Double pair comparison—A new method to determine how occupant characteristics affect fatality risk in traffic crashes
Abstract
A new method to determine how occupant characteristics affect fatality risk in traffic crashes is developed. The method, which uses data from the Fatal Accident Reporting System (FARS), focuses on two occupants, a “subject” occupant and an “other” occupant. The probabilities of a fatality to the subject occupant when that occupant has one of two characteristics are compared. The other occupant serves essentially a normalizing, or exposure estimating, role. The method uses only fatality frequency data—no external exposure information is required, and it is relatively free from uncertain assumptions. It has wide applicability; examples of potential applications include investigating car occupant fatality risk as a function of sex, age, alcohol use or motorcyclist fatality risk as a function of helmet use. The first application is to determining the effectiveness of safety belts in preventing car occupant fatalities, as described in the paper following this paper.
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Cited by (115)
New cars on the highways: Trends in injuries and outcomes following ejection
2020, Surgery Open ScienceAlthough ejections from motor vehicles are considered a marker of a significant mechanism and a predictor of severe injuries and mortality, scant recent data exist to validate these outcomes. This study investigates whether ejections increase the mortality risk following a motor vehicle crash using data that reflect the introduction of new vehicles to the streets of a large city in the United States.
The Trauma and Emergency Medicine Information System of Los Angeles County was queried for patients ≥ 16 years old admitted following a motor vehicle crash between 2002 and 2012. Ejected patients were compared to nonejected. Primary outcome was mortality. A logistic regression model was used to identify predictors of mortality and severe trauma.
A total of 9,742 (6.8%) met inclusion criteria. Of these, 449 (4.6%) were ejected; 368 (82.0%) were passengers and 81 (18.0%) were drivers. The rate of ejection decreased linearly (6.1% in 2002 to 3.4% in 2012). Compared to nonejected patients, ejected patients were more likely to require intensive care unit admission (43.7% vs 22.1%, P < .01), have critical injuries (Injury Severity Score > 25) (24.2% vs 7.3%, P <.01), require emergent surgery (16.3% vs 8.0%, P <.01), and expire in the emergency department (3.6% vs 1.2%, P <.01). Overall mortality was 3.6%: 9.6% for ejected and 3.3% for nonejected patients (P <.01). In a logistic regression model, ejection and extrication both predicted mortality (adjusted odds ratio: 1.83, P <.01 and 1.87, P <.01, respectively). Ejection also predicted critical injuries (Injury Severity Score > 25) with adjusted odds ratio of 2.48 (P <.01).
Ejections following motor vehicle crash have decreased throughout the years; however, they remain a marker of critical injuries and predictive of mortality.
Alcohol is a predictor of mortality in motor vehicle collisions
2019, Journal of Safety ResearchIt is well recognized that driving while intoxicated increases the probability of a motor-vehicle collision (MVC). The effect of alcohol on the chance of surviving the MVC is less clear. Method: Using data from the Fatality Analysis Reporting System (FARS) we conducted analyses for the outcome of mortality using alcohol and other variables as predictors. We also selected alcohol positive (AP) and alcohol negative (AN) persons from the same MVC and vehicle to control for confounding characteristics. Results: The odds ratio (OR) for mortality for alcohol positive drivers was 2.57, (p < 0.001 for all the following OR). Other harmful predictive factors were age OR 1.01 per year, vehicle age OR 1.05 per year, male sex OR 1.23, avoidance maneuver OR 1.09, speed related OR 2.89, rollover mechanism OR 2.75, and collision with a fixed object OR 6.70. Protective factors were proper restraint use – OR 0.19 and collision with another moving vehicle, OR 0.21. In the multivariate analysis the OR of mortality for AP vs AN was 1.46. Proper restraint use (OR 0.27) remained protective along with collision with another moving vehicle. When AP and AN persons from the same MVC and the same vehicle were compared, the adjusted OR’s for mortality were 1.46 and 2.08, respectively. Conclusions: Alcohol is an independent predictor of mortality in an MVC. Proper restraint use is the strongest protective factor. This finding allows a more complete understanding of the risks of driving while intoxicated, not only a higher probability of an MVC, but decreased survival once the MVC occurs. Practical Applications: Identification of alcohol as an independent predictor of mortality in an accident may improve risk assessment and influence drivers to avoid driving while intoxicated.
The risk of whiplash-induced medical impairment in rear-end impacts for males and females in driver seat compared to front passenger seat
2013, IATSS ResearchThe objective of this study was to study whiplash injury outcome in front-seat occupants in rear-end impacts using double paired comparison technique. The combination of gender, seated position, and outcome was analyzed. Folksam, a Swedish insurance company, has a database of whiplash injuries. A questionnaire was used to collect study data. The response rate was 81%. The inclusion criteria included medical impairment one year after the impact, as judged by medical specialists. The study included rear-end impacts between 1990 and 1999 that resulted in at least one permanent neck injury impairment; in total, 430 impacts with 860 occupants and 444 impairments. Of those suffering impairment, 302 were female and 142 male; 235 were seated in the driver's seat and 209 in the front passenger seat. Relative risk estimates for impairing whiplash injury, by gender and seated position:
- 1.
Driver male (DM)/passenger female (PF) relative risk = 0.5 n = 218
- 2.
Driver male (DM)/passenger male (PM) relative risk = 1.4 n = 57
- 3.
Driver female (DF)/passenger female (PF) relative risk = 2.5 n = 102
- 4.
Driver female (DF)/passenger male (PM) relative risk = 4.6 n = 67.
Females had a relative risk of medical impairment of 3.1 compared to men after adjustment for the average increased risk in the driver position. The driver position had a doubled relative risk compared to the front passenger position. As a conclusion it may be of value to take risk differences between male and female occupants and between driver and front passenger positions into account in future automotive car and seat construction.
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Inverse propensity weighting to adjust for bias in fatal crash samples
2013, Accident Analysis and PreventionThe Fatality Analysis Reporting System (FARS) has data from all areas of the United States, but is limited to fatal crashes. The National Automotive Sampling System–General Estimates System (NASS–GES) includes all types of serious traffic crashes, but is limited to a few sampling areas. Combining the strengths of these two samples might offset their limitations.
Logistic regression (allowing for sample design, and conditional upon selected person-, event-, and geographic-level factors) was used to determine the propensity (PFC) for each injured person in 2002–2008 NASS–GES data to be in a fatal crash sample. NASS–GES subjects injured in fatal crashes were then reweighted by a factor of WFC = (1/PFC) to create a “pseudopopulation”. The weights (WFC) derived from NASS–GES were also applied to injured subjects in 2007 FARS data to create another pseudopopulation. Characteristics and mortality predictions from these artificial pseudopopulations were compared to those obtained using the original NASS–GES sample. The sum of WFC for FARS cases was also used to estimate the number of crash injuries for rural and urban locations, and compared to independently reported data.
Compared to regression results using the original NASS–GES sample, unadjusted models based on fatal crash samples gave inaccurate estimates of covariate effects on mortality for injured subjects. After reweighting using WFC, estimates based upon the pseudopopulations were similar to results obtained using the original NASS–GES sample. The sum of WFC for FARS cases gave reasonable estimates for the number of crash injuries in rural and urban locations, and provided an estimate of the rural effect on mortality after controlling for other factors.
Weights derived from analysis of NASS–GES data (the inverse propensity for selection into a fatal crash sample) allow appropriate adjustment for selection bias in fatal crash samples, including FARS.
The expected number of road traffic casualties using stratified data
2010, Safety ScienceRoad safety policy plans often require robust calculation of the expected number of road casualties in a certain target year. The relevance of such estimations should be measured by their power to influence and support safety policy makers. Thus, techniques to evaluate the safety developments and the estimating methods must be sound, robust, and preferably accepted by both policy makers and the scientific community. In this paper, we concentrate on choosing an appropriate model used for the calculation, rather than on statistical techniques. We calculate a casualty rate from casualty data and mobility (distance travelled) data, which is extrapolated and subsequently multiplied by an expected future distance travelled. After correction for separately assessed effects of additional safety measures, the number of casualties is estimated. We investigate a method where this is done after both mobility data and casualty data are stratified into properly chosen subsets. Projecting these different trends generally leads to a result that differs from the projection of the aggregated data. Also, stratification enables incorporation in the estimation of explaining factors or additional measures related to a specific subset of the casualties. The principles of stratified projections are illustrated by three Dutch projections which were carried out between 2006 and 2008. Also, some preliminary results of further research on stratification are given. The results imply that the rates of change in casualty rate for different traffic modes or driver age, are not necessarily equal. We propose that these specific decreasing trends are a consequence of external influencing factors.
Risk and protection factors in fatal accidents
2010, Accident Analysis and PreventionThis paper aims at addressing the interest and appropriateness of performing accident severity analyses that are limited to fatal accident data. Two methodological issues are specifically discussed, namely the accident-size factors (the number of vehicles in the accident and their level of occupancy) and the comparability of the baseline risk. It is argued that – although these two issues are generally at play in accident severity analyses – their effects on, e.g., the estimation of survival probability, are exacerbated if the analysis is limited to fatal accident data. As a solution, it is recommended to control for these effects by (1) including accident-size indicators in the model, (2) focusing on different sub-groups of road-users while specifying the type of opponent in the model, so as to ensure that comparable baseline risks are worked with. These recommendations are applied in order to investigate risk and protection factors of car occupants involved in fatal accidents using data from a recently set up European Fatal Accident Investigation database (Reed and Morris, 2009). The results confirm that the estimated survival probability is affected by accident-size factors and by type of opponent. The car occupants’ survival chances are negatively associated with their own age and that of their vehicle. The survival chances are also lower when seatbelt is not used. Front damage, as compared to other damaged car areas, appears to be associated with increased survival probability, but mostly in the case in which the accident opponent was another car. The interest of further investigating accident-size factors and opponent effects in fatal accidents is discussed.