in the development of data mining accidents based prediction system. The output of this initial stage is the design of data warehouses that can provide periodic and incidental data to the data mining process, especially in the prediction of accidents. The method used to design data warehouse is Entity Relationship Diagram (ERD).


Author(s):  
Francisco Alonso ◽  
Sergio A. Useche ◽  
Eliseo Valle ◽  
Cristina Esteban ◽  
Javier Gene-Morales

Recent evidence suggests that driving behavior and traffic safety outcomes of parents may be influenced by the extent to which they receive information and education on road safety, as well as the fact of driving with their children on board, which may increase their risk perception. However, there are no studies specifically addressing the case of crashes suffered while driving with children. Hence, this study aimed to describe the relationship between road safety education-related variables and parents’ traffic safety outcomes while driving with children on board. For this cross-sectional study, data was retrieved from a sample composed of 165 Spanish parents—all of them licensed drivers—with a mean age of 45.3 years. Through binary logistic regression (logit) analysis, it was found that factors such as gender, having received road safety education (RSE), and having been sanctioned for the performance of risky driving behavior contribute to modulating the likelihood of suffering crashes while driving with children on board. Gender differences showed a riskier status for male parents. In this study, a set of risk factors explaining the involvement in traffic crashes when driving with children as passengers was identified among parents: gender, traffic sanctions, valuation, and exposure to road safety campaigns. Also, substantial limitations in the self-reported degree of received RSE were found, especially considering that risky driving behavior and traffic crash rates with children on board still have a high prevalence among parents.


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