Record Linkage of Crashes with Injuries and Medical Cost in Puerto Rico

Abstract

Cost considerations are critical in the analysis and prevention of traffic crashes. Integration of cost data into crash datasets facilitates the crash-cost analyses with all their related attributes. It is, however, a challenging task because of the lack of availability of unique identifiers across the databases and because of privacy and confidentiality regulations. This study performed a record linkage comparison between the deterministic and probabilistic approaches using attributes matching techniques with numerical distance and weight patterns under the Fellegi–Sunter approach. As a result, the deterministic algorithm developed using the exact match of the 14-digit police accident record number had an overall matching performance of 52.38% of real matched records, while the probabilistic algorithm had an overall matching performance of 70.41% with a quality measurement of the sensitivity of 99.99%. The deterministic approach was thus outperformed by the probabilistic approach by approximately 20% of records matched. The probabilistic matching with numerical variables seems to be a good matching strategy supported by quality variables. On record matching, a multivariable regression model was developed to model medical costs and identify factors that increase the costs of treating injured claimants in Puerto Rico.

An analysis of causative factors for road accidents using partition around medoids and hierarchical clustering techniques

“…By classifying seriously injured individuals into clusters, the study opened new possibilities for vehicle safety, potentially leading to improved safety features. In the survey of medical expenses and costs related to motor vehicle crashes in Puerto Rico, 11 K‐means clustering played a crucial role in grouping the data, facilitating the identification of the best cluster that maximized distance among groups and minimized distance within groups. This approach contributed to a better understanding of the factors influencing medical expenses and costs associated with injuries in road accidents.…”