In the present study, we found support for 3 substantive classes based on the response patterns on AUDIT in a cohort of Norwegian workers. Class 1 could adequately be described in terms of low-level alcohol consumption both with regards to frequency and intensity/‘binge drinking’, as well as reporting a very low probability of negative consequences (as measured by items 4–10 on AUDIT) related to their alcohol consumption. Class 2 was characterised by a higher level of consumption both in terms of frequency and intensity, but despite this, class 2 also had a relatively low probability of reporting any negative consequences related to their alcohol consumption. The last class, however, was characterised by even higher levels of frequency and intensity of consumption, as well as a rather high probability of reporting negative consequences of their consumption. Exploratory post-hoc analyses did not support additional classes in sub-group analyses.
In relation to the included covariates, important differences were observed across classes. For age, gender and education, class 1 was characterised by older age, a higher proportion of females and higher educational attainment, and class 3 by younger age, more males and lower educational attainment. Class 2 fell somewhere in between these two classes with regards to these factors. For income, class 2 was characterised by higher income compared to the other classes, and class 3 were characterised by the lowest level of income. Comparing work-related factors, the differences were less overarching, and occupational level only differed between class 1 and class 2, where the latter class was characterised by higher occupational level. For full-time employment, class 1 was more likely to report less 100% occupation compared to the remaining classes. In the adjusted models, four of the associations were rendered non-significant, but only small changes to the point estimates were observed indicating little confounding. No other differences were observed between classes for the included covariates.
Interpretation of findings
The three different classes we identified makes intuitively sense with regards to the relationship between alcohol consumption patterns (AUDIT items 1–3) and self-reported alcohol-related consequences (AUDIT items 4–10). Our results also yields indirect support for the conventional cut-point of 8 for alcohol-related problems as the central tendency scores (mean and median) of the class described as ‘higher-level consumption, prone to negative consequences’ (class 3) was close to the suggested cut-point while the other classes central tendency scores were well below . The general distribution of age, gender and education across classes is in line with previous findings from the WIRUS-study where cut-points and the sum score of AUDIT was used . With regards to findings from other studies, we found that income and occupational level is independently differentially associated with the retained classes in mostly expected ways [3, 12, 32]. However, we also found that those with lower occupational level (compared to class 2), less income (compared to class 2) and less than 100% employment (compared to class 2 and 3) are more likely to belong to the low risk class (class 1), even though they are more likely to report higher education in this class (compared to class 2 and 3). This could be due to gender differences in the association between education and occupational for women) , and not a reflection of actual disparities between education and the other socioeconomic indicators in relation to alcohol. This notion was, however, not reflected in our findings as there was only small changes from unadjusted to adjusted estimates, despite the tendency for socioeconomic factors to cluster and co-vary. Residual confounding is however always an issue and it is possible that inclusion of other unmeasured covariates would have yielded greater evidence for confounding.
Some previous studies have adjusted for drinking patterns when investigating the association between SES and alcohol-related consequences [12, 15]. Our findings indicate that individual drinking patterns is intrinsically related to self-reported negative consequences, as the only identified class (class 3) with a high probability of reporting negative consequences of their alcohol habits also was the class characterised by substantially higher volume of consumption (quantity and frequency, item 1 and 2) as well as frequency of binge drinking (item 3). This suggests that the level of alcohol consumption and consumption patterns, and the consequences cannot be understood separately from each other. Class 3 also differed on key demographic and socioeconomic indicators from the other classes in meaningful ways – characterised by lower age, more males, lower education and lower income. Taken together, the clear relationship between alcohol consumption patterns (AUDIT items 1–3), self-reported alcohol-related consequences (AUDIT items 4–10) and socioeconomic status suggest that adjusting for alcohol consumption when investigating the association between socioeconomic status and alcohol harm may be futile due to their interconnectedness. Rather, alcohol habits and alcohol-related factors should be seen in conjunction in further research and efforts should be made to identify meaningful patterns and assess status and objective measures of alcohol harm.
Strengths and limitations
The present study is characterised by several strengths. First, the large study size enabled identification of different sub-groups as determined by their alcohol consumption pattern and related consequences using latent class analysis. Relatedly, the study uses a compound measure of alcohol, which incorporates both aspects related to alcohol consumption (i.e. quantity, frequency and binge drinking), and self-reported negative consequences of the consumption (i.e. injury, memory loss, and reduced functioning). Second, our study included several measures of socioeconomic status such as educational attainment and income. This enabled a more detailed investigation into differential association between different classes and aspects of socioeconomic status. Several limitations are worth mentioning. First, given the low participation rate, our findings should be interpreted with caution, as they may not be representative of the whole invited sample. Due to data protection regulations, we are not able to compare non-participants and participants directly, but comparisons between the invited sample and the participants finds that gender composition among the participants are similar to the invited sample (p = 0.172). Those participating were, however, somewhat older compared to the invited sample (p < 0.001; 68.1% aged 40 or above among the participants versus 63.7% in the invited sample). The participants were recruited from a wide range of private and public enterprises. However, previous studies have reported that the WIRUS-sample is characterized by an overrepresentation of older, highly educated, and female employees compared to the entire Norwegian workforce [19, 21]. On the other hand, the WIRUS-sample can be considered more representative when it comes to the composition of gender and educational attainment among public and state sector employees. These considerations may limit the generalisability and external validity of the findings from the present study. Also, our findings are not necessarily generalisable to other populations. Most of our participants had an employment size of 100% or more, and the lack of variation thereof limited our ability to analyse differences with respect to employment size. Ideally, the distribution of employment size should be wider in order to investigate this indicator more fully. Furthermore, we were only able to discriminate between three broad levels of occupation. Ideally, a higher degree of differentiation would be preferable to shed more light on the role of education. Third, the WIRUS-study does not include questions regarding other life-style or health factors, such as smoking, diet, physical activity, general or mental health. Inclusion of such factors would have yielded more information regarding the relationship between the identified classes and measure of socioeconomic status, such as for instance abstention from drinking due to chronic health conditions or former alcohol-related problems. Fourth, despite the inclusion of several measures of socioeconomic status, even more measures would increase the value of our findings. This includes for instance area- or neighbourhood-based deprivation, home/car ownership as socioeconomic status is a complex phenomenon with many facets. Fifth, we were not able to identify a class defined by consistently very high scores across AUDIT-items. This would have been interesting based on previous studies which have highlighted high-risk alcohol groups (see for instance Lewer and colleagues ) as particularly interesting for understanding the alcohol-harm paradox. The range of AUDIT scores are limited (0–26), and less than 1% of participants scored more than 16 in total, and this is probably partly due to being recruited from a working population.
The findings from the latent class analyses identified a rather large class (≈15%) with both higher levels of alcohol consumption and a proneness to negative consequences related to their drinking. We were not able to further differentiate this group, and despite being more likely to report negative consequences must be regarded as within the lower end of the risk spectrum as described by Babor and colleagues . That would translate into simple advice as the recommended intervention in a public health perspective . Our findings further highlight a need for differentiation of various aspects of alcohol-related harm and behaviour, and demonstrate a differential association of individual factors often used to gauge SES. Specifically, our findings indicate that being young, male, having low educational attainment and low income were associated with particular exposure to both high levels of alcohol consumption and alcohol-related harm. Such knowledge may have practical implications for alcohol-preventive efforts within the frame of workplace interventions. For instance, companies that to a large extent employ individuals associated with these sociodemographic characteristics, should make alcohol-preventive efforts an overall priority both in terms of general alcohol education, but also more targeted approaches for those that report both higher levels of alcohol consumption and a proneness to negative consequences related to their alcohol habits.
Future direction: the alcohol harm paradox
As mentioned in the introduction, both ‘differential exposure’ and ‘differential vulnerability’ is relevant when trying to understand mechanisms underlying social inequalities in health. Regarding alcohol harm and differential vulnerability, several aspects may be relevant. First, it is plausible that other life-style factors, such as smoking, unhealthy diet and less physical activity, increases the vulnerability for exposure to alcohol. A recent study concluded for instance that combinations of lifestyle factors such as smoking, excessive alcohol consumption, poor diet and low physical activity is associated with an excess risk for poor health in socioeconomically deprived populations . Despite such findings, Katikreddi and colleagues (2017) reported that smoking and obesity could not help explain the increased alcohol-attributable harm observed among lower versus higher socioeconomic groups . Studies informed by twin studies also indicate that alcohol habits are associated with socioeconomic indicators that cannot be explained by shared exposures or a range of potential confounders . Second, other factors such as chronic stress , stressful life-events [35, 36], stigma , mental health problems  and poor health in general  may put individuals in lower socioeconomic status groups at a higher risk for alcohol harm despite similar consumption patterns. Third, a more limited access to high-level quality health care [40, 41] or less efficient application of available health information (‘health literacy’) [42, 43] may also result in excess exacerbation of symptoms or conditions attributable to alcohol in groups with lower socioeconomic status compared to groups higher on the socioeconomic ladder. The abovementioned vulnerability-factors may modify the relationship between alcohol exposure and alcohol-related harm in complex ways. We are not aware of studies which have specifically investigated this and future research need to further address differential exposure and differential vulnerability as a potential mechanism. In that respect an evidence review, Roche and colleagues highlighted some of the challenges with respect to giving an overview of social inequality in alcohol-related health :
The use of different measures of socioeconomic status as well as alcohol use across studies
Different determinants can interact with each other in a multitude of ways
The different components of socioeconomic status can act as mediators for each other
Disadvantaged groups may be encumbered by several risk factors, which in turn can interact and modify each other
Navigating these abovementioned factors in future research will provide further knowledge about the true relationship between alcohol consumption patterns, alcohol harm and socioeconomic status.