Abstract Background For alcohol, the association with socioeconomic status (SES) is different than for other public health challenges – the associations are complex, and heterogeneous between socioeconomic groups. Specifically, the relationship between alcohol consumption per se and adverse health consequences seems to vary across SES. This observation is called the ‘alcohol harm paradox’. This study aims to describe different patterns of alcohol use and potential problems. Next, the associations between sub-groups characterized by different patterns of alcohol use and potential problems, and age, gender, educational level, full-time employment, occupational level and income is analysed. Methods Employing data from the ongoing cross-sectional WIRUS-study, N = 4311 participants were included in the present study. Individual response patterns of the ten-item Alcohol Use Disorders Identification Test (AUDIT) were analysed and latent class analysis (LCA) was used to identify latent groups. Next, the associations between the classes identified in the best fitting LCA-model and sociodemographic factors were analysed and presented. Results We identified three classes based on the response patterns on AUDIT. Class 1 was characterised by low-level alcohol consumption and very low probability of negative alcohol-related consequences related to their alcohol consumption. Class 2 was characterised by a higher level of consumption, but despite this, class 2 also had a relatively low proba Abstract Background For alcohol, the association with socioeconomic status (SES) is different than for other public health challenges – the associations are complex, and heterogeneous between socioeconomic groups. Specifically, the relationship between alcohol consumption per se and adverse health consequences seems to vary across SES. This observation is called the ‘alcohol harm paradox’. This study aims to describe different patterns of alcohol use and potential problems. Next, the associations between sub-groups characterized by different patterns of alcohol use and potential problems, and age, gender, educational level, full-time employment, occupational level and income is analysed. Aims The overall aim is to identify and describe sub-groups of alcohol consumption and potential alcohol-related problems and investigate how these sub-groups relate to sociodemographic factors, including indicators of SES. This will be accomplished by a) investigating the individual response patterns of AUDIT and identify latent groups using latent class analyses (LCA). Next, b) the associations between the classes identified in the best fitting LCA-model and sociodemographic factors will be analysed and presented. The sociodemographic factors included are age, gender, educational level, employment level, occupational level and income. The following statistical criteria were used to decide on the number of classes to retain: Consistent Akaike information criterion (AIC), Bayesian information criterion (BIC) and adjusted BIC (aBIC) [ 26 ], where lower values indicate better model fit (Table 1 ). Also, we used entropy to assess the quality of classification (ranging from 0 to 1 with higher values indicating better discrimination between classes), as well as the likelihood-ratio between the different models. The LCA was done iteratively, beginning with one class (i.e. similar response patterns across all participants), and increasing the number of classes up to 5. managerial role; OR 0.84), full-time employment (OR 0.79) and higher income (OR 0.95; Table 3 ). In the adjusted model the same pattern of associations were observed but the association with full-time employment was no longer statistically significant (see Fig. 2 for crude proportion across indicators of socioeconomic status and Table 3 ). When comparing class 1 with class 3, there was an increased odds of belonging to class 1 with increasing age (OR 1.65), being female (OR 3.30), having higher education (OR 1.38) and higher income (OR 1.13), while there was a decreased odds of having a full-time employment (OR 0.70). There was no difference between class 1 and 3 with regards to occupational level. In the adjusted model a similar pattern was observed, but the difference in income was no longer statistically significant (Table 3 ). When comparing class 2 with class 3, there was an increased odds of belonging to class 2 with increasing age (OR 1.19), being female (OR 1.94), having higher education (OR 1.22) and higher income (OR 1.19). There was no difference between class 2 and 3 with regards to occupational level or full-time employment. In the adjusted model, the associations were similarly patterned, but education was no longer significant. 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). 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 [ 2 ]: 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