Britiik Journal of AddUlum 79 il9S4) 173-180© 1984 Society for tbe Study of Addictionto Alcohol and other Drugs

Alcohol Consumption and the Incidence of Acute Alcohol-Related Problems

Alexander C. Wagenaar, Ph.D.The University of Michigan, Transportation Research Institute, Ann Arbor,Michigan 48109, U.S.A.

SummaryResearchers in recent years have suggested that control of the aggregate level of alcohol consumption is an important componentof a comprehensive programme for the prevention of alcohol-related problems. The present study focused on ihe relationshipbetween alcohol consumption and perhaps the most significant acute alcohol-related problem, motor vehicle crashes. Dataanalyzed included monthly frequencies of alcohol-related property damage and injury producing motor vehicle crashes andmonthly distribution of beer, wine, and distilled spirits in the State of Michigan. To increase confidence that observed correlationsrepresented causal relationships between aggregate consumption and crash involvement, variance in each time series explainableon the basis of its own past history was filtered out first using ARIMA models, with the remaining variance used in analysesof the consumption!crash relationship. Using cross-lagged correlation methods, significant relationships were found betweenwholesale beer and wine distribution in a given month, and the number of alcohol-related motor vehicle crashes one month later.

In the past decade researchers have addressed the issue ofwhether the overall level of alcohol consumption in acommunity is related to the level of alcohol-relatedproblems experienced by that community. If it is, oneapproach to the prevention of alcohol-related problemsmay be to reduce the aggregate quantity of alcoholconsumed [1]. Most studies have focused on therelationship of aggregate consumption to chronic healthproblems associated with alcohol consumption such asliver cirrhosis. Numerous studies have revealed a positiverelationship between aggregate alcohol consumption andcirrhosis rates [2,3,4]. This study examined the relation-ship between the overall level of alcohol use and theincidence of a major acute alcohol-related problem, motorvehicle crashes involving drinking drivers.

This study was designed to overcome three importantlimitations of the extant research. First, the studyreported here uses a longitudinal or time-series approach.Many past studies have been cross-sectional designs,relating alcohol consumption levels in various jurisdic-tions to their level of alcohol problems. While multivariatemethods are coming into increased use, there alwaysremain additional confounding variables distinguishingjurisdictions that may account for observed relationshipsbetween alcohol consumption and problems. One way to

reduce the confounding-variable probiem is to hold amultitude of such factors constant by analyzing a singlejurisdiction over time.

Most time-series studies of the relationship betweenalcohol consumption and alcohol-related problems werebased on simple correlations and did not adequatelycontrol for the effects of long-term trends and cyclesbefore measuring the relationship between alcoholconsumption and damage. Therefore, the second way inwhich this study was designed to overcome limitations ofpast research was through controlling for the effects oflong-term patterns, particularly seasonal cycles, whenmeasuring the effects of changes in alcohol consumption.Such controls were accomplished by using Box-Jenkinstime-series-analysis methods.

Finally, most studies to date have focused on long-termchronic drinking, measured by liver cirrhosis rates, as thealcohol-related health problem most likely to be infiuen-ced by aggregate consumption patterns. The studyreported here, in contrast, focused on acute alcohol-related problems. In most western countries, motorvehicle crashes are a leading cause of morbidity andmortality for the first half of the life-span. Approximatelyhalf of all traffic fatalities and a quarter of all serious crash-induced injuries involve drinking drivers. Therefore, the

174 Alexander C. Wagenaar

Table 1. Cross-correlations between alcoholic beverage sales and alcohol-related motor vehicle accidents, raw data

Number of months lagVariables

0

- .27.28.27.52.72.56

1

- .18.36.36.52.15

- . 0 3

2

.00

.39

.44

.42- .02- .16

3

.20

.38

.22

.10

.22- .03

4

.53

.40

.08

.07- .19- . 18

5

.58

.21

.14

.13- .09

.07

6

.49- .02

.21

.30

.17

.22

Beer and property-damage accidentsBeer and injury accidentsWine and property-damage accidentsWine and injury accidentsSpirits and property-damage accidentsSpirits and injury accidents

Note: Standard error for all coefficients is .10.

frequency of drinking drivers involved in motor vehiclecrashes was used as a measure of acute alcohol-relatedproblems that may be affected by changes in aggregateconsumption.

MethodsThe main question addressed in this study is: does achange in the total quantity of alcoholic beverages

800

distributed in a given jurisdiction affect the incidence ofalcohol-related motor vehicle crashes? Independent vari-ables were the total quantities of beer, wine, and distilledspirits sold in the State of Michigan each month fromJanuary 1972 through December 1980. These data,obtained from the Michigan Liquor Control Commissionand Michigan Beer and Wine Wholesalers Association,reflect distribution at the wholesale rather than retail level.

40024 36 48 60 72

Month: I^January 197296 108

Figure 1. Michigan apparent beer consumption.

Alcohol Consumption and Acute Alcohol-Related Problems 175

The two dependent variables were the number ofdrinking drivers involved in (1) property-damage or (2)injury-producing motor vehicle crashes each month forthe same jurisdiction and time period. Records on allmotor vehicle crashes rerwrted to any police jurisdictionin Michigan from 1972 through 1980 were obtained fromthe Michigan Department of State Police. Determinationof whether or not the driver was drinking at the time of thecrash was based on information provided hy pmlice officerswho investigated the crashes. Michigan includes aseparate forced-choice item on a statewide standardizedcrash report that requires the investigating officer toidentify the driver as nondrinking or drinking. This itemis not dependent on (1) any complex judgement thealcohol caused the crash, (2) whether the driver wasarrested for driving under the influence ofalcohol, and (3)results of any blood-alcohol-concentration test that mayhave been administered. Because this item is not based onthese other factors that rely even more on policejudgement, the resulting data are a reasonably goodindicator of the presence of alcohol in crashes. Past

research on this measure of alcohol involvement inMichigan crashes revealed a high degree of convergentvalidity when compared with indirect measures such assingle-vehicle crashes occurring at night [5].

Analyses consisted of cross-lagged correlation methodscombined with the Auto-Regressive Integrated MovingAverage (ARIMA) modeling strategy developed by Boxand Jenkins [6,7]. Cross correlations are measures ofassociation between two time-series variables with one ofthe variables lagged a particular number of data points.For example, a simple correlation between the monthlysales of alcohol and the monthly number of crasheddrivers would measure the degree of simultaneousassociation between alcohol sales and crashes. A crosscorrelation at lag 1 would measure the degree ofassociation between sales of alcohol in any given monthand tbe number of crashed drivers 1 month later. Cross-correlation methods take advantage of the time-orderednature of the variables to insure that changes in theindependent variable occur before associated changes inthe dependent variable. The combination of the Box-

4500

0>

4000-

3500-

O 3000

2500-

2000-

1500

priyert

Movlnfl Avaraga

12 24 36 48 60 72 84

Month: l=January 1972Figure 2. Drinking drivers involved in property damage traffic accidents.

96 108

176 Alexander C. Wagenaar

Jenkins ARIMA models with cross-correlation methodsfurther strengthens the analyses; the ARIMA modelscontrol for the potentially confounding influence of long-standing trends and cycles that are a reflection of causallyextraneous variables.

ResultsBivariate correladons between each alcohol consumptionindicator and alcohol-related traffic crashes wereexamined. Because alcoholic beverage sales weremeasured at the wholesale level, a lag of 2 weeks to 2months was expected between observed alcoholdistribution and its hypothesized effect on traffic crashes.To take this lag structure into account, the simultaneouscorrelation (i.e., lag 0) and cross-correlations (at lags 1through 6 months) were calculated for each relationshipexamined. Results, shown in Table 1, indicated largepositive correlations between the quantities of beer, wine,spirits distributed at the wholesale level in a given monthand the numbers of drinking drivers involved in injuryand property-damage traffic crashes over the subsequent

3,000

2.500-

several months. With only one exception (beer andproperty damage crashes at lag 0), all significantcorrelation coefficients are positive. These results areconsistent with the hypothesis that the amoutit ofalcoholic beverages sold is associated with the number ofcrash-involved drinking drivers.

The first row of Table 1 shows the large positive correla-tions between beer sales and property-damage crashes atlags of 4 to 6 months. These relationships are at too long ofa lag to represent the effect of aggregate beer consumptionon driving. An important confounding factor is thepresence of seasonal cycles that characterize both beer salesand property-damage crashes. Beer sales tend to be highestin the late summer months (Fig. 1), while alcohol-relatedproperty damage crashes are most numerous in December(Fig. 2). These two out-of-phase cycles probably accountfor the high correlations at lags of 4 to 6 months.

Correlations between beer sales and injury crashes arehigh at lags 0 through 4. While these correlations mayreflect an effect of alcohol sales on crash involvement, theyare also contaminated by the normal seasonal cycles in

in

c

Xi

cuo

2.000-

1.500-

1,000-

50048 60 72

Month: 1=January 1972Figure 3. Drinking drivers involved in injury traffic accidents.

84 96 108

Alcohol Consumption and Acute Alcohol-Related Problems 177

both beer sales and crashes. Alcohol-related injury-producing crashes, like alcohol-related property damagecrashes, are most numerous in December. The last 6months of each year are generally characterized byincreasing numbers of crash-involved drinking drivers,with December consistently the peak month (Fig. 3). Therelatively high-crash-frequency months of late summerand fall overlap the months of highest beer sales, causingthe moderately high correlations between the beer andaccident time series.

Because cycles in both beer sales and alcohol-relatedcrashes arc partially a result of exogenous seasonal effects,the high correlation between beer sales and alcohol-related crashes cannot simply be interpreted as a causaleffect of beer sales on crashes. Exogenous seasonal effectsrefer to the many differences in both drinking patternsand driving patterns due to the time of year underconsideration. Vacation travel, recreational driving,weather conditions, holidays, and other seasonal factorsinfiuence both drinking and driving-after-drinking. As aresult of these confounding factors, a confluence of cycles

2.000

1.800-

in these two variables cannot be unambiguously attributedto a causal effect between the two series.

A high degree of association was also found betweenwine sales (Fig. 4) and both prop>erty-damage and injury-producing crashes (at lags 0 through 2; see Table 1). Aswith beer sales, these correlations may represent a causaleffect of wine sales on the frequency of drinking driversinvolved in crashes. However, the confounding seasonaleffects makes such a conclusion premature.

The relationship between spirits sales and the numberof crash-involved drinking drivers is significantly dif-ferent than beer or wine. The large correlations are limitedto concurrent (lag 0) relationships between spirits salesand both property-damage and injury-producing crashesinvolving drinking drivers. An examination of spirits sales(Fig. 5) indicates that most of the variation in sales is dueto a very large amount of spirits sold in December of eachyear (compared to the average monthly sales the rest of theyear). Since the number of drinking drivers involved incrashes also frequently peaks in December, a highconcurrent correlation between spirits sales and crashes is

c

"6o

"Oco(/)IIox:

1,600 -

1.400-

1.200-

1,000 -

800-

60012 24 36 48 60 72 84

Month: l=January 1972Figure 4. Michigan apparent wine consumption.

T '

96 108

178 Alexander C. Wagenaar

3500

V)

100012 24 36 48 60 72

Month: 1=January 197284

Figure 5. Michigan apparent spirits consumption.

observed. It is certainly plausible to attribute rhe highlevels of alcohol-related crashes in December to highspirits consumption levels. However, there may also beother factors occurring in December, other than highspirits sales, that may contribute to the high crash rates.

In short, interpretation of the correlations shown inTable I is complicated by the infiuence of confoundingseasonal effects. Numerous exogenous effects that occurdifferentially across months of the year are reflected inseasonal cycles in alcohol consumption and alcohol-related traffic crashes. To more adequately assessunderlying relationships between alcoholic beverage salesand alcohol-related traffic crashes, the seasonal effects ineach variable were controlled through the use of Auto-Regressive Integrated Moving Average (ARIMA) time-series models. The iterative model building proceduressuggested by Box and Jenkins were followed. Specificmodeling results are shown in the Appendix for thosefamiliar with these methods. The models accounted for all

of the observed regularities (primarily seasonal cycles) ineach variable and explained 59 to 98 per cent of thevariance in the original series. Residuals from each modelrepresented random month-to-month variation inbeverage sales or traffic crashes that cannot be predictedon the basis of past history. The independentlydistributed residuals from each model were cross-correlated in the same way as the original data. Resultingcross-correlations are shown in Table 2.

It is important to realize the implications of filteringeach variable by an ARIMA model before computing thecross-correlations, even if the reader is not familiar withARIMA modeling methods. ARIMA models of alcoholsales and crash involvement typically account for aboutthree-fourths of the variance in these indicators byBecause measurement and other random error is a muchlarger proportion of the residual variance than the originalseries, analyses of the ARIMA residuals provided aconservative test of possible causal effects. Moreover, any

Alcohol Consumption and Acute Alcohol-Related Problems 179

Table 2. Cross-correlations between alcoholic beverage sales and alcohol-relaied motor vehicle crashes, controlling for seasonaleffects

VariablesNumber of months lag

0

.15

.20

.03

.04

.06

.02

1

.13

.24

.30

.33

.10- .01

2

- . 08- .02- .19- . 0 4- . 0 9- .08

3

- . 2 3- .10

.02- .04- .04- .08

4

.22

.11

.08

.02

.00

.11

5

.15

.07

.08- .12-.11- . 0 6

6

.02

.02

.03

.09- . 08

.03

Beer and property-damage accidentsBeer and injury accidentsWine and property-damage accidentsWine and injury accidentsSpirits and property-damage accidentsSpirits and injury accidents

Note: Standard error for all coefficients is . 10.

causal effects betweeti alcohol sales and crash involvementthat are reflected in the similar cycles in both variables areignored when analyzing the residual cross-correlations. Ifthe number of drinking drivers involved in crashes followsa regular cycle because alcohol sales follow such a cycle, thiseffect is not measured in the residual cross-correlations.Only the relationship between random month-to-monthchanges in sales and similar changes in alcohol-relatedcrashes are measured. As a restilt, these analyses must beconsidered a conservative test of the relationship betweenaggregate alcohol sales and traffic crashes.

As expected, the residual cross-correlations in Table 2are smaller than the raw cross-correlations in Table 1.Statistically significant correlations remain, however,even after controlhng for seasonal effects through the useof ARIMA models. Beer sales and the frequency ofalcohol-related property-damage crashes are negativelycorrelated at lag 3 and positively correlated at lag 4 (seefirst row of Table 2). Reasons for this lag structure are notclear. The effect of wholesale distribution of beer ontraffic crashes was expected within 2 to 10 weeks, as thebeverage is consumed, not 4 months later. While anadequate explanation of the lag 3 and 4 results is notavailable, they provided no clear support for thehypothesis that the level of beer sales is directly related tothe frequency of alcohol-related crash involvement.

Results for residual cross-correlations between beersales and injury-producing traffic crashes, however, areconsistent with hypothesized effects. Significant concur-rent and lag 1 positive correlations were found betweenbeer sales and the number of drinking drivers involved ininjury crashes, after controlling for seasonal effects.Correlations at lags 2 through 5 were not significant, asexpected.

Examination of the relationship between wine sales andboth property-damage and in jury-producing crashesrevealed significant correlations only at lag 1 (Table 2).The observed .30 and .33 correlations are moderatelyhigh, and indicate that distribution of wine at the

wholesale level is related to the number of drinking driversinvolved in crashes one month later. The lack of asignificant concurrent relationship between wine sales andcrashes, as was found for bear sales and injury crashes,may reflect the longer lead time from wholesaledistribution to consumption for wine than beer.

Finally, the large simultaneous correlation betweenspirits sales and alcohol-related crashes seen in Table 1disappears when the seasonal effects are controlled (Table2). The ARIMA model for spirits distribution accountsfor 98 per cent of the total variance (see appendix); thesmall amount of remaining variance may partially explainthe lack of a significant relationship between spiritsdistribution and alcohol-related crashes. In any event, thisfinding confirms the suggestion made earlier that the highlag 0 correlation is a reflection of similar seasonal cycles inboth spirits sales and alcohol-related crashes.

DiscussionResults reported here provide considerable support forthe hypothesis that the aggregate quantity of alcoholicbeverages consumed in a community is related to thefrequency of drinking drivers involved in motor vehiclecrashes. Part of the association between alcohol consump-tion and alcohol-related crash involvement is due tocongruent annual cycles in both drinking and alcohol-related crashes. Such congruent cycles may reflect a causaleffect of aggregate drinking on crash involvement. On theother hand, they may also be due to the common influenceof exogenous seasonal factors. After controlling forseasonal effects, significant relationships remained be-tween beer and wine consumption and the number ofdrinking drivers involved in crashes. That is, a randomchange in the quantity of beer or wine distributed in agiven month is positively related to the number of crash-involved drinking drivers in that same month and thefollowing month. The lag structure observed wasconsistent with that expected, given the nature of thewholesale alcohol distribution indicators used.

180 Alexander C. Wagenaar

Distilled spirits distribution is highly correlated withthe number of crash-involved drinking drivers. The highcorrelation reflects a similar seasonal cycle in spiritsconsumption and alcohol-related crashes. After elimina-ting the seasonal cycles from both spirits distribution andcrashes, no significant cross-correlation remains. Thecyclic similarity between spirits consumption and trafficcrashes may reflect a causal relationship between the twovariables. However, the presence of other confoundingseasonal factors means a causal interpretation must bemade cautiously.

Further research is needed. The cross-lagged correla-tion approach used here should be extended by developingand testing parametric time-series (i.e., transfer function)models. Such models should include multiple indepen-dent variables likely to influence the frequency of alcohol-related crash involvement. Important variables mightinclude general macroeconomic conditions, motor vehicletravel mileage, and changing patterns of driving,drinking, and driving-after-drinking. Analyses of motorvehicle crashes not related to alcohol have revealed thecomplexity of developing such multivariate time-seriesmodels [8]. The multiple independent or interactiveeffects of factors such as economic conditions on drinkingbehaviour and driving behaviour further cotnpiicate suchanalyses. Nevertheless, initial findings rejxtrted here ofsignificant relationships between aggregate alcohol con-sumption and the incidence of alcohol-related crashes,even after filtering out trends and cycles, indicate thepotential utility of this line of research.

Results of the present study demonstrate that there aresignificant relationships between the aggregate level ofalcoholic beverage consumption in a community and theincidence of acute alcohol-related problems. If theserelationships reflect an underlying causal relationshipbetween consumption levels and the incidence of acutealcohol-related problems, measures aimed at reducing theaggregate consumption of alcoholic beverages willsignificantly reduce the frequency of acute alcohol-relatedproblems such as traffic crashes. The best way todetermine whether the significant observed relationshipsbetween aggregate alcohol consumption and acutealcohol-related problems identified here reflect causalrelationships is through continued careful evaluation ofalternative policies designed to reduce alcohol consump-tion and associated damage. The research reported heredemonstrates that a focus on policies designed to reduceaggregate consumption may be a useful approach for theprevention of alcohol-related problems.

References1 Bniim, K., Edwards, G., Lumio, M. and others. (1975).

Alcohol Control Policies in Public Health Perspective. Helsinki,Finland: The Finnish Foundation for Alcohol Studies.

2 Popham, R., Schmidt, W. and deLint, J. (1978).Government Control Measures to Prevent HazardousDrinking. In: I. A. Ewing and B. A. Rouse (eds.), Drinking:A Icohol in A merican Society —- Issues and Current Research, pp.239-266. Chicago: Nelson-Hall.

3 Aldoory, S. (1979/80). ABC Laws: An Overview. AUoholHealth and Research World, 4, 2-10.

4 Skog, O-J. (1980). Liver Cirrhosis Epidemiology: SomeMethodological Problems. Brittsh Journal of Addiction, 75,227-243.

5 Wagenaar, A. C. (1983). Alcohol, Young Drivers, and TrafficAccidents. Lexington, Massachusetts: D. C. Heath and Co.

6 Cook, T. D. , Dintzer, L. and Mark, M. M. (1980). TheCausal Analysis of Concomitant Time Series. In: L. Bickman(ed.). Applied Social Psychology Annual, vol. 1, pp. 93-135.Beverly Hills, California: Sage Publications.

7 Box, G. E. P. and Jenkins, G. M. (1976). Time SeriesAnalysis: Forecasting and Control. Revised Edition. SanFrancisco: Holden-Day.

8 Wageoaar, A. C. (1984). Effects of MacroeconomicConditions on the Incidence of Motor Vehicle Accidents.Accident Analysis and Prevention, 16,191-205.

AppendixAutQ-regressive integrated moving average models for alcoholicbeverage sales and traffic accidents

Beer distribution

(1 - . + .30B)u, + 3825

Adjusted R^ = .74 Q = 27 with 21 d.f.

Wine distribution

.^ (1 - .83B'')u, + 37995

1-B '^

Adjusted R^ = .59 Q - 24 with 22 d.f.

Spirits distribution

Y _( l - .84B '^ )u , - f 21086

1 - B'^

Adjusted R^=.98 Q = 13 wiih22d.f.

Alcohol-related property-damage accidents

Adjusted R^ = .74

Alcohol-related injury accidents

Q = 9 with 22 d.f.

Adjusted R^ = .70 Q = 18 with 22 d.f.