Socioeconomic and sociodemographic inequalities and their association with road traffic injuries

Abstract

Increasing evidence suggests that neighborhood-based measures of socioeconomic status are correlated with traffic injury. The main objective of this study is to determine the differences in associations between predictive variables and injury crashes (i.e. including injury and fatal crashes). This study makes a novel contribution by establishing the association between traffic casualties and socio-demographic, socioeconomic characteristics, traffic exposure data and road network variables, at the neighborhood-level while categorized by different genders and transport mode; 'car driver', 'car passenger' and 'active mode users' (i.e. pedestrians and cyclists). In this study an activity-based transportation model called FEATHERS (Forecasting Evolutionary Activity-Travel of Households and their Environmental RepercussionS) is utilized to produce exposure measures. Exposure measures are in the form of production/attraction trips for several traffic analysis zones (TAZ) in Flanders, Belgium. Analyzing crashes at a neighborhood-level provides important information that enables us to compare traffic safety of different neighborhoods. This information is used to identify safety problems in specific zones and consequently, implementing safety interventions to improve the traffic safety condition. This can be carried out by associating casualty counts with a number of factors (i.e. developing crash prediction models) which have macro-level characteristics, such as socio-demographic and network level exposure. The results indicate that socioeconomic variables are differently associated with casualties of different travel modes and genders. For instance, income level of residence of a TAZ is a significant predictor of male car driver injury crashes while it does not significantly contribute to the prediction of female car driver injury crashes.

Are There Changes in Inequalities in Injuries? A Review of Evidence in the WHO European Region

“…After screening 1274 records a total of 27 studies [8,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56] met the criteria for inclusion, as shown in Figure 1. Seven were cross-country and 20 country-specific.…”

“…Table 3 presents studies reporting social inequalities in injuries in Europe in more recent years, stratified first according to whether they were cross-country [41,42,43,44,45] or within-country [47,48,49,50,51,52,53,54,55,56]. The studies that considered more than one cause are inserted in each section of the table for which they provide results.…”

“…From among the four studies dealing with road traffic injuries within countries, two are from England [51,54], one from Belgium [52], and one emphasized the social distribution of non-use of car seats as a risk factor across Slovenia [53]. All but the latter were ecological studies.…”

“…All but the latter were ecological studies. Looking at the income based (two categories) distribution of road traffic injury mortality among men and women from Flanders, Pirdavani et al [52] found a negative association between income level and casualties, with significance varying based on road user type and sex. In England, at country level and for the period 2010–2011, Hughes et al [51] found almost threefold odds of emergency department attendances for (any) road traffic injury in children aged 0-14 years from most deprived areas compared to those from least deprived ones (adjusted OR 2.77; p < 0.001).…”

Spatial crash prediction models: an evaluation of the impacts of enriched information on model performance and the suitability of different spatial modeling approaches

“…In this context, crash analysis has been performed at different levels of aggregation, depending on their purposes, e.g. microscopic, for a specific road segment or intersection, or at macroscopic-level for a larger area, such as a municipality (Pirdavani, Daniels, van Vlierden, Brijs, & Kochan, 2017). We suggest Huang et al (2016) for more detailed information concerning both aggregation levels.…”