The finding of extensive alcohol use in mid-adolescents is consistent with recent UK survey data. For instance, these report that most young people by age 16 have tried alcohol by age 16, that there seem to be little difference in alcohol across the ages for girls and boys and, by age 15 ∼40% of children will be drinking weekly (Currie et al., 2008; NHS Information Centre, 2009). What these cross-sectional surveys, however, fail to capture is how drinking patterns evolve for different subgroups of children. While it is true that alcohol consumption increases during adolescence—there are different patterns of use—with some children engaged in high-frequency and high-consumption drinking. It is notable that our cohort and other recent surveys fail to detect differences between girls and boys due most likely to increase in consumption by girls. For example, the recent report of the Adult Psychiatric Morbidity Survey (NHS Information Centre, 2007) reported comparative increases in alcohol dependence in young women relative to men aged 16–24.

The categorization of adolescent alcohol use as high, medium and low in terms of both frequency and quantity of consumption may seem self-evident and the added value of using latent class analysis to derive these categories may not be immediately apparent. Nevertheless, there are a number of advantages of this data-driven mixture-modelling approach. The first is the ability to deal with a large number of different response patterns—there are 125 of these for typical consumption in the complete-case data set alone; secondly this approach allows for the inclusion of more participants as a partial set of responses is permitted—it would not be trivial to assign respondents to groups by hand if their response pattern was incomplete; thirdly, by modelling latent class assignment in a probabilistic way, uncertainty can be preserved allowing all respondents to be assigned irrespective of how unusual their response pattern may be.

Previous work describing patterns of drinking in adolescents has used a variety of measures including frequency of heavy use (Tucker et al., 2003; Jackson et al., 2005), frequency of any use (Blozis et al., 2007), recent quantity of ethanol consumed (Wiesner et al., 2007) or composite measures derived from a series of questions on alcohol use and misuse (e.g. Komro et al., 2010). This work has typically led to the extraction of between two and five classes, e.g. two (Li et al., 2002), three (Flory et al., 2004), four (Jackson et al., 2008) and five (Tucker et al., 2003). It has recently been argued that the number of classes identified in such analyses may be sensitive to a various aspects of the data and chosen model (Jackson and Sher, 2005, 2006, 2008) and that an apparent phenotypic variation within and between study samples may partly reflect differences in the number and type of measures used and the age-range modelled rather than true differences in substance use behaviour between cohorts. As far as we are aware, the current study is the first in the UK to apply these methods to adolescent alcohol use. We have found that changing patterns of drinking frequency and typical consumption between 13 and 15 can each be adequately summarized by three classes of behaviour. While the class prevalence differed depending on the repeated measure employed, the association between class membership and key risk factors for alcohol use was robust to the variables chosen as was the shape and interpretation of the patterns of use extracted. In a their paper which employed similar measures of drinking frequency and consumption, Casswell et al. (2002) concluded that quantity and frequency of drinking in adolescence have different trajectories; however, our own work does not fully support this finding. A possible explanation would be that their models spanned the ages of 18–26 years where a wider range of drinking patterns would be expected.