**IV. RESULTS**

** A. Blood Alcohol Concentration**

Table 1 presents the mean BACs measured immediately before and after each test
battery. As can be seen in the table, the mean for each battery was within 0.002% BAC of
the levels specified in the design of the experiment. The mean of 0.098% measured at
battery 2 is for moderate and heavy drinkers only. Batteries 3 through 7 include all __S__s.

| |||

Mean BACs (%) | |||

Test Battery | Pre-Battery | Post-Battery | Battery Average |

1 | 0.000 | 0.000 | 0.000 |

2 | 0.102 | 0.094 | 0.098 |

3 | 0.082 | 0.073 | 0.078 |

4 | 0.063 | 0.055 | 0.059 |

5 | 0.044 | 0.035 | 0.040 |

6 | 0.024 | 0.015 | 0.020 |

7 | 0.001 | 0.000 | 0.001 |

**B. Sensitivity of Study Measures**

It was necessary to determine whether the response measures for both SIM and DAT
had proved to be sufficiently sensitive for detection of the differential effects of alcohol as
a function of age, gender and drinking practice. The placebo scores were examined for this
purpose and, in fact, demonstrated that the measures were capable of detecting differences
between __S__ classifications on these three variables. Although the results are of considerable
interest, their presentation is deferred to a subsequent publication to avoid distraction from
the main thrust of the experiment.

**C. Ethanol Clearance Rate**

The ethanol clearance rate is the BAC decline over time after alcohol absorption from
the intestinal tract is complete. The rate varies as a function of age, gender, and drinking
practices. The rate for male __S__s in this experiment was 0.0149% and the rate for female __S__s
was 0.0184%. The rates by age group were 0.0156% for __S__s ages 19-20, 0.0156% for __S__s ages
21-24, 0.0168% for __S__s ages 25-50, and 0.0183% for __S__s ages 51-69. The ethanol clearance
rates for __S__s as a function of drinking practices varied from 0.0157% for light drinkers to
0.0165% for moderate drinkers and 0.0176% for heavy drinkers.

The ethanol clearance rate shows an increase with greater frequency of alcohol consumption. This variability is due to the stimulation by alcohol of the production of a liver enzyme. Although the clearance rate for the moderate drinkers in this experiment was found to be very close to that which was anticipated, the rate for light drinkers was higher and the rate for heavy drinkers was lower than expected. The finding strongly suggests an under-representation of the lighter drinkers of the light-drinking category and of the heavier drinkers of the heavy-drinking category. The latter result may be the consequence of the SCRI practice of excluding alcoholics from alcohol experiments.

**D. Sequence and Order Effects**

__ S__s in this experiment received a placebo treatment and an alcohol treatment on test
days separated by one week. In this repeated measures design, it is necessary to examine the
data to determine whether the sequence of treatments affected the results. Was the effect of
alcohol different for __S__s who received placebo first and alcohol second in comparison to __S__s
who received treatments in the reverse order? A difference would indicate a sequence effect.

An additional question asks whether the average performance on test day one differs
from the average performance on test day two, perhaps due to the difference in practice on
the tests. A difference would indicate an order effect. It should be noted, however, that
given the training sessions, such differences would be small, and the number of __S__s per cell
would limit the power of the test to detect such effects.

**1. Sequence Effect**

Half the __S__s received treatments in the sequence placebo-alcohol, and half the __S__s received treatments in the sequence alcohol-placebo. The mean scores for
the two sequences were examined with statistical tests for each response measure at
each BAC and across BACs. Table AP-I-3 presents the statistical analysis. Twelve
response measures for SIM and DAT were examined. None of the tests for overall
sequence effects were statistically significant. Two of 64 tests at separate BACs were
significant at the .05 level. The finding of only two statistically significant tests of
76 total tests (12 across BACs plus 64 at separate BACs) suggests that the two were
random occurrences, and it is concluded that there is no evidence of a sequence effect
that might influence data analysis.

**2. Order Effects**

All response measures were examined for an order effect. That is, did the
average performance score on test day 1 differ from the average performance score
on test day 2? Figures AP-I-1a and AP-I-1b present the mean change in score from
baseline for each measure for the two test days. Each figure has two lines, one
representing the difference scores for each test time for day 1 and one representing
the difference scores for day 2. Recall that a difference scores is the post-treatment
test score minus the pre-treatment test score. For each line half the __S__s are under
placement treatment, and half are under alcohol treatment.

Table AP-I-4 presents the results of a statistical analysis, which controlled for the variables of alcohol, battery and the alcohol X battery interaction, and examined whether the mean difference scores on days 1 and 2 differed. Five of the nine response variables were not statistically significant, but four were. Three of the four significant measures were worse on day one and one was worse on day two. These somewhat contradictory results clearly cannot rule out the possibility of an order effect. Since the treatments in the study were counterbalanced, however, the existence of an order effect would have no influence on the analysis of the alcohol, age, gender and drinking practice variables or their interactions. Such an effect would only limit statements about the impairment by alcohol of each individual.

**E. Alcohol Effects Analysis**

One hundred sixty-eight __S__s were tested six (or seven) times at two sessions, one with
a placebo treatment and one with an alcohol treatment (Table AP-I-5). The DAT provided
three response measures: reaction time to peripheral signals, number of incorrect responses
to peripheral signals, and error on the tracking task in central vision. The SIM provided six
response measures: lane deviation variability, speed variability, number of collisions, number
of times over the speed limit, reaction time to peripheral signals, and number of incorrect
responses to peripheral signals. Additionally, two performance indices were created by
combining all measures for the DAT into one composite score and combining all measures
for the SIM into another composite score. These two composites were also combined to
create a single index of overall performance.

Figure 2 presents the average raw scores for the three DAT measures, and Figure 3
presents the average raw scores for the six SIM measures. In both figures the scores are
shown by battery (seven for DAT, six for SIM) and by treatment condition. Battery 1 is the
pre-test or the pre-treatment test. Battery 2 is the first post-treatment test at 0.10% BAC
when only moderate and heavy drinkers were tested. Light drinkers were first tested post-treatment at battery 3 when all __S__s were tested at 0.08% BAC. The mean BAC for battery 4
was 0.06%. For battery 5 it was 0.04% and for battery 6 it was 0.02%. The BAC was 0.00%
for battery 7 when the only test was DAT.

The raw scores, which do not take pre-treatment performance into account, reveal impairment of all DAT and SIM measures at all positive BACs in comparison to performance in the placebo condition. If the curves are mentally adjusted so that the placebo and alcohol curves begin at the same point, it will be seen that the differences in SIM scores are even greater than they appear in the figures. In all comparisons, the adjustment produces greater separation of the alcohol and placebo curves. In order to take into account the variability of the pre-test scores on the two test days, the comprehensive statistical analysis used an impairment score, as described below.

Note in the succeeding tables, that the measure "number of incorrect responses to peripheral signals" for both DAT and SIM appears in two forms. The measure is tabled both as number of errors and as percent errors. The percent measure was generated, because there was a statistical question as to whether the measure, number of errors, would be normally distributed. As it developed, the results of statistical tests were the same for both measures but rather than re-create the tables both are included. Note, however, that in all subsequent discussions, the response error measure is counted only once for DAT and once for SIM.

**1. Impairment Scores**

An impairment score was created for the statistical analysis of the alcohol
effect. An impairment score is defined as the performance score on the alcohol
treatment day at a given BAC minus the comparable placebo score minus the
differences in the pre-treatment test scores on the two test days. Thus, the
impairment score takes into account the time-of-day factor at which testing occurred
under the two treatments, and it also takes into account variations in a __S__'s overall
performance from day to day.

__S__s were tested at BACs from 0.08% to 0.00%. Moderate and heavy drinkers
only (n=112) were also tested at 0.10% BAC (Table AP-I-1). Impairment scores
were created for each __S__ at each BAC for the nine original single response measures
and three composite scores. The advantage of the latter is that they take a larger slice
of the performance information into account and, therefore, provide a more stable
measure.

Table 2 shows the percent of __S__s whose impairment score was poorer under
alcohol than under placebo. Table 3 shows the results of the statistical test of the null
hypothesis, which states that fifty percent of the __S__s would have performed worse
under alcohol if alcohol had no effect. To be redundant, the null hypothesis states
that there is no difference between active and placebo treatments, and that by chance
half the scores will be poorer on the alcohol treatment day, and half the scores will
be poorer on the placebo treatment day.

| ||||||

Measurement |
BAC (%) | |||||

.00 |
.02 | .04 | .06 | .08 | .10 | |

DAT Reaction Time | 50 | 50 | 63 | 70 | 80 | 90 |

DAT Number Incorrect (%) | 39 | 37 | 45 | 54 | 58 | 71 |

DAT Number Incorrect (#) | 41 | 38 | 44 | 54 | 58 | 70 |

DAT Tracking Error | 53 | 60 | 68 | 77 | 79 | 83 |

SIM Reaction Time | . | 52 | 58 | 72 | 72 | 78 |

SIM Number Incorrect (%) | . | 52 | 54 | 63 | 72 | 73 |

SIM Number Incorrect (#) | . | 53 | 56 | 65 | 73 | 78 |

SIM Speed Deviation | . | 55 | 55 | 61 | 64 | 66 |

SIM Lane Deviation | . | 70 | 77 | 88 | 90 | 88 |

SIM Collisions | . | 43 | 56 | 65 | 72 | 75 |

SIM Times Over Speed Limit | . | 57 | 65 | 78 | 79 | 86 |

DAT Performance Index | 52 | 56 | 67 | 82 | 83 | 91 |

SIM Performance Index | . | 68 | 71 | 84 | 88 | 96 |

DAT+SIM Performance Index | . | 68 | 79 | 88 | 92 | 93 |

The scores in Table 2 were tested for statistical significance with a two-tailed
binomial distribution test. If the null hypothesis were true and alcohol and placebo
treatments were equal in effects, half the impairment scores would be positive and
half would be negative. Examining the binomial distribution for 168 __S__s reveals that
the probability is less than .05 that as many as 58 percent of the __S__s would show a
positive impairment score if the null hypothesis were true, or conversely that as few
as 42 percent would show a negative impairment score. For 112 __S__s (the number
tested at 0.10% BAC), the probability is less than .05 that as many as 60 percent
would have an alcohol score worse than the placebo score. Thus, the null hypothesis
is rejected if as many as 58 or 60 percent of the __S__s exhibit poorer performance under
alcohol.

Table 3 gives the exact probability for tests of the null hypothesis for each
response measure at each BAC. Beginning with 0.02% BAC, two of the nine single
response measures and two of the three composite measures showed statistically
significant poorer performance under alcohol.

| ||||||

Measurement |
BAC (%) | |||||

.00 | .02 | .04 | .06 | .08 | .10 | |

DAT Reaction Time | .938 | 1.000 | .001 | .001 | .001 | .001 |

DAT Number Incorrect (%) | .004 | .001 | .165 | .355 | .045 | .001 |

DAT Number Incorrect (#) | .016 | .001 | .123 | .355 | .031 | .031 |

DAT Tracking Error | .395 | .014 | .001 | .001 | .001 | .001 |

SIM Reaction Time | . | .537 | .031 | .001 | .001 | .001 |

SIM Number Incorrect (%) | . | .643 | .280 | .001 | .001 | .001 |

SIM Number Incorrect (#) | . | .440 | .123 | .001 | .001 | .001 |

SIM Speed Deviation | . | .217 | .217 | .003 | .001 | .001 |

SIM Lane Deviation | . | .001 | .001 | .001 | .001 | .001 |

SIM Collisions | . | .064 | .123 | .001 | .001 | .001 |

SIM Times Over Speed Limit | . | .090 | .001 | .001 | .001 | .001 |

DAT Performance Index | .588 | .123 | .001 | .001 | .001 | .001 |

SIM Performance Index | . | .001 | .001 | .001 | .001 | .001 |

DAT+SIM Performance Index | . | .001 | .001 | .00 | .001 | .001 |

At 0.04% BAC, five of the single response measures and all three composite scores show statistically significant alcohol impairment. At 0.06% BAC eight of the nine measures and all composite scores show statistically significant alcohol impairment. At 0.08% and 0.10% BAC all single and composite scores show statistically significant alcohol impairment.

As suggested by the graphs and supported by the statistical analyses, the
overwhelming majority of __S__s were significantly impaired by alcohol on some
important measures beginning at 0.02% BAC, the lowest level tested. The number
of __S__s who were impaired by alcohol increased as BACs increased. Also, in general
the magnitude of the impairment increased with increasing BAC (Figures 2 and 3).

Several of the single response variables showed a slight deviation from the finding of maximum impairment at 0.10% BAC in that the greatest impairment occurred at 0.08% BAC. To investigate this phenomenon, which initially was believed to be due to the light drinkers being first tested at 0.08% BAC, raw scores were examined separately for light, moderate, and heavy drinkers. For all drinking practices groups, greater impairment on some responses occurred at the second post-treatment testing. That is, for moderate and heavy drinkers, there was more impairment on a few of the responses at 0.08% than at 0.10%, and for light drinkers there was more impairment on some variables at 0.06% than at 0.08%.

It should be noted that since the behavioral tests began at the same time for
all __S__s, the moderate and heavy drinkers were tested at 0.10% at the time of day when
light drinkers were tested at 0.08%. It is possible, therefore, that a time-linked factor
increased impairment at the second post-treatment test. Perhaps some source of
stimulation offset impairment at the first post-treatment test, or possibly a circadian
interaction produced a greater decrement at the next test time. The issue cannot be
resolved from the data, but the analyses make it clear that it was the order of testing
rather than BAC that caused the variation in the magnitude of impairment. For the
majority of measures, the expected relationship of greater impairment with higher
BAC was found. Figures AP-I-3a to AP-I-10b, which present raw DAT and SIM
scores separately for light, moderate and heavy drinkers, illustrate that for most
measures the greatest impairment occurred at the highest BACs.

Finally, __S__s were tested on DAT only when their BACs returned to 0.00%.
Three of the four DAT response measures showed no alcohol effect; the percent of
__S__s with more impairment after alcohol was roughly equal to the percent of __S__s with
more impairment after placebo. There was, however, an unusual effect for the
remaining measure, number of errors in detecting peripheral signals. In a statistically
significant deviation, performance was better than what would be expected. Whether
this result reflects some time-linked factor or a rebound effect cannot be determined.
Since it was only one of four response measures, it also cannot be predicted that it
would occur upon retest.

**2. Age, Gender and Drinking Practice Effects**

As discussed above, these data indicate that alcohol, even at 0.02% BAC,
produces impairment in some important measures in the majority of __S__s. This section
considers whether impairment by alcohol varies as a function of age, gender or
drinking practices.

The original design for this study would have supported a complete factorial
analysis of variance with age, gender and drinking practices as the main effect and
with the alcohol treatments nested within each cell of the factorial design. All __S__s
were to have received sufficient alcohol to achieve a peak 0.11% BAC. As discussed
in an earlier section, however, it was determined that many light drinkers probably
would experience nausea or more severe effects at that level of alcohol. As a
consequence, the peak BAC for light drinkers was set at 0.09%. It was further
determined that a standard analysis of variance of main effects, which would sum the
effect of a variable across all levels, might obscure small effects which occurred at
certain BACs and not at others. It was decided, therefore, to simplify the analysis by
examining each alcohol level as a separate factorial design. This analysis also
removed the problems of the interactions with the different BACs, which would have
required another dimension in the factorial design

The five factorial designs for statistical analysis of the impairment scores at
the five BACs from 0.02% to 0.10% are shown in the matrix below. With only
heavy and moderate drinkers at 0.10% BAC, the design is a 4 (Age) X 2 (Gender)
X 2 (Drinking Groups) factorial. With all __S__s and three drinking groups at 0.08%,
0.06%, 0.04%, and 0.02%, the design is 4 X 2 X 3.

BAC |
DESIGN |

0.10% | Age (4) X Gender (2) X Moderate and Heavy Drinkers (2) |

0.08% |
Age (4) X Gender (2) X Light, Moderate and Heavy Drinkers (3) |

Table 4 summarizes the mean impairment score for each response variable within age, gender, and drinking practice. These data, which are across all BACs, are not the basis of the statistical significance tests. They merely provide an overview of the variability in impairment by alcohol with the three groupings. The scores are in the original response measure dimension, adjusted for baseline; for example, reaction time is in seconds. The figures in Appendix II show the original impairment scores at each BAC for each of the single response variables by each of the categories within age, gender, and drinking practices. The figures in Appendix III show the impairment scores at each BAC for the three DAT, SIM, and DAT+SIM composite scores.

| ||||||||||

Measurement |
Subject Groups | |||||||||

Age |
Drinking |
Gender | All | |||||||

19-20 | 21-24 | 25-50 | 51-69 | Light | Mod | Heavy | Female | Male | ||

DAT Reaction Time | 0.50 | 0.37 | 0.53 | 0.33 | 0.43 | 0.30 | 0.36 | 0.34 | 0.53 | 0.43 |

DAT Number Incorrect (%) | 1.65 | 1.72 | 1.43 | 0.76 | 1.94 | 0.73 | 1.16 | 1.05 | 1.73 | 1.39 |

DAT Number Incorrect (#) | 0.41 | 0.41 | 0.57 | 0.25 | 0.54 | 0.29 | 0.29 | 0.24 | 0.58 | 0.41 |

DAT Tracking Error | 0.42 | 0.37 | 0.29 | 0.21 | 0.22 | 0.35 | 0.28 | 0.40 | 0.25 | 0.32 |

SIM Reaction Time | 0.30 | 0.26 | 0.24 | 0.06 | 0.21 | 0.14 | 0.21 | 0.22 | 0.22 | 0.22 |

SIM Number Incorrect (%) | 6.66 | 5.03 | 4.71 | 1.04 | 4.42 | 1.61 | 5.47 | 4.05 | 4.67 | 4.36 |

SIM Number Incorrect (#) | 0.72 | 0.64 | 0.57 | 0.08 | 0.58 | 0.20 | 0.55 | 0.50 | 0.50 | 0.50 |

SIM Speed Deviation | 0.33 | 0.81 | 0.42 | 0.22 | 0.42 | 0.21 | 0.66 | 0.73 | 0.16 | 0.44 |

SIM Lane Deviation | 0.35 | 0.55 | 0.46 | 0.56 | 0.46 | 0.53 | 0.40 | 0.48 | 0.48 | 0.48 |

SIM Collisions | 2.73 | 3.74 | 3.15 | 6.50 | 3.67 | 4.06 | 3.46 | 4.16 | 3.90 | 4.03 |

SIM Times Over Speed Limit | 3.68 | 6.30 | 4.89 | 4.81 | 4.80 | 4.14 | 4.81 | 4.88 | 4.96 | 4.92 |

DAT Performance Index | 0.47 | 0.38 | 0.42 | 0.28 | 0.31 | 0.33 | 0.32 | 0.38 | 0.39 | 0.39 |

SIM Performance Index | 0.51 | 0.61 | 0.52 | 0.46 | 0.51 | 0.47 | 0.48 | 0.53 | 0.53 | 0.53 |

DAT+SIM Performance Index | 0.54 | 0.54 | 0.50 | 0.40 | 0.46 | 0.43 | 0.43 | 0.49 | 0.50 | 0.50 |

Table AP-I-6 presents the mean scores for each classification within each of the three main grouping factors. The scores have been standardized; that is, they have been transformed in terms of standard errors of the mean so that they have a common metric. In addition, the probability level appearing after each mean value indicates whether the mean impairment score within that category, when divided by the standard error of the mean of that category, is statistically significant. The tabled probability values are defined as follows: zero = probability less than .10, 1 = probability less than .05, 2 = probability less than .01, and 3 = probability less than .001.

Finally, Table AP-I-7 gives the test results for the mean effects of age, gender
and drinking practices and their interactions. At each BAC, for each of the three
main effects and four interaction terms, there were 48 statistical tests. Composite
measures were excluded.* *Thus,* *there are nine single measure (6 from SIM, 3 from
DAT) for five BACs plus three DAT measures at zero BAC for a total of 48 tests.

Table 5 summarizes the number of tests that were significant at the .05 level for each factor and the interactions. Six tests are significant for age, four for gender, five for drinking practices, two for the age X gender interaction, and five for the age X drinking practices interaction.

| ||

Effect | Tests (Total Number) |
Tests p.05 (Number) |

Age | 48 | 6 |

Gender | 48 | 4 |

Drinking Practice | 48 | 5 |

Age X Gender | 48 | 2 |

Age X Drinking Practice | 48 | 5 |

Gender X Drinking Practice | 48 | 0 |

Age X Gender Drinking Practice | 48 | 0 |

Thus, of 336 statistical tests performed to evaluate differential alcohol effects
as a function of age. gender, or drinking practices, only 22 reached the .05
significance level. Given random performance variability, some statistical tests will
be significant by chance even if there were no true underlying performance
differences as a function of the experimental variable. An experiment-wide
judgement of the number of findings expected to be significant at the .05 level by
chance is difficult, because in the repeated measures design the same __S__s were used
in all tests. An approximation, however, assuming independence of statistical tests
and using Fisher's exact test, indicates that six positive significant tests out of 48 are
required to reach at least a .05 level. Five significant tests only reach a .18
probability level.

Only the age variable approaches overall significance. Even within the age variable, however, six significant test among four response variables at three BACs occurred in no consistent pattern. It is concluded, therefore, that within the limits of the population represented by the study sample, there is no significant evidence that either age, gender, or drinking practice produces a differential response to the impairing effects of alcohol.

As noted earlier, no __S__s were younger than 19 years of age nor over 70, nor did
the sample include alcohol abstainers, heavy alcohol abusers, or alcoholics. Thus,
the conclusions are limited by the sample, but the characteristics of the sample likely
represent the characteristics of 80 - 90 percent of the driving public who will take a
drink.

To re-state the finding, for the population represented by the study sample,
which demonstrated impairment in driving skills beginning at 0.02% BAC,
differences in age, gender, and drinking practices provide no mitigation of
impairment. Had the experiment used many more __S__s to greatly increase the power
of the statistical tests, some of the small differences might have reached statistical
significance. From a social point of view, that would be irrelevant to the study
findings, because the actual differences would remain small in comparison to the
overall effects of alcohol.

The tables and figures in the appendix support these conclusions. In a non-significant trend, the oldest drivers' response to alcohol appeared dissimilar to the response of the other three groups. There was, however, no consistent direction since the oldest drivers were least impaired on four measures and most impaired on two measures. Males and females split the measures on which they were more impaired with no evidence of any gender superiority. Among drinking practice groups, light drinkers showed a tendency toward more impairment, but it was small and non-significant. Moderate and heavy drinkers were indistinguishable in degree of impairment. Even if these trends had been statistically significant, they were so small as to be socially irrelevant.