One Drink to a Lifetime of Drinking: Temporal Structures of Drinking Patterns

One Drink to a Lifetime of Drinking: TemporalStructures of Drinking Patterns

Paul J. Gruenewald, Marcia Russell, John Light, Rob Lipton, John Searles, Fred Johnson, Maurizio Trevisan,Jo Freudenheim, Paola Muti, Ann Marie Carosella, and Thomas H. Nochajski

This article presents the proceedings of a symposium at the 2001 Research Society on AlcoholismMeeting in Montreal, Canada. The cochairs were Paul J. Gruenewald and Marcia Russell. The focus of thesymposium was on mathematical, methodological, and statistical approaches to the assessment of drinkingpatterns from short (daily and monthly) to very long periods (the life course) of time. The researchpresented in the symposium argues that (1) model-based approaches to analyzing drinking patterns canprovide comprehensive bases for assessing drinking risks, (2) data acquisition technologies that track dailydrinking over long periods of time can illuminate unique features of drinking associated with abuse anddependence, and (3) retrospective data can be used to assess life-course trajectories of drinking associatedwith chronic problem outcomes. Each of the presentations points toward an integrated approach tounderstanding acute and chronic risks related to alcohol use. The presentations were (1) Mathematicalmodels of current drinking, by Paul J. Gruenewald and Fred Johnson; (2) Mathematical models of drinkingproblems, by John Light and Rob Lipton; (3) Patterns of drinking ascertained from daily data aggregatedacross 24 months, by John Searles; and (4) Cognitive lifetime drinking histories and natural histories ofdrinking, by Marcia Russell, Paul J. Gruenewald, Fred Johnson, Maurizio Trevisan, Jo Freudenheim, PaolaMuti, Ann Marie Carosella, and Thomas H. Nochajski.

Key Words: Drinking Patterns, Drinking Problems, Stochastic Drinking Theory, Cognitive LifetimeDrinking Histories, Interactive Voice Response.

OVER THE PAST decade, technological and theoreti-cal developments have enabled, for the first time,

comprehensive descriptions and analyses of drinking pat-terns and problems over many different temporal scales.This work has begun to lead researchers away from piece-meal empirical characterizations of drinking patterns toinformed measurement models of drinking behaviors. Un-derstanding how features of the temporal pattern of drink-ing underlie drinking measures, relate to drinking risks, andsupport the emergence of drinking problems over the lifecourse will lead to more accurate assessments of the spe-cific features of alcohol use related to specific problemoutcomes. That these assessments can take place overweeks, months, years, and lifetimes is an important contri-bution of recent studies. The presentations given in thissymposium outline some of the advances made in this areaand provide a guide to scientific enterprise in the next

decades. The presentations lead from small-scale examina-tions of the etiology of drinking (Gruenewald) to large-scale studies of lifetimes of drinking (Russell et al.). Alongthe way, examinations are provided of the assessment ofdose-response functions over months of drinking (Lightand Lipton) and stability and daily change in drinkingpatterns over years (Searles). This work builds on the ef-forts of many researchers to measure and understand therelationships between drinking patterns and problems inthe latter half of the 20th century.

Survey assessments of past month and yearly drinkinghabits have been a mainstay of epidemiological and clinicalresearch on alcohol use and related problems for manydecades. Dating from the work of Straus and Bacon (1953),these methods rely on the judgments of respondents tocharacterize their own drinking behaviors. Respondents tellus how often they drink, how much they typically consume,and, sometimes, how often they drink too much (e.g., Ca-halan et al., 1969). They tell us at what age they starteddrinking (Dawson et al., 1995; Grant, 1997), providing amarker for drinking onset, and whether or not they thinkthat their drinking is a problem. Responses on these sur-veys are used to characterize drinker types (Clark andHilton, 1991) and sometimes to classify drinkers by theirdrinking patterns (Wechsler et al., 1994). In general-population samples, the reliability and validity of thesesurvey-based measures are generally good, although there

From the Prevention Research Center (PJG, MR, JL, RL, FJ), Berkeley,California; the University of Vermont (JS), South Burlington, Vermont;University of Buffalo (MT, JF, PM, AMC), Buffalo, New York; and ResearchInstitute on Addiction (THN), Buffalo, New York.

Received for publication March 22, 2002; accepted April 1, 2002.Supported by NIAAA Grants P50 AA06282 (PJG, RL, FJ), R21 AA11684

(MR), R01 AA09684 (JS), and R01 AA11954 (JS).Reprint requests: Paul J. Gruenewald, Prevention Research Center, 2150

Shattuck Ave., Ste. 900, Berkeley, CA 94704; Fax: 510-644-0594; E-mail:[email protected]

Copyright © 2002 by the Research Society on Alcoholism.

0145-6008/02/2606-0916$03.00/0ALCOHOLISM: CLINICAL AND EXPERIMENTAL RESEARCH

Vol. 26, No. 6June 2002

916 Alcohol Clin Exp Res, Vol 26, No 6, 2002: pp 916–925

have always been some difficulties with underreporting(Midanik, 1995). Other lines of research, more clinicallyoriented, build on the measurement of drinking patterns intime; they prospectively assess days of drinking over weeksor months by using drinking diaries (Lemmens et al., 1992)or rely on retrospective recall to assess drinking patternsover longer periods of time [timeline follow-back (TLFB);Sobell and Sobell, 1992]. Again, these methods of acquiringdata seem generally reliable and valid.

Remarkably, much of this work has been pursued in theabsence of a theoretically based measurement model ofdrinking behaviors. This situation has arisen because muchabout drinking is commonplace (approximately 70% of usdo so), the temporal nature of drinking behaviors seemsintuitively transparent, and arguments about the reliabilityand validity of different measurement strategies can bedealt with, at least superficially, by using formalized statis-tical models of these concepts. The fact that our models ofdrinking behavior are intuitive and statistical has not beenan obvious impediment to progress. That we are able tocorrelate measures of drinking and find their reliability tobe good suggests that we are measuring a relatively stablebehavior. Thus, the epidemiological literature on drinkingpatterns and problems has focused on the assessment ofhazards related to drinking, finding those drinking prob-lems that are related to drinking measures (Midanik et al.,1996). This actuarial task provides indices of the relation-ships between measures of specific drinking patterns andproblems, but it does not tell us how drinking is related tothese problems or whether, indeed, if we measured drink-ing some other way we should find the same or somedifferent relationship. Consequently, when researchers askwhich drinking measures matter most for the prediction ofa problem outcome, the questions that arise are (1) Are thevarious measures measuring the same or different things?(2) How are the measures related to one another, and why?and (3) What is the temporal scale along which appropriatemeasures should be taken? In large part, these questionshave not been asked or answered by researchers in thefield.

The four papers presented in this symposium begin toask these questions and suggest a few very preliminaryanswers. Models of drinking are new to the field but wouldseem to provide a way of looking into the nonlinear rela-tionships expected to exist between drinking measures(Gruenewald). Such models provide a way of looking be-yond assessments of the hazards related to drinking to thedetermination of dose-response functions for drinkingproblems (Light and Lipton). Accurate temporal assess-ments of daily drinking patterns over longer periods of timeprovide the basis for constructing models of the (otherwiseunobservable) temporal dynamics of drinking patterns andproblems (Searles). Retrospective assessments of life-course trajectories of drinking provide the opportunity tomodel the large-scale temporal dynamics of drinking thatare themselves related to the very large scale problems of

chronic use (e.g., cardiovascular disease, alcohol abuse, andalcohol dependence; Russell et al.). Central to each of thepapers is a concern for the drinking process—how drinkingand related risks can be differently characterized over timeby using survey methods, daily self-reports, and retrospec-tive techniques. At each different temporal scale—days,weeks, months, years, and decades—they show how differ-ent data acquisition technologies and analytical approachescan be used to inform our understanding of human drink-ing behavior. They suggest a future for these areas ofresearch that is both robust and exciting.

MATHEMATICAL MODELS OF CURRENT DRINKING

Paul J. Gruenewald and Fred Johnson

Survey assessments of current drinking patterns gener-ally look at drinking over the past month or year. Theseassessments most often inquire into the typical frequenciesand quantities with which drinkers use alcohol (the stan-dard quantity/frequency method) or assess continueddrinking or graduated frequency measures that ask drink-ers how often they have used alcohol in the past month oryear and how often they have consumed two or more, threeor more, and so on, drinks. The last measure applied toheavy drinking has been particularly popular (e.g., “Howoften have you had five or more drinks in the pastmonth?”). Each of these survey questions asks the respon-dent to provide a summary of a behavior that takes place intime and is somewhat fragmented. When we look at thedrinking of individuals from day to day, we see a pattern ofdrinking on some days, not drinking on others, and drinkingto different levels on different days. A 28-day string ofdrinking and nondrinking days for one hypothetical drinkeris shown in Fig. 1a.

The information from these temporal strings of behaviorcan be summarized in various ways. The distribution oftimes between drinking events, the arrival time distribution,displays the regularity, or lack thereof, with which individ-uals initiate drinking (Fig. 1b). The drinking exposure dis-tribution characterizes the number of days on which indi-viduals drink a given dose of alcohol (Fig. 1c). The arrivaltime distribution is the distribution of times between drink-ing events; for example, if one drank every Saturday andonly on Saturday, the arrival time distribution would have asingle peak at 7 days. The drinking exposure distribution isthe distribution of the number of drinking events by drink-ing levels (the number of occasions on which one consumedone, two, three, and so on, drinks); if one drank only threedrinks on every occasion, the drinking exposure distribu-tion would have a single peak at three drinks.

Although these distributions can be developed from di-ary or timeline data that track drinking from day to day,they cannot be directly obtained from one-shot surveys thatrely on retrospective summaries of drinking. However, sur-vey measures of continued drinking provide the opportu-nity to infer some information about the inter–arrival time

TEMPORAL STRUCTURES OF DRINKING PATTERNS 917

and exposure distributions. Continued drinking data char-acterize the probability that an individual drinker will con-tinue to consume alcohol to any dosage level (i.e., anynumber of drinks) on any drinking occasion. These data canbe used to provide an assessment of continued drinkingprobabilities by using a parametric approximation to thisdistribution (Fig. 2a), and estimates based on this approx-imation can be differenced to obtain an inferred drinkingexposure distribution (Fig. 2b). The respondent in Fig. 2drank 1 or more drinks on 10 occasions and went on tocontinue to drink 2 or more drinks on 10 occasions; byimplication, there was no occasion on which this drinkerhad only 1 drink. Differencing the empirical approximationshows that the expected rate of drinking 1 drink alone is 0.2per 28 days, a suitably low rate for that period of time.

The drinking exposure distribution displayed in Fig. 2b isnotably the same and different from that shown in Fig. 1c.The same type of information is portrayed (the number ofdays on which a given dose of alcohol will be consumed),but the distribution is essentially continuous. The underly-ing theory of the approach, stochastic drinking theory(Gruenewald et al., 1996), argues that any particular se-quence of drinking events (Fig. 1a) is but one realization ofan underlying, partially stochastic drinking process. It isexpected that a second series of observations on the sameindividual would be derived from the same exposure dis-tribution but would not identically represent it (being onerealization of a probabilistic process).

This particular argument was anticipated by earlier work(Duffy and Alanko, 1992; Gruenewald, 1991) and is distin-guished by providing a theoretical model of the bases of alldrinking measures. Considering the example in Fig. 2b,drinking frequency is the sum of all exposures regardless ofdose. Mean, median, and modal drinking levels are the

mean, median, and mode of the exposure distribution (re-ports of “typical” drinking seem to be some compromise ofthese measures; Gruenewald and Nephew, 1994). Totalvolume is the sum of exposures weighted by dose. Measuresof heavy drinking are the sum of exposures to the right ofany dose (e.g., five or more drinks). Abstention is reflectedin a distribution of zero exposures.*

Applying this approach to data available from 1008 col-lege drinkers, the value of using exposure distributions tounderstand drinking patterns can be demonstrated. Forexample, it is widely known that male college drinkers drinkmore than female college drinkers. They may do so by drink-ing more at all drinking levels (all doses) or by distributingtheir drinking in different ways (at different doses). As shownin Fig. 3a, exposure distributions for men and women aredifferent; men drink more at higher levels but also less atlower levels. As a second example, it is also widely acceptedthat upper-class undergraduates drink more than those atentry into college. Figure 3b shows the dramatic increase inlower levels of use (from one to three drinks) that takes placeacross the college years (Fig. 3b), yet it emphasizes the greateramount of heavy drinking observed among freshmen. At dos-age levels of five or more drinks, freshmen slightly exceed allother grade levels in frequencies of drinking.

Little differences such as those observed for freshmencan have a big effect on drinking risks. Thus, differences indrinking between groups may also be displayed by using theinformation provided by exposure distributions to calculatereturn times to drinking—a measure of average time fromone heavy drinking event to another (defined across alldosage levels). Return times can then be used to charac-terize expected values for weekly, monthly, and quarterlydrinking. As shown in Fig. 3c, return times for men andwomen are very different at higher drinking levels. Femaleundergraduates will drink four or more drinks approxi-mately once a month. Male undergraduates will drink fouror more drinks nearly once a week. Male undergraduateswill drink eight or more drinks approximately once amonth; female undergraduates do this fewer than fourtimes a year. As shown in Fig. 3d, return times for drinkingbeyond five or more drinks are shorter for freshman thanfor any other grade level in this cohort of college respon-dents, and it is expected that most college students willdrink six or more drinks approximately once a month.

MATHEMATICAL MODELS OF DRINKING PROBLEMS

John Light and Rob Lipton

Many studies have documented relationships betweendrinking and a wide variety of alcohol-related problems insuch diverse realms as personal health, social relationships,and alcohol-related injury (Clark and Hilton, 1991). Unfor-

*Note that shared correlations among different drinking measures reflecttheir shared theoretical basis as sums of differently weighted drinkingexposures.

Fig. 1. Components of a comprehensive model of drinking. (a) The time courseof drinking for one drinker over 28 days. (b) The inter–arrival time distribution fordrinking days. (c) The distribution of drinking quantities (exposure distribution).

918 GRUENEWALD ET AL.

tunately, measurement methods well suited to demonstrat-ing the presence of such relationships are less appropriatefor investigating more refined questions, for instance, thelevel of risk associated with specific doses of alcohol. Yetwithout answers to such questions, it is difficult for re-searchers to offer informed advice on the public healthconsequences of alcohol use. Correlations between stan-dard measures of alcohol use and related problems imply arather vague public health message: drink less and lessoften. This is rather uninformative. The important ques-tions to answer are how much less and how much less often.Answers to these questions can be obtained only throughthe use of suitably defined measures of dose, exposure, andrisk.

Environmental epidemiologists refer to the relationshipbetween dose-specific exposures and environmental haz-ards as a dose-response function. Dose-response functionsrepresent a high level of descriptive sophistication and assuch are extremely useful (often critical) for addressing publichealth and policy issues regarding more- or less-risky patternsof exposure to any environmental hazard (Hertz-Picciotto,1998). However, requirements for data that can be used toestimate such relationships are correspondingly demanding.In the case of drinking, one must be able to measure thenumber of times an individual in a population at risk has beenexposed to a given dose of alcohol. This requires definitions ofdose and exposures that are unambiguous and over which riskfunctions can be estimated. In this respect, assessments of riskfunctions related to standard measures of drinking patterns(Midanik et al., 1996) reflect hazards related to drinking, notdose-response relationships. A measure of dose is missing ineach case. Greater drinking frequencies are indifferent todose, greater drinking quantities represent an average ofdoses, and heavy drinking measures an arbitrary aggregateacross dose levels. Consequently, functions relating thesemeasures to risks for alcohol-related outcomes represent

rather vague attributions of risks to usage levels.† However,drinking exposure distributions discriminate doses from expo-sures and provide the only way to assess dose-specific rela-tionships of drinking patterns to related problems by usingsurvey data.

In principle, the approach to this problem is simple. Withdrinking exposure distributions estimated for a large num-ber of survey respondents, we can estimate the populationaverage risk associated with a given dose (number of drinkson a specific occasion) by regressing a suitably definedproblem count or index over the estimated exposures ateach dosage level. This would result in a set of regressioncoefficients, each of which would represent an estimate ofthe population mean responsiveness of the problem inquestion to exposures at each dosage level. From such ananalysis, we would know, for example, how much a singleoccasion of drinking five drinks would contribute to therate of reported hangovers in the population of drinkers.We would also have the opportunity to examine the em-pirically defined dose-response function relating drinkinglevels to problem outcomes (Fig. 4a). This plan is fatallycompromised, however, by the fact that exposure data arestrongly intercorrelated. Individuals who have relativelymore exposures at one dosage level (d) are also more likelyto have been exposed at similar dosage levels (d � 1). Infact, when we attempt to estimate parameters for suchmodels, as shown in Fig. 4b, estimates of the coefficients ofthe dose-response functions exhibit the classic pattern ofoscillating signs and increasing magnitudes associated with

†Significantly, as pointed out by Stockwell et al. (1997), increases inmoderate drinking may be related to risks for problem outcomes only throughassociated increases in heavy drinking. With use of standard measures, theargument that increases in moderate drinking uniquely affect drinking risks(Midanik et al., 1996) may be specious.

Fig. 2. Extraction of exposure distribution from continued drinking data. (a) Observed and fitted values of continued drinking for one drinker. (b) Derived exposuredistribution for one drinker.


severe multicollinearity. The lack of independence in theexposure estimates is problematic.

Three analytical procedures are available to handle mul-ticollinearity in these data: constrained estimates of dose-response functions, principal components analysis of dose-exposure relationships, and Stein-like estimators that canbe used to extract independent dose-specific exposure in-formation (Judge et al., 1985). The first procedure is modelbased and requires a priori definitions of suitable dose-response functions. The second is model free but may bebiased under conditions in which doses and exposures arenot metrically well defined. The third is numerically de-manding and requires additional assumptions about theindependence of doses and exposures. The second choice isappealing because it does not require a priori definition ofthe form of the dose-response function and, under thecurrent definitions of dose, exposure, and response, canprovide unbiased estimates of dose-related effects. Withthis approach, principal components analysis can be ap-plied to the measures of doses and exposures for a popu-

lation of respondents and used to extract independent di-mensions of these measures on the basis of conditionindices (Belsley et al., 1980; Greene, 1993). Rates of prob-lem outcomes can then be regressed over this lower-dimensional orthogonal representation of dose exposures,and coefficients from this model can be used to reconstructdose-response functions.

By using the same data on 1008 college drinkers fromwhich exposure distributions were derived in the prior pre-sentation, four principal components were found to besufficiently independent to enable modeling of the problemoutcomes (condition indices less than 30; Belsley et al.,1980). They accounted for 97% of the variance in thedose-exposure data. Problem scores for three classes ofproblems were derived for each college-age drinker repre-senting mild (eight problems, e.g., hangovers, missing aclass), moderate (nine problems, e.g., unplanned sexualactivity, performing poorly on a test), and severe (sevenproblems, e.g., driving while under the influence of alcohol,get into physical fights) problems. By regressing the prob-

Fig. 3. Exposure distributions and return times for men and women (a and c) and college students in different grades (b and d).


lem scores over the principal components scores and thenusing coefficients from this model to estimate dose-specificresponse effects, the dose-response functions presented inFig. 4c were obtained. These functions represent the spe-cific risks associated with specific drinking events averagedacross the population of college drinkers. Thus, 1 occasionof drinking 6 drinks would be related to 2.2 serious, 3.3moderate, and 3.8 mild problems. The functions show abimodal distribution of risks related to different drinkinglevels, with risks peaking at two and six drinks per occasion.As shown in the figure, indices of mild, moderate, andsevere drinking problems exhibit roughly the same dose-response shapes. Serious problems are less likely at everydosage level than moderate or mild problems. This patternof effects suggests a two-population model of the sources ofdrinking risks; most of the individuals who report drinkingtwo and only two drinks relatively often may be inexperi-enced or infrequent drinkers, whereas those reportingthree-and-only-three-drink occasions (and higher) may bemore experienced and frequent drinkers.

Given the preliminary nature of the data presented here

and the complexity of the modeling exercise necessary toconstruct dose-response functions, much future work willbe necessary to begin to understand dose-response rela-tionships in human populations. However, as this presen-tation suggests, these relationships are discoverable withsurvey data and may lead to a new understanding of prob-lems related to alcohol use. The relationships among drink-ing patterns and problems are undoubtedly complex andwill take more time and greater sophistication to sort out.It seems clear to us that advances in measurement meth-odology are a necessary component of this process. Themeasurement system we have presented in this analysisshould be viewed as a more powerful “microscope” for thepurposes of such investigations.

PATTERNS OF DRINKING ASCERTAINED FROM DAILYDATA AGGREGATED ACROSS 24 MONTHS

John Searles

Valid and reliable quantitative measurement of alcoholconsumption has long been a major goal for clinicians and

Fig. 4. Estimating dose-response functions from drinking data. (a) Theoretical dose-response function. (b) Effects of multicollinearity on estimates of dose-responserelationships by using the ordinary least-squares regression model. (c) Estimates of dose-response relationships by using principal components of drinking exposures.


researchers alike. Allen and Litten (1992) have suggestedfour contexts in which accurate assessment of consumptionis important: (1) analyzing the effectiveness of treatmentfor alcohol disorders, (2) predicting future alcohol-relatedproblems, (3) the effect of consumption on other seriousmedical conditions, and (4) public safety issues (e.g., absti-nence by individuals engaged in public transportation).Closely tracking consumption over relatively long time pe-riods would add considerably to the usefulness of thesetypes of data, because changes in drinking patterns couldthen potentially be ascertained, and the influence of exog-enous variables on those changes could be assessed.

We have adapted a methodology to make long-termtracking of daily consumption feasible and practical. Theinteractive voice response (IVR) system is a computer-based telephone system in which callers respond to re-corded questions by using the telephone keypad. Subjectsreport their alcohol consumption and other data aboutassociated variables (e.g., stress, health, mood, and so on)each day. There are several advantages to this system:

1. It uses the nearly ubiquitous touch-tone telephone toenter data on a daily basis.

2. Data are entered directly into a secure database withouttranscription errors.

3. Individuals can be monitored for call compliance daily,and any problems can be solved quickly.

4. Different sets of questions can be asked on drinking daysand nondrinking days.

5. Individuals can call any time of the day or night to reporttheir data for the previous day.

Our first attempt at using the IVR involved 51 subjectswho called the system daily for 112 consecutive days(Searles et al., 1995). After the success of that study, weimplemented a project to observe 33 men for 2 years on adaily basis. The latter study forms the basis of this presen-tation. At the initiation of this study, 22 subjects had alifetime DSM-IV diagnosis of alcohol abuse or depen-dence, whereas 11 did not.

Subjects were asked daily about their consumption ofalcohol (separately for beer, wine, and liquor), level ofintoxication, reasons for (or for not) drinking, stress, mood,and health for the duration of the 2-year study. Call com-pliance rates were quite high: over the course of 731 con-secutive days, we received calls on the day they were due93.8% of the time. We allowed subjects to call in misseddays up to 7 days past the due date, and this accounted foranother 5% of data collected. Therefore, we obtained98.8% of the possible data. It should be noted that subjectswere closely monitored throughout the study and werecontacted if they did not call the system for two consecutivedays. Also, all subjects were compensated for their calls andcould maximize their reward with consistent callingrecords. Subjects were also personally interviewed every 3months during the study.

The daily call took approximately 2 min to complete.

Although reports on drinking days took somewhat longerto complete than on nondrinking days (117 vs. 111 sec),subjects reported drinking on 58.2% of all days. Figure 5shows the daily drinking pattern over the course of the 2years. The peaks represent weekends and holidays. Figure6 presents the mean drinks reported for each day of theweek for the two groups of subjects (no diagnosis andabuse/dependence). As can be seen, the diagnosable groupdrank significantly more on all days, and both groups in-creased their drinking on the weekends. This was primarilya beer-drinking group of men: beer consumption was re-ported on 86.5% of drinking days, liquor on 26.8% ofdrinking days, and wine on 14% of drinking days.

At quarterly interviews for the first year, subjects wereadministered the TLFB to compare the retrospective re-ports of the TLFB with the near-concurrent reports of the

Fig. 5. Summary of daily IVR reports for 2 years.

Fig. 6. Drinking quantities by day of week and diagnostic status. Ab/Dep,abuse or dependence.


IVR (Searles et al., 2000, 2002). We found that subjectssignificantly underreported their drinking and the numberof heavy-consumption days (defined as reporting five ormore drinks) on the TLFB compared with the IVR (meandrinks: 5.2 for IVR and 3.7 for TLFB, p � 0.05; percentageof heavy drinking days: 47.7% for IVR and 31.9% forTLFB, p � 0.05). This further underscores the value ofobtaining daily data over long time periods.

These data allowed us to examine in detail the relation-ship between potential mediating factors and the consump-tion of alcohol. We found, for example, that there was nosubstantial direct relationship between daily stress ratingand drinking (r � 0.09), daily mood rating and drinking(r � 0.19), or daily health rating and drinking (r � 0.14).However, we also found that this relationship varied as afunction of diagnostic status and whether subjects drank ornot. Thus, for those diagnosable, stress increased, moodincreased, and health rating decreased on nondrinkingdays. The opposite pattern occurred in the nondiagnosablegroup. This suggests a complex relationship that heretoforehas not been studied. Further research in this area could bequite fruitful in more accurately specifying the stress-consumption relationship.

We are pursuing other avenues of research with the IVRsystem. Current projects include pain management, theIVR as an adjunct to a brief intervention by a primary careprovider, and the IVR as part of treatment for individualswith more severe alcohol disorders.

COGNITIVE LIFETIME DRINKING HISTORIES ANDNATURAL HISTORIES OF DRINKING

Marcia Russell, Paul J. Gruenewald, Fred Johnson,Maurizio Trevisan, Jo Freudenheim, Paola Muti, Ann MarieCarosella, and Thomas H. Nochajski

Despite their undoubted relevance to chronic diseaseand alcohol problems, lifetime drinking patterns haverarely been assessed because of challenges related to thefeasibility of gathering such data, their validity, and theiranalysis. As part of the work undertaken by the NIAAAAlcohol Research Center on Clinical and Medical Epide-miology of Alcohol, a computer-assisted personal inter-view, the Cognitive Lifetime Drinking History (CLDH),was developed to address the issue of feasibility. This in-terview synthesized several well-established cognitive tech-niques for improving memory of past events that have beenused in previous measures, i.e., the Lifetime Drinking His-tory (Skinner and Sheu, 1982), the TLFB (Sobell and So-bell, 1992), and a retrospective approach used in studies offetal alcohol syndrome (Russell et al., 1994). Respondentsconstructed and used a calendar of lifetime events to aidmemory during the administration of the CLDH. Life in-tervals during which drinking was relatively homogeneouswere defined by asking when a regular pattern of drinkingbegan and when it changed. Lists of alcoholic beveragesand models were used to determine which beverages re-

spondents drank over their lifetimes, their usual drink sizeof each beverage, and whether drink size changed over thelifetime. This provided memory cues, as well as informationused to calculate absolute alcohol intakes and tailor thecomputer-assisted interview to each respondent’s drinkinghistory. For each homogeneous drinking interval defined,respondents were asked about their drinking patterns. Aperson who drank less often than weekly during an intervalwas simply asked how often he or she drank and how muchduring a typical month. But individuals who drank weeklyor more often were asked separate quantity/frequencyquestions for Fridays, Saturdays, Sundays, and weekdays.During this process, respondents were encouraged to recalltheir usual activities on different days of the week andwhether alcohol drinking was involved. Asking about spe-cific days of the week relieved respondents of the mentalburden of having to average their intakes over days whendifferent amounts were usually drunk and captured data ondrinking patterns. Everyone was asked quantity/frequencyquestions about times when they drank more than usual,how often they were intoxicated, the proportion of drinksthey had with meals/snacks/without eating; the proportionof beers representing different types of beer, such as light/regular/malt liquor, and so on; and the proportion of winesrepresenting fortified versus table wines.

A test-retest study was conducted demonstrating thatlifetime estimates of total ounces of alcohol consumed andtimes intoxicated on the basis of CLDH assessments madeat least a week apart were highly correlated, with coeffi-cients ranging from 0.73 to 0.83 (Russell et al., 1997).Subsequently, the CLDH was successfully used in case-control studies of alcohol and chronic diseases, includingcoronary heart disease, lung cancer, and breast cancer, aswell as in studies of the epidemiology of alcoholism intreated and untreated populations.

A number of articles and presentations have been basedon summary measures, such as lifetime ounces of alcohol orlifetime frequency of intoxication (list available from M.Russell upon request). However, to fully exploit the retro-spective, longitudinal nature of the data, it is necessary toincorporate the concepts of time, course, and progressioninto a single measure. Preliminary results from an investi-gation of the feasibility of using trajectory analysis for thispurpose are presented here. Findings were based on 2991individuals over the age of 50 years who participated incase-control studies of chronic disease. The CLDH pro-vided estimates of average drinks per month from the agesof 5 to 50 years for each respondent. The mean number ofchanges in drinking patterns and drinks per month wereplotted by age and sex, but the main focus of the presen-tation was on cluster analyses that identified five groups ofindividuals who had similar drinking profiles, i.e., similarchanges in their drinking patterns, from ages 5 through 50.The magnitude of the analytical task can be appreciated ifone considers that, with only three possible drinking pat-terns over this period of time, there are 345 (approximately


2.95 � 1021) possible trajectories. With continuous mea-sures of drinking, the number of possible trajectories is,indeed, limitless (Land et al., 1996).

As illustrated in Fig. 7, by age 50, alcohol consumption inthis population had converged into two levels of intake:22% of the population abstained or drank very little,whereas 78% averaged approximately one drink a day.However, there were substantial differences in the drinkingtrajectories that culminated in these two levels. One groupof abstainers drank heavily through their 20s and graduallytapered off during their 30s to very low intakes by their 40s(group 1; n � 382). The other group averaged approxi-mately a drink a day in their late teens but decreaseddrinking during their 20s to abstinent levels by age 27(group 2; n � 280). Despite the fact that both groupsabstained or drank very little by age 50, group 1 clearly hada much higher exposure to alcohol over a longer period oftime than group 2.

The majority of individuals who were drinking moder-ately at age 50 got there by increasing their intakes rapidlyduring their late teens to a peak at age 21, after whichintakes declined slightly during their 20s to a plateau during

their 30s, and another slight decline occurred during their40s (group 3; n � 1348). Group 4 (n � 702) started drinkingapproximately 5 to 6 years later than group 3 and reachedtheir peak intakes in their mid 30s, after which drinkingdeclined at a rate parallel to that of group 3. In contrast,group 5 (n � 279) started drinking later yet, in their mid30s, and their intakes peaked in their late 40s, decliningsomewhat by age 50. Thus, despite rather similar alcoholintakes at age 50, drinkers in group 3 would have beenexposed to relatively high average levels of alcohol over amuch longer period than drinkers in group 5, with drinkersin group 4 intermediate in their exposure. These substan-tive differences in lifetime exposure are not reflected byalcohol intakes at age 50. This dramatically illustrates theneed to look beyond current drinking patterns to investi-gate the effect of lifetime drinking patterns on health. Italso suggests the importance of studying environmental andpersonal factors that influence the shape of these lifetimedrinking trajectories.

The results of this rather simple clustering procedure dem-onstrate that there are common drinking trajectories amongthe respondents and that there are substantive differences in

Fig. 7. Lifetime drinking trajectories for five different drinker groups (empirically defined K-means cluster solution).


these trajectories relevant to important questions about therelation of alcohol consumption to health and factors influ-encing lifetime drinking patterns. Our next step will be toapply more sophisticated trajectory analyses (Land et al.,1996; Nagin and Land, 1993) to statistically assess the validityand reliability of the clusters derived from these data and toprovide a multivariate context in which to assess persons’memberships in specific trajectories and variables related tothese memberships.

CONCLUSIONS

The research enterprises represented by the presenta-tions in this symposium all provide indications of importantdirections for research into the etiology of alcohol use andrelated problems in human populations. Model-driven per-spectives on patterns of alcohol use can not only help clarifythe relationships between different drinking measures, butcan also provide new bases upon which to assess drinkingpatterns and risks related to alcohol use. Data acquisitiontechnologies that track drinking patterns in real time pro-vide opportunities for assessing the validity of differentdata acquisition systems (e.g., the IVR versus TLFB) andimportant information on the microstructure of the relation-ships between stress and alcohol use. The CLDH provides thebasis for discoveries relating different life courses of drinkingto chronic outcomes related to alcohol. From individualdrinking events to lifetimes of drinking, alcohol plays impor-tant roles in human development, individual and social behav-ior, and the occurrence of acute and chronic problems relatedto drinking. Understanding these relationships will requiregoing beyond the standard corpus of drinking measures, sur-vey designs, and data acquisition systems to the developmentof comprehensive integrated models of use throughout the lifecourse.

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One Drink to a Lifetime of Drinking: Temporal Structures of Drinking Patterns