Evaluating pedestrian crossing safety: Implementing and evaluating a convolutional neural network model trained on paired aerial and subjective perspective images.

With pedestrian crossings implicated in a significant proportion of vehicle-pedestrian accidents and the French government's initiatives to improve pedestrian safety, there is a pressing need for efficient, large-scale evaluation of pedestrian crossings. This study proposes the deployment of advanced deep learning neural networks to automate the assessment of pedestrian crossings and roundabouts, leveraging aerial and street-level imagery sourced from Google Maps and Google Street View. Utilizing ConvNextV2, ResNet50, and ResNext50 models, we conducted a comprehensive analysis of pedestrian crossings across various urban and rural settings in France, focusing on nine identified risk factors.

Background incidence rates of health outcomes in populations at risk for Lyme disease using US administrative claims data.

Background: Background incidence rates (IRs) of health outcomes in Lyme disease endemic regions are useful to contextualize events reported during Lyme disease vaccine clinical trials or post-marketing. The objective of this study was to estimate and compare IRs of health outcomes in Lyme disease endemic versus non-endemic regions in the US during pre-COVID and COVID era timeframes.

Methods: IQVIA PharMetrics® Plus commercial claims database was used to estimate IRs of 64 outcomes relevant to vaccine safety monitoring in the US during January 1, 2017-December 31, 2019 and January 1, 2020-December 31, 2021.

Chronic medical conditions and their association with crash risk and changes in driving habits: a prospective study of the GAZEL cohort.

Objectives: To assess crash risk and driving habits associated with chronic medical conditions among drivers entering old age.

Design: Prospective cohort study.

Setting: French cohort GAZEL.