Gyöngyvér Salavecz

Tarani Chandola

Hynek Pikhart

Nico Dragano

Johannes Siegrist

Karl-Heinz Jöckel

Raimund Erbel

Andrzej Pajak

Gyongyver Salavecz

Martin Bobak

Maria Kopp

Work Stress and Health in Western European and Post-Communist Countries: an East-West Comparison Study

Keywords: psychosocial stress, work stress, effort-reward imbalance, self-rated health

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

There is a consistent evidence from cross-sectional and prospective studies on the association of poor health with the hazards of work stress. However, the majority of research is derived from Western European societies. [START_REF] Bosma | Two alternative job stress models and the risk of coronary heart disease[END_REF][START_REF] Stansfeld | Psychosocial work characteristics and social support as predictors of SF-36 health functioning: the Whitehall II study[END_REF][START_REF] Peter | High effort, low reward, and cardiovascular risk factors in employed Swedish men and women: baseline results from the WOLF Study[END_REF][START_REF] Niedhammer | Effort-reward imbalance model and self-reported health: cross-sectional and prospective findings from the GAZEL cohort[END_REF][START_REF] Melchior | Do psychosocial work factors and social relations exert independent effects on sickness absence? A six year prospective study of the GAZEL cohort[END_REF][START_REF] Van Der Doef | The Job Demand-Control (-Support) Model and psychological well-being: a review of 20 years of empirical research[END_REF][START_REF] Siegrist | The measurement of effort-reward imbalance at work: European comparisons[END_REF][START_REF] Tsutsumi | A review of empirical studies on the model of effort-reward imbalance at work: reducing occupational stress by implementing a new theory[END_REF][START_REF] Vegchel | Reviewing the effort-reward imbalance model: drawing up the balance of 45 empirical studies[END_REF] There are only a few studies on the association of work stress with different health outcomes in Central and Eastern European post-communist countries. [START_REF] Bobak | Association between psychosocial factors at work and non-fatal myocardial infarction in a population-based case-control study in Czech men[END_REF][START_REF] Pikhart | Psychosocial work characteristics and self rated health in four post-communist countries[END_REF][START_REF] Pikhart | Social and psychosocial determinants of self-rated health in Central and Eastern Europe[END_REF][START_REF] Pikhart | Psychosocial factors at work and depression in three countries of Central and Eastern Europe[END_REF][START_REF] Hasselhorn | Effort-reward imbalance among nurses in stable countries and in countries in transition[END_REF][START_REF] Bobak | The association between psychosocial characteristics at work and problem drinking: a cross-sectional study of men in three Eastern European urban populations[END_REF][START_REF] Tobiasz-Adamczyk | Psychosocial work conditions as predictors of quality of life at the beginning of older age[END_REF][START_REF] Kopp | Psychosocial determinants of premature cardiovascular mortality differences within Hungary[END_REF][START_REF] Kopp | Work stress and mental health in a changing society[END_REF][START_REF] László | Work-related stress factors and menstrual pain: a nation-wide representative survey[END_REF] There are also large differences in morbidity and mortality rates between post-communist and Western European countries. The gap between East and West did not change much after the transition to market economies -mortality rates are still much higher in post-communist countries. Life expectancy for men is still 59 years in Russia, 69 years in Hungary and it is 76 years in the United Kingdom. [START_REF] Central | Average life expectancy at birth[END_REF] It has been hypothesised that, in addition to higher levels of unhealthy behaviours (excessive alcohol consumption, smoking, poor nutrition etc.) in post-communist countries, stressful experience related to core social roles (including the work role) could contribute towards explaining some of these differences in health between Western and post-communist societies. [START_REF] Kopp | Work stress and mental health in a changing society[END_REF][START_REF] Bobak | East-West mortality divide and its potential explanations: proposed research agenda[END_REF][START_REF] Kopp | Psychosocial risk factors, inequality and self-rated morbidity in a changing society[END_REF][START_REF] Kopp | Psychosocial determinants of premature health deterioration in a changing society: the case of Hungary[END_REF][START_REF] Kopp | Chronic stress and social changes, socioeconomic determination of chronic stress[END_REF] In many formerly socialist countries, persons of working age are especially vulnerable. [START_REF] Weidner | The gender gap in heart disease: lessons from Eastern Europe[END_REF] Socioeconomic and labour market differences between Eastern and Western European countries are also pronounced. For example, GDP per capita, total health expenditure per capita and unemployment rates are all less favourable in Eastern Europe compared to Western countries (e.g. in Poland the GDP per capita was 11,861 $, total health expenditure per capita was 805$ and unemployment rates was 17.7 % while in the United Kingdom these indicators were 32,860$, 2,508$ and 4.8% in 2005). [START_REF]Key Economic Indicators[END_REF] Countries with different socioeconomic condition could have different capabilities in buffering the effect of psychosocial stress which may increase the risk of poor health. It is therefore possible that the psychosocial work stressors may have a stronger effect on health in Eastern Europe compared to the West. [START_REF] Bobak | East-West mortality divide and its potential explanations: proposed research agenda[END_REF][START_REF] Bobak | Socioeconomic factors, perceived control and self-reported health in Russia. A cross-sectional survey[END_REF][START_REF] Siegrist | Adverse health effects of effort-reward imbalance -applying the model to Eastern Europe[END_REF] The focus of this paper is to compare the effect of work stress, measured in terms of effort-reward-imbalance, on self-rated health in the working populations of two Western European (United Kingdom, Germany), and four post-communist Central and Eastern European countries (Czech Republic, Poland, Hungary, Russia). The effort-reward imbalance model is one of the frequently tested concepts in work stress research. Its core assumption is that the imbalance between high efforts spent and low rewards received in turn (in terms of salary, esteem, promotion prospects and job security) leads to an increased risk of poor health.

In this model, a distinction is made between extrinsic and intrinsic sources of effort. [START_REF] Siegrist | Adverse health effects of high-effort/low-reward conditions[END_REF] The extrinsic construct shows the relation between the demands at work and the rewards received.

The intrinsic component indicates the motivations of working people to perform and achieve.

In this latter regard, a risky pattern of coping, 'overcommitment', was identified.

Overcommitted people spend excessive efforts at work, striving for control and success, but, by doing so, they run the risk of premature exhaustion and breakdown. Thus, the model assumes that the extrinsic and intrinsic components are independently associated with poor health. An alternative proposition is that there is an interaction between the two components.

We have tested both these hypotheses.

METHODS

Study population

This study used cross sectional data on working populations collected in four studies in six European countries. These studies are described below.

HAPIEE Study (Czech Republic, Poland, Russia)

The HAPIEE (Health, Alcohol and Psychosocial factors In Eastern Europe) study comprises three cohorts, in Russia, Poland and in the Czech Republic. The study has been described in detail elsewhere. [START_REF] Peasey | Determinants of cardiovascular disease and other non-communicable diseases in Central and Eastern Europe: rationale and design of the HAPIEE study[END_REF] The cohorts consist of random samples of men and women aged 45-69 years at baseline, selected from population registers of selected cities. A total of 28,947 individuals completed the questionnaire with an overall response rate of 61%. The study sample was restricted to persons who were still employed and under 65 years old (n=12,616).

Ethical approval for the study was obtained from the University College London Medical School Committee on the ethics of human research.

Hungarian Epidemiological Panel (Hungary)

The Hungarian Epidemiological Panel (HEP) is the second phase of Hungarostudy 2002, a national prospective survey of the adult Hungarian population. [START_REF] Susanszky | Methodology of the Hungarian Epidemological Panel (A Hungrostudy Egészség Panel követéses vizsgálat módszertana)[END_REF] A battery of questionnaires on psychosocial and demographic characteristics was administered during a home interview. The sampling frame was the National Population Register including men and women. Both towns with a population of more then 10,000, as well as a random sample of smaller villages were included in the sample. From the first phase (n=12,643), 8,008 individuals gave consent to participate in the second phase. In HEP the overall response rate was 72%. We restricted our study sample to the middle aged working population years) who completed the questionnaire on effort-reward imbalance in the second phase conducted in 2005-2006 (n=1,449). The study was approved by the Ethical Committee of Semmelweis University.

Whitehall II Study (United Kingdom)

The Whitehall II study is a prospective investigation of socioeconomic, psychosocial, behavioural, and biomedical health determinants in a cohort of 10,368 male and female British civil servants. [START_REF] Marmot | Cohort Profile: the Whitehall II study[END_REF] Data collection started in 1985-1988 (phase 1) and has continued until phase 7 (2002-04). At phase 7, there was a 72% response rate for the overall data collection, but only 3,413 were in employment and of those, 2,995 completed the questions on effort-reward imbalance and were under 65 years old. Furthermore, as questions on educational qualifications were only asked at phase 5, the sample reduced further to 2,625.

Ethical approval for the Whitehall II study was obtained from the University College London Medical School Committee on the ethics of human research.

The Heinz Nixdorf Recall Study (Germany)

The Heinz Nixdorf Recall (HNR) Study is a population-based prospective cohort study. Apart of medical examinations to measure coronary atherosclerosis and cardiovascular risk factors, face-to-face interviews were conducted, including questions about socio-economic conditions, occupation and work stress. [START_REF] Schmermund | Assessment of clinically silent atherosclerotic disease and established an novel risk factors for predicting myocardial infarction and cardiac death in healthy, middle-aged subjects[END_REF] The study base was the general German population living in three large adjacent cities (Bochum, Essen, Mülheim/Ruhr) of the densely populated Ruhrarea in Germany. 4814 males and females aged 45 to 74 years, randomly selected from statutory lists of residence, participated in the study (response of 56%). Details of recruitment and sample characteristics have been described elsewhere. [START_REF] Stang | Baseline recruitment and analyses of nonresponse of the Heinz Nixdorf Recall Study: identifiability of phone numbers as the major determinant of response[END_REF] Baseline examination took place between 2000-2003. For this analyses the sample was restricted to the working population, under 65 years, who completed the questions of effort-reward imbalance (n=1,805). The HNR study was approved by the Institutional Review Committee (Essen) and the German Council For Radiation Safety.

Measurements

We used the self rated health question as a measure of poor health. The question was answered by a 5-point Likert scale. This question was shown to predict subsequent deterioration in health and mortality in a number of prospective studies. [START_REF] Idler | Self-rated health and mortality: a review of twenty-seven community studies[END_REF][START_REF] Benjamin | Self-reported health and adult mortality risk: an analysis of cause-specific mortality[END_REF] The question was formulated as follows 'Over the last 12 months would you say your health has been?'.

The possible answers were: very good, good, average, poor, very poor. The results were coded from 1 to 5 from very good to very poor health. In the different countries the questions and the scale of self-rated health were identical, with the exception of a slight change in the wording in Hungarian and German language. We dichotomised self-rated health scale. Poor and very poor health were coded as poor health.

We used a shortened version of the effort-reward imbalance questionnaire, containing questions with the original wording. These questions were included in every study (Appendix 1). [START_REF] Siegrist | The measurement of effort-reward imbalance at work: European comparisons[END_REF] The scale 'effort' comprised 2 (out of 5 in the original instrument) items, the scale 'reward' 5 (out of 11) items, and the scale 'overcommitment' 5 (out of 6) items. In two studies where the original scale was also available (HAPIEE study, HNR study) the Pearson correlation between the original and the shortened scales were between 0.85 and 0.98.

Cronbach alphas of the shortened scales of effort, reward and overcommitment ranged from 0.58 to 0.76 in the four studies. These numbers suggest that short versions of all 4 measures (effort, reward, effort-reward imbalance and overcommitment) are as valid as the original measures.

A ratio of the two scales effort and reward was computed (ERI ratio) indicating the degree of the imbalance between the two; thus, this is considered a core estimate of stressful experience at work. [START_REF] Siegrist | Adverse health effects of high-effort/low-reward conditions[END_REF] The sum of the five four-point Likert scale items of overcommitment varies from 4 to 20, with higher scores indicating a higher tendency towards excessive striving at work. For the purpose of this comparative study, we divided the ERI ratio into country-specific quartiles. Similarly, the scores of overcommitment were divided into quartiles. The quartiles with highest scores of ERI ratio and overcommitment (quartile 4) indicated the most stressful work condition. We calculated odds ratios of poor health according to these measures of work stress where the quartile indicating no or low stress served as the reference category.

We used age, educational attainment and occupational status as covariates. Education levels were split into three categories: primary or less, secondary, and university or college degree. Occupation status was classified into three categories: managerial, self employed and non managerial.

The age range of the whole study sample was from 35 to 65 years. The age ranges in different studies were slightly different: in Hungary it started from 35, in Germany, Russia, Poland and in the Czech Republic from 45 and in the UK at 50 years. We tested whether our results differ when we restricted study samples to 45-65 or 50-65 years. As there were no differences between the results in the full and restricted samples, we decided to keep our original study samples.

Statistical Methods

The cross-sectional analysis explored the associations between psychosocial factors at work and the prevalence of poor self-rated health in two Western-European and four postcommunist countries. Kruskal-Wallis test and ANOVA was used to measure the differences of work stress and poor health among countries depending on the normal distribution of the scales. Chi-square test was applied to analyze the differences of the proportion of poor health and the adverse psychosocial characteristics of work. Multivariate logistic regression analyses were used to determine the main and the interaction effects of the components of effortreward imbalance model on poor self-rated health. We used gender, age, education level and occupation groups as covariates in the regression model. Data analysis was conducted with SPSS, version 13 (SPSS Inc., Chicago, USA).

RESULTS

Table 1. describes the number of participants and the prevalence of poor self-rated health in different age groups, educational level and occupational status and in both genders in each country. Older participants in Russia, Poland and Hungary (>50 years) had significantly poorer health than younger ones. In every country there was inverse association between educational level and self-rated health. The lowest prevalence of poor health was found in managers, except in Russia where self-employed workers showed the lowest proportion of poor health. ). For both these measures, there were significant linear trends, with higher odds of poor health among those with higher extrinsic (effort-reward imbalance) or intrinsic (overcommitment) level of stressful experience at work.

The effects of ERI and overcommitment were significantly smaller in Russia and Poland than in the remaining countries (p for interaction <0.05). Persons in the highest quartile of the effort-reward ratio had more than three times higher odds of poor health, compared to the lowest quartile in Hungary and in the Czech Republic. In the other two post-communist countries, these odds ratios were smaller than 1.5. Similarly, high 'overcommitment' was associated with higher odds ratios of poor health in Hungary, Czech, Germany and the UK than in Poland and Russia.

Finally, we have tested the hypothesis that the effect of effort-reward imbalance is modified by the level of overcommitment. However, we did not find significant and consistent interactions between these two variables (not shown in table ). Western European populations, we did not observe a systematic difference in the magnitude of effect between East and West, despite the fact that the Hungarian and the Czech sample exhibited particularly high levels of work stress and its strong effect on poor health. These effects, however, were weaker in Russia and Poland. Increasing odds for poor health with increasing levels of work stress were seen for both the extrinsic (effort-reward ratio) and the intrinsic (overcommitment) components of the model. These findings are in line with previous results in western countries and with an earlier report from four post-communist countries. [START_REF] Godin | A prospective study of cumulative job stress in relation to mental health[END_REF][START_REF] Dragano | Organisational downsizing and work stress: testing synergistic health effects in employed men and women[END_REF][START_REF] Eum | Psychometric properties of the Korean version of the job content questionnaire: data from health care workers[END_REF][START_REF] Siegrist | Quality of work, well-being, and intended early retirement of older employees -baseline results from the SHARE Study[END_REF][START_REF] Preckel | Effort-reward-imbalance, overcommitment and self-reported health: Is it the interaction that matters?[END_REF][START_REF] Pikhart | Psychosocial work characteristics and self rated health in four post-communist countries[END_REF] The fact that Russian and Polish data did not fit with our initial assumption of a stronger association of work stress with health in Central and Eastern European compared to

Western European countries may have several explanations. First, it is possible, although unlikely, that the job composition differed between countries in a way that fewer jobs were experienced as stressful in Poland and Russia. Second, the thresholds of what constitutes a stressful experience may vary as a function of exposure to material deprivation. As material deprivation is clearly more pronounced in Russia and Poland, psychosocial aspects in general may be evaluated as less pertinent than material adversity. [START_REF] Bobak | Socioeconomic factors, material inequalities, and perceived control in self-rated health: cross-sectional data from seven post-communist countries[END_REF] Third, the method of data collection may have played a role. Data were collected at home in the three CEE countries and in the offices in the Western countries, whereas Russian participants were invited to the clinic for an interview. It is possible that this context reinforced a tendency towards underreporting stressful experience. However, none of these explanations seems satisfactory, particularly since previous studies, using different data, also found weak or no associations of work stress with health in Poland and in Russia. This observations clearly call for further research. [START_REF] Pikhart | Social and psychosocial determinants of self-rated health in Central and Eastern Europe[END_REF][START_REF] Pikhart | Psychosocial factors at work and depression in three countries of Central and Eastern Europe[END_REF] Given the cross-sectional design, the temporal direction of the observed association is not entirely clear. Poor health may affect the tendency towards reporting higher level of workrelated stress, although the latter was measured as a rather complex algorithm (effort-reward ratio) which would not be easily understood by the subjects as a measure of work stress. It is also possible that unobserved underlying response style (e.g. negative affect) may have affected both measures in a systematic way. Although we cannot exclude such reporting bias, at least three reasons suggest that its impact was limited. First, the association of effort-reward imbalance with self-reported health was previously documented in prospective epidemiological studies. [START_REF] Stansfeld | Psychosocial work characteristics and social support as predictors of SF-36 health functioning: the Whitehall II study[END_REF][START_REF] Niedhammer | Effort-reward imbalance model and self-reported health: cross-sectional and prospective findings from the GAZEL cohort[END_REF][START_REF] Kuper | When reciprocity fails: effort-reward imbalance in relation to coronary heart disease and health functioning within the Whitehall II study[END_REF] Second, this association remained significant when adjusted for reporting bias as negative affectivity or personality characteristics (such as perceived control). [START_REF] Pikhart | Psychosocial work characteristics and self rated health in four post-communist countries[END_REF][START_REF] Kopp | Work stress and mental health in a changing society[END_REF] Third, the measurement of effort-reward imbalance was shown to be stable over time, evaluated in terms of factorial invariance, thus weakening the argument that this measure may mainly reflect characteristics of the respondent at the time of data collection. [START_REF] Jonge | Factorial invariance and stability of the effortreward imbalance scales: A longitudinal analysis of two samples with different time lags[END_REF] As this study used an abbreviated version of the original instrument, single scales or subscales could not be compared across countries. This limitation prevented us from analysing the role of job insecurity and low control over occupational status, including promotion prospects, in more details. This is unfortunate, since there is high prevalence of job instability and unemployment, and strong association with adverse health, in some CEE countries, particularly in Hungary. [START_REF] Kopp | Psychosocial determinants of premature health deterioration in a changing society: the case of Hungary[END_REF] When comparing Eastern with Western Europe, it should be stressed that unemployment at the time of data collection was relatively high in several Western countries including Germany. Further analysis of the Heinz Nixdorf Study found that the risk of poor health was the highest in the subgroup of employees who had a low occupational status (and unstable jobs) and who exhibited a high level of psychosocial stress at work. [START_REF] Wege | When does work stress hurt? Testing the interaction with socioeconomic status in the Heinz Nixdorf Recall Study[END_REF] In this study, we found no evidence of an interaction between effort-reward ratio and over-commitment. This result is in line with a majority of recent studies testing this hypothesis. [START_REF] Vegchel | Reviewing the effort-reward imbalance model: drawing up the balance of 45 empirical studies[END_REF][START_REF] Preckel | Effort-reward-imbalance, overcommitment and self-reported health: Is it the interaction that matters?[END_REF] There is an emerging consensus that the two components contribute to the elevated risk of poor health independently.

The study samples included in the present analyses were recruited from predominantly urban populations. The results therefore cannot be generalised to the whole working populations in the respective countries. In Poland and Russia in particular, working conditions in rural populations differ considerably from urban areas and are unlikely to be appropriately represented in our studies. In Whitehall II study the participants were mainly white collar government employees so the results cannot be representative for manual workers, although previous comparative studies support the generalizability of Whitehall II findings. [START_REF] Marmot | Cohort Profile: the Whitehall II study[END_REF][START_REF] Singh-Manoux | What does self rated health measure? Results from the British Whitehall II and French Gazel cohort studies[END_REF][START_REF] Singh-Manoux | Gender differences in the association between morbidity and mortality among middle-aged men and women[END_REF] These limitations are balanced by several strengths of the study. Most importantly, findings document a consistent association of effort-reward imbalance at work and of a high degree of work-related overcommitment with poor self-rated health in four post-communist and two Western European countries. To our knowledge, no previous study has documented this association, using a valid theoretical model of psychosocial stress at work and collecting data on the basis of an identical study protocol in all six countries. If confirmed by prospective evidence, these findings underline the importance of improving the quality of work and employment by structural measures with a tangible impact at a European level.

What is already known on this subject?

There is an evidence linking psychosocial work stress with poor health in Western societies but little is known about the effect of work stress in the former communist countries. To our knowledge, this is the first study which directly compares the effect of work stress of Western European and post-communist countries.

What does this study add?

Effort-reward imbalance at work and overcommitment were associated with poor health in all the six samples. Although there were differences in the magnitude of the effects between countries, there does not seem to be a systematic difference between Eastern and Western European countries. Improvement of the quality of employment may have a major impact on health of working people in both Eastern and Western Europe.

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Table 1 . Demogrhaphic characteristics of the study samples

 1

		Czech
		Republic		Russia		Poland		Hungary		Germany	United Kingdom
			% poor		% poor		% poor		% poor		% poor		% poor
	Age (years)	N=3920	health	N=4515	health	N=4181	health	N=1449	health N=1805	health	N=2624	health
	<49	1277	4.9	1309	11.5	1424	4	972	3.9	612	11.1	*	*
	>50	2643	6.1	3206	14.2	2757	7.5	477	9	1193	13.1	2624	4.70
	p value for chi
	square		0.08		0.02		<0.001		<0.001		0.2		*
	Education
	Primary	235	11.5	208	13.5	179	10.6	186	12.40	93	28	197	8.60
	Secondary	2958	5.8	2749	14.9	2230	6.5	966	5.30	1217	12.8	1080	5.00
	University	727	3.2	1558	10.7	1772	5.6	297	2.40	495	8.5	1347	3.90
	p value for chi
	square		<0.001		<0.001		0.02		<0.001		<0.001		0.01
	Occupation
	Non manager	2483	6.4	3274	14.4	2475	6.6	1145	6.00	1222	14.6	175	6.30
	Manager	1061	4.1	1161	10.8	1240	4.8	164	2.40	332	8.4	2449	4.60
	Self-employed	376	5.1	80	8.8	466	9.0	140	5.70	251	6.8	*	*
	p value for chi
	square		0.03		0.003		0.004		0.17		<0.001		0.31
	Sex
	Male	2017	6.4	2296	8.6	2241	6.2	634	5.4	1065	9.6	2002	4.3
	Female	1903	4.9	2219	18.3	1940	6.4	815	5.8	740	16.5	622	6.1
	p value for chi
	square		0.04		<0.001		0.82		0.74		<0.001		0.06

Table 2

 2 shows the mean values, standard deviations and medians of self-rated health and work stress scales. We found significant differences in mean values and medians of work stress scales and self-rated health between countries (p<0.001). The lowest self-rated health

	mean, which indicated better health was observed in the British sample (2.03); the highest
	mean in the Russian sample (2.99). The median of the ERI ratio was the lowest in Russia and
	the highest in Germany, and the prevalence of ERI above 1 (i.e. effort higher than reward)
	was highest among Hungarian workers. Overcommitment was highest in the Czech Republic
	and lowest in Russia.

Table 2 . Descriptive statistics of the scales

 2

		Self-Rated Health		Effort-Reward Ratio		Overcommitment
							Proportion
		Mean	SD	Median Mean	SD	Median	above 1	Mean	SD	Median
	Czech
	Republic	2.48	0.69	2	0.58	0.52	0.4	11.60%	12.1	2.5	12
	Russia	2.99	0.54	3	0.37	0.26	0.24	3.10%	10.22	3.52	10
	Poland	2.47	0.72	2	0.64	0.58	0.44	16,5%	11.33	2.81	11
	Hungary	2.49	0.71	2	0.67	0.53	0.52	19.00%	11.29	3.24	11
	Germany	2.49	0.89	2	0.61	0.31	0.6	8.70%	10.82	3.52	11
	United
	Kingdom	2.03	0.82	2	0.46	0.26	0.31	3.20%	11.18	2.8	11
	The odds ratios of poor health by quartile of effort-reward imbalance and overcommitment,
	estimated for each country separately and adjusted for gender, age, education and
	employment grade, are shown in Table 3. The highest quartiles of ERI ratio and
	overcommitment indicate the most stressful work condition (ERI4, OC4

Table 3 . Odds Ratios (OR) and 95% CIs of risk of poor health by work characteristics. Adjusted for sex, age, educational level and employment grade.

 3 This study found associations of work stress, measured by the effort-reward imbalance model, and poor self-rated health in middle-age male and female working populations in all six countries. When comparing four Central and Eastern European (CEE) countries with two

		Czech Republic	Russia	Poland	Hungary	Germany	United Kingdom
	Effort-reward
	Imbalance	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)
	ERI1 (lowest quartile)	1	1	1	1	1	1
	ERI2	1.58 (0.97-2.58)	1 (0.77-1.3)	0.98 (0.66-1.44)	1.52 (0.7-3.27)	1.44 (0.92-2.25)	0.93 (0.51-1.68)
	ERI3	1.84 (1.14-2.98)	1.06 (0.8-1.38)	1.19 (0.8-1.73)	1.48 (0.67-3.28)	1.33 (0.86-2.05)	1.03 (0.57-1.88)
	ERI4 (highest quartile)	3.64 (2.33-5.68)	1.39 (1.08-1.8)	1.48 (1.02-2.14)	3.84 (1.93-7.67)	2.33 (1.55-3.49)	2.48 (1.49-4.12)
	p for trend	<0.001	0.01	0.024	<0.001	<0.001	<0.001
	Overcommitment	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)
	OC1 (lowest quartile)	1	1	1	1	1	1
	OC2	0.72 (0.41-1.24)	1.14 (0.89-1.46)	0.83 (0.58-1.2)	0.93 (0.41-2.1)	1.19 (0.75-1.87)	1.01 (0.53-1.94)
	OC3	1.55 (1.04-2.32)	1.14 (0.85-1.52) 1.26 (0.85-1.86)	1.56 (0.78-3.1)	1.72 (1.15-2.59)	1.52 (0.86-2.67)
	OC4 (highest quartile)	2.93 (2-4.28)	1.75 (1.35-2.26) 1.37 (0.94-2.01)	2.38 (1.16-4.86)	2.34 (1.57-3.5)	2.84 (1.63-4.94)
	p for trend	<0.001	<0.001	0.01	0.01	<0.001	<0.001

ACKNOWLEDGMENTS

The HAPIEE study like to thank local collaborators and interviewers in the Czech Republic, Poland and Russia. The Hungarian Epidemiological Panel (HEP) thank for all members of the HEP study team, the network of district nurses and the Median Institute for the home interviews, the National Population Register for the selection of the sample. The Whitehall II study thank all participating civil service departments and their welfare, personnel, and establishment officers; the Occupational Health and Safety Agency; the Council of Civil Service Unions; all participating civil servants in the Whitehall II study; and all members of the Whitehall II study team. The Heinz Nixdorf Recall study thank the Heinz Nixdorf Foundation (Chairman: Dr. jur. G. Schmidt) for generous support of the Heinz Nixdorf Study. We are indebted to the investigative group and the study personnel, in particular E.M. Beck, Dr. S. Moebus, Prof. Dr. A. Stang, U. Roggenbuck, and U. Slomiani. FUNDING The HAPIEE study has been funded by grants from the Wellcome Trust (Reference numbers 064947/Z/01/Z and 081081/Z/06/Z), from the US National Institute on Aging (1R01 AG23522-01), and from the MacArthur Foundation. The HEP study was supported by the National Research Fund (OTKA) projects (73754/2008), and the Scientific Committee of Health Sciences (ETT 100/2006). The Whitehall II study has been supported by grants from the Medical Research Council; Economic and Social Research Council; British Heart Foundation; Health and Safety Executive; Department of Health; National Heart Lung and Blood Institute (HL36310), US, NIH: National Institute on Aging (AG13196), US, NIH; Agency for Health Care Policy Research (HS06516); and the John D and Catherine T MacArthur Foundation Research Networks on Successful Midlife Development and Socioeconomic Status and Health. The Heinz Nixdorf Recall Study was supported by the German Ministry of Education and Science. Assessment of psychosocial factors and neighborhood level information is funded by the German Research Council (DFG; Project SI 236/8-1 and SI 236/9-1).

COMPETING INTEREST

The authors declare that they have no competing interests.

APPENDIX

Items of Effort-Reward Imbalance Scale:

Effort: 1.

I have constant time pressure due to a heavy work load. 2.

I have many interruptions and disturbances while performing my job.

Reward:

3.

My job promotion prospects are poor. 4.

I have experienced or I expect to experience an undesirable change in my work situation.

5.

My employment security is poor.

6.

Considering all my efforts and achievements, I receive the respect and prestige I deserve at work.

7.

Considering all my efforts and achievements, my job promotion prospects are adequate.

Overcommitment:

8.

As soon as I get up in the morning I start thinking about work problems. 9.

When I get home, I can easily relax and 'switch off' work. 10.

People close to me say I sacrifice too much for my job. 11.

Work rarely lets me go, it is still on my mind when I go to bed. 12.

If I postpone something that I was supposed to do today I'll have trouble sleeping at night.


Dr Alessio Zambon
email: alessio.zambon@unito.it

Antony Morgan

Dr Carine Vereecken

Sabina Colombini

Dr Will Boyce

Dr Joanna Mazur

Prof Patrizia Lemma

Prof Franco Cavallo

Keywords: adolescents, health behaviour, psychosocial factors, social capital

Background -Social networks have been recognised as an important factor for enhancing the health of people and communities. Bridging social capital, characterised by numerous and varied weak ties, exemplifies a particular type of network which can help people reach their goals and improve their health. This paper seeks to contribute to the evidence base on the use of positive social networks for young people's health by exploring the importance of club participation in predicting the health and health related behaviours of 15 year old girls and boys across Europe and North America. Methods -Data derive from the 2005-06 WHO collaborative "Health Behaviour in School-aged Children (HBSC)" study to establish the relationships between different types of club and a range of health outcomes (self perceived health, wellbeing and symptoms) and health related behaviours (smoking, drinking). We used multi-level logistic regression to assess the independent effects of club participation by controlling for gender and socio-economic position. We compared data across 6 countries to explore the consistency of the relationships found. Results -All the considered outcomes, both in terms of perceived health and wellbeing and health behaviours were associated with participation in formal associations. The associations are in the expected direction (participating corresponding to better health) except for some particular association types, but participating in any whichever association, or more than one, is always correlated with better health. Conclusions -Participation in formal associations seems supportive for good health and health behaviours in adolescence, and should be promoted in this age group.

What is already known on this subject?

There is some evidence that participation in clubs can enhance health status, however in the main current evidence relates to adult populations..

What does this study add?

This paper builds on the existing evidence in two ways. Firstly it broadens the existing evidence base by exploring the relationship between club participation and young people's health. Secondly it uses data from different countries to test whether the relationships hold in different contexts. . The study is also original as it explores the associations between different clubs and health and its related behaviours. The results are helpful to policy makers by confirming the need to increase the possibilities for participation at an early age and for providing some hints as to the potential for involvement in different types of clubs..

Licence for Publication

The Corresponding Author has the right to grant on behalf of all authors and does grant on behalf of all authors, an exclusive licence (or non exclusive for government employees) on a worldwide basis to the BMJ Publishing Group Ltd and its Licensees to permit this article (if accepted) to be published in JECH editions and any other BMJPGL products to exploit all subsidiary rights, as set out in our licence(http://jech.bmjjournals.com/ifora/licence.dtl).

Introduction

Social approaches to improving health and reducing health inequalities are currently high on the agenda. Recent reports by WHO set out a systematic approach to understanding and acting on the social determinants of health. They highlight the need to understand the socially determined conditions where people grow, live, work and age [START_REF]Statement of the Commission on Social Determinants of Health[END_REF] . This paper aims to contribute to this goal by investigating the relative importance of young people's participation in clubs and organisations, in doing so it contributes to the evidence base on approaches to addressing the social determinants of health. Whilst some argue that health is equalised in adolescence 2 3 , the recent international report of the WHO Health Behaviour in School Aged Children [START_REF] Currie | Inequalities in young people's health: international report from the HBSC 2006/06 survey. WHO Policy Series: Health policy for children and adolescents Issue 5[END_REF] highlights that in 2005/6 there were large inequalities in young people's health and health-related behaviours across Europe and North America and strong relationships between adolescents' health and the socioeconomic status of their families. The concept of 'social capital' has been put forward as a construct which can help to explain these relationships, but its multi-faceted nature has caused some difficulties in assessing its role and potential. In part these difficulties arise because its components cut across many preexisting concepts: sense of community, sociability, social networking, civic engagement etc. Morgan and Swann [START_REF] Morgan | Social capital for health: issues of definition, measurement and links to health[END_REF] argue that for social capital to be useful, further empirical work needs to be carried out on its constituent parts towards a theory of how to build social capital for improved, sustained and more equal health for all in the future. Aldridge and Halpern 6 have argued that individuals have needs for different types of social capital at different points in their lives. Two such types are bridging and bonding social capital 7 8 : Bonding social capital refers notionally to those strong inward looking ties that are characterised by homogeneous groups linking people of the same gender, age or race. Whereas bridging social capital refers to weaker and more diverse ties linking similar people at the same level of hierarchy. Most research on social capital focuses on bonding social capital referring to strong ties in the community providing a means to health through social support. The notion of bridging social capital fits with Granovetter's 9 10 which suggests that weak ties are an important resource in making possible mobility opportunities, allowing individuals to accumulate a broader set of connections gaining mutual respect and social inclusion. Kim and colleagues [START_REF] Kim | Bonding versus bridging social capital and their associations with self rated health: a multi-level analysis of 40 US communities[END_REF] studied the relationship between bonding social capital and bridging social capital on health and found that both were important, however according to Granovetter 9 10 the benefits of weak ties as a social resource have much more potential for health than strong and lasting ties. Weak ties are important to individual health as they can open up access to a wide range and amount of information, access to services and other resources, whilst strong bonding networks tend to be more inward looking and in particular cases they can produce harmful effects [START_REF] Portes | Social capital: Its origins and applications in modern sociology[END_REF] . Many authors have highlighted the importance of weak ties. Bourdieu expresses the use of weak ties in building elites [START_REF] Bourdieu | The forms of capital[END_REF] ; Putnam [START_REF] Putnam | Explaining Institutional Success: The Case of Italian Regional Government[END_REF] and Kawachi [START_REF] Kawachi | Social cohesion, social capital and health[END_REF] promote them as a way of building integrated multi-cultural communities; and Berkman [START_REF] Berkman | Social integration, social networks, social support, and health[END_REF] and Swann [START_REF] Swann | Social capital for health[END_REF] explicitly introduce the concept in the context of action on the social determinants of health by highlighting how promoting weak ties can enhance individual social skills and resources and community integration across different groups. According to Bronfenbrenner's 18 theory of human development, child environment is conceived as a set of nested structures representing the microsystem, mesosystem, exosystem and macrosystemusing this framework, young people's participation in clubs may be seen as a way of extending the mesosystem (a set of interconnecting environments). Bourdieu 13 19 20 , with his construct of habitus, purports that those in higher social classes are more likely to encourage participation in structured groups. Participation in associations and structured groups has already been used as a measure of social integration and a resource for health 16 21 suggesting that club participation can be a resource for building larger and more diverse networks, which can promote a sense of belonging beyond the usual familial and everyday groups. This paper aims to test whether young people's participation in clubs is associated with better health and healthier behaviours and whether these associations are modified by the social position of adolescents. Increasing our knowledge about the benefits of club participation can contribute to the evidence base on the relative importance of bonding and bridging social capital -or at least be clearer about in what circumstances one maybe more relevant than the other.

Methods

We used data from the 2005-06 'Health Behaviour in School-aged Children (HBSC)' study to establish the relationships between different types of club and a range of health (self perceived health and wellbeing and symptoms) and health related behaviours (smoking, drinking). This is an international WHO collaborative study about health status, health behaviours and their socioeconomic determinants in school aged children, carried out through self-administered questionnaires. Questionnaires are administered in schools to 11, 13 and 15 year old children in each of the 41 participating countries; samples are representative of the populations in those age groups with 95%C.I. of +/-3%, mean sample size of about 1500 children per age group. The sampling unit is the school, we have therefore a two level cluster sample (pupils in schools in countries). Of the 41 HBSC countries, only 6 included the optional package of questions relating to club participation in their national survey and some of them only for 15 year old boys and girlstherefore only data from these countries have been included in the study. Measures Participation in clubs -Participate (yes or no) in different kinds of associations: sport club, voluntary service, political associations, cultural associations, church groups, youth clubs, others. Item recoded into a three categories variable: no association, one, two or more.

Family affluence scale (FAS) -It is used as a measure 22 23 of socio-economic position. It comprises 4 items: "Does your family own a car, van or truck?" (no; yes; yes, two or more); "Do you have your own bedroom for yourself?" (no; yes); "During the past 12 months, how many times did you travel away on holiday with your family?" (not at all; once; twice; more than twice); "How many computers does your family own?" (none; one; two; more than two). The final FAS scale, created by the sum of these scores, is then divided into three levels: low, medium, high. Perceived Health -Children ranked their health status on a four point scale, recoded into: "excellent" versus all the rest. Well being -Cantrill Ladder 24 : it assesses how satisfied the respondents are with their life on an eleven point scale (0 to 10). A dichotomous variable was used -those scoring 7 and above considered to be satisfied with their lives. Symptoms -The HBSC Symptom Check List (HBSC-SCL) [START_REF] Haugland | Subjective health in adolescence -Reliability and validity of survey methods[END_REF] was administered: how often respondents experience one or more of the eight items list: headache, stomach-ache, back ache, feeling low, irritability or bad temper, feeling nervous, difficulties in getting to sleep, and feeling dizzy. Response categories: about every day; more than once a week; about every week; about every month; and rarely or never. Haugland & Wold 25 reported high test-retest reliability of the eight items and qualitative analyses showed acceptable validity. Hetland et al. 2002 [START_REF] Hetland | Subjective health complaints in adolescence: dimensional structure and variation across gender and age[END_REF] demonstrated that HBSC-SCL has two distinct subscales on physical and psychological symptoms but that it is also valid as a one-dimensional construct. For the purposes of this paper, a single dichotomous variable was created: at least one symptom at least once a week and less than this. Smoking -"How often do you smoke tobacco at present?". Possible responses: "every day", "at least once a week, but not every day", "less than once a week" or "never" and were recoded into "smokers" (every day) and "non-smokers". Alcohol -"How often have you been drunk", response option: "never", "once", '2-3 times", "4-10 times" and "more than 10 times", recoded into "less than twice", versus "twice or more". Healthy diet -How often respondents ate fruit and vegetables (never through every day and more than once a day), recoded into a dichotomous variable (daily fruit and vegetables intake; less than daily). A variable on soft sugary drinks is made up in the same way. Physical Activity -"On how many days in the past week were you physically active for 60 minutes or more" (0 days up to "7 days"). The dichotomous variable created was: physically active for less than 5 days a week vs more than that. TV Watching -How many hours children usually watched television, responses categories were "none at all", "about half an hour a day", "about 1 hour a day", "about 2 hours a day" up to "about 7 or more hours a day". Recoded into: "≤ 2 hours a day" versus more Analysis Multilevel logistic regression analyses were conducted with adolescents nested within schools within countries (three-level random intercept model). For each outcome variable a separate model was fitted with club participation as independent variable and controlling for gender and FAS. MLwiN 2.02 software was used [START_REF] Rasbash | Centre for Multilevel Modelling[END_REF] .

Results

A description of the sample's characteristics is summarised in Table 1. The distribution of FAS is as expected from the national economies: high FAS is more represented in England (59.1% of boys, 54.6% of girls) and Canada (52.8% and 49.9%), less represented in Romania (14.7% and 8.66%). In Romania and Belgium, there is a higher tendency for adolescents to be involved in just one association (between 45 and 50%), where in Canada, England, Italy and Poland this figure is less (32 and 39%). However the percentage of adolescents who participate in more than one association is consistent across all countries, ranging from 28.5% in Italy to 37.5% in Romania. Table 4 shows the odds ratios for the influence of club participation on health and related outcomes and how this varies according to type of club and across outcomes. The probability of adolescents perceiving the own health as excellent is higher in those who go to sports clubs, those belonging to political organisation and those belonging to at least one association, no matter which one (O.R. = 1.18). Similarly those who have a high rating of life satisfaction are more likely to report being involved in sports clubs (O.R. = 1.51) and cultural organisations (O.R. = 1.18) and are more likely to belong to at least one organisation. Those who report at least one symptom at least once a week are less likely to go to a sports club (O.R. = 0.74) and less likely to be involved in more than one association (O.R. = 0.77). There is an increased probability of reporting symptoms for those involved in voluntary service, cultural and other clubs.

Overall the probability of adolescents smoking is lower for those participating in at least one association (O.R. = 0.8) and even lower if they participate in more than one association (O.R. = 0.64). But adolescents who are involved in a political organisation or who attend youth clubs are more likely to smoke (O.R.s = 1.74 and 1.66). Sports, political and youth clubs increase the risk of having been drunk, while cultural and religious groups decrease it. In general, however, participating in clubs and associations seems to be protective from drunken behaviour: belonging to one club (O.R. = 0.82), belonging to more than one club (O.R. = 0.74). Club participation in general decreases the likelihood of consuming soft drinks daily: membership of one club (O.R. = 0.79) and membership of more than one (O.R. = 0.67). However, attendance at sport clubs, political organisations and youth groups increases consumption. Those involved in sports or cultural groups are more likely to eat fruit and vegetables daily but, in general, being involved in any club increases the likelihood of eating fruit and vegetables regularly (O.R. = 1.23) and even more for 2 clubs or more (O.R. = 1.59) As might be expected, those who are involved in sports clubs are much more likely to report higher levels of physical activity (O.R. = 2.95); however, there is no significant impact on levels of physical activity for other types of club. Adolescents are less likely to be sedentary (as characterised by watching television more than 2 hours a day) if they are involved in clubs (O.R. = 0.82 and 0.66 for being in one club and more than one club respectively).

Discussion

In general, adolescents who are involved regularly in some sort of club report better health, are more likely to have healthier lifestyles and less likely to engage in risk taking behaviour. This paper adds value to the findings from previous research 16 21 by exploring the relative importance of different types of clubs for health and how they vary by gender and socio-economic groups, and verifying its consistency across countries representing Northern, Southern and Eastern Europe and North America.

Of course, the cross-sectional nature of HBSC limits our ability to detect the casual direction of any of the relationships found. Whether or not young people get involved in certain types of clubs may be determined by their existing levels of health or characteristics. For example, young people who participate in sports clubs may already have higher levels of health than those not participating.

However in this instance we would argue that being involved in this type of club provides more opportunities to maintain physical activity and indeed increase social skills and social ability which are both known to be health enhancing. . It might also be argued that those who are regularly involved in political or activist groups are already dissatisfied with their lives -participation in this instance driven by the need to change either their immediate or wider environment. Qualitative research to understand young peoples motivations for participation would help us to further interpret the findings presented here.. , The selection hypothesis is an important source of bias in our results and should be taken into account when considering them. Our interpretation, of the findings concurs with Bourdieu's 13 19 20 notion of social capital, which highlights the tendency of dominant groups (here: high FAS) to value large group participation, increasing access to information and to supportive networks from a wider range of sources (also resources for health): in our study, adolescents who came from wealthier backgrounds were more likely to participate in a range of clubs and more often.

As might be expected, boys in general were more likely to participate in clubs, especially sports clubs, whereas girls were more likely to participate in those organisations (for example voluntary service) which foster the development of supportive networks for families and the community. Overall, our data support the hypothesis that participation in clubs is important for the health of young people, irrespective of the types of club that they are involved in. However there are some differences in the types of organisations that are important. There is a consistent direction of association between club participation and perceived health and well being, fruit and vegetable consumption, physical activity and watching TV; but for other outcomes, some clubs seem to be more important than others and indeed some even have a negative effect on health or health related behaviour. For example, being involved in youth clubs seems to increase the likelihood of adolescent smoking and drinking behaviour. Nonetheless, even for these outcomes, there is a significant protective effect of belonging to one or more clubs which seems to outweigh the observed negative effects. This may of course be due to sample size -the overall effect is made up of several associations whose effect, when looked at alone, is not statistically significant. Two main patterns emerge from the findings: firstly, health and healthy behaviours tend almost in every case to be enhanced by any kind of club participation; and second, for risky behaviours, some particular clubs may produce less favourable outcomes (e.g. the effect of political and youth groups on smoking and drunkenness). One could assume that, in these types of clubs, peer pressure has an influence, making behaviours such as smoking and drinking more acceptable and cool, but no effect on healthy behaviours such as eating fruit and vegetables.. These initial findings provide some evidence that being involved in enlarged networks of different types is beneficial for health and that the notion of bridging social capital maybe a useful tool to support health development policies and practice. Further qualitative research can help us understand the mechanisms through which different types of club foster health development and therefore are also helpful to policy makers, about which types of social capital are appropriate to invest in, within and between different population groups, age and gender and in different country contexts.

Our findings support the notion that encouraging participation in a range of associations is a useful and beneficial policy goal especially for young people, increasing their facility to access and become part of wide ranging networks. This encouragement needs to be fostered by listening to the voices of young people, to ensure that it is not seen as an oppressive request, as seen historically in totalitarian societies. Participation should therefore not be seen as a dogmatic 'must do' regime, instead, by highlighting the range of benefits that can be accrued from participation it may be seen as a resource for young people in achieving their future health potential. Club participation however is not without its potential problems -peer pressure for example can lead to negative consequences for some adolescents (higher uptakes of smoking and drinking) and some cases can lead to those strong ties associated with teen gangs. Nonetheless, given our findings show that club participation has an effect on health even after controlling for social background (as measured by FAS) , it is suggested that club participation helps to build connections across the social spectrum and therefore has the potential to contribute to a reduction in health inequalities.

It is clear from our data that club participation can be supportive of health and healthy lifestyles.

Better policy and programme formulation, which seeks to understand the types of clubs that are acceptable to young people, and that finds ways of improving accessibility to them, will maximise the opportunities for the benefits of participation to be realised. Teachers and parents have a role to play in understanding the motivations for getting involved -proper involvement of young people which supports their role in creating and managing clubs provides the best possible chance for enhancing larger social networks which are health promoting irrespective of a young person's social background.

Table 1 -Sample description (total HBSC): distribution of the sample in countries by gender and FAS

 1

				Fl. Belgium	Canada England	Italy	Poland Romania	Total
		Low FAS	n %	74 9.67	86 8.17	50 7.58	122 18.37	330 30.73	247 42.59	909 18.95
	Boy	Medium FAS	n %	312 40.78	411 39.03	220 33.33	318 47.89	464 43.20	248 42.76	1973 41.14
		High FAS	n	379	556	390	224	280	85	1914

Table 2

 2 shows the proportion of adolescents participating in each type of club. Participation in sports clubs is by far the most popular everywhere: from 57.2% in Flemish Belgium to 18.9% in Romania. Voluntary service is higher in Canada (22.4%) compared to all other countries, England being the next highest with only 13.4%; being involved in political organisations is low, the highest being in Poland (8.3%), no other country reaches higher than 4%; Polish adolescents also score higher in membership of cultural organisations (36.6%), whilst in other countries it is between 10% and 16%; the highest participation rate in church groups is in Italy and Poland (more than 17%) whereas in Belgium (3.9%) it is much lower and in other countries between 12-14%. Youth clubs are most popular in Flemish Belgium (25.5%), followed by England, Italy and Romania, all around

20%, Canada and then Poland around 10-15%. Almost a third (32.7%) of English adolescents and 22.9% of Romanian ones selected the 'other clubs or associations' but much fewer in other countries (between 10-12%). Non participation is higher in Canada, England, Italy and Poland (around 30%), then Belgium (20.7%) and lastly Romania at16.0%.

Table 2 -Participation in different kinds and number of clubs by countries (for each type of association it is reported how many children answered that they participate in it; in the last three rows, the number of associations is treated separately and is mutually exclusive with respect to one another)

 2 Table3summarises the effects of gender and family affluence (FAS) on participation prevalence. FAS differences are significant for every kind of association but more pronounced for sport clubs: 22.5% in low FAS children, 39.3% in middle and 55.3% in the high FAS group. In addition, those who participate in more than one club are likely to come from wealthier families: 39.

			Fl Belgium	Canada	England Italy	Poland	Romania Total
	Sport clubs	n	835	1157	604	521	700	269	4086
		%	57.23	52.00	48.71	44.04	30.92	18.85	41.70
	Voluntary service n	100	499	159	83	282	143	1266
		%	7.24	22.43	13.40	7.09	12.62	10.32	13.21
	Political org.	n	26	35	34	46	185	38	364
		%	1.90	1.57	2.90	3.92	8.27	2.76	3.81
	Cultural org.	n	214	277	193	147	824	149	1804
		%	15.60	12.45	16.29	12.56	36.61	10.76	18.82
	Church groups	n	53	302	146	206	389	195	1291
		%	3.86	13.57	12.32	17.59	17.30	14.02	13.46
	Youth clubs	n	350	257	238	224	344	270	1683
		%	25.47	11.55	20.00	19.11	15.34	19.27	17.52
	Other clubs	n	126	378	385	125	274	319	1607
		%	10.50	16.99	32.74	12.01	12.28	22.87	17.34
	No associations	n	291	669	366	393	712	149	2580
		%	20.74	30.63	28.31	33.08	31.21	16.04	27.81
	One associations n	686	747	449	457	737	432	3508
		%	48.90	34.20	34.73	38.47	32.31	46.50	37.81
	2 or more	n	426	768	478	338	832	348	3190

Table 3 -Participation in different kinds and number of clubs by gender and FAS (for each type of association it is reported how many children answered that they participate in it; last three rows, the number of associations is treated separately and is mutually exclusive with respect to one another)

 3

						Males			Females			Total
	FAS		Low	Medium	High	Low	Medium	High	Low	Medium	High
	Sport clubs*		n	279	920	1124	188	667	858	467	1587	1982
				%	32.44	48.99	61.25	15.41	30.84	49.11	22.45	39.27	55.33
	Voluntary			n	61	152	233	140	315	358	201	467	591
	service*			%	7.27	8.31	13.10	11.60	14.77	20.94	9.82	11.79	16.94
	Political org.	1	n	40	83	66	45	64	59	85	147	125
				%	4.78	4.53	3.73	3.74	3.01	3.46	4.16	3.72	3.60
	Cultural org.*	n	104	265	303	245	440	433	349	705	736
				%	12.43	14.48	17.07	20.25	20.60	25.28	17.05	17.78	21.10
	Church groups*	n	125	207	214	218	283	234	343	490	448
				%	14.81	11.28	12.07	18.06	13.25	13.69	16.72	12.34	12.87
	Youth clubs	2	n	126	323	348	195	351	318	321	674	666
				%	14.95	17.58	19.50	16.08	16.46	18.67	15.61	16.98	19.09
	Other clubs*	n	140	301	357	158	320	317	298	621	674
				%	17.05	16.82	20.65	13.49	15.79	19.29	14.96	16.27	19.99
	No associations* n	273	476	374	369	678	391	642	1154	765
				%	35.09	26.58	20.46	35.69	33.51	22.51	35.43	30.26	21.46
	One association n	296	737	751	349	705	632	645	1442	1383
				%	38.05	41.15	41.08	33.75	34.85	36.38	35.60	37.81	38.79
	2 or more			n	209	578	703	316	640	714	525	1218	1417
	associations	%	26.86	32.27	38.46	30.56	31.64	41.11	28.97	31.93	39.75
	* Differences are statistically significant for both gender and FAS with p<0.01
	1 Difference statistically significant only by gender with p<0.01
	2 Difference statistically significant only by FAS with p<0.01

Table 4 -Multilevel logistic model for each outcome variable considered (O.R. and 95% Confidence Interval; 15yo only). 1 st group: control variables (gender and FAS,); 2 nd group: 1 st set of determinants (club participation: participation in each club type vs not participation in that club); 3 rd group: 2 nd set of determinants (number of clubs the person participates to). Bold values mean statistical significance (p<0.05).

 4

		Excellent	High life-	Health				Fruit and veg	Physical
		health	satisfaction	complaints	Smoke	Drunkenness	Soft drinks	daily	activity	TV >2h/day
		O.R.	O.R.	O.R.	O.R.	O.R.	O.R.	O.R.	O.R.	O.R.
	Girl vs boy	0.49	0.67	1.79	0.99	0.72	0.63	1.74	0.52	0.81
	95%CI	(0.44-0.54)	(0.61-0.74)	(1.63-1.97)	(0.89-1.1)	(0.66-0.79)	(0.57-0.69)	(1.56-1.94)	(0.48-0.58)	(0.74-0.89)
	Mid FAS vs
	low	1.14	1.79	0.83	1.03	1.10	1.27	1.34	1.07	0.92
	95%CI	(0.99-1.30)	(1.59-2.00)	(0.74-0.94)	(0.89-1.18)	(0.97-1.25)	(1.12-1.45)	(1.14-1.57)	(0.94-1.22)	(0.82-10.4)
	High FAS vs
	low	1.43	2.61	1.74	1.08	1.29	1.18	1.80	1.27	0.86
	95%CI	(1.24-1.65)	(2.28-2.98)	(0.65-0.84)	(0.93-1.26)	(1.12-1.48)	(1.03-1.36)	(1.52-2.13)	(1.10-1.46)	(0.74-0.96)
	Sport clubs	2.01	1.51	0.74	0.99	1.31	1.22	1.17	2.95	1.07
	95%CI	(1.75-2.32)	(1.31-1.75)	(0.65-0.85)	(0.84-1.16)	(1.14-1.51)	(1.05-1.42)	(1.01-1.36)	(2.57-3.39)	(0.94-1.22)
	Voluntary
	serv.	1.02	1.12	1.20	1.01	1.03	0.86	1.15	1.02	0.92
	95%CI	(0.87-1.21)	(0.94-1.32)	(1.03-1.41)	(0.83-1.22)	(0.88-1.21)	(0.72-1.03)	(0.97-1.35)	(0.88-1.19)	(0.80-1.07)
	Political org.	1.36	0.78	1.24	1.74	1.55	1.38	0.98	1.17	0.98
	95%CI	(1.07-1.74)	(0.60-1.00)	(0.97-1.59)	(1.33-2.28)	(1.21-1.99)	(1.06-1.78)	(0.74-1.30)	(0.91-1.49)	(0.78-1.24)
	Cultural org.	0.94	1.18	1.18	0.84	0.80	0.86	1.32	0.94	0.79
	95%CI	(0.81-1.09)	(1.01-1.39)	(1.02-1.36)	(0.71-1.00)	(0.69-0.93)	(0.73-1.01)	(1.13-1.54)	(0.81-1.08)	(0.69-0.90
	Religious
	groups	0.93	0.95	1.02	0.64	0.54	0.87	1.04	1.04	0.92
	95%CI	(0.80-1.09)	(0.80-1.11)	(0.87-1.19)	(0.53-0.78)	(0.46-0.64)	(0.73-1.03)	(0.88-1.23)	(0.89-1.21)	(0.79-1.06)
	Youth clubs	0.87	0.97	1.11	1.66	1.27	1.32	0.94	1.08	1.09
	95%CI	(0.74-1.01)	(0.83-1.14)	(0.96-1.29)	(1.40-1.98)	(1.09-1.48)	(1.13-1.56)	(0.80-1.10)	(0.94-1.25)	(0.95-1.25)
	Other clubs	1.01	0.92	1.20	1.07	1.15	1.00	1.05	1.15	1.04
	95%CI	(0.87-1.18)	(0.79-1.08)	(1.04-1.40)	(0.90-1.27)	(0.99-1.33)	(0.85-1.18)	(0.90-1.23)	(0.99-1.33)	(0.91-1.20
	1ass. Vs none	1.18	1.19	0.88	0.80	0.82	0.79	1.23	1.08	0.82
	95%CI	(1.00-1.38)	(1.03-1.39)	(0.77-1.02)	(0.68-0.95)	(0.70-0.95)	(0.68-0.93)	(1.03-1.45)	(0.92-1.25)	(0.71-0.94)

Acknowledgements: 1. HBSC is an international study carried out in collaboration with WHO Europe. The International Coordinator of the 2005/06 survey was Professor Candace Currie from the University of Edinburgh and the Data Bank Manager was Dr. Oddrun Samdal from the University of Bergen. The 2005/06 survey was conducted by Principal Investigators in 41 countries. For details, see http://www.hbsc.org". 2. Italian Ministry of Health provided funding with program "ex 60%". 3. Carine Vereecken is post-doctoral researcher funded by the FWO-Flanders