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Inequalities in smoking in the Czech Republic: Societal or individual effects?
Health & Place 17 (2011) 215–221
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Inequalities in smoking in the Czech Republic: Societal or individual effects? Jana Spilkova´ a,1, Dagmar Dzu´rova´ a,n, Hynek Pikhart b,2 a b
Charles University in Prague, Faculty of Science, Department of Social Geography and Regional Development, Albertov 6, 128 43 Prague 2, Czech Republic University College London, Department of Epidemiology and Public Health, 1-19 Torrington Place, London WC1E 6BT, United Kingdom
a r t i c l e in f o
abstract
Article history: Received 16 March 2010 Received in revised form 17 September 2010 Accepted 2 October 2010 Available online 14 October 2010
Smoking constitutes one of the main public health problems worldwide. In the Czech Republic, one of the post-communist countries undergoing societal transition, there was a significant decrease in smoking prevalence during 1985–1997, followed by certain stagnation in prevalence of smokers. The most serious problem is the smoking among young population and socially disadvantaged groups. This paper examines social inequalities in smoking in the Czech population using multilevel approach. Data were analysed by multilevel modelling using smoking in the past, current smoking and current moderate/heavy smoking as outcomes of interest. Men were significantly more likely to be smokers than women. Further, the analysis confirmed that current smoking is the most common among young people. Education was strongly inversely related to all smoking outcomes. Smoking was also significantly more reported by divorced and unemployed individuals. While the association between small-area characteristics and smoking was limited, smoking was more common in the areas with higher unemployment and higher proportion of non-Czech nationals. & 2010 Elsevier Ltd. All rights reserved.
Keywords: Smoking Czech Republic Inequalities Multilevel analysis
1. Introduction In most post-communist countries of Central and Eastern Europe, including the Czech Republic, social and political changes during the transition period after 1990 brought changes in the life style and health behaviours of many individuals, including changes in alcohol consumption, drug use and cigarette smoking. Smoking has been repeatedly shown as one of the major mortality risk factors. Lung cancer accounts for the highest number of deaths related to smoking, and is closely followed by other diseases, such as atherosclerosis, coronary artery disease, angina pectoris and myocardial infarction, or cerebrovascular disease. Deaths from neoplasms attributable to tobacco smoking in the Czech Republic represented 12% in 2000 and 10.5% in 2002 of the total number of deaths for men and 2.1% (2000) and 2.7% (2002) for women (Sovinova´ et al., 2008a). For cardiovascular disease, these numbers were 9.8% and 13.8% (men), and 3.2% and 7.1% (women) of total number of deaths in those years. Similar situation was found among hospitalisation cases. The financial costs of the hospital treatment for smoking related diseases were estimated to be 6.1 billion CZK in 2002 (Sovinova´ et al., 2008a). While a significant decrease in smoking prevalence was reported for Czech males between 1985 and 1997—49–37% among 35–64 years old men based on WHO MONICA results (Skodova´ et al., 2000),
n
Corresponding author. Tel.: + 420 221 951 390; fax: +420 224 920 657. E-mail addresses: [email protected] (J. Spilkova´), [email protected] (D. Dzu´rova´), [email protected] (H. Pikhart). 1 Tel.: + 420 221 951 388; fax: +420 224 920 657. 2 Tel.: + 44 20 7679 1906; fax: + 44 20 7813 0280. 1353-8292/$ - see front matter & 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.healthplace.2010.10.003
the prevalence remained almost the same for Czech females (1985–1997 change from 28% to 26%). Later reports by the Czech National Institute of Public Health (Sovinova´ et al., 2008b, 2010) summarizing the smoking prevalence between 1997 and 2007, resp. 2009, showed virtually constant prevalence of approximately 30% in the age group 15–64 years. Social inequalities in smoking in this period were similar to those observed in the number of other European countries, for example showing higher proportion of smokers among persons with lower education for both genders. The smoking prevalence, including young people and teenagers, remains at a high level, while the protection of non-smokers against passive smoking remains low, in spite of increasing activity of non-government organizations and selected groups of health professionals (preparation and implementation of primary prevention projects, or a treatment of somatic and psycho-social addictions). Generally, there are some anti-smoking measures in operation,3 such as the law prohibiting advertisement of tobacco products or prohibition of smoking in closed public areas, in public transport (including stations), in schools, in entertainment areas and inside healthcare facilities. The existing laws are not, however, strictly followed. For example, it has been reported that over half of 3 Legal measures related to tobacco control in the Czech Republic: law no. 305/2009 (measures against the damage caused by tobacco products, alcoholic beverages and other addictive products and protection of non-smokers), 132/2003 (regulation and prohibition of advertising of tobacco products), law no. 231/2001 (regulation of radio and television broadcasting with parts related to smoking and tobacco products), decree no. 113/2005 (health warnings on packaging and other requirements on tobacco products) and 272/2005 (sale, pricing and consumer prices related to tobacco products), etc.
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the factors and determinants influencing smoking habits in the Czech population using multilevel modelling. We tested the following hypotheses: (i) Individual socioeconomic characteristics affect smoking habits of study respondents. (ii) Area-level socioeconomic characteristics affect independently individual smoking in a way that people living in more socially disadvantaged areas have more risky health behaviour than people living in less disadvantaged areas. (iii) Area-level characteristics affect smoking habits of privileged and underprivileged individuals differentially (presence of cross-level interaction). 2. Data and methods The ‘‘Czech Health and Life Style Study’’ was conducted by the Institute of Health Information and Statistics of the Czech Republic (IHIS CR) in collaboration with INRES–SONES public opinion agency in 2003. Data were collected by face-to-face interviews using the EMCDDA (European Monitoring Centre for Drugs and Drug Addiction) questionnaire (EMCDDA, 2002). Cluster sampling was used for sample selection. Total of 235 electoral wards were randomly selected in the country. In each electoral ward, data were collected from 15 randomly selected individuals (with exception of one ward where data on 16 individuals were collected). As there were two or more wards selected in some municipalities, 161 municipalities were finally represented in the study. Because of small-area data availability, municipalities represent the smallest analytic unit for this analysis. Together, the study covers municipalities with more than 4.23 mil inhabitants in different size categories, the smallest with 96 inhabitants (15 respondents) and the largest with 1.17 mil inhabitants (405 respondents). The sample of respondents corresponds with the Czech population structure in terms of regional, sex and age structure. Small deviations were found in the distribution by marital status, education and economic activity. The sample included 3526 persons, aged 18–64 years at the time of the survey. The response rate was 68.2%. The questions focused on self-rated health, long-term illness, mental health and the substance abuse related behaviours (smoking, drinking alcohol, drug use). Basic demographic and socioeconomic data were also collected (such as education, marital status, occupational status). Smoking status was self-reported. The three outcome measures of smoking behaviour used in this paper were based on the frequency of tobacco consumption: current smoking, current high smoking, and ever smoking. Current smoking was estimated by a question: ‘‘Do you currently smoke cigarettes?’’ Respondents selected one answer from the following six options: (i) yes, occasionally, (ii) yes, daily 1–5 cigarettes, (iii) yes, daily 6–20 cigarettes, (iv) yes, daily more than 20 cigarettes, (v) not smoking cigarettes, but smoking cigars and
0.700 0.600 0.500 0.400 0.300 0.200 0.100 0.000
19
91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06
relative price of cigarette package in % of average month income
all smokers under 15 years of age regularly purchase cigarettes in common retail outlets, and 72% of them reported never having been restricted in their purchases because of their age. Over 80% of children younger than 15 years of age reported that they have been exposed to tobacco advertising (Sovinova´ and Cse´my, 2004). In addition to limited adherence to the law, the economic pressure on smokers is small. There is a clear decreasing trend of relative price of cigarette package (Fig. 1) showing the ineffectiveness of the anti-smoking policies and lack of general interest in public health issues. Overall, according to the tobacco consumption, the Czech Republic resembles the post-communist countries rather than EU countries (WHO, 2004). As already mentioned, smoking is one of the behavioural risk factors most affecting health of individuals, and personal smoking habits has been repeatedly suggested to be related to socioeconomic position of individuals (Mackenbach et al., 2008; Lahelma et al., 1997; Osler et al., 2000; Bartley et al., 2000; Borrell et al., 2000). Socioeconomic inequalities in smoking have widened and persisted in the last decades (Graham, 2009; Giskes et al., 2005). In the case of the Czech Republic, higher prevalence of non-smokers in the groups with better educational attainment was repeatedly reported (e.g. Bobak et al., 1997; Dragano et al., 2007). Based on the data of CIDI/ICD-10 survey (1998–99) in the Czech Republic, it was shown that both employment status and marital status (and community size in women) had significant effect on individual’s smoking (Dzu´rova´ et al., 2008). The results of another comparative study (Schaap et al., 2008) also suggests larger socioeconomic inequalities in quitting smoking in the Czech Republic compared with elsewhere. There has been recently an increased interest in the effects of area characteristics and local context on health and health behaviours including smoking (Helmert et al., 2001; Picket and Pearl, 2001; Cummins et al., 2007; Duncan et al., 1993; Karvonen and Rimpela, 1996). While people of similar socioeconomic status tend to be clustered in particular neighbourhoods (MacIntyre et al., 1993), the contextual influences on health and health behaviour might be also important. As a result, individuals living in deprived areas might have higher risk of poor health and health behaviour even if their individual socioeconomic position is taken into account (Diez-Roux, 2003; Pickett and Pearl, 2001). Various papers aimed to contribute to a general discussion on the importance of individual risk factors and the area characteristics through multilevel analysis, a method that allows to evaluate these differential effects (Chaix et al., 2004; Smith et al., 1998; van Lenthe and Mackenbach, 2006, Duncan et al., 1996; Twigg et al., 2000; Twigg and Moon, 2002; Monden et al., 2006; Fukuda et al., 2005). The aim of this paper is to focus on the smoking prevalence in different demographic and socioeconomic groups and to evaluate
Fig. 1. The development of relative price of cigarette package in percents of average month income in the transitional period. Source: Czech Statistical Office.
´ et al. / Health & Place 17 (2011) 215–221 J. Spilkova
(vi) non-smokers. Binary dependent variable was constructed combining answers (i)–(v) as a positive answer. Current moderate or heavy smoking was constructed from the same question. Respondents answering (iii) or (iv) (smoking of 6 or more cigarettes daily) were defined as high smokers. Those who said they were current nonsmokers were asked whether they smoked in the past. Responses were: (i) yes, occasionally, (ii) yes, daily 1–5 cigarettes, (iii) yes, daily 6–20 cigarettes, (iv) yes, daily more than 20 cigarettes, (v) not smoking cigarettes, but smoking cigars and (vi) non-smokers. Binary variable was constructed, and ever smokers were defined as those who either reported being current smokers or those who answered (i)–(v) to question on smoking in the past. A range of demographic, socioeconomic and health-related covariates were used as potential explanatory variables. Among demographic and socioeconomic variables we used gender (men and women), age (18–29, 30–39, 40–49, 50–64 years old), marital status (married/cohabitating, single, divorced, widowed), education (four categories based on the highest achieved education level: university degree, secondary, vocational, primary), and economic activity (employed/self-employed, pensioners, students, housewife/maternal leave, unemployed and others). Three variables were used to help control for the underlying health status: self-rated health (5-point scale ranging from excellent to poor), long-term illness (yes, no) and emotional disorders (yes, no). The small-area contextual variables were used to characterize socioeconomic dimensions of the municipalities of residence of the respondents. We used data from the 2001 Census, obtained from the Czech Statistical Office. The individuals from survey were linked with the Census database and subjects were assigned an ID number of the municipalities. Using this area ID number, the survey subjects were linked with 161 municipalities, and 5 characteristics were derived from the Census: (i) proportion of people with university education, (ii) proportion of divorced persons, (iii) proportion of inhabitants reporting no religious attachment, (iv) proportion of people with nationality other than Czech, and (v) proportion of unemployed persons. Additionally, we used mean persons/household ratio for each municipality, and we categorized municipalities into five groups based on their population size. The crude relationships between the independent and dependent variables were initially assessed by cross-tabulations. Then we used random intercept logistic regression with two levels (individual and small-area level) in multilevel regression analysis. In such way, the modelling strategy accounts for the hierarchical structure of the dataset. First, both individual-level socioeconomic characteristics and area-level measures were assessed with the respect to their effect on the odds of individual habits of smoking in a crude analysis. Then, other individual-level indicators (used as proxy measures of individuals’ physical and psychological health) were included into the model to control for potential confounding. Third, we built model using all individual-level variables. We have checked for colinearity between social and demographic variables. While the individual-level social and demographic variables did not show high correlation we did find relatively high correlations (in the magnitude of 0.4–0.5) at
217
the area level. Thus, finally, one by one, area-level characteristics were added into the model with all individual-level characteristics present. The additional models were tested for potential individual-level interactions and cross-level interactions between area and individual-level variables. Data were analysed using Stata 10 software (Stata Corp., College Station, USA) applying the same process of analysis as in Dzu´rova´ et al. (2010).
3. Results Descriptive characteristics of the study population are shown in Tables 1 and 2, area-based variables are described in Table 3. Table 1 shows smoking habits in study sample by gender. The gender distribution of the sample was almost equal (1766 men and 1760 women). The proportion of current smokers in our sample was 37.3% (44.9% for males and 20.7% for women). The percentage of smokers who have ever smoked was 57.3%. The distribution of socio-demographic characteristics and their associations with smoking habits is summarized in Table 2. Men prevail as current smokers, moderate/heavy smokers and ever smokers. While current smoking is most common between 18 and 29 years the ever smoking prevalence was almost equal in all age groups. Divorced people smoked more (in terms of all three outcomes) than any other group based on marital status. This difference was particularly large for current moderate/heavy smoking (29.6% of smokers among divorced compared to 16.9, 20.7 and 14.1% among married, single and widowed individuals). Smoking prevalence was inversely associated with education for all three outcomes. People with higher education reported smoking substantially less frequently than people with lower education (pvalue for trend in OR, not shown in Table 2, o0.001 for all three outcomes). In relation to economic activity, smoking was most common among unemployed individuals. While moderate/heavy smoking was not common among students (and the proportion of moderate/heavy smokers was, for example, lower than among pensioners) this was not the case for current smoking—more than 35% of students reported smoking habits. Table 3 describes the basic information about studied municipalities and shows the unadjusted association between study outcomes and municipality-level characteristics. The study included 59 municipalities with less than 2000 inhabitants, 64 municipalities with 2–20,000 inhabitants, 33 municipalities with 20–100,000 inhabitants, four cities with 100–500,000 inhabitants and the capital city of Prague with more than one million of inhabitants (the category of cities between 500,000 and 1 million of inhabitants is missing because there is no city of such size in the country). Table 4 shows the effects of the individual socioeconomic characteristics on the smoking habits. After adjustment for all characteristics in the table and self-rated health, long-standing illness, and mental health, all the three outcomes show strong association with gender. Current smoking is statistically significantly associated with age: risk of
Table 1 Smoking habits in sample by gender, N ¼ 3526 cases. Smoking habits
Current smoking Ever smoked Current smoking 45/day (‘‘moderate/heavy smoking’’)
Total N ¼3526
Men N ¼ 1766
Women N ¼ 1760
yes no yes no yes
1314 2212 2019 1507 692
37.3% 62.7% 57.3% 42.7% 19.6%
792 974 1189 577 460
44.9% 55.1% 67.3% 32.7% 26.1%
522 1238 830 930 232
29.7% 70.3% 47.2% 52.8% 13.2%
no
2834
80.4%
1306
73.9%
1528
86.8%
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Table 2 Association between smoking habits and socio-demographic variables (%, unadjusted odds ratio and 95% confidence intervals). Variable
N
Current smoking
Ever smoking
Current moderate/heavy smoking
%
OR
95%CI
%
OR
95%CI
%
OR
95%CI
Gender Men Women
1766 1760
44.8 29.7
1 0.51
0.45–0.59
67.3 47.2
1 0.42
0.37–0.48
26.0 13.2
1 0.42
0.36–0.51
Age 18–29 30–39 40–49 50–64
1012 739 730 1045
44.0 37.6 41.0 27.9
1 0.78 0.89 0.49
0.64–0.95 0.73–1.08 0.40–0.59
56.9 55.8 60.7 56.3
1 0.97 1.19 0.98
0.80–1.19 0.98–1.45 0.82–1.17
19.5 19.6 22.6 17.7
1 1.02 1.23 0.88
0.80–1.30 0.97–1.55 0.71–1.11
Marital status Married/cohabiting Single Divorced Widowed
1849 980 497 192
32.2 44.6 45.9 25.5
1 1.71 1.76 0.70
1.45–2.01 1.44–2.17 0.49–0.98
55.8 57.2 65.8 48.4
1 1.04 1.49 0.71
0.89–1.23 1.21–1.85 0.52–0.96
16.9 20.7 29.6 14.1
1 1.27 2.07 0.78
1.04–1.55 1.64–2.61 0.51–1.20
Education Primary Vocational Secondary University
819 1325 936 446
48.0 40.0 30.1 24.4
1 0.72 0.46 0.34
0.60–0.86 0.38–0.56 0.26–0.44
64.0 60.5 51.6 47.1
1 0.86 0.59 0.48
0.72–1.04 0.48–0.72 0.38–0.61
26.7 21.6 15.1 10.3
1 0.75 0.48 0.31
0.61–0.92 0.38–0.61 0.22–0.43
Economic activity Employed Pensioner Student Housewife Unemployed Other
2346 453 252 136 321 18
38.2 19.7 35.3 30.1 56.1 29.3
1 0.46 0.89 0.68 2.05 0.81
0.36–0.59 0.68–1.17 0.47–1.00 1.62–2.61 0.30–2.19
57.2 50.3 50.0 49.3 71.3 61.1
1 0.84 0.73 0.71 1.83 2.04
0.68–1.03 0.56–0.95 0.49–1.01 1.41–2.38 0.71–5.87
20.2 12.7 11.5 9.6 32.7 19.7
1 0.68 0.51 0.41 1.91 1.08
0.51–0.90 0.34–0.77 0.23–0.74 1.47–2.48 0.35–3.36
Total
3,526
Table 3 Association between smoking habits and municipality-level characteristics (N ¼ 161 municipalities, unadjusted odds ratio and 95% confidence intervals).
Municipality size o2000 2000–19,999 20,000–99,999 100,000–499,999 1,000,000 +
% % % % %
university education divorced without religion with non-Czech nationality unemployed
Number (%)
Current smoking
Ever smoking
Current moderate/ heavy smoking
59 64 33 4 1
1 (ref) 1.06 (0.85–1.31) 1.17 (0.92–1.49) 1.22 (0.82–1.82) 1.00 (0.53–1.89)
1 (ref) 0.98 (0.78–1.24) 1.13 (0.87–1.48) 1.20 (0.74–1.97) 0.98 (0.43–2.27)
1 (ref) 0.99 (0.76–1.28) 0.95 (0.71–1.28) 1.16 (0.72–1.88) 1.05 (0.49–2.28)
Mean (SD)
Per 1% increase
Per 1% increase
Per 1% increase
5.6 (2.9) 7.1 (2.5) 56.6 (16.5) 4.9 (3.3) 8.4 (3.9)
0.99 1.03 1.01 1.03 1.03
0.99 (0.96–1.02) 1.02 (0.98–1.06) 1.00 (1.00–1.01) 1.03 (1.00–1.06) 1.02 (1.00–1.05)
0.99 1.01 1.00 1.03 1.04
(36.6) (39.8) (20.5) (2.5) (0.6)
current smoking decreases with increasing age (p for trend of OR so0.001). All the models confirm the increased risk of smoking among divorced as already seen in crude analyses. The effects of education remained statistically significant with the decreasing proportions of smokers related to higher education. In relation to economic activity, compared to employed individuals, the lower risk of smoking has been found among students and pensioners (not for ever smoking for pensioners) while the risk of smoking is increased for unemployed. The models using all the individual characteristics and municipality-level variables at bottom part of Table 4 show that unemployment variable is important for current smoking in both levels, individual and municipality. Thus, the current and moderate/heavy smokers are found more often not only among the
(0.96–1.01) (0.99–1.07) (1.00–1.01) (1.00–1.06) (1.01–1.05)
(0.95–1.02) (0.97–1.06) (0.99–1.01) (1.00–1.07) (1.01–1.06)
unemployment persons, but also among the people living in the municipalities with higher unemployment rates. The nationality proved to be statistically significantly associated with current smoking, showing that there are more people currently smoking in the municipalities with higher percentage of non-Czech citizens, thus with higher ethnic heterogeneity. The variance components of the regression models provide valuable information regarding the variation between-municipality areas. As mentioned previously, top block of Table 4 shows that 3.1% (current smoking), 5.7% (ever smoking) and 4.3% (moderate/high smoking) of the total variance in the outcome resides betweenmunicipalities after the adjustment for individual variables. This small but statistically significant proportion of variance at the area level was
´ et al. / Health & Place 17 (2011) 215–221 J. Spilkova
219
Table 4 Multilevel logistic regression (adjusted odds ratios and 95% confidence intervals) of smoking habits, N ¼ 3 525 individuals nested within N ¼161 municipalities. Individual-level variables Variable
Gender Men Women Age 18–29 30–39 40–49 50–64 P for linear trend of OR Marital status Married/cohabiting Single Divorced Widowed Education Primary Vocational Secondary University P for linear trend of OR Economic activity Employed Pensioner Student Housewife Unemployed Other
sbU r
Current smoking
Ever smoking
Current moderate/heavy smoking
Adj. ORa
95%CI
p
Adj. ORa
95%CI
p
Adj. ORa
95%CI
p
1 0.52
0.45–0.61
o 0.001
1 0.41
0.36–0.48
o 0.001
1 0.43
0.35–0.52
o 0.001
1 0.76 0.86 0.52
0.59–0.98 0.65–1.14 0.39–0.70
0.04 0.30 o 0.001 o 0.001
1 0.85 0.99 0.82
0.66–1.09 0.75–1.31 0.61–1.10
0.20 0.94 0.19 0.33
1 0.9 1.02 0.79
0.66–1.22 0.72–1.43 0.56–1.14
0.49 0.92 0.21 0.29
1 1.30 1.67 1.04
1.02–1.67 1.34–2.07 0.71–1.52
0.03 o 0.001 0.85
1 0.97 1.44 0.80
0.76–1.23 1.15–1.79 0.57–1.12
0.78 0.001 0.20
1 1.23 1.92 0.93
0.92–1.66 1.50–2.45 0.58–1.49
0.17 o 0.001 0.77
1 0.69 0.44 0.33
0.57–0.84 0.36–0.55 0.25–0.43
o 0.001 o 0.001 o 0.001 o 0.001
1 0.83 0.64 0.49
0.68–1.01 0.52–0.79 0.38–0.64
0.06 o 0.001 o 0.001 o 0.001
1 0.71 0.51 0.32
0.57–0.88 0.39–0.66 0.22–0.46
0.002 o 0.001 o 0.001 o 0.001
0.41–0.74 0.38–0.74 0.56–1.29 1.07–1.80 0.27–2.21
o 0.001 o 0.001 0.45 0.01 0.62
0.64–1.08 0.52–0.99 0.72–1.55 1.14–2.01 0.70–6.14
0.17 0.05 0.77 0.004 0.19
0.48–0.94 0.26–0.65 0.34–1.16 1.00–1.77 0.31–3.35
0.02 o 0.001 0.14 0.05 0.97
OR
95%CI
p
0.98 0.142 0.041 1.01 0.149 0.043 1.00 0.148 0.043 1.03 0.139 0.041 1.04 0.113 0.033 0.72 0.144 0.042
0.95–1.01
0.22
0.96–1.05
0.78
0.99–1.01
0.65
0.99–1.06
0.14
1.01–1.07
0.007
0.44–1.19
0.21
0.74–1.28 0.66–1.24 0.65–1.82 0.39–2.03
0.83 0.54 0.75 0.79 0.78
1 0.55 0.53 0.85 1.39 0.77 0.105 0.031
1 0.83 0.72 1.06 1.52 2.07 0.200 0.057
1 0.67 0.41 0.63 1.33 1.02 0.149 0.043
Municipality-level variables Adjustedb
% university
s2U r % divorced
s2U r % without religion
s2U r % non-Czech nationality
s2U r % Unemployed
s2U r Persons/household
s2U r
Adjustedb
OR
95%CI
0.98 0.099 0.029 1.03 0.103 0.030 1.00 0.101 0.030 1.03 0.093 0.028 1.03 0.074 0.022 0.68 0.101 0.030
0.95–1.01
0.16
0.99–1.07
0.16
0.99–1.01
0.14
1.00–1.06
0.04
1.01–1.06
0.003
0.45–1.03
0.07
0.84–1.32 0.88–1.47 0.78–1.84 0.43–1.70
0.64 0.33 0.4 0.66 0.42
Community size o 2,000 2,000–19,999 20,000–99,999 100,000–499,999 1,000,000 + P for linear trend of OR
1 1.06 1.14 1.20 0.86
s2U r
0.100 0.029
p
Variance components: Level 2 variance s2U. Intra-class correlation r. a b
Mutually adjusted for all individual-level variables in the table. Adjusted for all individual-level variables.
Adjustedb
OR
95%CI
0.99 0.199 0.057 1.02 0.199 0.057 1.00 0.198 0.057 1.03 0.191 0.055 1.03 0.186 0.053 0.77 0.197 0.056
0.96–1.02
0.49
0.97–1.06
0.48
0.99–1.01
0.24
0.99–1.06
0.11
0.99–1.05
0.06
0.49–1.21
0.25
0.76–1.25 0.84–1.48 0.70–2.00 0.37–2.22
0.85 0.44 0.53 0.82 0.48
1 0.98 1.12 1.18 0.90 0.195 0.056
p
1 0.97 0.91 1.09 0.89 0.144 0.042
220
´ et al. / Health & Place 17 (2011) 215–221 J. Spilkova
only partly explained by municipality-level characteristics; however it has been reduced particularly when unemployment rate in the municipality was taken into consideration.
4. Discussion The results of this analysis show that men are significantly more likely to be smokers, which is slightly in contrast with the trends described in the introduction stressing the increasing smoking among young females showing a certain lagging of the Czech Republic behind the behavioural trends in other countries (Graham, 2009; Myers and MacPherson, 2009, etc.), however this can be the result of a time lag between higher adolescent female smoking and higher adult female smoking. The analysis confirmed that current smoking is the most common among young people. Education is strongly inversely related to all three smoking outcomes (as reported also by e.g. Bobak et al., 1997; Dragano et al., 2007). Smoking was also more reported by divorced and unemployed individuals. These results also confirm previous results (Dzu´rova´ et al., 2008) in a similar study of mental disorder and substance abuse showing mainly employment and marital status as highly significant indicators of future risk behaviours (nevertheless, the relation between marital status and smoking in particular has not been previously extensively studied). While the association between small-area level variables and smoking were limited, it is still possible to say that smoking is more common in areas with higher unemployment and with higher proportion of people with non-Czech nationality. The unemployment has been associated with smoking both at the individual and the aggregated level, suggesting that smoking represents a strategy to cope with stress caused by the individual economic situation and unsatisfactory social environment. There are several possible reasons for the association between smoking and proportion of non-Czech nationals in the municipalities. The effect might be compositional because many non-Czech nationals come from the post-Soviet countries or south-east Asia, the regions with high prevalence of smoking. It may also be contextual effect suggesting that the proportion of non-Czech nationals acts as another proxy measure for overall social situation of the municipality. We can also hypothesize that higher smoking in unemployed people is the result of the recent changes in the anti-tobacco legislative. Smoking is prohibited in the work place and also in most of the public spaces, therefore the employed people have nowadays less opportunity to smoke than those who are without a job and it is possible that presence of smoke-free workplaces might affect smoking behaviour (Fichtenberg and Glanz, 2002). While it is possible to say that multilevel design is great advantage of this project, the study has also few limitations. The main limitation is cross-sectional design of the study not allowing to make causal inference on the association between socio-demographic indicators used in the project and smoking outcomes. While the response rate was around 68% and it is likely that proportion of individuals from low socioeconomic groups and with more risky health behaviours were not included, we believe that the response rate is relatively high compared to other contemporary studies. We, however, believe that interpretation of the results must be cautious in terms of generalizing for the whole population of the country. Finally, we were able to use only whole municipalities as second-level area data and the size of municipalities differs substantially from small towns or villages to capital Prague. While we would be able to get second level data for smaller units (such as electoral wards) it was not possible to do linkage between individual survey data and area-based data on smaller than municipality level (the individual dataset is anonymised and the only information available was municipality code). Neighbourhoods, rather than municipalities, might be more
important area-level units influencing smoking behaviours of individuals but this study did not allow to test this possibility. The popularity of smoking among young people was confirmed by this study. There is an urgent need of an effective tobacco control strategy among young adults. This must be focused on the social environment. The evidence shows that interpersonal peer influence and role modelling are significant determinants of smoking among adolescents (Griesler and Kandel, 1998). Among others alcohol drinking and lack of interest in educational process remain important influential factors affecting adolescent smoking (Pinilla et al., 2002). 5. Policy implications Although the prevalence of smoking and the average number of cigarettes per single person per annum in the Czech Republic are among the highest within the European framework, the attention devoted to this issue is not sufficient. This is probably the reflection of perception of smoking by the whole society seeing smoking as socially acceptable. Knowledge of health effects of smoking is increasing among the Czech society. Part of the society starts to view smoking as a serious danger to health as well as a socially unacceptable behaviour, but a long-term trend showing decrease in smoking prevalence among population has not been observed yet (Sovinova´ et al., 2010). The currently existing anti-smoking measures can be considered as insufficient. Despite the fact that the Czech Republic is closing gap within the EU countries in the levels of economic indicators, and successfully continues its transformation process, it is still belonging to countries seriously lagging behind the trends of developed world in the area of public health policies (and mainly those concerning risk behaviours). The Czech Republic has made a commitment to meet one of the goals of the WHO Health 21 project (WHO, 1998), which is to reduce undesirable effects of alcohol, drugs and tobacco by 2015 (goal 12). Government decision no. 1046 from October 2002, related to this project, started a number of activities to meet this goal. These were, among others, efficient development of legal measures, changes in tax policy, prohibition of advertising, protection of non-smokers against passive smoking, prevention support, availability of addiction treatment, expert advice and implementation of the European action plan ‘‘Europe without Tobacco’’. The results of this paper give the evidence that particularly socially disadvantaged groups must be targeted in any anti-smoking policies as these are groups with the highest risk of smoking behaviour. This idea corresponds with the conclusions of Graham (2009, p. 15) who claims that ‘‘policy synergies are important, with equity-oriented tobacco control policies both supporting and supported by equityoriented social and economic policies’’.
Acknowledgements This work was supported by the Fogarty International Centre (Finance and Delivery of Mental Health Services in Central and Eastern Europe, NIH Research Grant D43 TW005810) and the Ministry of Education, Youth and Sport of the Czech Republic (MSM 0021620831). References Bartley, M., Fitzpatrick, R., Firth, D., Marmot, M., 2000. Social distribution of cardiovascular disease risk factors: change among men in England 1984–1993. J. Epidemiol. Community Health 54, 806–814. Bobak, M., Skodova, Z., Pisa, Z., Poledne, R., Marmot, M., 1997. Political changes and trends in cardiovascular risk factors in the Czech Republic, 1985–92. J. Epidemiol. Community Health 51, 272–277.
´ et al. / Health & Place 17 (2011) 215–221 J. Spilkova
Borrell, C., Rue, M., Pasarin, I., Rohlfs, I., Ferrando, J., Fernandez, E., 2000. Trends in social class inequalities in health status, health-related behaviors, and health services utilization in a southern European urban area (1983–1994). Prev. Med. 31, 691–701. Chaix, B., Guilbert, P., Chauvin, P., 2004. A multilevel analysis of tobacco use and tobacco consumption levels in France: are there any combination risk groups? Eur. J. Public Health 14 186–190. Cummins, S., Curtis, S., Diez-Roux, A., Macintyre, S., 2007. Understanding and representing ‘‘place’’ in health research: a relational approach. Social Sci. Med. 65, 1825–1838 Dragano, N., Bobak, M., Wege, N., Peasey, A., Verde, P.E., Kubinova, R., et al., 2007. Diez-Roux, A.V., 2003. Residential environments and cardiovascular risk. J. Urban Health 80 (4), 569–589. Dragano, N., Bobak, M., Wege, N., Persey, A., Verde, P.E., Kubinova, R., et al., 2007. Neighbourhood socioeconomic status and cardiovascular risk factors: a multilevel analysis of nine cities in the Czech Republic and Germany. BMC Public Health 7, 255. Duncan, C., Jones, K., Moon, G., 1993. Do places matter? A multi-level analysis of regional variations in health-related behaviour in Britain. Soc. Sci. Med. 37, 725–733. Duncan, C., Jones, K., Moon, G., 1996. Health-related behaviour in context: a multilevel modelling approach. Soc. Sci. Med. 42, 817–830. Dzu´rova´, D., Brazˇinova´, A., Dragomirecka´, E., 2008. Mental health status of the Czech and Slovak populations. In: Scheffler, R.M., Potu˚cˇek, M. (Eds.), Mental health care reform in the Czech and Slovak Republice, 1989 to the Present. Karolinum Press, Prague, pp. 39–80. Dzu´rova´, D., Spilkova´, J., Pikhart, H., 2010. Social inequalities in alcohol consumption in the Czech Republic: a multilevel analysis. Health and Place 16, 590–597. EMCDDA, 2002. Handbook for surveys on drug use among the general population. Final project report. EMCDDA, Lisabon. Fichtenberg, C.M., Glanz, S.A., 2002. Effect of smoke-free workplaces on smoking behaviour: systematic review. BMJ 325, 188. Fukuda, Y., Nakamura, K., Takano, T., 2005. Accumulation of health risk behaviours is associated with lower socio-economic status and women’s urban residence: a multilevel analysis in Japan. BMC Public Health 53, 5. Griesler, P.C., Kandel, D.B., 1998. Ethnic differences in correlates of adolescent cigarette smoking. J. Adolescent Health 23, 167–180. Giskes, K., Kunst, A.E., Benach, J., Borrell, C., Costa, G., Dahl, E., et al., 2005. Trends in smoking behaviour between 1985 and 2000 in nine European countries by education. J. Epidemiol. Community Health 59, 395–401. Graham, H., 2009. Women and smoking: understanding socioeconomic influences. Drug Alcohol Depend. 104S, 11–16. Helmert, U., Borgers, D., Bammann, K., 2001. Social determinants of smoking behavior in Germany: results of a 1995 micro-census (in German). Sozial und ¨ Praventivmedizin 46, 172–181. Karvonen, S., Rimpela, A., 1996. Socio-regional context as a determinant of adolescents’ health behaviour in Finland. Soc. Sci. Med. 43, 1467–1474. Lahelma, E., Rahkonen, O., Berg, M.A., et al., 1997. Changes in health status and health behavior among Finnish adults 1978–1993. Scand. J. Work Environ. Health 23 (Suppl. 3), 85–90. Macintyre, S., Maciver, S., Sooman, A., 1993. Area, class and health: should we be focusing on places or people? Journal of Social Policy 22, 213–234.
221
Mackenbach, J.P., Stirbu, I., Roskam, A.J., Schaap, M.M., Menvielle, G., Leinsalu, M., Kunst, A.E., 2008. European Union Working Group on socioeconomic inequalities in health. Socioeconomic inequalities in health in 22 European countries. N. Engl. J. Med. 358, 2468–2481. Monden, C.W.S., van Lenthe, F.J., Mackenbach, J.P., 2006. A simultaneous analysis of neighbourhood and childhood socio-economic environment with self-assessed health and healthrelated behaviours. Health Place 12, 394–403. Myers, M.G., MacPherson, L., 2009. Coping with temptations and adolescent smoking cessation: an initial investigation. Nicotine Tobacco Res. 11, 940–944. Osler, M., Gerdes, L.U., Davidsen, M., et al., 2000. Socioeconomic status and trends in risk factors for cardiovascular diseases in the Danish MONICA population, 1982–1992. J. Epidemiol. Community Health 54, 108–113. Picket, K.E., Pearl, M., 2001. Multilevel analyses of neighbourhood socio-economic context and health outcomes: a critical review. J. Epidemiol. Community Health 55, 111–122. Pinilla, J., Gonza´les, B., Barber, P., Santana, Y., 2002. Smoking in young adolescents: an approach with multilevel discrete choice models. J. Epidemiol. Community Health 56, 227–232. Schaap, M.M., Kunst, A.E., Leinsalu, M., Regidor, E., Ekholm, O., Dzurova, D., et al., 2008. Effect of nationwide tobacco control policies on smoking cessation in high and low educated groups in 18 European countries. Tobacco Control 17, 248–255. Skodova´, Z., Cı´fkova´, R., Ada´mkova´, V., Duskova´, A., Hauserova´, G., Hejl, Z., et al., 2000. Development of smoking habits in the population of the Czech Republic from ˇ ˇ ´ ch 139, 143–147. 1985 to 1997/98. Casopis Le´kaˇru˚ Cesky Smith, G.D., Hart, C., Watt, G., Hole, D., Hawthorne, V., 1998. Individual social class, area-based deprivation, cardiovascular disease risk factors, and mortality: the Renfrew and Paisley Study. J. Epidemiol. Community Health 52, 399–405. Sovinova´, H., Cse´my, L., 2004. Smoking behaviour of Czech adolescents: results of the Global Youth Tobacco Survey in the Czech Republic. Central Eur. J. Public Health 12, 26–31. Sovinova´, H., Cse´my, L., Procha´zka, B., Kottnauerova´, S., 2008a. Smoking-attributable mortality in the Czech Republic. J. Public Health 16, 37–42. ˇ Sovinova´, H., Sadı´lek, P., Cse´my, L., 2008b. Vy´voj prevalence kuˇra´ctvı´ v populaci CR, ˇ k problematice kouˇrenı´ (obdobı´ 1997–2007). Sta´tnı´ na´zory a postoje obcˇanu˚ CR zdravotnı´ u´stav, Prague. Sovinova´, H., Sadı´lek, P., Cse´my, L., 2010. Vy´voj prevalence kuˇra´ctvı´ v dospˇele´ ˇ ˇ k problematice kouˇrenı´ (obdobı´ populaci CR, na´zory a postoje obcˇanu˚ CR 1997–2009). Sta´tnı´ zdravotnı´ u´stav, Prague. Twigg, L., Moon, G., 2002. Predicting small area health-related behavior: a comparison of multilevel synthetic estimation and local survey data. Soc. Sci. Med. 54, 931–937. Twigg, L., Moon, G., Jones, K., 2000. Predicting small-area health-related behaviour: a comparison of smoking and drinking indicators. Soc. Sci. Med. 50, 1109–1120. van Lenthe, F.J., Mackenbach, J.P., 2006. Neighbourhood and individual socioeconomic inequalities in smoking: the role of physical neighbourhood stressors. J. Epidemiol. Community Health 60, 699–705. WHO Regional Office for Europe. Health21., 1998. An introduction to the health for all policy framework for the WHO European Region. WHO, Copenhagen. WHO, 2004. The Tobacco Atlas. WHO, Copenhagen.
Contents lists available at ScienceDirect
Health & Place journal homepage: www.elsevier.com/locate/healthplace
Inequalities in smoking in the Czech Republic: Societal or individual effects? Jana Spilkova´ a,1, Dagmar Dzu´rova´ a,n, Hynek Pikhart b,2 a b
Charles University in Prague, Faculty of Science, Department of Social Geography and Regional Development, Albertov 6, 128 43 Prague 2, Czech Republic University College London, Department of Epidemiology and Public Health, 1-19 Torrington Place, London WC1E 6BT, United Kingdom
a r t i c l e in f o
abstract
Article history: Received 16 March 2010 Received in revised form 17 September 2010 Accepted 2 October 2010 Available online 14 October 2010
Smoking constitutes one of the main public health problems worldwide. In the Czech Republic, one of the post-communist countries undergoing societal transition, there was a significant decrease in smoking prevalence during 1985–1997, followed by certain stagnation in prevalence of smokers. The most serious problem is the smoking among young population and socially disadvantaged groups. This paper examines social inequalities in smoking in the Czech population using multilevel approach. Data were analysed by multilevel modelling using smoking in the past, current smoking and current moderate/heavy smoking as outcomes of interest. Men were significantly more likely to be smokers than women. Further, the analysis confirmed that current smoking is the most common among young people. Education was strongly inversely related to all smoking outcomes. Smoking was also significantly more reported by divorced and unemployed individuals. While the association between small-area characteristics and smoking was limited, smoking was more common in the areas with higher unemployment and higher proportion of non-Czech nationals. & 2010 Elsevier Ltd. All rights reserved.
Keywords: Smoking Czech Republic Inequalities Multilevel analysis
1. Introduction In most post-communist countries of Central and Eastern Europe, including the Czech Republic, social and political changes during the transition period after 1990 brought changes in the life style and health behaviours of many individuals, including changes in alcohol consumption, drug use and cigarette smoking. Smoking has been repeatedly shown as one of the major mortality risk factors. Lung cancer accounts for the highest number of deaths related to smoking, and is closely followed by other diseases, such as atherosclerosis, coronary artery disease, angina pectoris and myocardial infarction, or cerebrovascular disease. Deaths from neoplasms attributable to tobacco smoking in the Czech Republic represented 12% in 2000 and 10.5% in 2002 of the total number of deaths for men and 2.1% (2000) and 2.7% (2002) for women (Sovinova´ et al., 2008a). For cardiovascular disease, these numbers were 9.8% and 13.8% (men), and 3.2% and 7.1% (women) of total number of deaths in those years. Similar situation was found among hospitalisation cases. The financial costs of the hospital treatment for smoking related diseases were estimated to be 6.1 billion CZK in 2002 (Sovinova´ et al., 2008a). While a significant decrease in smoking prevalence was reported for Czech males between 1985 and 1997—49–37% among 35–64 years old men based on WHO MONICA results (Skodova´ et al., 2000),
n
Corresponding author. Tel.: + 420 221 951 390; fax: +420 224 920 657. E-mail addresses: [email protected] (J. Spilkova´), [email protected] (D. Dzu´rova´), [email protected] (H. Pikhart). 1 Tel.: + 420 221 951 388; fax: +420 224 920 657. 2 Tel.: + 44 20 7679 1906; fax: + 44 20 7813 0280. 1353-8292/$ - see front matter & 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.healthplace.2010.10.003
the prevalence remained almost the same for Czech females (1985–1997 change from 28% to 26%). Later reports by the Czech National Institute of Public Health (Sovinova´ et al., 2008b, 2010) summarizing the smoking prevalence between 1997 and 2007, resp. 2009, showed virtually constant prevalence of approximately 30% in the age group 15–64 years. Social inequalities in smoking in this period were similar to those observed in the number of other European countries, for example showing higher proportion of smokers among persons with lower education for both genders. The smoking prevalence, including young people and teenagers, remains at a high level, while the protection of non-smokers against passive smoking remains low, in spite of increasing activity of non-government organizations and selected groups of health professionals (preparation and implementation of primary prevention projects, or a treatment of somatic and psycho-social addictions). Generally, there are some anti-smoking measures in operation,3 such as the law prohibiting advertisement of tobacco products or prohibition of smoking in closed public areas, in public transport (including stations), in schools, in entertainment areas and inside healthcare facilities. The existing laws are not, however, strictly followed. For example, it has been reported that over half of 3 Legal measures related to tobacco control in the Czech Republic: law no. 305/2009 (measures against the damage caused by tobacco products, alcoholic beverages and other addictive products and protection of non-smokers), 132/2003 (regulation and prohibition of advertising of tobacco products), law no. 231/2001 (regulation of radio and television broadcasting with parts related to smoking and tobacco products), decree no. 113/2005 (health warnings on packaging and other requirements on tobacco products) and 272/2005 (sale, pricing and consumer prices related to tobacco products), etc.
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the factors and determinants influencing smoking habits in the Czech population using multilevel modelling. We tested the following hypotheses: (i) Individual socioeconomic characteristics affect smoking habits of study respondents. (ii) Area-level socioeconomic characteristics affect independently individual smoking in a way that people living in more socially disadvantaged areas have more risky health behaviour than people living in less disadvantaged areas. (iii) Area-level characteristics affect smoking habits of privileged and underprivileged individuals differentially (presence of cross-level interaction). 2. Data and methods The ‘‘Czech Health and Life Style Study’’ was conducted by the Institute of Health Information and Statistics of the Czech Republic (IHIS CR) in collaboration with INRES–SONES public opinion agency in 2003. Data were collected by face-to-face interviews using the EMCDDA (European Monitoring Centre for Drugs and Drug Addiction) questionnaire (EMCDDA, 2002). Cluster sampling was used for sample selection. Total of 235 electoral wards were randomly selected in the country. In each electoral ward, data were collected from 15 randomly selected individuals (with exception of one ward where data on 16 individuals were collected). As there were two or more wards selected in some municipalities, 161 municipalities were finally represented in the study. Because of small-area data availability, municipalities represent the smallest analytic unit for this analysis. Together, the study covers municipalities with more than 4.23 mil inhabitants in different size categories, the smallest with 96 inhabitants (15 respondents) and the largest with 1.17 mil inhabitants (405 respondents). The sample of respondents corresponds with the Czech population structure in terms of regional, sex and age structure. Small deviations were found in the distribution by marital status, education and economic activity. The sample included 3526 persons, aged 18–64 years at the time of the survey. The response rate was 68.2%. The questions focused on self-rated health, long-term illness, mental health and the substance abuse related behaviours (smoking, drinking alcohol, drug use). Basic demographic and socioeconomic data were also collected (such as education, marital status, occupational status). Smoking status was self-reported. The three outcome measures of smoking behaviour used in this paper were based on the frequency of tobacco consumption: current smoking, current high smoking, and ever smoking. Current smoking was estimated by a question: ‘‘Do you currently smoke cigarettes?’’ Respondents selected one answer from the following six options: (i) yes, occasionally, (ii) yes, daily 1–5 cigarettes, (iii) yes, daily 6–20 cigarettes, (iv) yes, daily more than 20 cigarettes, (v) not smoking cigarettes, but smoking cigars and
0.700 0.600 0.500 0.400 0.300 0.200 0.100 0.000
19
91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06
relative price of cigarette package in % of average month income
all smokers under 15 years of age regularly purchase cigarettes in common retail outlets, and 72% of them reported never having been restricted in their purchases because of their age. Over 80% of children younger than 15 years of age reported that they have been exposed to tobacco advertising (Sovinova´ and Cse´my, 2004). In addition to limited adherence to the law, the economic pressure on smokers is small. There is a clear decreasing trend of relative price of cigarette package (Fig. 1) showing the ineffectiveness of the anti-smoking policies and lack of general interest in public health issues. Overall, according to the tobacco consumption, the Czech Republic resembles the post-communist countries rather than EU countries (WHO, 2004). As already mentioned, smoking is one of the behavioural risk factors most affecting health of individuals, and personal smoking habits has been repeatedly suggested to be related to socioeconomic position of individuals (Mackenbach et al., 2008; Lahelma et al., 1997; Osler et al., 2000; Bartley et al., 2000; Borrell et al., 2000). Socioeconomic inequalities in smoking have widened and persisted in the last decades (Graham, 2009; Giskes et al., 2005). In the case of the Czech Republic, higher prevalence of non-smokers in the groups with better educational attainment was repeatedly reported (e.g. Bobak et al., 1997; Dragano et al., 2007). Based on the data of CIDI/ICD-10 survey (1998–99) in the Czech Republic, it was shown that both employment status and marital status (and community size in women) had significant effect on individual’s smoking (Dzu´rova´ et al., 2008). The results of another comparative study (Schaap et al., 2008) also suggests larger socioeconomic inequalities in quitting smoking in the Czech Republic compared with elsewhere. There has been recently an increased interest in the effects of area characteristics and local context on health and health behaviours including smoking (Helmert et al., 2001; Picket and Pearl, 2001; Cummins et al., 2007; Duncan et al., 1993; Karvonen and Rimpela, 1996). While people of similar socioeconomic status tend to be clustered in particular neighbourhoods (MacIntyre et al., 1993), the contextual influences on health and health behaviour might be also important. As a result, individuals living in deprived areas might have higher risk of poor health and health behaviour even if their individual socioeconomic position is taken into account (Diez-Roux, 2003; Pickett and Pearl, 2001). Various papers aimed to contribute to a general discussion on the importance of individual risk factors and the area characteristics through multilevel analysis, a method that allows to evaluate these differential effects (Chaix et al., 2004; Smith et al., 1998; van Lenthe and Mackenbach, 2006, Duncan et al., 1996; Twigg et al., 2000; Twigg and Moon, 2002; Monden et al., 2006; Fukuda et al., 2005). The aim of this paper is to focus on the smoking prevalence in different demographic and socioeconomic groups and to evaluate
Fig. 1. The development of relative price of cigarette package in percents of average month income in the transitional period. Source: Czech Statistical Office.
´ et al. / Health & Place 17 (2011) 215–221 J. Spilkova
(vi) non-smokers. Binary dependent variable was constructed combining answers (i)–(v) as a positive answer. Current moderate or heavy smoking was constructed from the same question. Respondents answering (iii) or (iv) (smoking of 6 or more cigarettes daily) were defined as high smokers. Those who said they were current nonsmokers were asked whether they smoked in the past. Responses were: (i) yes, occasionally, (ii) yes, daily 1–5 cigarettes, (iii) yes, daily 6–20 cigarettes, (iv) yes, daily more than 20 cigarettes, (v) not smoking cigarettes, but smoking cigars and (vi) non-smokers. Binary variable was constructed, and ever smokers were defined as those who either reported being current smokers or those who answered (i)–(v) to question on smoking in the past. A range of demographic, socioeconomic and health-related covariates were used as potential explanatory variables. Among demographic and socioeconomic variables we used gender (men and women), age (18–29, 30–39, 40–49, 50–64 years old), marital status (married/cohabitating, single, divorced, widowed), education (four categories based on the highest achieved education level: university degree, secondary, vocational, primary), and economic activity (employed/self-employed, pensioners, students, housewife/maternal leave, unemployed and others). Three variables were used to help control for the underlying health status: self-rated health (5-point scale ranging from excellent to poor), long-term illness (yes, no) and emotional disorders (yes, no). The small-area contextual variables were used to characterize socioeconomic dimensions of the municipalities of residence of the respondents. We used data from the 2001 Census, obtained from the Czech Statistical Office. The individuals from survey were linked with the Census database and subjects were assigned an ID number of the municipalities. Using this area ID number, the survey subjects were linked with 161 municipalities, and 5 characteristics were derived from the Census: (i) proportion of people with university education, (ii) proportion of divorced persons, (iii) proportion of inhabitants reporting no religious attachment, (iv) proportion of people with nationality other than Czech, and (v) proportion of unemployed persons. Additionally, we used mean persons/household ratio for each municipality, and we categorized municipalities into five groups based on their population size. The crude relationships between the independent and dependent variables were initially assessed by cross-tabulations. Then we used random intercept logistic regression with two levels (individual and small-area level) in multilevel regression analysis. In such way, the modelling strategy accounts for the hierarchical structure of the dataset. First, both individual-level socioeconomic characteristics and area-level measures were assessed with the respect to their effect on the odds of individual habits of smoking in a crude analysis. Then, other individual-level indicators (used as proxy measures of individuals’ physical and psychological health) were included into the model to control for potential confounding. Third, we built model using all individual-level variables. We have checked for colinearity between social and demographic variables. While the individual-level social and demographic variables did not show high correlation we did find relatively high correlations (in the magnitude of 0.4–0.5) at
217
the area level. Thus, finally, one by one, area-level characteristics were added into the model with all individual-level characteristics present. The additional models were tested for potential individual-level interactions and cross-level interactions between area and individual-level variables. Data were analysed using Stata 10 software (Stata Corp., College Station, USA) applying the same process of analysis as in Dzu´rova´ et al. (2010).
3. Results Descriptive characteristics of the study population are shown in Tables 1 and 2, area-based variables are described in Table 3. Table 1 shows smoking habits in study sample by gender. The gender distribution of the sample was almost equal (1766 men and 1760 women). The proportion of current smokers in our sample was 37.3% (44.9% for males and 20.7% for women). The percentage of smokers who have ever smoked was 57.3%. The distribution of socio-demographic characteristics and their associations with smoking habits is summarized in Table 2. Men prevail as current smokers, moderate/heavy smokers and ever smokers. While current smoking is most common between 18 and 29 years the ever smoking prevalence was almost equal in all age groups. Divorced people smoked more (in terms of all three outcomes) than any other group based on marital status. This difference was particularly large for current moderate/heavy smoking (29.6% of smokers among divorced compared to 16.9, 20.7 and 14.1% among married, single and widowed individuals). Smoking prevalence was inversely associated with education for all three outcomes. People with higher education reported smoking substantially less frequently than people with lower education (pvalue for trend in OR, not shown in Table 2, o0.001 for all three outcomes). In relation to economic activity, smoking was most common among unemployed individuals. While moderate/heavy smoking was not common among students (and the proportion of moderate/heavy smokers was, for example, lower than among pensioners) this was not the case for current smoking—more than 35% of students reported smoking habits. Table 3 describes the basic information about studied municipalities and shows the unadjusted association between study outcomes and municipality-level characteristics. The study included 59 municipalities with less than 2000 inhabitants, 64 municipalities with 2–20,000 inhabitants, 33 municipalities with 20–100,000 inhabitants, four cities with 100–500,000 inhabitants and the capital city of Prague with more than one million of inhabitants (the category of cities between 500,000 and 1 million of inhabitants is missing because there is no city of such size in the country). Table 4 shows the effects of the individual socioeconomic characteristics on the smoking habits. After adjustment for all characteristics in the table and self-rated health, long-standing illness, and mental health, all the three outcomes show strong association with gender. Current smoking is statistically significantly associated with age: risk of
Table 1 Smoking habits in sample by gender, N ¼ 3526 cases. Smoking habits
Current smoking Ever smoked Current smoking 45/day (‘‘moderate/heavy smoking’’)
Total N ¼3526
Men N ¼ 1766
Women N ¼ 1760
yes no yes no yes
1314 2212 2019 1507 692
37.3% 62.7% 57.3% 42.7% 19.6%
792 974 1189 577 460
44.9% 55.1% 67.3% 32.7% 26.1%
522 1238 830 930 232
29.7% 70.3% 47.2% 52.8% 13.2%
no
2834
80.4%
1306
73.9%
1528
86.8%
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218
Table 2 Association between smoking habits and socio-demographic variables (%, unadjusted odds ratio and 95% confidence intervals). Variable
N
Current smoking
Ever smoking
Current moderate/heavy smoking
%
OR
95%CI
%
OR
95%CI
%
OR
95%CI
Gender Men Women
1766 1760
44.8 29.7
1 0.51
0.45–0.59
67.3 47.2
1 0.42
0.37–0.48
26.0 13.2
1 0.42
0.36–0.51
Age 18–29 30–39 40–49 50–64
1012 739 730 1045
44.0 37.6 41.0 27.9
1 0.78 0.89 0.49
0.64–0.95 0.73–1.08 0.40–0.59
56.9 55.8 60.7 56.3
1 0.97 1.19 0.98
0.80–1.19 0.98–1.45 0.82–1.17
19.5 19.6 22.6 17.7
1 1.02 1.23 0.88
0.80–1.30 0.97–1.55 0.71–1.11
Marital status Married/cohabiting Single Divorced Widowed
1849 980 497 192
32.2 44.6 45.9 25.5
1 1.71 1.76 0.70
1.45–2.01 1.44–2.17 0.49–0.98
55.8 57.2 65.8 48.4
1 1.04 1.49 0.71
0.89–1.23 1.21–1.85 0.52–0.96
16.9 20.7 29.6 14.1
1 1.27 2.07 0.78
1.04–1.55 1.64–2.61 0.51–1.20
Education Primary Vocational Secondary University
819 1325 936 446
48.0 40.0 30.1 24.4
1 0.72 0.46 0.34
0.60–0.86 0.38–0.56 0.26–0.44
64.0 60.5 51.6 47.1
1 0.86 0.59 0.48
0.72–1.04 0.48–0.72 0.38–0.61
26.7 21.6 15.1 10.3
1 0.75 0.48 0.31
0.61–0.92 0.38–0.61 0.22–0.43
Economic activity Employed Pensioner Student Housewife Unemployed Other
2346 453 252 136 321 18
38.2 19.7 35.3 30.1 56.1 29.3
1 0.46 0.89 0.68 2.05 0.81
0.36–0.59 0.68–1.17 0.47–1.00 1.62–2.61 0.30–2.19
57.2 50.3 50.0 49.3 71.3 61.1
1 0.84 0.73 0.71 1.83 2.04
0.68–1.03 0.56–0.95 0.49–1.01 1.41–2.38 0.71–5.87
20.2 12.7 11.5 9.6 32.7 19.7
1 0.68 0.51 0.41 1.91 1.08
0.51–0.90 0.34–0.77 0.23–0.74 1.47–2.48 0.35–3.36
Total
3,526
Table 3 Association between smoking habits and municipality-level characteristics (N ¼ 161 municipalities, unadjusted odds ratio and 95% confidence intervals).
Municipality size o2000 2000–19,999 20,000–99,999 100,000–499,999 1,000,000 +
% % % % %
university education divorced without religion with non-Czech nationality unemployed
Number (%)
Current smoking
Ever smoking
Current moderate/ heavy smoking
59 64 33 4 1
1 (ref) 1.06 (0.85–1.31) 1.17 (0.92–1.49) 1.22 (0.82–1.82) 1.00 (0.53–1.89)
1 (ref) 0.98 (0.78–1.24) 1.13 (0.87–1.48) 1.20 (0.74–1.97) 0.98 (0.43–2.27)
1 (ref) 0.99 (0.76–1.28) 0.95 (0.71–1.28) 1.16 (0.72–1.88) 1.05 (0.49–2.28)
Mean (SD)
Per 1% increase
Per 1% increase
Per 1% increase
5.6 (2.9) 7.1 (2.5) 56.6 (16.5) 4.9 (3.3) 8.4 (3.9)
0.99 1.03 1.01 1.03 1.03
0.99 (0.96–1.02) 1.02 (0.98–1.06) 1.00 (1.00–1.01) 1.03 (1.00–1.06) 1.02 (1.00–1.05)
0.99 1.01 1.00 1.03 1.04
(36.6) (39.8) (20.5) (2.5) (0.6)
current smoking decreases with increasing age (p for trend of OR so0.001). All the models confirm the increased risk of smoking among divorced as already seen in crude analyses. The effects of education remained statistically significant with the decreasing proportions of smokers related to higher education. In relation to economic activity, compared to employed individuals, the lower risk of smoking has been found among students and pensioners (not for ever smoking for pensioners) while the risk of smoking is increased for unemployed. The models using all the individual characteristics and municipality-level variables at bottom part of Table 4 show that unemployment variable is important for current smoking in both levels, individual and municipality. Thus, the current and moderate/heavy smokers are found more often not only among the
(0.96–1.01) (0.99–1.07) (1.00–1.01) (1.00–1.06) (1.01–1.05)
(0.95–1.02) (0.97–1.06) (0.99–1.01) (1.00–1.07) (1.01–1.06)
unemployment persons, but also among the people living in the municipalities with higher unemployment rates. The nationality proved to be statistically significantly associated with current smoking, showing that there are more people currently smoking in the municipalities with higher percentage of non-Czech citizens, thus with higher ethnic heterogeneity. The variance components of the regression models provide valuable information regarding the variation between-municipality areas. As mentioned previously, top block of Table 4 shows that 3.1% (current smoking), 5.7% (ever smoking) and 4.3% (moderate/high smoking) of the total variance in the outcome resides betweenmunicipalities after the adjustment for individual variables. This small but statistically significant proportion of variance at the area level was
´ et al. / Health & Place 17 (2011) 215–221 J. Spilkova
219
Table 4 Multilevel logistic regression (adjusted odds ratios and 95% confidence intervals) of smoking habits, N ¼ 3 525 individuals nested within N ¼161 municipalities. Individual-level variables Variable
Gender Men Women Age 18–29 30–39 40–49 50–64 P for linear trend of OR Marital status Married/cohabiting Single Divorced Widowed Education Primary Vocational Secondary University P for linear trend of OR Economic activity Employed Pensioner Student Housewife Unemployed Other
sbU r
Current smoking
Ever smoking
Current moderate/heavy smoking
Adj. ORa
95%CI
p
Adj. ORa
95%CI
p
Adj. ORa
95%CI
p
1 0.52
0.45–0.61
o 0.001
1 0.41
0.36–0.48
o 0.001
1 0.43
0.35–0.52
o 0.001
1 0.76 0.86 0.52
0.59–0.98 0.65–1.14 0.39–0.70
0.04 0.30 o 0.001 o 0.001
1 0.85 0.99 0.82
0.66–1.09 0.75–1.31 0.61–1.10
0.20 0.94 0.19 0.33
1 0.9 1.02 0.79
0.66–1.22 0.72–1.43 0.56–1.14
0.49 0.92 0.21 0.29
1 1.30 1.67 1.04
1.02–1.67 1.34–2.07 0.71–1.52
0.03 o 0.001 0.85
1 0.97 1.44 0.80
0.76–1.23 1.15–1.79 0.57–1.12
0.78 0.001 0.20
1 1.23 1.92 0.93
0.92–1.66 1.50–2.45 0.58–1.49
0.17 o 0.001 0.77
1 0.69 0.44 0.33
0.57–0.84 0.36–0.55 0.25–0.43
o 0.001 o 0.001 o 0.001 o 0.001
1 0.83 0.64 0.49
0.68–1.01 0.52–0.79 0.38–0.64
0.06 o 0.001 o 0.001 o 0.001
1 0.71 0.51 0.32
0.57–0.88 0.39–0.66 0.22–0.46
0.002 o 0.001 o 0.001 o 0.001
0.41–0.74 0.38–0.74 0.56–1.29 1.07–1.80 0.27–2.21
o 0.001 o 0.001 0.45 0.01 0.62
0.64–1.08 0.52–0.99 0.72–1.55 1.14–2.01 0.70–6.14
0.17 0.05 0.77 0.004 0.19
0.48–0.94 0.26–0.65 0.34–1.16 1.00–1.77 0.31–3.35
0.02 o 0.001 0.14 0.05 0.97
OR
95%CI
p
0.98 0.142 0.041 1.01 0.149 0.043 1.00 0.148 0.043 1.03 0.139 0.041 1.04 0.113 0.033 0.72 0.144 0.042
0.95–1.01
0.22
0.96–1.05
0.78
0.99–1.01
0.65
0.99–1.06
0.14
1.01–1.07
0.007
0.44–1.19
0.21
0.74–1.28 0.66–1.24 0.65–1.82 0.39–2.03
0.83 0.54 0.75 0.79 0.78
1 0.55 0.53 0.85 1.39 0.77 0.105 0.031
1 0.83 0.72 1.06 1.52 2.07 0.200 0.057
1 0.67 0.41 0.63 1.33 1.02 0.149 0.043
Municipality-level variables Adjustedb
% university
s2U r % divorced
s2U r % without religion
s2U r % non-Czech nationality
s2U r % Unemployed
s2U r Persons/household
s2U r
Adjustedb
OR
95%CI
0.98 0.099 0.029 1.03 0.103 0.030 1.00 0.101 0.030 1.03 0.093 0.028 1.03 0.074 0.022 0.68 0.101 0.030
0.95–1.01
0.16
0.99–1.07
0.16
0.99–1.01
0.14
1.00–1.06
0.04
1.01–1.06
0.003
0.45–1.03
0.07
0.84–1.32 0.88–1.47 0.78–1.84 0.43–1.70
0.64 0.33 0.4 0.66 0.42
Community size o 2,000 2,000–19,999 20,000–99,999 100,000–499,999 1,000,000 + P for linear trend of OR
1 1.06 1.14 1.20 0.86
s2U r
0.100 0.029
p
Variance components: Level 2 variance s2U. Intra-class correlation r. a b
Mutually adjusted for all individual-level variables in the table. Adjusted for all individual-level variables.
Adjustedb
OR
95%CI
0.99 0.199 0.057 1.02 0.199 0.057 1.00 0.198 0.057 1.03 0.191 0.055 1.03 0.186 0.053 0.77 0.197 0.056
0.96–1.02
0.49
0.97–1.06
0.48
0.99–1.01
0.24
0.99–1.06
0.11
0.99–1.05
0.06
0.49–1.21
0.25
0.76–1.25 0.84–1.48 0.70–2.00 0.37–2.22
0.85 0.44 0.53 0.82 0.48
1 0.98 1.12 1.18 0.90 0.195 0.056
p
1 0.97 0.91 1.09 0.89 0.144 0.042
220
´ et al. / Health & Place 17 (2011) 215–221 J. Spilkova
only partly explained by municipality-level characteristics; however it has been reduced particularly when unemployment rate in the municipality was taken into consideration.
4. Discussion The results of this analysis show that men are significantly more likely to be smokers, which is slightly in contrast with the trends described in the introduction stressing the increasing smoking among young females showing a certain lagging of the Czech Republic behind the behavioural trends in other countries (Graham, 2009; Myers and MacPherson, 2009, etc.), however this can be the result of a time lag between higher adolescent female smoking and higher adult female smoking. The analysis confirmed that current smoking is the most common among young people. Education is strongly inversely related to all three smoking outcomes (as reported also by e.g. Bobak et al., 1997; Dragano et al., 2007). Smoking was also more reported by divorced and unemployed individuals. These results also confirm previous results (Dzu´rova´ et al., 2008) in a similar study of mental disorder and substance abuse showing mainly employment and marital status as highly significant indicators of future risk behaviours (nevertheless, the relation between marital status and smoking in particular has not been previously extensively studied). While the association between small-area level variables and smoking were limited, it is still possible to say that smoking is more common in areas with higher unemployment and with higher proportion of people with non-Czech nationality. The unemployment has been associated with smoking both at the individual and the aggregated level, suggesting that smoking represents a strategy to cope with stress caused by the individual economic situation and unsatisfactory social environment. There are several possible reasons for the association between smoking and proportion of non-Czech nationals in the municipalities. The effect might be compositional because many non-Czech nationals come from the post-Soviet countries or south-east Asia, the regions with high prevalence of smoking. It may also be contextual effect suggesting that the proportion of non-Czech nationals acts as another proxy measure for overall social situation of the municipality. We can also hypothesize that higher smoking in unemployed people is the result of the recent changes in the anti-tobacco legislative. Smoking is prohibited in the work place and also in most of the public spaces, therefore the employed people have nowadays less opportunity to smoke than those who are without a job and it is possible that presence of smoke-free workplaces might affect smoking behaviour (Fichtenberg and Glanz, 2002). While it is possible to say that multilevel design is great advantage of this project, the study has also few limitations. The main limitation is cross-sectional design of the study not allowing to make causal inference on the association between socio-demographic indicators used in the project and smoking outcomes. While the response rate was around 68% and it is likely that proportion of individuals from low socioeconomic groups and with more risky health behaviours were not included, we believe that the response rate is relatively high compared to other contemporary studies. We, however, believe that interpretation of the results must be cautious in terms of generalizing for the whole population of the country. Finally, we were able to use only whole municipalities as second-level area data and the size of municipalities differs substantially from small towns or villages to capital Prague. While we would be able to get second level data for smaller units (such as electoral wards) it was not possible to do linkage between individual survey data and area-based data on smaller than municipality level (the individual dataset is anonymised and the only information available was municipality code). Neighbourhoods, rather than municipalities, might be more
important area-level units influencing smoking behaviours of individuals but this study did not allow to test this possibility. The popularity of smoking among young people was confirmed by this study. There is an urgent need of an effective tobacco control strategy among young adults. This must be focused on the social environment. The evidence shows that interpersonal peer influence and role modelling are significant determinants of smoking among adolescents (Griesler and Kandel, 1998). Among others alcohol drinking and lack of interest in educational process remain important influential factors affecting adolescent smoking (Pinilla et al., 2002). 5. Policy implications Although the prevalence of smoking and the average number of cigarettes per single person per annum in the Czech Republic are among the highest within the European framework, the attention devoted to this issue is not sufficient. This is probably the reflection of perception of smoking by the whole society seeing smoking as socially acceptable. Knowledge of health effects of smoking is increasing among the Czech society. Part of the society starts to view smoking as a serious danger to health as well as a socially unacceptable behaviour, but a long-term trend showing decrease in smoking prevalence among population has not been observed yet (Sovinova´ et al., 2010). The currently existing anti-smoking measures can be considered as insufficient. Despite the fact that the Czech Republic is closing gap within the EU countries in the levels of economic indicators, and successfully continues its transformation process, it is still belonging to countries seriously lagging behind the trends of developed world in the area of public health policies (and mainly those concerning risk behaviours). The Czech Republic has made a commitment to meet one of the goals of the WHO Health 21 project (WHO, 1998), which is to reduce undesirable effects of alcohol, drugs and tobacco by 2015 (goal 12). Government decision no. 1046 from October 2002, related to this project, started a number of activities to meet this goal. These were, among others, efficient development of legal measures, changes in tax policy, prohibition of advertising, protection of non-smokers against passive smoking, prevention support, availability of addiction treatment, expert advice and implementation of the European action plan ‘‘Europe without Tobacco’’. The results of this paper give the evidence that particularly socially disadvantaged groups must be targeted in any anti-smoking policies as these are groups with the highest risk of smoking behaviour. This idea corresponds with the conclusions of Graham (2009, p. 15) who claims that ‘‘policy synergies are important, with equity-oriented tobacco control policies both supporting and supported by equityoriented social and economic policies’’.
Acknowledgements This work was supported by the Fogarty International Centre (Finance and Delivery of Mental Health Services in Central and Eastern Europe, NIH Research Grant D43 TW005810) and the Ministry of Education, Youth and Sport of the Czech Republic (MSM 0021620831). References Bartley, M., Fitzpatrick, R., Firth, D., Marmot, M., 2000. Social distribution of cardiovascular disease risk factors: change among men in England 1984–1993. J. Epidemiol. Community Health 54, 806–814. Bobak, M., Skodova, Z., Pisa, Z., Poledne, R., Marmot, M., 1997. Political changes and trends in cardiovascular risk factors in the Czech Republic, 1985–92. J. Epidemiol. Community Health 51, 272–277.
´ et al. / Health & Place 17 (2011) 215–221 J. Spilkova
Borrell, C., Rue, M., Pasarin, I., Rohlfs, I., Ferrando, J., Fernandez, E., 2000. Trends in social class inequalities in health status, health-related behaviors, and health services utilization in a southern European urban area (1983–1994). Prev. Med. 31, 691–701. Chaix, B., Guilbert, P., Chauvin, P., 2004. A multilevel analysis of tobacco use and tobacco consumption levels in France: are there any combination risk groups? Eur. J. Public Health 14 186–190. Cummins, S., Curtis, S., Diez-Roux, A., Macintyre, S., 2007. Understanding and representing ‘‘place’’ in health research: a relational approach. Social Sci. Med. 65, 1825–1838 Dragano, N., Bobak, M., Wege, N., Peasey, A., Verde, P.E., Kubinova, R., et al., 2007. Diez-Roux, A.V., 2003. Residential environments and cardiovascular risk. J. Urban Health 80 (4), 569–589. Dragano, N., Bobak, M., Wege, N., Persey, A., Verde, P.E., Kubinova, R., et al., 2007. Neighbourhood socioeconomic status and cardiovascular risk factors: a multilevel analysis of nine cities in the Czech Republic and Germany. BMC Public Health 7, 255. Duncan, C., Jones, K., Moon, G., 1993. Do places matter? A multi-level analysis of regional variations in health-related behaviour in Britain. Soc. Sci. Med. 37, 725–733. Duncan, C., Jones, K., Moon, G., 1996. Health-related behaviour in context: a multilevel modelling approach. Soc. Sci. Med. 42, 817–830. Dzu´rova´, D., Brazˇinova´, A., Dragomirecka´, E., 2008. Mental health status of the Czech and Slovak populations. In: Scheffler, R.M., Potu˚cˇek, M. (Eds.), Mental health care reform in the Czech and Slovak Republice, 1989 to the Present. Karolinum Press, Prague, pp. 39–80. Dzu´rova´, D., Spilkova´, J., Pikhart, H., 2010. Social inequalities in alcohol consumption in the Czech Republic: a multilevel analysis. Health and Place 16, 590–597. EMCDDA, 2002. Handbook for surveys on drug use among the general population. Final project report. EMCDDA, Lisabon. Fichtenberg, C.M., Glanz, S.A., 2002. Effect of smoke-free workplaces on smoking behaviour: systematic review. BMJ 325, 188. Fukuda, Y., Nakamura, K., Takano, T., 2005. Accumulation of health risk behaviours is associated with lower socio-economic status and women’s urban residence: a multilevel analysis in Japan. BMC Public Health 53, 5. Griesler, P.C., Kandel, D.B., 1998. Ethnic differences in correlates of adolescent cigarette smoking. J. Adolescent Health 23, 167–180. Giskes, K., Kunst, A.E., Benach, J., Borrell, C., Costa, G., Dahl, E., et al., 2005. Trends in smoking behaviour between 1985 and 2000 in nine European countries by education. J. Epidemiol. Community Health 59, 395–401. Graham, H., 2009. Women and smoking: understanding socioeconomic influences. Drug Alcohol Depend. 104S, 11–16. Helmert, U., Borgers, D., Bammann, K., 2001. Social determinants of smoking behavior in Germany: results of a 1995 micro-census (in German). Sozial und ¨ Praventivmedizin 46, 172–181. Karvonen, S., Rimpela, A., 1996. Socio-regional context as a determinant of adolescents’ health behaviour in Finland. Soc. Sci. Med. 43, 1467–1474. Lahelma, E., Rahkonen, O., Berg, M.A., et al., 1997. Changes in health status and health behavior among Finnish adults 1978–1993. Scand. J. Work Environ. Health 23 (Suppl. 3), 85–90. Macintyre, S., Maciver, S., Sooman, A., 1993. Area, class and health: should we be focusing on places or people? Journal of Social Policy 22, 213–234.
221
Mackenbach, J.P., Stirbu, I., Roskam, A.J., Schaap, M.M., Menvielle, G., Leinsalu, M., Kunst, A.E., 2008. European Union Working Group on socioeconomic inequalities in health. Socioeconomic inequalities in health in 22 European countries. N. Engl. J. Med. 358, 2468–2481. Monden, C.W.S., van Lenthe, F.J., Mackenbach, J.P., 2006. A simultaneous analysis of neighbourhood and childhood socio-economic environment with self-assessed health and healthrelated behaviours. Health Place 12, 394–403. Myers, M.G., MacPherson, L., 2009. Coping with temptations and adolescent smoking cessation: an initial investigation. Nicotine Tobacco Res. 11, 940–944. Osler, M., Gerdes, L.U., Davidsen, M., et al., 2000. Socioeconomic status and trends in risk factors for cardiovascular diseases in the Danish MONICA population, 1982–1992. J. Epidemiol. Community Health 54, 108–113. Picket, K.E., Pearl, M., 2001. Multilevel analyses of neighbourhood socio-economic context and health outcomes: a critical review. J. Epidemiol. Community Health 55, 111–122. Pinilla, J., Gonza´les, B., Barber, P., Santana, Y., 2002. Smoking in young adolescents: an approach with multilevel discrete choice models. J. Epidemiol. Community Health 56, 227–232. Schaap, M.M., Kunst, A.E., Leinsalu, M., Regidor, E., Ekholm, O., Dzurova, D., et al., 2008. Effect of nationwide tobacco control policies on smoking cessation in high and low educated groups in 18 European countries. Tobacco Control 17, 248–255. Skodova´, Z., Cı´fkova´, R., Ada´mkova´, V., Duskova´, A., Hauserova´, G., Hejl, Z., et al., 2000. Development of smoking habits in the population of the Czech Republic from ˇ ˇ ´ ch 139, 143–147. 1985 to 1997/98. Casopis Le´kaˇru˚ Cesky Smith, G.D., Hart, C., Watt, G., Hole, D., Hawthorne, V., 1998. Individual social class, area-based deprivation, cardiovascular disease risk factors, and mortality: the Renfrew and Paisley Study. J. Epidemiol. Community Health 52, 399–405. Sovinova´, H., Cse´my, L., 2004. Smoking behaviour of Czech adolescents: results of the Global Youth Tobacco Survey in the Czech Republic. Central Eur. J. Public Health 12, 26–31. Sovinova´, H., Cse´my, L., Procha´zka, B., Kottnauerova´, S., 2008a. Smoking-attributable mortality in the Czech Republic. J. Public Health 16, 37–42. ˇ Sovinova´, H., Sadı´lek, P., Cse´my, L., 2008b. Vy´voj prevalence kuˇra´ctvı´ v populaci CR, ˇ k problematice kouˇrenı´ (obdobı´ 1997–2007). Sta´tnı´ na´zory a postoje obcˇanu˚ CR zdravotnı´ u´stav, Prague. Sovinova´, H., Sadı´lek, P., Cse´my, L., 2010. Vy´voj prevalence kuˇra´ctvı´ v dospˇele´ ˇ ˇ k problematice kouˇrenı´ (obdobı´ populaci CR, na´zory a postoje obcˇanu˚ CR 1997–2009). Sta´tnı´ zdravotnı´ u´stav, Prague. Twigg, L., Moon, G., 2002. Predicting small area health-related behavior: a comparison of multilevel synthetic estimation and local survey data. Soc. Sci. Med. 54, 931–937. Twigg, L., Moon, G., Jones, K., 2000. Predicting small-area health-related behaviour: a comparison of smoking and drinking indicators. Soc. Sci. Med. 50, 1109–1120. van Lenthe, F.J., Mackenbach, J.P., 2006. Neighbourhood and individual socioeconomic inequalities in smoking: the role of physical neighbourhood stressors. J. Epidemiol. Community Health 60, 699–705. WHO Regional Office for Europe. Health21., 1998. An introduction to the health for all policy framework for the WHO European Region. WHO, Copenhagen. WHO, 2004. The Tobacco Atlas. WHO, Copenhagen.