We used the FARS dataset, a world-renowned recording system for fatal crashes that: (1) occur in all US states (as well as the District of Columbia and Puerto Rico); (2) happen on public roadways; and (3) result in a death within 30 days of the crash (US DOT 2003). It also contains details pertaining to health (eg, physical or mental conditions); physical environmental factors (eg, vehicles or highway design); social environmental factors (eg, passengers); and behavioral factors (eg, alcohol use or safety belt use).

For this cross-sectional analysis, we designated Younger Drivers (aged 35–54 years) as our controls, and Older Drivers (aged 65 and older) as our cases. With 6,445 Older Drivers and 20,077 Younger Drivers, there was sufficient statistical power to detect differences between these groups. Because significant variation exists within age categories (Foley et al 2002; Langford et al 2006), we tested age as a confounding and moderating variable.

In addition to age, inclusion in the study depended on drivers being involved in motor vehicle crashes during 2003. We excluded drivers with missing data for age or injury severity (dependent variable); drivers that do not normally travel on public roads (snowmobiles or farm equipment); and drivers of heavy trucks (school buses or motor homes); and vehicles with unknown body types. The final sample size was 19,782 with 14,083 Younger Drivers and 5,774 Older Drivers.

From the 178 FARS variables, we excluded 146 variables that (1) did not contribute significant information (eg, unique identifiers); (2) did not meet the scope of our research questions (eg, vehicle identification number); (3) that had large counts of missing data (eg, drug test results); or (4) were prone to multicolinearity. As a result, the remaining 32 variables were conceptually congruent with the domains of the PPMHP and consistent with our previous work on older driver safety (Awadzi et al 2006; Classen, Garvan et al 2006). Given their categorical and ordinal nature, we were able to collapse variables with several value categories. For example the variable most harmful event originally had 57 values from which we created four categories. Our dependent variable injury severity was collapsed (from its original eight categories) into a dichotomous Yes/No outcome.

Using SPSS 14.0 [SPSS, 2005] we performed a descriptive analysis, bivariate analysis, and binary logistic regression. After inspecting the data for distribution and completeness we excluded variables with more than 8% missing data from subsequent analyses. We used bivariate analyses to (1) examine the relationships of the 32 explanatory variables to the outcome variable, and to (2) observe the relationship between age-related licensure renewal policies and injury outcome. The variable license state was collapsed by factors (age-renewal policies, reduced renewal cycle, in-person renewal, and vision/medical testing) to ascertain differences in injury rates among states with and without these policies. We conducted a binary logistic regression analysis using age as an interaction variable. Using the SPSS enter option, we keyed our 32 retained FARS variables into the analysis. We inspected the model fit by considering the Hosmer and Lemeshow goodness-of-fit test statistic (good fit indicated by statistic not being significant at p < 0.05), and by considering the explained variance through the Nagelkerke R-Square.

For our meta-synthesis, we included studies that: (1) were conducted in the US; (2) drew perceptions from older driver stakeholders; and (3) provided rich narrative results pertaining to needs, factors influencing safety, and safety priorities of older driver stakeholders. The six studies to meet our criteria illustrated stakeholder perspectives on safety factors, including decision making (Sterns et al 1997; Johnson 1998, 2002), automobile use and alternative transportation (Kerschner and Aizenburg 1999), driving reduction and cessation (Kostyniuk and Shope 1998), and the consequences of mobility and driving (Burkhardt et al 1998). These studies represented the perspectives of 690 participants (older adults who were still driving, former drivers, families, friends and professionals) across a wide geographic area of more than 10 states. The research reports and journal articles were prepared, imported into the ATLAS.ti (Muhr 2004) software program, and analyzed to identify themes and concepts.

We analyzed the studies descriptively and profiled each study by funding source, research questions, study method, and respondents (age, socio-economic status and geography of residence). Our content analysis used the structural model for older driver safety (Classen, Garvan et al 2006), to guide (but not limit) our coding of themes, categories and concepts. Using ATLAS.ti we analyzed the data using both deductive coding (use of a coding rubric based on the structural model) and inductive coding (open coding to capture new themes in the primary studies). We employed an iterative process to analyze the studies. First we read and coded the primary studies attending to the research context, questions asked, design and theoretical influences (single case analysis). Then, we used the constant comparison method (Strauss and Corbin 1998) among all six studies to identify and illustrate common themes as well as contrasting results. Next, we identified over-arching themes by exploring relationships among the studies. Finally, we developed a meta-synthesis model to illustrate the joint findings and new concepts.

To interpret the main findings (risk and protective factors) pertaining to older driver safety, within a relevant context, we used a mixed methods approach (Creswell 2005). Within the PPMHP framework, we compared and integrated the findings from the qualitative meta-synthesis with the quantitative results (findings from the binary logistic regression model). The interpretation of this expanded coverage process (Denzin 1989) allowed us to identify the most important risk and protective factors for older driver safety, resulting in a framework to plan a targeted intervention.

The expanded coverage design (Denzin 1989) entailed simultaneously collecting quantitative and qualitative data, then analyzing the data separately, before integrating and comparing the data (Tashakkori and Teddlie 1998). We used a multi-method meta-matrix (Tashakkori and Teddlie 1998) to demonstrate confirmability and/or completeness of the data, and as a means of ensuring acceptability across the two research paradigms. Table 1 presents a multi-method meta-matrix template used to indicate the PPMHP domain, and report comparison and integration of a significant finding from the logistic regression model with the comparable qualitative finding(s) from the meta-synthesis. The information yields an integrated whole and discusses implications for intervention planning.

The sample included 29% (5,744) older drivers. Gender comprised 66.6% (3,827) males and 33.4% (1,917) females. About 85% experienced an injury from the crash. Most crashes (94.9%) were non-alcohol related. In most cases, older drivers wore a restraint (72.5%), were registered vehicle owners (85.5%), and had a valid drivers’ license for the vehicle they operated during the crash (97.5%). Crashes generally occurred on straight roads (84.4%), in dry road conditions (84.4%), and during daylight hours (79.5%).

Although almost all the variables were significant at p ≤ 0.05 performing the bivariate analysis enabled us to further collapse multicolinear variables. For age-related licensing policies (enabling domain), we found: (1) Older drivers with licenses from states with age-renewal policies had significantly lower injury percentages compared to older drivers from states with no age-renewal policies (p ≤ 0.01); (2) There was no significant difference in injury rates between states with reduced renewal cycles and those with no reduced renewal cycles (p = 0.59). (3) Drivers with licenses from states with in-person renewal policies had significantly lower injury rates compared to older drivers from states with no in-person renewal policies (p ≤ 0.01) and; (4) There was no significant difference (p ≤ 0.46) in injury rates between states with and without test requirements (vision, medical, and road) for older drivers.

From the 32 variables identified as conceptually congruent with the PPMHP model, we had representation in five domains: health (2 variables), behavior (3 variables), environment (21 variables), reinforcing (5 variables), and predisposing (1 variable). The Hosmer and Lemoshow test (p = 0.68), indicated that the model fits the data well; and the Nagelkerke R-Square indicated that 57.2% of the variance in the outcome variable was explained by the data. Table 2 displays the binary logistic regression model showing the significant age interactions and explanatory variables from the five domains of the PPMHP, with the dependent variable (injury yes/no). We observed 20 statistically significant associations: four with age-interaction effects and 16 main effects (no statistically significant differences between younger and older drivers). The interaction effects related to the environmental domain (registered vehicle owner, principal point of impact, and number of occupants) and the reinforcing domain (number of other previous motor vehicle convictions). For the registered vehicle owner variable, older drivers who were not the registered owner of the vehicle were 53% (p = 0.05) less likely to be injured in the crash compared to drivers who were registered vehicle owners. For the principal point of impact variable, (angle at which the vehicle was struck using the clock method), two levels of risk emerged: the 1–3 0’clock angle (OR = 1.61) and 7–9 0’clock angle (OR = 4.75). For the variable number of vehicle occupants a protective marginally significant association emerged for older drivers with two or more occupants present (OR = 0.60).

After controlling for age, 6 main effects were evident: health (1 variable), behavioral (3 variables), environmental (11 variables) and predisposing (1 variable) domains. Compared to males (health domain), females had an increased risk (OR = 1.51) of injury in motor vehicle crashes. Risk factors from the behavioral domain were: (1) not having a valid drivers’ license (OR = 1.39); alcohol (OR = 2.00); and restraint system use (OR = 6.20). Factors from the environmental domain included roadway surface conditions, vehicle body type, and most harmful event. Compared to dry roadway surfaces, crashes in adverse roadways (eg, snow and rain) were 1.5 times more likely to result in injury. For vehicle body type, in relation to SUVs, automobile and automobile derivatives were risks for injury (OR = 2.00), while vans, trucks, and light pickup were protective (OR = 0.77). Most harmful event pertained to major events for vehicles involved in crashes. In contrast with collision with an object that was not fixed, drivers who collided with a fixed object had 249 times the likelihood of being injured, while drivers in motor vehicles in transport had 31 times the odds of being injured. Most harmful events with non-collision crashes had 266 times the odds of resulting in injury. Protective environment factors included absence of traffic control devices (OR = 0.79), traveling in urban areas (compared to rural) (OR = 0.61) and airbag not deployed (OR = 0.25). From the predisposing domain, vehicle maneuver (actions before initiation of the crash) indicated that compared to going straight, lane-related crashes (OR = 0.64), maneuvers (OR = 0.59), and making a left turn (OR = 0.66) had protective effects.

Quantitative: For each previous motor vehicle conviction, older drivers are 35% less likely to be injured in a crash.

Qualitative: Families intervened with protective measures based on crashes or incidents involving the police. For example, after the parent was in an accident, the son-in-law would no longer allow this parent to drive. In another case, after a call from the police, a daughter took the keys away from her parent (Sterns et al 1997, p 57).

Comparison or integration: Findings from both datasets support that protective steps have been taken by the driver themselves (quantitative), or by the families (qualitative) after convictions, crashes, violations, or incidents with police involvement. These protective steps were associated with impacting (reinforcing) safer driving or leading to driving cessation.

Discussion: Consistent with the literature (Coughlin et al 2004; NHTSA 2006a) a history of an adverse event was somehow associated with a safer outcome: decreased risk of injuries, or driving cessation.

Implications: To further understand the history of violations, convictions, or crashes and their impact (reinforcing) on safe driving, further research (case-control or historical cohort) is necessary. However these findings suggest that formal (police) or informal (families) agents of the older drivers’ social network have a role in crash or injury prevention. Thus continued police or family involvement are necessary to (1) monitor, (2) influence decision making, (3) establish rules, (4) and enforce driving restrictions for risk reductions in unsafe driving outcomes.

Quantitative: Compared to older drivers without any passengers in the vehicle, those with two or more passengers were 40% less likely to sustain injuries following a motor vehicle crash.

Qualitative: Some older drivers drove alone to prevent distraction, while others choose to drive with a companion for longer trips, or reported that passengers were observing and giving feedback on their driving errors.

Comparison or integration: Compared to older drivers without any passengers in the vehicle, older drivers with two or more passengers were 40% less likely to sustain injuries following a motor vehicle crash. Individual responses are congruent to the quantitative finding when driving with one or more companions. Conversely, limited qualitative data show that driving alone can be of benefit for the older driver in avoiding distractions (Hing et al 2003).

Discussion: Within the PPMHP, the social environment encompasses the presence of passengers. Consistent with the literature (Burkhardt et al 1998; Hing et al 2003; Bédard and Meyers 2004) and the qualitative findings, at least some older adults are aware of the protectiveness of driving (way finding or alerting drivers to avoid adverse events) with passengers. Conversely, if older drivers perceive passengers to be a distraction (Burkhardt et al 1998; Hing et al 2003; Bédard and Meyers 2004), that may pose a safety risk.

Implications: These findings have implications for intervention planning by which the presence of passengers may be protective or distractive to safer driving outcomes. We think that the older drivers’ response to passenger cues is a function of their cognitive status and conditions under which driving occur, and must be examined as such.

Quantitative: Compared to older drivers who are registered vehicle owners, those who are not registered owners of vehicles are 31% less likely to be injured in crashes.

Qualitative: None.

Comparison or integration: The protective nature of driving a vehicle other than one’s own may partly be explained by considering a subgroup of older drivers that would most likely drive (a) a rented vehicle; (b) the vehicle of another member; or (c) a company vehicle (Janicak 2003). This group may represent the younger old groups, be in better physical or mental (cognitive) health, have better visual perceptual abilities, or have an increased sense of responsibility.

Discussion: A sub-analysis confirmed our thinking that drivers who drove other vehicles were of the younger age group (p=0.01, mean age 74.15, SD=6.55) compared to those who drove vehicles registered in their names (p=0.01, mean age 76.29, SD=7.20).

Implications: No implications for the intervention plan.

Quantitative: Compared to the 12 o’clock angle, older drivers who were impacted at the 1–3 o’clock angle were 2 times more likely to be injured in crashes, but those who were impacted at the 7–9 o’clock angle were 5 times more likely to be injured in crashes.

Qualitative: None.

Comparison or integration: This finding shows the significance of two angles of impact associated with the highest risks of injury for older drivers. Principal impact is related to dynamic interactions of many components such as kinetic forces, speed, distance, velocity, vehicle crashworthiness, objects impacted (fixed vs moving), and maneuvers executed. To understand the details of the principal impact, sub-analysis including other interaction variables such as (1) speed, (2) most harmful event (collision with fixed objects or moving objects) and (3) vehicle maneuver is needed.

Discussion: Although no qualitative findings exist to clarify the older drivers’ experience on principal impact, we realized, from the quantitative data, that relationships exist among the two angles of impact and risk of injuries. Conducting an interaction analysis, such as a log linear analysis, may help us understand the underlying mechanisms of injury post-crash.

Implication: Although this finding has no implications for the intervention plan, it has research implications. Interaction effects of the environment (vehicle and objects), with behavior (speed) and predisposing factors (vehicle maneuver) need to be examined.

Quantitative: Compared to all drivers who were in crashes between 9 PM and 7 AM, those who drove in the daylight hours were 28% (8 AM–1 PM) and 37% (2 PM–8 PM) less likely to be injured in crashes.

Qualitative: Several individual quotes support application of self-restriction for nighttime driving (Burkhardt et al 1998, p 132; Kostyniuk and Shope 1998, p 10; Kerschner et al 1999, p 58) or that drivers feel more comfortable to drive during the day time hours (Burkhardt et al 1998, p 73).

Comparison or integration: The quantitative data show protective effects for daytime driving for all drivers, and the qualitative findings show that older drivers prefer daytime trips and self-restrict for nighttime driving.

Discussion: Our findings are congruent with the existing literature (Finison and Dubrow 2002; Baker et al 2003; NHTSA 2006b), in support of self-regulation strategies as they pertain to daytime driving for older adults.

Implication: These include continued education about the protective nature of daytime driving, and further research to examine the components (health, vehicle, environment) underlying the preference for not driving during these darker hours. If older adults are to drive safer and longer, societal implications include making services and activities available to accommodate them during daytime hours when it is safer to drive. For example, businesses could extend daytime service hours with more “matinee” shows and “early bird” opportunities across a variety of settings. Also, society must ensure provision of acceptable alternative transportation options if older adults should choose to participate in night time activities.

Quantitative: Compared to drivers traveling on non-junctions (eg, rail road crossing or bridges), those who had intersection related crashes were 41% less likely to be injured in a crash.

Qualitative: An older driver stated that he pulled out from a side street and was broad-sided (Kostyniuk and Shope 1998, p 31); a family member of another reported that “one side of her car is all banged up from taking turns too tight” (Sterns et al 1997, p 43). In another case a driver stated that he needed the presence of a stoplight before crossing highways (Burkhart et al 1998, p 72).

Comparison or integration: The protective nature of intersection-related crashes to injury may partially be explained by (1) complex environments being more structured and requiring increased vigilance from drivers, or (2) intersections acting as speed reducing mechanisms. A combination of the increased vigilance and slower speeds among drivers may explain the protective nature of intersections. From the qualitative data an older driver called for a more structured environment (a stoplight before crossing a highway) to feel safer, but families and friends observed that intersection negotiation is somehow associated with minor traffic collisions, near misses, or dents in the body of vehicles.

Discussion: Two recently published studies on highway design and safe driving performance (Classen et al 2006; Shechtman et al 2006) showed that roads with enhanced intersections (using the Federal Highway Administration’s guidelines recommendations proposed by Staplin et al 2001) generally benefit the safe driving performance of younger and older drivers alike. The findings from analyzing the crash dataset illustrated the protective nature between the physical environment and safer driving outcomes at intersections. However, the qualitative data show that (1) minor crashes occur at intersections, and (2) failure to negotiate intersections safely helped families identify unsafe driving behavior.

Implications: These findings bring a new awareness of the protective nature of intersections to injury (structuring the environment and decreasing speed) for all drivers, but also a realization that more complex driving environments (intersections) are related to minor collisions for older drivers.

Quantitative: Compared to crashes occurring at functioning traffic control devices, those drivers who crashed in the absence of these devices were 40% less likely to be injured.

Qualitative: Families and friends commented that older drivers had problems with interpretation of traffic lights, eg, some slowed down or stopped for a green light while others failed to stop or ignored red lights (Sterns et al 1997, p 22); that failure to stop at stop signs was indicative of unsafe driving behavior for older drivers (Kershner and Aizenberg 1999, p 88); and that others reported on having inadequate traffic signs and signals (Kershner and Aizenberg 1999, p 52).

Comparison or integration: The finding that the absence of the traffic control device is protective for all drivers represents co-existence of other factors such as complexity of the environment, higher populated areas, and increases in traffic flow. These factors must be taken into consideration in interpreting this finding. The qualitative findings indicate that older drivers may have difficulty interpreting traffic control devices, which may potentially be indicative of a decline in cognition, thus indicating partial congruence with the quantitative finding. However, in some circumstances, such as crossing a highway, older drivers feel safer with the presence of a traffic control device.

Discussion: The significance of the physical environment is evident as it pertains to traffic control devices. The quantitative data clearly show the protective effect of the absence of traffic control devices and the qualitative data (family and friends) help to explain the confusion that occurs among some older drivers in the presence of traffic control devices; yet limited data suggest that some older drivers rely on traffic control devices. The interactions underlying the person factors (eg, cognition) in combination with environmental factors (eg, highway design) may be better explained in an experimental study.

Implications: Continued education on demands of complex environments may increase the risk for some, but be protective for others.

Quantitative: Compared to going straight, drivers who performed a variety of maneuvers had a lesser chance of being injured in a traffic crash (lane related 36%; other maneuvers: leaving a parked position, making a right turn, and avoiding objects 41%; making a left turn 34%).

Qualitative: Various lane related errors were noted, eg, older drivers weave in and out of lanes and straddle the line frequently (Sterns et al 1997, p 22): “I had the neighbors calling to say grandpa drives right down the middle of the street” (Sterns et al 1997, p 44); or changing lanes too fast (Kershner and Aizenberg 1999, p 56).

Comparison or integration: We ascertained that, compared to going straight, drivers who performed a variety of maneuvers had a lesser chance of being injured in a traffic crash (lane related 36%; other maneuvers 41%; making a left turn 34%). Conversely, in some cases, we have reports from families and friends that older drivers commit lane-related errors that may endanger them.

Discussion: We deduced that the physical environment may enhance (quantitative findings) or endanger (qualitative findings) safe driving. Thus, the physical environment is linked to driving behavior, specifically driver skill and competence (predisposing).

Implications: Drivers may need to undergo assessment for determining driver competence. A variety of self-assessments (Driving Decisions Workbook (Eby et al 2000); or Roadwise Review (AAA 2005)) and professional assessments (American Medical Association (Wang et al 2003)) are available.

Societal implications: These findings extend to society as they call for environmental modifications such as highway design (Staplin et al 2001) or improved crashworthiness of vehicles (NHTSA 2006c).

Quantitative: Compared to male drivers, female drivers were 1.5 times more likely to be injured in a crash.

Qualitative: Older women, whose spouses have died, voiced feelings of insecurity due to a lack of driving experience, and about their driving skills and competencies (Sterns et al 1997, p 41).

Comparison or integration: Compared to male drivers, female drivers had an increased risk of crashes, injuries, and fatalities; and these findings are supported by older driver literature (Finison and Dubrow 2002; Baker et al 2003; Bauer et al 2003; NHTSA 2004). The qualitative data (Sterns et al 1997) are in concert with these findings and bring the understanding that safety risks may be remedied by enhancing the driving skills of female drivers.

Discussion: These findings target females as a high risk group.

Implication: Targeted interventions may be directed towards females to enhance their driving skills and competencies. Strategies may include driver education, driving refresher courses, proper ergonomics (car fit) or proper vehicle selection, and self or professional assessment methods.

Quantitative: Compared to dry road conditions drivers were 1.5 times more likely to be injured in crashes with adverse road conditions.

Qualitative: Family members related that their at-risk older driver had limited him or herself to driving during the day, in certain kinds of traffic, and in good weather (Sterns et al 1997, p 49); avoided driving in ice and snow (Kostyniuk and Shope 1998, p 9); reduced or eliminated night driving in the rain (Kostyniuk and Shope 1998, p 9); or pulled over in the rain (Burkhardt et al 1998, p 72).

Comparison or integration: Compared to dry road conditions, drivers were 1.5 times more likely to be injured in crashes with adverse road conditions. From the qualitative findings, we have testimonies from older drivers and their families that they mostly employ self-restriction strategies to avoid driving in adverse conditions, when the roads are more likely to be wet or slippery.

Discussion: Road surface conditions, a component of the physical environment, yielded an increased likelihood of injuries for all drivers if the roads were not dry. This finding is supported by the qualitative data, but contradicts previous findings (Finison and Dubrow 2002; Baker et al 2003; Awadzi et al 2006) suggesting that crashes are more likely to occur under optimal conditions. However, we clearly see a relationship among the physical environment, predisposing domain (eg, making safer choices) and driving behaviors (manifestation of self-regulatory strategies).

Implication: These findings have educational implications, especially as it pertains to those groups not using self-regulatory strategies.

Quantitative: An interesting finding emerged as we examined how state policies influenced safety outcomes in driving for older drivers. Drivers with licenses from states with age renewal policies (p < 0.01), and those from states with in-person renewal (p ≤ 0.01) had lower percentages of injury compared to those from states with no age-related renewal policies or no in-person renewal policies. No significant differences existed in injury rates between states with and without reduced renewal cycles (p = 0.59), and states with and without test requirements (p ≤ 0.46) (vision, medical and road).

Qualitative: While seniors were concerned about lack of age limits on license renewal by mail, they also expressed that licensing procedures based on age could be discriminatory (Kerschner and Aizenberg, 1999). Seniors tended to have strong opinions either for or against additional testing based on age (Kerschner and Aizenberg, 1999). A second focus group study with a greater percentage of former drivers had respondents that were more in favor of age-based licensing procedures. “It would be okay, I would like it [to have driving tests every two to four years]” (Burkhardt 1998, p 179).

Comparison or integration: The quantitative data show an overall relationship between age-related licensing policies and decreased injury among older drivers. However, upon further analyses, only one out of three age-related policies showed a significant decrease in injury. The qualitative data show that seniors recognize the impact of licensing procedures on safety. However, they felt that changes in licensing targeting older adults infused discrimination, and if applied, changes should pertain to all drivers. The quantitative and qualitative data support age-related licensing policies as a potential avenue for influencing older driver safety.

Discussion: From the perspective of the PPMHP, licensing procedures are enabling factors that have the potential to influence safety. In-person renewal has been associated with reduced fatality rates among drivers over 85 years, with other age-renewal policies such as vision and road tests insignificantly associated with injury, and confirmed through previous research (Grabowski et al 2004). However, Levy et al (1995) found that state mandated vision tests were associated with reduced fatal crash risk for drivers 70 years and older. Further research is needed to clarify specific licensing procedures associated with reduced injury and fatality risk.

In the absence of available qualitative data, some significant quantitative findings (protective and risk factors) could not be compared or integrated. Protective factors (reduction in risk of injury) pertaining to the physical environment included vehicle body type (light pickups and vans), not traveling on the national highway system, traveling in urban areas, and not having the airbags deployed. Inherent to some of these factors were the use of passive protective strategies, for example, vehicle body type and travel in urban areas (more structured environment), which has implications for education in an intervention plan. However we also recognize that these variables may be proxies for other underlying factors. For example driving at low speed may partly explain the protective nature of not traveling on the national highway system, or not having the airbags deployed; which of course underscores the continued need for proper speed enforcement. Risk factors pertained to the physical environment and unsafe behavior and included not having a compliant drivers’ license, using alcohol, not using restraints, traveling on all days of the week except Sundays, and experiencing harmful events (collision with a fixed object, collision while the motor vehicle was in transport, and rollover crashes). Although most of these risk factors were not surprising we were amazed by the magnitude of the most harmful events where drivers had up to a 266 increased likelihood of sustaining injuries in crashes. Many educational and law enforcement strategies already exist to manage most of these risk factors and we support the continuation of those.

Implication: The effects of the most harmful event are perhaps best prevented by a combination of strategies pertaining to environmental redesign (eg, replacing concrete divides with cable barriers), legislation mandating crashworthiness (eg, electronic stability control or anti-rollover technology in all motor vehicles by 2009) (NHTSA 2006c), and continued use of protective devices (safety belts) and observance of traffic laws.