Accepted Manuscript Title: Breath Alcohol Elimination Rate as a function of Age, Gender, and Drinking Practice Authors: Dary D. Fiorentino Herbert Moskowitz PII: DOI: Reference:
S0379-0738(13)00435-0 http://dx.doi.org/doi:10.1016/j.forsciint.2013.09.017 FSI 7359
To appear in:
FSI
Received date: Revised date: Accepted date:
30-4-2013 31-8-2013 18-9-2013
Please cite this article as: D.D. Fiorentino, H. Moskowitz, Breath Alcohol Elimination Rate as a function of Age, Gender, and Drinking Practice, Forensic Science International (2013), http://dx.doi.org/10.1016/j.forsciint.2013.09.017 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
BREATH ALCOHOL ELIMINATION RATE
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ABSTRACT The objective of this study was to determine whether breath alcohol elimination rate varies as a
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function of age, gender, and drinking practice, factorially combined. Eighty-four men and 84 women drank enough alcohol to produce peak BrACs of .110 g/210L for heavy and moderate
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drinkers and BrACs of .090 g/210L for light drinkers. An Intoxilyzer 5000 was used to generate
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the concentration-time profiles. Mean (M) elimination rates (g/210L/h) were found to be higher for women (N = 84, M = .0182, SD = .0033) than for men (N = 84, M = .0149, SD = .0029),
an
F(1, 144) = 57.292, p < .001; higher for heavy drinkers (N = 56, M = .0176, SD = .0038) than for light and moderate drinkers combined (N = 112, M = .0160, SD = .0032), F(1, 144) =
M
12.434, p < .01; and higher for older subjects (51-69 years, N = 42, M = .0180, SD = .0038) than younger subjects (19-50 years, N = 126, M = .0161, SD = .0033), F(1, 144) = 14.324, p < .001.
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None of the two-way interactions (age x gender, age x drinking practice, gender x drinking practice) or the three-way interaction (age x gender x drinking practice) was statistically
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significant. Limitations of the current study and suggestions for further research are discussed.
Keywords: Alcohol
Elimination rate Drunk driving
Forensic science Pharmacokinetics Retrograde extrapolation
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1. Introduction
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Retrograde extrapolation is often performed to estimate the blood alcohol concentration (BAC) or breath alcohol concentration (BrAC) at a time prior to a known measurement [1, 2].
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When performed, it is based on the assumptions that at the time of interest the alcohol had been fully absorbed, was uniformly distributed in the body, and was being cleared at a constant rate
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independent of the alcohol concentration in the body, a zero-order process [1]. In this study we
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build on the latter assumption. Specifically, we examined whether breath alcohol elimination rates vary as a function of gender, drinking practice, and age.
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In general, alcohol elimination rates in humans vary as a function of genetic and environmental factors [3], and have a range from .0100 to .0350 g/dL/h [1]. There is conclusive
ed
evidence that women clear alcohol at a faster rate than men [4, 3]. The gender difference in elimination rate has been attributed to the relationship between liver size, which is approximately
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the same between men and women, and the volume of distribution of alcohol, which tends to be
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smaller in women than in men [5, 6]; resulting in women having proportionally more liver tissue per unit of volume of distribution. There is also conclusive evidence that alcohol elimination rates increase with drinking experience [7], and can be especially high for alcoholics [1]. Much less is understood about the effects of age on alcohol elimination rate. Initial reports suggested that older male mice cleared alcohol at a much lower rate than other groups [8], but subsequent studies in humans have found both no differences with increasing age [9] and faster elimination rates with increasing age (Schweitzer; cited in Dubowski [10]). Clearly, more research is needed in this area.
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To the best of our knowledge, this is the first study to examine the effects of gender, drinking practice, and age, factorially combined, on breath alcohol elimination rates. On the basis of prior research, we made the following predictions. First, we expected a main effect of
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gender, with women clearing alcohol at a faster rate than men. Second, we expected a main
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effect of drinking practice, with heavier drinkers clearing alcohol at a faster rate than light drinkers. Third, we expected a main effect of age. In theory, if the volume of distribution
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decreases with advancing age, and if the rate of hepatic functioning remains relatively constant with age, then elimination rates for older people should be faster than younger people, for the
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same reasons described above for gender. Fourth, we expected a statistically significant interaction between age and gender. The basis for that prediction was a commonly-used formula
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for the estimation of total body water—which is essentially the volume of distribution of
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alcohol—that uses age as one of the predictors for men but not for women [11]. Finally, we made no specific predictions regarding the interaction between age and drinking practice, the
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2. Method
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interaction between drinking practice and gender, and the three-way interaction.
2.1 Experimental Design
Data were collected on the basis of 4 x 3 x 2 between-subjects design. The independent variables were age group (19-20, 21-24, 25-50, and 51-69 years), drinking practice (light, moderate, and heavy drinkers), and gender (males and females). The dependent variable was elimination rate.
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2.2
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Subjects Eighty four men and eighty four women participated as paid subjects in the study. Each
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subject was assigned to one of 24 cells, defined by the two categories of gender (84 males and 84 females), the three categories of drinking practice (56 light drinkers, 56 moderate drinkers, 56
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heavy drinkers), and the four categories of age group (42 between 19 and 20 years, 42 between
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21 and 24 years, 42 between 25 and 50, and 42 between 51 and 69 years of age). Thus, each cell consisted of seven participants. The 42 participants in the 19-20 age group were tested in
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Toronto, Ontario, Canada, where the law allows administration of alcohol to that age group. The age groups were selected to represent four classes of drivers involved in fatal motor
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vehicle crashes: drivers under the age of 21 years, who are not legally allowed to purchase alcohol in the US; drivers between the ages of 21 to 24 years, who have the highest level of
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involvement in alcohol-impaired driving fatalities; and drivers between the ages of 25 to 50 and between the ages of 51 to 69, who have declining levels of involvement in alcohol-impaired
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2.3 Apparatus
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driving fatalities [12]. Note that the age ranges in the four groups were not equal.
2.3.1 Breath Alcohol Tester. The BrACs were measured with an Intoxilyzer 5000 (CMI, Owensboro, KY). The instrument and its performance characteristics have been described extensively elsewhere [13, 14] and will not be described here in detail. Briefly, however, the device collects a sample of breath, passes an infrared light through the sample, and measures the amount of light absorbed by the alcohol contained in the sample. Three filters (3.39, 3.48, and 3.80 μM) are used to determine the concentration of alcohol, the presence of acetone, and the reference value. For the purpose of the study, one Intoxilyzer 5000 was borrowed from the
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manufacturer. Initial calibration was performed by the manufacturer with a wet bath simulator, and subsequent calibration was verified with a dry gas simulator (.080 g/210L) on site every
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Monday morning before data collection. For each breath alcohol test, subjects were asked to blow into a mouthpiece a slow,
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steady breath, until told to stop. A research associate waited a minimum of six seconds before
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instructing the subjects to stop blowing into the mouthpiece. The instrument automatically monitored the suitability of the breath sample.
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2.3.2 Pregnancy Tests. To prevent administration of alcohol to pregnant women, female subjects were required to provide a urine specimen and the specimens were screened for hCG,
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the pregnancy hormone. The pregnancy tests used for the study were purchased over the counter
2.4 Alcohol
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and were administered precisely following the manufacturers’ instructions.
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Subjects arrived in a fasted state at the laboratory and were asked to drink enough alcohol
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to produce a peak BrAC of .110 g/210L for moderate and heavy drinkers and .090 g/210L for light drinkers. The lower dose for the light drinkers reduced the probability of adverse reactions to the alcohol.
2.5 Procedures
Subjects were recruited with newspaper ads, Internet postings, and referrals. An initial telephone interview determined eligibility for the study. Applicants were screened in terms of health history, current health status, and use of alcohol and other drugs. The Cahalan, Cisin and Crossley’s Quantity-Frequency-Variability (QFV) scale [15] was used to classify applicants into
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the three drinking categories (light, moderate, and heavy). Applicants who were categorized as borderline in any QFV category were excluded from the study. Special efforts were made to exclude chronic alcohol abusers. Pregnancy, disease, evidence of substance abuse, and the taking
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of medications that interacted with alcohol resulted in exclusion from the study.
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Experimental sessions began at 8:00 am. Subjects were transported from their residence
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to the laboratory via taxi. They were instructed to arrive at the laboratory in a fasted state (no food and beverages in the previous four hours). Upon arrival, each subject gave informed
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consent to participate in the study and received a copy of the signed Informed Consent. A breath alcohol test, an additional administration of the QFV, a pregnancy test for females,
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cardiovascular measures within acceptable ranges (systolic blood pressure = 120 ± 30 mmHg, diastolic blood pressure = 80 ± 20 mmHg, heart rate = 70 ± 20), and verification of age and
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gender with formal identification confirmed eligibility for the study. The alcoholic beverage was 80 proof vodka and orange juice. Subjects were asked to
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drink the total dose in 30 minutes, consuming each of three equal-sized drinks in 10 minutes.
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After the 30 minutes of drinking, the subjects sat in a quiet area, away from food and other beverages. After 20 minutes from the end of drinking, the measurement of the BrAC began, and continued at regular intervals until the BrAC dropped to zero. On average, for each subject, BrACs were measured five times per hour, or once every 12 minutes. After the BrAC dropped to zero, the subjects were debriefed, paid $250, and transported back home. Subjects were not privy to their measured BrACs until their participation in the study was complete. 2.6 Regulatory Approval
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All study procedures were approved by the Institutional Review Board of the performing organization.
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2.7 Determination of Elimination Rates Determination of elimination rates was conducted as typically reported in the literature
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[1]. For each subject, the BrAC data were entered into a spreadsheet, and the BrAC readings
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were plotted as a function of time in minutes from the start of drinking (see Figure 1). The typical curve showed the BrAC rising, reaching a peak, and then declining in an approximately
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linear fashion. A best-fit line was determined on the basis of the BrACs between .070 and .040 g/210L, the portion of the BrAC curve after the diffusion-equilibration phase and before the
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rectilinear decline phase changes to a curvilinear function. The .070 to .040 g/210L BrAC range was the general rule, but visual inspection of the figure at times required that the range of data
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points be changed to avoid inconsistent readings such as an unexplained spike or drop. The elimination rate was then calculated by dividing the Y Intercept by the X Intercept divided by 60.
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Because the selection of the data points for the best-fit line was a subjective process, all 168
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elimination rates were determined twice, independently, by two different people. 3. Results
Prior to analysis, elimination rate was examined for normality and outliers. As shown in Figure 2, the distribution of elimination rates was fairly normal. Significance testing for kurtosis (.642) showed that it was not statistically different from zero, z = 1.72, p > .05. Significance testing for skewness (.677) showed that it was statistically different from zero, z = 3.62, p < .001, but because of the large sample size the deviation in normality was not believed to substantively affect the analyses.
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Figure 2 also shows the clear presence of an outlier (elimination rate of .0307 g/210L/h, z = 3.82, p < .001). The subject was a 63 year old woman who was 71 inches tall and weighed 260 lbs (1.80 m, 118 Kg, BMI = 38.4). She was categorized as a moderate drinker. The extreme
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elimination rate was changed to the grand mean for all cases (including the outlier). To
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determine whether this substitution substantively affected the results, all subsequent analyses
were conducted twice, once with the outlier and once without. Because the pattern of results for
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the two sets of analyses were the same, the substitution was retained and only the results without
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the outlier are reported in the following sections. No multivariate outliers were detected. Analysis of the 4 x 3 x 2 between-subjects design was conducted with multiple
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regression, as described by Cohen [16] and Pedhazur [17], using the statistical software SPSS 20. In general, elimination rate was the dependent variable, and age group, drinking practice, and
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gender were the independent variables. The expression of group membership in the three independent variables was accomplished with orthogonal coding. For each main effect and
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interaction, the test of significance was conducted by sequentially regressing elimination rate
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first on all the effects minus the effect of interest, and then with all the effects including the effect of interest, with R2 change on the last step as the basis of analysis. For each effect, the coded vectors were entered into the equation as a set. As shown in Tables 1 and 2, females (N = 84, M = .0182 g/210L/h, SD = .0033 g/210L/h) had faster elimination rates than males (N = 84, M = .0149 g/210L/h, SD = .0029 g/210L/h), for a statistically significant main effect of gender, F(1, 144) = 57.292, p < .001. Differences in elimination rates between light drinkers (N = 56, M = .0157 g/210L/h, SD = .0032 g/210L/h), moderate drinkers (N = 56, M = .0162 g/210L/h, SD = .0032 g/210L/h), and heavy drinkers (N =
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56, M = .0176 g/210L/h, SD = .0038 g/210L/h) produced a statistically significant main effect of drinking practice, F(2, 144) = 6.662, p < .01, with no statistically significant differences between light drinkers and moderate drinkers, F(1, 144) = .890, p > .05, but with statistically
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significant differences between light and moderate drinkers together (N = 112, M = .0160
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g/210L/h, SD = .0032 g/210L/h) versus heavy drinkers, F(1, 144) = 12.434, p < .01.
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Differences between 19-20 (N = 42, M = .0158 g/210L/h, SD = .0032 g/210L/h), 21-24 (N = 42, M = .0155 g/210L/h, SD = .0026 g/210L/h), 25-50 (N = 42, M = .0168 g/210L/h, SD =
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.0033 g/210L/h), and 51-69 year olds (N = 42, M = .0180 g/210L/h, SD = .0038 g/210L/h) produced a statistically significant main effect of age group, F(3, 144) = 6.088, p < .01. As
M
expected, there were no statistically significant differences between 19-20 and 21-24 year olds, F(1, 144) = .060, p > .05; and between 19-24 (N = 84, M = .0157 g/210L/h, SD = .0032
ed
g/210L/h), and 25-50 year olds, F(1, 144) = 3.881, p > .05; but there were statistically significant differences between 19-50 (N = 126, M = .0161 g/210L/h, SD = .0033 g/210L/h) and
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51-69 year olds, F(1, 144) = 14.324, p < .001.
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None of the interactions were statistically significant. Note, however, that one of the six vectors of the drinking practice x age group interaction and one of the six vectors of the gender x drinking practice x age group interaction were statistically significant. Because in the age groups with large age ranges (25-50 and 51-69) subject recruiting procedures yielded average age differences between males and females and between light, moderate, and heavy drinkers, there was a concern that age was contributing to the effects of gender and drinking practice on elimination rate. To allay those concerns, an analysis of covariance was conducted on elimination rate, with gender and drinking practice as the
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independent variables and with transformed chronological age (1/Age) as the covariate. The pattern of results, a statistically main effect of gender, a statistically significant main effect of drinking practice, and a not statistically significant gender x drinking practice interaction were
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consistent with the results of the main analysis, further supporting the hypothesis that gender and
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drinking practice affect elimination rate, independently from the effects of age.
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4. Discussion
The objective of this study was to determine whether alcohol elimination rate varies as a
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function of gender, drinking practice, and age. We tested four specific hypotheses. First, as expected, it was found that women clear alcohol at a faster rate than men. Second, as expected, it
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was found that heavy drinkers clear alcohol at a faster rate than light and moderate drinkers. Third, as expected, it was found that older people clear alcohol faster than younger people,
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although it should be noted that in the current sample differences were evident only in the 19-50 versus 51-69 age groups. It is proposed that both the gender- and age-related differences in
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elimination rates are attributable to the relationship between liver size and the volume of
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distribution of alcohol. Fourth, although we expected the interaction between age and gender to be statistically significant, it was not. The basis of the prediction was a commonly-used formula for the estimation of total body water that uses age as a predictor for men but not for women. The formula predicts that over the life span the volume of distribution of alcohol diminishes for men but not for women. Assuming that hepatic functioning remains fairly constant over time, that would result in faster elimination rates as a function of age for men but not for women. As shown in Figure 3, this was not what was found. Further study is needed to explain this potential
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discrepancy. It should be noted that there are formulas for total body water that have age as a predictor for both men and women (i.e., [18, 19, 20]).
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The ranges in breath elimination rates from this study are consistent with the extant scientific literature on the topic [21, 22], but the results must be interpreted with caution for the
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following reasons. First, the uneven age ranges and the sequential nature of the four age groups
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presented logical and statistical problems that may limit the usefulness of the results. For example, the comparison between 19-24 and 25-50 age groups approached statistical
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significance, but the comparison includes subjects who are one year apart and thus not likely to differ. A more useful approach would have been to have equal-range age groups separated by
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intervals of a few years.
Second, the study had 24 experimental cells, each with seven participants. The small
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sample size per individual cell may have provided insufficient statistical power in the tests of the interactions. In fact, power analyses of the interactions showed that none exceeded observed
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power of .53. Future research should address this limitation of the current study.
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Third, although the subjects were asked to arrive to the laboratory in a fasted state, there was no independent way of determining whether that was the case. There is overwhelming evidence that ingesting alcohol with a full stomach reduces the bioavailability of the dose, resulting in lower peak BACs, increased times to peak BAC, and smaller areas under the curve [23, 24]. There is also evidence that food increases the elimination rate of alcohol [25, 26]. Thus, if some subjects violated study procedures and ate before the study session, it is likely that their elimination rates would have been higher than the elimination rates of subjects who did not eat.
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Fourth, determination of drinking practice was based on self-report, which can be unreliable. The double administration of the QFV may have screened out some study applicants that were inconsistent, but it is possible that some may have qualified by consciously or
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unconsciously misreporting their alcohol consumption.
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Finally, the dosing procedures were designed to produce peak BrACs of .090 g/210L for
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light drinkers and .110 g/210L for moderate and heavy drinkers. It is possible that that the difference in dosing procedures between the groups may have had an effect of the elimination
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rates. Further, it must be noted that the peak BACs from this study are much lower that the BACs typically encountered in the real world, where 58% of drivers involved in fatal crashes have
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BACs of .15 g/dL or greater [12].
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5. Conclusions
Currently, for forensic purposes, it is recommended that a suspect’s BAC be extrapolated
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using the range in elimination rate between .010 and .025 g/dL/h [1]. This study provides evidence that breath alcohol elimination rate varies as a function of age, gender, and drinking
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practice. If replicated and further evaluated, the findings from the current study may help define a narrower range of elimination rates based on the suspects’ characteristics.
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Declaration statement
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To be written after the review process.
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[25] A.W. Jones, K.A. Jonsson, Food-induced lowering of blood-ethanol profiles and increased rate of elimination immediately after a meal, J Forensic Sci 39 (1994) 1084-1093.
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elimination rates in healthy men and women, J Clin Pharmacol 41 (2002) 1345-1350.
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Table 1
Elimination Rate (g/210L/h) by Age Group, Drinking Practice, and Gender
group
practice
Males
M
Light
7
.0152
Moderate
7
Heavy
Light
Females and males
N
M
SD
N
M
SD
.0027
7
.0137
.0020
14
.0144
.0024
.0178
.0035
7
.0143
.0024
14
.0161
.0034
7
.0170
.0030
7
.0168
.0042
14
.0169
.0035
21
.0166
.0031
21
.0149
.0032
42
.0158
.0032
7
.0157
.0014
7
.0134
.0020
14
.0146
.0021
7
.0176
.0023
7
.0139
.0025
14
.0158
.0030
Ac c
Moderate
SD
d
N
All 21-24
Females
ep te
19-20
Gender
an
Drinking
M
Age
Heavy
7
.0197
.0031
7
.0134
.0027
14
.0166
.0043
All
21
.0177
.0028
21
.0136
.0023
42
.0156
.0033
Page 18 of 25
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BREATH ALCOHOL ELIMINATION RATE
All
7
Moderate
7
.0176
.0021
7
Heavy
7
.0217
.0026
7
All
21
.0187
.0031
Light
7
.0207
.0036
Moderate
7
.0182
M
Heavy
7
All Light Moderate Heavy All
cr
.0023
.0142
.0022
14
.0155
.0026
.0146
.0026
14
.0161
.0027
.0158
.0014
14
.0187
.0037
us
.0168
an
7
.0148
.0021
42
.0168
.0033
7
.0162
.0034
14
.0185
.0041
.0044
7
.0160
.0033
14
.0171
.0039
.0204
.0026
7
.0163
.0035
14
.0184
.0037
21
.0198
.0036
21
.0162
.0032
42
.0180
.0038
28
.0171
.0033
28
.0147
.0026
56
.0157
.0032
28
.0178
.0030
28
.0147
.0027
56
.0162
.0032
28
.0197
.0032
28
.0156
.0032
56
.0176
.0038
.0182
.0033
84
.0149
.0029
168
.0165
.0035
d
21
ep te
51-69
Light
Ac c
25-50
19
84
Page 19 of 25
BREATH ALCOHOL ELIMINATION RATE
20
Table 2 Analysis of Variance Through Multiple Regression R2 Change
F Change
.224
57.292
Drinking practice
.052
6.662
Light v. Moderate (2)
.003
Light and Moderate v. Heavy (3)
.049
p .000
ip t
Gender (1)
cr
Source
.002
.347
12.434
.001
6.088
.001
.060
.807
3.881
.051
.056
14.324
.000
.007
.908
.406
.000
.127
.722
.007
1.689
.196
.018
1.560
.202
.015
3.807
.053
1x5
.003
.749
.388
1x6
.000
.124
.725
.031
1.322
.251
2x4
.000
.083
.773
2x5
.002
.430
.513
2x6
.016
4.025
.047
Age group
.071 .000
an
19-20 v. 21-24 (4)
.015
19-20, 21-24, and 25-50 v. 51-69 (6)
ed
Gender x Drinking practice
Ac ce
1x 4
pt
1x3 Gender x Age group
M
19-20 and 21-24 v. 25-50 (5)
1x2
Drinking practice x Age group
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.890
Page 20 of 25
BREATH ALCOHOL ELIMINATION RATE
21
.000
.024
.878
3x5
.006
1.573
.212
3x6
.007
1.798
.182
.032
1.376
.228
1x2x4
.000
.037
1x2x5
.001
1x2x6
.008
1x3x4
.018
1x3x5
.005
.000
cr
.594
2.113
.148
4.552
.035
1.217
.272
.055
.815
M
1x3x6
.847
.285
us
an
Gender x Drinking practice x Age group
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3x4
Note. Group membership in the three independent variables was accomplished with orthogonal
ed
coding. For each variable, the coded vectors are labeled in parentheses. Vectors representing the
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interactions were generated by cross multiplying the vectors in parentheses.
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BREATH ALCOHOL ELIMINATION RATE
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Best Fit Line 0.140
Measured BAC
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Y Intercept
0.120
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0.080 0.060
Data range for best-fit line
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BrAC (g/210L)
0.100
0.040
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0.020 0.000 0
100
200
300
400
X Intercept
500
600
M
Time from First Drink (Minutes)
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Figure 1. Example of chart used to determine elimination rates. In general, the BrAC readings between .040 and .070 were used to generate a best-fit line, which was then extrapolated to the
Ac ce
(X intercept / 60).
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X-axis and Y-axis. The elimination rate was calculated with the following formula: Y intercept /
Page 22 of 25
BREATH ALCOHOL ELIMINATION RATE
pt
ed
M
an
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23
Ac ce
Figure 2. Frequency distribution of elimination rates of ethanol from breath (g/210 L/h) in 168 subjects (84 males and 84 females) aged 19-69 years comprised of light, moderate and heavy drinkers.
Page 23 of 25
BREATH ALCOHOL ELIMINATION RATE
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M
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24
Figure 3. Mean alcohol elimination rate (g/210L/h) as a function of age group and gender. Age groups are 1 = 19-20, 2 = 21-24, 3 = 25-50, and 4 = 51-69.
Page 24 of 25
BREATH ALCOHOL ELIMINATION RATE
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Dary D. Fiorentino1,*
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Herbert Moskowitz2
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Breath Alcohol Elimination Rate as a function of Age, Gender, and Drinking Practice
2
Present address is DF Consulting, 8115 Mammoth Avenue, Van Nuys, CA 91402, USA
Deceased
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1
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Southern California Research Institute, 11914 West Washington Blvd., Los Angeles, CA 90066, USA
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*Corresponding author. Tel +1 310 390 8481; Fax +1 310 390 8482. E-mail address: [email protected].
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S0379-0738(13)00435-0 http://dx.doi.org/doi:10.1016/j.forsciint.2013.09.017 FSI 7359
To appear in:
FSI
Received date: Revised date: Accepted date:
30-4-2013 31-8-2013 18-9-2013
Please cite this article as: D.D. Fiorentino, H. Moskowitz, Breath Alcohol Elimination Rate as a function of Age, Gender, and Drinking Practice, Forensic Science International (2013), http://dx.doi.org/10.1016/j.forsciint.2013.09.017 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
BREATH ALCOHOL ELIMINATION RATE
1
ABSTRACT The objective of this study was to determine whether breath alcohol elimination rate varies as a
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function of age, gender, and drinking practice, factorially combined. Eighty-four men and 84 women drank enough alcohol to produce peak BrACs of .110 g/210L for heavy and moderate
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drinkers and BrACs of .090 g/210L for light drinkers. An Intoxilyzer 5000 was used to generate
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the concentration-time profiles. Mean (M) elimination rates (g/210L/h) were found to be higher for women (N = 84, M = .0182, SD = .0033) than for men (N = 84, M = .0149, SD = .0029),
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F(1, 144) = 57.292, p < .001; higher for heavy drinkers (N = 56, M = .0176, SD = .0038) than for light and moderate drinkers combined (N = 112, M = .0160, SD = .0032), F(1, 144) =
M
12.434, p < .01; and higher for older subjects (51-69 years, N = 42, M = .0180, SD = .0038) than younger subjects (19-50 years, N = 126, M = .0161, SD = .0033), F(1, 144) = 14.324, p < .001.
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None of the two-way interactions (age x gender, age x drinking practice, gender x drinking practice) or the three-way interaction (age x gender x drinking practice) was statistically
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significant. Limitations of the current study and suggestions for further research are discussed.
Keywords: Alcohol
Elimination rate Drunk driving
Forensic science Pharmacokinetics Retrograde extrapolation
Page 1 of 25
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1. Introduction
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Retrograde extrapolation is often performed to estimate the blood alcohol concentration (BAC) or breath alcohol concentration (BrAC) at a time prior to a known measurement [1, 2].
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When performed, it is based on the assumptions that at the time of interest the alcohol had been fully absorbed, was uniformly distributed in the body, and was being cleared at a constant rate
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independent of the alcohol concentration in the body, a zero-order process [1]. In this study we
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build on the latter assumption. Specifically, we examined whether breath alcohol elimination rates vary as a function of gender, drinking practice, and age.
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In general, alcohol elimination rates in humans vary as a function of genetic and environmental factors [3], and have a range from .0100 to .0350 g/dL/h [1]. There is conclusive
ed
evidence that women clear alcohol at a faster rate than men [4, 3]. The gender difference in elimination rate has been attributed to the relationship between liver size, which is approximately
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the same between men and women, and the volume of distribution of alcohol, which tends to be
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smaller in women than in men [5, 6]; resulting in women having proportionally more liver tissue per unit of volume of distribution. There is also conclusive evidence that alcohol elimination rates increase with drinking experience [7], and can be especially high for alcoholics [1]. Much less is understood about the effects of age on alcohol elimination rate. Initial reports suggested that older male mice cleared alcohol at a much lower rate than other groups [8], but subsequent studies in humans have found both no differences with increasing age [9] and faster elimination rates with increasing age (Schweitzer; cited in Dubowski [10]). Clearly, more research is needed in this area.
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BREATH ALCOHOL ELIMINATION RATE
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To the best of our knowledge, this is the first study to examine the effects of gender, drinking practice, and age, factorially combined, on breath alcohol elimination rates. On the basis of prior research, we made the following predictions. First, we expected a main effect of
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gender, with women clearing alcohol at a faster rate than men. Second, we expected a main
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effect of drinking practice, with heavier drinkers clearing alcohol at a faster rate than light drinkers. Third, we expected a main effect of age. In theory, if the volume of distribution
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decreases with advancing age, and if the rate of hepatic functioning remains relatively constant with age, then elimination rates for older people should be faster than younger people, for the
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same reasons described above for gender. Fourth, we expected a statistically significant interaction between age and gender. The basis for that prediction was a commonly-used formula
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for the estimation of total body water—which is essentially the volume of distribution of
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alcohol—that uses age as one of the predictors for men but not for women [11]. Finally, we made no specific predictions regarding the interaction between age and drinking practice, the
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2. Method
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interaction between drinking practice and gender, and the three-way interaction.
2.1 Experimental Design
Data were collected on the basis of 4 x 3 x 2 between-subjects design. The independent variables were age group (19-20, 21-24, 25-50, and 51-69 years), drinking practice (light, moderate, and heavy drinkers), and gender (males and females). The dependent variable was elimination rate.
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BREATH ALCOHOL ELIMINATION RATE
2.2
4
Subjects Eighty four men and eighty four women participated as paid subjects in the study. Each
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subject was assigned to one of 24 cells, defined by the two categories of gender (84 males and 84 females), the three categories of drinking practice (56 light drinkers, 56 moderate drinkers, 56
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heavy drinkers), and the four categories of age group (42 between 19 and 20 years, 42 between
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21 and 24 years, 42 between 25 and 50, and 42 between 51 and 69 years of age). Thus, each cell consisted of seven participants. The 42 participants in the 19-20 age group were tested in
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Toronto, Ontario, Canada, where the law allows administration of alcohol to that age group. The age groups were selected to represent four classes of drivers involved in fatal motor
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vehicle crashes: drivers under the age of 21 years, who are not legally allowed to purchase alcohol in the US; drivers between the ages of 21 to 24 years, who have the highest level of
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involvement in alcohol-impaired driving fatalities; and drivers between the ages of 25 to 50 and between the ages of 51 to 69, who have declining levels of involvement in alcohol-impaired
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2.3 Apparatus
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driving fatalities [12]. Note that the age ranges in the four groups were not equal.
2.3.1 Breath Alcohol Tester. The BrACs were measured with an Intoxilyzer 5000 (CMI, Owensboro, KY). The instrument and its performance characteristics have been described extensively elsewhere [13, 14] and will not be described here in detail. Briefly, however, the device collects a sample of breath, passes an infrared light through the sample, and measures the amount of light absorbed by the alcohol contained in the sample. Three filters (3.39, 3.48, and 3.80 μM) are used to determine the concentration of alcohol, the presence of acetone, and the reference value. For the purpose of the study, one Intoxilyzer 5000 was borrowed from the
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BREATH ALCOHOL ELIMINATION RATE
5
manufacturer. Initial calibration was performed by the manufacturer with a wet bath simulator, and subsequent calibration was verified with a dry gas simulator (.080 g/210L) on site every
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Monday morning before data collection. For each breath alcohol test, subjects were asked to blow into a mouthpiece a slow,
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steady breath, until told to stop. A research associate waited a minimum of six seconds before
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instructing the subjects to stop blowing into the mouthpiece. The instrument automatically monitored the suitability of the breath sample.
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2.3.2 Pregnancy Tests. To prevent administration of alcohol to pregnant women, female subjects were required to provide a urine specimen and the specimens were screened for hCG,
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the pregnancy hormone. The pregnancy tests used for the study were purchased over the counter
2.4 Alcohol
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and were administered precisely following the manufacturers’ instructions.
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Subjects arrived in a fasted state at the laboratory and were asked to drink enough alcohol
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to produce a peak BrAC of .110 g/210L for moderate and heavy drinkers and .090 g/210L for light drinkers. The lower dose for the light drinkers reduced the probability of adverse reactions to the alcohol.
2.5 Procedures
Subjects were recruited with newspaper ads, Internet postings, and referrals. An initial telephone interview determined eligibility for the study. Applicants were screened in terms of health history, current health status, and use of alcohol and other drugs. The Cahalan, Cisin and Crossley’s Quantity-Frequency-Variability (QFV) scale [15] was used to classify applicants into
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BREATH ALCOHOL ELIMINATION RATE
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the three drinking categories (light, moderate, and heavy). Applicants who were categorized as borderline in any QFV category were excluded from the study. Special efforts were made to exclude chronic alcohol abusers. Pregnancy, disease, evidence of substance abuse, and the taking
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of medications that interacted with alcohol resulted in exclusion from the study.
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Experimental sessions began at 8:00 am. Subjects were transported from their residence
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to the laboratory via taxi. They were instructed to arrive at the laboratory in a fasted state (no food and beverages in the previous four hours). Upon arrival, each subject gave informed
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consent to participate in the study and received a copy of the signed Informed Consent. A breath alcohol test, an additional administration of the QFV, a pregnancy test for females,
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cardiovascular measures within acceptable ranges (systolic blood pressure = 120 ± 30 mmHg, diastolic blood pressure = 80 ± 20 mmHg, heart rate = 70 ± 20), and verification of age and
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gender with formal identification confirmed eligibility for the study. The alcoholic beverage was 80 proof vodka and orange juice. Subjects were asked to
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drink the total dose in 30 minutes, consuming each of three equal-sized drinks in 10 minutes.
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After the 30 minutes of drinking, the subjects sat in a quiet area, away from food and other beverages. After 20 minutes from the end of drinking, the measurement of the BrAC began, and continued at regular intervals until the BrAC dropped to zero. On average, for each subject, BrACs were measured five times per hour, or once every 12 minutes. After the BrAC dropped to zero, the subjects were debriefed, paid $250, and transported back home. Subjects were not privy to their measured BrACs until their participation in the study was complete. 2.6 Regulatory Approval
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BREATH ALCOHOL ELIMINATION RATE
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All study procedures were approved by the Institutional Review Board of the performing organization.
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2.7 Determination of Elimination Rates Determination of elimination rates was conducted as typically reported in the literature
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[1]. For each subject, the BrAC data were entered into a spreadsheet, and the BrAC readings
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were plotted as a function of time in minutes from the start of drinking (see Figure 1). The typical curve showed the BrAC rising, reaching a peak, and then declining in an approximately
an
linear fashion. A best-fit line was determined on the basis of the BrACs between .070 and .040 g/210L, the portion of the BrAC curve after the diffusion-equilibration phase and before the
M
rectilinear decline phase changes to a curvilinear function. The .070 to .040 g/210L BrAC range was the general rule, but visual inspection of the figure at times required that the range of data
ed
points be changed to avoid inconsistent readings such as an unexplained spike or drop. The elimination rate was then calculated by dividing the Y Intercept by the X Intercept divided by 60.
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Because the selection of the data points for the best-fit line was a subjective process, all 168
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elimination rates were determined twice, independently, by two different people. 3. Results
Prior to analysis, elimination rate was examined for normality and outliers. As shown in Figure 2, the distribution of elimination rates was fairly normal. Significance testing for kurtosis (.642) showed that it was not statistically different from zero, z = 1.72, p > .05. Significance testing for skewness (.677) showed that it was statistically different from zero, z = 3.62, p < .001, but because of the large sample size the deviation in normality was not believed to substantively affect the analyses.
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BREATH ALCOHOL ELIMINATION RATE
8
Figure 2 also shows the clear presence of an outlier (elimination rate of .0307 g/210L/h, z = 3.82, p < .001). The subject was a 63 year old woman who was 71 inches tall and weighed 260 lbs (1.80 m, 118 Kg, BMI = 38.4). She was categorized as a moderate drinker. The extreme
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elimination rate was changed to the grand mean for all cases (including the outlier). To
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determine whether this substitution substantively affected the results, all subsequent analyses
were conducted twice, once with the outlier and once without. Because the pattern of results for
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the two sets of analyses were the same, the substitution was retained and only the results without
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the outlier are reported in the following sections. No multivariate outliers were detected. Analysis of the 4 x 3 x 2 between-subjects design was conducted with multiple
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regression, as described by Cohen [16] and Pedhazur [17], using the statistical software SPSS 20. In general, elimination rate was the dependent variable, and age group, drinking practice, and
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gender were the independent variables. The expression of group membership in the three independent variables was accomplished with orthogonal coding. For each main effect and
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interaction, the test of significance was conducted by sequentially regressing elimination rate
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first on all the effects minus the effect of interest, and then with all the effects including the effect of interest, with R2 change on the last step as the basis of analysis. For each effect, the coded vectors were entered into the equation as a set. As shown in Tables 1 and 2, females (N = 84, M = .0182 g/210L/h, SD = .0033 g/210L/h) had faster elimination rates than males (N = 84, M = .0149 g/210L/h, SD = .0029 g/210L/h), for a statistically significant main effect of gender, F(1, 144) = 57.292, p < .001. Differences in elimination rates between light drinkers (N = 56, M = .0157 g/210L/h, SD = .0032 g/210L/h), moderate drinkers (N = 56, M = .0162 g/210L/h, SD = .0032 g/210L/h), and heavy drinkers (N =
Page 8 of 25
BREATH ALCOHOL ELIMINATION RATE
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56, M = .0176 g/210L/h, SD = .0038 g/210L/h) produced a statistically significant main effect of drinking practice, F(2, 144) = 6.662, p < .01, with no statistically significant differences between light drinkers and moderate drinkers, F(1, 144) = .890, p > .05, but with statistically
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significant differences between light and moderate drinkers together (N = 112, M = .0160
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g/210L/h, SD = .0032 g/210L/h) versus heavy drinkers, F(1, 144) = 12.434, p < .01.
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Differences between 19-20 (N = 42, M = .0158 g/210L/h, SD = .0032 g/210L/h), 21-24 (N = 42, M = .0155 g/210L/h, SD = .0026 g/210L/h), 25-50 (N = 42, M = .0168 g/210L/h, SD =
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.0033 g/210L/h), and 51-69 year olds (N = 42, M = .0180 g/210L/h, SD = .0038 g/210L/h) produced a statistically significant main effect of age group, F(3, 144) = 6.088, p < .01. As
M
expected, there were no statistically significant differences between 19-20 and 21-24 year olds, F(1, 144) = .060, p > .05; and between 19-24 (N = 84, M = .0157 g/210L/h, SD = .0032
ed
g/210L/h), and 25-50 year olds, F(1, 144) = 3.881, p > .05; but there were statistically significant differences between 19-50 (N = 126, M = .0161 g/210L/h, SD = .0033 g/210L/h) and
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51-69 year olds, F(1, 144) = 14.324, p < .001.
Ac ce
None of the interactions were statistically significant. Note, however, that one of the six vectors of the drinking practice x age group interaction and one of the six vectors of the gender x drinking practice x age group interaction were statistically significant. Because in the age groups with large age ranges (25-50 and 51-69) subject recruiting procedures yielded average age differences between males and females and between light, moderate, and heavy drinkers, there was a concern that age was contributing to the effects of gender and drinking practice on elimination rate. To allay those concerns, an analysis of covariance was conducted on elimination rate, with gender and drinking practice as the
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BREATH ALCOHOL ELIMINATION RATE
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independent variables and with transformed chronological age (1/Age) as the covariate. The pattern of results, a statistically main effect of gender, a statistically significant main effect of drinking practice, and a not statistically significant gender x drinking practice interaction were
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consistent with the results of the main analysis, further supporting the hypothesis that gender and
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drinking practice affect elimination rate, independently from the effects of age.
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4. Discussion
The objective of this study was to determine whether alcohol elimination rate varies as a
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function of gender, drinking practice, and age. We tested four specific hypotheses. First, as expected, it was found that women clear alcohol at a faster rate than men. Second, as expected, it
M
was found that heavy drinkers clear alcohol at a faster rate than light and moderate drinkers. Third, as expected, it was found that older people clear alcohol faster than younger people,
ed
although it should be noted that in the current sample differences were evident only in the 19-50 versus 51-69 age groups. It is proposed that both the gender- and age-related differences in
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elimination rates are attributable to the relationship between liver size and the volume of
Ac ce
distribution of alcohol. Fourth, although we expected the interaction between age and gender to be statistically significant, it was not. The basis of the prediction was a commonly-used formula for the estimation of total body water that uses age as a predictor for men but not for women. The formula predicts that over the life span the volume of distribution of alcohol diminishes for men but not for women. Assuming that hepatic functioning remains fairly constant over time, that would result in faster elimination rates as a function of age for men but not for women. As shown in Figure 3, this was not what was found. Further study is needed to explain this potential
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discrepancy. It should be noted that there are formulas for total body water that have age as a predictor for both men and women (i.e., [18, 19, 20]).
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The ranges in breath elimination rates from this study are consistent with the extant scientific literature on the topic [21, 22], but the results must be interpreted with caution for the
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following reasons. First, the uneven age ranges and the sequential nature of the four age groups
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presented logical and statistical problems that may limit the usefulness of the results. For example, the comparison between 19-24 and 25-50 age groups approached statistical
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significance, but the comparison includes subjects who are one year apart and thus not likely to differ. A more useful approach would have been to have equal-range age groups separated by
M
intervals of a few years.
Second, the study had 24 experimental cells, each with seven participants. The small
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sample size per individual cell may have provided insufficient statistical power in the tests of the interactions. In fact, power analyses of the interactions showed that none exceeded observed
pt
power of .53. Future research should address this limitation of the current study.
Ac ce
Third, although the subjects were asked to arrive to the laboratory in a fasted state, there was no independent way of determining whether that was the case. There is overwhelming evidence that ingesting alcohol with a full stomach reduces the bioavailability of the dose, resulting in lower peak BACs, increased times to peak BAC, and smaller areas under the curve [23, 24]. There is also evidence that food increases the elimination rate of alcohol [25, 26]. Thus, if some subjects violated study procedures and ate before the study session, it is likely that their elimination rates would have been higher than the elimination rates of subjects who did not eat.
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Fourth, determination of drinking practice was based on self-report, which can be unreliable. The double administration of the QFV may have screened out some study applicants that were inconsistent, but it is possible that some may have qualified by consciously or
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unconsciously misreporting their alcohol consumption.
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Finally, the dosing procedures were designed to produce peak BrACs of .090 g/210L for
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light drinkers and .110 g/210L for moderate and heavy drinkers. It is possible that that the difference in dosing procedures between the groups may have had an effect of the elimination
an
rates. Further, it must be noted that the peak BACs from this study are much lower that the BACs typically encountered in the real world, where 58% of drivers involved in fatal crashes have
M
BACs of .15 g/dL or greater [12].
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5. Conclusions
Currently, for forensic purposes, it is recommended that a suspect’s BAC be extrapolated
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using the range in elimination rate between .010 and .025 g/dL/h [1]. This study provides evidence that breath alcohol elimination rate varies as a function of age, gender, and drinking
Ac ce
practice. If replicated and further evaluated, the findings from the current study may help define a narrower range of elimination rates based on the suspects’ characteristics.
Page 12 of 25
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Declaration statement
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To be written after the review process.
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References
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[1] A.W. Jones, Evidence-based survey of the elimination of ethanol from blood with
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applications in forensic casework, Forensic Sci. Int. 200 (2010) 1-20.
[2] A.W. Jones, Biomarkers of recent drinking, retrograde extrapolation of blood-alcohol
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[6] P.Y. Kwo, V.A. Ramchandani, S. O’Connor, D. Amann, L.G. Carr, K. Sandrasegaran, K.K. Kopecky, T.K. Li, Gender differences in alcohol metabolism: relationship to liver volume and effect of adjusting for body mass, Gastroenterology 115 (1998) 1552-1557. [7] C.L. Winek, K.L. Murphy, The rate and kinetic order of ethanol elimination, Forensic Sci. Int. 25 (1984) 159-166.
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[9] A. W. Jones, A. Neri, Age-related differences in blood ethanol parameters and subjective feelings of intoxication in healthy men Alcohol & Alcoholism 20 (1985) 45-52.
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[10] K.M. Dubowski, Absorption, distribution and elimination of alcohol: Highway safety
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[12] National Highway Traffic Safety Administration, Alcohol-Impaired Driving, Report No.
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[13] P.M. Harding, R.H. Laessig, P.H. Field, Field performance of the Intoxilyzer 5000: a
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comparison of blood- and breath-alcohol results in Wisconsin drivers. J Forensic Sci 35 (1990)
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[14] A.W. Jones, K.M. Beylich, A. Bjorneboe, J. Ingum, J. Morland, Measuring ethanol in blood and breath for legal purposes: variability between laboratories and between breath-test instruments, Clin Chem 38 (1992) 743-747. [15] D. Cahalan, I.H Cisin, H.M. Crossley, American Drinking Practices, College and University Press, Connecticut, 1969. [16] J. Cohen, Multiple regression as a general data-analytic tool, Psych. Bull. 70 (1968) 426443.
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[17] E.J. Pedhazur, Multiple Regression in Behavioral Research, third ed., Wadsworth, Florida, 1997.
ip t
[18] G.M. Chertow, J.M. Lazarus, N.L. Lew, L. Ma, E.G. Lowrie, Development of a populationspecific regression equation to estimate total body water in hemodialysis patients, Kidney Int. 51
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(1997) 1578-1582.
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[19] W.C. Chumlea, S.S. Guo, C.M. Zeller, N.V. Reo, R.N. Baungartner, P.J. Garry, J. Wang, R.N. Pierson, S.B. Heymsfield, R.M Siervogel, Total body water reference values and prediction
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equations for adults, Kidney Int. 59 (2001) 2250-2258.
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[20] A.C. Johansson, O. Samuelsson, P.O. Attman, I. Bosaeus, B. Haraldsson, Limitations in anthropometric calculations of total body water in patients on peritoneal dialysis, J. Am. Soc.
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Nephrol. 12 (2001) 568-573.
[21] E. Martin, W. Moll, P. Schmid, L. Dettli, The pharmacokinetics of alcohol in human breath,
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venous and arterial blood after oral ingestion, Eur J Clin Pharmacol 26 (1984), 619-626.
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[22] M. Pavlic, P. Grubweiser, K. Libiseller, W. Rabl Elimination rates of breath alcohol, Forensic Sci. Int. 171 (2007) 16-21. [23] Y. Lin, D.J Weidler, D.C. Garg, J.G. Wagner, Effects of solid food on blood levels of alcohol in man, Res. Commun. Chem. Pathol. Pharmacol. 13 (1976) 713-722. [24] A.J. Sedman, P.K. Wilkinson, E. Sakmar, D.J. Weidler, J.G Wagner, Food effects on absorption and metabolism of alcohol, J. Stud. Alcohol 37 (1976) 1197-1214.
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[25] A.W. Jones, K.A. Jonsson, Food-induced lowering of blood-ethanol profiles and increased rate of elimination immediately after a meal, J Forensic Sci 39 (1994) 1084-1093.
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[26] V.A. Ramchandani, P.Y Kwo, T.K. Li, Effect of food and food composition on alcohol
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pt
ed
M
an
us
cr
elimination rates in healthy men and women, J Clin Pharmacol 41 (2002) 1345-1350.
Page 17 of 25
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18
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Table 1
Elimination Rate (g/210L/h) by Age Group, Drinking Practice, and Gender
group
practice
Males
M
Light
7
.0152
Moderate
7
Heavy
Light
Females and males
N
M
SD
N
M
SD
.0027
7
.0137
.0020
14
.0144
.0024
.0178
.0035
7
.0143
.0024
14
.0161
.0034
7
.0170
.0030
7
.0168
.0042
14
.0169
.0035
21
.0166
.0031
21
.0149
.0032
42
.0158
.0032
7
.0157
.0014
7
.0134
.0020
14
.0146
.0021
7
.0176
.0023
7
.0139
.0025
14
.0158
.0030
Ac c
Moderate
SD
d
N
All 21-24
Females
ep te
19-20
Gender
an
Drinking
M
Age
Heavy
7
.0197
.0031
7
.0134
.0027
14
.0166
.0043
All
21
.0177
.0028
21
.0136
.0023
42
.0156
.0033
Page 18 of 25
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BREATH ALCOHOL ELIMINATION RATE
All
7
Moderate
7
.0176
.0021
7
Heavy
7
.0217
.0026
7
All
21
.0187
.0031
Light
7
.0207
.0036
Moderate
7
.0182
M
Heavy
7
All Light Moderate Heavy All
cr
.0023
.0142
.0022
14
.0155
.0026
.0146
.0026
14
.0161
.0027
.0158
.0014
14
.0187
.0037
us
.0168
an
7
.0148
.0021
42
.0168
.0033
7
.0162
.0034
14
.0185
.0041
.0044
7
.0160
.0033
14
.0171
.0039
.0204
.0026
7
.0163
.0035
14
.0184
.0037
21
.0198
.0036
21
.0162
.0032
42
.0180
.0038
28
.0171
.0033
28
.0147
.0026
56
.0157
.0032
28
.0178
.0030
28
.0147
.0027
56
.0162
.0032
28
.0197
.0032
28
.0156
.0032
56
.0176
.0038
.0182
.0033
84
.0149
.0029
168
.0165
.0035
d
21
ep te
51-69
Light
Ac c
25-50
19
84
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Table 2 Analysis of Variance Through Multiple Regression R2 Change
F Change
.224
57.292
Drinking practice
.052
6.662
Light v. Moderate (2)
.003
Light and Moderate v. Heavy (3)
.049
p .000
ip t
Gender (1)
cr
Source
.002
.347
12.434
.001
6.088
.001
.060
.807
3.881
.051
.056
14.324
.000
.007
.908
.406
.000
.127
.722
.007
1.689
.196
.018
1.560
.202
.015
3.807
.053
1x5
.003
.749
.388
1x6
.000
.124
.725
.031
1.322
.251
2x4
.000
.083
.773
2x5
.002
.430
.513
2x6
.016
4.025
.047
Age group
.071 .000
an
19-20 v. 21-24 (4)
.015
19-20, 21-24, and 25-50 v. 51-69 (6)
ed
Gender x Drinking practice
Ac ce
1x 4
pt
1x3 Gender x Age group
M
19-20 and 21-24 v. 25-50 (5)
1x2
Drinking practice x Age group
us
.890
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BREATH ALCOHOL ELIMINATION RATE
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.000
.024
.878
3x5
.006
1.573
.212
3x6
.007
1.798
.182
.032
1.376
.228
1x2x4
.000
.037
1x2x5
.001
1x2x6
.008
1x3x4
.018
1x3x5
.005
.000
cr
.594
2.113
.148
4.552
.035
1.217
.272
.055
.815
M
1x3x6
.847
.285
us
an
Gender x Drinking practice x Age group
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3x4
Note. Group membership in the three independent variables was accomplished with orthogonal
ed
coding. For each variable, the coded vectors are labeled in parentheses. Vectors representing the
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pt
interactions were generated by cross multiplying the vectors in parentheses.
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BREATH ALCOHOL ELIMINATION RATE
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Best Fit Line 0.140
Measured BAC
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Y Intercept
0.120
cr
0.080 0.060
Data range for best-fit line
us
BrAC (g/210L)
0.100
0.040
an
0.020 0.000 0
100
200
300
400
X Intercept
500
600
M
Time from First Drink (Minutes)
ed
Figure 1. Example of chart used to determine elimination rates. In general, the BrAC readings between .040 and .070 were used to generate a best-fit line, which was then extrapolated to the
Ac ce
(X intercept / 60).
pt
X-axis and Y-axis. The elimination rate was calculated with the following formula: Y intercept /
Page 22 of 25
BREATH ALCOHOL ELIMINATION RATE
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ed
M
an
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23
Ac ce
Figure 2. Frequency distribution of elimination rates of ethanol from breath (g/210 L/h) in 168 subjects (84 males and 84 females) aged 19-69 years comprised of light, moderate and heavy drinkers.
Page 23 of 25
BREATH ALCOHOL ELIMINATION RATE
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ed
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24
Figure 3. Mean alcohol elimination rate (g/210L/h) as a function of age group and gender. Age groups are 1 = 19-20, 2 = 21-24, 3 = 25-50, and 4 = 51-69.
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Dary D. Fiorentino1,*
an
Herbert Moskowitz2
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Breath Alcohol Elimination Rate as a function of Age, Gender, and Drinking Practice
2
Present address is DF Consulting, 8115 Mammoth Avenue, Van Nuys, CA 91402, USA
Deceased
ed
1
M
Southern California Research Institute, 11914 West Washington Blvd., Los Angeles, CA 90066, USA
Ac ce
pt
*Corresponding author. Tel +1 310 390 8481; Fax +1 310 390 8482. E-mail address: [email protected].
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