Health Education Research, Vol. 15, No. 5, 569-580,
October 2000
© 2000 Oxford University Press
Consumed with worry: `unsafe' alcohol consumption and self-reported problem drinking in England
Institute for the Geography of Health, Department of Geography, University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth PO1 3AS, UK
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Abstract |
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Using data from the 1994 Health Survey for England, logistic multivariate multilevel modelling techniques are used to investigate the simultaneous effect of individual demographic characteristics and socio-structural factors on self-reported problem drinking as revealed by CAGE scores and `unsafe' levels of alcohol consumption. Whilst the influence of key socio-structural variables is broadly similar for both unsafe alcohol consumption and high CAGE scores, there are notable exceptions when results are examined by tenure group: those in the rented sector are more likely to be problem drinkers as revealed by CAGE, but less likely to consume `unsafe' amounts of alcohol. Both dimensions of drinking behaviour are influenced by the consumption patterns of others in the household, with both likelihoods increasing as the average consumption of others in the household rises. After taking into account individual compositional variables, the research indicates that there is very little evidence for geographical variation remaining in these two dimensions of drinking behaviour. It is found that the proportion of the population whose drinking behaviour may be classed as (potentially) problematic via the CAGE responses is substantially less than the proportion consuming above recommended `safe' levels. The research concludes, however, that the two measures are broadly similar in their relationship to social and structural variables. Tenure provides an exception to this conclusion and indicates a continuing need to take account of housing circumstances in developing an understanding of drinking behaviour.
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Introduction |
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TopAbstractIntroductionMeasuring drinking behaviourMethodsResults and discussionConclusionsReferences |
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Whilst it is commonly accepted that low to moderate levels of alcohol consumption may be beneficial to health (Joint Working Group, 1995), long-term high alcohol consumption has a negative impact on health status (Anderson et al., 1993
). The Department of Health has published guidelines relating to `safe' levels of consumption and these currently stand at between 3 and 4 units a day for men and between 2 and 3 for women (Interdepartmental Working Group, 1995). In addition, it is stated that drinking regularly 4 units or more per day for men and 3 units per day or more for women will increase risks to health. Approximately 27% of men and 11% of women are consuming above these limits (Alcohol Concern, 1999). Health of the Nation goals were set to reduce these to 18 and 7%, respectively, for men and women by the year 2005 (Department of Health, 1992). The 1999 White Paper on public health did not adjust these goals (Secretary of State for Health, 1999). To help achieve success in reaching these targets, it is useful for groups such as community alcohol teams and the various voluntary bodies providing alcohol-related services to know more about the types of people who are likely to consume unsafe amounts of alcohol, and where or in what type of area they may be located. There are, however, difficulties in the measurement and recording of drinking behaviour. This paper addresses these issues by examining information from the Health Survey for England (HSE) on two measures relating to problem drinking: alcohol consumption and responses to a series of questions (known as the `CAGE questions') which attempt to identify an individual with (potentially) problematic drinking behaviour.
The paper falls into three sections. The first is concerned with the measurement of drinking behaviour, and discusses consumption measures and the CAGE questionnaire. The second section outlines the methods employed in the present study. It provides brief descriptions of the data source and the method of multivariate multilevel modelling. The third section of the paper presents the empirical results of the study. It compares and contrasts the influence of individual demographic and socio-structural factors in explaining high CAGE scores and high alcohol consumption patterns. It goes on to examine the relationships between individuals and the geographical contexts in which they live with respect to the two dimensions of drinking behaviour, and their covariance across district health authorities (DHAs) and postcode sectors.
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Measuring drinking behaviour |
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Patterns of alcohol consumption at the national level are derived via government-sponsored surveys such as the HSE (Prescott-Clarke and Primatesta, 1998
). In the HSE, the respondent is asked to state how often they have consumed various types of alcoholic beverage (e.g. shandy, beer, wine, spirits, etc.) over the last 12 months (e.g. `every day', `once or twice a week', `once or twice a year', etc.). The respondent is then asked to state how much s/he usually drinks of this particular type of alcohol in any given day. This information is used to generate an approximate weekly count of units of alcohol. Information from the HSE is used to monitor progress towards national public health targets.
As Sutton and Godfrey (Sutton and Godfrey, 1995) note, data used in the analyses of consumption patterns may be problematic, and may suffer from measurement error and measurement bias. There are, for example, difficulties surrounding the attempt to derive weekly estimates on the `usual' amount drunk by asking people to recall their drinking behaviour over a 12 month period. People's patterns of drinking vary tremendously from week to week, and individuals are often unaware of what constitutes (say) a strong beer or lager and may get confused over the amounts consumed. Furthermore, for various reasons, individuals may choose not to be truthful about their consumption patterns and underestimate their regular drinking level (Ehrens and Hedges, 1999).
An alternative or validating measure that can be used to assess levels of problematic drinking is the CAGE score. This score is based on the responses to a set of questions, known as the CAGE questions, which were originally developed by Ewing and Rouse as a tool to help identify alcoholics in a hospital setting (Ewing and Rouse, 1970; Ewing, 1984). These questions are included in the 1994 HSE (Colhoun and Prescott-Clarke, 1996). Interviewees are asked to indicate a `yes' or `no' response to six statements about drinking—three on physical dependency and three on social attitudes. Work with CAGE has shown that a positive response on two or more questions indicates a high likelihood of the presence of problematic drinking (Mayfield et al., 1974).
Several authors have evaluated the CAGE questions. Mayfield et al. (Mayfield et al., 1974) found that the questions correctly identified 87% of alcoholic psychiatric patients. Masur and Monteiro (Masur and Monteiro, 1983) found a 62% success rate. Escobar et al. (Escobar et al., 1995) found CAGE had the greatest efficacy and discriminating power for diagnosing patients with alcoholism in primary care when compared to other diagnostic tests. A similar result was found when comparing CAGE questions with various chemical marker tests (Girela et al., 1994). Lee and DeFrank (Lee and DeFrank, 1988) have found CAGE to be a more successful indicator of self-reported drinking in men compared to women. They and Davidson (Davidson, 1987) also levy some criticism at CAGE in so far as time scales are not explicitly defined in the questions and the same score or emphasis can, for example, be allocated to a person who has taken an `eye opener' in the last few days and someone who took such a drink several years ago. To this end, when comparing CAGE with another screening test—the Michigan Alcohol Screening Test (MAST)—it has been concluded that CAGE best identifies alcohol dependence in the previous year (Watson et al., 1995).
The majority of work involving CAGE centres on the identification of (potential) problem drinkers in a clinical setting with relatively small numbers of individuals. There has only been a limited amount of work, which has systematically described the socio-structural patterns of CAGE scores amongst the general population. Twigg et al. (Twigg et al., 1998), for example, have compared the overall proportion of problematic drinkers in England and Québec, and provide a detailed description of Québecois people who score highly on CAGE. One of the main reasons for the absence of systematic community-level studies using CAGE has been that extensive national survey information has been unavailable until relatively recently. This shortcoming has been countered by the inclusion of CAGE in major government sponsored surveys. A second reason is that CAGE has traditionally been regarded as a tool to be used in a clinical setting. However, Spencer et al. (Spencer et al., 1987) have found CAGE to be useful in screening for alcohol problems in the general community and Colhoun et al. (Colhoun et al., 1997) have used CAGE as a validation measure in an investigation of the collectivity of drinking in England. Dent et al. (Dent et al., 1995) compared CAGE responses to the Readiness to Change questionnaire (RCQ) and argue that CAGE is a useful tool not only for identifying patients who are problematic drinkers but also patients who are ready to make changes to address their drinking problems. There is therefore a body of work that suggests that CAGE has utility as a measurement tool in community settings both inside and outside the USA where it was originally derived.
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Methods |
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This paper considers the covariation of high CAGE scores and unsafe levels of alcohol consumption. It also examines the joint relationship of both these outcome measures to socio-structural variables. It has been widely illustrated that a number of variables, as well as age and gender, are important in explaining alcohol consumption patterns at the individual level (Karvonen, 1995
; Marmot, 1997). Others have recognized the additional importance of contextual influences in the form of drinking patterns of other members of the family or household (Rice et al., 1998; Sutton and Godfrey, 1995). Our approach was therefore to compare and contrast the effects of the main socio-structural variables of gender, age, social class, tenure and marital status on the likelihood of consuming unsafe amounts of alcohol and on the probability of scoring highly on CAGE, while incorporating the effect of household context by including a household drinking index (Sutton and Godfrey, 1995). Additionally, a dummy variable was used to indicate whether the individual was from a single person household.
Evidence also exists to suggest that geographical area of residence may influence consumption even after the compositional mix of an area has been taken into account (Blaxter, 1990; Williams and Debakey, 1992; Duncan et al., 1993a; Karvonen, 1995). This contextual influence may be perhaps best characterized by reference to the possible impact of `cultural milieu' on drinking behaviour. To this end, the drinking behaviour of individuals can only fully be understood by reference to the context in which the behaviour is learned and performed on a day-to-day basis (Dorn, 1980). While one such context is undoubtedly the household, another is the area of a person's residence. A popular manifestation of this latter effect would be the reputed existence of `heavy-drinking' environments. Macintyre et al. (Macintyre et al., 1993) develop these arguments further within a broader framework and question whether public health should focus on people or places.
The influence of area or ecological context is investigated here by analysing data from the 1994 HSE in a multilevel model. The 1994 HSE is the fourth wave of an annually repeated cross-sectional survey in which interviewing takes place continuously throughout the year. It uses a multi-stratified design and is deemed to be a representative sample of the English population (Colhoun and Prescott-Clarke, 1996). A similar set of questions relating to core topics is asked each year and questions on alcohol consumption and drinking behaviour make up part of this core. In total, approximately 15 800 individuals in the 1994 HSE supplied information on their drinking behaviour, although only those who stated that they drink `regularly' were asked the CAGE questions. Both the alcohol consumption variable and the results of the CAGE questions were transformed into dichotomous variables. Women consuming more than 14 units per week were classed as `unsafe' drinkers; for men this threshold was raised to 21 units. These definitions of problem drinking were chosen to reflect traditional government guidelines regarding unit consumption and the literature on CAGE. All individuals answering `yes' to two or more of the CAGE questions were classed as high CAGE.
In the final analysis 13 685 individuals provided information on the two measures of drinking behaviour and a summary of the results is shown as a cross-tabulation in Table I. Table I indicates that the majority of `unsafe' drinkers (82%) do not score highly on CAGE. This would suggest that CAGE cannot be used as an indicator of levels of unsafe consumption using current Department of Health definitions. However, as expected, most of the `safe' drinkers (98%) do not score highly on CAGE. Table II shows a gender breakdown of the relationship between CAGE score and consumption. This confirms a general positive trend, and the expected distinction between men and women.
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The major substantive research advance in this paper concerns the simultaneous investigation of both alcohol consumption patterns and high CAGE scores in a multivariate multilevel model. This approach provides a number of advantages over the use of separate multilevel models for each dimension of drinking behaviour (Duncan, 1997
). First, the relative influence of any one variable can be assessed simultaneously for each dimension of drinking behaviour. Much of the literature, for example, points to strong gender differences in alcohol consumption (Blaxter, 1990; Duncan et al., 1993a; Twigg et al., 2000), but do these also exist for problem drinkers as revealed by CAGE? Indeed, do gender differences occur across both dimensions but with different strength and/or direction of influence? Second, not only can these questions be explored more efficiently for both dimensions in a single multivariate model, the approach can also allow for statistical testing of the differential co-presence of compositional effects. Whilst varying effects for any one compositional variable could be obtained from separate models, the statistical significance of any differences can only be gauged when the models are run simultaneously. Third, the multivariate multilevel approach allows for a simultaneous assessment of the importance of place with respect to both measures of alcohol related behaviour. For example, the proportion of people with CAGE problems may vary substantially from place to place, but consumption may not. In the model reported in this paper, the measurement of both outcomes on a logit scale makes these processes of comparison and significance testing relatively straightforward. It is, however, possible to run mixed multivariate models with outcomes measured on different scales (Duncan, 1997). Further, and perhaps more importantly, a major advantage of modelling the two responses in a single multilevel model is the ability to estimate higher-level covariance terms. This joint covariance can illustrate the extent and manner in which the two drinking behaviours covary across geographies. We would expect areas with high proportions of CAGE problem drinkers to be characterized by high levels of alcohol consumption but, through an analysis of residuals from the higher levels, we can investigate those places or types of place that do not fit this pattern. There may, for example, be areas or types of area where consumption is high, but the population is not acknowledging problematic drinking patterns. Conversely, there may be areas where consumption is relatively low, but self-diagnosed problem drinking is declared to be relatively high.
A number of individual variables were used in the model to explain both dimensions of drinking behaviour. These were age, gender, social class, tenure, marital status and household drinking behaviour. Table III provides the sample breakdown of these variables and also indicates the levels of `unsafe' consumption and problematic CAGE scores for each variable. The household drinking index was calculated as described by Sutton and Godfrey (Sutton and Godfrey, 1995) and summarizes the drinking habits of other individuals within the household. Where there is only one person in the household, the value is set to the average for the index.
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Apart from age and the household drinking index, all of the variables used in this paper were categorical in nature and, for these, dummies were created that were modelled against a base category denoting the modal characteristics for each variable. Age, which ranged from 16 to 97, was used as a continuous measure and centred on mean age. The overall base category in the analysis was a woman, aged 45, from social class I or II, who was an owner occupier with a mortgage and was married or cohabiting. The household drinking index was also centred and a dummy variable indicating single person households was included in the model to allow investigation of the influence of such households. Table IV
summarizes the variables used in the analysis.
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MLn software was used to carry out the multivariate multilevel models (Rasbash and Woodhouse, 1995
). A four-level model was calibrated with level 1 representing the response set (CAGE and units consumed; n = 27 370) and level 2 the individuals (n = 13 685). Level 3 made use of the primary sampling unit (PSU) codes (n = 712). These are based on postcode sectors and whilst they remain un-named, they can still be used in the model because individuals can be grouped according to PSU. Whilst conclusions cannot be made about drinking behaviour within identifiable PSUs, variation remaining at this level after taking account of individual composition can be determined. Incorporating the PSU as a level in the analysis enables account to be taken of the clustering inherent in the survey sample design of the HSE. At the same time, it also allows for an assessment of possible small-scale `neighbourhood' effects on drinking behaviour. PSUs are approximately the same size as electoral wards (8000–10 000 households). Other possibly more relevant small-scale area identifiers were not available. Level 4 is represented by named DHAs (n = 177) and any variation remaining at this level can be estimated and residuals identified for named geographical regions. DHAs were again included for design reasons and also for their relevance to policy and the planning of alcohol-related services. |
Results and discussion |
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The results are shown in Table V
. The first row in Table V represent the results for the constant term and show the estimated logit for the base category on the CAGE questions (CAGE) and on levels of alcohol consumption (SAFE). Using a pseudo Z-test, both of these logit estimates can be regarded as significant as they are more than twice their standard error. More sense of the values can, however, be made by taking an anti-logit to obtain an estimate of the probability of these events occurring. Thus the probability of a base category respondent scoring highly on CAGE or being an unsafe drinker is approximately 0.03 (i.e. a 3% chance) and 0.14 (i.e. a 14% chance), respectively. The remaining coefficients are interpreted with respect to these constants and indicate differential effects for each variable allowing for other variables and the multilevel structure. For example, people above average age are less likely to be classed as problematic drinkers, either by reference to CAGE or units consumed. This effect of age is significant for both the CAGE questions (AGECAGE) and unsafe alcohol consumption (AGESAFE), but the influence of age on the CAGE variable is slightly stronger than its effect on unsafe drinking behaviour. Although the magnitude of the difference between the CAGE and the consumption coefficients is relatively small, further testing using a contrast hypothesis testing procedure based on the 
2 statistic (Duncan et al., 1993b) reveals that it is statistically significant (P 
0.05). This suggests that age has a more moderating effect on problematic drinking as revealed by CAGE compared to unit consumption. Non-linear functions of age were explored but were found not to have an effect on the model.
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The effect of being male on each of the responses is both positive and statistically significant (MALECAGE and MALESAFE). By adding these terms to the constants and taking anti-logits, it can be estimated that the probability for a male scoring highly on CAGE or being an unsafe drinker rises to 7 and 33%, respectively. There is a slightly higher gender effect with regard to levels of consumption but this difference is not statistically significant. Age–gender interactions were also included in the model (AGESEXCAGE and AGESEXSAFE) but these were found not to be significant. CAGE and consumption thus appear to perform similarly with regard to their association with gender. Being male is clearly linked to both high levels of consumption and to higher levels of self-assessed problem drinking. These results offer individual-level confirmation of the ecologically based conclusions of the study by Colhoun et al. (Colhoun et al., 1997
) on regional prevalences of heavy drinking and CAGE-defined problem drinking. The effect of marital status is statistically significant, but its influence is not as strong as gender. Being single has a positive effect that is of similar strength for both CAGE (SINGCAGE) and safe consumption (SINGSAFE). For a single woman (in contrast to the typical individual, a married woman), the chance of scoring highly on CAGE rises to 4 and 18% for unsafe consumption. The implication here is that the presence of a spouse or partner has a moderating effect that is similar for both dimensions of drinking behaviour. There are expected differentials between men and women in terms of the `protective' effect of marriage or cohabitation: for CAGE-defined problem drinking, marriage or cohabitation provides an additional one percentage `protection' for men, for the consumption measure there is a two percentage point effect.
Lower social class (i.e. social class IV/V) tends to reduce the probability for both dimensions of problematic drinking. The relationship, however, is not significant for the CAGE variable (IV/VCAGE). Being in social class IV or V reduces the chances of consuming unsafe levels of alcohol to approximately 11% for an otherwise stereotypical respondent (IV/VSAFE). The same pattern also arises for the manual (3MCAGE and 3MSAFE) and non-manual social class III groups (3NMCAGE and 3NMSAFE). In the latter case, both dimensions of drinking behaviour have logit values that are statistically significant. Further statistical testing confirms that there is no significant difference in magnitude between the two dimensions: being from a non-manual social class III background has a similar effect on CAGE score and patterns of unsafe alcohol consumption. Taken together, these findings challenge popular stereotypes of problem drinking as a characteristic of lower social class individuals. They may well reflect hidden unmeasured relationships with disposable income, the non-participation of people with serious drinking problems in surveys such as the HSE or class-related bias in estimating drinking. For those individuals where class cannot be assigned (CLMISSCAGE and CLMISSAFE), a significant reduction in both the probability of scoring highly on CAGE and in the probability of being categorized as an unsafe drinker is shown. This appears particularly influential for the CAGE variable, where the probability is reduced to approximately 1%. However, statistical testing indicates no significant difference between the two logit values.
In terms of tenure, the logit values for owning outright (OWNOUTCAGE and OWNOUTSAFE) are not statistically significant, and it can be assumed that there are no real differences between this group and the base category (i.e. owned through a mortgage). However, the values given for local authority/housing association renting (LAHACAGE and LAHASAFE) are significant and interestingly the direction of influence is different for the two dimensions of drinking behaviour. A similar situation also occurs for the private rented sector where again the result is to reduce the chances of being an unsafe consumer (PRIVSAFE) but increase the probability of scoring highly on CAGE (PRIVCAGE). Unlike social class, the missing category for tenure (TMISSCAGE and TMISSAFE), which also contains those few individuals who live `rent-free', is not significant on either of the dimensions of drinking behaviour.
The findings concerning rented tenures are of significance. Whilst being in the LAHA tenure category reduces the probability of being an unsafe drinker (as revealed by units consumed—LAHASAFE) to approximately 12%, the chances of being a problem drinker as revealed by CAGE is increased to just over 4% (LAHACAGE). Statistical testing reveals that these within-tenure differences are unlikely to have occurred through chance processes. It would seem therefore that people from rented tenures are less likely to report high levels of consumption but more likely to be concerned about their drinking habits. Explanations for this finding are likely to be complex and, in the context of this paper, only speculations can be made. It may be that the relative poverty associated with rented tenures leads to a lessened ability to afford alcohol. This hypothesis is strengthened by the reduction in the probability of being an unsafe drinker that is also experienced by people in lower social classes. At the same time, it is possible that people in rented tenures are more likely to under-report their levels of consumption. Higher levels of CAGE-defined problem drinking may result from the impact of differential patterns of drinking and consequential greater awareness of its problematic aspects among people in rented tenures—evidence suggests that binge drinking, for example, is more common among people with characteristics associated with rented tenures (Moore et al., 1994).
The influence of household on drinking behaviour is summarized by reference to the drinking habits of other members of a respondent's household. The derived drinking index (INDEXCAGE and INDEXSAFE) is influential on both dimensions of drinking. Both logit values are significant and positive but the value for unsafe/safe levels of consumption (INDEXSAFE) is almost twice that of the CAGE response. For a unit increase in the drinking index, the chances of consuming unsafe amounts of alcohol for the otherwise typical individual increase to 21%. The chances of a high score on CAGE increase to just 4%. The effect of single person households on the two dimensions of drinking behaviour can be estimated using the logit values for the dummy variables used to denote such households (ONEHSCAGE and ONEHSAFE). These are found to be positive for both dimensions but neither is statistically significant. Their influence on the response variables is minimal.
Table VI indicates the estimates of the higher level variances from the multivariate, multilevel analysis with level 4 representing the 177 DHAs and level 3 representing the 712 PSUs. It should be noted here that the figures relate to the variances and covariances of the model intercepts. Random slopes were not allowed for in the analyses. The covariance terms (SAFE/CAGE) measure the degree to which the two dimensions of problem drinking are related within the contexts of different levels. These terms cannot, however, be considered in isolation. If there is to be a meaningful relationship, in terms of covariation, then the two component aspects of drinking behaviour should also exhibit a significant degree of variation within the relevant level. Table VI indicates that there is significant intercept variation, albeit small, across DHAs for the SAFE response variable but not for the CAGE variable. There cannot therefore be any meaningful covariance at this level notwithstanding the fact that the reported negative covariance is, in any case, not statistically significant. This suggests that, at the DHA level there is little unexplained contextual variation. The compositional mix of an area (i.e. the characteristics of its individuals) is of most importance in explaining geographical variation in both CAGE scores and levels of unsafe consumption. This finding underlines the importance of going beneath the ecological analysis reported in the earlier study by Colhoun et al. (Colhoun et al., 1997). Moreover, though there is a very small negative covariance at this level, the lack of significance of that covariance and one of its component parts (CAGE/CAGE) indicates that there is no evidence that there are DHAs where the population is on average, drinking unsafe amounts of alcohol but not scoring highly on CAGE. At the PSU level there appears to be significant positive covariance suggesting that, at that level, areas with high CAGE scores also have high levels of consumption after taking account of the compositional mix. This finding is, however, undermined by a lack of significance in the two component parts of the covariance. In summary, Table VI suggests that there is very little ecological context or `real' geography to problem drinking as revealed by CAGE scores or units consumed. Instead, variation is determined by the characteristics of the people living in those geographical areas.
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Conclusions |
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The nature and extent of drinking behaviour is an important consideration in the formulation of public health strategy. This paper has explored the dimensions of drinking behaviour through an innovative multivariate multilevel analysis of two distinct measures of that behaviour. By undertaking simultaneous analysis of `unsafe' drinking as measured by number of units consumed and self-reported problem drinking as reflected by high CAGE scores, the paper has been able to distinguish between quantitative aspects of drinking behaviour and more qualitative perceptions of approaches to drinking.
The analysis presented within the paper has suggested that age, sex, marital status, class and household drinking patterns all exert similar influences on both dimensions of drinking behaviour. Consumption levels are reduced and CAGE scores lowered for older people, women and married (or cohabiting) people. Levels rise and CAGE scores increase for those in the higher social classes, a trend that challenges notions that people from higher classes may be less likely to define heavy drinking as problematic. As expected, those individuals living in households where other members are consuming high levels of alcohol are also themselves more likely to be heavy drinkers and classed as a (potentially) problematic drinker according to the CAGE questionnaire. This overall picture of similarity between the two dimensions of drinking behaviour does not hold for tenure. Rented tenures appear to exert different influences on each dimension such that people in rented accommodation are more likely to have high CAGE scores and less likely to drink to unsafe levels. Given the spatial concentration of these tenure groups in the UK, this finding has implications for the delivery of alcohol-related services and health promotion.
It is acknowledged that the measurement of drinking behaviour is a problematic and contested field. The analysis presented in this paper has employed established definitions of problem drinking drawn from government policy and published literature. It must be noted that these definitions are conservative and may exaggerate the number of unsafe drinkers with serious problems of alcohol misuse. The focus of the paper is not, however, on the utility of these survey instruments but rather with an investigation of the covariation of the two aspects of drinking behaviour which they purport to measure. In this regard the paper has provided a substantive contribution indicating the particular nature of that covariation taking account of the influence of contextual circumstances.
Key analytical gains from the simultaneous multivariate multilevel approach are evident in the above findings. Only by considering consumption levels and CAGE scores within the same model can we directly compare the two behaviours and assess the relative influence of any one variable on both dimensions of drinking behaviour at the same time. This is particularly evident in the application of simultaneous significance testing across all variables for each dimension of drinking behaviour. In the reported analysis, application of this procedure resulted in a recognition that age and household levels of drinking have statistically distinct relationships with each measure of problem drinking—albeit in the same direction. This procedure also confirmed the existence of a differential influence exerted by the rented tenures on each aspect of drinking behaviour. Analysis of higher-level intercept variation and covariation was also facilitated by the multivariate approach and revealed that there was little evidence for regional cultures of drinking. Once individual characteristics have been taken into account, there is also no significant evidence that there are places where CAGE scores and consumption do not co-vary as expected.
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Received on October 19, 1999; accepted on March 5, 2000
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