Social Inequality and Substance Use and Problematic ...

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Social Inequality and Substance Use and Problematic Gambling Among Adolescents and Young Adults: A Review of Epidemiological Surveys in Germany Dieter Henkel*,1 and Uwe Zemlin2 1

Institute of Addiction Research, Frankfurt University of Applied Sciences, Frankfurt a.M., Germany

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AHG Klinik Wilhelmsheim, Oppenweiler, Germany Abstract: The current review provides an overview of socioepidemiological research in Germany about the prevalence of addictive behaviours (smoking, binge and hazardous drinking, consumption of cannabis and other illegal drugs, the non-medical use of prescription drugs and problematic gambling) among adolescents (11-17 years) and young adults (18-25 years), also differentiating between different socioeconomic status (SES) indicators (attended school type, family affluence, parental occupational Dieter Henkel status, parental SES, employment status) and migration background. The authors evaluated data from ten national surveys and one regional survey conducted between 2002 and 2012, which included different samples. The trends over this time frame reveal that the proportion of adolescents who smoke tobacco, show problematic patterns of alcohol consumption, use cannabis or other illegal drugs has generally declined over the investigated time span in Germany. The results nevertheless suggest that some strong associations still exist between social inequalities and the prevalence of substance use. The detailed results are summarised in twelve tables. The main results are as follows: 1) Low SES (school type, employment status) was consistently associated with more cigarette smoking, and, where such data was available, this pattern was observed in both males and females. 2) With regard to family affluence, two surveys show that boys with low and middle FAS are significantly less likely to have binge drinking experience compared to boys with high FAS. There were no significant associations between problematic alcohol use and parental SES, and not all results of the surveys show that binge drinking is more prevalent among HS-students. Employment status was associated with gender differences; problematic patterns of alcohol consumption were significantly more prevalent among young unemployed males compared to GY-students (secondary high school/grammar school) of the same age. The opposite was true for females. Another consistent finding was that among adolescents and young adults with a Turkish/Asian migration background, a problematic use of alcohol was significantly less common compared to adolescents and young adults of the same age without a migration background. 3) In terms of the consumption of cannabis, the unemployed and students with low educational level (`Hauptschule´) emerge as high-risk groups. 4) The results concerning problematic gambling significantly overlap with and reflect the findings of international research: being male, of low educational attainment, unemployed, receiving social welfare, and having a migration background significantly increased the risk of problematic gambling habits. 5) The highest lifetime prevalence rates for the consumption of illegal drugs (other than cannabis) were observed among students with low educational level. It should be noted that other SES indicators, in addition to school type, have not been examined to date. The review concludes by outlining gaps and future research areas, as well as presenting several implications for prevention initiatives.

Keywords: Social inequality, SES, tobacco, alcohol, cannabis, illegal drugs, prescription drugs, problematic gambling, adolescents, young adults, Germany. 1. INTRODUCTION Social inequality is a multi-faceted phenomenon. It characterizes the unequal distribution of property and power, income and wealth, education and qualification, participation in the labour market, access to health provisions and social security, as well as opportunities to partake in social and cultural activities. The specific social situation of each individual is furthermore determined by his status as native resident or resident with a migration background.

*Address correspondence to this author at the Ebertsbronn 31, D-97996 Niederstetten, Germany, Tel: (49)7932-604786. E-mail: [email protected]

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Social epidemiology aims to document the prevalence of addictive behaviours in different social groups. The generated insight provides important and necessary insights for targeted drug prevention activities. The international research about adolescent and young adults has focused on a specific number of factors that contribute to social inquality: socioeconomic status (SES: income, education, occupation), employment status (employed/unemployed), material deprivation/poverty, and migrationstatus. Hanson and Chen [1] produced a review summarizing the findings from 97 epidemiological studies about tobacco, alcohol and cannabis consumption in Western countries. These authors concluded that adolescents with low SES (e.g.

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Current Drug Abuse Reviews, 2016, Vol. 9, No. 1

family income, parental education or occupation) are much more likely to smoke than high SES adolescents. However, country-specific differences play an important role. For example, SES was a stronger predictor of smoking in northern European countries. However, smoking and SES were not significantly related in southern European countries [2, 3]. In terms of alcohol and cannabis consumption, Hanson and Chen [1] concluded that SES is not significantly linked to alcohol and cannabis consumptions during adolescence. However, problematic patterns of consumption (e.g. binge drinking, hazardous drinking) were not the focus of this research. In contrast, the meta-analysis by Lemstra and colleagues [4], summarizing findings across a sample of 200,000 adolescents in the USA, Great Britain, New Zealand, Finland, and Italy, did observe a significantly higher prevalence rate for both cannabis and alcohol risk behaviour among adolescents with low SES (parental income, parental education and occupation) in comparison to adolescents with higher SES. However, it is unclear how cannabis and alcohol risk behaviours were conceptualized. Evidence from the European literature about binge drinking shows that this pattern of alcohol use is much more common among adolescents with a low educational level, particularly in the nothern and middle parts of Europe compared to the southern parts [5]. A review of the international literature (e.g. USA, New Zealand, France, European countries) [6] concluded the following with regard to employment status: Unemployed adolescents and young adults were found to have a significantly higher rate of substance use and abuse compared to their employed counterparts. Problematic behaviours include binge and hazardous drinking, illegal drug use, cannabis dependence and smoking. Furthermore, based on twenty longitudinal studies the review [6] demonstrates that job loss/unemployment is a risk factor for young adults to develop substance use problems and substance use disorders (smoking, alcohol and cannabis use). For example, a longitudinal study from New Zealand showed that long-term unemployment significantly increases the risk of alcohol and drug abuse/dependence [76]. Another study provided further support for this as well, showing that low socioeconomic status in childhood exacerbates the risk for heavy episodic drinking and smoking [7]. And finally, a representative study covering different European countries (N=4,695) further revealed that unemployment was a discrete predictor of binge drinking among adolescents, even when controlling for age, gender and education [47]. Social and material deprivation tends to be multidimensional; various factors come into play. These include low educational status, poor employment status (precarious work, unemployment), low income/living on social welfare, precarious housing, and being socially isolated. Representative US studies link deprivation and consumption of various substances. Baumann et al. [8] found that increasing levels of deprivation made it more likey that individuals smoked tobacco, abused alcohol and used psychotropic drugs (prescribed or non-prescribed). Results from a longitudinal study show that when adolescents experienced poverty over extended periods of time during their adolescence, they were more likely to drink heavily [9]. Fettes et al. [10] further observed that substance use was

Henkel and Zemlin

higher among youths involved in some welfare scheme, specifically in terms of cannabis use over the life span and other illegal drugs. Problematic gambling was the focus of a comprehensive review of the international literature [11]. This review considers a number of sociodemographic factors by critically evaluating 27 empirical studies. The authors identified several potential risk factors for problematic gambling: being an adolescent or young adult, being male, being unemployed, receiving social welfare, having a low level of academic achievement, and having a migration background (e.g. in US studies: African-American, Hispanic or Asian background). The epidemiological research has consistently shown that substance use rates of migrant or ethnic minority groups often differ significantly in two ways. First, the prevalence rates between these groups and majority ethnic groups tend to be quite different. And second, prevalence rates also differ significantly between the migrant and ethnic groups as well. This also applies to adolescents and young adults [see, for example, 12 and 13]. As a result, factors such as migration and ethnic background represent important sociodemographic variables to be considered in ths research as they provide a complete and sufficiently differentiated picture about the type of addictive problems that are experienced by different groups in the population. Germany is one of the richest countries in the world. Nevertheless, a number of serious social problems exist. According to recent figures, around 2 million resp. 18% of all adolescents under the age of 18 are living in povertystricken families. In addition, around 280.000 (6%) young adults between 18 and 25 years of age are currently unemployed. And 5.6 million individuals in this age group have a migration background (came to Germany as a child or have at least one parent who move to Germany from abroad) [14]. These circumstances highlight the importance of researching the relationship between social unequality and addictive behaviours among different groups in Germany. 2. AIMS The aim of the present review is to provide an overview of current socioepidemiological research in Germany, focusing on two questions in particular: (1) To what extent are the prevalence rates of addictive behaviours (such as tobacco smoking, the problematic consumption of alcohol, cannabis and other illegal drugs, the non-medical use of prescription drugs as well as problematic gambling) among adolescents and young adults linked to indicators of social inequality? (2) To what extent have social inequalities on substance use changed (increased or decreased) over time since 2003? To date, no other review has summarised the empirical evidence from Germany in order to address these questions on the basis of different representative surveys. In addition, the current review will specify research gaps and summarise implications for future prevention programmes. 3. METHODS The authors conducted an extensive literature search between January and March 2015 using the following electronic Databases: Pubmed; PsychIndex, Google, Google

Social Inequality and Substance Use and Problematic Gambling


scholar. The search covered all publications between 2000 and 2015. The keywords and related search terms were as follows: Germany/German, survey, adolescents, young adults, social inequality, socioeconomic status (SES), educational level, family income/affluence, migration background, employment status, smoking/tobacco, binge drinking, heavy/hazardous alcohol consumption, illegal drugs, cannabis, prescription drugs, problematic gambling, health behaviour, prevalence. Additional papers and surveys were identified via citations in other papers. This process was repeated until no additional publications could be located. The final selection included some papers that had not been peer-reviewed. Most of the prevalence rates reported in German surveys (see Tables 2-13) were obtained from scientific research reports produced and published by research institutions rather than peer-reviewed articles. Only publications in German or English were considered.

prescription drugs and problematic gambling; (e) studies on alcohol were limited to those that revealed problematic patterns of use (binge or hazardous drinking); (f) the studies included at least one indicator of social inequality (SES). Using these criteria, 10 surveys with different samples were identified. 4. SURVEYS The chosen epidemiological studies included the following surveys; these can be grouped into four categories (Table 1).

The present review draws on studies that met the following criteria: (a) The studies were conducted in Germany between 2000 and 2015; (b) they included nationally representative population-based cross-sectional samples; (c) the samples included participants between the ages of 11 to 17 (adolescents) or 18 to 25 (young adults); (d) the surveys measured prevalence rates of smoking, alcohol consumption, illegal drug/cannabis use, non-medical use of Table 1.

1)

HBSC-surveys (`Health Behavior in School-aged Children´, University of Bielefeld, www.hbscgermany.de). Another representative HBSC-survey was conducted for the state of North Rhine-Westphalia (17.6 million inhabitants) to examine problematic gambling. This HBSC-PG-survey was also included in the present review even though it was not based on a nationally representative sample because the survey included information that was not included in other surveys.

2)

DA-surveys (`Drug Affinity Studies´) from the Federal Centre for Health Promotion (BZgA, Cologne, www.bzga.de).

German socio-epidemiological surveys: sample size, age, SES-indicators/migration background, addictive behaviours Surveys

N

Age

SES-Indicators,

Addictive Behaviours

Migration Background HBSC 2010

HBSC 2006

HBSC 2002

HBSC-PG 2002 DA 2012

DA 2010

KiGGS 2006

4,978

11-15

school type

S, BD

family affluence

S, BD, C

migration background

S, BD

7,274

11-15

school type

S, BD, C

2,455

15

parental occupational status

S, BD, C

family affluence

S, BD, C

school type

S, BD, C


S, BD, C

family affluence

S, BD, C

5,650

5,009

11-15

13-15

school type

PG


PG S, BD, HC, C

1,942

12-17

school type

3,057

18-25


BD, HC, C

employment status

S, BD, HC, C

2,686

12-17

school type

S, BD, HC

4,251

18-25

employment status

S, BD, HC


BD, HC

6,813

3

14-17

school type

S, C

parental SES

S, C


S, C

KiGGS 2012

5,258

11-17

parental SES

S, BD, HC

ESPAD 2011

6,192

15-16

school type

S, BD, HC, C, ID, PD, PG

ESPAD 2007

12,448

15-16

school type

S, BD, HC, C, ID, PD

ESPAD 2003

11,043

15-16

school type

S, BD, HC, C, IG, PD

Note: S smoking, BD binge drinking, HC hazardous alcohol consumption, C cannabis consumption, ID illegal drug use except cannabis, PD non-medical use of prescription drugs, PG problematic gambling.

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3)

ESPAD-surveys (`The European School Survey Project on Alcohol and other Drugs`, Institute for Therapy Research IFT, Munich, www.ift.de).

4)

KiGGS-Surveys 2006 and 2012 (`Children and Adolescents Health Survey´, Robert Koch-Institut RKI, Berlin, www.kiggs-studie.de).

Henkel and Zemlin

outlined by the DSM-IV [18]. The 2011 ESPADsurvey recorded only the frequencies with which participants engaged in different types of gambling activities during the last 12 months (e.g., card games, gambling machines, sport bets). 5)

The ESPAD-surveys also asked students as to whether or not they had ever consumed any illegal drugs (not including cannabis), such as amphetamines, ecstasy, LSD, cocaine, crack, heroin and psychotropic mushrooms. The present review describes the frequency of consumption of any of these drugs over the lifespan.

6)

The ESPAD-surveys also asked pupils about their non-medical use of prescription drugs, specifically, whether they had ever consumed tranquilizer/sedatives.

4.1. Definitions of Substance Use and Problematic Gambling Overall, the surveys examined the following specific characteristics of substance use and problematic gambling (assessed via self-report in all surveys): 1)

2)

3)

4)

Regular smoking, daily smoking, heavy smoking (>10/>20 cigarettes per day), age at which individuals first started smoking, electronic cigarette smoking (ecigarettes with liquids containing nicotine) and second-hand smoking among non-smokers (based on individuals spending time at least several times a week in facilities where others smoke). In terms of alcohol consumption, the present review considered two patterns of consumption which can be viewed as clearly problematic: (a) binge drinking (heavy episodic drinking) was examined in all surveys (except the data collected by KiGGS-survey 2006) using the same international standard [5] to define binge drinking as behaviour that leads to intoxication due to the consumption of five (or six, KiGGs-survey 2012) or more units in one sitting; (b) hazardous consumption of alcohol (alcohol consumption that leads to long-term health risks) as indicated by an average daily intake of more than 12g/20g for girls or 24g/30g of pure alcohol for boys (according to DA-surveys/ESPAD-surveys). The authors of the KiGGS-survey 2012 [8] assessed hazardous consumption by using the internationally established brief screening test AUDIT-C [15]. In terms of cannabis consumption, the frequency of consumption was examined in terms of three measures: prevalence over the lifespan, use during the last 12 months and/or the last 30 days. The 2007 and 2011 ESPAD-surveys also included the six-item Cannabis Abuse Screening Test (CAST) by Legleye et al. [16]. Based on this measure, when individuals score four or more out of six points (one for each question), their cannabis consumption is considered problematic as it may result in addiction. Problems listed in the CAST include: (a) consuming cannabis on one’s own, (b) consuming cannabis in the morning, (c) experiencing problems due to consuming cannabis, (d) particulary memory-related problems, (e) advice from friends or family to reduce the consumption of cannabis, and (f) trying and failing to reduce one’s consumption. Problematic gambling always involves financial stakes and risks being taken. The 2002 HBSC-PGsurvey defined adolescents as problematic gamblers if their behaviour met the criteria outlined in the DSMIV-MR-J according to Fisher [17]. These criteria were adapted to reflect gambling-specific addiction, as

4.2. Indicators of Social Inequality/SES The current surveys consider different criteria of social inequality. These criteria are labelled ‘indicators’ of socioeconomic status (SES) in the present review. These indicators were self-reported and include, in addition to school type (as most surveys were conducted in school settings), the following variables: 1)

Family affluence, as measured by the Family Affluence Scale (FAS) [19]. The FAS consists of four different items: a) family ownership of one or more automobiles, b) number of family holidays taken over the last 12 months, c) separate bedrooms for children and adolescents (yes/no) and d) number of computers in the home. In the HBSC-surveys, the FAS was used to differentiate between low, middle and high levels of family affluence [2]. There were several studies of FAS that suggest acceptable validity [20-22]. Furthermore, there is strong consistency in the associations found between FAS and health outcomes across countries and between survey cycles [2].

2)

Type of school attended at the secondary level. This is often seen as an indicator of the students’ current and future SES [23]. The German school system comprises four different school types: a) `Hauptschule´ (abbreviated as HS) = low educational level, b) `Realschule´ (RS) = middle level, c) `Gymnasium´ (GY) (secondary high school/grammar school) = high level; d) comprehensive schools (`Gesamtschule´) were also included in the surveys. However, the latter do not fit clearly in with the hierarchical structure of education associated with HS-, RS- and GY-schools. As a result, this school type was not included in the present review.

3)

Parental occupational status.

4)

Parental socioeconomic status, measured by income, education and occupation.

5)

Employment status (employed/unemployed).

6)

Migration background. The surveys also asked adolescents whether or not one or both of their parents had emigrated from another country/region to Germany (e.g., Turkey/Asia) or, more generally, whether they had


any migration background (HBSC-surveys, KiGGSsurveys). It is important to be mindful of the social situation of migrants in Germany; a migration background does not only reflect cultural and ethnic differences. It is also associated with hierarchical social inequality. Low SES status is less prevalent (22% versus 54%) among native Germans than German residents with a migration background [23]. 4.3. Trend Data The ESPAD-surveys provide trend data for a time span of nine years (2003-2011) that show how the prevalence of smoking, binge and hazardous drinking, cannabis use, illegal drug use (except cannabis), and non-medical use of prescription drugs changes over time. The survey data also allows to differentiate the results for different school types. Although the data was collected at different points, the same indicators of social inequality and also the same substance use measures were applied (in contrast to the HBSC- and KiGGS-surveys). 5. RESULTS


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4)

Low SES adolescents started to smoke considerably earlier, for example, 32% of the HS-students started before they reached 12 years of age, compared with 24% of the RS-students and 14% of the GY-students [24].

5)

Unemployed young adults were significantly more likely to smoke e-cigarettes, although the prevalence rate of 4.5% observed for a time period of 30 days is low (Table 2).

6)

Non-smoking adolescents with low SES (school type, parental SES) are significantly more often affected by second-hand smoking than GY-students (KiGGSsurvey 2006, Table 2). The prevalence rates for RS students tends to lie between the rate for HS- and GYstudents.

7)

With regard to the KiGGS-survey 2006, adolescents with migration background were significantly less likely to smoke (OR 0.5, Table 2). But the 2010 HBSC findings show the opposite. A differentiated examination is not available due to the lack of information about countries or regions of origin.

8)

Several analyses were conducted to assess whether or not the prevalence rates actually correlated with SES and the extent to which this relationship may or may not have been confounded by potential covariates such as gender and age (Table 2). These logistic regression analyses revealed that each of the two variables (school type, employment status) was consistently related to smoking, independently from the covariates mentioned above (see OR, Table 2).

Given that different substance use behaviours and problematic gambling may have different associations with the above mentioned indicators of social inequality, the relationships between substance use and SES indicators were examined separately for each individual behaviour, resulting in five sections on the consumption of tobacco, alcohol, cannabis, other substances, and problematic gambling. 5.1. Tobacco

5.1.2. Trends (2003-2011)

Germany is one of the countries where SES and smoking tend to be closely linked:

The proportion of smokers among adolescents in most Western European countries, including Germany, has significantly declined in the first decade of the 21st century [25]. According to the DA-surveys of the BZgA, which first started to examine tobacco consumption using representative samples of German adolescents in 1979, the proportion of smokers fell considerably among the 12 to 17 year olds. The decisive change came about in 2001. From 2001 to 2011, the smoking rate declined from 30% to 11% and 12% respectively among boys and girls [36]. These are, by far, the lowest percentages ever obtained across three decades of data collection. The findings of the ESPAD-surveys [24], the HBSC-surveys [28], and the KiGGS-suveys [27] confirm this trend.

1)

A consistent trend emerges, regardless of the period when the survey data was collected and which particular smoking characteristic were examined: Smoking rates are significantly higher among the unemployed and adolescents who attended schools with low educational level (HS). For example, the propensity to take up smoking tends to several times higher among HS-students (OR 4.6 for boys, 3.4 for girls) than among GS-students (reference OR 1.0), adjusted for age, migration background, and parental SES (KiGGs 2006, Table 2: OR).

2)

In contrast, no consistent relationship was observed between smoking, family affluence, and parental occupational status. Only the KiGGS-survey 2012 shows a significant relationship between parental SES and smoking (Table 2).

3)

The proportion of heavy smokers (>10/>20 cigarettes per day) is significantly higher amongst adolescents with low SES (HS-students, unemployed) compared to GY-students (the reference group) of the same age (Tables 2 and 4). The prevalence rates of the employed young adults are also significantly higher, but considerably lower compared to the unemployed (DA-surveys 2010/2012, see also the results for daily smoking, Table 2).

The causes for the change in smoking rates have not yet been examined empirically, in Germany or in other countries. Yet, at least in Germany, this reduction may be traced back to several measures that were introduced in Germany since 2002. These include: 1)

The incremental, but overall very pronounced, increase in tax rates for tobacco, resulting in an almost three-fold price increase from 1980 to 2011.

2)

Legal changes that raised the minimum age for buying and consuming tobacco from 16 to 18 years of old.

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Table 2.

Henkel and Zemlin

Prevalence rates (%) and Odds ratios (OR) for smoking among adolescents and young adults by SES and migration background.

Surveys, Age, Refs.

HBSC 2010

Tobacco Consumption Characteristics

P%

P%

OR

OR

♂

♀

11-15

low

8.0

8.6

1.3 ns

2.8 *)

[68] [69]

middle

4.7

3.9

1.2 ns

1.0 ns

high (ref)

2.8

3.9

1.0

1.0

family affluence

♂

♀

♂

♀

11-15

low

9.0

13.5

1.0 ns

1.4 ns

[53]

middle

7.9

9.7

0.9 ns

1.2 ns

high (ref)

7.7

7.5

1.0

1.0

family affluence

♂

♀

♂

♀

HBSC 2002

smoking ≥1x/week

smoking ≥1x/week

♂

♀

11-15

low

17.8

18.1

1.0 ns

1.0 ns

[64]

middle

14.9

16.1

0.9 ns

1.0 ns

high (ref)

14.6

13.9

1.0

1.0


♂

♀

♂

♀

11-15

low

9.0

11.7

1.0 ns

0.9 ns

[53]

middle

8.1

9.7

0.9 ns

0.8 ns

high (ref)

7.0

8.8

1.0

1.0


♂

♀

♂

♀

HBSC 2006

HBSC 2002

regular smoking

smoking ≥1x/week

11-15

low

16.0

18.0

0.9 ns

1.2 ns

[64]

middle

14.4

15.2

0.9 ns

1.1 ns

high (ref)

14.0

12.9

1.0

1.0

♂

♀

♂

♀

KiGGS 2006

current smoking

parental SES

14-17

low

36.4

39.1

0.9 ns

1.8 ns

[65]

middle

30.1

33.0

0.9 ns

1.5 ns

high (ref)

25.8

21.6

1.0

1.0

parental SES

♂

♀

♂

♀

11-17

low

8.5

7.7

2.7 *)

7.0 *)

[38]

middle

5.1

5.6

1.7 *)

5.4 *)

high (ref)

3.1

1.0

1.0

1.0

school type

♂

♀

♂

♀

KiGGS 2012

HBSC 2010

daily smoking

regular smoking

11-15

HS

12.2 *)

15.5 *)

[7ß]

RS

6.7 ns

7.6 ns

GY (ref)

2.3

2.2

school type

♂

♀

♂

♀

all others

10.2

13.5

3.1 *)

3.0 *)

GY (ref)

4.6

5.8

1.0

1.0

school type

♂

♀

♂

♀

2.9 *)

2.5 *)

1.0

1.0

HBSC 2006

regular smoking

11-15 [53] HBSC 2002

smoking ≥1/week

OR adj. for

family affluence

HBSC 2006

daily smoking

SES-Indicators, Migration Background

11-15

all others

18.5

19.2

[64]

GY (ref)

8.5

9.5

a, m

a, o, s

a, ö, s

a, g, m

a, f, s

a, m, s

a

a, f, o

a, f, o



7

(Table 2) contd…..

Surveys, Age, Refs.

DA 2012


P%

P%

OR

OR

♂+♀

♂+♀

12-17

HS

10.2

*) OR np

[40]

RS

4.0

*) OR np

GY (ref)

0.5

1.0

school type

♂+♀

♂+♀

12-17

HS

7.5

*) OR np

[72]

RS

3.6

ns OR np

GY (ref)

1.3

1.0

school type

♂+♀

♂+♀

12-17

HS

4.2

*) OR np

[40]

RS

0.2

ns OR np

GY (ref)

0.1

1.0

DA 2012

DA 2010

daily smoking

>20 cigarettes/daily

school type

♂+♀

♂+♀

12-17

HS

5.0

*) OR np

[72]

RS

0.9

ns OR np

GY (ref)

0.5

1.0

school type

♂

♀

KiGGS 2006


current smoking

♂

a, g

a, g

a, g

a, g

♀

14-17

HS

42.2

44.6

4.6 *)

3.4 *)

[65]

RS

31.4

33.5

2.8 *)

1.7 *)

GY (ref)

17.6

23.1

1.0

1.0

DA 2012

employment status

♂+♀

♂+♀

18-25

unemployed

41.9

*) OR np

[40]

employed

29.1

*) OR np

GY (ref)

6.7

DA 2010

daily smoking

♂+♀

♂+♀

18-25

unemployed

49.0

*) OR np

[72]

employed

30.1

ns OR np

GY (ref)

8.0

1.0

employment status

♂+♀

♂+♀

18-25

unemployed

18.0

*) OR np

[40]

employed

6.6

*) OR np

GY (ref.)

0.3

1.0

employment status

♂+♀

♂+♀

18-25

unemployed

17.0

*) OR np

[72]

employed

6.8

*) OR np

GY (ref.)

0.5

1.0

DA 2010

DA 2012



regular e-cigarette

employment status

♂+♀

a, m, p

1.0

employment status

DA 2012

daily smoking

OR adj. for

school type

DA 2010

daily smoking


a, g

a, g

a, g

♂+♀

smoking last 30 days 18-25

unemployed

4.5

*) OR np

[40]

employed

1.6

ns OR np

GY (ref.)

0.8

1.0

a, g

8


Henkel and Zemlin


Surveys, Age, Refs.

KiGGS 2006


second-hand smoking

SES-Indicators, Migration Background parental SES

P%

P%

OR

OR

for ♂

♀

♂

♀

14-17

low

59.6

58.7

3.3 *)

3.2 *)

[73]

middle

50.9

50.6

2.2 *)

2.2 *)

high (ref)

34.8

33.4

1.0

1.0

KiGGS 2006

♂

♀

♂

♀

14-17

HS

36.9

50.4

1.8 *)

1.5 *)

[27]

RS

27.7

30.1

1.6 *)

1.7 *)

GY (ref)

13.4

17.8

1.0

1.0

♂

♀

♂

♀

KiGGS 2006

second-hand smoking

current smoking

school type


14-17

yes

26.4

24.1

0.5 *)

0.5 *)

[65]

no (ref)

32.0

33.4

1.0

1.0


♂+♀

14-17

both parents

12.8 ns

[23]

one parent

16.5 ns

no (ref)

16.9


♂

♀

♂

♀

11-15

both parents

7.9

3.8

1.9 *)

1.0 ns

[68] [69]

one parent

6.0

6.1

2.4 *)

0.7 ns

no (ref)

3.0

4.3

1.0

1.0

KiGGS 2006

HBSC 2010

current smoking

smoking ≥1x/week

OR adj.

a, m

a, m

a

♂+♀

a, f

Note: P % prevalence rate, *) p 0.05, chi2-test/OR logistic regression adjusted for covariates, a (age), g (gender), f (FAS), m (migration background), o (parental occupational status), p (parental SES), s (school type), (ref) reference, nss no test for statistical significance was conducted, np OR-value not published.

3)

Tobacco advertising restrictions.

5.2. Alcohol

4)

The implementation of a widespread smoking ban in all public buildings, on all means of transport, schools, restaurants, pubs and so on since the introduction of a number of laws banning smoking in 2007.

A review of the results outlined in Tables 5-7 show that the relationship between SES and alcohol consumption is less clear compared to the relationship between SES and smoking:

These measures were supported by awareness campaigns involving mass media and led to greater public recognition of tobacco smoking as a problematic behaviour. One can assume that these campaigns also led to a significant change in attitudes about smoking among children and adolescents [27, 28]. The question remains as to whether these measures have also changed the smoking rates for different social groups. The results from the surveys can be summarized as follows: The developments can be differentiated according to different school types (Tables 3 and 4). Some noticeable general trends emerged, indicating a decline. However, these trends were influenced by school type. The ESPAD-surveys [24] show that the proportion of pupils who smoke declined in all three school types since 2003 (Table 3). However, by 2011, the HS-smoking rate was back to 19.2 percentage points above the rates observed for GY-students). And since 2003, the difference between the prevalence rate of heavy smoking (>10 cigarettes per day) among smoking HSstudents and GY-students increased from 7.4 percentage points in 2003 to 16.5 points in 2011 (Table 4).

1)

With regard to family affluence (FAS), two HBSCsurveys (2002, 2006) show that boys with low and middle FAS are significantly less likely to have binge drinking experience compared to boys with high FAS. Differences in parental occupational status and parental SES, on the other hand, were not associated with different prevalence rates observed for binge drinking or hazardous alcohol consumption (Table 5).

2)

With regards to European findings, a negative relationship exists between the prevalence of binge drinking and the level of education. That is, the prevalence rates rise as the level of education declines [5]. The results of German surveys do not provide similarly consistent findings (Tables 5-7). According to the DA- surveys, no significant school type differences exist in terms of binge drinking prevalence rates (Table 5). In contrast to this, the 2002, 2006 and 2010 HBSC-surveys and the ESPADsurveys reveal substantially lower prevalence rates for binge drinking among GY-students in comparison to HS-students resp. to HS- plus RS-students. The 2006 HBSC-survey also shows gender differences (Tables


Table 3.


9

Trend data 2003-2011, 30-days-prevalence rates (%) for smoking among adolescents (age 15-16 years) by SES (school type).

ESPAD- Surveys

HS Prevalence Rate %

RS Prevalence Rate %

GY Prevalence Rate %

Difference PP HS-GY

2003

55.8 *)

50.4 *)

37.2 *)

18.6 nss

2007

42.3 ns

38.9 ns

29.9 ns

12.4 nss

2011 (ref)

45.6

35.2

26.4

19.2 nss

Difference PP

10.2

15.2

10.8

2003-2011 Note: *) p 0.05, OR logistic regression adjusted for age, gender, OR-value not published, (ref) reference, nss no test for statistical significance was conducted, PP percentage points [24].

Table 4.

Trend data 2003-2011, 30-days-prevalence rates (%) for smoking >10 cigarettes per day among smoking adolescents (age 15-16 years) by SES (school type).

ESPAD-Surveys



GY Prevalence Rate

2003

31.1

26.0

13.9

7.4 nss

2007

29.6

24.4

14.6

15.0 nss

2011

23.7

15.7

7.2

16.5 nss

10.3 nss

6.7 nss

Difference PP

7.4 nss

Difference PP HS-GY

2003-2011 Note: nss no test for statistical significance was conducted, PP percentage points [24] [74] [33].

5 and 6). No systematic relationship appears to exist between hazardous alcohol use and school type (Tables 5 and 7). 3)

HS-students reported more severe problems associated with alcohol consumption than their GYcounterparts (ESPAD 2011) [24]. The problems ranged from exhibiting aggressive behaviour, engaging in unprotected sexual intercourse, to poor school performance, personal experience of violence as well as problems with the police. These problems are typical consequences of binge drinking [5].

4)

No significant relationships were found between the employment status of 18 to 25 year olds, their rate of binge drinking and risky alcohol consumption (Table 5). However, the relationships between these variables become more distinctive when gender differences are also considered. The rate of high risky alcohol consumption (hazardous alcohol use plus binge drinking ≥1x/last 30 days) is significantly higher among young unemployed men (BZgA-survey 2010) [29], while the opposite is true for women (Table 5).

5)

Results related to migration background show the following consistent trends: Problematic patterns of alcohol use are less frequently observed among adolescents and young adults who have Turkish, Asian or Middle Eastern/Northern African backgrounds. These findings apply to binge drinking, hazardous as well as exceedingly risky alcohol consumption (the latter includes both hazardous and binge drinking, Table 5). This pattern, where examined to date, appears to be similar for both males and females.

5.2.1. Trends (2003-2011) The mass media in Germany (TV, newspapers, and Internet outlets) often discusses the issue of alcohol consumption among adolescents in very dramatic terms. These reports are based on the number of children and adolescents admitted to hospital due to acute alcohol intoxication. The number of 10 to 20 year olds being admitted to hospital for this reason has more than doubled between 2000 and 2013 from around 9,500 cases to more than 23,000 cases per year [30]. Still, this relatively small and special group of adolescents may not be representative of the alcohol consumption patterns in adolescents as a whole. Representative data from the ESPAD- and DA-surveys show, in fact, a drop in hazardous alcohol use among adolescents overall, as well as a declining or unchanged prevalence rate in terms of binge drinking [24, 29]. This is observed for both genders. The evaluations of the 2002, 2006 and 2010 HBSC-surveys confirm these findings [28]. Similarly trends were observed at an international level. The proportion of adolescents who consumed alcohol significantly declined in various European and North American countries between 2002 and 2010 [31], including Germany. Unfortunately it was not analysed to what extent this trend differed among various SES groups [31]. With regard to social inequality (attended school type), the ESPAD-surveys revealed the following: The prevalence rates associated with hazardous alcohol consumption significantly declined among HS- and RS-students between 2003 and 2011 respectively, and between 2007 and 2011 among GY-students (Table 7). The differences in prevalence rates between the three school types also diminished over

10


Table 5.

Henkel and Zemlin

Prevalence rates (%) and Odds Ratios (OR) for binge drinking and hazardous alcohol consumption among adolescents and young adults by SES and migration background.

Surveys, Age, Refs.

Alcohol Consumption Characteristics


P%

P%

HBSC 2010

binge drinking ≥4x/lifetime

family affluence

♂

♀

11-15

low

8.1 nss

7.4 nss

[68]

middle

6.6 nss

4.5 nss

high (ref)

6.3 nss

4.9 nss

HBSC 2006

♂

♀

♂

♀

♂

♀

low

12.4

12.2

0.6 *)

0.9 ns

[53]

middle

14.0

12.2

0.8 ns

1.0 ns

high (ref)

15.7

11.5

1.0

1.0

♂

♀

♂

♀

binge drinking ≥ 2x/lifetime

family affluence

OR

11-15

HBSC 2002


OR

family affluence

11-15

low

17.7

14.4

0.7 ns

0.9 ns

[64]

middle

17.4

14.9

0.7 *)

1.0 ns

high (ref)

22.1

14.9

1.0

1.0

♂

♀

♂

♀

HBSC 2006



11-15

low

13.7

11.2

1.0 ns

0.9 ns

[53]

middle

14.5

11.8

0.9 ns

0.9 ns

high (ref)

13.8

13.2

1.0

1.0

♂

♀

♂

♀

HBSC 2002



11-15

low

16.9

14.3

0.8 ns

1.0 ns

[64]

middle

20.3

15.8

1.1 ns

1.2 ns

high (ref)

20.0

14.6

1.0

1.0

parental SES

♂

♀

♂

♀

11-17

low

12.9 ns

8.0 ns

[39]

middle

13.8 ns

11.3 ns

high (ref)

12.0

7.4

♂

♀

♂

♀

KiGGS 2012

KiGGS 2012

binge drinking ≥1x/last month

hazardous alcohol consumption AUDIT-C Score ≥5/4 boys/girls

parental SES

11-17

low

15.1

15.6

0.7 ns

0.8 ns

[39]

middle

15.0

17.7

0.9 ns

1.1 ns

high (ref)

16.1

14.5

1.0

1.0

♂

♀

♂

♀

HBSC 2010


school type

11-15

HS

22.3 *)

16.5 *)

[70]

RS

14.4 ns

12.5 ns

GY (ref)

10.9

10.6

HBSC 2006

♂

♀

♂

♀

11-15

all others

15.9

12.5

1.7 *)

1.5 ns

[53]

GY (ref)

10.6

11.1

1.0

1.0

HBSC 2002



school type

♂

♀

♂

♀

11-15

school type all others

19.7

15.5

1.7 *)

1.4 *)

[64]

GY (ref)

16.8

12.8

1.0 ns

1.0

OR adj. for

a, o, s

a, o, s

a, f, s

a, f, s

a

a, f, o

a, f, o



11


Surveys, Age, Refs.



P%

DA 2012


P%

OR

OR

school type

♂ + ♀

♂+♀

12-17

HS

14.3

ns OR np

[75]

RS

12.1

ns OR np

GY (ref)

11.5

1.0

school type

♂+♀

♂+♀

12-17

HS

5.2

ns OR np

[75]

RS

3.3

ns OR np

GY (ref)

2.6

1.0

school type

♂

♀

12-17

HS

3.6

0.4

ns OR np

ns OR np

[29]

RS

2.7

1.6

ns OR np

ns OR np

GY (ref)

2.6

1.7

1.0

1.0

employment status

♂ + ♀

♂ + ♀

18-25

unemployed

18.7

ns OR np

[75]

employed

14.8

ns OR np

GY (ref)

14.0

1.0

DA 2012

DA 2010

hazardous alcohol consumption


♂

OR adj. for

a, g

a, g

♀

+ binge drinking ≥1x/last month

DA 2012

DA 2012


employment status

♂ + ♀

♂ + ♀

18-25

unemployed

41.7

ns OR np

[75]

employed

42.3

ns OR np

GY (ref)

44.0

1.0

DA 2010



employment status

♂

♀

♂

a, e, m

a, g

♀

+ binge drinking ≥1x/last month 18-25

unemployed

22.4

1.4

*) OR np

*) OR np

[29]

employed

15.4

8.9

ns OR np

ns OR np

GY (ref)

9.2

7.4

1.0

1.0

DA 2012


♂ + ♀

♂ + ♀

12-17

Western Europe

8.6

ns OR np

[75]

Eastern Europe

2.5

ns OR np

Turkey, Asia

0.1

*) OR np

no (ref)

3.2

1.0


♂+ ♀

♂ + ♀

DA 2012



18-25

Western Europe

16.0

ns OR np

[75]

Eastern Europe

13.0

ns OR np

5.3

*) OR np

Turkey, Asia no (ref)

17.2

1.0


♂+ ♀

♂ + ♀

12-17

Western Europe

14.7

ns OR np

[75]

Eastern Europe

12.5

ns OR np

2.6

*) OR np

DA 2012


Turkey, Asia no (ref)

12.9

1.0

a, m. s

a, e, s

a, e, s

a, e, s

12


Henkel and Zemlin


Surveys, Age, Refs.



P%

DA 2012



♂+ ♀

♂ + ♀

18-25

Western Europe

29.1

ns OR np

[75]

Eastern Europe

36.1

ns OR np

Turkey, Asia

16.0

*) OR np

no (ref)

47.7

1.0


♂

♀

12-17

Turkey/Asia

0.2

1.8

*) OR np

*) OR np

[29]

Eastern Europe

5.6

2.3

*) OR np

ns OR np

Western Europe

4.8

15.1

ns OR np

*) OR np

no (ref)

6.6

2.3

1.0

1.0


♂

♀

♂

♀

18-25

Turkey/Asia

8.3

0.7

*) OR np

*) OR np

[29]

Eastern Europe

17.2

4.9

ns OR np

ns OR np

Western Europe

17.8

4.4

ns OR np

ns OR np

no (ref)

18.6

9.5

1.0

1.0

♂

♀

DA 2010


P%

OR

♂

OR

OR adj. for

a, e, s

♀


DA 2010


a, e, s


HBSC 2010



♂

♀

11-15

both parents

7.6 nss

4.1 nss

[68]

one parent

11.6 nss

4.5 nss

no (ref)

5.8

a, e, s

-

5.1

Note: P % prevalence rate, *) p 0.05, chi2-test/ OR logistic regression adjusted for covariates, a (age), g (gender),s (school type), e (employment status), f (FAS), m (migration background), o (parental occupational status), s (school type), (ref) reference, nss no test for statistical significance was conducted, np OR-value not published.

time. With regard to binge drinking, a remarkable difference (11.4 percentage points) in prevalence rates persists in 2011 for HS-students compared to GY-students (Table 6). Unfortunately no test for statistical significance was conducted.

1)

Adolescents’ use of cannabis does not systematically correlate with family affluence, socioeconomic or occupational status of the parents (Table 8). See also [32].

2)

Migration background does not appear to have a significant effect on cannabis consumption among the 12 to 17 year olds or the 18 to 25 year olds (Table 8). However, information about gender differences was missing.

3)

No significant differences emerged in terms of cannabis consumption and school type (Table 8). But

5.3. Cannabis The situation in Germany is provided in Tables 8-10. They show the following results. Table 6.

Trend data 2003-2011, prevalence rates (%) for binge drinking ≥3x/last month among adolescents (age 15-16 years) by SES (school type).

ESPAD-surveys




Difference PP HS-GY

2003

37.0 ns

32.0 ns

18.6 ns

18.4 nss

2007

36.4 ns

34.9 *)

23.5 ns

12.9 nss

2011 (ref)

33.2

29.7

21.8

11.4 nss

3.8

2.3

3.2

Difference PP 2003-2011

Note: *) p 0.05, OR logistic regression adjusted for age, gender, OR-value not published, (ref) reference, nss no test for statistical significance was conducted , PP percentage points [24]


Table 7.


13

Trend data 2003-2011, prevalence rates (%) for hazardous alcohol consumption among adolescents (age 15-16 years) by SES (school type).

ESPAD-surveys




2003

13.1 *)

15.1 *)

9.8 ns

3.3 nss

2007

12.4 ns

14.7 *)

10.6 *)

1.8 nss

2011 (ref)

9.5

10.2

8.7

Difference PP

3.9

4.9

1.1

Difference PP HS-GY

0.8 nss

2003-2011 Note: hazardous consumption = average daily intake >12g/>24g pure alcohol (boys/girls), *) p 0.05, OR logistic regression adjusted for age, gender, OR-value not published, (ref) reference, nss no test for statistical significance was conducted, PP percentage points [24]

again, no information was available about gender differences. 4)

5)

In an effort to support prevention, it is important to identify risky forms of cannabis consumption as well as those groups who may be at risk. The 2007 and 2011 ESPAD-surveys [24, 33] are the only ones that examined risky cannabis consumption with the help of the Cannabis Abuse Screening Test (CAST) by Legleye et al. [16]. According to the 2011 results, the problematic consumption (CAST ≥4) is obviously unequally distributed (Tables 8-10). The prevalence rate is highest among HS-students (2.9%). With respect to past-year use, the percentage of problematic consumers among cannabis consuming HS-students was 17%, almost four times higher than the percentage of problematic consumers among cannabis consuming GY-students (Tables 8 and 9). In other words: when HS-students start consuming cannabis, they are much more likely to develop a problematic pattern of cannabis use, which may then lead to later cannabis addiction. In addition to HS-students, another risk group includes the 18 to 25 year olds unemployed (DAsurvey 2012, Table 8). Their 30 days prevalence rate of 14.5% is significantly higher than the prevalence rate of 3.1% reported for GY-students of similar age, and the prevalence rate is three times higher for those who consume cannabis on a regular basis (that is, weekly), even after adjusting for age and gender (Table 8).

5.3.1. Trends (2003-2011) According to an international study by Hublet et al. [25], the proportion of adolescents consuming cannabis declined significantly in Western European countries between the years of 2002 and 2010. The consumption of cannabis among adolescents and young adults in Germany has also declined for both males and females since 1997 [34]. While the 1997 prevalence rate among 12 to 17 year olds was around 5% (based on consumption of cannabis over the course of 30 days), this rate had declined to 2% by 2012. A similar decline from around 10% to 6% was observed for 18 to 25 year olds. Evaluations based on the HSBC-surveys confirm this trend [28]. Whether or not these changes are similar for the different SES groups can only be answered in relation to school type.

The ESPAD-surveys [24] indicate a significant and substantial decline for the 30 days prevalence rates for pupils from all three school types from 2003 to 2011. At the same time, the differences in prevalence rates between the school types shrank almost entirely over the same time frame (Table 9). The problematic cannabis consumption as assessed by CAST reveals a contrasting development over time. Table 10 shows little change in prevalence rates among RS- and GYstudents in 2007 and 2011 (no data was available for 2003). However, the rate increases significantly for HS-students. When considering the change in prevalence rates across all HS-students, an overall increase is observed (from 0.7% to 2.9%). This increase appears to be most pronounced among those HS-students who had consumed cannabis over 12 months (rate increased from 5.0% to 17.0%). Examining and following this noteworthy trend will be an important task in future research. 5.4. Other Substances With regard to the consumption of illegal drugs (excluding cannabis), a number of knowledge gaps exist that may need to be addressed in future work. The only indicator of social inequality that has been examined so far is school type. The results of the 2003 ESPAD-survey suggest that the prevalence rate of illegal drug use other than cannabis across the life span of HS- and RS-students was around 15%, double the rate observed for GY-students (Table 11). In 2011 the same difference persists for HS-students compared to GY-students. The prevalence rate has only declined significantly for RS-students between 2003 and 2011, but not in other student groups. To date, only the ESPAD-surveys examined the nonmedical use of prescription drugs. As is shown in Table 12, the prevalence rate across the lifespan for the use of tranquilizers or sedatives increased significantly among HSstudents between 2003 and 2011. The difference in prevalence rates observed for HS-students compared to GYstudents also appears to become more pronounced over the same time frame. 5.5. Problematic Gambling The results from the 2002 HBSC-PG-survey [35] and the 2011 ESPAD-survey [24] provide informations about

14


Table 8.

Henkel and Zemlin

Prevalence rates (%) and Odds ratios (OR) for cannabis consumption among adolescents and young adults by SES and migration backround

Surveys, Age, Refs.

HBSC 2010

Cannabis Consumption

SES-Indicator,

Characteristics

Migration Background

≥1x/last month

P%

P%

OR

OR

♀

♂

family affluence

♂

♀

11-15

low

6.7 ns

5.9 ns

[68]

middle

4.9 ns

2.5 ns

high (ref)

3.9

2.7

family affluence

♂+♀

♂+♀

15

low

2.3

2.2 ns

[53]

middle

2.4

2.3 ns

high (ref)

1.2

1.0

HBSC 2006

HBSC 2002

≥2x/last 12 months

a, o, s

family affluence

♂

♀

♀

♂

11-15

low

20.2

11.0

1.0 ns

0.6 ns

[64]

middle

22.1

16.8

1.3 ns

0.9 ns

high (ref)

23.0

14.0

1.0

1.0


♂+♀

♂+♀

15

low

2.2

0.6 ns

[53]

middle

1.6

0.5 ns

high (ref)

2.7


♂

♀

♀

♂

11-15

low

20.4

14.5

1.0 ns

0.8 ns

[64]

middle

22.3

17.2

1.2 ns

0.9 ns

high (ref)

24.5

14.8

1.0

1.0

parental SES

♂

♀

♀

♂

14-17

low

13.8

8.8

0.9 ns

0.8 ns

[65]

middle

15.4

10.4

1.1 ns

0.9 ns

high (ref)

13.3

10.5

1.0

1.0

school type

♂

♀

♀

♂

14-17

HS

15.6

12.0

1.5 ns

1.5 ns

[65]

RS

13.9

7.8

0.9 ns

1.3 ns

GY (ref)

12.7

9.8

1.0

1.0

school type

♂+♀

♂+♀

15

all others

2.3

1.2 ns

[53]

GY (ref)

1.8

1.0

school type

♂

♀

♀

♂

11-15

all others

23.1

14.5

1.0 ns

1.2 ns

[64]

GY (ref)

20.0

15.5

1.0

1.0

school type

♂

♀

♀

♂

14-17

HS

15.6

12.0

1.0 ns

1.4 ns

[65]

RS

13.9

7.8

1.0

1.0

GY (ref)

12.7

9.8

HBSC 2006

HBSC 2002

KiGGS 2006

KiGGS 2006

HBSC 2006

HBSC 2002

KiGGS 2006









OR adj. for

a, o, s

a, f, s

a, f, s

a, m, s

a, m, p

a, f, o

a, f, o

a, m, p



15


Surveys, Age, Refs.

Cannabis Consumption

SES-Indicator,

P%

Characteristics

Migration Background

CAST Score ≥4

school type

♂+♀

15-16

HS

2.9 nss

[24]

RS

1.8 nss

GY

0.7 nss

school type

♂+♀

15-16

HS

17.0 nss

[24]

RS

9.7 nss

GY

4.5 nss

ESPAD 2011

ESPAD 2011

DA 2012

CAST Score ≥4

P%

OR

♂+♀

(last 12 months user)

school type

♂+♀

♂+♀

HS

2.3

ns OR np

[34]

RS

1.3

ns OR np

GY (ref)

1.6

1..0

employment status

♂+♀

♂+♀

18-25

unemployed

14.5

*) OR np

[34]

employed

4.5

ns OR np

GY (ref)

3.1

1.0

DA 2012

≥1x/last 30 days

≥1x/last 30 days

regular

employment status

♂+♀

unemployed

9.3

*) OR np

[34]

employed

3.8

ns OR np

GY (ref)

3.1

1.0


♂+♀

12-17

Turkey, Asia

1.2

ns OR np

[34]

Eastern Europe

0.4

ns OR np

Western Europe

2.3

ns OR np

no (ref)

1.7

1.0

DA 2012

≥1x/last 30 days

≥1x/last 30 days

a, g

a, g

♂+♀


♂+♀

18-25

Turkey, Asia

2.4

ns OR np

[34]

Eastern Europe

8.0

ns OR np

Western Europe

7.8

ns OR np

no (ref)

5.7

1.0

a, g

♂+♀ a, g


♂

♀

♀

♂

14-17

yes

13.9

7.9

0.9 ns

0.8 ns

[65]

no (ref)

14.9

10.4

1.0

1.0

KiGGS 2006


a, g

♂+♀

18-25

DA 2012

OR adj. for

♂+♀

12-17

DA 2012

OR

a, p, s

Note: P % prevalence rate, *) p 0.05, , chi2-test/OR logistic regression adjusted for covariates, a (age), g (gender), e (employment status), f (FAS), m (migration background), o (parental occupational status), p (parental SES), s (school type), (ref) reference, nss no test for statistical significance was conducted, np OR-value not published.

16


Table 9.

Henkel and Zemlin

Trend data 2003-2011, 30-days-prevalence rates (%) for cannabis consumption among adolescents (age 15-16 years) by SES (school type).

ESPAD-surveys




Difference PP HS-GY

2003

14.9 *)

12.9 *)

12.7 *)

2.2 nss

2007

7.8 ns

7.7 ns

7.2 ns

0.6 nss

2011 (ref)

8.0

7.7

7.8

0.2 nss

Difference PP

6.9

5.2

4.9


Table 10. Trend data 2003-2011, prevalence rates (%) for problematic cannabis consumption (CAST ≥4) among adolescents (age 15-16 years) by SES (school type). ESPAD-surveys




Difference PP HS-GY

2007

0.7 *)

1.6 ns

1.1 ns

0.4 nss

2011 (ref)

2.9 ns

1.8

0.7

2.2 nss

Difference PP

2.2

0.2

0.4

5.0 *)

9.9 ns

6.2 ns

all students

2003-2011 last 12 months consumer 2007 2011 (ref)

17.0

9.7

4.5

Difference PP

12.0

0.2

1.7

0.5 nss 12.5 nss

2003-2011 Note: *) p 0.05, logistic regression adjusted for age, gender, OR-value not published, ref reference, nss no test for statistical significance was conducted, PP percentage points [24].

problematic gambling. These surveys differentiate between two indicators of social inequality: attended school type and migration background. The results indicate the following (Table 13). 1)

With respect to school types, the prevalence rates increase with decreasing educational level and are highest for HS-students (Table 13). As shown by the data obtained in the ESPAD-surveys [24], this observation also applies to all types of gambling forms that have been investigated, as well to problematic gambling as assessed by DSM-IV-MR-J by Fisher [17, 35]. According to this data, around 13% of HS-students reported problematic gambling

behaviours; this rate is twice the prevalence rate observed among GY-students (6.3%). These percentages are based on the number of adolescents that were involved in gambling requiring money over the course of the last 12 months. 2)

Problematic gambling and migration background also appear to be closely linked to one another (Table 13). The percentage of problem gamblers assessed by DSM-IV-MR-J by Fisher [17, 35] among adolescents whose parents are not originally from Germany is, at around 17%, more than twice as high as the percentage of 7% observed for the reference group without such migration background. Similarly, the

Table 11. Trend data 2003-2011, lifetime-prevalence rates (%) for using illegal drugs (except cannabis) among adolescents (age 1516 years) by SES (school type). ESPAD-surveys



2003

15.5 ns

15.2 *)

7.7 ns

7.8 .nss

2007

12.6 ns

12.5 ns

8.8 ns

3.8 nss

2011 (ref)

14.1

10.7

6.8

7.3 nss

1.4

4.5

0.9

Difference PP


Difference PP HS-GY




17

Table 12. Trend data 2003-2011, lifetime-prevalence rates (%) for non-medical use of prescription drugs (tranquilizer/sedativa) among adolescents (age 15-16 years) by SES (school type). ESPAD-surveys




Difference PP HS-GY

2003

1.7 *)

1.4 ns

1.5 ns

0.2 .nss

2007

2.9 ns

3.1 ns

2.5 ns

0.4 nss

2011 (ref)

4.0

2.2

1.6

2.4 nss

Difference PP

2.3

0.8

0.1

2003-2011 Note: *) p 0.05, OR logistic regression adjusted for age, gender, OR-value not published, (ref) reference, nss no test for statistical significance was conducted, PP percentage points [24]

prevalence rate of around 14% for adolescents who have only one parent with a migration background is also very high. These prevalence rates are related to those adolescents who participated in gambling activities over the last 12 months. Schmidt and Kähnert [35] examined which sociodemographic characteristics are prevalent among problem gamblers, as diagnosed by DSM-IV-MR-J by Fisher [17]. Based on the data from the 2002 HBSC-PG-survey, the authors found that problem gamblers are predominantly male adolescents (82% of gamblers), HS-students (41%), and adolescents with Turkish or Eastern European background (46%). 6. DISCUSSION The findings regarding tobacco smoking are supported by the results of the current 2014 BZgA-survey. This survey also observed higher smoking consumption among adolescents with low educational levels and unemployed young adults [36]. Overall, the findings regarding smoking suggest the following: Adolescents with low SES (school type, employment status) are more likely to experience significant tobaccorelated health problems. Even when they do not smoke themselves, they suffer more health issues as a result of being more frequently exposed to second-hand smoke; this issue also pertain to adolescents with low parental SES. The risks of tobacco addiction are also more pronounced for adolescents with low SES (school type). International studies have shown that the likelihood of becoming addicted on tobacco over time and the increase in health risks can be linked to how early individuals start to smoke tobacco [24, 37]. Smoking is expensive and thus reduces the smokers’ means available to finance health (e.g., sport), education, leisure and other activities. As such, smoking may reduce health-related and other opportunities to participate in various activities. Understandably, adolescents with low SES (particularly the unemployed) are disproportionally affected due to having less financial means than other groups. The present findings provide substantial support that more prevention policies are needed that also address the role of social inequality in relation to tobacco consumption, focusing specifically on the needs of more socially disadvantaged targets groups such as HS-students,

adolescents with low parental SES and unemployed young adults. The data that was presented in Tables 2-4 had originated from cross-sectional studies. In contrast to longitudinal studies, such studies do not allow us to draw conclusions about cause-and-effect relationships. Only one longitudinal study was conducted in Germany. This study examined the effect of job loss on smoking behaviour [38]. The study was based on a nationwide representative sample (SOEP) and the applied method of analysis was robust against selection and reverse causality. The study found that young non-smokers were more likely to start smoking due to job loss. This effect was found especially among individuals who were disadvantaged with respect to SES. Smoking can be associated with different motives (e.g., to reduce tension or perceived school stress, to be seen as a grown-up, and to obtain the recognition from the peer group) [39, 40], The extent to which the motives differ across different social groups has not so far been examined in Germany. However, a number of records suggest that adolescents with low SES tend to grow up in a social environment where smoking is substantially more common, such as in the family, at school and in peer groups, than in other social groups. According to the 2011 ESDAP-survey [24], half of all HS-students (49%) indicated that they were part of a peer group where almost everybody smoked tobacco. This proportion is significantly lower among GY-students (20%). The international literature also shows that peer smoking is the strongest predictor of adolescent smoking [41]. Likewise, adolescents with low SES are also disproportionally more likely to have parents or siblings who smoke [42]. And children in families that smoke are at greater risk to start smoking as well [44]. The common occurrence of smoking in the social environment appears to promote individual’s emulation of smoking as this behaviour is perceived as the socially accepted norm, rather than problem behaviour. One hypothesis is that the socially unequal distribution of tobacco smoking may be due to some extent to tobacco advertising. No investigations to date have examined the effects of advertising on smoking rates among different social groups. However, the use of suggestive pictures and symbols in advertising (e.g., reflecting happiness, affluence, freedom, adventure, success and wealth) may resonate more so with adolescents that have lower SES as they are less likely to experience these experiences themselves.

18


Henkel and Zemlin

Table 13. Prevalence rates (%) for gambling among adolescents by SES (school type) and migration background. Surveys, Age, Refs.

Gambling Characteristics

SES-Indicator, Migration Background

ESPAD 2011

gambling machine ≥1x/last month

school type

Prevalence Rate %

15-16

HS

10.8 nss

[24]

RS

7.2 nss

GY

3.6 nss

ESPAD 2011

gambling machine (casino) ≥1x/last month

school type

15-16

HS

5.1 nss

[24]

RS

3.0 nss

GY

1.1 nss

ESPAD 2011

poker, card games (Internet) ≥1x/last month

school type

15-16

HS

10.9 nss

[24]

RS

7.2 nss

GY

3.6 nss

ESPAD 2011

card games (casino) ≥1x/last month

school type

15-16

HS

6.7 nss

[24]

RS

3.6 nss

GY

2.8 nss

ESPAD 2011

sport bets ≥1x/last month

school type

15-16

HS

8.5 nss

[24]

RS

2.8 nss

GY

2.3 nss

HBSC-PG 2002

DSM-IV-MR-J Score ≥4

school type

(last 12 months gambler) 13-15

HS

12.9 *)

[35]

RS

8.4 ns

GY (ref)

6.3

HBSC-PG 2002

DSM-IV-MR-J Score ≥4


(last 12 months gambler) 13-15

both parents

17.3 *)

[35]

one parent

13.6 *)

no (ref)

6.7

Note: *) p 0.05, (ref) reference, nss no test for statistical significance was conducted.

The tobacco control and prevention measures in Germany have led to an overall reduction of tobacco consumption across all three school types. However, tobacco consumption continues to be significantly influenced by social inequality; this remains unchanged. This also means there is no cause for optimism; the role of social inequality in smoking prevalence is unlikely to diminish and disappear on its own. A low score on the FAS scale measuring family affluence does not indicate poverty, that is, a life of relative poverty spent on social welfare. Poverty is closely associated with alcohol abstinence. Nationally representative studies show that parents who live in poverty are more likely to abstain from alcohol than prosperous ones. For example, some

research revealed abstinence rates of 15% resp. 42% among fathers resp. mothers living in poverty and having children less than fifteen years of age; for prosperous fathers and mothers, these rates were 2% and 16%, respectively [42]. The findings of a more recent study corroborate the close relationship between poverty and abstinence [45]. The negative correlation between income and abstinence is further supported by international studies [46]. This leads to the assumption that a large proportion of adolescents that grow up in poverty-stricken families may also be exposed to positive role models in that their parents are less likely to drink. This leads us to hypothesise that the prevalence rates of problematic alcohol use among adolescents in these families are significantly lower than amongst other groups


classified as having a low FAS in the HBSC-surveys. Future research should examine this hypothesis further. The fact that the prevalence of binge drinking is more pronounced among unemployed individuals corresponds with the results of international studies (see section 1). The low alcohol prevalence rates among the adolescents and young adults with migration background can be attributed to the fact that most of these individuals originated from countries where Islam is the predominant religion, and hence countries where alcohol consumption tends to be, compared to European countries, significantly lower overall. For example, the annual amount of pure alcohol reportedly consumed among Turkish individuals aged 15 or over is only 3.6 litres, compared to 13 litres per capita in Germany [30]. In reference to adolescents from Eastern Europe, however, the results in Table 5 are less conclusive. According to the 2010 HBSC-survey, the prevalence rate associated with binge drinking does not appear to depend on whether or not one or both parents are migrants. However, the relationship between these variables has not been examined statistically. Regarding the drop of the alcohol and cannabis use over time, it stands to reason that this drop is linked to the increase in preventative measures employed since 2000. Whether or not a causal relationship exists will be difficult to determine, however. There is only one longitudinal study from Germany which examined the risk factors for the development of problematic cannabis consumption. Von Sydow et al. [48] examined which factors may predict the development of cannabis addiction (DSM-IV) in a four-year study from a sample of 2,446 participants between the ages of 14 to 24. The participants had all consumed cannabis before the study commenced, but were not dependent on it. The authors included more than 50 different sociodemographics and psychosocial characteristics in their analysis. Seven of these were significant predictors of cannabis addiction: starting to consume cannabis at a young age, low self-esteem, consuming other illegal drugs, death of a parent before the age of 15, severe mental problems, poor financial position (e.g., in the case of unemployment) and low SES. And yet, gender did not have a significant effect. The sociodemographical profiles for adolescent and adult problem gamblers are very similar. Problem gamblers (adolescents and adults) are more likely to include indivdiuals with lower levels of education and a migration background [12, 13]. Two additional characteristics found among adult gamblers in Germany is that they are more likely to be unemployed [26, 12] and have a monthly household income below 1,000 Euros [49], an income that in Germany puts them very close to the relative poverty line. International research reveals similar findings (see Introduction): being unemployed, receiving social welfare, being younger than 25 years old, being male, having attained only a low level of education resp. of academic achievement, and having a migration background are all significant risk factors for problematic gambling [11]. This means that efforts aimed at the prevention of problematic gambling among adolescents and young adults should pay particular attention to these sociodemographic characteristics.


19

Young problematic gamblers also report lower psychological well-being and life satisfaction [35]. They also tend to experience more strain and critical life events that they struggle to cope with, such as problems and conflicts with the family, divorce or separation of parents, unemployment in the family, and being overwhelmed with school demands [35]. Some of these problems may, however, represent both consequences as well as causes of problematic gambling. The primary reasons for gambling can be very different: gambling for enjoyment/excitement, to make or maintain friends, to alleviate feelings of depression, to escape problems, and to make money [51]. According to a regionally representative survey of adolescents, conducted in the City of Hamburg/Germany, adolescents gamble predominantly in order to win money [13]. It is not surprising to note that those who have less financial means are more likely to be problematic gamblers (HS-students compared to GY-students, the unemployed and poor). Greater financial means support a higher standard of living. For adolescents, this also often translates into greater independence from their parents, more social recognition and increased self-esteem. Improving their life circumstances through education and work is an important goal for many migrants. When migrants are blocked from achieving these goals, they may be more tempted to improve their circumstances by turning to more risky gambling. This means the high prevalence rate observed for adolescent migrants may be a cause and expression of their unsatisfactory or failed integration into society. Future research should consider the degree of social integration among migrants as another potential predictor of their problematic gambling. It is important to note that there has been little research in Germany to date that has examined the relationship between social inequality, problematic gambling, and prescription drug use. While the ESPAD surveys asked respondents whether or not they used prescription drugs, respondents were not asked about the dosis and within which time frame the prescription drugs were consumed. With regard to gambling, the ESPAD surveys only provide information about the frequency of gambling with which students are engaged in different types of gambling activities during the past 12 months. Gambling frequency alone is not an indicator of problem gambling. Other indicators include loss of control over gambling behaviour and and gambling with increasing amounts of money [52]. All German surveys considered attended school types an indicator of the students’ SES. But it is also right in pointing out that schools are social institutions which may influence problematic substance use themselves [2]. It is possible that the school types differ in terms of specific aspects, which may potentially influence substance use (e.g., school satisfaction, interactions between different student groups related to integration or discrimination of students with a migration background, aggressive student behaviours such as bullying at school). The close relationship between substance use and general school success suggests that the school experience itself can play an important role. Based on data from the 2006 HSBC-survey, Nickel et al. [53] were able to show that students with academic performance below school

20


Henkel and Zemlin

average were three times more likely to smoke, compared to students who performed well in school. A similar picture emerges for binge drinking and cannabis use (although these findings are probably influenced by reciprocal and interactive processes as well.

6)

Finally, information about gender differences is often not included in the available surveys in Germany. Yet, such statistics are important to test as to whether or not, and to what degree, prevention efforts need to consider genderspecific trends.

8. PREVENTION EFFORTS

7. FUTURE RESEARCH AREAS 1)

Not all studies utilised multivariate logistic regression to control for covariates such as age, gender and migration status. It is recommended that this approach represents a methodological standard that ought to be adopted in all future research.

2)

Current studies on migration background usually include broad and generic categories (e.g., region of origin such as Asia or Western Europe). More detail may be desirable as this may enable researchers to gain a better understanding of current findings. Having this data is of crucial importance given the significant numbers and diverse backgrounds of today’s migrants in Germany.

3)

Longitudinal studies examining the causal relationship between addictive behaviours and SES are missing in the current research in Germany. For example, binge drinking can be seen as both the cause and the consequence of unemployment [6]. Being able to clarify and differentiate cause and consequence is particularly important for preventative efforts as these should target the causes, rather than the consequences, of a problematic behaviour.

4)

Some existing research suggests that problematic substance use may be functional for the mastery of developmental tasks in adolescence [2]. Some individuals may take up smoking so as to be accepted as adults, a decision that is often linked when they find it difficult to separate themselves from their parents and lack autonomy. On the other hand, binge drinking may lead to greater social recognition and acceptance by their peer group. Research that is able to delineate concrete life circumstances of the different SES groups to such functional substance use may provide further starting points for the development of targeted, potentially-group specific, intervention measures.

5)

Future socioepidemiological research should also study the Novel Psychoactive Substances (so called `legal highs´ such as `spice´) [54, 55]. The same research suggestion can also be applied to the study of substances for cognitive neurological enhancement (such as Ritalin®). The use of these substances has probably increased in Germany, particularly among students and employed young adults. This trend will become a more important issue to consider in future work [56].

Finally, given the large number of adolescents living in poverty in Germany, it is important to expand the currently examind sociodemographic characteristics to include poverty and social/material deprivation.

Despite the fact that a number of research gaps still remain to be explored, the presented socioepidemiological data suggests starting points for future prevention efforts: Overall, the data suggest a general decline in the number of adolescents who smoke, show problematic patterns of alcohol consumption, use cannabis or other illegal drugs over the past years. Nonetheless, social inequalities continue to play a substantial role in both substance use and problematic gambling. These findings suggest that prevention policies need to pay special and systematic attention to social inequalities when planning, implementing and evaluating measures as these appear to be important influences on problematic substance abuse and gambling among adolescents and young adults. So far, prevention policies in Germany do not consider these factors. Addressing and reducing the impact of social inequalities on addictive behaviours is important for a variety of reasons. On the one hand, it is a matter of providing equal opportunities to society and ensuring social justice. On the other hand, health research has shown that social inequality affects health-related behaviours negatively not only during adolescence (e.g., in terms of nutrition, exercise, smoking, alcohol consumption etc.), but that the effects of social inequality may endure and lead to poorer health in adulthood as well [53]. By addressing the social inequalities already during adolescence, it should also be possible to reduce the number of adults who adopt addictive behaviours. Several social differences are particularly relevant in terms of such behaviours, such as tobacco smoking among men and women [58], alcohol addiction among men [59], and problematic gambling, also among men [49]. These issues tend to be more common among those who are disadvantaged in society, by either having lower educational or income levels and/or by experiencing unemployment. Another argument can be made in support of employing prevention measures that target specific, socially disadvantaged groups. In terms of the economic costs, a disproportionate percentage of patients with severe alcoholand tobacco-related health problems come from socially disadvantaged parts of the population. This is also the case for patients treated for alcohol and drug addiction as well as problematic gambling. As a result, socially disadvantaged groups tend to require more support, leading to significantly higher costs for the health care system. These findings apply in particular to patients with lower educational levels, who are unemployed and on social welfare [60, 61]. The effectiveness of preventative measures ought to be assessed based on the degree to which they result in a substantial decline or even elimination of social inequalities in terms of substance use and problematic gambling. Up to now, little research has investigated which kinds of prevention efforts were effective for different social groups. For example, evidence suggests that price increases lead


groups with lower incomes to reduce their tobacco consumption more so than other groups [62]. In order for a prevention measure to be effective, it has to reach all potential target groups. Several strategies can be employed to achieve this. One approach is the indirect recruitment of target groups (e.g., via mass media, Internet, posters, letters). Another strategy is direct recruitment, which involves contacting and finding potential recipients (e.g., by seeking them out, visiting or calling them directly). The direct recruitment strategy is a better option when social inequality is a concern, as indirect methods may not be as successful in recruiting those who are socially disadvantaged, e.g. have low education, are unemployed, or have a migration background [63, 77]. One reason for this is that adolescents and young adults with low SES may not recognise their substance use behaviour (e.g., their smoking) as being potentially problematic. In addition, they may not be as motivated to participate in prevention efforts or to seek these actively [66, 77].


may thus not have been included in this review. Second, not all survey-studies were peer-reviewed and not all have calculated resp. published odds ratios (OR), which limits the conclusions that can be drawn from these surveys. Third, the most recent survey data were collected in 2011/2012. It is therefore not known to what extent the prevalence rates of the different social groups may have changed since. CONFLICT OF INTEREST The authors have no financial or other conflicts to declare. ACKNOWLEDGEMENTS The author would like to thank Debora Jeske for her assistance with the manuscript. REFERENCES

A particularly useful strategy is on-site implementation, that is, direct intervention in the actual institutions that are known to, or already have contact with, socially disadvantaged children and adolescents [66] [77]. Without doubt, schools represent ideal locations as they have contact with children and adolescents with migration background, families with low SES, as well as adolescents facing potential unemployment. Schools may therefore serve as a good setting to initiate prevention measures aimed at reducing the social inequalities that play a role in addictive behaviours.

[1]

School-based prevention programmes should focus, in particular, on HS-students. Concentrating on these school settings is justified not only by the epidemiological results presented in this review but also the general agreement of most researchers [2] [64-66]. Of course, targeted prevention efforts will only produce substantial and sustainable effects when they are implemented as early as possible in the form of longitudinal and complementary measures in the school setting, ideally also enabling students to contribute to the continuous improvement and evaluation of these measures.

[5]

The unemployed represent another group that may be more likely to prone to several different risks. The recommendation of employing intervention measures on-site also applies to this group, too, in so far as this might include institutions that the unemployed will naturally use and maintain contact with. First and foremost, this will include the employment centres (e.g., `Jobcenter´ in Germany). Since 2005, jobcenter advisors in Germany are required, by law, to take note when their advisees may be suffering from substance use, and where appropriate, supporting the referral of their advisees to appropriate addiction treatment facilities (§ 16a SGB II in the German Social Code). In practice, this option has rarely been used to date [67]. This means that many important opportunities for prevention through the early recognition of addictive behaviours and early intervention exist, but these may not always be recognised or utilised as such. LIMITATIONS This review has three limitations. First, some socioepidemiological studies may not have been identified and

21

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[36]

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[49] [51] [52]

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Accepted: June 30, 2015