Personal, temporal and spatial characteristics of seriously injured crash-involved seat belt non-users in Hawaii

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Abstract

The characteristics of crash-involved seat belt non-users in a high use state (Hawaii) are examined in order to better design enforcement and education programs. Using police crash report data over a 10-year period (1986–1995), we compare belted and unbelted drivers and front seat occupants, who were seriously injured in crashes, in terms of personal (age, gender, alcohol involvement, etc.) and crash characteristics (time, location, roadway factors, etc.). A logistic regression model combined with the spline method is used to analyze and categorize the salient differences between users and non-users. We find that unbelted occupants are more likely to be male, younger, unlicensed, intoxicated and driving pickup trucks versus other vehicles. Moreover, non-users are more likely than users to be involved in speed-related crashes in rural areas during the nighttime. Passengers are 70 times more likely to be unbelted if the driver is also unbelted than passengers of vehicles with belted drivers. While our general findings are similar to other seat belt studies, the contribution of this paper is in terms of a deeper understanding of the relative importance of various factors associated with non-use among seriously injured occupants as well as demonstrating a powerful methodology for analyzing safety problems entailing the categorization of various groups. While the former has implication for seat belt enforcement and education programs, the latter is relevant to a host of other research questions.

Introduction

The seat belt is one of the most effective devices for saving lives and reducing injuries to the occupants of vehicles involved in crashes. Despite mandatory seat belt laws and numerous safety programs that have been introduced to increase belt use nationwide, use rates remain at approximately 69% (NHTSA, 1998a). Seat belt use rates in the US are significantly lower than Canada, Australia and several Western European countries that have achieved compliance rates of more than 90% (NHTSA, 1997). Front seat occupants in Victoria, Australia had use rates of over 97% and rear seat passengers had use rates of 94% (Diamantopoulou et al., 1997). Even drivers in their 20s with the lowest use rates had a 95% compliance rate.

The benefits of seat belt use have been well documented. Lap/shoulder belts reduce the risk of fatal injury to front seat passenger occupants by 45% and reduce the risk of moderate–critical injury by 50% (NHTSA, 1998a). The crash outcome data evaluation system (CODES) study, moreover, has demonstrated that the average inpatient charge for unbelted drivers involved in crashes is more than 55% higher than the average charge for belted drivers (NHTSA, 1996).

Laws and their enforcement may affect seat belt use rates. At present, 11 states have primary enforcement laws that enable police to stop and cite motorists in violation of belt use laws. Secondary enforcement laws, in which motorists must violate some other traffic law before they can be stopped and cited, exist in 38 states. Secondary enforcement law states have use rates that are on the average 15% lower than primary enforcement states (NHTSA, 1998a). Enforcement practices, moreover, may also affect belt use. In Hawaii, for example, it has been shown that cumulative number of citations issued explained more than half of the monthly variation in seat belt use (Kim, 1991).

In order to plan and implement effective programs for increasing seat belt use, more reliable data on belt use are needed. Most jurisdictions use observational studies to determine belt use rates. These methods typically involve observing and recording seat belt use of occupants as well as vehicle type, location and roadway characteristics. Observational studies are costly to implement. It is difficult to achieve control over sampling. In addition to geographic sampling considerations, a variety of other temporal and roadway factors (road class, speed limit, volume, etc.) may also influence belt use. The problem, for example, of unbelted drivers on rural roads has been documented (Li et al., 1999). Belt use and attitudes towards the use of seat belts (Hamed and Easa, 1998) may also be influenced by trip purpose, distance from home, accident experience or other factors, such as the number of occupants in the vehicle. While some people may “belt up” all the time, there is also evidence of selective belt use (NHTSA, 1997).

Another approach to gathering data on seat belt use is through attitudinal surveys. However, there are validity problems with self-reported use studies. Self-reported seat belt use is typically 12–25% higher than observed use (NHTSA, 1998b). As documented in earlier studies, there are significant differences between observed and self-reported seat belt use (Kim, 1999). One study found that 10% of motorists who described themselves as using belts “all the time” did not use belts at least once while driving during the past week (NHTSA, 1997).

In spite of these difficulties associated with data collection, more information about seat belt non-users is needed. One way to better identify the characteristics and behaviors of non-users is to more closely examine police crash report data. However, there are problems and limitations with police crash data. Estimates of seat belt use from police crash data can be unreliable, particularly for minor crashes (O’Day, 1993, Hunter et al., 1993). Police crash investigators typically rely on interviews with drivers and passengers to determine whether or not belts were used in minor crashes (Figgers and Nash, 1989).

In many states, admitting to police that drivers or occupants were not belted could lead to traffic fines or increases in auto insurance premiums. This is the so-called “lie factor” in traffic safety (Kim, 1999). According to the police data in Hawaii, over the period 1986–1995, 96.8% of all crash involved drivers and front seat passengers age 5 and over used seat belts. This rate is much higher than the annual average of 76.9% based on observational studies conducted during the same period. However, police crash data includes a valuable source of information for seat belt use studies. Crash data are readily available. Crash data are also population-based, with detailed information on occupants and the crash environment.

Police officers derive information on restraint use by examining the condition of the vehicle and the extent of the occupants’ injuries in serious crashes (O’Day, 1993, Hunter et al., 1993, Figgers and Nash, 1989). A previous study has demonstrated that severely injured motorists are less likely to “lie” to the police (Kim, 1999). Based on data consisting of 369 drivers for which crash and hospital records were linked in 1990, the police reported a belt use rate of 89.9% among the drivers with minor injuries. However, hospital records indicated that only 58.3% of the drivers were actually belted. The discrepancy between police and hospital records narrowed significantly as injury levels increased. The police reported a belt use rate of 76.6% and hospital records showed a rate of 73.4% among drivers who had incapacitating or fatal injuries (Kim, 1999).

By restricting our analysis of police data to seriously injured occupants, that is only those who received KABC0 scores of K (fatal injury) or A (incapacitating injury) and removing cases with B (non-incapacitating injury), C (possible injury) and 0 (no injury) scores, the reliability of belt use data increases greatly. Our analysis, therefore, focuses on the characteristics of unbelted drivers and front seat occupants involved in serious, injury producing crashes in which a police report has been filed. This is the critical population for which injury prevention strategies and law enforcement efforts should be targeted.

The purpose of this paper is to examine the characteristics of seriously injured occupants seat belt non-users. There have been many studies on seat belt use. Those studies generally provide descriptive statistics on seat belt use rates by characteristics, such as age group, gender, race, vehicle type, etc. This paper provides models of seat belt non-use as a function of temporal and spatial factors as well as the personal characteristics of occupants (Kim et al., 1995). We begin by comparing the characteristics of users and non-users in terms of demographic variables and crash, roadway, temporal and spatial factors. We use a spline method to better control for the age variable and then build a logistic regression model for explaining the likelihood of being a non-user. We conclude with some observations on how relationships between age, demographic, crash and other variables might be useful in the design of programs to increase belt use.

Section snippets

Data sources

Data for this paper were collected as part of the Hawaii Crash Outcome Data Evaluation System (CODES) project funded by the US Department of Transportation, National Highway Traffic Safety Administration (NHTSA). The CODES project facilitated the acquisition, cleaning, editing and linkage of crash and injury outcome (EMS transport and hospital records) files.

The analysis is based on police reports of vehicle crashes over a 10-year period between 1986 and 1995. The quality of police-reported

Personal characteristics of serious crash-involved seat belt non-users

Table 1 shows the comparison of personal characteristics between two groups, seat belt non-users and users who were seriously injured in crashes. Drivers and front seat passengers are analyzed separately. The significance of the difference between seat belt non-users and users is identified by z-test statistics.

Table 1 shows the proportional distribution of seat belt use by driver and passenger gender. Males are over-represented among unbelted drivers. More than 76% of unbelted drivers are

Discussion

This paper investigated the relationships between seat belt use and personal, temporal and spatial characteristics. However, our analysis is limited to the examination of seriously injured crash-involved drivers and front seat passengers. Since, seriously injured occupants are more likely to be unbelted, our analysis may have some selectivity bias if we were to apply our results to the entire motorist population. Seriously injured occupants, however, need special attention since they should be

Conclusions

The analysis reveals that the characteristics of seat belt non-users can not only be identified, but also used to develop a statistical model for understanding the relative effects of age, gender, risk-taking behaviors and other crash variables. Our study, therefore, contributes to a more detailed understanding of the complex relationships between driver, vehicle, roadway and environmental factors and seat belt non-use among seriously injured occupants. We think that the logistic regression

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