Day: July 10, 2014

Geo-Based Statistical Models for Vulnerability Prediction of Highway Network Segments

/ Matt Artz / 1 Comment

isprsISPRS International Journal of Geo-Information, 2014, 3(2), 619-637

By Keren Pollak, Ammatzia Peled, and Shalom Hakkert

“This study describes four statistical models—Poisson; Negative Binomial; Zero-Inflated Poisson; and Zero-Inflated Negative Binomial—which were devised in order to examine traffic accidents and estimate the best probability estimating model in terms of future risk assessment at interurban road sections. The study was conducted on four sets of fixed-length sections of the road network: 500, 750, 1000, and 1500 m. The contribution of transportation and spatial parameters as predictors of road accident rates was evaluated for all four data sets separately. In addition, the Empirical Bayes method was applied. This method uses historical accidents information, allowing regression to the mean phenomenon so as to improve model results.

Expected number of accidents comparing real number of accidents and predicted number after applying EB method (road section of 500 m)—observation 3000 until 3300.

Expected number of accidents comparing real number of accidents and predicted number after applying EB method (road section of 500 m)—observation 3000 until 3300.

“The study was performed using Geographic Information System (GIS) software. Other analyses, such as statistical analyses combined with spatial parameters, interactions, and examination of other geographical areas, were also performed. The results showed that the short road sections data sets of 500 and 750 m yielded the most stable models. This allows focused treatment on short sections of the road network as a way to save resources (enforcement; education and information; finance) and potentially gain maximum benefit at minimum investment. It was found that the significant parameters affecting accident rates are: curvature of the road section; the region and traffic volume. An interaction between the region and traffic volume was also found. ”

Long-Term Exposure to Fine Particulate Matter: Association with Nonaccidental and Cardiovascular Mortality in the Agricultural Health Study Cohort

/ Matt Artz / 1 Comment

EHP_newEnvironmental Health Perspectives, Published 01 June 2014

By Scott Weichenthal, Paul J. Villeneuve, Richard T. Burnett, Aaron van Donkelaar, Randall V. Martin, Rena R. Jones, Curt T. DellaValle, Dale P. Sandler, Mary H. Ward, and Jane A. Hoppin

“Background

Few studies have examined the relationship between long-term exposure to ambient fine particulate matter (PM2.5) and nonaccidental mortality in rural populations.

Objective

We examined the relationship between PM2.5 and nonaccidental and cardiovascular mortality in the U.S. Agricultural Health Study cohort.

Methods

The cohort (n = 83,378) included farmers, their spouses, and commercial pesticide applicators residing primarily in Iowa and North Carolina. Deaths occurring between enrollment (1993–1997) and 30 December 2009 were identified by record linkage. Six-year average (2001–2006) remote-sensing derived estimates of PM2.5 were assigned to participants’ residences at enrollment, and Cox proportional hazards models were used to estimate hazard ratios (HR) in relation to a 10-μg/m3 increase in PM2.5 adjusted for individual-level covariates.

Spatial distribution of participants and estimated PM2.5 concentrations (μg/m3) in North Carolina (left) and Iowa (right). Dots outside state lines reflect the small number of participants who were enrolled in the study but lived outside North Carolina or Iowa.

Spatial distribution of participants and estimated PM2.5 concentrations (μg/m3) in North Carolina (left) and Iowa (right). Dots outside state lines reflect the small number of participants who were enrolled in the study but lived outside North Carolina or Iowa.

Results

In total, 5,931 nonaccidental and 1,967 cardiovascular deaths occurred over a median follow-up time of 13.9 years. PM2.5 was not associated with nonaccidental mortality in the cohort as a whole (HR = 0.95; 95% CI: 0.76, 1.20), but consistent inverse relationships were observed among women. Positive associations were observed between ambient PM2.5 and cardiovascular mortality among men, and these associations were strongest among men who did not move from their enrollment address (HR = 1.63; 95% 0.94, 2.84). In particular, cardiovascular mortality risk in men was significantly increased when analyses were limited to nonmoving participants with the most precise exposure geocoding (HR = 1.87; 95% CI: 1.04, 3.36).

Conclusions

Rural PM2.5 may be associated with cardiovascular mortality in men; however, similar associations were not observed among women. Further evaluation is required to explore these sex differences.”