Before the CHMSL requirement was extended from passenger cars to light trucks (pickup trucks, vans and sport utility vehicles), there were questions if CHMSL would be as effective in trucks as in cars, or even if it would be effective in trucks at all. By now, CHMSL have been in light trucks for several years. Statistical analyses of calendar year 199496 crash records from six States indicate that light trucks equipped with CHMSL were approximately 5 percent less likely to be struck in the rear than light trucks without CHMSL. This point estimate of CHMSL effectiveness in light trucks is statistically significant, and it is very close to the longterm effectiveness in passenger cars (4.3 percent). Nevertheless, the uncertainty in the light truck estimate precludes the inference that CHMSL are as effective in trucks as in cars.
4.1 Analyses of six State files: method
The launch of CHMSL on light trucks differs from their introduction on passenger cars in several important features that will influence the analysis method.
When passenger cars were first equipped with CHMSL, model year 1986, the lamps were a new experience for the motoring public. The initial reaction to the lamps was perhaps quite different from what would happen when drivers became acclimatized to them. That made it critical to estimate effectiveness separately in each calendar year, to distinguish between the initial and the longterm effectiveness. By the time that the first CHMSL appeared on light trucks  in the MY 1991 Dodge Caravan, Plymouth Voyager, Chrysler Town & Country and Ford Explorer  the public was already acclimatized to the lamps (on cars). It is less important to estimate effectiveness separately in each calendar year, since it may be assumed that the longterm effectiveness was reached immediately.
CHMSL implementation in passenger cars was nearly simultaneous: MY 1986 in all models (except Cadillacs and very few others). In light trucks, CHMSL were phased in over a threeyear period, MY 199194, since some manufacturers installed them on certain models up to three years before the MY 1994 effective date. We must know the specific makemodel of each truck to determine if it had CHMSL (unlike cars, where it was basically good enough just to know the model year). State files that include the Vehicle Identification Number (VIN) on their crash records allow identification of truck makemodels by decoding of the VIN. NHTSA has access to six State files that include VINs, document the impact location on the vehicle, and are available for some or all of the calendar years 199496:
Florida  Maryland  Missouri 
North Carolina  Pennsylvania  Utah 
The Indiana and Texas files employed in Chapter 2 include "makemodel" codes, but they are not detailed enough to distinguish between all the specific truck models addressed in this chapter; the Virginia file does not contain makemodel information.
The basic contingency table analyses of Chapters 2 and 3 compared the proportion of crashes that were rear impacts in all cars of the first year with CHMSL  MY 1986  to the corresponding proportion in all cars of the last year without CHMSL  MY 1985. Here, too, we will compare all trucks of the first year with CHMSL to all trucks of the last year without CHMSL, but both of these groups will include a mix of model years, since different models got CHMSL in different years.
The "vehicle age effect"  the trend of decreasing proportions of rear impacts as cars get older, for reasons unrelated to CHMSL  was calibrated for passenger cars in Chapter 2 by looking at this trend for cars 015 years old in many calendar years of data. It cannot be assumed that light trucks will have the same "vehicle age effect" as cars. At the same time, it is not necessarily appropriate to use a similar calibration procedure for trucks as for cars. The method of Chapter 2 assumes that CHMSL were introduced in all vehicles at the same time (correct for cars, but not for trucks). In addition, major changes in the truck population since 1985 raise doubts whether the effect in recent years should be calibrated from older data. Instead, the method for handling the vehicle age effect will be essentially the same as in Farmer's evaluation of passenger car CHMSL [9]. This method, as will be described below, relies entirely on data for the last two MY without CHMSL and the first two MY with CHMSL.
The starting point for the analysis is a list of makemodels that were equipped with CHMSL. Information on CHMSL installations was obtained from Chrysler, Ford, General Motors, Mazda, Nissan and Toyota. The makemodels of pickup trucks, vans and sport utility vehicles (SUV) included in the analyses are listed in Table 41. For example, Dodge Caravan and Plymouth Voyager were equipped with CHMSL in MY 1991; thus, the last two MY without CHMSL are 198990 and the first two MY with CHMSL are 199192. Note that all GM and Mazda trucks, and a substantial proportion of the other trucks, got CHMSL in MY 1994, simultaneous with the Federal requirement.
Excluded from the analyses are the Jeep Grand Cherokee, Ford Explorer, Ford Windstar and Toyota T100, which had CHMSL from their inception, and which did not have a similar predecessor vehicle without CHMSL. Also excluded is the Dodge Ram Van and Wagon, which got CHMSL in the middle of MY 1993 (thus making it impossible to define a first MY with CHMSL and an adjacent last MY without CHMSL). On the other hand, Table 41 includes models that got CHMSL as part of a major redesign, as long as the redesign did not fundamentally change the model's size, function or market class (e.g., Dodge Ram Pickup, Ford fullsized van/wagon). Also included are models that merely changed their names, without any substantial redesign (e.g., Chevrolet KBlazer to Tahoe).
As stated above, the range of model years on the data file must include the first two MY with CHMSL. For the many makemodels that got CHMSL in 1994, that is only possible in the CY 1995 and 1996 data files. Those two calendar years are the mainstay of the analysis. Since the sample drawn from those two CY alone is somewhat limited, CY 1994 data have been added to the analysis. However, the CY 1994 data is restricted to the makemodels that had at least 2 MY with CHMSL by 1994  i.e., the models that got CHMSL in 1993 or earlier. For example, none
Mfr.  Model  Last 2 MY
without CHMSL 
First 2 MY
with CHMSL 
Chrysler  Caravan/Voyager  198990  199192 
Grand Caravan / Town&Country / Grand Voyager  198990  199192  
Dakota  199293  199495  
Ram pickup  199293  199495  
Cherokee (excl. Grand Cherokee)  199293  199495  
Wrangler  199293  199495  
Ford  F pickup  199091  199293 
Fullsized van/wagon  199091  199293  
Bronco (fullsized)  199091  199293  
Ranger  199192  199394  
Aerostar  199293  199495  
GM  C/K pickup  199293  199495 
S/T pickup  199293  199495  
Fullsized van / wagon  199293  199495  
Astro / Safari van  199293  199495  
Lumina APV / Silhouette / TransSport  199293  199495  
K Blazer / Yukon / Tahoe  199293  199495  
Suburban  199293  199495  
S/T Blazer/Jimmy  199293  199495  
Geo Tracker  199293  199495  
Mazda  Pickup  199293  199495 
MPV  199293  199495  
Nissan  Pathfinder  199192  199394 
Pickup  199293  199495  
Toyota  Previa  199192  199394 
Landcruiser  199192  199394  
Pickup (compact)  199293  199495  
4Runner  199293  199495 
of the GM trucks are included in the CY 1994 data. As of December 1997 data were available for the following calendar years:
Florida  9496  Maryland  9496  Missouri  9496 
North Carolina  94  Pennsylvania  9496  Utah  9495 
Crash involvements are tabulated by impact type: rear vs. other. Trucks with unknown impact locations and parked trucks are excluded from the analysis. The definitions of "rear impact" are:
Florida  impact = 7,8,9 
Maryland  impact & veh_dam1 both 712, or one is 712 and one is blank 
Missouri  veh_dam1 = 7,8,9 
North Carolina  veh_dam1 = 1013 
Pennsylvania  impact = 4,5,6,7,8 
Utah  impact & impact2 both 79, or one is 79 and one is blank 
The vehicles deleted because they were "parked" are:
Florida  veh_man1 = 8,9 
Maryland  veh_man1 = 10 
Missouri  veh_man1 = 13 or veh_man2 = 13 or veh_man3 = 13 
North Carolina  contfact = 7,10,11 
Pennsylvania  veh_man1 = 19 
Utah  veh_man1 = 11 
These definitions are the same as in Chapter 2 (except in the case of North Carolina; data from that State were not used in the Chapter 2 analyses). The basic tabulation of crash involvements of vehicles listed in Table 41, by impact type and YT, the truck's model year relative to the transition MY to CHMSL for that makemodel, combines the data for all six States in all the CY that these data were available:
YT  REAR IMPACT?  
NO  YES  
2 (Two MY before CHMSL)  X(2)  R(2) 
1 (Last MY before CHMSL)  X(1)  R(1) 
0 (First MY with CHMSL)  X(0)  R(0) 
1 (Second Mywith CHMSL)  X(1)  R(1) 
For example, since the Dodge Caravan got CHMSL in 1991, YT = 2 when MY = 1989; the MY 1989 cases go into the X(2) and R(2) cells, the MY 1990 cases into the X(1) and R(1) cells, the MY 1991 cases into the X(0) and R(0) cells, and the MY 1992 cases into the X(1) and R(1) cells. Since the Dodge Dakota got CHMSL in 1994, the MY 1992 cases go into the X(2) and R(2) cells, the MY 1993 cases into the X(1) and R(1) cells, etc.
The unadjusted effect of CHMSL is the actual, observed reduction in the log odds of a rear impact from the last MY before CHMSL (YT = 1) to the first MY with CHMSL (YT = 0):
Generally speaking, E_{ U} will understate the actual benefit of CHMSL, because there is a "vehicle age effect" working in the opposite direction. That effect can be estimated by comparing trucks of the second year to the first year before CHMSL (when neither had CHMSL); or by comparing trucks of the first year to the second year with CHMSL (when both had CHMSL). The age effect is the average of those two statistics:
The adjusted effect of CHMSL is the difference of the unadjusted effect and the age effect:
Since DEL_{ AVG} is usually negative, E_{ A} will generally be more positive than E_{ U}. Finally, the percentage reduction of rear impacts attributed to CHMSL is calculated by converting the log odds ratios back to odds ratios:
The statistical significance of the adjusted effectiveness estimate for CHMSL is tested by performing a CATMOD analysis of the preceding 4 x 2 tabulation of YT by impact location [32]. The dichotomous dependent variable is REARIMP (1 for rear impacts, 0 for other locations). The independent variables are YT (4 groups, but treated as a linear or "direct" variable with values 2, 1, 0 and 1) and CHM (= 0 when YT = 2 or 1; = 1 when YT = 0 or 1). Essentially, YT models the "vehicle age effect" and CHM models the true effect of the lamps after controlling for vehicle age. The effect of the lamps is significant when the coefficient for CHM has a statistically significant ^{ 2} value in the CATMOD analysis.
The preceding analysis could be biased because it aggregates data from all the States. For example, preCHMSL trucks could be overrepresented in a State where vehicles of all types have lowerthanaverage proportions of rear impact crashes. To adjust for biases like that, a CATMOD analysis is performed on the 6 x 4 x 2 table of State by YT by impact location. The dependent variable is again REARIMP. The independent variables are State (a nominal variable with six categories), YT (a linear variable), and CHM (a dichotomous variable). The effect of the lamps is significant, after controlling for State and the vehicle age effect, when the coefficient for CHM has a statistically significant ^{ 2} value in this analysis.
4.2 Overall effectiveness
The basic table of crash involvements by impact type and YT, the truck's model year relative to the transition MY to CHMSL, combining the data for six States in all CY, looks like this:
REAR IMPACT?  
YT Frequency
Row Percent 
NO  YES 
2 (Two MY before CHMSL)  27871
74.21 
9685
25.79 
1 (Last MY before CHMSL)  29882
73.90 
10554
26.10 
0 (First MY with CHMSL)  35116
74.40 
12080
25.60 
1 (Second MY with CHMSL)  33195
73.78 
11796
26.22 
Although the table includes over 44,000 rear impact cases, the sample is nevertheless much smaller than what was available for the passenger car analyses of Chapter 2 (where a table spanning MY 8487 and aggregating the eight States would have included 800,000 rear impact cases over a ten CY period, or an average of 80,000 per calendar year). Correspondingly, we can expect the overall effectiveness estimate based on this table to be less precise than the passenger car estimate in any single calendar year, let alone an aggregation of calendar years, such as 198995.
It is obvious from an inspection of the row percentages that the estimate of CHMSL effectiveness will be positive. The percent of crash involvements that were rear impacts declined from 26.10 in the last MY before CHMSL to 25.60 in the first MY with CHMSL  despite a vehicle age trend in the opposite direction, as evidenced by an increase in the percentage of rear impacts from YT 2 to YT 1 and another increase from YT 0 to YT 1.
The unadjusted effect of CHMSL is:
The statistics used for estimating the "vehicle age effect" are:
The adjusted effect of CHMSL is the difference of the unadjusted effect and the age effect:
The percentage reduction of rear impacts attributed to CHMSL is calculated by converting E_{ A} back to an odds ratio:
This point estimate is very close to the longterm, 198995 effectiveness estimate for passenger car CHMSL, 4.3 percent (see Table 26). The similarity of the point estimates, however, may be deceptive, because the light truck estimate is subject to considerable sampling error (whereas the passenger car estimate had 95 percent confidence bounds ranging from 2.9 to 5.7 percent). When the statistical significance of the light truck estimate is tested by a CATMOD analysis of the preceding 4 x 2 table with YT and CHM as the independent variables, the coefficient for CHM has ^{ 2} = 4.51, slightly more than the ^{ 2} needed for statistical significance at the twosided .05 level (3.89  the critical value customarily used in NHTSA evaluations). Similarly, in the CATMOD analysis of the 6 x 4 x 2 table of State by YT by impact location, the coefficient for CHM has ^{ 2} = 4.62, again somewhat more than the ^{ 2} needed for statistical significance.
Confidence bounds for effectiveness may be obtained by noting that the standard deviation of the CHM coefficient in the first CATMOD analysis is .0245. In other words, the confidence bounds on E_{ A} are .0508 ± (1.96 x .0245): a range from .0028 to .0988. When these changes in the log odds ratio are converted to percentage reductions in rear impacts, the confidence bounds for effectiveness, E_{ %} range from 0.3 percent to 9.4 percent.
These levels of uncertainty preclude definitive inferences about the effectiveness of CHMSL for light trucks. There may be some support for temporarily accepting, as a "null" hypothesis, that CHMSL are about equally effective in trucks than cars. But it is also conceivable that they are less effective in trucks than in cars, or, for that matter, more effective than in cars.
4.3 Effectiveness by truck type and size
Given that all the light truck data combined were not really sufficient for a precise effectiveness estimate, limited returns can be expected if the data are subdivided by truck type or size. Nevertheless, it is worth doing at least a preliminary analysis to see if there are any conspicuous outliers (in either a positive or a negative direction).
Table 42 performs the basic effectiveness analysis separately for pickup trucks, sport utility vehicles (SUV's) and vans. For example, an inspection of the row percents in the top section of
PICKUP TRUCKS
REAR IMPACT? 
DEL_{BEFORE} = .0455
E_{u} = .0223 DEL_{AFETR} = .0394 DEL_{AVG} = .0425 E_{A} = .0648 

YT Frequency
Row Percent 
NO  YES  
2 (Two MY before CHMSL)  13978
75.25 
4597
24.75 

1 (Last MY before CHMSL)  14878
74.39 
5121
25.61 

0 (First MY wtih CHMSL)  20897
74.82 
7034
25.18 

1 (Second MY with CHMSL)  18370
74.07 
6432
25.93 
SPORT UTILITY VEHICLES
REAR IMPACT? 
DEL_{BEFORE} = .0196
E_{u} = .0006 DEL_{AFETR} = .0352 DEL_{AVG} = .0274 E_{A} = .0268 

YT Frequency
Row Percent 
NO  YES  
2 (Two MY before CHMSL)  4165
74.07 
1458
25.93 

1 (Last MY before CHMSL)  4737
73.69 
1691
26.31 

0 (First MY wtih CHMSL)  5994
73.68 
2141
26.32 

1 (Second MY with CHMSL)  5665
72.99 
2096
27.01 
observed effectiveness = E_{ %} = 2.64 percent
VANS
REAR IMPACT? 
DEL_{BEFORE} = .0235
E_{u} = .0314 DEL_{AFETR} = .0101 DEL_{AVG} = .0067 E_{A} = .0247 

YT Frequency
Row Percent 
NO  YES  
2 (Two MY before CHMSL)  9728
72.83 
3630
27.17 

1 (Last MY before CHMSL)  10267
73.29 
3742
26.71 

0 (First MY wtih CHMSL)  8225
73.90 
2905
26.10 

1 (Second MY with CHMSL)  9160
73.70 
3268
26.30 
Table 42 makes it clear that the CHMSL effectiveness estimate will be positive. The percentage of crash involvements that were rear impacts dropped from 25.61 to 25.18 in the model year that CHMSL were introduced, in spite of the fact that it was on a rising trend the year before, and rose again the year after. The unadjusted log odds reduction for CHMSL is .0223 and the average age effect is .0425. The point estimate is that CHMSL reduced rear impacts in pickup trucks by 6.27 percent. This reduction is not statistically significant: in the CATMOD analysis of the 4 x 2 table for pickup trucks, with YT and CHM as the independent variables, the coefficient for CHM has ^{ 2} = 3.64.
Similarly, the middle section of Table 42 analyzes the crash involvements of SUV's. The point estimate is that CHMSL reduced rear impacts in SUV's by 2.64 percent. This reduction is not statistically significant: in the CATMOD analysis, the coefficient for CHM has ^{ 2} = 0.23. The lower section indicates a 2.44 percent reduction of rear impacts for CHMSL in vans. This point estimate is also nonsignificant (^{ 2} = 0.22).
The differences between the three point estimates, 6.27, 2.64 and 2.44 percent, are very much in the "noise" range. In a CATMOD analysis of the 3 x 4 x 2 table of truck type by YT by impact location, with TRKTYP, YT, CHM as the independent variables, the coefficient for the interaction term TRKTYP * CHM had ^{ 2} = 0.67. That falls well short of the ^{ 2} needed for statistical significance. In other words, no significant differences in effectiveness by truck type were detected.
Table 43 compares the effect of CHMSL in compact and fullsized light trucks. The observed effectiveness of CHMSL in compact pickup trucks, SUV's and vans is 3.34 percent. This point estimate is not statistically significant (^{ 2} = 1.41). The effectiveness in fullsized light trucks, 7.28 percent, is likewise nonsignificant (^{ 2} = 3.32).
The differences between these two point estimates, 3.34 and 7.28 percent, is in the "noise" range. In a CATMOD analysis of the 2 x 4 x 2 table of truck size by YT by impact location, the coefficient for the interaction term SIZE * CHM had a nonsignificant ^{ 2} = 0.66.
While the data are still insufficient to distinguish the CHMSL effects in different types or sizes of trucks, one trend in the results is encouraging. All five point estimates (pickup, SUV, van, compact, fullsized) were positive. In four of the five analyses, even the unadjusted CHMSL effects were positive, showing a reduction in the proportion of rear impacts in the model year that CHMSL were first installed.
COMPACT TRUCKS
REAR IMPACT? 
DEL_{BEFORE} = 0084
E_{u} = .0129 DEL_{AFETR} = .0339 DEL_{AVG} = .0211 E_{A} = .0340 

YT Frequency
Row Percent 
NO  YES  
2 (Two MY before CHMSL)  17719
73.63 
6347
26.37 

1 (Last MY before CHMSL)  20245
73.46 
7313
26.54 

0 (First MY wtih CHMSL)  22500
73.71 
8023
26.29 

1 (Second MY with CHMSL)  20192
73.05 
7448
26.95 
FULLSIZED TRUCKS
REAR IMPACT? 
DEL_{BEFORE} = .0226
E_{u} = .0448 DEL_{AFETR} = .0391 DEL_{AVG} = .0308 E_{A} = .0756 

YT Frequency
Row Percent 
NO  YES  
2 (Two MY before CHMSL)  10152
75.26 
3338
24.74 

1 (Last MY before CHMSL)  9637
74.83 
3241
25.17 

0 (First MY wtih CHMSL)  12616
75.67 
4057
24.33 

1 (Second MY with CHMSL)  13003
74.94 
4348
25.06 