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

Materials and Methods

Data on the alcohol consumption among youth and risk factors are obtained from the Stockholm Student Survey, a repeated cross-sectional self-report questionnaire completed every second year by high-school students in Year 9 (aged 15–16 years) and Year 11 (aged 18–19 years) in the Stockholm municipality. The survey, financed by the Education Department, is conducted during the spring period, in which the questionnaire is completed anonymously by students during class time before being returned in a sealed envelope to teachers. Students absent from school because of illness were posted a questionnaire to be completed at home and then returned by regular mail. The survey includes ~350 questions covering demographic information, alcohol and drug use (frequency, quantity and type) and various risk/protective factors for alcohol misuse, including criminal behaviour, psychosocial health, truancy, school and parental support (El-Khouri et al., 2005). The questionnaire is the largest youth alcohol and drug survey in Stockholm and is used to monitor important changes in health-related behaviour. Participation in the survey is mandatory for all public schools, which comprise ~90% of all schools in Stockholm. Independent (fee paying) schools participate voluntarily.

Participants

As the survey was completed during school hours and supervised by a class room teacher, the response rates were high; ranging between 75 and 80% across all six survey years (2000, 2002, 2004, 2008 and 2010). The student participants represent ~56% of all young people in their age group living in Stockholm. As the total number of schools in Stockholm (including independent schools) expanded considerably between 2000 and 2010, so did the number of students who completed the survey—from 8915 students and 76 schools in 2000 to 15,746 students and 182 schools in 2010. Approximately, equal numbers of males and females in both school years participated. As the survey was anonymous, non-responders could not be followed up for comparison purposes. Separate analyses were conducted to examine whether changes over time in the number of participating schools influenced the results, but no effects were found (data not shown).

Alcohol Consumption

The frequency and the quantity of alcohol consumption during the past 12 months were assessed on a 12-item questionnaire. Questions about the quantity of alcohol were answered on a 9-point scale; for example, 'When you drink wine, approximately how much do you normally drink?' with responses ranging from 1 corresponding to 'less than a glass <15 centilitres' to 9 'more than three bottles.' The frequency scale follows the same format; for example, 'How often have you drunk wine during the past 12 months?' With responses ranging from 1 corresponding to 'every day' to 9 'I have not drunk wine at all during the past 12 months'. Per capita consumption (in centilitres) was determined by multiplying the quantity and the frequency of reported consumption from these scales. Changes in binge drinking over time were assessed with a single question: 'How often have you consumed the following amounts of alcohol during a single occasion?': at least one bottle of wine, five to six shots of spirits or four cans of strong beer (or six cans of medium strength beer). This is an established measure, used in annual alcohol surveys in Sweden since 1972, and roughly equivalent to 'five drinks' (CAN, 2010). Estimates of the yearly frequency of binge drinking were determined by converting statement response alternatives into numerical scores; for example, 'a few times per year' became 'three times per year', etc.

Risk Factors for Alcohol Misuse

Thirteen risk factors for alcohol misuse were identified in the questionnaires on the basis of previous research. Our main interest was whether or not there was a polarization in the risk factor total score, with changes over time following a pattern similar to that of the consumption data. We used a theory-driven approach to identify the 13 risk factors, focussing both on the international literature (Hawkins et al., 1992; Petridis et al., 1995; Zufferey et al., 2007; Merline et al., 2008) and recent Swedish studies of youth alcohol consumption (El-Khouri et al., 2005; Bränström et al., 2008; Danielson et al., 2010) to guide our selection. Risk factors (e.g. parental provision of alcohol, having friends who drink, truancy from school, etc.) are circumstances or personal characteristics that are presumed to increase the likelihood of hazardous or harmful drinking. Our goal was to determine whether or not the total number of risk factors had changed significantly over time in the entire population surveyed, compared with the heaviest drinkers. Here, we defined 'heavy drinkers' as young people who consumed 20 l or more alcohol per year, which is approximately the level at which the polarization effect in consumption emerges in the aggregated data. We expected to see a reduction in the total number of risk factors among the majority of students (those drinking <20 l of alcohol per year), but an increase among the heaviest drinkers, between 2000 and 2010, following a trend consistent with the consumption data changes. The 13 risk factors selected were present in each of the six survey rounds. Questions with more than two response alternatives were dichotomized as shown in Table 1. One item 'anti-social behaviour' was composed of nine separate items. A total anti-social score was calculated and then dichotomized so that approximately one-third of the highest responses were coded as 1 (risk factor present).

Data Analysis

The survey data were 'cleaned' by removing clearly inappropriate responses from students who indicated the highest possible score on every item in both the alcohol and the drug questionnaires (<1% of responses). Examination of the distribution of alcohol consumption revealed that few students drank >100 l of pure alcohol per year. On the basis of this analysis, we determined that a 100 l cut-off was reasonable because it excluded students who deliberately exaggerated their drinking habits. For comparison, however, we also examined changes in consumption levels over time using a 30 and 50 l cut-off, including and excluding the abstainers, and found that the trends were similar regardless of which cut-off was used. As the consumption data were heavily skewed, and so not to violate parametric test assumptions, we log-transformed the data before conducting t-tests and tests of the homogeneity of variance. Data imputation was considered unnecessary because of the high questionnaire completion rate (above 95% on most items), and the high number of study participants.

Changes in mean consumption and binge drinking were examined for 6 years: 2000, 2002, 2004, 2006, 2008 and 2010. To look more closely at changes at different levels of consumption over time, we report changes in the centilitres of alcohol consumed each year by percentile ranks (from the 1st to the 99th). Changes in consumption are reported by school Years (9 and 11) and gender. As the data were cross-sectional and different student populations were surveyed each year, we used independent sample t-tests (with Bonferroni adjustment for multiple comparisons) to examine the significance of changes in per capita consumption between years, with our main focus on changes that occurred between 2000 and 2010. Changes in both the shape (skewness and kurtosis) and the dispersion of the data [SD, coefficient of variation (CV)] are reported in Table 2 , Table 3 , Table 4 and Table 5 . Changes in the dispersion of the consumption data and the risk factor total score were also assessed with Levene's test, which determines the statistical significance of changes in the spread or homogeneity of the data between 2000 and 2010. Increased dispersion means more heterogeneous data, and vice versa. We also explored changes in the total number and dispersion of risk factors over time, on the basis of the risk factor total score, including the percentage of respondents with different numbers of risk factors for alcohol misuse. As the consumption data were heavily skewed, we used Spearman's non-parametric bivariate correlation to test the relationship between alcohol consumption and the risk factor total score. All the analyses were performed using SPSS version 20.0.

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