Literature DB >> 25643104

Small-area spatiotemporal analysis of pedestrian and bicyclist injuries in New York City.

Charles DiMaggio1.   

Abstract

BACKGROUND: This study quantifies the spatiotemporal risk of pedestrian and bicyclist injury in New York City at the census tract level over a recent 10-year period, identifies areas of increased risk, and evaluates the role of socioeconomic and traffic-related variables in injury risk.
METHODS: Crash data on 140,835 pedestrian and bicyclist injuries in 1908 census tracts from 2001 to 2010 were obtained from the New York City Department of Transportation. We analyzed injury counts within census tracts with Bayesian hierarchical spatial models using integrated nested Laplace approximations. The model included variables for social fragmentation, median household income, and average vehicle speed and traffic density, as well as a spatially unstructured random effect term, a spatially structured conditional autoregression term, a first-order random walk-correlated time variable, and an interaction term for time and place. Incidence density ratios, credible intervals, and probability exceedances were calculated and mapped.
RESULTS: The yearly rate of crashes involving injuries to "pedestrians" (including bicyclists) decreased 16.2% over the study period, from 23.7 per 10,000 population to 16.2 per 10,000. The temporal term in the spatiotemporal model indicated that much of the decrease over the study period occurred during the first 4 years of the study period. Despite an overall decrease, the model identified census tracts that were at persistently high risk of pedestrian injury throughout the study period, as well as areas that experienced sporadic annual increases in risk. Aggregate social, economic, and traffic-related measures were associated with pedestrian injury risk at the ecologic level. Every 1-unit increase in a standardized social fragmentation index was associated with a 19% increase in pedestrian injury risk (incidence density ratio = 1.19 [95% credible interval = 1.16 - 1.23]), and every 1 standardized unit increase in traffic density was associated with a 20% increase in pedestrian injury risk (1.20 [1.15 - 1.26]). Each 10-mile-per-hour increase in average traffic speed in a census tract was associated with a 24% decrease in pedestrian injury risk (0.76 [0.69 - 0.83]).
CONCLUSIONS: The risk of a pedestrian or bicyclist being struck by a motor vehicle in New York City decreased from 2001 to 2004 and held fairly steady thereafter. Some census tracts in the city did not benefit from overall reductions or experienced sporadic years of increased risk compared with the city as a whole. Injury risk at the census tract level was associated with social, economic, and traffic-related factors.

Mesh:

Year:  2015        PMID: 25643104     DOI: 10.1097/EDE.0000000000000222

Source DB:  PubMed          Journal:  Epidemiology        ISSN: 1044-3983            Impact factor:   4.822


  16 in total

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Authors:  Matthew C Morris; Miriam Marco; Kathryn Maguire-Jack; Chrystyna D Kouros; Brooklynn Bailey; Ernesto Ruiz; Wansoo Im
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Authors:  D Alex Quistberg; Eric J Howard; Philip M Hurvitz; Anne V Moudon; Beth E Ebel; Frederick P Rivara; Brian E Saelens
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7.  Multilevel models for evaluating the risk of pedestrian-motor vehicle collisions at intersections and mid-blocks.

Authors:  D Alex Quistberg; Eric J Howard; Beth E Ebel; Anne V Moudon; Brian E Saelens; Philip M Hurvitz; James E Curtin; Frederick P Rivara
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8.  Bayesian hierarchical spatial models: Implementing the Besag York Mollié model in stan.

Authors:  Mitzi Morris; Katherine Wheeler-Martin; Dan Simpson; Stephen J Mooney; Andrew Gelman; Charles DiMaggio
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9.  Development and Validation of a Google Street View Pedestrian Safety Audit Tool.

Authors:  Stephen J Mooney; Katherine Wheeler-Martin; Laura M Fiedler; Celine M LaBelle; Taylor Lampe; Andrew Ratanatharathorn; Nimit N Shah; Andrew G Rundle; Charles J DiMaggio
Journal:  Epidemiology       Date:  2020-03       Impact factor: 4.860

10.  Fine scale analysis of malaria incidence in under-5: hierarchical Bayesian spatio-temporal modelling of routinely collected malaria data between 2012-2018 in Cameroon.

Authors:  Celestin Danwang; Élie Khalil; Dorothy Achu; Marcelin Ateba; Moïse Abomabo; Jacob Souopgui; Mathilde De Keukeleire; Annie Robert
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