Changes in Attitudes toward Tobacco Smoking and Factors Associated with Quitting in 9-Year Observation of PURE Poland Cohort Study
1
Department of Population Health, Wroclaw Medical University, Bujwida St. 44, 50-345 Wrocław, Poland
2
Calisia University, 62-800 Kalisz, Poland
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(11), 6564; https://doi.org/10.3390/ijerph19116564
Submission received: 21 April 2022
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Revised: 18 May 2022
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Accepted: 23 May 2022
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Published: 27 May 2022
(This article belongs to the Special Issue Community and Family-Focused Public Health and Sustainable Development: 2nd Edition)
Abstract
:(1) Background: This study aims to examine changes in tobacco smoking prevalence in the PURE Poland cohort study over the 9-year follow-up period. Moreover, it attempts to identify socio-demographic factors that affect changes in attitudes towards tobacco smoking. (2) Methods: The PURE Poland cohort study—baseline was performed in 2007–2010 and covered 2036 participants, including urban (59.4%) and rural (40.6%) residents of Lower Silesia, Poland. The following study reports the results of 1690 participants who took part in both the baseline (2007–2010) study and 9-year follow-up (2016–2019). (3) Results: There was a 3.5% decrease in current smokers during the analyzed period (from 20.2% at the baseline study to 16.7% in the 9-year follow-up). Living in rural area increased the likelihood of being a current smoker by more than 1.5-fold (OR = 1.65 CI = 1.26–2.14) and decreased the likelihood of being a former smoker (OR = 0.70 CI = 0.57–0.86). In the 9-year follow-up period, more women were current smokers than men (17.2% vs. 16.0%) and women had lower chances of being former smokers than men (OR = 0.77 CI = 0.62–0.95). People with a primary education had 1.5-fold higher likelihood of being a current smoker (OR = 1.45 CI = 1.03–2.05). Nearly 11% significant increase in the percentage of current smokers was observed in the oldest age group (1.9% in the baseline study vs. 12.6% in the follow-up period). (4) Conclusions: The results obtained during 9 years of observation indicate the necessity of intensifying anti-tobacco programs especially targeting women, elderly population, people with lower level of education, rural residents, and the unemployed.
1. Introduction
Tobacco smoking is one of the major modifiable predictors of premature mortality. According to the WHO, currently 1.3 billion people in the world smoke tobacco. Tobacco smoking causes more than eight million deaths each year. The reason for 7.2 million of these deaths is direct tobacco use, while approximately 1.2 million deaths are the result of passive smoking [1]. Globally, the WHO European Region has the highest adult smoking prevalence rate (28%), including one of the highest prevalence rates in women (19%) [1].
Since the 1990s, there has been a steady decline in the proportion of smokers in Poland. With the introduction of strong regulations in the 1990s, Poland was named a tobacco-control leader in Europe [2]. Over the period from 1990–1994, 51% of men and 25% of women smoked tobacco daily [3]. The WHO global report on trends in the prevalence of tobacco use from 2000–2025 estimates that the age-standardized proportion of current cigarette smokers in 2018 in Poland was 24.3% of all adults and 20.2% of adult women. On the contrary, in Iceland, which is a European country with the lowest percentage of current smokers, 11.0% of all adults and 11.4% of adult women smoked cigarettes [4]. Tobacco smoking remains a major public health issue in Poland, resulting in disability, reduced economic development, and premature mortality. According to the European Commission, approximately 50% of those who smoke tobacco die prematurely, resulting in an average loss of 14 life years [5]. Samet and Buran estimated that approximately 80,000 deaths in Poland were linked to tobacco smoking in 2017 [6]. According to a study on liability and the health costs of smoking, the estimated premature mortality costs in 2009 in Poland were EUR 56,183 million or 10.33% of GDP (gross domestic product) [7]. However, smoking cessation—especially before the age of 40—may dramatically reduce the risk of death and greatly improve quality of life. Therefore, it is critical to encourage all current smokers to quit smoking in order to reduce the disease burden and tobacco smoking-related mortality [8].
As tobacco smoking attitudes vary by age, sex, place of residence (urban–rural), and level of education and income [8,9,10,11,12,13,14,15], monitoring the tobacco smoking prevalence and initiating research concerning the determinants and consequences of tobacco smoking are essential for the development and implementation of effective health policy programs resulting in the reduction of tobacco smoking rates [9].
The PURE (Prospective Urban Rural Epidemiology) Poland study cohort, which was established in 2007–2010, has been the basis for unique nationwide studies concerning tobacco smoking patterns in urban (Wroclaw) and rural (Wroclaw area) populations. Our previous analyses revealed that tobacco smoking prevalence in the PURE Poland population only varies by age, place of residence, and level of education [15,16]. This study aims to examine changes in tobacco smoking prevalence in the PURE Poland cohort study over the 9-year follow-up period. Moreover, it attempts to identify socio-demographic factors that affect changes in attitudes towards tobacco smoking.
2. Materials and Methods
The PURE study includes 225,000 participants from 27 high-, middle-, and low-income countries [17]. The PURE Poland cohort study was performed in 2007–2010 and covered 2036 participants (1277 women and 758 men; age: 30–85 years), including urban (59.4%) and rural (40.6%) residents of Lower Silesia, Poland. The recruitment of cohort volunteers was announced in the Polish media (TV, radio, local press). Participants were selected to obtain a sample of the community that is as representative as possible [18,19]. All participants were assessed according to the PURE project protocol, which was described in detail elsewhere [18,19]. Every visit in the study center included a questionnaire study (individual health, family, household, food frequency, and international physical activity questionnaires), a blood draw, blood pressure measurement, and anthropometric measurements [18,19]. After the 9-year follow-up period, the re-contact rate was 84.2%. The following study reports the results of 1690 participants who took part in both the baseline study and 9-year follow-up (2016–2019). The flowchart diagram shows different stages of recruiting the final study population (Figure 1). Participants who died/resigned from the study between baseline and follow-up and who were not reached by phone were excluded from this analysis. If participants lacked any data regarding attitudes towards tobacco smoking, they were also excluded from the analysis.
More than half of the participants were women, 64.8%, and urban residents, 59.4%. Participants were divided into three birth-year cohort: those born before 1940 (aged 67–85), 1940–1960 (aged 47–69) and 1961–1979 (aged 30–48). During the period covered by this study, the median age increased by 9 years and 75% of respondents were aged under 70. Detailed characteristics of the study population in the baseline study and after the 9-year follow-up are shown in Table 1.
The tobacco smoking history was assessed both in the baseline study and after the 9-year follow-up. The participants selected one of three possible responses: “I used tobacco products in the past”; “I currently use tobacco products” or “I have never used tobacco products”. Worldwide, specific criteria for the tobacco smoking history were added to the 9-year follow-up. In the baseline study, regular use of tobacco products was defined as consumption of at least one tobacco product per day. In the 9-year follow-up, current smokers were defined as participants who have smoked at least 100 cigarettes in their lifetime and currently smoke cigarettes daily or on certain days (non-daily). In addition, in the 9-year follow-up, never-smokers were defined as participants who have never smoked or who have smoked fewer than 100 cigarettes in their lifetime. Moreover, for the purpose of detailed analyses, 410 individuals who were former smokers on the first examination and on the final (ninth) examination were identified in the group of former smokers as “successful quitters”, whereas 84 individuals who were current smokers on the first examination and former smokers on the second examination were identified as “quitters”. The questionnaire regarding tobacco smoking referred to traditional tobacco products (cigarettes).
The data were analyzed using the chi-square test and logistic regression test, whereas the smoking status during the follow-up period was analyzed by sex, birth cohort, place of residence, and level of education. In detailed analyses, the following self-reported health problems were considered in the baseline study: smoking-related factors (such as cough for at least 2 weeks, wheezing or whistling in the chest, early morning cough with chest tightness), respiratory diseases (including chronic obstructive pulmonary disease (COPD), asthma, tuberculosis (TB)), cardiovascular diseases (CVD) (including cardiac infarction, coronary artery disease, heart failure, hypertension, and other heart diseases), and stroke. Furthermore, pack-years, presence of diabetes and hypertension, body weight, Alternate Healthy Eating index (AHEI) score, physical activity, and employment status were included in the analyses. Pack-years were calculated by multiplying the number of packs of cigarettes smoked per day by the number of years the person has smoked. The diabetes group consisted of individuals whose fasting plasma glucose was 126 mg/dL (7.0 mmol/L) or higher or those who self-reported diabetes diagnosis and diabetic treatment. The hypertension group consisted of individuals whose the average of three measurements met the ESC criteria (systolic blood pressure ≥ 140 and/or diastolic blood pressure ≥ 90 mmHg) or those who self-reported hypertension diagnosis and treatment. Patients were advised to sit quietly and rest for 5 min before blood pressure measurement. The appropriate cuff size was selected. The measurements were separated by 5-min breaks. Body mass was assessed using the body mass index (BMI). The participants were divided into four groups according to their BMI values: underweight (BMI < 18.5 kg/m2), normal body weight (BMI 18.5−24.99 kg/m2), overweight (BMI 25.00–29.99 kg/m2), and obesity (BMI ≥ 30.00 kg/m2). AHEI-2010 score was calculated according to methodology that was described by Chiuve et al. [20,21]. Physical activity was assessed using the International Questionnaire of Physical Activity (IPAQ)—long version [22].
The variables that were associated with current smoking at a significance level of p-value < 0.1 in the univariate analysis were included in multivariate modeling using the backward conditional regression method, where a two-sided p-value < 0.05 was considered statistically significant. The statistical analysis was performed using the STATISTICA software version 13.3 (TIBCO. Software Inc., Palo Alto, CA, USA)). The multivariate logistic regression analysis of the smoking status was performed using variables with p-value less than 0.05 in the univariate analysis. A backwards-stepwise method was applied using variables retained in the model if their logistic p-value likelihood ratio was less than 0.05. The combination of independent variables that gave the best explanation of the outcome (using the R2 statistics) was adopted.
Each participant gave informed consent to participate in the study. All human studies were reviewed by the appropriate bioethics committee, and they were conducted in accordance with the ethical standards contained in a relevant version of the 1964 Declaration of Helsinki (Positive opinion of the Bioethics Committee of the Wroclaw Medical University No. KB-443/2006).
3. Results
There was a statistically significant, 3.5% decrease in current smokers during the period under analysis (from 20.2% in the baseline study to 16.7% in the 9-year follow-up period). In the baseline study, there were 20.2% current smokers, 31.5% former smokers, and 48.3% of never-smokers. After the 9-year follow-up period, the percentage of current smokers decreased to 16.7%, former smokers increased to 45.4%, and never-smokers decreased slightly to 47.9% (Table 2). Participants whose pack-years were ≥3.8 had over 3-fold higher risk (odds ratio (OR) = 3.36; confidence interval (CI) 2.59–4.40), and those who declared tobacco smoking in the baseline study were 75 times more likely to be current smokers in the 9-year follow-up period (OR = 75.2 CI = 51.5–110) (Table 3). Moreover, the likelihood was higher in participants who, in the baseline study, declared early morning cough with chest tightness, wheezing, or whistling in the chest or cough for at least 2 weeks (Table 3, Figure 2). In contrast, former smokers at the baseline study and the employed had a higher likelihood of being former smokers (Table 4, Figure 3).
Attitudes towards tobacco smoking were statistically significantly differentiated by sex, birth cohort, place of residence, and level of education (Table 2).
There was a statistically significant, 6.0%, decrease in the percentage of male current smokers from 22.0% in the baseline study to 16.0% in the follow-up period. Moreover, it should be noted that there was a statistically significant—8.4%—increase in the percentage of never smokers, resulting from the change in the definition of current smokers. This means that 50 male participants who declared in the baseline study to be never smokers smoked less than 100 cigarettes in their lifetime. For women, there was non-significant decrease in smoking prevalence (from 19.2% in the baseline study to 17.2% in the follow-up period). However, there was a significant 7.3% increase in the percentage of former smokers and a decrease in the percentage of never smokers by 5.3% (Table 2). This means that 58 women started tobacco smoking during the period under analysis and smoked at least 100 cigarettes and/or continued smoking. However, sex differences were not statistically significant (p >0.05). Sex was not a significant differentiator of being a current smoker (OR = 1.09 CI = 0.60–1.99) (Table 3). On the other hand, the 9-year follow-up revealed that being female reduces the likelihood of being a former smoker (OR = 0.77 CI = 0.62–0.95) (Table 4). In the multivariate regression analysis, sex was a non-significant factor of being a former smoker (Figure 3).
Age is a significant differentiator of smoking patterns. In the baseline study, there was an increase in the percentage of current smokers as the age of the participants decreased. The highest percentage of current smokers (23.9%) was observed in the youngest individuals who were born in 1961–1979. In contrast, after the 9-year follow-up period, current smoking was the most prevalent in those born between 1940–1960 (17.3%). In terms of those who were born in 1961–1979, there was a significant 7.7% decrease in the percentage of current smokers (Table 2). Despite this, age was found to be a non-significant predictor of being a current smoker (OR = 0.70 CI = 0.39–1.28 in <1940 birth cohort and OR = 1.14 CI = 0.87–1.51 in 1940–1960 birth cohort) (Table 3). In contrast, those born in 1940–1960 are nearly one-third more likely to be a former smoker compared to others (OR = 1.27 CI 1.02–1.57) (Table 4).
A very disturbing, nearly 11% statistically significant increase in the percentage of current smokers was observed in the oldest age group (1.9% in the baseline study vs. 12.6% in the follow-up period). Because of the low size of the group of participants who were born before 1940, these results should be interpreted with caution (Table 2).
The percentage of current smokers was higher in rural than urban residents (25.1% vs. 16.8% in the baseline study and 20.7 vs. 14.0% in the follow-up period, respectively). Only for rural residents was there a statistically significant increase in the percentage of former smokers (24.8% in the baseline study vs. 30.6% in the follow-up period) (Table 2). Living in rural area independently increased the likelihood of being a current smoker by more than 1.5 times (OR = 1.65 CI = 1.26–2.14) (Figure 2). In the univariate logistic regression analysis, rural residency decreased the likelihood of being a former smoker (OR = 0.70 CI = 0.57–0.86) (Table 4).
The lowest percentage of current smokers was found in the higher education group (15.1% in the baseline study vs. 12.6 in the follow-up period). Excluding those with a primary education, in whom the percentage of current smokers increased from 17.7% in the baseline study to 21.6% in the follow-up period, there was a decrease in current smokers in all other groups (Table 2). The level of education significantly affected the likelihood of being a current smoker or a former smoker. People with a primary education had nearly 1.5 times the likelihood of being current smokers (OR = 1.45 CI = 1.03–2.05) (Table 3). On the other hand, secondary education was a factor that increased the likelihood of being a former smoker (OR = 1.28 CI 1.04–1.56), whereas vocational education was a factor that decreased it (OR = 0.73 CI 0.55–0.97) (Table 4).
In the following section, the factors that affect successful smoking cessation or quitting smoking between baseline and follow-up are analyzed in detail.
Baseline demographics for 459 former smokers on the first examination according to smoking status on the final examination conducted 9 years later are shown in Table 5. In general, participants who remained former smokers in the 9-year follow-up period (successful quitters) had a higher AHEI score, a lower MET (multiples of the resting metabolic rate) score for walking from place to place, a lower MET score for transportation-related physical activity, and they walked less during the day in their leisure time.
4. Discussion
This study examines changes in tobacco smoking prevalence in the PURE Poland cohort study over the 9-year follow-up period. Moreover, it attempts to identify socio-demographic factors that affect changes in attitudes towards tobacco smoking. The results revealed a small but statistically significant decrease in the percentage of current smokers. There has been a systematic decrease in the percentage of current smokers in Poland since the 1990s [15,23,24,25]. The decrease was more observed in men; the youngest people, who were born in 1961–1979; and those with a vocational education. The reports published by the European Commission reveal that the percentage of current smokers across the EU decreased by 6.0% in 2006–2017 and by 2.0% in 2017–2020 [24,25]. In our cohort, the decrease was 3.5% in the 9-year follow-up period. Conforming to our results, other studies observed a similar decrease in tobacco smoking prevalence in Poland [14,26]. Several factors could have contributed to the overall decrease in smoking prevalence, including introduction of anti-tobacco governmental regulations [23] as well as increasing availability of cytisine, which increases the likelihood of smoking cessation [27,28]. An analysis of data published under the “Actual problems and events” study conducted by the Public Opinion Research Centre reveals that 21.8% of general Polish population smoked cigarettes in 2019 [29]. This percentage is higher than in our population (16.7%). The lower percentage of current smokers in this study compared to the general Polish population study was already observed in the baseline study (2007–2010). According to Global Adult Tobacco Survey (GATS) data, 30.3% of Polish population smoked cigarettes in 2010, whereas 20.2% of our cohort did at the time [30]. These differences may be due to discrepancies in age groups of participants. The GATS study included participants aged 15 and older, while our cohort includes individuals aged 30 and older. Moreover, Pesce et al. [10] revealed that in the 2000s, the peak in smoking prevalence in Europe was particularly pronounced in people around the age of 30 and in Eastern Europe in those aged 26. The impact of age differences in the study groups may be confirmed by the fact that at the beginning of our follow-up, there was a decrease in smoking prevalence with the increase of age. The highest percentage of current smokers was found in the youngest participants, who were born in 1961–1979.
Sex was a factor that significantly differentiated tobacco smoking prevalence in this study. In the baseline study, the percentage of current smokers was higher in men compared to women (22.0% vs. 19.2%), while after the 9-year follow-up period, more women were current smokers (17.2% vs. 16.0%). Other studies of the Polish population reveal higher smoking prevalence in men over the entire follow-up period [13,14,29,30]. The higher smoking prevalence in women in our study may be partially explained by the fact that our population is generally older than Polish population. Pinkas et al., observed the highest smoking prevalence in women aged 30–39 and 50–59 years [31]. Moreover, after the 9-year follow-up period, women who participated in our study were less likely to be former smokers than men (OR = 0.77 CI = 0.62–0.95), which is consistent with the results of other Polish study [14] (women: OR = 0.76Cl = 0.55–1.07 in urban area in 2012 and OR = 0.50 Cl = 0.35–0.72 in rural area in 2012). Furthermore, women were less likely to quit smoking during the follow-up period. Higher decline in smoking prevalence in men versus women has been already observed in Europe [32]. It has been estimated that smoking-attributable mortality in women may surpass smoking-attributable mortality in men, which indicates that women should be targeted in anti-tobacco campaigns and interventions [32]. On the other hand, there was a positive—more than 7%—increase in the percentage of ex-smokers in women (from 25.9% to 33.2%). The highest decline in the percentage of current smokers in study participants was observed in the youngest birth cohort—1961–1979 (from 23.9% to 16.2%). However, the 1940–1960 birth cohort participants were most likely to be former smokers over the 9-year follow-up period, whereas those aged under 61 were three times more likely to quit smoking during the follow-up period. These results correspond to the EU report’s results [25] which revealed that 22.0% of 40–54-year-olds and 30% of 55-year-olds and older quit smoking.
In this study, rural residents were more likely to be current smokers both in the baseline study and during the 9-year follow-up period; however, this percentage decreased over the follow-up period in both areas (from 25.1% to 20.7% in rural area and from 16.8% to 14.0% in urban area). Living in rural area increased the likelihood of being a current smoker, decreased the likelihood of being a former smoker, and decreased the likelihood of successful smoking cessation after the 9-year follow-up period by more than two times. Our results correspond to those obtained by Sozańska et al.’s [14] study conducted in the same region of Poland. On the other hand, studies including the general Polish population in the same period of time as our follow-up reveal that urban residents are more frequently current smokers than rural residents [29,30]. The differences observed in terms of tobacco smoking attitudes between urban and rural residents may indicate regional variations within the Polish population. Other global studies also found a higher percentage of current smokers in rural residents [33,34].
According to our results, tobacco smoking attitudes vary by the level of education. The percentage of current smokers both in the baseline study and over the 9-year follow-up period decreased as the level of education increased. A secondary education increased the likelihood of being a former smoker nearly 1.3-fold, while a vocational education decreased this likelihood by nearly one-third. Moreover, it should be noted that during the follow-up period, participants with a primary education were least likely to quit smoking. The obtained results are consistent with the results of studies conducted in the general Polish population and other studies according to which smoking prevalence was higher in individuals with a lower education [24,25,35,36]. In both 2010 and 2019, individuals with a higher education were the smallest group of current smokers in Poland (24.5% and 8.3%, respectively) [29,30]. Women with a secondary education had two times higher likelihood of being former smokers (OR = 2.01 CI = 1.15–3.32), whereas men with a higher education had more than four times higher likelihood of being former smokers (OR = 4.22 CI = 1.67–10.66) [29].
In addition, we searched for other predictors that affect tobacco smoking attitudes in the study population over the 9-year follow-up period. The most significant factors that were associated with being current smoker, in addition to living in rural area, included a higher number of pack-years and a smoking-related factor, such as cough for at least 2 weeks. In contrast, factors that increased the likelihood of maintaining the status of a former smoker (successful quitter), in addition to living in urban area, were associated with fewer pack-years and amount of free time spent on walking (walking during leisure time >120 min per week and <90 min per day). Factors that increased the likelihood of smoking cessation during 9 years of observation in our cohort, in addition to male sex, were more pack-years, age <61 years, and being employed. The Polish population studies described by Kaleta at al. [13], as well as other studies, indicate a positive effect of being employed on tobacco smoking attitudes. According to other studies, unemployed men smoked cigarettes significantly more frequently on a daily basis than employed ones (OR = 1.8 CI = 1.4–2.4) [13,25,36,37]. In contrast, diagnosis of respiratory diseases, CVD, stroke, diabetes, or hypertension in the baseline study had no effect on smoking prevalence. An analogous lack of correlation was found by Tonnesen at al., following a 10-year follow-up of 12,283 participants in the Copenhagen General Population Study [38].
An interesting observation from our study concerns the increase in the prevalence of current smokers in the oldest age group. This birth cohort was small, and therefore, the conclusions should be interpreted with caution. This observation draws attention to the fact that elderly population should not be omitted in the anti-tobacco preventive programs. The elderly population may still believe that if they continued to smoke throughout their life, there is no point is quitting at their current age, whereas smoking cessation at any age decreases the risk of premature mortality and should be strongly encouraged [39,40]. Considering demographic changes towards the aging population, there is an urgent need to target the elderly with the anti-tobacco campaigns.
When relating the results of our study to the general population, some limitations must be taken into consideration, e.g., the lack of biochemical verification of non-smoker status. According to West et al.’s findings, this may contribute to approximately 4% lower rates of tobacco smoking prevalence [41]. On the other hand, this was a study that did not focus on tobacco smoking attitudes but on many other health parameters; the study participants did not have strong reasons to not declare their actual smoking status. Another factor is the limited size of the group of oldest participants (birth cohort <1940, n = 103) that limits drawing inferences. Due to the global PURE study design, the population <30 years of age was not analyzed. The analyses did not include the income level that is considered to be one of the factors that affect tobacco smoking attitudes [42]. However, the level of education (primary, vocational, secondary, higher) was analyzed in detail and professional activity was taken into consideration, which correlates to a large extent with the level of income earned in Poland. The questionnaires in the PURE study focused on traditional tobacco products (cigarettes) and lacked questions regarding the electronic cigarettes or vaping. As the use of these products gains popularity, it is one of the limitations of our study. Although we observe the changes in attitudes toward tobacco smoking, the study did not investigate the methods of smoking cessation (e.g., pharmacotherapy), so we cannot draw conclusions on the most successful methods.
On the other hand, the study strength is that it is a longitudinal cohort study. The application of identical methodology at each follow-up makes it possible to infer changes in tobacco smoking attitudes over the long-term follow-up of the same individuals at different stages of their life.
5. Conclusions
The results obtained during the 9-year follow-up indicate the necessity of intensifying anti-tobacco programs especially targeting women, less educated people, rural residents, the unemployed, and the elderly population. Preventive programs should also be directed towards former smokers to encourage them to maintain their status since our observations indicate that some participants relapsed over time.
Author Contributions
Conceptualization, K.P.-Z.; formal analysis, K.P.-Z.; methodology, K.P.-Z. and K.Z.; visualization, K.P.-Z.; writing—original draft, K.P.-Z.; writing—review and editing, P.P. and K.P.-Z. All authors have read and agreed to the published version of the manuscript.
Funding
The main PURE study and its components are funded by the Population Health Research Institute, the Canadian Institutes of Health Research, Heart and Stroke Foundation of Ontario, and through unrestricted grants from several pharmaceutical companies, Poland substudy: Polish Ministry of Science and Higher Education (grant no. 290/W-PURE/2008/0), Wroclaw Medical University. Additionally, presented research results, were conducted within the framework of the topic: “Analiza rozpowszechnienia czynników ryzyka chorób cywilizacyjnych ze szczególnym uwzględnieniem narażenia na dym tytoniowy w populacji dorosłych mieszkańców województwa dolnośląskiego oraz dzieci w wieku szkolnym” (Eng. “Analysis of the prevalence of risk factors of non-communicable diseases with particular emphasis on tobacco smoke exposure in the population of adult residents of the Lower Silesia Province and school-age children”) as per records in SIMPLE system number SUB. C. 300.21.009. We declare that funding body had no role in the design of the study; collection, analysis, or interpretation of data and no role in writing the manuscript in this section.
Institutional Review Board Statement
All human studies have been reviewed and approved by the Bioethics Committee of the Wroclaw Medical University (Positive opinion no. KB-443/2006) and have therefore been performed in accordance with the ethical standards laid down in an appropriate version of the 1964 Declaration of Helsinki.
Informed Consent Statement
Written and informed consent was obtained from all individual participants included in the study.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Acknowledgments
The authors of this article would like to express their gratitude to Witold Zatoński for his expertise and assistance throughout all aspects of this study.
Conflicts of Interest
The authors of hereby article declare no conflict of interest.
References
- WHO Regional Office for Europe. Tobacco—Overview. Available online: https://www.euro.who.int/en/health-topics/disease-prevention/tobacco (accessed on 14 December 2021).
- Ding, N.; Sang, Y.; Chen, J.; Ballew, S.H.; Kalbaugh, C.A.; Salameh, M.J.; Blaha, M.J.; Allison, M.; Heiss, G.; Selvin, E.; et al. Cigarette Smoking, Smoking Cessation, and Long-Term Risk of 3 Major Atherosclerotic Diseases. J. Am. Coll. Cardiol. 2019, 74, 498–507. [Google Scholar] [CrossRef] [PubMed]
- Zatoński, W.; Przewoźniak, K.G.J. Palenie Tytoniu w Polsce: Obraz Zjawiska, Nowe Trendy i Wyzwania. In Palenie Tytoniu: Aspekty Medyczne Psychiczne i Duchowe; Jaworski, R., Ed.; Płocki Instytut Wydawniczy: Płock, Poland, 2006; pp. 111–120. Available online: https://docplayer.pl/18211051-Poradnik-korzystania-z-programu-mendeley.html (accessed on 28 August 2021).
- WHO Global Report on Trends in Prevalence of Tobacco Use 2000–2025, Third Edition. Available online: https://www.who.int/publications/i/item/who-global-report-on-trends-in-prevalence-of-tobacco-use-2000-2025-third-edition (accessed on 25 August 2021).
- Attitudes of Europeans towards Tobacco and Electronic Cigarettes—Publications Office of the EU. Available online: https://op.europa.eu/en/publication-detail/-/publication/942834a3-678b-11eb-aeb5-01aa75ed71a1/language-en/format-PDF/source-search (accessed on 26 August 2021).
- Samet, J.; Buran, M. The burden of avoidable disease from air pollution: Implications for prevention. J. Health Inequalities 2020, 6, 2–6. [Google Scholar] [CrossRef]
- Jarvis, A.; Vincze, M.; Falconer, B.; Garde, A.; Geber, F.; Daynard, R. A Study on Liability and the Health Costs of Smoking, DG SANCO (2008/C6/046), Updated final report, London. 2012. Available online: www.ghkint.com (accessed on 27 August 2021).
- Jha, P.; Ramasundarahettige, C.; Landsman, V.; Rostron, B.; Thun, M.; Anderson, R.N.; McAfee, T.; Peto, R. 21st-Century Hazards of Smoking and Benefits of Cessation in the United States Abstract. N. Engl. J. Med. 2013, 368, 341–350. [Google Scholar] [CrossRef] [Green Version]
- Bednarski, B.; Goryński, P.; Łata, E.; Parchimowicz, T.; Przewoźniak, K.; Wojtyniak, B. The Current Status of the Tobacco Epidemic in Poland, Copenhagen. 2009. Available online: https://www.euro.who.int/__data/assets/pdf_file/0006/68064/E92470.pdf (accessed on 28 August 2021).
- Pesce, G.; Marcon, A.; Calciano, L.; Perret, J.L.; Abramson, M.J.; Bono, R.; Bousquet, J.; Fois, A.G.; Janson, C.; Jarvis, D.; et al. Time and age trends in smoking cessation in Europe. PLoS ONE 2019, 14, e0211976. [Google Scholar] [CrossRef] [Green Version]
- Giovino, G.A.; Mirza, S.A.; Samet, J.M.; Gupta, P.C.; Jarvis, M.J.; Bhala, N.; Peto, R.; Zatonski, W.; Hsia, J.; Morton, J.; et al. Tobacco use in 3 billion individuals from 16 countries: An analysis of nationally representative cross-sectional household surveys. Lancet 2012, 380, 668–679. [Google Scholar] [CrossRef]
- Asharani, P.V.; Seet, V.A.L.; Abdin, E.; Kumar, F.D.S.; Wang, P.; Roystonn, K.; Lee, Y.Y.; Cetty, L.; Teh, W.L.; Verma, S.; et al. Smoking and mental illness: Prevalence, patterns and correlates of smoking and smoking cessation among psychiatric patients. Int. J. Environ. Res. Public Health 2020, 17, 5571. [Google Scholar] [CrossRef]
- Kaleta, D.; Makowiec-Dabrowska, T.; Dziankowska-Zaborszczyk, E.; Fronczak, A. Prevalence and socio-demographic correlates of daily cigarette smoking in poland: Results from the global adult tobacco survey (2009–2010). Int. J. Occup. Med. Environ. Health 2012, 25, 126–136. [Google Scholar] [CrossRef]
- Sozańska, B.; Pearce, N.; Błaszczyk, M.; Boznański, A.; Cullinan, P. Changes in the prevalence of cigarette smoking and quitting smoking determinants in adult inhabitants of rural areas in Poland between 2003 and 2012. Pub. Health 2016, 141, 178–184. [Google Scholar] [CrossRef]
- Połtyn-Zaradna, K.; Zatońska, K.; Basiak, A.; Sozańska, B.; Gaweł-Dąbrowska, D.; Wołyniec, M.; Szuba, A.; Zatoński, W. Sociodemographic characteristic of changes in smoking patterns in rural and urban population of PURE Poland study: Findings from 6-year follow up. BMC Public Health 2019, 19, 6. [Google Scholar] [CrossRef]
- Połtyn-Zaradna, K.; Basiak, A.; Zatońska, K.; Wołyniec, M.; Dąbrowska, D.G.; Szuba, A.; Zatoński, W.A. Prevalence of tobacco smoking among participants of PURE Poland study. J. Health Inequalities 2016, 2, 42–47. [Google Scholar] [CrossRef]
- PURE. Population Health Research Institute of Canada. Available online: https://www.phri.ca/research/pure (accessed on 15 June 2021).
- Teo, K.; Chow, C.K.; Vaz, M.; Rangarajan, S.; Yusuf, S. The Prospective Urban Rural Epidemiology (PURE) study: Examining the impact of societal influences on chronic noncommunicable diseases in low-, middle-, and high-income countries. Am. Heart J. 2009, 158, 1–7. [Google Scholar] [CrossRef]
- Zatońska, K.; Zatoński, W.A.; Szuba, A. Prospective urban and rural epidemiology Poland—Study design. J. Health Inequalities 2016, 2, 136–141. [Google Scholar] [CrossRef]
- Chiuve, S.E.; Fung, T.T.; Rimm, E.B.; Hu, F.B.; McCullough, M.L.; Wang, M.; Stampfer, M.J.; Willett, W.C. Alternative Dietary Indices Both Strongly Predict Risk of Chronic Disease. J. Nutr. 2012, 142, 1009–1018. [Google Scholar] [CrossRef] [Green Version]
- Basiak-Rasała, A.; Połtyn-Zaradna, K.; Wołyniec, M.; Szuba, A.; Zatońska, K. Assessment of diet quality according to AHEI-2010 in the PURE Poland study. J. Health Inequalities 2019, 5, 174–179. [Google Scholar] [CrossRef]
- Craig, C.L.; Marshall, A.L.; Sjöström, M.; Bauman, A.E.; Booth, M.L.; Ainsworth, B.E.; Pratt, M.; Ekelund, U.L.; Yngve, A.; Sallis, J.F.; et al. International physical activity questionnaire: 12-Country reliability and validity. Med. Sci. Sports Exerc. 2003, 35, 1381–1395. [Google Scholar] [CrossRef] [Green Version]
- Zatoński, W.; Zatoński, M.; Janik-Koncewicz, K.; Połtyn-Zaradna, K.; Wijatkowska, K.; Marciniak, A. Hundred years of cigarette smoking in Poland: Three phases of the tobacco epidemic. J. Health Inequalities 2017, 3, 118–122. [Google Scholar] [CrossRef]
- European Commision. Special Eurobarometer 458. Attitudes of Europeans towards Tobacco and Electronic Cigarettes. 2017. Available online: https://data.europa.eu/data/datasets/s2146_87_1_458_eng?locale=en (accessed on 15 October 2021).
- European Commision. Special Eurobarometer 506. Attitudes of Europeans towards Tobacco and Electronic Cigarettes. 2021. Available online: https://data.europa.eu/data/datasets/s2240_506_eng?locale=en (accessed on 15 October 2021).
- Polakowska, M.; Kaleta, D.; Piotrowski, W.; Topór-Mdry, R.; Puch-Walczak, A.; Niklas, A.; Bielecki, W.; Kozakiewicz, K.; Pająk, A.; Tykarski, A.; et al. Tobacco smoking in Poland in the years from 2003 to 2014 Multi-centre National Population Health Examination Survey (WOBASZ). Pol. Arch. Intern. Med. 2017, 127, 91–99. [Google Scholar]
- Tutka, P.; Vinnikov, D.; Courtney, R.J.; Benowitz, N.L. Cytisine for nicotine addiction treatment: A review of pharmacology, therapeutics and an update of clinical trial evidence for smoking cessation. Addiction 2019, 114, 1951–1969. [Google Scholar] [CrossRef]
- Zatoński, W.; Zatoński, M. Cytisine versus nicotine for smoking cessation. N. Engl. J. Med. 2015, 372, 1072. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Janik-Koncewicz, K.; Zatoński, W.; Zatońska, K.; Stępnicka, Z.; Basiak-Rasała, A.; Zatoński, M.; Połtyn-Zaradna, K. Cigarette smoking in Poland in 2019: The continuing decline in smoking prevalence. J. Health Inequalities 2020, 6, 87–94. [Google Scholar] [CrossRef]
- World Health Organization. Global Adult Tobacco Survey Poland 2009–2010; WHO: Geneva, Switzerland, 2010. [Google Scholar]
- Pinkas, J.; Kaleta, D.; Zgliczyński, W.S.; Lusawa, A.; Wrześniewska-Wal, I.; Wierzba, W.; Gujski, M.; Jankowski, M. The Prevalence of Tobacco and E-Cigarette Use in Poland: A 2019 Nationwide Cross-Sectional Survey. Int. J. Environ. Res. Public Health 2019, 16, 4820. [Google Scholar] [CrossRef] [Green Version]
- Janssen, F.; El Gewily, S.; Bardoutsos, A. Smoking epidemic in Europe in the 21st century. Tob. Control 2021, 30, 523–529. [Google Scholar] [CrossRef] [PubMed]
- Doescher, M.P.; Jackson, J.E.; Jerant, A.; Hart, L.G. Prevalence and Trends in Smoking: A National Rural Study. J. Rural Health 2006, 22, 112–118. [Google Scholar] [CrossRef]
- Doogan, N.; Roberts, M.; Wewers, M.; Stanton, C.; Keith, D.; Gaalema, D.; Kurti, A.; Redner, R.; Cepeda-Benito, A.; Bunn, J.; et al. A growing geographic disparity: Rural and urban cigarette smoking trends in the United States. Prev. Med. 2017, 104, 79–85. [Google Scholar] [CrossRef]
- Schnohr, C.; Højbjerre, L.; Riegels, M.; Ledet, L.; Larsen, T.; Schultz-Larsen, K.; Petersen, L.; Prescott, E.; Grønbæk, M. Does educational level influence the effects of smoking, alcohol, physical activity, and obesity on mortality? A prospective population study. Scand. J. Public Health 2016, 32, 250–256. [Google Scholar] [CrossRef]
- Vogli, R.D.; Santinello, M. Unemployment and smoking: Does psychosocial stress matter? Tob. Control. 2005, 14, 389–395. [Google Scholar] [CrossRef] [Green Version]
- Barbeau, E.M.; Krieger, N.; Soobader, M.-J. Working Class Matters: Socioeconomic Disadvantage, Race/Ethnicity, Gender, and Smoking in NHIS 2000. Am. J. Public Health 2004, 94, 269. [Google Scholar] [CrossRef]
- Tonnesen, P.; Marott, J.L.; Nordestgaard, B.; Egil Bojesen, S.; Lange, P. Secular trends in smoking in relation to prevalent and incident smoking-related disease: A prospective populationbased study. Tob. Induc. Dis. 2019, 17, 72. [Google Scholar] [CrossRef]
- Gellert, C.; Schöttker, B.; Brenner, H. Smoking and all-cause mortality in older people: Systematic review and meta-analysis. Arch. Intern. Med. 2012, 172, 837–844. [Google Scholar] [CrossRef] [Green Version]
- Müezzinler, A.; Mons, U.; Gellert, C.; Schöttker, B.; Jansen, E.; Kee, F.; O’Doherty, M.G.; Kuulasmaa, K.; Freedman, N.D.; Abnet, C.C.; et al. Smoking and All-cause Mortality in Older Adults: Results from the CHANCES Consortium. Am. J. Prev. Med. 2015, 49, e53–e63. [Google Scholar] [CrossRef]
- West, R.; Zatonski, W.; Przewozniak, K.; Jarvis, M.J. Can We Trust National Smoking Prevalence Figures? Discrepancies between Biochemically Assessed and Self-Reported Smoking Rates in Three Countries. Cancer Epidemiol. Prev. Biomark. 2007, 16, 820–822. [Google Scholar] [CrossRef]
- McCarthy, M.; Siahpush, M.; Shaikh, R.A.; Sikora Kessler, A.; Tibbits, M. Social Disparities in Unaided Quit Attempts among Daily Current and Former Smokers: Results from the 2010–2011 Tobacco Use Supplement to the Current Population Survey. Nicotine Tob. Res. 2016, 18, 1705–1710. [Google Scholar] [CrossRef] [PubMed]
Figure 1.
Flow chart of the Pure Poland study participants (n = 2036) and the final study population (n = 1690).
Figure 1.
Flow chart of the Pure Poland study participants (n = 2036) and the final study population (n = 1690).
Figure 2.
Multivariable logistic regression analysis of predictors of current smoking after the 9-year follow-up period.
Figure 2.
Multivariable logistic regression analysis of predictors of current smoking after the 9-year follow-up period.
Figure 3.
Multivariable logistic regression analyses of predictors of former smoking after the 9-year follow-up period.
Figure 3.
Multivariable logistic regression analyses of predictors of former smoking after the 9-year follow-up period.
Figure 4.
Multivariable logistic regression analyses of predictors of successful smoking cessation after the 9-year follow-up period.
Figure 4.
Multivariable logistic regression analyses of predictors of successful smoking cessation after the 9-year follow-up period.
Figure 5.
Multivariable logistic regression analyses of predictors of smoking cessation after the 9-year follow-up period.
Figure 5.
Multivariable logistic regression analyses of predictors of smoking cessation after the 9-year follow-up period.
Table 1.
Characteristics of 1690 participants of the PURE Poland study.
| Sex | Total n = 1690 | Urban n = 1004 | Rural n = 686 | |||
|---|---|---|---|---|---|---|
| Man n (%) | 595 (35.2%) | 366 (36.5%) | 229 (33.4%) | |||
| Women n (%) | 1095 (64.8%) | 638 (63.5%) | 457 (66.6%) | |||
| Baseline 2007–2010 | 9-year study | Baseline 2007–2010 | 9-year study | Baseline 2007–2010 | 9-year study | |
| Man | ||||||
| Age: median (range) | 55 (45–61) | 64 (54–70) | 55 (47–61) | 64 (56–70) | 53 (45–61) | 62 (54–70) |
| Birth-year cohort | ||||||
| <1940 | 35 (5.9%) | 13 (3.6%) | 22 (9.6%) | |||
| 1940–1960 | 379 (63.7%) | 251 (68.6%) | 128 (55.9%) | |||
| 1961–1979 | 181 (30.4%) | 102 (27.8%) | 79 (34.5%) | |||
| Women | ||||||
| Age: median (range) | 55 (48–61) | 64 (57–70) | 55 (49–60) | 64 (58–69) | 53 (47–61) | 62 (56–70) |
| Birth-year cohort | ||||||
| <1940 | 68 (6.2%) | 21 (3.3%) | 47 (10.3%) | |||
| 1940–1960 | 757 (69.1%) | 478 (74.9%) | 279 (61.1%) | |||
| >1960 | 270 (24.7%) | 139 (21.8%) | 131 (28.7%) | |||
| Total | ||||||
| Age: median (Q1–Q3) | 55 (47–61) | 64 (56–70) | 55 (49–61) | 64 (58–70) | 53 (46–61) | 62 (55–70) |
| Birth-year cohort | ||||||
| <1940 | 103 (6.1%) | 34 (3.4%) | 69 (10.1%) | |||
| 1940–1960 | 1136 (67.2%) | 729 (72.6%) | 407 (59.3%) | |||
| >1960 | 451 (26.7%) | 241 (24.0%) | 210 (30.6%) | |||
| Education level | ||||||
| Primary | 232 (13.7%) | 34 (3.4%) | 198 (28.9%) | |||
| Vocational | 267 (15.8%) | 59 (5.9%) | 208 (30.3%) | |||
| Secondary | 675 (40,0%) | 448 (44.6%) | 227 (33.1%) | |||
| Higher | 516 (30.5%) | 463 (46.1%) | 53 (7.7%) | |||
Table 2.
General characteristics of attitudes towards tobacco smoking in 1690 participants of the PURE Poland study.
Table 2.
General characteristics of attitudes towards tobacco smoking in 1690 participants of the PURE Poland study.
| Characteristics | Ever Smokers | Never Smokers | p-Value | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Current Smokers | Former Smokers | Total | ||||||||||||||
| Baseline 2007–2010 | 9-Year Study | p-Value | Baseline 2007–2010 | 9-Year Study | p-Value | Baseline 2007–2010 | 9-Year Study | Baseline 2007–2010 | 9-Year Study | (a) | (b) | (c) | (d) | (e) | (f) | |
| Total n = 1690 % (95% Cl) | 341 20.2 (18.3–22.1) | 283 16.7 (15.0–18.5) | 0.010 | 532 31.5 (29.3–33.7) | 598 35.4 (33.1–37.7) | 0.018 | 873 51.7 (49.3–54.0) | 881 52.1 (49.7–54.5) | 817 48.3 (46.0–50.7) | 809 47.9 (45.5–50.3) | - | - | 0.010 | 0.810 | - | - |
| Sex | ||||||||||||||||
| Men n = 595 % (95% Cl) | 131 22.0 (18.7–25.3) | 95 16.0 (13.0–18.9) | 0.013 | 248 41.7 (37.7–45.6) | 234 39.3 (35.4–43.3) | 0.484 | 379 63.7 (59.8–67.6) | 329 55.3 (51.3–59.3) | 216 36.3 (32.4–40.2) | 266 44.7 (40.7–48.7) | <0.001 | <0.001 | 0.004 | 0.004 | 0.043 | 0.062 |
| Women n =1095 % (95% Cl) | 210 19.2 (16.8–21.5) | 188 17.2 (14.9–19.4) | 0.263 | 284 25.9 (23.3–28.5) | 364 33.2 (30.5–36.0) | 0.001 | 494 45.1 (42.2–48.1) | 552 50.4 (47.4–53.4) | 601 54.9 (51.9–57.8) | 543 49.6 (46.6–52.6) | <0.001 | 0.015 | ||||
| Birth-year cohort | ||||||||||||||||
| <1940 n = 103 % (95% Cl) | 2 1.9 (0.0–4.6) | 13 12.6 (6.2–19.0) | 0.005 | 31 30.1 (21.2–39.0) | 33 32.0 (23.0–41.1) | 0.764 | 33 32.0 (23.0–41.1) | 46 44.7 (35.1–54.3) | 70 68.0 (58.9–77.0) | 57 55.4 (45.7–64.9) | <0.001 | <0.001 | 0.009 | 0.086 | 0.004 | 0.018 |
| 1940–1960 n = 1136 % (95% Cl) | 231 20.3 (18.0–22.7) | 197 17.3 (15.1–19.5) | 0.090 | 391 34.4 (31.7–37.2) | 422 37.1 (34.3–40.0) | <0.001 | 622 54.8 (51.9–57.6) | 619 54.5 (51.6–57.4) | 514 45.2 (42.4–48.1) | 517 45.5 (42.6–48.4) | <0.001 | 0.902 | ||||
| 1961–1979 n = 451 % (95% Cl) | 108 23.9 (20.0–27.9) | 73 16.2 (12.8–19.6) | 0.008 | 108 23.9 (20.0–27.9) | 143 31.7 (27.4–36.0) | <0.001 | 216 47.9 (43.3–52.5) | 216 47.9 (43.3–52.5) | 233 51.7 (47.1–56.3) | 235 52.1 (47.5–56.7) | <0.001 | 0.998 | ||||
| Place of residence | ||||||||||||||||
| Urban n = 1004 % (95% Cl) | 169 16.8 (14.5–19.1) | 141 14.0 (11.9–16.2) | 0.098 | 362 36.1 (33.1–39.0) | 388 38.6 (35.6–41.7) | 0.281 | 531 52.9 (49.8–56.0) | 529 52.7 (49.6–55.8) | 473 47.1 (44.0–50.2) | 475 47.3 (44.2–50.4) | <0.001 | 0.240 | 0.180 | 0.964 | <0.001 | 0.612 |
| Rural n = 686 % (95% Cl) | 172 25.1 (21.8–28.3) | 142 20.7 (17.7–23.7) | 0.081 | 170 24.8 (21.6–28.0) | 210 30.6 (27.2–34.1) | 0.039 | 342 49.9 (46.1–53.6) | 352 51.3 (47.6–55.1) | 344 50.1 (46.4–53.9) | 334 48.7 (44.9–52.4) | 0.027 | 0.627 | ||||
| Level of Education | ||||||||||||||||
| Primary n = 232 % (95% Cl) | 41 17.7 (12.8–22.6) | 50 21.6 (16.3–26.8) | 0.332 | 61 26.3 (20.6–32.0) | 76 32.8 (26.7–38.8) | 0.215 | 102 44.0 (37.6–50.4) | 126 54.3 (47.9–60.7) | 130 56.0 (49.6–62.4) | 106 45.7 (39.3–52.1) | <0.001 | <0.001 | 0.083 | 0.033 | 0.002 | 0.013 |
| Vocational n = 267 % (95% Cl) | 78 29.2 (23.8–34.7) | 50 18.7 (14.0–23.4) | 0.019 | 69 25.8 (20.6–31.1) | 79 29.6 (24.1–35.1) | 0.443 | 147 55.1 (49.1–61.0) | 129 48.3 (42.3–54.3) | 120 44.9 (39.0–50.9) | 138 51.7 (45.7–57.7) | 0.018 | 0.141 | ||||
| Secondary n = 675 % (95% Cl) | 144 21.3 (18.2–24.4) | 118 17.5 (14.6–20.3) | 0.117 | 247 36.6 (33.0–40.2) | 262 38.8 (35.1–42.5) | 0.494 | 391 57.9 (54.2–61.7) | 380 56.3 (52.6–60.0) | 284 42.1 (38.3–45.8) | 295 43.7 (40.0–47.4) | 0.199 | 0.582 | ||||
| Higher n = 516 % (95% Cl) | 78 15.1 (12.0–18.2) | 65 12.6 (9.7–15.5) | 0.283 | 155 30.0 (26.1–34.0) | 181 35.1 (31.0–39.2) | 0.162 | 233 45.2 (40.9–49.4) | 246 47.7 (43.4–52.0) | 283 54.8 (50.6–59.1) | 270 52.3 (48.0–56.6) | 0.174 | 0.454 | ||||
(a) Chi-square test comparing current smokers, former smokers, and never smokers at baseline 2007; (b) chi-square test comparing ever smokers and never smokers at baseline 2007–2010; (c) chi-square test comparing current smokers, former smokers, and never smokers at baseline 2007–2010 and 9-year study; (d) chi-square test comparing ever smokers and never smokers at baseline 2007–2010 and 9-year study; (e) chi-square test comparing current smokers, former smokers, and never smokers in 9-year study; (f) chi-square test comparing ever smokers and never smokers in 9-year study.
Table 3.
Odds ratios and 95% confidence intervals of predictors of current smoking after the 9-year follow-up period.
Table 3.
Odds ratios and 95% confidence intervals of predictors of current smoking after the 9-year follow-up period.
| Parameters | Current Smokers CS9y (9-Year Study) | Chi-Square Test: p-Value | OR (95% CI) * | |
|---|---|---|---|---|
| Yes n = 283 | No n = 1407 | |||
| Current smoker (baseline): | <0.001 | |||
| Yes | 234 (82.7%) | 84 (6.0%) | 75.2 (51.5–110) | |
| No | 49 (17.3%) | 1323 (94.0%) | 1.00 (ref.) | |
| Former smoker (baseline): | <0.001 | |||
| Yes | 49 (17.3%) | 410 (29.1%) | 0.51 (0.37–0.71) | |
| No | 234 (82.7%) | 997 (70.9%) | 1.00 (ref.) | |
| Sex: | 0.573 | |||
| Women | 188 (66.4%) | 907 (64.5%) | 1.09 (0.83–1.43) | |
| Men | 95 (33.6%) | 500 (35.5%) | 1.00 (ref.) | |
| Place of residence: | <0.001 | |||
| Rural | 142 (50.2%) | 544 (38.7%) | 1.60 (1.24–2.06) | |
| Urban | 141 (49.8%) | 863 (61.3%) | 1.00 (ref.) | |
| Birth-year cohort <1940: | 0.307 | |||
| Yes | 13 (4.6%) | 90 (6.4%) | 0.70 (0.39–1.28) | |
| No | 270 (95.4%) | 1317 (93.6%) | 1.00 (ref.) | |
| Birth-year cohort 1941–1960: | 0.384 | |||
| Yes | 197 (69.6%) | 939 (66.7%) | 1.14 (0.87–1.51) | |
| No | 86 (30.4%) | 468 (33.3%) | 1.00 (ref.) | |
| Primary level of education: | 0.040 | |||
| Yes | 50 (17.7%) | 181 (12.9%) | 1.45 (1.03–2.05) | |
| No | 233 (82.3%) | 1226 (87.1%) | 1.00 (ref.) | |
| Vocational level of education: | 0.392 | |||
| Yes | 50 (17.7%) | 217 (15.4%) | 1.18 (0.84–1.65) | |
| No | 233 (82.3%) | 1190 (84.6%) | 1.00 (ref.) | |
| Secondary level of education: | 0.567 | |||
| Yes | 118 (41.7%) | 558 (39.7%) | 1.09 (0.84–1.41) | |
| No | 165 (58.3%) | 849 (60.3%) | 1.00 (ref.) | |
| Cough for at least 2 weeks: | 0.009 | |||
| Yes | 74 (26.2%) | 269 (19.1%) | 1.50 (1.11–2.01) | |
| No | 209 (73.8%) | 1138 (80.9%) | 1.00 (ref.) | |
| Wheezing or whistling in the chest: | 0.049 | |||
| Yes | 35 (12.4%) | 119 (8.5%) | 1.53 (1.02–2.28) | |
| No | 248 (87.6%) | 1288 (91.5%) | 1.00 (ref.) | |
| Early morning cough with chest tightness: | 0.006 | |||
| Yes | 30 (10.6%) | 83 (5.9%) | 1.89 (1.22–2.93) | |
| No | 253 (89.4%) | 1324 (94.1%) | 1.00 (ref.) | |
| Respiratory diseases: | 0.502 | |||
| Yes | 14 (4.9%) | 84 (6.0%) | 0.82 (0.46–1.67) | |
| No | 269 (95.1%) | 1323 (94.0%) | 1.00 (ref.) | |
| CVD: | 0.074 | |||
| Yes | 41 (14.5%) | 267 (19.0%) | 0.72 (0.51–1.03) | |
| No | 242 (85.5%) | 1140 (81.0%) | 1.00 (ref.) | |
| Stroke: | 0.256 | |||
| Yes | 7 (2.5%) | 19 (1.4%) | 1.85 (0.77–4.45) | |
| No | 276 (97.5%) | 1388 (98.6%) | 1.00 (ref.) | |
| Diabetes: | 0.297 | |||
| Yes | 22 (7.8%) | 86 (6.1%) | 1.29 (0.80–2.11) | |
| No | 261 (92.2%) | 1321 (93.9%) | 1.00 (ref.) | |
| Hypertension: | 0.888 | |||
| Yes | 101 (35.7%) | 496 (35.3%) | 1.02 (0.78–1.33) | |
| No | 182 (64.3%) | 911 (64.7%) | 1.00 (ref.) | |
| Overweight or obese: | 0.311 | |||
| Yes | 192 (67.8%) | 997 (70.9%) | 0.87 (0.66–1.14) | |
| No | 91 (32.2%) | 410 (29.1%) | 1.00 (ref.) | |
| Employed: | 0.560 | |||
| Yes | 157 (55.5%) | 807 (57.4%) | 0.93 (0.72–1.20) | |
| No | 126 (44.5%) | 600 (42.6%) | 1.00 (ref.) | |
| Smoking pack years: | <0.001 | |||
| ≥3.8 | 190 (67.1%) | 532 (37.8%) | 3.36 (2.56–4.40) | |
| <3.8 | 93 (32.9%) | 875 (62.2%) | 1.00 (ref.) | |
* Bold values are statistically significant.
Table 4.
Odds ratios and 95% confidence intervals of predictors of former smoking after the 9-year follow-up period.
Table 4.
Odds ratios and 95% confidence intervals of predictors of former smoking after the 9-year follow-up period.
| Parameters | Former Smokers FS9y | Chi-Square Test: p-Value | OR (95% CI) * | |
|---|---|---|---|---|
| Yes n = 598 | No n = 1092 | |||
| Current smoker (baseline): | <0.001 | |||
| Yes | 84 (14.1%) | 234 (21.4%) | 0.60 (0.46–0.79) | |
| No | 514 (85.9%) | 858 (78.6%) | 1.00 (ref.) | |
| Former smoker (baseline): | <0.001 | |||
| Yes | 410 (68.6%) | 49 (4.5%) | 46.4 (33.2–64.9) | |
| No | 188 (31.4%) | 1043 (95.5%) | 1.00 (ref.) | |
| Sex: | 0.014 | |||
| Women | 364 (60.9%) | 731 (66.9%) | 0.77 (0.62–0.95) | |
| Men | 234 (39.1%) | 361 (33.1%) | 1.00 (ref.) | |
| Place of residence: | <0.001 | |||
| Rural | 210 (35.1%) | 476 (43.6%) | 0.70 (0.57–0.86) | |
| Urban | 388 (64.9%) | 616 (56.4%) | 1.00 (ref.) | |
| Birth-year cohort <1940: | 0.531 | |||
| Yes | 33 (5.5%) | 70 (6.4%) | 0.85 (0.56–1.31) | |
| No | 565 (94.5%) | 1022 (93.6%) | 1.00 (ref.) | |
| Birth-year cohort 1940–1960: | 0.034 | |||
| Yes | 422 (70.6%) | 714 (65.4%) | 1.27 (1.02–1.57) | |
| No | 176 (29.4%) | 378 (34.6%) | 1.00 (ref.) | |
| Primary level of education: | 0.438 | |||
| Yes | 76 (12.7%) | 155 (14.2%) | 0.88 (0.66–1.18) | |
| No | 522 (87.3%) | 937 (85.8%) | 1.00 (ref.) | |
| Vocational level of education: | 0.037 | |||
| Yes | 79 (13.2%) | 188 (17.2%) | 0.73 (0.55–0.97) | |
| No | 519 (86.8%) | 904 (82.8%) | 1.00 (ref.) | |
| Secondary level of education: | 0.021 | |||
| Yes | 262 (43.8%) | 414 (37.9%) | 1.28 (1.04–1.56) | |
| No | 336 (56.2%) | 678 (62.1%) | 1.00 (ref.) | |
| Employed: | 0.012 | |||
| Yes | 366 (61.2%) | 598 (54.8%) | 1.30 (1.06–1.60) | |
| No | 232 (38.8%) | 494 (45.2%) | 1.00 (ref.) | |
| Cough for at least 2 weeks: | 0.236 | |||
| Yes | 112 (18.7%) | 231 (21.2%) | 0.86 (0.67–1.10) | |
| No | 486 (81.3%) | 861 (78.8%) | 1.00 (ref.) | |
| Wheezing or whistling in the chest: | 0.250 | |||
| Yes | 61 (10.2%) | 93 (8.5%) | 1.22 (0.87–1.71) | |
| No | 537 (89.8%) | 999 (91.5%) | 1.00 (ref.) | |
| Early morning cough with chest tightness: | 0.841 | |||
| Yes | 39 (6.5%) | 74 (6.8%) | 0.96 (0.64–1.43) | |
| No | 559 (93.5%) | 1018 (93.2%) | 1.00 (ref.) | |
| Respiratory diseases: | 0.944 | |||
| Yes | 35 (5.9%) | 63 (5.8%) | 1.02 (0.66–1.55) | |
| No | 563 (94.1%) | 1029 (94.2%) | 1.00 (ref.) | |
| CVD: | 0.147 | |||
| Yes | 120 (20.1%) | 188 (17.2%) | 1.21 (0.94–1.56) | |
| No | 478 (79.9%) | 904 (82.8%) | 1.00 (ref.) | |
| Stroke: | 0.901 | |||
| Yes | 9 (1.5%) | 17 (1.6%) | 0.97 (0.43–2.18) | |
| No | 589 (98.5%) | 1075 (98.4%) | 1.00 (ref.) | |
| Diabetes: | 0.504 | |||
| Yes | 35 (5.9%) | 73 (6.7%) | 0.87 (0.57–1.32) | |
| No | 563 (94.1%) | 1019 (93.3%) | 1.00 (ref.) | |
| Hypertension: | 0.143 | |||
| Yes | 225 (37.6%) | 372 (34.1%) | 1.17 (0.95–1.44) | |
| No | 373 (62.4%) | 720 (65.9%) | 1.00 (ref.) | |
| Overweight or obese: | 0.252 | |||
| Yes | 431 (72.1%) | 758 (69.4%) | 1.14 (0.91–1.42) | |
| No | 167 (27.9%) | 334 (30.6%) | 1.00 (ref.) | |
| Smoking pack years: | <0.001 | |||
| >3.8 | 337 (56.4%) | 385 (35.3%) | 2.37 (1.93–2.91) | |
| <3.8 | 261 (43.6%) | 707 (64.7%) | 1.00 (ref.) | |
* Bold values OR are statistically significant.
Table 5.
Baseline demographics of 459 former smokers on the first examination in 2007–2010 according to smoking status on the final examination in 2016–2019.
Table 5.
Baseline demographics of 459 former smokers on the first examination in 2007–2010 according to smoking status on the final examination in 2016–2019.
| Parameters | Successfully Quit Smoking (9-Year Study) | p-Value | |
|---|---|---|---|
| Yes n = 410 | No n = 49 | ||
| Age: | 0.516 | ||
| M ± SD | 55.3 ± 8.9 | 54.5 ± 9.4 | |
| Me (Q1; Q3) | 56 (50; 61) | 55 (48; 61) | |
| Min–Max | 29–80 | 36–77 | |
| Age of starting cigarette smoking: | 0.344 | ||
| M ± SD | 19.4 ± 3.8 | 19.5 ± 4.2 | |
| Me (Q1; Q3) | 19 (18; 20) | 20 (18; 20) | |
| Min–Max | 10–47 | 8–40 | |
| Smoking pack years: | 0.501 | ||
| M ± SD | 14.0 ± 13.6 | 16.9 ± 17.4 | |
| Me (Q1; Q3) | 10 (5; 20) | 10 (5; 24) | |
| Min–Max | 0.1–100 | 0.1–72 | |
| Average number of cigarettes smoked per day: | 0.729 | ||
| M ± SD | 14.3 ± 9.4 | 15.4 ± 10.8 | |
| Me (Q1; Q3) | 11 (10; 20) | 15 (8; 20) | |
| Min–Max | 1–80 | 1–40 | |
| Duration of cigarette smoking (years): | 0.508 | ||
| M ± SD | 17.9 ± 10.4 | 18.7 ± 10.3 | |
| Me (Q1; Q3) | 18 (10; 25) | 20 (10; 25) | |
| Min–Max | 1–50 | 1–40 | |
| SBP (mm Hg): | 0.667 | ||
| M ± SD | 147 ± 21 | 145 ± 19 | |
| Me (Q1; Q3) | 144 (132; 158) | 144 (130; 154) | |
| Min–Max | 100–230 | 115–195 | |
| DBP (mm Hg): | 0.804 | ||
| M ± SD | 87 ± 11 | 86 ± 12 | |
| Me (Q1; Q3) | 86 (79; 93) | 84 (78; 95) | |
| Min–Max | 52–130 | 66–124 | |
| Weight (kg): | 0.968 | ||
| M ± SD | 78.5 ± 15.5 | 80.1 ± 18.8 | |
| Me (Q1; Q3) | 77 (67; 88) | 75 (66; 92) | |
| Min–Max | 48–136 | 54–130 | |
| BMI (kg/m2): | 0.763 | ||
| M ± SD | 28.6 ± 4.9 | 28.6 ± 5.8 | |
| Me (Q1; Q3) | 27.9 (24.9; 31.4) | 27.8 (25.6; 30.1) | |
| Min–Max | 19.1–46.7 | 17.9–47.0 | |
| AHEI (score): | 0.043 | ||
| M ± SD | 32.4 ± 7.5 | 30.2 ± 7.3 | |
| Me (Q1; Q3) | 31.5 (26.9; 37.5) | 29.6 (24.9; 35.2) | |
| Min–Max | 17.1–54.4 | 19.2–53.6 | |
| Minutes/day you walk during your leisure time: | 0.039 | ||
| M ± SD | 71.6 ± 57.8 | 113.4 ± 113.2 | |
| Me (Q1; Q3) | 60 (30; 90) | 65 (40; 130) | |
| Min–Max | 0–450 | 20–480 | |
| MET score for walking to go from place to place: | 0.047 | ||
| M ± SD | 614 ± 689 | 656 ± 534 | |
| Me (Q1; Q3) | 300 (120; 900) | 600 (300; 900) | |
| Min–Max | 0–3000 | 0–2160 | |
| Transportation related physical activity MET score: | 0.031 | ||
| M ± SD | 1270 ± 1480 | 1518 ± 1272 | |
| Me (Q1; Q3) | 765 (396; 1521) | 1386 (594; 1848) | |
| Min–Max | 0–9891 | 90–6030 | |
Table 6.
Odds ratios and 95% confidence intervals of predictors of successful smoking cessation after the 9-year follow-up period.
Table 6.
Odds ratios and 95% confidence intervals of predictors of successful smoking cessation after the 9-year follow-up period.
| Parameters | Successfully Quit Smoking (9-Year Study) | Chi-Square Test: p-Value | OR (95% CI) * | |
|---|---|---|---|---|
| Yes n = 410 | No n = 49 | |||
| Sex: | 0.775 | |||
| Women | 243 (59.3%) | 28 (57.1%) | 1.09 (0.60–1.99) | |
| Men | 167 (40.7%) | 21 (42.9%) | 1.00 (ref.) | |
| Place of residence: | 0.005 | |||
| Urban | 304 (74.1%) | 27 (55.1%) | 2.34 (1.28–4.28) | |
| Rural | 106 (25.9%) | 22 (44.9%) | 1.00 (ref.) | |
| Age: | 0.303 | |||
| ≥56 years | 216 (52.7%) | 22 (44.9%) | 1.37 (0.75–2.48) | |
| <56 years | 194 (47.3%) | 27 (55.1%) | 1.00 (ref.) | |
| Primary level of education: | 0.384 | |||
| Yes | 45 (11.0%) | 8 (16.3%) | 0.63 (0.28–1.43) | |
| No | 365 (89.0%) | 41 (83.7%) | 1.00 (ref.) | |
| Vocational level of education: | 0.205 | |||
| Yes | 43 (10.5%) | 2 (4.1%) | 2.75 (0.65–11.7) | |
| No | 367 (89.5%) | 47 (95.9%) | 1.00 (ref.) | |
| Secondary level of education: | 0.152 | |||
| Yes | 190 (46.3%) | 28 (57.1%) | 0.65 (0.36–1.18) | |
| No | 220 (53.7%) | 21 (42.9%) | 1.00 (ref.) | |
| Cough for at least 2 weeks: | 0.538 | |||
| Yes | 69 (16.8%) | 6 (12.2%) | 1.45 (0.59–3.54) | |
| No | 341 (83.2%) | 43 (87.8%) | 1.00 (ref.) | |
| Wheezing or whistling in the chest: | 0.606 | |||
| Yes | 41 (10.0%) | 3 (6.1%) | 1.70 (0.51–5.72) | |
| No | 369 (90.0%) | 46 (93.9%) | 1.00 (ref.) | |
| Early morning cough with chest tightness: | 1.000 | |||
| Yes | 19 (4.6%) | 2 (4.1%) | 1.14 (0.26–5.06) | |
| No | 391 (95.4%) | 47 (95.9%) | 1.00 (ref.) | |
| Respiratory diseases: | 1.000 | |||
| Yes | 27 (6.6%) | 3 (6.1%) | 1.09 (0.32–3.70) | |
| No | 383 (93.4%) | 46 (93.9%) | 1.00 (ref.) | |
| CVD: | 0.702 | |||
| Yes | 81 (19,8%) | 8 (16,3%) | 1.26 (0.57–2.80) | |
| No | 329 (80,2%) | 41 (83,7%) | 1.00 (ref.) | |
| Stroke: | 1.000 | |||
| Yes | 8 (2,0%) | 1 (2,0%) | 0.96 (0.12–7.80) | |
| No | 402 (98,0%) | 48 (98,0%) | 1.00 (ref.) | |
| Diabetes: | 0.437 | |||
| Yes | 26 (6.3%) | 5 (10.2%) | 0.60 (0.22–1.63) | |
| No | 384 (93.7%) | 44 (89.8%) | 1.00 (ref.) | |
| Hypertension: | 0.467 | |||
| Yes | 162 (39.5%) | 22 (44.9%) | 0.80 (0.44–1.46) | |
| No | 248 (60.5%) | 27 (55.1%) | 1.00 (ref.) | |
| Overweight or obese: | 0.533 | |||
| Yes | 301 (73.4%) | 38 (77.6%) | 0.80 (0.39–1.62) | |
| No | 109 (26.6%) | 11 (22.4%) | 1.00 (ref.) | |
| Employed: | 0.938 | |||
| Yes | 245 (59.8%) | 29 (59.2%) | 1.02 (0.56–1.87) | |
| No | 165 (40.2%) | 20 (40.8%) | 1.00 (ref.) | |
| Smoking pack years: | 0.008 | |||
| <3.8 | 149 (36.3%) | 8 (16.3%) | 2.93 (1.34–6.41) | |
| ≥3.8 | 261 (63.7%) | 41 (83.7%) | 1.00 (ref.) | |
| AHEI (score): | 0.005 | |||
| ≥25.5 | 331 (80.7%) | 31 (63.3%) | 2.43 (1.30–4.57) | |
| <25.5 | 79 (19.3%) | 18 (36.7%) | 1.00 (ref.) | |
| Walk during your leisure time: | 0.015 | |||
| <90 min/day | 193 (73.7%) | 17 (53.1%) | 2.47 (1.17–5.21) | |
| ≥90 min/day | 69 (26.3%) | 15 (46.9%) | 1.00 (ref.) | |
| Walk during your leisure time: | 0.085 | |||
| ≥120 min/week | 179 (68.3%) | 17 (53.1%) | 1.90 (0.91–3.99) | |
| <120 min/week | 83 (31.7%) | 15 (46.9%) | 1.00 (ref.) | |
| Walking from place to place: | 0.033 | |||
| <693 MET | 182 (55.3%) | 15 (37.5%) | 2.06 (1.05–4.06) | |
| ≥693 MET | 147 (44.7%) | 25 (62.5%) | 1.00 (ref.) | |
| Physical activity related to transport: | 0.016 | |||
| <753 MET | 171 (49.1%) | 12 (29.3%) | 2.33 (1.15–4.72) | |
| ≥753 MET | 177 (50.9%) | 29 (70.7%) | 1.00 (ref.) | |
* Bold values OR are statistically significant.
Table 7.
Odds ratios and 95% confidence intervals of predictors of smoking cessation after the 9-year follow-up period.
Table 7.
Odds ratios and 95% confidence intervals of predictors of smoking cessation after the 9-year follow-up period.
| Parameters | Smoking Quitters n = 84 | Continued Smokers n = 234 | A vs. B p-Value | OR (95% CI) * |
|---|---|---|---|---|
| Sex: | ||||
| Men | 37 (44.0%) | 74 (31.6%) | 0.040 | 1.70 (1.02–2.84) |
| Women | 47 (56.0%) | 160 (68.4%) | 1.00 (ref.) | |
| Age (years): | ||||
| < 61 years | 80 (95.2%) | 187 (79.9%) | 0.001 | 5.03 (1.75–14.2) |
| ≥ 61 years | 4 (4.8%) | 47 (20.1%) | 1.00 (ref.) | |
| Level of education: | 0.112 | |||
| Primary | 6 (7.1%) | 42 (17.9%) | 0.35 (0.13–0.94) | |
| Vocational | 39 (46.4%) | 90 (38.5%) | 1.06 (0.57–1.98) | |
| Secondary | 17 (20.3%) | 48 (20.5%) | 0.87 (0.41–1.83) | |
| Higher | 22 (26.2%) | 54 (23.1%) | 1.00 (ref.) | |
| Cough for at least 2 weeks | 21 (25.0%) | 68 (29.1%) | 0.477 | 0.81 (0.46–144) |
| Wheezing or whistling in the chest | 14 (16.7%) | 32 (13.7%) | 0.504 | 1.26 (0.64–2.50) |
| Early morning cough with chest tightness | 12 (14.3%) | 28 (12.0%) | 0.582 | 1.23 (0.59–2.54) |
| Respiratory diseases | 2 (2.4%) | 11 (4.7%) | 0.526 | 0.49 (0.11–2.28) |
| CVD | 15 (17.9%) | 33 (14.1%) | 0.410 | 1.32 (0.68–2.58) |
| Stroke | 1 (1.2%) | 6 (2.6%) | 0.680 | 0.46 (0.05–3.86) |
| Diabetes | 2 (2.4%) | 17 (7.3%) | 0.176 | 0.31 (0.07–1.37) |
| Hypertension | 22 (26.2%) | 79 (33.8%) | 0.201 | 0.70 (0.40–1.21) |
| Overweight or obese | 60 (71.4%) | 154 (65.8%) | 0.347 | 1.30 (0.75–2.24) |
| Employed | 61 (72.6%) | 128 (54.7%) | 0.004 | 2.20 (1.27–3.79) |
| Smoking pack years ≥ 3.8 | 76 (90.5%) | 149 (63.7%) | <0.001 | 5.42 (2.50–11.8) |
* Bold values OR are statistically significant.
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Połtyn-Zaradna, K.; Psikus, P.; Zatońska, K. Changes in Attitudes toward Tobacco Smoking and Factors Associated with Quitting in 9-Year Observation of PURE Poland Cohort Study. Int. J. Environ. Res. Public Health 2022, 19, 6564. https://doi.org/10.3390/ijerph19116564
AMA Style
Połtyn-Zaradna K, Psikus P, Zatońska K. Changes in Attitudes toward Tobacco Smoking and Factors Associated with Quitting in 9-Year Observation of PURE Poland Cohort Study. International Journal of Environmental Research and Public Health. 2022; 19(11):6564. https://doi.org/10.3390/ijerph19116564
Chicago/Turabian StylePołtyn-Zaradna, Katarzyna, Piotr Psikus, and Katarzyna Zatońska. 2022. "Changes in Attitudes toward Tobacco Smoking and Factors Associated with Quitting in 9-Year Observation of PURE Poland Cohort Study" International Journal of Environmental Research and Public Health 19, no. 11: 6564. https://doi.org/10.3390/ijerph19116564
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