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RESEARCH REPORT

doi:10.1111/add.12741

The association between family affluence and smoking among 15-year-old adolescents in 33 European countries, Israel and Canada: the role of national wealth Timo-Kolja Pförtner1, Irene Moor1, Katharina Rathmann1, Anne Hublet2, Michal Molcho3, Anton E. Kunst4 & Matthias Richter1 Institute of Medical Sociology, Health Services Research, and Rehabilitation Science, Medical Faculty, University of Cologne, Cologne, Germany,1 Department of Public Health, Ghent University, Ghent, Belgium,2 Health Promotion Research Centre, School of Health Sciences, National University of Ireland Galway, Galway, Ireland3 and Department of Public Health, AMC, University of Amsterdam, Amsterdam, the Netherlands4

ABSTRACT Aims To examine the role of national wealth in the association between family affluence and adolescent weekly smoking, early smoking behaviour and weekly smoking among former experimenters. Design and Participants Data were used from the Health Behaviour in School-aged Children (HBSC) study conducted in 2005/2006 in 35 countries from Europe and North America that comprises 60 490 students aged 15 years. Multi-level logistic regression was conducted using Markov chain Monte Carlo methods (MCMC) to explore whether associations between family affluence and smoking outcomes were dependent upon national wealth. Measurement Family Affluence Scale (FAS) as an indicator for the socio-economic position of students. Current weekly smoking behaviour is defined as at least weekly smoking (dichotomous). Early smoking behaviour is measured by smoking more than a first puff before age 13 years (dichotomous). Weekly smoking among former experimenters is restricted to those who had tried a first puff in the past. Findings The logistic multi-level models indicated an association of family affluence with current weekly smoking [odds ratio (OR) = 1.088; 95% credible interval (CrI) = 1.055–1.121, P < 0.001], early smoking behaviour (OR = 1.066; CrI = 1.028–1.104, P < 0.001) and smoking among former experimenters (OR = 1.100; CrI = 1.071– 1.130; P < 0.001). Gross domestic product (GDP) per capita was associated positively and significantly with the relationship between family affluence and current weekly smoking (OR = 1.005; CrI = 1.003–1.007; P < 0.001), early smoking behaviour (OR = 1.003; CrI = 1.000–1.005; P = 0.012) and smoking among former experimenters (OR = 1.004; CrI = 1.002–1.006; P < 0.001). The association of family affluence and smoking outcomes was significantly stronger for girls. Conclusions The difference in smoking prevalence between rich and poor is greater in more affluent countries. Keywords smoking.

Adolescence, cross-national comparison, HBSC, national wealth, socio-economic status, tobacco

Correspondence to: Timo-Kolja Pförtner, Institute of Medical Sociology, Medical Faculty, Martin Luther University, Magdeburger Street 8, 06112 HalleWittenberg, Germany. E-mail: [email protected] Submitted 1 July 2013; initial review completed 27 September 2013; final version accepted 9 September 2014

INTRODUCTION Smoking is the leading cause of preventable mortality world-wide, and is a major risk factor for six of eight leading causes of death: ischaemic heart disease, cerebrovascular disease, lower respiratory infections, chronic obstructive pulmonary disease, tuberculosis and lung cancers [1–3]. Parallel with the increase in standards of © 2014 Society for the Study of Addiction

living, adult smoking has declined in developed countries during the last 50 years [2]. During the same time period, inequalities in smoking associated with socio-economic status (SES) were growing among adults in wealthier countries [2,4–6]. Recently, some authors pointed out that socio-economic inequalities in smoking are emerging in low-income countries [7,8]. However, most studies have shown that the association between SES and adult Addiction 110, 162–173

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smoking—indicated with higher prevalence of smokers among less socio-economically advantaged groups—are stronger in more affluent countries [4,9–15]. Although a number of studies emphasize that wealthier countries face larger inequalities in smoking by SES only a few studies have analysed this assumption, with contradictory findings. Pampel & Denney [4] used data from the World Health Survey, including more than 250 000 individuals from 50 low- to upper-middleincome nations. They found that increases in national wealth (GDP) widen the educational inequalities in smoking, especially among young adults and men, and to a lesser extent among older people and women. Another study by Pampel [16], based on the Eurobarometer in 2006, found that educational inequalities increased in relationship to the growth of national wealth among females, but not among males. Finally, in 2000 Schaap et al. [17] did not find a relationship between national wealth and inequalities in smoking associated with educational status among females for 19 European countries. These contradictory findings may be attributed to different factors, such as the geographic coverage of the studied countries, the survey years and the operationalization of SES. With regard to socio-economic inequalities in smoking among adolescents, a number of international overviews have been made, but these studies did not analyse the role of national wealth [18–20]. Instead, most studies so far have assessed how individual characteristics and tobacco control interventions contribute to the association between socio-economic status and adolescent smoking [5,21]. However, the consideration of national wealth can help to provide more insights into the evolution and development of socio-economic inequalities in adolescent smoking [4,16]. As smoking and inequalities in smoking among adults has been found to be related to economic development, it is important to know whether this association can be found for adolescents. This study can provide implications for future trends in inequalities in adolescent smoking, especially for less wealthy countries where economic development and prosperity are lagging behind economically advanced countries [22]. The key objective of this study is to investigate whether wealthier countries have smaller inequalities in adolescent smoking according to family affluence, a proxy measure for SES in adolescents. Our study aims to answer the following research questions: (i) does the association between family affluence and aspects of smoking vary across countries; and (ii) to what extent is the relationship between family affluence and smoking associated with GDP per capita? All analyses were stratified by gender to consider differences in smoking among boys and girls [18,20]. We will focus on three measures of smoking [23]: (i) current weekly © 2014 Society for the Study of Addiction

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smoking, (ii) smoking behaviour before age 13 and (iii) weekly smoking among former experimenters. While the first two aspects of smoking refer to the overall sample population, the latter is restricted to those who had tried a first puff in the past to identify adolescents who have established a regular smoking pattern. Overall, these measures reflect different stages of a smoking career that allow identification of adolescents with a higher risk of early and sustained smoking behaviour [24,25].

METHODS Study population The analyses are based on data of the Health Behaviour in School-aged Children study (HBSC). The HBSC study is a World Health Organization collaborative cross-national study collecting nationally representative data on adolescents every 4 years since 1982 in a growing number of countries in Europe and North America [26]. The aim of the HBSC study is to increase the understanding of health, health-related behaviour and the social contexts of young people aged 11, 13 and 15 years [27,28]. Research groups in 38 countries took part in the 2005/06 survey, adhering to an internationally agreedupon protocol [29]. The survey is based upon a selfcompleted questionnaire that is administered in schools by teachers. Participation was voluntary and consent was obtained from school administrators, parents and children. Each participating country employed a multistage sample procedure with the school or class being the sampling unit. According to the international HBSC report, the response rate on school, class and pupil levels exceeded 80% in 2005/06 [27]. Ethical approval was obtained for each national survey according to the national guidance and regulations at the time of data collection. Three countries were excluded from the analyses: Turkey, where questions on smoking were not surveyed; Greenland, where reliable data on GDP per capita were not available; and the United States, where 57% of the sample returned missing values. Thirty-five countries were included, and a full sample (15-year-olds (n) = 60 490) and a subsample (former smoking experimenters (n) = 32 581) were considered according to the specific smoking measure (see Table 1). For the present study, we analysed data only for 15-year-olds. Prevalence rates of weekly smoking in younger age groups were too low for analyses (i.e. 0.9% in Norway and Macedonia among 13-year-olds). Tobacco smoking In accordance with Kuipers et al. [23], three outcome measures were used: weekly smoking, early smoking Addiction 110, 162–173

Austria Belgium Bulgaria Canada Croatia Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Israel Italy Latvia Lithuania Luxembourg Malta Netherlands Norway Poland Portugal Romania Russian Federation Slovakia Slovenia Spain Sweden Switzerland Ukraine Macedonia United Kingdom Total

36 534.8 34 192.7 11 063.4 36 862.9 16 794.7 23 231.3 35 999.2 19 137.7 33 096.8 31 274.2 33 503.2 26 733.1 18 274.7 35 849.0 42 473.7 23 957.4 30 359.0 14 973.0 16 031.7 78 382.6 22 192.8 38 025.6 53 778.4 15 053.4 22 937.0 11 118.4 14 949.2 18 357.0 25 422.8 30 332.7 35 657.5 40 881.8 6226.20 8547.38 35 065.8 27 922.1

1398 2739 1602 2210 1559 1591 1460 1553 1606 2171 2353 1292 1091 1778 1528 1802 1214 1227 1806 1410 326 1330 1468 2228 1313 1508 2354 1118 1504 2948 1310 1451 1706 1835 4701 60 490

24.6 16.7 27.3 6.9 23.6 19.4 15.8 26.4 22.9 16.6 15.7 16.3 21.4 12.3 17.4 13.2 19.9 28.9 25.7 16.5 20.7 15.6 8.9 18.4 8.9 20.1 26.7 18.9 20.2 14.5 7.7 15.1 34.5 13.8 13.2 17.9

29.5 16.6 36.1 10.0 28.3 23.5 14.8 18.4 21.5 20.7 22.1 15.7 21.5 12.6 18.4 5.7 20.4 22.0 17.9 21.3 24.1 20.5 12.0 14.2 11.9 11.8 21.0 14.9 16.6 21.5 9.5 15.1 18.6 14.5 21.5 18.3

27.3 16.6 32.0 8.6 26.1 21.4 15.3 22.4 22.2 18.6 19.0 15.9 21.4 12.5 17.9 8.5 20.2 25.2 21.8 18.9 22.4 18.1 10.4 16.2 10.6 15.1 23.6 16.7 18.4 18.0 8.6 15.1 25.6 14.1 17.4 18.1

At least weekly smoking (%) GDP per capita in Total number PPP (int. US$) of students Boys Girls Total 33.4 15.8 24.0 8.7 23.7 30.8 13.1 50.3 23.5 16.7 22.5 9.9 23.5 8.2 19.0 8.0 12.1 39.3 36.2 22.8 11.0 17.9 14.5 29.9 15.7 17.1 28.8 26.9 19.0 12.2 9.9 24.8 35.1 10.1 13.8 20.4

Boys 34.0 14.5 21.5 10.7 17.0 24.5 11.4 29.8 15.3 16.8 22.7 6.2 18.5 3.7 18.8 3.0 8.2 23.2 19.1 18.9 14.2 20.4 10.5 16.9 13.1 9.3 18.2 18.2 13.3 13.1 11.2 16.6 14.0 5.1 17.6 15.6

Girls 33.8 15.2 22.7 9.8 20.1 27.9 12.3 40.1 19.1 16.7 22.6 7.9 20.7 5.9 18.9 4.8 10.1 30.6 27.6 20.9 12.6 19.2 12.6 23.0 14.2 12.2 22.9 22.3 16.1 12.6 10.5 20.6 23.4 7.6 15.7 17.9

Total

Early smoking behaviour before age 13 (%)

923 1304 1065 805 956 1098 751 1142 963 1159 1336 554 703 652 746 608 641 942 1398 816 149 645 629 1306 590 733 1490 622 815 1432 494 850 1238 665 2361 32 581

39.0 35.3 45.0 20.4 39.4 27.4 30.5 33.6 37.4 32.1 29.3 38.0 34.2 33.2 38.1 29.1 38.8 35.5 31.0 29.4 48.6 33.6 22.1 29.2 19.7 38.0 41.1 31.3 37.0 33.0 21.7 24.4 43.1 38.1 30.4 33.4

43.0 34.6 50.4 25.9 45.1 35.0 28.9 26.9 36.6 37.6 36.9 36.5 32.6 34.9 35.1 21.2 37.7 30.2 24.8 35.7 49.4 40.7 26.3 25.9 26.6 26.1 33.8 28.8 30.8 40.4 23.9 27.3 27.7 39.8 37.8 33.9

41.3 35.0 48.1 23.5 42.6 31.1 29.7 30.5 37.0 34.9 33.4 37.2 33.3 34.1 36.6 25.2 38.2 32.8 28.1 32.6 49.0 37.4 24.2 27.6 23.6 31.0 37.2 30.1 33.9 37.1 22.9 25.8 35.2 39.0 34.7 33.6

Weekly smoking among former experimenters (%) Total number of former smoking experimenters Boys Girls Total

Table 1 Description of the population and country-level variable: gross domestic product (GDP) per capita, number of boys, girls and total prevalence of at least weekly smoking, early smoking behaviour and smoking among former smoking experimenters.

164 Timo-Kolja Pförtner et al.

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Addiction 110, 162–173

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initiation and weekly smoking among former experimenters. Variables were based on three items in the HBSC questionnaire (Table 1): ‘have you ever smoked tobacco (at least one cigarette, cigar or pipe)’; ‘at which age did you first smoke a cigarette (more than a puff)’; and ‘how often do you smoke at present?’. Students were defined as current weekly smokers if they indicated smoking at least weekly at present [30]. Early smoking behaviour was defined as having smoked a cigarette before age 13 [31]. Weekly smoking among former experimenters refers to all students who had tried a first puff in the past and differentiates between those who currently smoke at least weekly and those who currently smoke less than weekly or not smoke at all. In accordance with Everett et al. [32], the latter was selected to examine the dynamic character of the progression from smoking experimentation to addiction. Socio-economic position Socio-economic position was measured using the Family Affluence Scale (FAS) [33]. The FAS is a validated index of material affluence based on a composite of four household items: ‘does your family have a car or van’ [‘no’ (2) ‘yes, one’ (1) ‘yes, two or more’ (0)]; ‘do you have your own bedroom’ [‘yes’ (0), ‘no’ (1)]; ‘how many computers does your family own’ [‘none’ (3), ‘one’ (2), ‘two’ (1), ‘more than two’ (0)]; and ‘during the past 12 months, how many times did you travel away on holiday with your family?’ [‘not at all’ (3), ‘once’ (2), ‘twice’ (1), ‘more than twice’ (0)]. The validity of the FAS has been addressed in several studies [34,35], and the score has been used in previous research on adolescent smoking [30,36]. In accordance with previous debate on the cross-country comparability of the FAS [37,38], we used an additive affluence score that has been standardized for each country individually. For each country, the score has a mean of zero and a standard deviation of 1 in order to have comparable information across countries. Higher scores indicate lower levels of family affluence. National wealth To estimate national wealth, we used information on GDP in 2006 based on purchasing power parity (PPP), converted to current international dollars from the World Bank database (http://data.worldbank.org/). Statistical analyses Logistic regression models were used to assess associations between family affluence and each of the three smoking indicators taken as dependent variables. Multi© 2014 Society for the Study of Addiction

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variate models for each smoking indicator were built adjusting for gender, using cluster-robust standard errors according to Huber & White [39]. The association between family affluence and smoking outcomes was analysed for countries combined and each country separately (Fig. 1). Multi-level logistic models were used to investigate the cross-country variability of the association between family affluence and smoking (level 1), as well as the role of national wealth (level 2) in this relationship [40]. The national GDP level was centred on its grand mean for a meaningful interpretation of the effect of family affluence [41]. Models were fitted using Markov chain Monte Carlo (MCMC) [42] simulations in MLwiN version 2.28 [43] (University of Bristol, UK) interfaced with Stata (StataCorp, TX, USA) [44]. Inferences were drawn from chains of length 50 000 after a burn-in of 500 with the means and 95% confidence intervals (CIs) from the posterior distribution of the parameters with the data held fixed. To quantify the cross-country variation in smoking, both an intraclass correlation coefficient (ICC) and a median odds ratio (MOR) were calculated [45]. The ICC indicates the proportion of the unexplained variation in the probability of smoking that can be attributed to differences between countries. The MOR indicates the increased (median) odds of smoking if an adolescent changes from a country to a different country with higher odds of smoking. Using the country-level variance of the multi-level model, the MOR computes the OR for each pair of individuals with the same individual covariates but residing in different countries (the higher odds are always placed in the numerator). This procedure yields a cumulative distribution function of OR and the MOR is the median of this distribution [45]. All multi-level analyses are based on three consecutive models that were compared using the deviance information criterion (DIC), with smaller values indicating an improvement in model fit (with a difference of at least 7 units) [46]. The first model is an intercept-only model that included no explanatory variables. The second model included family affluence and gender (individual-level model). In the third model, GDP per capita and its interaction with family affluence were considered (full model). Results of logistic multi-level regression were used to estimate the predicted probabilities of smoking by GDP per capita for different FAS groups (Fig. 2) [47]. Finally, we applied sensitivity analysis and re-estimate the models excluding Norway and Luxembourg, with high levels of GDP per capita, to determine whether or not results remained unaltered. All regression analyses were carried out with Stata version 13.0 (StataCorp, TX, USA). Addiction 110, 162–173

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15000-25000

25000-35000

35000-40000

Overall

Luxembourg

Ireland

Norway

Switzerland

Netherlands

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Canada

Iceland

Denmark

Sweden

Belgium

United Kingdom

Finland

Germany

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Greece

Israel

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Malta

Portugal

Estonia

Slovakia

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< 15000

Hungary

Lithuania

Latvia

Poland

Russia

Bulgaria

Romania

Ukraine

2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6

Macedonia

Odds ratio of weeklysmoking by less family affluence

Weekly smoking

>40000

GDP per capita, PPP (current international $)

Luxembourg

Overall

Norway

Ireland

Switzerland

Netherlands

Austria

Canada

Denmark

35000-40000

Overall

25000-35000

Iceland

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Belgium

United Kingdom

Finland

Germany

France

Italy

Spain

Greece

Slovenia

Israel

Czech Republic

Portugal

Malta

Estonia

Slovakia

15000-25000

Luxembourg

< 15000

Hungary

Croatia

Lithuania

Poland

Latvia

Russia

Bulgaria

Romania

Ukraine

2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6

Macedonia

Odds ratio of eatly smoking before age 13 by less family affluence

Early smoking behavior

>40000

GDP per capita, PPP (current international $)

< 15000

15000-25000

25000-35000

Norway

Ireland

Switzerland

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Austria

35000-40000

Canada

Denmark

Iceland

Sweden

Belgium

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Finland

Germany

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Slovenia

Israel

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Portugal

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Hungary

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Lithuania

Poland

Latvia

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Bulgaria

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2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6

Macedonia

Odds ratio of weekly smoking among former experimenters

Weekly smoking among former experimenters

>40000

GDP per capita, PPP (current international $)

Figure 1 Country-wise logistic regression for different measures of smoking by family affluence among 15-year-olds, gender-adjusted odds ratio (95% confidence interval) [Health Behaviour in School-aged Children (HBSC) 2005/06]. GDP = gross domestic product; PPP = purchasing power parity

RESULTS Figure 1 shows the results of logistic regressions for different measures of smoking and family affluence among 15-year-olds per country. Overall, family affluence appeared as significant risk factor in weekly smoking (OR = 1.087; 95% CI = 1.064–1.121; P < 0.001) and in smoking among former experimenters (OR = 1.093; CI = 1.068–1.130; P < 0.001). A decrease of family affluence by 1 standard deviation was associated with an increase in the risk of weekly smoking by approximately 9% among all adolescents and among former experimenters. For both samples, we observe a strong variation in the direction and effect size of family affluence on weekly smoking. Among all adolescents and among © 2014 Society for the Study of Addiction

former experimenters, the association of family affluence with weekly smoking was stronger in wealthier countries. The association of family affluence with early smoking behaviour was smaller on average (OR = 1.060; CI = 1.037–1.083; P < 0.001), and was not statistically significant in the majority of countries. Multi-level models were conducted to identify whether the interaction of GDP per capita (PPP) with the association between family affluence and aspects of smoking was significant (see Supporting information, Tables S1– S3). The intercept-only models indicated the countrylevel variance in weekly smoking (ICC = 0.045/MOR = 1.459), early smoking behaviour (ICC = 0.100/ MOR = 1.780) and smoking among former experimenters (ICC = 0.023/MOR = 1.306). Table 2 illustrates the Addiction 110, 162–173

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Table 2 Multi-level logistic regression for different measures of smoking among 15-year-olds [Health Behaviour in School-aged Children (HBSC) 2005/06].

Intercept Individual level Family affluencea Sex (reference: boys) National level GDP per capita/1000 Cross-level interaction GDP per capita/1000 × family affluence Number of observations Level 1 Level 2

Weekly smoking

Early smoking behaviour before age 13

Weekly smoking among former experimenters

OR (95% CrIa)

OR (95% CrIa)

OR (95% CrIa)

0.212*** (0.184–0.243)

0.239*** (0.197–0.293)

0.497*** (0.451–0.548)

1.088*** (1.055–1.121) 1.023 (0.980–1.066)

1.066*** (1.028–1.104) 0.714*** (0.684–0.745)

1.100*** (1.071–1.130) 0.999 (0.953–1.046)

0.991* (0.981–1.001)

0.996 (0.983–1.011)

0.996 (0.989–1.002)

1.005*** (1.003–1.007)

1.003* (1.000–1.005)

1.004*** (1.002–1.006)

60 490 35

60 490 35

32 581 35

OR = odds ratio; SD = standard deviation; MOR = median odds ratio; Crl = credible interval. a95% credible interval; if the interval contains 1.0, there are no significant differences; GDP = gross domestic product. *P < 0.05; ***P < 0.001.

full model of the cross-level interaction of national wealth (country level) with family affluence and adolescent smoking (individual level). National wealth was associated positively and significantly with the risks of smoking by family affluence. For example, the risk to smoke weekly at present increased by 8.8% with a decrease of family affluence of 1 standard deviation. In addition, the risk of smoking weekly at present for less affluent adolescents rose on average by 0.5%, with a rise of GDP per capita by 1000 international dollars in PPP. A similar cross-level interaction was found for risks of smoking by family affluence among smoking experimenters and was less strong for early smoking behaviour. In the final multi-level models, the deviance decreased substantially for current weekly smoking (−110.26 from 56 132.26), early smoking behaviour (−298.42 from 54 497.95) and smoking among former experimenters (−80.66 from 41 222.71) compared to the intercept-only model. A somewhat slight reduction of the variance in risks of smoking by family affluence was found for weekly smoking (from 0.008 to 0.003), early smoking behaviour (from 0.008 to 0.005) and smoking among former experimenters (from 0.004 to 0.001) after considering GDP per capita (PPP). However, as the total variance of risks in smoking by family affluence was somewhat low across countries, the explanatory power of GDP per capita was already restricted. There were almost no gender differences in the association between GDP per capita, family affluence and smoking. We observed significantly smaller inequalities in smoking by family affluence only among boys (see Table 3). Figure 2 illustrates the probabilities of smoking by family affluence and GDP per capita according to the © 2014 Society for the Study of Addiction

logistic multi-level results in Table 2. We observe that inequalities in smoking by family affluence increase with levels of GDP per capita. The largest inequalities were observed for smoking among former experimenters. We also observe a grouping of countries by GDP per capita separating the eastern European countries (below 30 000 international US$) from most of the remaining countries. Luxembourg and Norway showed the highest levels of GDP per capita and appeared as outliers according to Fig. 2. Sensitivity analyses showed that the results did not change when these wealthiest countries were excluded from analyses. All analyses for weekly smoking were repeated for current daily smoking behaviour (see Supporting information Tables S1 and S3). The results indicated a stronger association of family affluence with daily smoking, but were similar with regard to the cross-level interaction of GDP per capita with family affluence and weekly smoking.

DISCUSSION This study provides novel results on the relationship between national wealth, family affluence and adolescent smoking. The results show that weekly smoking, early smoking behaviour and weekly smoking among former experimenters were, on average, more prevalent among students from less affluent families, especially among girls. For all three smoking measures, the association with family affluence tended to be stronger in wealthier countries. The strongest variations by national wealth were observed for inequalities in weekly smoking by family affluence, among both all adolescents and former experimenters. Addiction 110, 162–173

© 2014 Society for the Study of Addiction

OR = odds ratio; Crl = credible interval. a95% credible interval; if the interval contains 1.0, there are no significant difference. bSignificant gender differences exist only in the association between family affluence and weekly smoking, early smoking behaviour and weekly smoking among former experimenters but did not apply for their cross-level interaction with gross domestic product (GDP) per capita (results not shown). *P < 0.05; **P < 0.01; ***P < 0.001.

17 072 35 15 509 35

1.003* (1.000–1.006) 1.004** (1.001–1.006)

1.000 (0.991–1.008) 0.991** (0.984–0.998)

1.058** (1.018–1.099) 1.142*** (1.106–1.179)

0.491*** (0.446–0.539) 0.489*** (0.438–0.545)

Intercept 0.213*** (0.185–0.243) 0.212*** (0.181–0.248) 0.237*** (0.186–0.293) 0.169*** (0.138–0.207) Individual level Family affluence 1.045* (1.006–1.085) 1.140*** (1.095–1.187) 1.048** (1.011–1.088) 1.084** (1.032–1.139) National level GDP per capita/1000 0.985** (0.975–0.996) 0.997 (0.987–1.007) 0.989 (0.974–1.004) 1.001 (0.986–1.016) Cross-level interaction GDP per capita/1000 × family affluence 1.003* (1.000–1.006) 1.005** (1.002–1.007) 1.000 (0.998 −1.003) 1.004* (1.001–1.008) Number of observations Level 1 28 907 31 583 28 907 31 583 Level 2 35 35 35 35

OR (95% CrIa) OR (95% CrIa) OR (95% CrIa) OR (95% CrIa) OR (95% CrIa) OR (95% CrIa)

Boys Girls Boys Girls Boys

Weekly smoking among former experimenters Early smoking behaviour before age 13 Weekly smoking

Table 3 Multi-level logistic regression for different measures of smoking among 15-year-olds, stratified by genderb [Health Behaviour in School-aged Children (HBSC) 2005/06].

Girls

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Comparison with previous research and interpretation Our results are in line with previous studies that reported socio-economic inequalities in adolescent smoking to be more common in northern and western Europe [18,19]. The identified patterns are consistent with the smoking epidemic theory [48,49]. Wealthier countries are in an advanced stage of the tobacco epidemic, in which smoking has become less common among wealthy groups [2,4,5,13,14,50–60]. Accordingly, socioeconomic inequalities in smoking were smaller in southern and eastern Europe than in northern and western Europe. The eastern and southern European countries might lag behind the smoking epidemic, as inequalities in smoking are emerging more recently or are still favouring the less wealthy groups [10,18]. Other cross-national studies also indicated this geographical pattern for adolescents and adults [3,9–12]. The literature offers a number of explanations as to why inequalities in smoking are stronger in affluent countries. It has been assumed that socio-economically advantaged adolescents in industrialized countries might be more likely to consider and invest in individual future health [6,14]. According to Inglehart [61], affluent countries have higher levels of self-expression and quality of life [62]. In these countries, adolescents from high-income families tend to be more likely to have these expectations for their future life and, thus, to show healthy behaviours more frequently, compared to worseoff adolescents [39,63–65]. Another explanation relates to adolescents’ ability to afford cigarettes [14]. In poor countries, cigarettes might be too expensive for adolescents in low socio-economic settings, thus preventing them from smoking. However, the affordability of cigarettes is generally high in low- and middle-income countries compared to high-income countries [66–68]. Alternatively, aspects of tobacco control policies may have contributed to the larger socio-economic inequalities in smoking in affluent countries [15]. Wealthier countries have a higher level of implementation of tobacco control policies [30]. To the extent that better-off adolescents benefited more from past tobacco policies, this would have widened inequalities in smoking [69]. However, the evidence regarding the effect of interventions on inequalities in smoking is not conclusive [69,70]. Finally, it has been suggested that the economic development of a country is linked to a diffusion of norms of smoking [50]. In less wealthy countries, smoking has the symbolic meaning of status distinction for high-status groups. As smoking rates increase in the rest of the population, high-status groups move on to new symbols of societal distinction as, for example, health diets and life-styles. Simultaneously, in these countries smoking might have become an undesirable Addiction 110, 162–173

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Weekly smoking Predicted probability

25

20

15

10 0

10,000

20,000

30,000

40,000 50,000 GDP per capita

FAS (country mean)

60,000

70,000

80,000

90,000

70,000

80,000

90,000

70,000

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Early smoking behavior Predicted probability

25

20

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Weekly smoking among former experimenters Predicted probability

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Figure 2 Predicted probabilities of aspects of smoking by family affluence among 15-year-olds, at gender means level [Health Behaviour in School-aged Children (HBSC) 2005/06]. Predicted probabilities are based on results of logistic multi-level regression in Table 2. Horizontal symbol-pairs represent the country according to its level in gross domestic product (GDP) per capita. Family Affluence Scale (FAS) is standardized with a country mean at zero and a standard deviation of 1, indicating a decrease of FAS with higher scores

behaviour that stigmatizes and identifies marginal lower-status groups [71]. The association of national income with inequalities in smoking did not significantly differ by gender. © 2014 Society for the Study of Addiction

However, we observed smaller inequalities in smoking by family affluence among boys. This result is in accordance with previous research, and it appears that gender is an important factor in developing interventAddiction 110, 162–173

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ions against socio-economic inequalities in smoking [72–80]. Methodological considerations The strengths of the study are the cross-national data collected from a large number of European and North American countries, allowing for a broad cross-country comparison of family affluence and adolescent smoking. The variety of smoking indicators permitted detailed analyses of the impact of national wealth on inequalities in adolescent tobacco use. However, some limitations also need to be acknowledged. First, despite the statistical significance, the low effect size value of FAS indicates that differences in smoking by family affluence are not very large. Rather low effect sizes call into question the practical implications of such findings, but are in line with previous studies on adolescent inequalities in health and health behaviour by FAS [18]. Furthermore, as family affluence is indicated by a continuous variable, the indicator does not capture the total range of risks in smoking by family affluence. In this regard, across countries the differences in risks of smoking by family affluence were statistically significant, but not very large. This might result from the restricted number of countries, which refers only to middle- and high-income countries. Therefore, further research should take into account low-, middle- and high-income countries. Secondly, in our study, a main problem may arise from social desirability among affluent adolescents [81], which might be associated with the diffusion of norms of smoking. Therefore, affluent adolescent from less wealthy countries might be less influenced by the social desirability of non-smoking compared to their peers in richer countries. It is unknown to what extent social desirability affects reporting on smoking in adolescence, but all students were assured their anonymity in the survey. Thirdly, the analysed data were collected in 2005/06 and identified patterns might have changed in time due, for example, to the financial crisis in late 2007 [82,83]. Future research should consider more recent data to assess the dynamics and the present situation of the association between national wealth, adolescent smoking and SES. Finally, geographical confounding may be present, as the countries were not selected randomly and the data consist mainly of high-income countries. Therefore, current study findings require additional analyses taking into account a higher number of countries from low-, middle- and high-income countries. CONCLUSIONS Socio-economic inequalities in adolescent smoking are of specific interest for the public health domains, as they © 2014 Society for the Study of Addiction

play an important role in explaining socio-economic inequalities in health among adults. Moreover, as the majority of adult smokers began smoking before age 18, it is highly relevant to identify factors that are linked to smoking and socio-economic inequalities in smoking among adolescents. The present study indicated two patterns: while socio-economic inequalities in different aspects of smoking were higher in wealthier countries, the absolute rates in adolescent smoking were higher in less affluent countries. Different implications are associated with these findings. Wealthier countries should increase efforts in tackling smoking among adolescents from disadvantaged families by establishing tailored policies and interventions that consider multiple and proactive approaches, especially at the network and school levels [84]. In contrast, in less wealthy countries, general interventions against adolescent smoking need to be increased and extended for low socio-economic groups. Learning from wealthier countries means establishing tailored interventions against smoking among low socioeconomic groups at an early stage of the policy development to avoid large inequalities in less affluent countries. However, as the differences in risks of smoking by family affluence were somewhat low across countries, study implications should be considered carefully. Further research is required to confirm our findings by focusing on low-, middle- and high-income countries.

Declaration of interests This research was funded by the European Union’s Seventh Framework Programme for Research and Technological Development (FP7) (grant no. 278273-2). The authors have no connection with the tobacco industry or any body substantially funded by one of these organizations. There are no contractual constraints on publishing imposed by the funder.

Acknowledgements The Health Behaviour in School-aged Children (HBSC) study is an international survey conducted in collaboration with the WHO Regional Office for Europe. The current International Coordinator of the study is Candace Currie, CAHRU, University of St Andrews, Scotland. The data bank manager is Oddrun Samdal, University of Bergen, Norway. The data collection in each country was funded at the national level. We are grateful for the financial support offered by the various government ministries, research foundations and other funding bodies in the participating countries and regions. This work on this paper is part of the project ‘Tackling socioeconomic inequalities in smoking (SILNE)’, which is funded by the European Commission, DirectorateAddiction 110, 162–173

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Supporting information Additional Supporting information may be found in the online version of this article at the publisher’s web-site: Table S1 Stepwise multi-level logistic regression models for daily and weekly smoking among 15-year-olds [Health Behaviour in School-aged Children (HBSC) 2005/06]. Table S2 Stepwise multi-level logistic regression models for early smoking behaviour among 15-year-olds [Health Behaviour in School-aged Children (HBSC) 2005/06]. Table S3 Stepwise multi-level logistic regression models for daily and weekly smoking former experimenters [Health Behaviour in School-aged Children (HBSC) 2005/06].

Addiction 110, 162–173

The association between family affluence and smoking among 15-year-old adolescents in 33 European countries, Israel and Canada: the role of national wealth.

To examine the role of national wealth in the association between family affluence and adolescent weekly smoking, early smoking behaviour and weekly s...
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