Police narrative reports: Do they provide end-users with the data they need to help prevent bicycle crashes?

Linking crash and breathalyzer data in Connecticut

OBJECTIVES

To document the process of linking breathalyzer and motor vehicle crash (MVC) data for the State of Connecticut using a unique identifier in the place of personal and private information.

METHODS

Deterministic linkage methodologies were utilized in Microsoft SQL Server to join 5,634 (of 6,650) breathalyzer records to corresponding MVC driver records for the period of January 1, 2017 to December 31, 2022. Differences between the linked and original datasets were documented by comparing the consistency of frequency and proportion distributions of key variables.

RESULTS

Proportions of annual records, alcohol breath tests, and refusals were nearly unchanged when comparing linked and original breathalyzer data. When examining variables in the original MVC driver records, there were differences in the within-group proportions for sex and age, with an overrepresentation of males and drivers aged 26-to-40 years old. For crash and injury severity, the linked dataset had lower proportions of more severe injury records when compared to the original MVC data. Additionally, 1,007 breathalyzer records were not matched with an associated MVC record.

CONCLUSIONS

Linkage methodology is sound and produced quality matches. The use of a unique identifier provided a strong match qualifier in the absence of personal and private data. Changes in proportions for age, sex, crash and injury severity align with previous research. Potential missed matches may be attributed to several factors outside of the linkage process, including data discrepancies and varied reporting practices. Future studies will further explore these differences and incorporate additional toxicology data as part of a continued effort to fuze crash, citation, toxicology, and public health data. The end result will be a holistic, comprehensive, and multifaceted database for transportation research and education.

Assessment of inequity in bicyclist crashes using bivariate Bayesian copulas

INTRODUCTION

Physical activity associated with active transport modes such as bicycling has major health benefits and can help to reduce health concerns related to sedentary lifestyles, such as cardiovascular disease, Type II diabetes, and obesity, as well as risks of colon and breast cancer, high blood pressure, lipid disorders, osteoporosis, depression, and anxiety. However, as a vulnerable user group, bicyclists experience negative health impacts of transportation policies and infrastructure, such as traffic crashes and exposure to air and noise pollution that is disproportionately distributed within low-income and underserved areas.

METHOD

This study used aggregated (block-group) bicyclist crash data from Harris County, Texas, to analyze how various equity measures are associated with both fatal and injury (FI) and no injury (property damage only) bicyclist crashes that occurred from 2010 to 2017. We used Bayesian bivariate copula-based random effects regression analysis to evaluate these associations. In contrast to more traditional univariate analysis, this novel methodology can consider the effects of factors of interest across different severity levels or crash types to fully understand their effects and how they may differ across categories.

RESULTS

The analysis results indicate that the bicyclist exposure, vehicle exposure, population demographics, population density, the percentage of African-Americans, and households below the poverty level are associated with both FI and PDO bicyclist crashes.

CONCLUSIONS

Although more location and context-specific analyses are required, this study's overall results once again conform with the findings and assumptions in bicycling safety literature that the low-income and racially diverse communities are prone to experience more bicyclist crashes.

PRACTICAL APPLICATIONS

The findings of this study may have implications for future transportation and planning policies. These findings can be used to guide the policies and strategies targeting the elimination of inequity in transportation-related health concerns.

Prediction of Severe Injury in Bicycle Rider Accidents: A Multicenter Observational Study

Introduction

This study aimed to establish a predictive model that includes physiological parameters and identify independent risk factors for severe injuries in bicycle rider accidents.

Methods

This was a multicenter observational study. For four years, we included patients with bicycle rider injuries in the Emergency Department-Based Injury In-depth Surveillance database. In this study, we regarded ICD admission or in-hospital mortality as parameters of severe trauma. Univariate and multivariate logistic regression analyses were performed to assess risk factors for severe trauma. A receiver operating characteristic (ROC) curve was generated to evaluate the performance of the regression model.

Results

This study included 19,842 patients, of whom 1,202 (6.05%) had severe trauma. In multivariate regression analysis, male sex, older age, alcohol use, motor vehicle opponent, load state (general and crosswalk), blood pressure, heart rate, respiratory rate, and Glasgow Coma Scale were the independent factors for predicting severe trauma. In the ROC analysis, the area under the ROC curve for predicting severe trauma was 0.848 (95% confidence interval: 0.830–0.867).

Conclusion

We identified independent risk factors for severe trauma in bicycle rider accidents and believe that physiologic parameters contribute to enhancing prediction ability.