GAO l~--l-_-“l”l_~l.l-~l- Novt~rtlht*r I. 1tN0 HIGHWAY SAFETY Fatalities in Light Trucks and Vans IllI Ml 142642 -- --.- United States GAO General Accounting Office Washington, DC. 20548 Program Evaluation and Methodology Division B-236722 November 14,1999 The Honorable Frank R. Lautenberg Chairman, Subcommitteeon Transportation and Related Agencies Committee on Appropriations United States Senate The Honorable William Lehman Chairman, Subcommitteeon Transportation and Related Agencies Committee on Appropriations Houseof Representatives In a letter dated November 21,1988, and through our subsequentdis- cussionswith your staff, you asked us to analyze the National Highway Traffic Safety Administration’s (NHTSA'S) Fatal Accident Reporting System (FAR@ to l compare passenger-carfatality rates to those for standard pickup trucks, small pickups, standard vans, small vans, and multipurpose vehicles (for example, all-terrain and 4-wheel-drive vehicles); and l compare the fatality experience for these vehicle types in two, more policy-relevant ways: (1) after statistically controlling (that is, adjusting) for non-vehicle-related factors (for example, driver and roadway variables), and (2) when only those accidentsinvolving roll- overs or side-impact collisions are considered. The analysis in this report complementsour earlier assessmentof NHTSA’S overall strategy for determining if certain Federal Motor Vehicle Safety Standards (FMVSS) should be extended to pickup trucks, vans, and multipurpose vehicles.’ This report also provides data relevant to the question of whether the risks associatedwith rollover and side-impact accidents warrant proposed regulations to require crush-resistant roofs and side-impact protection. ‘U.S. General Accounting Office, Motor Vehicle Safety: Passive Restraints Needed To Make Light Trucks Safer, GAO/RCED-90-66 (Washington, DC.: November 1989). Page 1 GAO/PEMDSl-S FataUties in Light Trucks and Vans B-220722 Fatality rates for different vehicle types do differ, as table 1 indicates. During 1986 and 1986, there were 206 fatalities for every million pas- sengercars registered.2The fatality rate was lower for small and stan- dard vans and standard pickups, and higher for small pickups and multipurpose vehicles. Table 1: Fatality Rate8 Combining 1985 and 1986 Data Fatality rate per million registered Vehicle type Fatalities vehicles Standard pickup 6,799 200 Small pickup 3,928 308 Standard van I,51 1 140 Small van 293 151 Multipurpose vehicle 1,639 217 Passenger car 47,789 206 Total 61,959 207 However, differences in fatality rates may not be solely attributable to vehicle type. While vehicle type probably contributes to differences in fatality rates, so do non-vehicle-related factors, such as whether the victim was wearing a seat belt or whether the crash occurred in an urban or rural setting. Unadjusted fatality rates are difficult to interpret becadsewe cannot tell how much of the difference between two rates is due to vehicle-type differences and how much is due to non-vehicle- related factors such as gender of the driver. For example, if men have higher accident rates than women, and if men are more likely to be drivers of certain types of vehicles than others, then a difference in fatality rates may be attributable partly to vehicle type and partly to the gender of the drivers3More policy-relevant information can be pro- duced by statistically controlling for such non-vehicle-related factors. Existing research indicates that a disproportionate share of single- vehicle-accident fatalities involves occupants of light trucks and multi- purpose vehicles. For example, the research suggeststhat the rollover tendenciesof light trucks and multipurpose vehicles may be higher as a ‘For our analysis of highway fatalities we used 1986-86 information from the Fatal Accident Reporting System (FARS), the latest FARS information available at the time we began our study. 3Appendix I discusses the relationship between vehicle type and eleven variables representing char- acteristics of drivers, roadway conditions, and accident circumstances. Highly significant differences exist among vehicle types in the likelihood of their involvlng a drinking driver, a driver under 26 years old, a male driver, a victim being ejected or wearing a safety belt, an accident occurring on weekends, involving multiple vehicles, occurring on rural or wet or curved roads, or off the road. (See appendix I.) Page 2 GAO/PEMDWS Fatalitiea in Light Truclrs and Vana B22iv22 result of inherent vehicle characteristics, such as a higher center of gravity in relation to vehicle track width. However, becausethe studies we reviewed have not controlled for the influence of different driver characteristics or vehicle uses,it has not been possible to conclude that higher fatality rates are due to the characteristics of the vehicle, inde- pendent of the foregoing confounding influences. Appendix II summa- rizes the existing research in this area. In our analysis, we controlled for, or held constant, two sets of non- vehicle-related factors: driver/victim characteristics and roadway/acci- dent characteristics. The driver/victim characteristics included restraint use by fatality (yes or no), sex of driver, age of driver (younger than 26 or 26 and older), and alcohol use by driver (drinking or not). The roadway/accident characteristics we controlled for included multiple- vehicle involvement (whether this factor was present), accident location (on or off the roadway), setting (urban or rural), roadway curvature (straight or curved), and pavement condition (wet or dry). Becauseof the Committee’s interest in rollover and side-impact colli- sions, we examined the fatalities associatedwith each separately. For each type of collision, we separately estimated the effect of driver/ victim characteristics and roadway/accident characteristics, and then, having controlled for these effects, we estimated the likelihood of fatali- ties occurring in each of the six vehicle types. Our full technical report, included as appendix I, describesthe statistical analyses we performed and our more detailed findings. Unfortunately, the information necessaryto calculate fatality rates, which are adjusted for each of our control factors, doesnot exist. While we know the number of registered vehicles within each vehicle type, and therefore can derive general fatality rates as we did in table 1, we do not have the necessarylevel of detailed “exposure” data to adjust these rates for driver or roadway conditions. We do not know, for example, how many miles small vans are driven by men, or on wet pave- ment, or by drinking drivers. Without such information, it is impossible to calculate fatality rates for different types of vehicles adjusted for non-vehicle-related fators. For this reason, we have expressedthe results of our analysis not as a comparison of fatality rates for different vehicle types, but rather as the relative odds of a fatality occurring in one particular type of vehicle as opposedtoother. For example, 6,401 passenger-carfatalities in our sample involved rollovers, and 28,493 did not. The odds, therefore, of a Page 2 GAO/PFiMbBM Fatalities in Light Trucka and Vaau 0286722 fatality occurring in a passenger-caraccident involving a rollover are .19 (6,401 divided by 28,493). By contrast, 610 fatalities in multipur- pose vehicles involved rollovers, while 668 did not. The odds for rol- lover fatalities in multipurpose vehicles, therefore, are 1.09 (610 divided by 668). By forming an odds ratio between the results of these two cal- culations, we can conclude that a fatality in a multipurpose vehicle is 6.74 (1.09 divided by .19) times more likely than a fatality in a pas- sengercar to involve a rollover. These calculations, however, do not account for the possible con- founding effect of other variables. For example, they do not take into account the possibility that drivers of multipurpose vehicles may be younger than passengercar drivers, or more liable to have been drinking, or more likely to be male, or less likely to be wearing a safety belt. They also do not consider the possibility that fatal accidents involving multipurpose vehicles may be more likely to take place on dry pavement, or in a rural area, or off the road, or on a curve, or involve only one vehicle. Any of these non-vehicle-related factors, or somecom- bination of them, could account, in whole or in part, for the greater like- lihood of rollover fatalities in one type of vehicle than in another. For this reason, we constructed statistical models which allowed for the possible influence of these factors and recalculated the odds for each vehicle type after adjusting for the non-vehicle-related factors. For these calculations, we used passengercars as the criterion (or reference) group. Table 2 presents the results of these analyses for fatalities involving rollover accidents- that is, the likelihood, relative to pas- sengercars, of a fatality occurring in each of five vehicle types. The data are presented (1) before adjustment for non-vehicle-related factors, (2) after adjustment for driver/victim characteristics, and (3) after adjustment for accident/roadway characteristics. Table 2: Fatallty Likelihood Ratio in Rollover Accidenta, Non-Passenger-Car Multip;;xww& Standard Small Small Standard Vehlcler Versus Parsenger Cars Variables controlled for van van pickup pickup None 5.74 1.89 1.21 2.73 2.47 Driver/victim characteristics 5.83 2.11 1.74 2.58 2.35 Accident/roadway characteristics 4.59 1.99 1.88 2.25 1.76 We can conclude that, in all of the special vehicle types we examined, fatalities are more likely than those occurring in a passengercar to have involved a rollover. This tendency is most pronounced for multipurpose Page 4 GAO/PEMD-91-S Fatalities in Ught Trucks and Vane E-286122 vehicles. While adjustments for the influence of driver/victim character- istics or for accident/roadway characteristics affect the magnitude of our estimates somewhat, the differential effect of vehicle type, and the lower likelihood of a fatality occurring in a passengercar than in any of these other vehicle types, persist. Table 3 presents parallel statistics for fatalities occurring in side-impact accidents. Fatalities in all the non-passenger-carvehicles in our analysis are less likely to have involved a side impact than those occurring in passengercars. This tendency persists even after adjustment for driver/ victim characteristics and for accident/roadway characteristics. Multi- purpose-vehicle, standard-van, and pickup fatalities are approximately half as likely, and small van fatalities slightly less than two thirds as likely, to have involved a side impact. Table 3: Fatallty Likelihood Ratio in Side- Impact Accidents, Non-Passenger-Car Muitip;;wx Standard Small Small Standard Vehicles Versus Passenger Cars Variables controlled for van van pickup pickup None 0.39 0.52 0.65 0.46 0.46 Driver/victim characteristics 0.42 0.53 0.60 0.50 0.50 Accident/roadway characteristics 0.49 0.54 0.60 0.53 0.54 The results of our analysis suggestthat the increasedlikelihood of fatal rollover accidents-including fatalities in all five light truck and van vehicle types-may be attributable to the vehicles themselves.This increased likelihood may be due to differences in vehicle configuration (for example, higher center of gravity), as well as to the absenceof spe- cific safety standards required for passengercars. Therefore, in the case of rollovers, our results provide somesupport for the proposed exten- sion and strengthening of federal standards concerning crush-resistant roofs for all five non-passenger-carvehicle types considered.4 Our results do not provide similar evidence for the extension of side- impact standards to those samevehicles. Here our results indicate that fatalities in non-passenger-carvehicles are less likely to involve side impacts than are passenger-carfatalities. Somecautions need to be applied in interpreting the results of our anal- ysis. First, while we have found that non-passenger-carfatalities are more likely than passenger-carfatalities to involve rollovers, and less 4NHTSA is now reviewing comments received from the notice of proposed rule-making. Page 5 GAO/PEMD-91-9 Fatalities in Light Trucks and Vans likely to involve side impacts, we cannot conclude that these differences are the result of differing protections afforded occupants in these types of accidents.(Indeed, our study was not designedto measure such effects.) Such an interpretation is consistent with our findings. How- ever, in the absenceof information on nonfatal accidents to parallel our fatality data, this linkage cannot be established. Second,as we noted earlier, we do not have “exposure” data at the level of detail neededto compute adjusted fatality rates. We do not know, for example, how many miles small vans are driven by men, or on wet pave- ment, or by drinking drivers. Consequently, we cannot estimate the like- lihood that a given number of miles traveled in one type of vehicle by a given driver type will result in a rollover fatality. Finally, our data do not allow us to estimate the effects of proposed safety features--so that although safety features such as crush-resis- tant roofs might reduce rollover injuries, without data on vehicles so equipped we cannot estimate the effectiveness of such features. While the foregoing limitations do not allow us to demonstrate conclu- sively that changesin specifications for certain vehicles would result in fewer highway fatalities, we believe our analysis offers persuasive evi- dencethat rollover fatalities are more likely, and side-impact fatalities less likely, to occur in non-passenger-carvehicles, and that these tenden- cies are vehicle-specific and cannot be attributed simply to driver, roadway, or accident characteristics. We conducted our analysis in Washington, D.C., and Kansas City, Mis- souri, between August 1988 and July 1990 in accordancewith generally acceptedgovernment auditing standards. Dr. Probir Roy of the Univer- sity of Missouri at KansasCity and Dr. Douglas Sloaneof the Catholic University of America assistedus in the development and application of our statistical model. We provided draft copies of this report to officials of NHTSA’S National Center for Statistics and Analysis and discussedwith them the study results. We incorporated their suggestionsas appropriate. We are sending copies to the Secretary of Transportation and other interested parties and will make copies available to others upon request, Page 6 GAO/PEMD-61-6 F&ditkm in LI&t Trucka and Vam B-226722 If you have any questions or would like additional information, please call me at (202) 276-1864.Major contributors to this report are listed in appendix III. Eleanor Chelimsky Assistant Comptroller General Page 7 GAO/PJ!MD91-9 FataUtiea in Light Tmck~ and Vana Contents Letter Appendix I 12 The Analysis of FAIRS Overview 12 Bivariate Relationship Between Rollovers, Side Impacts, 12 Data on Rollovers and and Vehicle Type Side Impacts: Bivariate Relationships Between Vehicle Type and 14 Methodology and SelectedDriver, Victim, and Accident Characteristics Hierarchical Models With Simultaneous Controls for 24 Detailed Findings Driver/Victim and Roadway/Accident Characteristics Higher Order Interactions 36 Appendix II 38 Results of Prior Relative Risk to Car and Light Truck Occupants,1987 Light Truck Safety: A Literature Review and Research 39 39 Research Outline, 1986 A Further Look at Utility Vehicle Rollovers, 1984 40 Estimates of the Potential Benefit of Extending Selected 41 PassengerCar Safety Standards to Light Trucks, Vans, and Multipurpose Vehicles, 1982 Side Impacts: An Analysis of Light Trucks, Intrusion, and 42 Injury in FARSand NCSSData, 1982 Comparison of Truck and Passenger-CarAccident Rates 42 on Limited-Access Facilities, 1981 RecentTrends in Van and Small Truck Safety, 1979 42 Unwarranted Delays by the Department of 43 Transportation to Improve Light Truck Safety, 1978 Appendix III 44 Major Contributors to This Report Tables Table 1: Fatality RatesCombining 1985 and 1986 Data 2 Table 2: Fatality Likelihood Ratio in Rollover Accidents, 4 Non-Passenger-CarVehicles Versus PassengerCars Table 3: Fatality Likelihood Ratio in Side-Impact 6 Accidents, Non-Passenger-CarVehicles Versus PassengerCars Page 8 GAO/PEMD91-8 Fatalities in Ught Trucks and Vans Table I. 1: ObservedFrequenciesof Motor-Vehicle 13 Fatalities Involving Rollovers and Side Impacts, by Vehicle Type, and Odds and Odds Ratios Derived From Them Table 1.2:ObservedFrequenciesof Motor Vehicle 15 Fatalities Involving SelectedCharacteristics, by Vehicle Type and Odds and Odds Ratios Derived From Them Table 1.3:ObservedFrequenciesin the Cross- 17 Classifications of Rollovers, With Selected Characteristics and Odds and Odds Ratios Derived From Them Table 1.4:ObservedFrequenciesin the Cross- 18 Classifications of Side Impacts, With Selected Characteristics and Odds and OddsRatios Derived From Them Table 1.6:Likelihood-Ratio Chi-Square(L2) Values 20 Associated With Several .Hierarchical Models Fitted to Three-Way Tables in Which Rollovers Are Cross- Classified by Vehicle Type and Selected Characteristics Table 1.6:Likelihood-Ratio Chi-Square(L2) Values 21 Associated With Several Hierarchical Models Fitted to Three-Way Tables in Which Side Impacts Are Cross-Classifiedby Vehicle Type and Selected Characteristics Table 1.7:Odds Ratios Indicating the Association of 22 Vehicle Type With Rollovers and Side Impacts, Before and After Controlling for Selected Characteristics Table 1.8:Likelihood-Ratio Chi-SquareValues and Other 24 Characteristics Associated With Hierarchical Models Fitted to the Six-Way Tables Formed by Cross- Classifying Rollovers and Side Impacts With Vehicle Type and SelectedDriver/Victim Characteristics Table 1.9:Expected FrequenciesUnder the Main-Effect 28 Model for the Six-Way Table in Which Rollovers Are Cross-Classifiedby Vehicle Type and Driver/Victim Characteristics Table I. 10: Odds Derived From the Main-Effect Model for 30 the Six-Way Table in Which Rollovers Are Cross- Classified by Vehicle Type and Driver/Victim Characteristics Page 9 GAO/PEMD-91-9 FataIitiea in Ltght Trucks and Vans contents Table I.1 1: Odds Ratios Derived From the Main-Effect 32 Model for the Six-Way Table in Which Rollovers Are Cross-Classifiedby Vehicle Type and Driver/Victim Characteristics Table I. 12: Likelihood-Ratio Chi-SquareValues and Other 34 Characteristics Associated With Hierarchical Models Fitted to the Six-Way Tables Formed by Cross- Classifying Rollovers and Side Impacts With Vehicle Type and SelectedAccident/Roadway Characteristics Table I. 13: Odds Ratios Describing the Associations of 36 Vehicle Type With Rollovers and Side Impacts, After Controlling for Driver/Victim Characteristics and Accident/Roadway Characteristics Table I. 14: Odds Ratios Describing Interactions of Vehicle 36 Type With Other Characteristics on Rollovers Table I. 16: Odds Ratios Describing Interactions of Vehicle 37 Type With Other Characteristics on Side Impacts Abbreviations our Department of Transportation FARS Fatal Accident Reporting System FMVSS Federal Motor Vehicle Safety Standards GAO General Accounting Office NCSS National Crash Severity Study NHTSA National Highway Traffic Safety Administration UMTRI University of Michigan Transportation ResearchInstitute Page 10 GAO/PEMD91-9 Fatalities in Light Trucks and Vans Page 11 Appendix I The Analysis of FARSData on RAlovers and SideImpacts: Methodology and Detailed Findings Our analysis of these data involved four interrelated steps, Overview 1. We examined the data for bivariate relationships between vehicle type and rollovers, and between vehicle type and side impacts. We found strong relationships in both cases. 2. We then looked at a series of two-way tables to explore whether cer- tain other variables that measured characteristics of drivers, fatality victims, accidents, and roadways were simultaneously related to both vehicle type and the outcomesof interest (rollovers and side impacts) in such a fashion that they could account for these bivariate relationships. We discovered strong relationships between several of these measures and both rollover and side-impact accidents. 3. We then considereda series of three-way tables that permitted us to control for the relationship between vehicle type and these characteris- tics, and between these characteristics and rollover and side-impact accidents,before reestimating the relationship between vehicle type and rollovers, and between vehicle type and side impacts. 4. When, in these three-way analyses,the associationsof vehicle type with both rollovers and side impacts persisted, we attempted finally to control for certain of these interrelated characteristics simultaneously. The persistenceof the initial vehicle type/rollover and vehicle type/ side-impact associationseven after the introduction of these mul- tivariate controls convincesus that they are not of a spurious nature, or at least are not readily accounted for by the set of control variables we have considered. The nature and magnitude of these associations,and the techniques we used to test and describe them, are discussedin the following sections. Our analysis began with a consideration of the fatal-accident data in Bivariate Relationship table I. 1, where the type of vehicle in which the fatality occurred-a six Between Rollovers, category variable contrasting multipurpose vehicles, standard vans, Side Impacts, and small vans, small pickups, standard pickups, and passengercars-is cross-classifiedby whether the fatality involved a rollover or a side Vehicle Type impact. The numbers given in the first two rows in table I. 1 represent * the number of fatalities, within each vehicle category, that did or did not involve a rollover, or that did or did not involve a side impact. For each of the two accident categoriesshown in the table, a likelihood ratio chi-square statistic (L2) is given. The large value of this test statistic for Page 12 GAO/PEMD91-9 Fatalitbs in Light Trucks and Vane The An&Ma of FARS Data on Itollovem and Side Impacta: Methodology and D&ailedFindlngs both tables indicates that the hypothesis that, among fatalities, rollovers and side impacts are unrelated to vehicle type can be easily rejected. Rollovers and side impacts, in other words, are strongly associatedwith vehicle type. Table 1.1:Observed Frequencier of Motor-Vehicle Fatalities Involving Rollovers and Side Impacts, by Vehicle Type, and Odds and Odds Ratios Derived From Them MultiP;#‘$i Standard Passenger Accldent category, oddr, and ratio Standard van Small van Small pickup pickup car Rollo”er~ 610 275 40 934 1,560 5,401 No rollover - ----~.--___ 558 763 171 1,806 3,345 28,493 -~-~.-- on rollover Odds 1.09 0.36 0.23 0.52 0.47 0.19 -~ Ratiob 5.74 1.89 1.21 2.73 2.47 Side impa@ 180 202 49 484 857 10.708 No side impact 988 836 162 2,256 4,068 23,186 Odds on side impact 0.18 0.24 0.30 0.21 0.21 0.46 Ratiob 0.39 0.52 0.65 0.46 0.46 aL2 = 1,685.41,5 df, P < .OOOl bAll ratios are expressed relative to passenger cars-that is, 1.09/0.19 = 5.74,0.36/0.19 = 1.89, and so on. These odds ratios can be interpreted in a reasonably straightforward fashion: Multipurpose-vehicle fatalities are 5.74 times as likely to have involved a rollover as passenger-car fatalities, standard-van fatalities 1.86 times as likely as passenger-car fatalities to have involved a rollover, and so on. ‘L* - 816.30,5 df, P < .OOOl The nature of these associationscan be described by calculating odds and odds ratios from the observed frequencies in the table. The odds of rollovers (or side impacts) having been involved in these fatalities were calculated for each type of vehicle. For multipurpose vehicles, for example, there were 610 fatalities that involved a rollover and 668 fatalities that did not, so the odds on fatalities involving a rollover in that vehicle type were 610/568 = 1.09. For every multipurpose-vehicle fatality that did not involve a rollover, in other words, there were 1.09 that did. Thus, for every 100 that did not, there were 109 that did. The odds on fatalities involving rollovers in other types of vehicles can be similarly calculated, and the values that result are given in the third row in each of the two accident categoriescontained in the table. The odds on fatalities involving a rollover were 0.36 for standard vans, 0.23 for small vans, and so on. To determine how strongly the odds on rollovers or side impacts are associatedwith vehicle type, we chosepassengercars as the criterion vehicle type and calculated the odds ratios, or relative odds on rollovers Page 13 GAO/PEMD-91-9 Fatalities in Light Trucks and Vans APpandirI TbeAmlyebofFARSlhitaonMlovenand sldeImjMct8:Metllodologyand &-Flndinlp being involved in other-vehicle-type fatalities versus fatalities involving passengercars. Theseodds ratios are provided in the last row in each of the two accident categoriesin table I. 1. For example, among rollover fatalities, the odds ratio between multipurpose vehicles and passenger cars is 6.74 (1.09/O.19). Similarly, the odds ratios comparing standard vans, small vans, and standard pickups to passengercars are 1.89 (0.36/ 0.19), 1.21 (0.23/0.19), 2.73 (0.62/0.19), and 2.47 (0.47/0.19), respec- tively. These odds ratios can be interpreted directly to mean that, for example, fatalities in multipurpose vehicles are 6.74 times as likely to have involved a rollover as fatalities in passengercars, fatalities in stan- dard vans 1.89 times as likely to have involved a rollover as passenger- car fatalities, and so on. The two full sets of odds ratios provided in table I.1 indicate that all non-passenger-carfatalities are more likely than passenger-carfatalities to have involved a rollover (by factors ranging from 1.21 to 5.74), and all non-passenger-carfatalities are less likely than passenger-carfatali- ties to have involved a side impact (by factors ranging from 0.66 to 0.39). Differences in the odds on rollovers are most pronounced between multipurpose vehicles and trucks versus passengercars, as are differ- encesin the odds on side impacts. The value of the chi-square statistics associatedwith these differences assuresus that they are due to more than sampling variability or chance. The fact that these strong associationsof vehicle type with rollovers Bivariate and side impacts are not attributable to chanceor random fluctuations Relationships Between doesnot necessarily imply that they are not spurious, or that they Vehicle Type and cannot be accounted for by other variables with which both vehicle type and rollovers or side impacts are jointly associated.It may be, for SelectedDriver, example, that the more pronounced tendency for multipurpose-vehicle Victim, and Accident fatalities (relative to passenger-carfatalities) to involve rollovers results Characteristics from the fact that multipurpose vehicles are more apt to be driven by males, and males are more likely to be involved in rollovers. Alterna- tively, drinking drivers may be more likely to be involved in rollovers or side impacts than nondrinking drivers, and certain vehicle-type fatali- ties may be more apt to involve drinking drivers. To gain a preliminary impression of the extent to which certain charac- teristics may be jointly related to vehicle type, and to rollovers and side impacts, we examined the simple paired associationsbetween a number of driver, victim, and accident characteristics and vehicle type, and Page 14 GAO/PEMD-91-9 Fatalities in Light Trucke and Vans The Au&d af FAUS Data on Rolloveru and side Impacts: Methodology and DetauedFIndIngfi between these characteristics and rollovers and side impacts. The cross- tabulations between these characteristics and vehicle type are presented in table 1.2,while the crosstabulations with rollovers and side impacts can be found in tables I.3 and 1.4. Table 1.2:Obrerved Frequencler of Motor Vehicle Fatalltleb Involving Selected Characteristics, by Vehicle Type and Odds and Odds Ratloa Derived From Them MultlP~lJ TN$ Standard Passenger Characterlrtlc, odds, and ratlo --- R Standard van Small van Small pickup pickup car Male’ -_- 984 884 152 2,348 4,478 23,395 Female -----.____. 184 154 59 392 447 10.499 Oddsonmale ---~-_.--- 5.35 5.74 2.58 5.99 10.02 2.23 Ratio 2.40 2.57 1.16 2.69 4.50 Under25b ~---___ 428 238 26 1,041 1,523 12,363 25andover 740 800 185 1.699 3,402 21,531 Oddsonunder25 ~~-.-~ -_..-_--.- 0.58 0.30 0.14 0.45 0.61 0.57 Ratio -~-I--- 1.01 0.53 0.25 0.79 1.07 DrinkingC ..-__-.--_.-.---_ 602 378 55 1,304 2,464 13,398 No drinkina 566 660 156 1,436 2,461 20.496 Odds on drinking -~-.~______- 1.06 0.57 0.35 0.91 1.00 0.65 Ratio I_-- .-..-----_- 1.63 0.88 0.54 1.40 1.54 No Restraintd __-II--.__. - -_.. -_--.-^__II__ 1,026 935 150 2,533 4,706 28,989 - Restraint 142 103 61 207 219 4,905 Odds on-.---.--_ -_.. no restraint 7.23 9.08 2.46 12.24 21.49 5.91 Ratio ---_-_.---._-... ..-- .--- 1.22 1.54 0.42 2.07 3.64 Ejection0 ----- _______ -- _.__. ---1 726 374 62 1,142 1,918 -- 8,066 No eiection --..----..-.--.-.-...-.--- 442 664 149 1,598 3,007 25,828 Odds on.__. -"~.__-." ejection -.--_ --.-- 1.64 0.56 0.42 0.71 0.64 0.31 Ratio 5.29 1.81 1.35 2.29 2.06 -__I Multi-vehicles' 354 533 143 1,161 1.883 19.345 Single I--_--.- vehicle 814 505 68 1,579 3,042 14,549 Odds on multi-vehicles -~---_---_ 0.43 1.06 2.10 0.74 0.62 1.33 Ratio ---.---- 0.32 0.80 1.58 0.56 0.47 WeekendQ -__-.I__.-..~ 707 529 102 1,478 2,691 17,919 Weekday ---"-... .---- ...-.-__ 461 509 109 1,262 2,234 15,975 Oddsonweekend -----____-_-~. 1.53 1.04 0.94 1.17 1.20 1.12 Ratio _-.--- ~I- 1.37 0.93 0.84 1.04 1.07 Rural" _-_ll_._.._.l__- "-._-.-- 880 719 135 2,094 3,924 22,274 Other _.----____-. 288 319 76 646 1,001 11,620 Odds on rural --_-...----.- u 3.06 2.25 1.78 3.24 3.92 1.92 Ratio 1.59 1.17 0.93 1.69 2.04 (continued) Page 15 GAO/PEMDSl-8 Fatalities in Light Trucks and Vana Appemdix I The Andyh of FABS Data on Rollovers and Side Impacts lbbthodology and Det.alledFindlnga Multipvul, pc:x Standard Passenger Characterlstlc, odds, and ratio 5: Standard van Small van Small pickup pickup car Off-road’ 663 433 62 1,328 2,668 14,154 --_---- Other 505 605 149 1,412 2,257 19,740 ---- ---- Odds on off-road 1.31 0.72 0.41 0.94 1.18 0.72 -____-.-.._--..- _-.___. - Ratio 1.82 1.oo 0.57 1.31 1.64 Curved roadi 411 262 54 971 1,654 9,878 -- Other 757 776 157 1,769 3,271 23,996 ___.__-.-- --- Odds on curved road 0.54 0.34 0.34 0.55 0.51 0.41 -.~ ....-- - Ratio 1.32 0.83 0.83 1.33 1.23 _-...__ Wet road” 195 232 40 466 838 7,389 ---__ Other 973 806 171 2,274 4,087 26,505 -__“I---~ Odds on wet road 0.20 0.29 0.23 0.20 0.21 0.28 ~---~.-- Ratin 071 1.04 0.82 0.71 0.75 aL2 - 1,660.49,5 df, P < .OOOl bL2 - 203.46,5df,P c .OOOl cL2 - 321.95,5 df,P < .OOOl dL2 = 604.24,5df,P < .OOOl eL2 - 1,437.06,5 df, P < .OOOl 'L* = 1,046.03,5 df,P < .OOOl gL2 = 35.67,5df, P < .OOOl hL2 - 540.53,5df,P < .OOOi 'L* * 393.65,5 df,P < .OOOl jL2 - 104.96,5df, P < .OOOl kL2 = 104.93,5df, P < .OOOi Page 16 GAO/PEMD-918 Fatalities in Light Trucks and Vans The Analysis of FARS Data on Rollovera and Side Impacts: Methodology and DetailedFlndlngE Table 1.3:Observed Frequencies In the Cross-Classltlcatlons of Rollovers, With Odds on Selected Characteristics and Odds and Characteristic Rollover No rollover rollover Odds ratio Odds Ratlos Derived From Them Malea 6,936 25,305 0.27 1.42 Female 1,904 9.831 0.19 Under 25b 4,020 11,599 0.35 1.75 25 and over 4,820 23,537 0.20 DrinkinaC 5.183 13.018 0.40 2.35 No drinking 3,657 22,118 0.17 No restraintd 8,373 29,966 0.28 3.11 Restraint 467 5,170 0.09 EiectioP 6,293 5.995 1.05 11.67 No ejection 2,547 29,141 0.09 Multi-vehicles’ 725 22,694 0.03 0.05 Sinale vehicle 8,115 12.442 0.65 Weekends 5,221 18.205 0.29 1.38 Weekday 3,619 16,931 0.21 Ruralh 7,100 22,926 0.31 2.21 Other 1.740 12.210 0.14 Off -road’ 7,207 12,101 0.60 8.57 Other 1,633 23,035 0.07 Curved roadj 4,021 9229 0.44 2.32 Other 4,819 25,907 0.19 Wet roadk 1,074 8,086 0.13 0.45 Other 7.766 27.050 0.29 aL2 - 15503,l df, P < .OOOl bL2 = 467.43,l df,P < .OOOl cL2 = lJ37.67, 1 df,P < OOOl dL2 =I 666.95, 1 df, P < .OOOl "L2 = 9,382.12,1 df, P < .OOOl 'L2 = 10,066.64,1 df,P < .OOOl QL2 = 149.91,1 df,P < .OOOl hL2 = 791.57,l df,P < .OOOl 'L* = 6,599.96, 1 df, P < 0001 IL* = 1,176.16,1 df, P < .OOOl kL2 = 557.04, 1 df,P < .OOOl Page 17 GAO/PEMD-91-8 Fatalities in Light Trucks and Vans L Appendix I The Analyda of FAR8 Data on Rollovers and Side Impacts: Methodology and DetailedFindlnga Table 1.4:Observed Frequencies in the Cross-Classifications of Side Impacts, No side Odds on Wlth Selected Characteristic8 and Odd8 Characteristic Side impact impact side impact Odds ratio and Odds Ratio8 Derived From Them Male8 8,595 23,646 0.36 0.73 Female 3,885 7,850 0.49 Under 25b 4.338 11.281 0.38 0.95 25 and over 8,142 20,215 0.40 DrinkingC 4,283 13,918 0.31 0.66 No drinking 8,197 17,578 0.47 No restraintd 10,392 27,947 0.37 0.63 Restraint 2;088 3:549 0.59 EjectiorP 2,740 9,548 0.29 0.26 No eiection 9,740 21.948 0.44 Multi-vehicles’ 8,542 14,967 0.57 2.33 Single vehicle 4,028 16,529 0.24 Weekend’J 6,267 17,159 0.37 0.86 Weekdav 6,213 14,337 0.43 Ruralh * 7,801 22,225 0.35 0.70 Other 4,679 9,271 0.50 Off -road’ 3.982 15,426 0.25 0.46 Other 8,599 161070 0.54 Curved roadi 3,066 10,190 0.30 0.68 Other 9,420 21,306 0.44 Wet roadk 3,290 5,870 0.56 1.56 Other 9,190 25,626 0.36 .L2 = 172.67,1 df, P < .OOOl %* - 4.37, 1 df, P * ,037 cL2 - 363.75,l df. P < .OOOl dL2 - 228.29,l df, P < .OOOl ‘L2 - 319.99,l df. P < DO01 ‘L* - 1,493.97,1 df, P < .ooOl gL2 - 6620,l df, P < 0001 “L* - 263.39,l df, P < DO01 ‘L* = 1,185.38, 1 df, P < .CUHN IL* - 267.09, 1 df, P C .OOOl kL2 - 312.75, 1 df, P < .OOOl Nearly all these crosstabulations reveal highly significant relationships, as indicated by the chi-square statistics associatedwith them. These tables also present the magnitude of this relationship as odds and odds Page 19 GAO/PJCMD-91-9 Fatalitlw in Light Tmcka and Vana The Anai,ysta of FARS Data on Rollovera and Side Impacta Methodology and Dm4mdFYndlngE ratios. These ratios can be interpreted as in table 1.1.For example, the driver of a multipurpose vehicle involved in a fatality was 2.39 (6.35/ 2.23) times more likely to have been male than the driver of a passenger car involved in a fatality.1 A rollover fatality was 1.42 (0.27/O.19) times more likely to have involved a male driver than a female driver.2 A detailed interpretation of each of these relationships appears unneces- sary, but in general these crosstabulations indicate the following: 1. Among vehicle fatalities, each of the following variables bears a sig- nificant relationship to vehicle type, to rollovers, and to side impacts: sex of driver, age of driver, whether the driver was drinking, whether the victim was using restraints, whether an ejection occurred, whether multiple vehicles were involved, whether the fatalities occurred on weekends,or on rural roads, or off-road, or on curved or wet roads. 2. Fatalities involving multipurpose vehicles, and both types of pickups, were more likely than passenger-carfatalities to have involved a male driver, a drinking driver, no restraint use, and an ejection, and more likely to have occurred off-road or on rural roads or curved stretches of roads. They were less likely, at the sametime, to have involved multiple vehicles and wet roads and, in the caseof pickups at least, a driver under the age of 25. Fatalities occurring in both types of van were also more likely than passenger-carfatalities to have involved a male driver and an ejection, but, unlike multipurpose vehicle and truck fatalities, they were less likely than passenger-carfatalities to have involved a drinking driver, or to have occurred off-road, or on a curved road. (See table 1.2.) 3. Fatalities involving rollovers were more likely to have involved a male driver, a driver under age 25, a drinking driver, no restraint use, and an ejection, and they were also more likely to have occurred on weekends,on rural roads, on curved roads, or off road. Fatalities involving rollovers were, at the sametime, less likely to have involved multiple vehicles or wet roads. Fatalities involving side impacts were, conversely, more likely to have involved multiple vehicles or wet roads, but less likely to have involved male drivers, younger drivers, drinking drivers, no restraint use, and ejection, or an accident that occurred off- road, on the weekend, or on a curved or wet road. (Seetables I.3 and 1.4.) ISee table 1.2. %ee table 1.3. Page 19 GAO/PEMD-9143 Fatalities in Light Trucka and Vans Appemdix I The Andy&~ of FARS Data on Rolloven, and Side Impacts! Methodology nnd DetalledFlndlnga Given the significance and magnitude of the associationsof these char- acteristics, both with vehicle type and with the outcomesof interest (rollovers and side impacts), we concluded that it was necessaryto con- trol for them in analyzing the association of vehicle type with rollovers and side impacts. In other words, we attempted first to identify the por- tion of the bivariate relationship between vehicle type and rollover or side impact that could be accounted for by the relationship between the personal and accident-related characteristics we considered,and then we attempted to determine if vehicle type significantly added to our understanding of the likelihood of rollovers or side impacts. We did this by constructing and analyzing a series of three-way tables in which vehicle type was cross-classifiedby rollover and one control vari- able at a time, and a similar set of tables for side impact and one control variable at a time. While we do not provide all of these three-way tables in this report, table I.6 presents the results of fitting selectedhierar- chical models to the rollover tables, and table I.6 contains the results of fitting similar models to the side-impact tables. Table 1.6~Likelihood-Ratio Chi-Square (L2) Valuer Associated With Several Chi-square value8 Hkrarchlcal Models Fitted to Three-Way [VC] [R] 11 [VC] [CR] 10 [VC] [CR] [VR] 5 Tables In Which Rollovers Are Crorr- de m;d?mm de rees of de reesot Classified by Vehicle Type and Selected Control variable f f!eeedom #eedom Characteristic8 Sex of driver 1,748.23 1,593.20 31.11 (.98) Age of driver 2,231.33 1,763.90 12.71 (.99) Drinking 2,876.09 1J38.41 26.72 (.99) Restraint use 2,226.74 1,557.79 6.05 (99) Ejection 10,097.19 715.07 47.68 (.99) Multi-vehicles 11,066.65 978.02 62.63 (.99) Weekend 1,819.83 1,669.92 2.30 l.99) Rural road 2,306.24 1,514.67 11.16j.99) Off -road 8.177.50 1.57754 190.21 t-98) Curved road 2,802.58 1,626.42 11.75 (.99) Wet road 2,211.57 1,654.54 33.33 (.99) Models are denoted, following convention, by the underlying marginals of the three-way tables they fit: V = vehicle type; R - rollover; C - the third (control) variable in each table (for example, sex, age, and so on). All models are described in detail in the text. Numbers in parentheses next to the chi-square values for the third model fitted to each table indicate the proportion of the variation in the odds on the fatality involving a rollover that is accounted for by that model. Page 20 0A0/PEMD-9143 Fatal&h in Light ‘hocka and Vans The AaJyrir, of FARS Data on R4lovere and Side Impacta; Methodology and DetdledFindlnge Table 1.6: Likelihood-Ratio Chi-Square (L2) Values Associated With Several Chi-squarwaiues Hierarchical Models Fitted to Three-Way PC1IS111 PC1PSI 1:a Ml [dcBsH~~]o: Tables in Which Side Impacts Are Cross- de rees oi de rees of Classified by Vehicle Type and Selected Control vafiable Breedom Breedom Breedom Characteristics Sex of driver 888.21 715.56 1.90 (.99) Age of driver 830.53 826.15 5.13 (.99) Drinking 1,117.37 753.62 3.54 (.99) Restraint use 991.21 762.92 14.77 i.98) Eiection 1.018.95 698.96 22.86 1.981 Multi-vehicles 2IO67.83 573.86 11.54 i.99; Weekend .880.29 815.09 5.09 i.99; Rural road 1 JlO7.87 744.48 8.20 (99) Off-road 1 s895.79 710.41 23.01 1.99) Curved road 1D63.36 796.27 9.37 (99) Wet road 1.098.39 785.64 7.33 I.991 Models are denoted, following convention, by the underlying marginals of the three-way tables they fit: V - vehide type: S = side impact; C = the third (control) variable in each table (for example, sex, age, and so on). All models are described in detail in the text. Numbers in parentheses next to the chi-square values for the third model fitted to each table indicate the proportion of the variation in the odds on the fatality involving a side impact that is accounted for by that model. Three models were fitted to all tables. The first was the logit-specified model of independence,which assertsthat in each table rollovers or side impacts are unrelated to either vehicle type or the control variable pres- ent. This model can be readily rejected in every case,as could have been anticipated from our two-way results. The independencemodel, more- over, is substantially and significantly improved upon by the second model we fit to the data, which assertsthat the control variables (but not vehicle type) are associatedwith rollovers and side impacts. (Note the significant reduction in chi-square values from the first to the secondcolumns of numbers in tables I.6 and 1.6,which correspond to these two models.) Additionally, the third model fitted to each of the tables-which allows vehicle type to be related to rollovers and side impacts after controlling for the association of each control variable with both vehicle type and these outcomes-significantly improves upon the secondmodel. This implies that the associationsof vehicle type with rollovers and side impacts persist after individual controls are introduced. While this third model does not, in every case,provide a reasonablefit to the data (indicating the presenceof significant three-way interac- tions), this is not surprising given the large sample being used Page 21 GAO/PEMD-9143 Fatalities in L&M Trucks and Vans Appendix I The Analysis of FARS Data on Rollover and Side Impacts: Methodology and DetailedFindings Table 1.7:Odds Ratios Indicating the Association of Vehicle Type With Rollover Rollovers and Side Impacts, Before and Multiwg;; After Controlling for Selected -Control variable Standard van Small van Characteristics (One at a Time) None 5.74 1.89 1.21 briver’s sex 5.64 1.85 1.23 Driver’s age 5.93 2.08 1.44 &inking 5.55 1.99 1.40 Restraint use 5.84 1.85 1.40 Ejection 3.21 1.53 1.08 Multi-vehicle 4.64 1.92 1.88 Weekend 5.68 1.92 1.25 Rural road 5.59 1.88 1.25 Off-road -~ 6.08 2.14 1.74 Curved road 5.81 2.01 1.28 Wet road 5.71 1.92 1.21 (approximately 44,000). A better indicator of whether this third model provides an adequate account of the associations present in the table can be obtained by determining how much of the variation in the odds on rollovers or side impacts it accounts for. This can be computed by dividing the difference between the chi-square value for the baseline model and the chi-square value for the third (main effect) model by the baseline model chi-square. For the three-way table involving rollovers, vehicle type, and sex of driver, for example, this calculation yields 0.98 [(1748.23 - 31.11)/1748.23]. Ninety-eight percent of the variation in rollovers, across the joint categories of vehicle type and driver sex, is accounted for by this model, which posits independent main effects of vehicle type on rol- lovers. In other words, the model stipulates an effect of vehicle type that is the same for both male and female drivers and an effect of sex that is the same for all vehicle types. Therefore, there is no com- pelling reason to take account of the significant three-way interac- tion that is present in this table. The same is true for the other three- way tables as well, inasmuch as in every case our main effect model accounts for better than 98 percent of the variation in each. Page 22 GAO/PEMD-918 Fatalities in Light Trucks and Vans m &slyele of FABB Data on Rollovers and Side Impacts Methodology and DetailedFindings Rollover Side impact Standard MultiP;erc$; Standard Standard Small pickup pickup van Small van Small pickup pickup ____. .__.__” 2.73 .-....__-. --.- 2.47 0.39 0.52 0.65 0.46 0.46 2.66 2.41 0.41 0.54 0.66 0.48 0.48 2.75 2.61 0.39 0.51 0.64 0.46 0.45 2.63 ..~..~-__-..“._--.-.-.-.------ 2.34 0.41 0.52 0.62 0.48 0.47 ..-- ..__....- -. .--2.61 ._^_ --._-.__-..-___ 2.32 0.40 0.53 0.62 0.48 0.47 2.09 .- ..-.-...-....-.---..- ._..--.-- 2.01 0.45 0.54 0.67 0.49 0.48 2.39 1.89 0.48 0.54 0.60 0.51 0.52 2.73 2.49 0.40 0.52 0.65 0.46 0.46 2.58 2.31 0.41 0.53 0.65 0.48 0.48 2.85 .._-___...I. ____..__ --.- .._-.. 2.23 0.43 0.52 0.60 0.48 0.49 2.66 .--...---..--- --_. “..-._-__ll_-..--- 2.46 0.40 0.51 0.65 0.47 0.46 2.68 2.44 0.40 0.52 0.66 0.47 0.46 We can use the expected frequencies under this model for each table and derive from them, as before, the odds on rollovers and side impacts, and the ratios of these odds (again with passengercars as the criterion vehicle type) acrossvehicle type. The results of these calculations are provided in table 1.7,which also provides the initial odds ratios (that is, those calculated in table I. 1, prior to controls). As table I.7 indicates, the odds ratios after adjusting for the effect of the individual control variables are not substantially different from the ratios derived without controls. Only the control for whether the victim was ejected alters appreciably our estimate of the relationship between vehicle type and rollovers, and even there sizable differences among vehicle types remain. For side impacts, no control variable, taken by itself, does much to alter our conclusion about the sizable differences between vehicle types. Page 23 GAO/PEMD-91-8 Fatalities in Light Trucks and Vans Appendix I The An&da of FAR8 Data on Rollovera and Side Impacts: Methodology and DetailedFindings Table 1.8: Likelihood-Ratio Chi-Square Values and Other Characteristics Asrociated With Hierarchical Models Fitted to the SIX-Way Table8 Formed by Crorr-Classifying Rollover8 and Side Model Marginal8 fitted Impacts Wlth Vehicle Type and Selected 1 [SADRV] [Z] Drlver/Vlctim Characteridlcs 2 [SADRV] [SADRZ] 3 [SADRV] [SADRZ] [VZ] 4 [SADRV] [SADRZ] [SVZ] 5 [SADRV] [SADRZ] [AVZ] 6 [SADRV] [SADRZ] [DVZ] 7 [SADRV] [SADRZ] [RVZ] 8 [SADRV] [SADRZ] [SVZ] [DVZ] It may be that while no control variable greatly attenuates the initial Hierarchical Models relationships we found when we consider them serially, they do so when With Simultaneous we consider them simultaneously. Unfortunately, the contingency table Controls for Driver/ approach demandedby the categorical nature of independent variables, combined with the small number of fatalities for certain vehicle types Victim and Roadway/ (especially small vans), doesnot permit us to build and analyze tables in Accident which all control variables are considered at once. We were able, how- Characteristics ever, to control for certain of these variables in blocks, by exploring two pairs of six-way tables in which rollovers, and then side impacts, were cross-classifiedby vehicle type and certain characteristics of drivers and victims, and then by vehicle type and certain characteristics of acci- dents and roadways. Table I.8 provides information about various hierarchical models fitted to the two six-way tables in which rollovers and side impacts are cross- classified by vehicle type and by the following characteristics of drivers and victims: sex of driver, age of driver, whether the driver had been drinking, and whether the victim was using restraints.3 3After analysis of the interrelationships of the control variables and consultation with NHTSA researchers, we decided to omit ejection from our control variables for these models. Restraint use is highly correlated with e&tion. The use of both control variables simultaneously would result in numerous empty or sparse cells. More substantively, ejection can itself be considered a function of vehicle type and therefore could introduce a spurious control into our analysis of the effect of vehicle type. Page 24 GAO/PEMDM-l3 FataWes in Light TN&E and Vana The Analyds of FARS Data on RoUovem and Side Impscts: Methodology and DetalledFindlnga Rollover Side Impact Proportlon oi Proportion of varlatlon De &eeyoi variation De?rE22 L* P explained r L* P explained 93 3,766s c.0001 .oo 93 1,371.23 < .ooo1 .oo 78 1,605.31 c.OOO1 57 78 737.02 < .oooi A6 73 128.72 <.OOl .97 73 99.75 .021 .93 66 103.09 .004 .97 66 98.32 a09 .93 66 120.39 <.OOl .97 66 91.34 ,031 .93 68 99.90 a07 .97 68 97.23 .012 .93 66 126.68 <.OOl .97 66 84.41 .086 94 63 80.24 .070 .98 S - driver’s sex; A - driver’s age; D = drinking driver; R = restraint use; V - vehicle type; Z - rollover or side impact The first model fitted to both tables was again the lo&it-specified model of independence,which allows vehicle type to be related to each of the control variables (that is, driver/victim characteristics) in the table but assertsthat rollovers and side impacts are independent of both vehicle type and all of these controls. The large values of chi-square associated with this model suggestthat it doesnot fit the data acceptably, and becauseit posits that the odds on rollovers and side impacts are the same acrossall of the joint categoriesof the factors in the table, it does not account for any of the variation in those odds. Model 2, which allows all factors except for vehicle type to be related in an uncon- strained or interactive fashion with rollovers and side impacts, improves significantly upon this first model and accounts for 67 and 46 percent, respectively, of the variation in the odds on rollovers and side impacts. More importantly, model 3 improves significantly upon model 2. After controlling for the associationsof the driver/victim characteristics with vehicle type, and the associationsof driver/victim characteristics with rollovers and side impacts, model 3 allows an association of vehicle type with rollovers and side impacts. The significant improvement of model 3 on model 2 implies that the vehicle type/rollover and vehicle type/side- impact associationspersist even after controlling for these characteris- tics simultaneously. Page 26 GAO/FEMD-@U3 FataUtien in Light Trucka and Vanm The Analyols of FARS Data on Rollover and Side Impacta Methodology snd De!taIledFIn~ Model 3 doesnot, strictly speaking, fit the data acceptably in either of these two tables (P < .06)-that is, a statistically significant amount of variation due to interactions among the control variables, vehicle type, and the outcome variable remains to be explained. Nevertheless,it does account for the large bulk of the variation in the odds on rollovers (97 percent) and side impacts (93 percent). Further analysis indicates that securing a model for rollovers that fits the data acceptably requires the inclusion of interactions between sex of driver, vehicle type, and rollovers, and between drinking driver, vehicle type, and rol1overs.4For side impacts, an acceptablefit of model to data is achieved by allowing an interaction between restraint use, vehicle type, and side impact.6We will discussthe nature of these interactions below, after discussingthe implications of model 3. However, it should be noted here that these interactions do not account for much of the variation in rollovers or side impacts, nor even much of that variation which is directly attributable to vehicle type. To reestimate the association of vehicle type with rollovers and side impacts after these simultaneous controls, we can calculate the expected frequencies under the third model fitted to the data in each of the four tables considered and derive from them the odds and odds ratios as before. Tables I.9 through I.1 1 contain an example of this procedure for a model of the effect of vehicle type on rollovers, after controlling for the effect of our driver/victim characteristics. In multivariate tables of this nature, we can calculate odds within categoriesof vehicle type and within categoriesin the joint distribution of the four other variables being controlled for. For multipurpose-vehicle fatalities, for example, among accidents involving male drivers under 25 who were drinking and not using restraints, the odds on rollovers having been involved were 2.047 (135.70/66.30). For passenger-carfatalities, the odds on roll- overs having occurred for that samegroup defined by that specific com- bination of control categorieswas 0.351(1206.71/3438.29). 4Note that models 4 and 6, which include these interactions one at a time, improve significantly on model 3, and that model 8, which includes both interactions, improves significantly on both models 4 and 6. 6Model 7 fits the data acceptably (P < .OS)and significantly improves on model 3. Page 26 GAO/PEMDz)l-8 Fatalities in Light Trucks and Vans AppcmUx I The Anelysb of FARS Data on Rdlovem and Side Impacts; Methodology and DetailedFLn~ As in our previous example, the odds ratio indicating the greater likeli- hood of rollovers for multipurpose-vehicle fatalities relative to pas- senger-carfatalities is obtained by dividing the former odds by the latter. For this case,the odds ratio of multipurpose vehicles to passenger cars is 5.83 (2.047/0.361). Odds on rollovers can similarly be obtained for other vehicle types, and ratios contrasting those odds can be obtained by using the passenger-carodds as the criterion.6 ‘The odds ratios relative to passenger cars calculated from this model remain the same for each vehicle type across each category of the control variables, since this constraint is specified in the model. These odds ratios would vary somewhat across categories in models that allow for vehick- type/control-variable interactions. Page 21 GAO/P-91-9 Fatalities in I&ht Trucks and Vans , Appendix I The Amlysin of FAJB Data on Rollovers end Side Impacts; Methodology and DetailedFlndings Table 1.9: Expected Frequenclea Under .a the Main-Effect Model for the Six-Way Table in Which Rollovers Are Cross- Clabsitied by Vehicle Type and Driver/ Sex Age Drinking Restraint used Vlctlm Charscterlstlcs Male Under 25 Yes No Yes No No Yes 25 and over Yes No Yes No No Yes Female Under 25 Yes No Yes No No Yes 25 and over Yes No Yes No No Yes Page 29 GAO/PEMD9143 Fatalities in Light Trucks and Vans Appendix I The Analyeie of FM18 Data on Rollovers and Side Impacte Methodology and DetsiledFin~ Odds on rollover Mult’p;;$yt Standard Small Small Standard Passenger Rollover .~ van van pickup pickup car Ye9 ’ %t E:E 1.89 E:E 345.92 1,206.71 No --- -__--. 3.11 420.08 33438.29 Yes 11.20 0.48 0.00 4.47 3.64 41.21 No 1_~ 12.80 1.52 0.00 11.53 10.36 274.79 Yes 35.13 136.36 191.40 655.46 No -_--.-- - !E 73.87 % 234.64 362.60 2,913.54 Yes 7.78 1.10 0.13 4.43 4.10 36.46 No 15.22 5.90 0.87 19.57 19.90 415.54 Yes it% 93.26 11.57 Z: 592.67 1,177.16 No 161.75 24.43 926.33 4,316.84 Yes 3.08 9.48 10.79 62.08 No --~ : ::i; 8.92 i::; 22.52 28.21 380.92 Yes 98.13 63.78 8.56 144.58 284.69 634.25 No .--~ 161.87 290.22 47.44 538.42 1,167.31 6,101.75 Yes 8.51 5.11 3.08 10.87 11.44 81.99 No - 29.49 48.89 35.92 85.13 98.56 1,658.Ol Yes 9.85 2.31 0.83 %: 26.88 285.93 No 4.15 2.69 1.17 28.12 702.07 Yes 0.53 i% i::i: 1.34 0.31 13.56 No 0.47 2.66 0.69 69.44 Yes :% 6.96 1.45 34.33 36.91 367.94 No 14.04 3.55 56.67 67.09 1,569.06 Yes 2.03 E E 1.44 0.60 20.51 No 5.97 9.56 4.40 352.49 ~- Yes 18.84 % 1.58 28.09 26.12 275.06 No 12.16 3.42 40.91 41.88 1,034.94 ies 0.21 0.49 0.46 13.36 No 2; 0.79 51::: 1.51 1.54 105.64 Yes 25.70 17.97 5.22 32.94 41.67 466.50 No 36.30 70.03 24.78 105.06 146.33 3,843.50 --- Yes 4.14 2.20 1.00 2.42 2.42 62.83 No 14.86 21.80 12.00 19.58 21.58 1,316.17 Page 29 GAO/PEMD-91-S Fatal&lea in Light Trucks and Vans I The Anal@ of FABlp Data on Rollovers and Side Impacts! Methodology and Detalled~ Table l.l& Odds Derived From.the Main- Effect Model for the Six-Way Table In Which Rollovers Are Cross-Clnsslfied by Vehicle Type and Driver/Vlctlm Sex Age Drinking Restraint used Characteristics Male Under 25 Yes No Yes No No Yes 25 and over Yes No Yes No No Yes Female Under 25 Yes No Yes No No Yes 25 and over Yes No Yes No No Yes Page 30 GAO/PEMD-91-9 Fatalitier in Light Trucke and Vans The Analyeio of FARS Data on Ibllovers and Side Impacts Methodology and DetailedFinding Odds on rollover Multipurpose Standard Small Small Standard Passenger Rollover _.. -vehicle . _.-- van van pickup pickup car Yes 2.047 0.742 0.608 0.907 0.823 0.351 No .-. ----- Yes 0.875 0.316 WA 0.388 0.351 0.150 No Yes 1.312 0.476 0.391 0.581 0.528 0.225 No Yes 0.511 0.186 0.149 0.226 0.206 0.088 No Yes 1.590 0.577 0.474 0.704 0.640 0.273 No _ . .-- ._.- Yes 0.951 0.345 0.282 0.421 0.382 0.163 No Yf?S 0.606 0.220 0.180 0.269 0.244 0.104 No Yes 0.289 0.105 0.086 0.128 0.116 0.049 No Yes 2.373 0.859 0.709 1.052 0.956 0.407 No Yes 1.128 WA 0.333 0.504 0.449 0.195 No --..-_I__ Yes 1.368 0.496 0.408 0.606 0.550 0.234 No .~ -. ..--. YC?S 0.340 0.124 0.099 0.151 0.136 0.058 No ~~.._ .._-.-- .-_. Yes 1.549 0.563 0.462 0.687 0.624 0.266 NO YCS 0.736 0.266 0.220 0.325 0.299 0.126 No Yes 0.708 0.257 0.211 0.314 0.285 0.121 No . .~ .---_-~- Yes 0.279 0.101 0.083 0.124 0.112 0.048 No Page 31 GAO/PEMD-91-3 Fatalities in Light Trucks and Vans The Analyela of FAR8 Data on Rollovera and Side hpach Methodology and DetalledFindlnge Table 1.11: Odda Ratlor Dwlved From the Maln-Effect Model for the SIX-Way Table In Which Rollovers Are Cross-Classified by Vehicle Type and Drlver/Vlctim sex Age Drinking Restrain‘t used Characterlstlcs Male Under 25 Yes No Yes No No Yes 25 and over Yes No No No Yes Female Under 25 Yes No Yes No No Yes 25 and over Yes No Yes No No Page 32 GAO/PEMD-91-3 Fatalities in Light Truclce and Vane The AnalysLe of FAN3 Data on Rollovem and slde&mnm~~odology and Odds ratios relative to passenger cars Multipur 008 Standard Small Small Standard Rollover _--.__-___- ...-_ -I .--____ ve Rlcle van van pickup pickup Yes No 5.83 2.11 1.74 2.58 2.35 No -..___-._l . _._-...----..-- 5.83 2.11 N/A 2.58 2.35 Yes No --I ._-._.- _-._..-.-_-._.-. 5.83 2.11 1.74 2.58 2.35 Yes No 5.83 2.1 1 1.74 2.58 2.35 Yes No .-._-- ...-._.--_-_.- _..- - . ..-- 5.83 2.11 1.74 2.58 2.35 Yes No _..-__--.-_.-.----- .__.__ ~ 5.83 2.11 1.74 2.58 2.35 Yes No 5.83 2.11 1.74 2.58 2.35 Yes No .-..- ~.-_- .._l___"~ 5.83 2.11 1.74 2.58 2.35 Zs 5.83 2.11 1.74 2.58 2.35 Yes No 5.83 N/A 1.74 2.58 2.35 Es ------ 5.83 2.11 1.74 2.58 2.35 Yes No 5.83 2.11 1.74 2.58 2.35 Yes No .--_.-. ._.-..."-l-_..-..- . . --.- __- 5.83 2.11 1.74 2.58 2.35 Yes No 5.83 - 2.11 1.74 2.58 2.35 Yes No __. ^__ - ..-_ ._ .-- -. ____. 5.83 -- 2.11 1.74 2.58 2.35 Yes No 5.83 2.11 1.74 2.58 2.35 Page 33 GAO/PEMD-91-3 Fatalities in Light Trucks and Vans Appendix I The helyedo of FARS Data on Rvllovem and Side Impncta: Methodology and Dc!tauedFindlngo hbk 1.12: Llkolihood-Ratio Chl-Square Values and Other Characterlrtlcs Aoaociated Wlth Hlerarchlcal Models Pltted to the Six-Way Tables Formed by Cross-Clasrlfying Rollovers and Sldo Model Marginale fitted Impacts With Vehlclo Type and Selected 1 [RMCWV] [Z] Accident/Roadway Characterlrtlcs 2 [RMCWV] [RMCWZ] 3 [RMCWV] [RMCWZ] [VZ] 4 [RMCWV] [RMCWZ] [RVZ] 5 [RMCWV] [RMCWZ] [MVZ] 6 [RMCWV] [RMCWZ] [CVZ] 7 [RMCWV] [RMCWZ] [WVZ] 8 [RMCWV] [RMCWZ] [MVZ] [CVZ] The results of such calculations can be summarized by the odds ratios provided in table I. 11. After controls were introduced for these driver/ victim characteristics, multipurpose-vehicle fatalities remained more than five times as likely as passenger-carfatalities to have involved a rollover. Both types of pickups, and standard vans as well, were more than twice as likely as passengercars to have involved a rollover, and small vans were almost twice as likely. When we fitted a series of models involving roadway and accident char- acteristics to our rollover and side-impact data, we reached conclusions about the preferred models similar to those derived from our driver/ victim models. Table I. 12 presents a summary of these hierarchical models. As Table I. 12 shows, for both the rollover and side-impact data, model 2 improves significantly on model 1, and model 3 improves significantly on model 2. Moreover, model 3 accountsfor the great bulk of the varia- tion in rollovers (99 percent) and side impacts (97 percent) and, in the caseof side impacts, it fits the data acceptably and is not improved on significantly by models 4 through 7, which include interaction terms. For the rollover table, none of the models (including three-way Page 34 GAO/PEMD-91-8 Fatalities In Light Truck6 and Vana Appendix I The Analyeb of FARS Data on Rollovers and Side Impacta: Methodology and DetaIledFYndlng6 Rollovsr Side impact Proportion of Proportion of De ftft$llol variation De rees of variation -.-----f .-.-- - L* P explained ?reedom L* P explained - ---~~-.-_ 94 12,148.64 <.OOOl .oo 94 2,716.21 < .OOOl .oo i.--_ _-.----- 79 986.70 <.OOOl .92 79 597.01 -=c.OOOl .78 --------- 74 177.19 -c .OOOl .99 74 93.27 .05 .97 ---..-.--- 69 167.83 < .Oool .99 69 85.29 .05 .97 _--._- 69 117.37 < ,001 .99 69 82.59 .lO .97 -- 69 - 165.11 < .OOOl .99 69 85.87 .05 .97 69 _..._._..._- A-..- 170.17 -K .OOOl .99 69 87.10 .05 .97 64 106.40 < .OOl .99 Note: R - rural road; M = multi-vehicle accident; C = curved road; W = wet road; V = vehicle type; 2 = rollover or side impact interactions) fit the data, which implies the existence of significant higher order-that is, four-way or five-way-interactions. Given the large proportion of variation explained by the models we have con- sidered, however, it seems reasonable to assume that such interac- tions are substantively trivial in spite of their statistical significance.’ A summary of the odds ratios obtained from each of our four preferred models is presented in table I. 13. We have already presented a detailed interpretation of the odds ratios for rollover models considering driver/ victim characteristics. We found similar results when we controlled for accident/roadway characteristics, although controlling for this set of characteristics doesdiminish our estimate of the differences between all type of vehicles (with the exception of small vans) and passengercars. These associationspersist, however, and remain quite sizable even after introducing these controls. ‘Among the models provided in table I. 12 for the rollover data, it would appear that the only three- way interaction of any importance is the one involving multi-vehicles, vehicle type, and rollovers, inasmuch as mode16, which includes that interaction, improves significantly on mode13 and is not itself improved on by mode18, which includes another interaction that had appeared significant in the absence of this one. This interaction will be discussed but caution must be applied in interpreting it. In spite of its statistical significance--which is achieved rather easily in working with samples of this size-it accounts for very little of the variation in the odds on rollovers. Page 36 GAO/PEMtbBl-8 Fatalities in Light Truclm and Vana Appendix I The Analyeie of FARS Data on Rollovers and Side Impacte: Methodology and DetailedFindjnga Table 1.13: Odd8 Ratios Dercrlblng the A88ociatlonr of Vehicle Type Wlth Ratios of odds on roiioverb, relative Roiioverr and Side impactr, After Muitip;;rc+t Standard Controiilng for Driver/Victim Variables controlled for van Small van Characteristic8 and Accident/Roadway Driver/victim characteristics 5.83 2.11 1.74 Characteristics (Derived From Main-Effects Accident/roadway characteristics 4.59 1.99 1.88 Models) The association of vehicle type with side impacts also persists after we control for either the driver/victim or accident/roadway characteristics, Multipurpose-vehicle, standard-van, and pickup fatalities are only roughly half as likely as passenger-carfatalities to have involved a side impact, and small-van fatalities are slightly less than two thirds as likely. The odds ratios estimating these associationsof interest that are Higher Order presented in table I. 13 were derived from models that constrain those Interactions associationsto be equally large acrossall categoriesof the control vari- ables employed. As we noted, however, there is someevidenceof certain interactions present. The nature of those interactions is demonstrated in tables I. 14 and I. 16, where we have reestimated odds ratios using inter- action models. Table 1.14: Odds Ratloe Describing interaction8 of Vehicle Type With Other Characteristics on Rollovers Ratios of odds on rollovers, relative to passenger cars MuiWy~~~ Standard Categories of interacting variables Standard van Small van Small pickup pickup Male drivers, drinking 1.50 1.35 2.27 2.31 Male drivers, not drinking Z:E 2.43 2.47 2.60 2.22 5.27 7.72 2.38 3.87 0.79 1.45 2; %I -Female -~ drivers, ..-- drinking not drinking ___---..--~.--__ Sir@+vehicle Multivehicle accident accident 9.74 3.87 4.13 1.68 2.28 1.82 3.14 2.14 ::Bs: Page 36 GAO/PEMD-91-S Fatalities in Light Trucks and Vane Appendix I The Analysla of FARS Data on Rollovers and Side Impact% Methodology and Detailed Findln@ to passenger cars Ratio of odds on side impacts, relative to passenger cars Small Standard Mu”‘iyy~ Small Standard pickup _. __-_. --_--_-- pickup Standard van Small van pickup pickup _.2.58 .._ -I 2.35 _._--__ ~~-__ 0.42 0.53 0.60 0.50 0.50 2.25 1.76 0.49 0.54 0.60 0.53 0.54 Wenoted, for example, that it appeared that the vehicle type/rollover association interacted with sex of driver, whether the driver was * drinking, and whether multiple vehicles were involved. This can be seen in the odds ratios presented in tables 1.14,and 1.15,as can the interac- tion of vehicle type with rollovers and side impacts. For all types of non- passengercars relative to passengercars, differences in the odds on fatalities involving rollovers appear to be more pronounced when drivers were female, when drivers were not drinking, and when multiple vehicles were involved. Also, non-passengercar/passengercar differ- encesin the odds on side impacts were more pronounced when restraints were used than when they were not. Table 1.1s: Odd8 Ratios Describing interactlone of Vehicle Type With Other Characteristics on Side impacts Ratio of odds on side impacts, relative to passenger cars MWygg Standard Categories of Interacting variables Standard van Small van Small pickup pickup No restraint use 0.43 0.59 0.56 0.50 0.51 Restraint use 0.37 0.22 0.68 0.47 0.39 As discussedpreviously, however, the improvement in our under- standing of rollovers or side impacts afforded by these models, while statistically significant, is slight. In only one case-that of female drivers of small vans who had been drinking-is the odds ratio to pas- ,. sengercars reversed. This anomaly should be considered a statistical artifact of the small number of small vans, and particularly of this sub- category of small-van fatalities, in our sample.s *Only ten of the nearly 44,000 cases in our sample fall into this subcategory. Page 37 GAO/PEMD-91-9 Fatalities in Light Truck and Vans I Appendix II &sub of Prior Research Researchersappear to agreethat a disproportionate share of the single- car-accident fatalities occurring on our nation’s streets and highways involve occupantsof small trucks and multipurpose vehicles. While study results suggestthat the rollover tendenciesof small trucks and multipurpose vehicles may reflect vehicle characteristics-such as a high center of gravity in relation to vehicle track width-every study cautions that various characteristics of drivers and vehicle use may affect the results. Becausethese factors have not been consideredin previous research,researchershave not been able to conclude that higher fatality rates are due to inherentcharacteristics of these vehicles. To determine what research has been done on the subject of light-truck, van, and multipurpose-vehicle safety, we searchedthe National Highway Traffic Safety Administration (NHTSA), Department of Trans- portation (nor), University of Michigan Transportation ResearchInsti- tute (UMTRI), and GAO libraries for research on highway safety, with specific emphasis on studies dealing with fatalities. These libraries col- lectively contain most of the research in the highway safety area. We identified over one hundred research citations pertaining to small truck and van safety, motor vehicle safety standards, and the extension of those standards to small trucks and vans. We reviewed all identified researchto determine its relevance to our analysis-specifically, to identify those studies that compared fatality experience by vehicle type and/or by vehicle type and type of impact (side impact or rollover). We found eight studies comparing the fatality experience of small trucks, vans, and multipurpose vehicles to that of passengercars, with emphasis on type of impact. Although most of the studies baaedtheir results on the type of vehicle involved in the accident (adjusted to reflect vehicle exposure), none of the studies accounted for the amount and type of use each vehicle type received-that is, annual number of miles driven in total or for specific purposes.Of the eight studies, four used registration data for the expo- sure measurement;one used vehicle production data combined with scrap-rate information as a proxy for estimating the number of vehicles in use; one limited itself to toll roads for which accurate exposure rates, using miles traveled, could be obtained; and two used no measure of exposure, basing their results on investigations of samplesof vehicles involved in accidents. Page 98 GAO/PEMD-918 Fatalities in Light TIUC~S ad Van13 Appendix II liewlta of Prior Besearch l light truck occupant-injury rates in multi-vehicle crasheswere generally lower than passengercar occupant-injury rates in multi-vehicle crashes; . light trucks have higher rollover and occupant-ejectionrates than do passengercars, which creates a greater potential for injuries to their occupants as compared to occupants of passengercars; and lastly, . light trucks as striking vehicles were found to have a greater tendency to injure occupants of the struck vehicle-that is, they appear to be more “aggressive” than passengercars. This study was a follow-up to a 1981 report by Reinfurt, et al., that A Further Look at analyzed the relative involvement in rollover crashesof utility vehicles Utility Vehicle (also referred to asjeeps), pickup trucks, and passengercars, using Rollovers, 1984 crash data from North Carolina (1973-78), Maryland (1974-78), and the Fatal Accident Reporting System (1978-79). The highlight of the results of the earlier study was that smaller vehicles generally had higher rates of rollover involvement than larger vehicles3 This follow-up study examined more recent crash data for North Caro- lina (1979-82) that included several additional utility-vehicle models for which data were previously inadequate or nonexistent.4Also, this report estimated vehicle-specific mileage exposure from newly available data. As was found in the earlier study, rollovers occurred approximately ten times as often in single-vehicle crashesas in multi-vehicle crashes. Among the vehicle groups, utility vehicles had, by a considerable margin, the highest involvement rate in single-vehicle rollover crashes; pickups and cars were equally invqlved in single-vehicle rollover crashes,at a rate of involvement that was considerably lower than that for utility vehicles. In addition, the study found that utility vehicles had serious or fatal driver injury rates that were approximately three times higher than the rates for pickups or passengercars. The authors stressedthat, as indicated by earlier literature, track width and center of gravity of a vehicle are very important factors with respect to rollovers.6Utility vehicles have a higher center of gravity and 3D. W. Reinfurt, et al., A Comparison of the Crash Experience of Utility Vehicles, pickup Trucks, and Passen er Cars (Chapel Hill, NC.: The University of North Carolina Highway Safety Research Center an t e as mgton, DC., Insurance Institute for Highway Safety, 1981). -Y3Ti%T 4D. W. Reinfurt, et. al., A Further Look at Utility Vehicle Rollovers (Chapel Hill, NC.: The University of North Carolina Highway Safety Research Center, 19i34,1986). ‘See J. W. Garrett, “A Study of Rollover in Rural United States Automobile Accidents,” Society of Automotive Engineers, Paper No. 680772,1969. Page 40 GAO/PEMD-91-9 FataMes in Light Trucks and Vane . Appendix II Re4YuIta of Prior Besearch In this report, side impacts of light trucks were studied using 1979 FARS Side Impacts: An data and data from NCSS.’ Vehicle types included within the light-truck Analysis of Light category were pickups, small vans, and large station wagons. Trucks, Intrusion, and Study results indicated that 12.6 percent of the fatalities in light trucks Injury in FARS and resulted from side impacts. Within the light-truck category, pickups NCSSData, 1982 accountedfor nearly 86 percent of the light truck side-impact fatalities, and small vans accounted for only 13.5 percent. The study further disclosedthat there is a high correlation between intrusion into the passengercompartment and serious injury in side- impacted vehicles. However, the authors stressedthat it is not clear whether the correlation is due to the intrusion by itself or to the greater impact severity associatedwith the intrusion. In this study, vehicles were classified into three broad categories-pas- Comparison of Truck sengercars, light trucks, and heavy trucks.a Vehicles were classified into and Passenger-Car three broad categories-passenger cars, light trucks, and heavy trucks. Accident Rates on The study conducted a nationwide survey of 1976 through 1978 acci- dent rates for 34 limited-accessfacilities. These included 21 toll express- Limited-Access ways and turnpikes and 13 bridges and tunnels for which accurate Facilities, 1981 exposure rates using vehicle miles traveled could be obtained. The results showed that the fatal-accident rate for light trucks on express- ways was significantly greater than that for passengercars. On the average,light trucks were involved in 2.36 times more fatal accidents than were passengercars for the samedistance traveled. This study examined whether small truck and van safety had been com- Recent Trends in Van promised due to the exclusion of these vehicle types from certain Fed- and Small Truck eral Motor Vehicle Safety Standards (FMVSS).~ For its exposure Safety, 1979 measurement,the study used vehicle production data combined with scrap-rate information to estimate the number of vehicles in use. Using data from the 1977 FARS data base,the study found that pickup trucks 7R. E. Scott, Side Impacts An Analysis of Light Trucks, Intrusion, and Injury in FARS and NCSSData (Ann Arbor:The University of Michigan Transportation Research Institute, 1982). 8W.E. Myers, “Comparison of Truck and Passenger-Car Accident Rates on Limited-Access Facilities,” Transport&Ion Research Record, 808 (1981), pp. 48-66. eJ. 0%~ and R. Kaplan, “Recent Trends ln Van and Small Truck Safety,” Society of Automotive meers Technical Paper, 1979. Page 42 GAO/PEMD-91-8 Fatalities in Light Trucks and Vans Appendix III Major Contributors to This Report Michael J. Wargo, Director Program Evalluation Richard 2‘. Barnes, Assistant Director and Methodology Roy R. Jones,Project Manager Division, Washington, ~~e$!~~!t~~@?~~~~or , DC. Kansas City Regio DeniseM. Wempe,Deputy Project Manager Office Donald L. Ficklin, Computer Analyst Thomas M. Cook, Project Staff Kimberli S. Hagberg, Project Staff (878264) Page 44 GAO/PEML%Bl-9 Fatalities in Light Trucka and Vans
Highway Safety: Fatalities in Light Trucks and Vans
Published by the Government Accountability Office on 1990-11-14.
Below is a raw (and likely hideous) rendition of the original report. (PDF)