Spatial analysis of alcohol-related motor vehicle crash injuries in southeastern Michigan

An integrated spatio-temporal approach to examine the consequences of driving under the influence (DUI) in crashes

Driving under the influence (DUI) is illegal in the United States because a driver's mental and motor skills can be seriously impaired by alcohol or drugs. Consequently, DUI violators' involvement in severe crashes is high. Motivated by the spatial and temporal nature of traffic crashes, this study introduces an integrated spatio-temporal approach to analyzing highway safety data. Specifically, this study estimates Geographically and Temporally Weighted Regression (GTWR) models to understand the consequences of DUI in crashes. GTWR can theoretically outperform traditional regression methods by accounting for unobserved heterogeneity that may be related to the location and time of a crash. Using Southeast Michigan crash data, this study finds that DUI is associated with a 25% higher likelihood of injury in a crash. The association between injury severity and DUI varies significantly across space and time. From the spatial aspect, DUI crashes in rural or small-town areas are more likely to cause injuries than urban crashes. From the temporal aspect, different times are associated with varying relationships between injury severity and DUI. If focusing on DUI crashes in late nights and early mornings, on Fridays, the entire northeast part from Clinton Charter Township to Port Huron is associated with severer injuries than other regions including Detroit's urban area and its south. On Mondays, the DUI crashes in the northwest are also more likely to cause severe injuries. The methodology introduced in this study takes advantage of modern computational tools and localized crash/inventory data. This method offers researchers and practitioners an opportunity to understand highway safety outcomes in great spatial and temporal details and customize safety countermeasures for specific locations and times such as saturation patrols.

Road traffic crash characteristics of drivers who take prescription medicines that carry a risk to driving

BACKGROUND

The specific features of crashes involving an alcohol-intoxicated driver have been extensively characterized, but no such data are available for crashes involving a driver who has used a prescription medicine, which could help to plan and target prevention and control initiatives. This study aimed to describe the characteristics of crashes involving drivers under the influence of prescription medicines.

METHODS

We took advantage of CESIR, a French record linkage study for which data were extracted and matched from three French national databases: police reports, the national police database of injurious crashes and the national health care insurance database (HCI database). The drivers included in the study were those involved in an injurious road crash in France from July 1, 2005 to December 31, 2015, whose national identity number, date of birth and sex allowed matching. Prescription medicines considered were those with the two highest levels of warning.

RESULTS

Similar crash profiles were found when drivers used alcohol or medicines, particularly with respect to injury severity, type of vehicle, type of collision, type of road and cross-track profile. Alcohol-related crashes were over-represented during weekends and in low-density areas and medicine-related crashes were over-represented during weekdays and in cities of fewer than 300 000 inhabitants. While a much higher strength of association with responsibility was found for alcohol than for medicines, the proportion of crashes with drivers using medicines was twice as high as crashes with drivers using alcohol.

CONCLUSION

The lower risk carried by medicines is therefore in part compensated by a higher prevalence of use, making medicines one of the hidden factors of road risk. Characterizing these crashes will therefore be useful to better focus road safety intervention on the prevention of driving under the influence of psychotropic medicines.

Bicyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates

INTRODUCTION

Bicyclists are among vulnerable road users with their safety a key concern. This study generates new knowledge about their safety by applying a spatial modeling approach to uncover non-stationary correlates of bicyclist injury severity in traffic crashes.

METHOD

The approach is Geographically Weighted Ordinal Logistic Regression (GWOLR), extended from the regular Ordered Logistic Regression (OLR) by incorporating the spatial perspective of traffic crashes. The GWOLR modeling approach allows the relationships between injury severity and its contributing factors to vary across the spatial domain, to account for the spatial heterogeneity. This approach makes use of geo-referenced data. This study explored more than 7,000 geo-referenced bicycle--motor-vehicle crashes in North Carolina.

RESULTS

This study performed a series of non-stationarity tests to identify local relationships that vary substantially across the spatial domain. These local relationships are related to the bicyclist (bicyclist age, bicyclist behavior, bicyclist intoxication, bicycle direction, bicycle position), motorist (driver age, driver intoxication, driver behavior, vehicle speed, vehicle type) and traffic (traffic volume).

CONCLUSIONS

Results from the regular OLR are in general consistent with previous findings. For example, an increased bicyclist injury severity is associated with older bicyclists, bicyclist being intoxicated, and higher motor-vehicle speeds. Results from the GWOLR show local (rather than global) relationships between contributing factors and bicyclist injury severity. Practical Applications: Researchers and practitioners may use GWOLR to prioritize cycling safety countermeasures for specific regions. For example, GWOLR modeling estimates in the study highlighted the west part (from Charlotte to Asheville) of North Carolina for increased bicyclist injury severity due to the intoxication of road users including both bicyclists and drivers. Therefore, if a countermeasure is concerned with the road user intoxication, there may be a priority for the region from Charlotte to Asheville (relative to other areas in North Carolina).

Pedestrian injury severity in motor vehicle crashes: An integrated spatio-temporal modeling approach

Traffic crashes are outcomes of human activities interacting with the diverse cultural, socio-economic and geographic contexts, presenting a spatial and temporal nature. This study employs an integrated spatio-temporal modeling approach to untangle the crashed injury correlates that may vary across the space and time domain. Specifically, this study employs Geographically and Temporally Weighted Ordinal Logistic Regression (GTWOLR) to examine the correlates of pedestrian injury severity in motor vehicle crashes. The method leverages the space- and time-referenced crash data and powerful computational tools. This study performed non-stationarity tests to verify whether the local correlates of pedestrian injury severity have a significant spatio-temporal variation. Results showed that some variables passed the tests, indicating they have a significantly varying spatio-temporal relationship with the pedestrian injury severity. These factors include the pedestrian age, pedestrian position, crash location, motorist age and gender, driving under the influence (DUI), motor vehicle type and crash time in a day. The spatio-temporally varying correlates of pedestrian injury severity are valuable for researchers and practitioners to localize pedestrian safety improvement solutions in North Carolina. For example, in near future, special attention may be paid to DUI crashes in the city of Charlotte and Asheville, because in such areas DUI-involved crashes are even more likely to cause severe pedestrian injuries that in other areas. More implications are discussed in the paper.

Taxicab crashes modeling with informative spatial autocorrelation

Maintaining taxi safety is one of the important goals of operating urban transportation systems. Taxicabs are often prone to higher crash risk due to their long-time exposure to the complicated and dynamic traffic environments in urban areas. Despite existing efforts in understanding the safety issues associated with these vehicles, there were still few attempts that have specifically examined the relationship between taxi-involved crashes and other multifaceted contributing factors. To this end, this paper aims to develop crash frequency models for analyzing taxi-involved crashes. In particular, the spatial autocorrelations between variables were explored and the Poisson conditional autoregressive (Poisson-CAR) models for taxi-involved crashes were proposed. Unlike previous safety studies that mainly consider distance as the key indicator of spatial correlation, the present paper introduced the use of massive taxi trip data for constructing a more informative spatial weight matrix. The developed models with the taxi trip-based weight matrix were tested by using the 2016 taxi trip data collected in Washington D.C. The modeling results highlight the key explanatory factors such as road density, taxi activity, number of bus stops, and land use. More importantly, it demonstrates that the proposed Poisson-CAR models with the taxi trip-based weight matrix outperformed both the non-spatial Poisson model and the Poisson-CAR models using conventional distance-based weight matrix. Moran's I tests further indicate that our proposed models have sufficiently accounted for the spatial autocorrelation of the residuals. Thus, it deserves to consider informative spatial weight matrices when applying spatial models in traffic safety studies.

Alcohol and non-alcohol-related motor vehicle crashes in Perth, Australia: Do alcohol outlets make a difference?

This study examined the effects of distance from alcohol outlets to motor vehicle crashes across the Perth metropolitan area. A retrospective population-based study was undertaken using measures of alcohol- and non-alcohol-related crashes, and their proximity to alcohol outlets, using a geographic information system. Two logistic regression models were developed with the following outcomes: i) crashes including drivers with BAC ≥ 0.05%, and ii) weekend single vehicle night-time crashes, a surrogate measure of alcohol-related crashes. The surrogate measures of non-alcohol-related crashes for these models were all day-time and single vehicle day-time crashes respectively. The major predictors of alcohol-related crashes were number of on-premise outlets and bottleshops in buffer zones up to 2 km, 2 km-5 km, 5 km-10 km and 10 km-20 km from crashes. The distance from the central business district (CBD) and sociodemographic factors were controlled for. The study included 341,467 crashes that occurred between 2005 and 2015. The highest crash incidence rates occurred in the CBD. The statistical models indicated that crashes with a higher number of on-premise outlets in adjacent buffer zones were more likely to be alcohol-related than non-alcohol-related crashes. Crashes with a higher number of on-premise outlets less than 2 km, 2 km-5 km, 5 km-10 km, and 10 km-20 km from the crashes were significantly more likely to be weekend single vehicle night-time crashes than day-time crashes (OR = 1.014; 95% CI:1.002-1.027, OR = 1.022; 95% CI:1.014-1.029, OR = 1.019; 95% CI:1.014-1.024, and OR = 1.017; 95% CI:1.014-1.020 respectively). There was some evidence that crashes with lower number of bottleshops in adjacent buffer zones were more likely to be alcohol-related crashes, although this was not consistent across both models and all buffer zones. When other predictors were controlled for, alcohol-related crashes were more likely to occur further from the CBD, than in the CBD. Recommendations about the timing and location of roadside alcohol testing are made.

Gate-violation behavior at highway-rail grade crossings and the consequences: Using geo-Spatial modeling integrated with path analysis

Drivers undertaking risky behaviors at highway-rail grade crossings are often severely injured in collisions with trains. Among these behaviors, gate-violation (referring to driving around or through the gates that were activated and lowered by an approaching train) seems to be one of the most dangerous actions a driver might take at a gated crossing; it may compromise the intended safety improvement made by adding gates at crossings. This study develops a nuanced conceptual framework that uses path analysis to explore the contributing factors to gate-violation behaviors and the correlation between gate-violation behaviors and the crash consequence - the driver injury severity. Further, using geo-spatial modeling techniques, this study explores whether the correlates of gate-violation behaviors and their associations with injury severity are stationary across diverse geographic contexts of the United States. Geo-spatial modeling shows that the correlates of gate-violation and its associations with injury severity vary substantially across the United States. Spatial variations in correlates of gate-violation and injury severity are mapped by estimating geographically weighted regressions; the maps can serve as an instrument for screening safety improvements and help identify regions that need safety improvements. For example, the results show that two-quadrant gates are more likely to have gate-violation crashes than four-quadrant gates in Iowa, Illinois, Wisconsin and Minnesota. These states may need to receive more attentions on the enforcement of inhibiting gate-violation at crossings with two-quadrant gates or have the priority over other states to upgrade these crossings to four-quadrant gates if financially feasible.

The Association between Regional Environmental Factors and Road Trauma Rates: A Geospatial Analysis of 10 Years of Road Traffic Crashes in British Columbia, Canada

Background

British Columbia, Canada is a geographically large jurisdiction with varied environmental and socio-cultural contexts. This cross-sectional study examined variation in motor vehicle crash rates across 100 police patrols to investigate the association of crashes with key explanatory factors.

Methods

Eleven crash outcomes (total crashes, injury crashes, fatal crashes, speed related fatal crashes, total fatalities, single-vehicle night-time crashes, rear-end collisions, and collisions involving heavy vehicles, pedestrians, cyclists, or motorcyclists) were identified from police collision reports and insurance claims and mapped to police patrols. Six potential explanatory factors (intensity of traffic law enforcement, speed limits, climate, remoteness, socio-economic factors, and alcohol consumption) were also mapped to police patrols. We then studied the association between crashes and explanatory factors using negative binomial models with crash count per patrol as the response variable and explanatory factors as covariates.

Results

Between 2003 and 2012 there were 1,434,239 insurance claim collisions, 386,326 police reported crashes, and 3,404 fatal crashes. Across police patrols, there was marked variation in per capita crash rate and in potential explanatory factors. Several factors were associated with crash rates. Percent roads with speed limits ≤ 60 km/hr was positively associated with total crashes, injury crashes, rear end collisions, and collisions involving pedestrians, cyclists, and heavy vehicles; and negatively associated with single vehicle night-time crashes, fatal crashes, fatal speeding crashes, and total fatalities. Higher winter temperature was associated with lower rates of overall collisions, single vehicle night-time collisions, collisions involving heavy vehicles, and total fatalities. Lower socio-economic status was associated with higher rates of injury collisions, pedestrian collisions, fatal speeding collisions, and fatal collisions. Regions with dedicated traffic officers had fewer fatal crashes and fewer fatal speed related crashes but more rear end crashes and more crashes involving cyclists or pedestrians. The number of traffic citations per 1000 drivers was positively associated with total crashes, fatal crashes, total fatalities, fatal speeding crashes, injury crashes, single vehicle night-time crashes, and heavy vehicle crashes. Possible explanations for these associations are discussed.

Conclusions

There is wide variation in per capita rates of motor vehicle crashes across BC police patrols. Some variation is explained by factors such as climate, road type, remoteness, socioeconomic variables, and enforcement intensity. The ability of explanatory factors to predict crash rates would be improved if considered with local traffic volume by all travel modes.

Evaluating the effects of a large increase in off-sale alcohol outlets on motor vehicle crashes: a time-series analysis

This study examined the effects on motor vehicle crashes of a policy change that led to the introduction of a very large number of off-sale alcohol outlets in Lubbock, Texas. Times-series analysis of total crashes and single-vehicle nighttime (SVN) crashes was used to compare the periods before and after the policy change in Lubbock and in a comparison area. The results of the analysis revealed some weak effects on total crashes, but no statistically significant effects were found for SVN crashes. Possible reasons for the essentially null findings of the current study regarding the effects of the policy change on motor vehicle crashes are discussed. These include the fact that there were a small number of off-sale outlets already present in the community and that motor vehicle travel immediately following alcohol consumption is less likely to occur with alcohol purchased from an off-sale outlet compared to an on-sale outlet.

Traffic crashes and alcohol outlets in a Brazilian state capital

OBJECTIVE

Restricting alcohol outlets is being considered as a measure for preventing alcohol-related crashes. However, in many developing countries, alcohol availability is not regulated and its influence on motor vehicle traffic crashes is unknown. This study explores the association between traffic crashes and alcohol outlets in a Brazilian city.

METHOD

Data were geocoded and exploratory analysis was conducted using the kernel density estimator. Two generalized additive models (GAMs) were implemented to predict the factors associated with alcohol-related crashes.

RESULTS

For 78 percent of the 3840 traffic crashes where the driver was a victim, there was at least one bar located within a 300-m radius. The median distances between an outlet were 124.4 and 130.7 m for a non-alcohol- and alcohol-related crashes, respectively (P =.13). The GAMs did not make evident any significant association between the outlet locations and alcohol-related crashes: the presence of at least one outlet was associated with alcohol-related crashes with an odds ratio (OR) of 0.94 (95% confidence interval [CI] = 0.75-1.17). Alcohol crashes are more likely to be observed among males (OR = 1.58; 95% CI = 1.21-2.06), young drivers vs. those aged 50 years+ (OR = 3.4; 95% CI = 1.79-6.43), and crashes with fatalities (OR = 1.73; 95% CI = 0.98-3.04).

CONCLUSIONS

Density of alcohol outlets was high all over the city and both alcohol- and non-alcohol-related crashes occurred near an outlet. The study helps to better understand the relationship between alcohol availability and traffic crashes in a middle-income country where licensing/zoning is absent and suggests that measures for restricting the physical availability of alcohol are necessary, even though further studies are still needed.

A spatial and temporal analysis of child pedestrian crashes in Santiago, Chile

This paper presents a spatial and temporal analysis of child pedestrian crash data in Santiago, Chile during the period 2000-2008. First, this study identified seven critical areas with high child pedestrian crash risk employing kernel density estimation, and subsequently, statistically significant clusters of the main attributes associated to these crashes in each critical area were determined in a geographic information systems environment. Moran's I index test identified a positive spatial autocorrelation on crash contributing factors, time of day, straight road sections and intersections, and roads without traffic signs within the critical areas during the studied period, whereas a random spatial pattern was identified for crashes related to the age attribute. No statistical significance in the spatial relationship was obtained in child pedestrian crashes with respect to gender, weekday, and month of the year. The results from this research aid in determining the areas in which enhanced school-age child pedestrian safety is required by developing and implementing effective enforcement, educational, and engineering preventive measures.

Reaching the hard-to-reach: a probability sampling method for assessing prevalence of driving under the influence after drinking in alcohol outlets

Drinking alcoholic beverages in places such as bars and clubs may be associated with harmful consequences such as violence and impaired driving. However, methods for obtaining probabilistic samples of drivers who drink at these places remain a challenge – since there is no a priori information on this mobile population – and must be continually improved. This paper describes the procedures adopted in the selection of a population-based sample of drivers who drank at alcohol selling outlets in Porto Alegre, Brazil, which we used to estimate the prevalence of intention to drive under the influence of alcohol. The sampling strategy comprises a stratified three-stage cluster sampling: 1) census enumeration areas (CEA) were stratified by alcohol outlets (AO) density and sampled with probability proportional to the number of AOs in each CEA; 2) combinations of outlets and shifts (COS) were stratified by prevalence of alcohol-related traffic crashes and sampled with probability proportional to their squared duration in hours; and, 3) drivers who drank at the selected COS were stratified by their intention to drive and sampled using inverse sampling. Sample weights were calibrated using a post-stratification estimator. 3,118 individuals were approached and 683 drivers interviewed, leading to an estimate that 56.3% (SE = 3,5%) of the drivers intended to drive after drinking in less than one hour after the interview. Prevalence was also estimated by sex and broad age groups. The combined use of stratification and inverse sampling enabled a good trade-off between resource and time allocation, while preserving the ability to generalize the findings. The current strategy can be viewed as a step forward in the efforts to improve surveys and estimation for hard-to-reach, mobile populations.

Spatial temporal modeling of hospitalizations for fall-related hip fractures in older people

The study determined the spatial temporal characteristics of fall-related hip fractures in the elderly using routinely collected injury hospitalization and sociodemographic data. There was significant spatial temporal variation in hospitalized hip fracture rates in New South Wales, Australia.

INTRODUCTION

The study determined the spatial temporal characteristics of fall-related hip fractures in the elderly using routinely collected injury hospitalization data.

METHODS

All New South Wales (NSW), Australia residents aged 65+ years who were hospitalized for a fall-related hip fracture between 1 July 1998 and 30 June 2004 were included. Bayesian Poisson regression was used to model rates in local government areas (LGAs), allowing for the incorporation of spatial, temporal, and covariate effects.

RESULTS

Hip fracture rates were significantly decreasing in one LGA, and there were no significant increases in any LGAs. The proportion of the population in residential aged care facilities was significantly associated with the rate of hospitalized hip fractures with a relative risk (RR) of 1.003 (95% credible interval 1.002, 1.004). Socioeconomic status was also related to hospitalized hip fractures with those in the third and fourth quintiles being at decreased risk of hip fracture compared to those in the least disadvantaged (fifth) quintile [RR = 0.837 (0.717, 0.972) and RR = 0.855 (0.743, 0.989) respectively].

CONCLUSIONS

There was significant spatial temporal variation in hospitalized hip fracture rates in NSW, Australia. The use of Bayesian methods was crucial to allow for spatial correlation, covariate effects, and LGA boundary changes.

Changing the density of alcohol outlets to reduce alcohol-related problems

Increasingly, it seems, legal and political debates regarding the granting of new liquor licences are turning to the issue of whether the number and density of alcohol outlets makes a difference in rates of alcohol consumption and alcohol-related harm. But what is the state of the evidence on this question? In this Harm Reduction Digest Livingston, Chikritzhs and Room review the research literature on the effects of density of alcohol sales outlets on alcohol consumption and alcohol-related problems; suggest a new way of conceptualising the relationships; and discuss the implications for reducing alcohol-related harm.

Income inequality and alcohol attributable harm in Australia

BACKGROUND

There is little research on the relationship between key socioeconomic variables and alcohol related harms in Australia. The aim of this research was to examine the relationship between income inequality and the rates of alcohol-attributable hospitalisation and death at a local-area level in Australia.

METHOD

We conducted a cross sectional ecological analysis at a Local Government Area (LGA) level of associations between data on alcohol caused harms and income inequality data after adjusting for socioeconomic disadvantage and remoteness of LGAs.The main outcome measures used were matched rate ratios for four measures of alcohol caused harm; acute (primarily related to the short term consequences of drinking) and chronic (primarily related to the long term consequences of drinking) alcohol-attributable hospitalisation and acute and chronic alcohol-attributable death. Matching was undertaken using control conditions (non-alcohol-attributable) at an LGA level.

RESULTS

A total of 885 alcohol-attributable deaths and 19467 alcohol-attributable hospitalisations across all LGAs were available for analysis. After weighting by the total number of cases in each LGA, the matched rate ratios of acute and chronic alcohol-attributable hospitalisation and chronic alcohol-attributable death were associated with the squared centred Gini coefficients of LGAs. This relationship was evident after adjusting for socioeconomic disadvantage and remoteness of LGAs. For both measures of hospitalisation the relationship was curvilinear; increases in income inequality were initially associated with declining rates of hospitalisation followed by large increases as the Gini coefficient increased beyond 0.15. The pattern for chronic alcohol-attributable death was similar, but without the initial decrease. There was no association between income inequality and acute alcohol-attributable death, probably due to the relatively small number of these types of death.

CONCLUSION

We found a curvilinear relationship between income inequality and the rates of some types of alcohol-attributable hospitalisation and death at a local area level in Australia. While alcohol-attributable harms generally increased with increasing income inequality, alcohol-attributable hospitalisations actually showed the reverse relationship at low levels of income inequality. The curvilinear patterns we observed are inconsistent with monotonic trends found in previous research making our findings incompatible with previous explanations of the relationship between income inequality and health related harms.

A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods

Numerous efforts have been devoted to investigating crash occurrence as related to roadway design features, environmental factors and traffic conditions. However, most of the research has relied on univariate count models; that is, traffic crash counts at different levels of severity are estimated separately, which may neglect shared information in unobserved error terms, reduce efficiency in parameter estimates, and lead to potential biases in sample databases. This paper offers a multivariate Poisson-lognormal (MVPLN) specification that simultaneously models crash counts by injury severity. The MVPLN specification allows for a more general correlation structure as well as overdispersion. This approach addresses several questions that are difficult to answer when estimating crash counts separately. Thanks to recent advances in crash modeling and Bayesian statistics, parameter estimation is done within the Bayesian paradigm, using a Gibbs Sampler and the Metropolis-Hastings (M-H) algorithms for crashes on Washington State rural two-lane highways. Estimation results from the MVPLN approach show statistically significant correlations between crash counts at different levels of injury severity. The non-zero diagonal elements suggest overdispersion in crash counts at all levels of severity. The results lend themselves to several recommendations for highway safety treatments and design policies. For example, wide lanes and shoulders are key for reducing crash frequencies, as are longer vertical curves.

Geographic variability in alcohol-related crashes in response to legalized Sunday packaged alcohol sales in New Mexico

On July 1, 1995 the state of New Mexico lifted its ban on Sunday packaged alcohol sales. Legislation lifting the ban included a local option allowing individual communities within the state to hold an election to reinstitute the ban on Sunday packaged alcohol sales. Previous research has shown a clear statewide increase in alcohol-related crash and crash fatality rates after the ban was lifted. The goal of this study is to measure county-level variability in changes in alcohol-related crash rates while adjusting for county socio-demographic characteristics, spatial patterns in crash rates and temporal trends in alcohol-related crash rates. Bayesian hierarchical binomial regression models were fit to the observed quarterly crash counts for all counties between July 1, 1990 and June 30, 2000. Results show marked variability in the impact of legalized Sunday packaged alcohol sales on alcohol-related crash rates. Relative risks of an alcohol-related crash for the post-repeal versus pre-repeal period vary across counties from 1.04 to 1.90. Counties with older population suffered a greater negative impact of legalized Sunday packaged alcohol sales. Counties with communities that quickly passed the local option to re-ban packaged sales on Sundays were able to mitigate most of the deleterious impact of increased alcohol availability that was observed across the state.

Fatal traffic injuries among pedestrians, bicyclists and motor vehicle occupants

The objective of this study was to investigate characteristic injuries of pedestrians and bicyclists (unprotected) compared to motor vehicle occupants' (protected) in fatal traffic accidents. Cases of 664 fatal traffic accidents (371 pedestrians, 45 bicyclists, and 248 motor vehicle occupants) were collected from 1999 to 2001 using the database of the Forensic Institute in Budapest. Autopsy reports were analyzed. Location of injuries, blood alcohol levels, seasonal distribution and natural diseases influencing accident outcome were evaluated. For statistical analysis, odds ratio (OR) with 95% confidence interval (CI) was used by a conditional logistic regression. There were substantial differences in distribution of injuries suffered by pedestrians, bicyclists and motor vehicle occupants. Among pedestrians and bicyclists there was a higher rate of head injuries, such as skull fractures, epidural haemorrhage, subdural haemorrhage, brain contusion, and injuries of the lower extremities. Thoracic damages, such as traumatic aortic rupture, hemothorax, and abdominal damages, like liver rupture were dominant in motor vehicle occupants. Considering existing natural diseases, coronary artery disease was the only one with higher occurrence among motor vehicle occupants 24 (9.7%) compared with pedestrians and bicyclist 36 (8.6%). These results underline the importance of preventive strategies in transportation, pointing out that different methods are necessary to reduce fatal injuries of various traffic participants.