Drinking Less But Greater Harm

Could Polarized Drinking Habits Explain the Divergence Between Alcohol Consumption and Harms Among Youth?

Mats Hallgren; Håkan Leifman; Sven Andréasson

Disclosures

Alcohol Alcohol. 2012;47(5):581-590. 

In This Article

Results

Alcohol Abstention Rates

Between 2000 and 2010, there has been a steady increase in the number of young people in Stockholm who abstain from alcohol completely (Fig. 2). The largest increase in abstention was among Year 9 females and males (up 83 and 82%, respectively).

Figure 2.

Percentage change in alcohol abstention rates. The figure shows changes over time in the percentage of young people who abstain from alcohol, by school year and gender.

Per Capita Alcohol Consumption

Changes in per capita consumption are shown in Fig. 3. Per capita consumption reduced between 2000 and 2010, mainly because of the increase in non-drinkers over the same period. However, even when abstainers are excluded, significant reductions were found for Year 9 males [t = 5.86(3409), P < 0.000], Year 9 females [t = 4.27(3559), P < 0.000] and Year 11 males [t = 3.91(3507), P < 0.000]. Year 11 females were the only group to increase their consumption between 2000 and 2010 (by 20%); however, the increase was not statistically significant.

Figure 3.

Changes in per capita alcohol consumption. The figure shows changes over time in per capita alcohol consumption (drinkers only), by school year and gender.

Table 2 , Table 3 , Table 4 and Table 5 illustrate important changes at different levels of drinking over the past decade. The data show consistent reductions in per capita consumption between 2000 and 2010 across most levels of drinking, except among the very heaviest drinkers where, in most cases, the trend reverses and consumption begins to increase over time. This pattern of consumption is most evident among Year 9 males ( Table 2 ), where reductions in drinking occur up to the 93rd percentile, and then consumption increases over time among the heaviest drinkers. A similar pattern was found among males and females in both school years, but the trend is stronger for Year 9 males, compared with Year 9 females. The switch to increasing consumption occurs at different levels of drinking, depending on school year and gender: for example, at the 93rd percentile for Year 9 males; at the 92nd percentile for Year 11 males and at the 40th percentile for Year 11 females.

Dispersion of Alcohol Consumption

Table 2 , Table 3 , Table 4 and Table 5 show changes in both the shape (skewness and kurtosis) and the dispersion of the consumption data between 2000 and 2010. The high SD in comparison with the mean indicates that the data are heavily skewed. Both the SD and the CV increased between 2000 and 2010 across all the four groups, except Year 9 females. The skewness of the data also increased over time (except for Year 9 females), indicating that more young people drink at very high levels, while the majority continue to drink less.

Using log-transformed data, we examined changes in the homogeneity of variance between 2000 and 2010. The results indicate significant increases in the dispersion or spread of the alcohol consumption data between 2000 and 2010 for Year 9 males (F = 15.84, P < 0.000), Year 9 females (F = 7.99, P < 0.005), year 11 males (F = 13.89, P < 0.000) and Year 11 females (F = 8.57, P < 0.003).

Binge Drinking

Changes in heavy episodic drinking (binge drinking) are particularly relevant because this pattern of consumption is often associated with serious acute harmful effects among young people (Babor et al., 2010). In the Stockholm Student Survey, binge drinking was defined as the consumption of at least one bottle of wine, five to six shots of spirits or four cans of full-strength beer (or six cans of medium strength beer), consumed during a single occasion. Changes in the estimated yearly frequency of binge drinking are shown in Fig. 4.

Figure 4.

Estimated yearly frequency of binge drinking. The figure shows changes over time in the estimated frequency of yearly binge drinking (number of times per year) by age and school year.

As might be expected, males reported binge drinking more frequently than females, and older adolescents binge drank more than younger adolescents. Year 11 females were the only group to significantly increase their binge drinking, from about 10 times per year in 2000 to 13 "times per year in 2010 [t = − 6.51(3501), P < 0.000]. By contrast, binge drinking decreased significantly among Year 9 males [t = − 3.67(3157), P < 0.000], while Year 9 females and Year 11 males remained fairly constant. The dispersion of binge drinking frequency scores, assessed by Levene's homogeneity of variance test, reduced significantly between 2000 and 2010 in Year 9 males (F = 26.05, P < 0.000) and increased significantly among Year 11 females (F = 20.24, P < 0.000).

To determine whether binge drinking increased over time among the heaviest alcohol consumers, we examined changes in the frequency of yearly binge drinking among the heaviest 5% of drinkers, compared with the remaining 95%. Binge drinking 'once per year or more' increased between 2000 and 2010 among the top 5% of young people by 14.5%, but decreased among the majority of lighter drinking students by 15.2%. This result might be expected because most of the consumption occurring in the top 5% of the distribution is heavy episodic drinking.

Risk Factors for Alcohol Misuse

There was a large and statistically significant correlation between the per capita alcohol consumption and the risk factor total score (r = 0.468, P < 0.000), indicating that as consumption increased, so did the number of risk factors. This relationship was present for both males (r = 0.463, P < 0.000) and females (r = 0.472, P < 0.000) when analysed separately. However, the association weakened considerably when only the heaviest drinkers were examined (i.e. >20 l per year) (r = 0.123, P < 0.000). Although the association remained significant among the heavy drinkers, possibly because of the high number of participants, the weak correlation coefficient indicates that only a small proportion of the total variance in scores could be explained by this relationship. Table 6 shows changes over time in the mean number of risk factors for alcohol misuse when all the participants were included in the analysis. The data indicate a trend towards fewer risk factors over time, but also some variability in scores between years. Unlike the consumption data, which increased in dispersion over time, indicating more heavy drinkers and a likely polarization effect, the dispersion of the risk factor total score reduced significantly between 2000 and 2010 (F = 20.32, P < 0.001) (Levine's test). This trend was observed for both school years and genders, and when drinkers only and all youth (including abstainers) were examined separately. Other measures of dispersion, including the coefficient of variation, indicated no significant changes in either direction.

Table 7 shows the mean number of risk factors by year for the heavy drinkers only (i.e. young people consuming 20 l or more alcohol). The heaviest drinkers report more risk factors for alcohol misuse compared with the majority of young people who consumed less alcohol, but with large variability between years. There were no significant changes in the number or dispersion of risk factors over time in this category.

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