Tropospheric Ozone in Tehran,Iran, during the last 20 years

Environmental Geochemistry and Health - Air pollution and its effects on human health and the environment are one of the main concerns in urban areas. This study focuses on the distribution and...     

Characterization and Optimization of Persian Gum/Whey Protein Bionanocomposite Films Containing Betanin Nanoliposomes for Food Packaging Utilization

Journal of Polymers and the Environment - In this study, composite packaging films were produced from relatively inexpensive materials including Whey protein isolate (WPI) and Persian gum (PG),...     

Driving exposure by driver age in Michigan

Background

This study compared driving exposure between two high-crash-risk groups (16–17 and 18-24-year-olds), with a low-crash-risk group (35-64-year-olds). In addition, patterns of association between driving exposure measures and demographic and driving behavior variables were examined.

Methods

Respondent's total miles, minutes, and trips driven were calculated within a 48-hour period, using state-wide survey data collected in 2004 and 2005.

Results

The youngest drivers drove fewer miles and minutes, but a comparable number of trips as the two older groups. Employment and high vehicle access were associated with greater driving exposure for 16-17-year-olds and 18-24-year-olds. Employment, high household income, large household size, and low vehicle access were associated with greater driving exposure for 35-64-year-olds. More driving was done alone than with passengers present and during the day than at night across all ages. There was a positive association between two driving exposure measures (miles and minutes driven) and demographic and driving behavior variables, which did not extend to trips driven.

Discussion

Driving exposure is directly related to stage of life. The entire sample of 16-17-year-old respondents were in high school, which directly influenced their driving times, destinations, and purpose. Those aged 18–24 years displayed driving behavior patterns that were closer to the older drivers, while retaining some differences. The oldest drivers were likely to be shouldering the greatest household responsibilities, and their greater driving exposure may reflect this reality.

Impact on industry

These findings provide new information about driving exposure for two high-risk and one low-risk group of drivers. They also raise concern over potential workplace safety issues related to teens’ higher driving exposure, and concomitant crash risk, related to being employed. Future research should examine this issue more carefully so that evidence based recommendations can be made to enhance the safety of teens who are employed, especially those who are employed as drivers.     

Covariability in three dimensions of teenage driving risk behavior: impaired driving,risky and unsafe driving behavior,and secondary task engagement

Objective: This research examined the extent to which teenagers who engaged in one form of risky driving also engaged in other forms and whether risky driving measures were reciprocally associated over time.

Methods: The data were from waves 1, 2, and 3 (W1, W2, and W3) of the NEXT Generation study, with longitudinal assessment of a nationally representative sample starting with 10th graders starting in 2009–2010. Three measures of risky driving were assessed in autoregressive and cross-lagged analyses: driving while alcohol/drug impaired (DWI), Checkpoints Risky Driving Scale (risky and unsafe driving), and secondary task engagement while driving.

Results: In adjusted autoregression models, the risk variables demonstrated high levels of stability, with significant associations observed across the 3 waves. However, associations between variables were inconsistent. DWI at W2 was associated with risky and unsafe driving at W3 (β = 0.21, P < .01); risky and unsafe driving at W1 was associated with DWI at W2 (β = 0.20, P < .01); and risky and unsafe driving at W2 is associated with secondary task engagement at W3 (β = 0.19, P < .01). Over time, associations between DWI and secondary task engagement were not significant.

Conclusions: Our findings provide modest evidence for the covariability of risky driving, with prospective associations between the Risky Driving Scale and the other measures and reciprocal associations between all 3 variables at some time points. Secondary task engagement, however, appears largely to be an independent measure of risky driving. The findings suggest the importance of implementing interventions that addresses each of these driving risks.