Cigarette smoking and perceived risk of cardiovascular disease in Iran
- Open Access
- 01.12.2025
- Research
Abstract
Introduction
Cigarette smoking is a well-established risk factor for cardiovascular disease (CVD), contributing significantly to the global burden of cardiovascular morbidity and mortality. Numerous epidemiological studies have consistently demonstrated a strong link between cigarette smoking and an increased risk of CVD. For example, the Barua et al. study showed that smoking plays a major role in premature coronary atherosclerosis and in accelerating atherosclerosis by increasing the oxidation of low-density lipoprotein (LDL) and damaging coronary endothelial vasodilation [1]. Long-term cohort studies, such as the Framingham Heart Study, led by Dawber, Meadors, and Moore, have provided compelling evidence that smoking is associated with a higher incidence of coronary heart disease (CHD) and stroke [2]. The harmful effects of smoking on the cardiovascular system are mediated through various mechanisms. Smoking is known to induce endothelial dysfunction, inflammation, and oxidative stress which contribute to the development and progression of atherosclerosis [3, 4, 5].
Cigarette smoking has been identified as a significant modifiable risk factor for ischemic and hemorrhagic strokes. Some studies like the Jackson Heart Study [6], Framingham study [7] and study by Shah et al. [8] have consistently highlighted the elevated risk of stroke among smokers.
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Cigarette smoking is a well-documented and significant risk factor for hypertension [9, 10, 11]. Hypertensive smokers are more likely to develop severe forms of hypertension, including malignant and renovascular hypertension, an effect likely due to accelerated atherosclerosis [12].
The results of a nationwide study that included 10,834 participants aged ≥ 15 years from Iran showed that the mean ± standard deviation of systolic blood pressure and history of cardiovascular symptoms in current smokers was higher than in nonsmokers [13]. The results of a cohort study,12 years follow-up in the capital city of Tehran, indicated that adjusted hazard ratios CVD in daily smoker men were 2.05 (95%CI: 1.57–2.67). In smokers with a history of using more than 21 cigarettes per day, the HR of CVD was 3.79 (95%CI: 2.25–6.37), with less risk observed in those who smoked fewer cigarettes per day [14]. The results of a study conducted by Ansari et al. in central parts of Iran revealed that the hypertension prevalence was 2.5 times higher in urban areas compared to rural areas with a significant difference as it increased to 3.5 times smoking factor was considered [15]. The findings of a population-based study of 3059 male individuals, aged ≥ 30 years, free of CVD at baseline, were evaluated for a median of 9.3 years and showed that being a past smoker significantly increased the risk of CVD events (HR = 2.42, CI = 1.28–0.56). Additionally, being a current smoker (more than 10 cigarettes a day) dramatically increased the risk of CVD/CHD events and total/CVD mortality. However, smoking less than 10 cigarettes per day only increased the risk of CVD and its mortality [16].The results of the Tehran Lipid and Glucose Study (TLGS) in the last two decades indicated that smoking in men was associated with an increased risk of hypertension (HR 1.26; CI 95% 0.98–1.63). Moreover, men, even smoking less than 10 cigarettes per day, were at increased risk for cardiovascular diseases by HR 2.12 (CI 95% 1.14–3.95) [17].
Understanding the relationship between perceived risk among cigarette smokers and cardiovascular disease (CVD) is crucial for developing effective public health interventions to reduce the burden of CVD. While research on this specific aspect is still ongoing, studies have explored the broader connection between perceived risk, smoking behavior, and cardiovascular health. Another study conducted by Weinstein et al. has investigated how individuals’ perception of the risks associated with smoking influences their smoking behavior [18]. For instance, a higher perceived risk of developing smoking-related health issues, including CVD, has been linked with increased motivation to quit and higher rates of smoking cessation attempts [19, 20].
Individuals with a lower perceived risk of CVD due to smoking may be less likely to adopt healthier behaviors or seek preventive measures [21]. This underlines the potential role of perceived risk in mediating the relationship between smoking and cardiovascular health [21, 22]. Despite the importance of increasing the studies on the perceived risk to reduce the burden of cigarette smoking, there is a dearth of studies in Iran addressing this topic. Therefore, this study was designed to assess the perceived risk of CVD among smokers, to enrich the existing body of knowledge.
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Methods
Procedure
This cross-sectional study employed a descriptive analysis approach within the population of smokers in Northeast Iran, Golestan province. The sample consisted of 380 smokers selected through snowball and network sampling methods between January and May 2023. Snowball and network sampling is a non-probability sampling technique that often is used to identify participants in hard-to-reach or marginalized populations. The process begins with a small group of initial participants, or “seeds,” who meet the study’s inclusion criteria. Participants were recruited at social gatherings or events, shopping malls, and parks, where they were invited to join the study by completing the questionnaires. Participants were also asked to connect the researchers to their friends and acquaintances who smoked cigarettes and were likely to participate in the study. It was explicitly communicated to all participants that the questionnaires were anonymous, and their responses will be confidential. The study’s protocol and ethical considerations were reviewed and approved by the ethics committee of Golestan University of Medical Sciences (Ethical number: IR.GOUMS.REC.1401.251).
Data collection tools
The data collection tool for this study was the smoking risk perception questionnaire, originally designed by Wong and Cappella [23] and Kotz et al. [24] and subsequently adapted for use in Iran by Zarghami et al., focusing on lung cancer [25]. In this study, the questionnaire was further modified by incorporating elements related to Myocardial Infarction (MI), Hypertension (HTN), and Stroke. The modifications were subjected to rigorous scrutiny and approval by eight experts in fields of medicine, health education, health promotion, epidemiology, and environmental health. The developed and modified questionnaire in the current study is provided as a supplementary file.
This comprehensive questionnaire included two sets of questions: the perceived risk of CVD including MI, HTN, and stroke, and participants’ assessments of the likelihood of surviving these diseases. The perceived risk section of the questionnaire consists of two questions assessing participants’ perceptions of developing MI, HTN, and stroke in the future compared to non-smokers of the same age. Respondents provide their answers on a scale ranging from zero to ten. A score of zero implies a belief that they are unlikely to develop these diseases and a score of ten shows a high probability of developing one or any of these morbidities in the future.
The second category of questions asked how likely they are to survive in the next 5 years if they are diagnosed with any of these diseases. A score of zero indicates that they think it is very likely that they will be alive after 5 years while the numbers 10 indicates that they think they will not be alive after 5 years of being diagnosed with any of these conditions (due to complications from these incidents). The sum of the scores of these two sections was considered as perceived risk, and the range of possible numerical scores was between 0 and 20.
To assess the stages of change in smoking, the questionnaire that was originally developed by Prochaska and DiClemente [26, 27] was utilized. It included 5 categories; (1) pre-contemplation stage: people who currently are smoking and do not intend to quit in the next 6 months, (2) contemplation stage: people who are considering smoking abstinence in the next 6 months, (3) preparation stage: people who intend to quit smoking in the next month, (4) action stage: people who quitted the smoking but no more than 6 months ago, and (5) maintenance stage: people who have not smoked for more than 6 months. This questionnaire was previously employed in the study conducted by Charkazi et al. in Iran [28]. Additionally, the Fagerström Nicotine Dependence Questionnaire (FTND), which was previously employed in Iran by Charkazi et al., was used to gauge the level of nicotine dependence [29, 30]. This tool (FTND) consisted of 6 questions with a total score ranging from 0 to 10. A score of ≥ 6 in FTND indicates high nicotine dependence [31].
Moreover, the demographic variables section of the questionnaire encompasses information such as age, gender, level of education, occupation, duration of smoking, number of cigarettes consumed daily, and age of smoking initiation. Regarding income level, we considered people with monthly income level below 10 million Tooman (equal to 170 US Dollars) as low income, 10–20 million Tooman as middle (171–240 US Dollars), and more than 20 million Tooman as high-income level (more than 240 US Dollars). In line with cigarette smoking intensity, we defined people who smoke less than 20 cigarettes per day as “light smokers” and who smoke 20 or more per day as “heavy smokers”.
Data analysis
The collected data were presented using descriptive statistical indicators such as frequency distribution, percentage, and average standard deviation. Scatter plots of the perceived risk of MI, Stroke, and HTN were generated for each predictor variable to validate the linear relationship between the predictors and the outcomes. To evaluate the relationship between demographic variables and the perceived risk of developing MI a multiple linear regression analysis was conducted. Similarly, a multiple linear regression analysis was employed to assess the relationship between demographic variables and the perceived risk of Stroke and HTN, separately. Moreover, we evaluated the multicollinearity of the predictor variables utilizing the Variance Inflation Factor (VIF). We implemented the Bonferroni correction, an established statistical technique designed to adjust for the multiple hypothesis testing problem. The data were analyzed using Stata version 17. A significance level of 0.05 was considered for the analysis.
Findings
The mean age of the participants was 35.57 ± 12.05 years, ranging from 18 to 79 years. The perceived risk score of MI was 10.68 ± 4.69 (out of 20). The scores related to stroke and HTN were 10.00 ± 4.65 and 10.89 ± 4.73, respectively. Among all of them, 293 individuals (77.1%) were male, and 217 individuals (57.1%) were married. A majority of them (n: 232 individuals, 61.1%), initiated smoking under the age of 20, while 148 individuals (38.09%) began smoking after the age of 20. Additionally, 179 participants (47.1%) used additional tobacco product (s), including Nass and hookah, alongside cigarettes.
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The duration of smoking varied, with 181 individuals (47.6%) smoked for less than 10 years. Regarding socioeconomic variables, 310 participants (81.6%) had low income, 143 individuals (37.6%) were self-employed, and 132 individuals (34.7%) held a university education. Concerning the stages of change in smoking, most of the participants, 240 people (63.2%), were in the pre-contemplation stage. The mean nicotine dependence score, measured by the Fagerström test, was 4.10 ± 2.51 out of 10 (Table 1).
Table 1
Demographic profile of the participants
Variable | n | % | MI perceived score Mean (sd) | Stroke perceived score Mean (sd) | HTN perceived score Mean (sd) | |
|---|---|---|---|---|---|---|
Gender | Male Female | 293 87 | 77.1 22.9 | 11.56 (3.78) 10.42 (4.90) | 9.61 (4.81) 11.31 (3.81) | 10.66 (5.07) 11.96 (3.26) |
Marital status | Unmarried Married | 163 217 | 42.9 57.1 | 10.98 (4.16) 10.46 (5.05) | 9.96 (4.15) 10.02 (5.00) | 11.17 (4.05) 10.67 (5.18) |
Smoking onset | < 20 years 20 years and more | 232 148 | 61.1 38.9 | 11.21 (4.58) 9.84 (4.75) | 10.36 (4.47) 9.43 (4.88) | 11.39 (4.74) 10.10 (4.62) |
Dual use | Sole cigarette Dual user | 201 179 | 52.9 47.1 | 10.89 (4.70) 10.44 (4.67) | 10.17 (4.71) 9.81 (4.59) | 11.12 (4.83) 10.62 (4.62) |
Employment | Unemployed Self-employed Nonskilled Worker Student Employer Homemaker | 19 143 78 67 34 39 | 5 37.6 20.5 17.6 8.9 10.3 | 9.58 (5.49) 10.08 (4.90) 10.91 (4.62) 10.99 (4.86) 11.68 (4.08) 11.62 (3.52) | 8.58 (5.04) 9.47 (4.70) 10.49 (4.40) 10.22 (4.96) 9.94 (4.68) 11.36 (3.93) | 9.21 (5.89) 10.33 (4.83) 11.01 (4.72) 11.31 (4.74) 11.76 (4.54) 12.05 (3.52) |
Education level | Illiterate Elementary Secondary High school dropout High school diploma Associate degree Bachelor’s degree Masters and higher degree | 20 39 64 31 94 34 54 44 | 5.3 10.3 16.8 8.2 24.7 8.9 14.2 11.6 | 9.65 (5.85) 11.92 (4.96) 10.68 (4.75) 10.74 (4.80) 10.64 (4.29) 10.73 (5.38) 10.57 (4.00) 10.18 (4.88) | 9.60 (5.96) 11.51 (3.80) 10.14 (4.86) 9.45 (4.53) 10.28 (4.39) 10.08 (5.05) 8.37 (4.27) 10.36 (4.83) | 9.50 (5.58) 12.58 (3.98) 10.87 (4.60) 11.54 (5.18) 10.89 (4.86) 10.85 (4.91) 10.12 (4.16) 10.75 (4.82) |
Income level (Monthly) | low (< 10 million Toman) Middle (10–20 million Toman) High (> 20 million Toman) | 310 63 7 | 81.6 16.6 1.8 | 10.70 (4.76) 10.68 (4.31) 9.57 (5.38) | 10.08 (4.73) 9.74 (4.36) 8.57 (3.27) | 10.94 (4.75) 10.74 (4.73) 9.71 (4.38) |
Smoking duration (year) | < 10 10–19 20–29 30 and more | 181 100 53 46 | 47.6 26.3 13.9 12.1 | 10.51 (4.50) 11.32 (4.52) 10.69 (5.26) 9.84 (5.07) | 10.04 (4.65) 10.33 (4.65) 10.11 (4.86) 8.91 (4.37) | 10.84 (4.54) 11.40 (4.54) 11.23 (5.21) 9.52 (5.20) |
Smoking intensity | Light smoker Heavy smoker | 339 41 | 89.2 10.8 | 10.64 (4.68) 11.00 (4.80) | 9.86 (4.69) 11.14 (4.13) | 10.84 (4.73) 11.24 (4.74) |
Perceived risk of MI
In the multiple linear regression analysis aimed at predicting the perceived risk of MI, the results revealed that the perceived risk score increases by 0.10 units for each additional year of age. Furthermore, individuals with primary education exhibited significantly higher perceived risk scores compared to those with no formal education.
Interestingly, individuals who had been smoking for 20–29 years and 30 years or more, as well as those who initiated smoking after the age of 20, demonstrated significantly lower perceived risk scores for MI. Meanwhile, dual users showed significantly lower perceived risk scores compared to those who are not dual users. These identified variables collectively contributed significantly to predicting the perceived risk of MI (F(26, 353) = 1.72, p <.0001, Adj R-squared = 0.05%). The model, therefore, accounts for approximately 5% of the variation in the perceived risk of MI (Table 2).
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Table 2
Results of multiple linear regression analysis to determine the relationship between perceived risk of MI and demographic variables
Variable | Coefficient | SE | p | 95% CI |
|---|---|---|---|---|
Fagerström test score | 0.06 | 0.11 | 0.456 | [-0.15, 0.28] |
Smoking intensity | ||||
Light smoker | Ref | |||
Heavy smoker | 0.84 | 0.85 | 0.320 | [-0.82, 2.52] |
Age | 0.10*** | 0.037 | 0.007 | [0.03, 0.17] |
Employment status | ||||
Unemployed | Ref | |||
Self-employed | 0.21 | 1.17 | 0.858 | [-2.10, 2.52] |
Worker | 1.04 | 1.23 | 0.399 | [-1.38, 3.46] |
Student | 1.91 | 1.36 | 0.163 | [-0.77, 4.59] |
Employer | 2.18 | 1.46 | 0.136 | [-0.69, 5.05 |
Homemaker | 1.27 | 1.52 | 0.403 | [-1.71, 4.26] |
Marital status | ||||
Unmarried | Ref | |||
Married | -0.67 | 0.62 | 0.280 | [-1.91, 0.55] |
Education level | ||||
Illiterate | Ref | |||
Elementary | 2.88** | 1.32 | 0.030 | [0.28, 5.48] |
Secondary | 1.72 | 1.24 | 0.168 | [-0.72, 4.17] |
High school dropout | 1.39 | 1.40 | 0.321 | [-1.36, 4.15] |
High school diploma | 1.06 | 1.21 | 0.382 | [-1.32, 3.45] |
Associate | 0.82 | 1.37 | 0.547 | [-1.87, 3.53] |
Bachelor degree | 0.85 | 1.33 | 0.525 | [-1.77, 3.47] |
Masters and higher degree | 0.19 | 1.44 | 0.896 | [-2.65, 3.03] |
Income levels (monthly) | ||||
Low | Ref | |||
Middle | 0.25 | 0.71 | 0.719 | [-1.14, 1.65] |
High | -1.06 | 1.83 | 0.564 | [-4.67, 2.55] |
Duration of smoking (year) | ||||
< 10 | Ref | |||
10–19 | -0.04 | 0.69 | 0.945 | [-1.42, 1.32] |
20–29 | -1.96** | 0.99 | 0.049 | [-3.91, -0.00] |
> 30 | -4.08*** | 1.33 | 0.002 | [-6.70, -1.40] |
Smoking onset age | ||||
< 20 | Ref | |||
20 and more | -2.10*** | 0.57 | < 0.001 | [-3.24, -0.96] |
Dual user | ||||
No | Ref | |||
Yes | -1.04** | 0.52 | 0.049 | [-2.08, -0.00] |
Gender | ||||
Female | Ref | |||
Male | -0.73 | 0.79 | 0.357 | [-2.30, 0.83] |
Smoking stages of change | ||||
precontemplation | Ref | |||
contemplation | -0.81 | 0.66 | 0.224 | [-2.12, 0.49] |
preparation | -0.08 | 0.67 | 0.900 | [-1.41, 1.24] |
Intercept | 7.69*** | 2.17 | < 0.001 | [3.41, 11.97] |
Number of observations | 380.00 | |||
R-squared | 0.11 | |||
Adjusted R-squared | 0.05 | |||
F statistic (26, 353), P value | 1.72, 0.0001 | |||
Dependent variable | MI perception |
Perceived risk of stroke
The multiple linear regression analysis aimed at predicting the perceived risk of stroke and the findings indicated that the perceived risk score increases by 0.10 units for each additional year of age. Conversely, those who have smoked for 20 years or more, and individuals who initiated smoking after the age of 20 exhibited significantly lower perceived risk scores for stroke. Individuals with primary education exhibited significantly higher perceived risk scores compared to those with no formal education. Moreover, female and heavy smokers revealed higher perceived risk scores for stroke compared to male and light smokers.
In summary, these identified variables collectively contributed significantly to predicting the perceived risk of stroke (F(26, 353) = 2.29, p <.0001, Adj R-squared = 0.08%). The model accounts for approximately 8% of the variation in the perceived risk of stroke (Table 3).
Table 3
Results of multiple linear regression analysis to determine the relationship between perceived risk of stroke and demographic variables
Variable | Coefficient | SE | p | 95% CI |
|---|---|---|---|---|
Fagerström test score | 0.09 | 0.10 | 0.392 | [-0.12, 0.30] |
Smoking intensity | ||||
Light smoker | Ref | |||
Heavy smoker | 1.78** | 0.82 | 0.032 | [0.15, 3.41] |
Age | 0.10*** | 0.036 | 0.006 | [0.03, 0.17] |
Job | ||||
Unemployed | Ref | |||
Self-employed | 0.05 | 1.14 | 0.959 | [-2.19, 2.30] |
Worker | 0.90 | 1.20 | 0.452 | [-1.45, 3.26] |
Student | 1.12 | 1.32 | 0.397 | [-1.48, 3.74] |
Employer | 1.09 | 1.42 | 0.441 | [-1.69, 3.89 |
Homemaker | 0.74 | 1.48 | 0.618 | [-2.17, 3.65] |
Marital status | ||||
Single | Ref | |||
Married | 0.24 | 0.611 | 0.689 | [-0.95, 1.44] |
Education level | ||||
Illiterate | Ref | |||
elementary | 2.32* | 1.28 | 0.071 | [-0.20, 5.85] |
secondary | 1.29 | 1.21 | 0.288 | [-1.09, 3.67] |
High school dropout | -0.19 | 1.36 | 0.936 | [-2.79, 2.57] |
High school diploma | 0.47 | 1.18 | 0.688 | [-1.84, 2.79] |
Associate | 0.09 | 1.33 | 0.942 | [-2.53, 2.73] |
Bachelor degree | -1.11 | 1.30 | 0.391 | [-3.67, 1.44] |
Masters and higher degree | 0.93 | 1.40 | 0.509 | [-1.83, 3.70] |
Income levels (monthly) | ||||
Low | Ref | |||
Middle | 0.50 | 0.69 | 0.462 | [-0.85, 1.87] |
High | -1.77 | 1.78 | 0.323 | [-5.28, 2.55] |
Duration of smoking (year) | ||||
< 10 | Ref | |||
10–19 | -0.79 | 0.68 | 0.244 | [-2.13, 0.54] |
20–29 | -2.23** | 0.96 | 0.022 | [-4.13, -0.32] |
> 30 | -5.01*** | 1.29 | < 0.001 | [-7.56, -2.46] |
Smoking onset age | ||||
< 20 | Ref | |||
20 and more | -1.86*** | 0.56 | 0.001 | [-2.97, -0.75] |
Dual user | ||||
No | Ref | |||
Yes | -0.93* | 0.514 | 0.069 | [-1.94, 0.07] |
Gender | ||||
Female | Ref | |||
Male | -2.00*** | 0.77 | 0.010 | [-3.53, -0.47] |
Smoking stages of change | ||||
Precontemplation | Ref | |||
Contemplation | -0.55 | 0.64 | 0.391 | [-1.83, 0.71] |
Preparation | 0.68 | 0.65 | 0.298 | [-0.60, 1.97] |
Intercept | 8.33*** | 2.11 | 0.001 | [4.16, 12.50] |
Number of observations | 380.00 | |||
R-squared | 0.14 | |||
Adjusted R-squared | 0.08 | |||
F statistic (26, 353), P value | 2.29, 0.0001 | |||
Dependent variable | Stroke perception |
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Perceived risk of HTN
In the multiple linear regression analysis aimed at predicting the perceived risk of HTN, the results revealed that the perceived risk score increases by 0.07 units for each additional year of age. Furthermore, individuals with primary education exhibited significantly higher perceived risk scores compared to those with no formal education. Additionally, employers and students demonstrated perceived risk scores of 3.18 and 2.74 points higher, respectively compared to those with unemployed smokers.
Interestingly, individuals who had been smoking for 30 years or more, as well as those who initiated smoking after the age of 20, demonstrated significantly lower perceived risk scores for HTN. Meanwhile, dual users showed significantly lower perceived risk scores compared to those who are not dual users. These identified variables collectively contributed significantly to predicting the perceived risk of HTN (F(26, 353) = 1.99, p <.0001, Adj R-squared = 0.06%). The model, therefore, accounts for approximately 6% of the variation in the perceived risk of HTN (Table 4).
Table 4
Results of multiple linear regression analysis to determine the relationship between perceived risk of HTN and demographic variables
Variable | Coefficient | SE | p | 95% CI |
|---|---|---|---|---|
Fagerström test score | 0.18 | 0.11 | 0.103 | [-0.03, 0.40] |
Smoking intensity | ||||
Light smoker | Ref | |||
Heavy smoker | 0.74 | 0.85 | 0.380 | [-0.92, 2.42] |
Age | 0.07** | [0.00, 0.15] | ||
Job | ||||
Unemployed | Ref | |||
Self-employed | 0.99 | 1.17 | 0.398 | [-1.31, 3.30] |
Unskilled Worker | 1.41 | 1.23 | 0.258 | [-1.00, 3.84] |
Student | 2.74** | 1.36 | 0.045 | 0.06, 5.43] |
Employer | 3.18** | 1.46 | 0.030 | [0.31, 6.05] |
Homemaker | 2.64* | 1.52 | 0.083 | [-0.34, 5.64] |
Marital status | ||||
Single | Ref | |||
Married | -0.59 | 0.62 | 0.343 | [-1.83, 0.63] |
Education level | ||||
Illiterate | Ref | |||
elementary | 3.09** | 1.32 | 0.020 | [0.49, 5.69] |
secondary | 1.66 | 1.24 | 0.182 | [-0.78, 4.12] |
High school dropout | 2.20 | 1.40 | 0.116 | [-0.55, 4.69] |
High school diploma | 1.06 | 1.21 | 0.380 | [-1.32, 3.45] |
Associate | 0.72 | 1.37 | 0.598 | [-1.97, 3.43] |
Bachelor degree | -0.14 | 1.33 | 0.912 | [-2.77, 2.48] |
Masters and higher degree | 0.34 | 1.44 | 0.809 | [-2.49, 3.19] |
Income levels (monthly) | ||||
Low | Ref | |||
Middle | 0.26 | 0.71 | 0.707 | [-1.13, 1.66] |
High | -1.92 | 1.83 | 0.297 | [-5.53, 1.69] |
Duration of smoking (year) | ||||
< 10 | Ref | |||
10–19 | -0.12 | 0.69 | 0.858 | [-1.49, 1.25] |
20–29 | -1.38 | 0.99 | 0.163 | [-3.34, 0.56] |
> 30 | -4.40*** | 1.33 | 0.003 | [-7.02, -1.77] |
Smoking onset age | ||||
< 20 | Ref | |||
20 and more | -2.02*** | 0.57 | 0.001 | [-3.14, -0.87] |
Dual user | ||||
No | Ref | |||
Yes | -1.36** | 0.52 | 0.010 | [-2.40, -0.32] |
Gender | ||||
Female | Ref | |||
Male | -0.34 | 0.79 | 0.663 | [-1.91, 1.21] |
Smoking stages of change | ||||
Precontemplation | Ref | |||
Contemplation | 0.13 | 0.66 | 0.838 | [-1.17, 1.44] |
Preparation | 0.25 | 0.67 | 0.708 | [-1.07, 1.58] |
Intercept | 7.16*** | 2.17 | 0.001 | [2.88, 11.45] |
Number of observations | 380.00 | |||
R-squared | 0.13 | |||
Adjusted R-squared | 0.06 | |||
F statistic (26, 353), P value | 1.99, 0.0001 | |||
Dependent variable | HTN perception |
Discussion
Risk perception stands as a cornerstone within numerous health behavior theories. The perception of smoking risk encapsulates an individual’s thoughts and emotions concerning the detrimental health effects linked with smoking [32]. The present study was meticulously designed and implemented to ascertain the perceived risk of cardiovascular diseases among cigarette smokers in Golestan province, located in the northern province of Iran. There are numerous studies indicating that the impact of smoking on populations exposed to air pollution is different and extensive research has shown that air pollution is a recognized risk factor for mortality [33]. However, this study has not looked at air pollution as one of the contributing factors because this province is the only province in Iran with forests and green coverage, and air pollution is not a major issue like other big cities (e.g., Tehran, Mashhad, etc.). The findings of this study indicate a moderate overall perception of the risk of cardiovascular diseases resulting from smoking among the participants. The researchers posit that a lower perceived risk associated with smoking and other tobacco products correlates with an increased likelihood of initiation and decreased motivation for smoking cessation. Conversely, a heightened perceived risk may act as a deterrent factor to initiation or, if already engaged, encourage cessation [34, 35, 36, 37]. Overall, the findings of the studies confirm that smokers tend to underestimate their risk of developing diseases because of smoking [21, 37]. However, a more detailed measure of occupation beyond the broad categories provided could capture specific work-related exposures or stress on factors that influence perceived risk. Incorporating information on physical activity levels, including frequency, intensity, and type of activity, could provide insights into how physical activity influences perceived risk. Assessing dietary factors like fruit and vegetable consumption, processed food intake, and dietary quality could potentially contribute to explaining variance in perceived risk. Measures of stress levels and coping mechanisms, such as social support or relaxation techniques, could reveal their influence on perceived risk. Incorporating information about pre-existing medical conditions, even those unrelated to CVD, could help clarify the influence of overall health status on perceived risk. A family history of CVD could reveal the potential impact of genetic predisposition on perceived risk. Measures of access to healthcare services, such as insurance status, could contribute to understanding the role of healthcare availability in perceived risk. Measures of health literacy could reveal how individuals’ understanding of health information influences their perceptions. By including these additional factors, the model could potentially capture a wider range of influences on perceived risk and lead to a more robust and accurate explanation of the variation in the outcome variable. Overall, these are the factors that must be considered when conducting future studies.
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The findings of the study revealed that smokers tend to underestimate their risk of developing cardiovascular diseases and lung cancer in comparison to non-smokers [38]. However, Iran has experienced some socio-cultural changes in the recent decade. These changes increased the young generation’s tendency to cigarette smoking which resulted in presenting smoking as a normal behavior. The results of a qualitative study conducted by Jafari et al. indicated that community predispositions like the normalization of smoking behavior, easy access and lower price of cigarettes than other entertainments, and selling cigarettes to under-age groups, were the main factors influencing women to smoke [39].
It must be noted that the actual ratio of male to female smokers in Iran is around 25.9% in men vs. 4.4% in women in 2021 [40]. However, since we aimed to investigate gender differences between smokers’ perceived risk, we aimed to recruit more females (22.9% female and 77.1% male participants. This means the percentage of female participants in this study (22.9%) is purposefully much higher than the actual percentage (4.4%) in the country. Additionally, role modeling of friends along with membership in groups were the most common reasons for cigarette smoking among Iranian females [39]. The results of another qualitative study in Iran showed that from the perspective of teachers, parents, and students, the most important factor in smoking in adolescents smoking was the feeling of looking good and getting attention from others [41]. The results of a qualitative study by Rezaei et al. revealed that the easy access and low price of cigarettes are effective factors in adolescents’ tendency to smoke [42].
Tehrani et al. study results showed that ignoring the side effects of cigarette smoking is one of the main factors of cigarette smoking among Iranian female adolescents. They concluded that the participants did not take these risks seriously and continued to smoke due to reasons: the first reason is that they may not have had enough information about smoking and its side effects. The second reason for their inattention can be due to the immediate enjoyment of smoking, which has led them to deny the side effects and consequences of smoking [43]. The sociocultural changes in smoking among Iranians have made cigarettes smoking more normal behavior which leads to the low perceived risk of smoking in general.
The findings of this study indicate that the perceived risk of cardiovascular diseases (such as heart attack, stroke, and high blood pressure) among the participants is lower than anticipated. Similar results have been replicated in other studies. For instance, the findings of Strecher et al.‘s study, which investigated the perceived risk of cardiovascular diseases among both smokers and non-smokers, revealed that while smokers are more inclined to perceive themselves at risk of heart attack and stroke, their perception remains lower compared to non-smokers while non-smokers exhibit an optimistic bias [44]. The findings of the study by Desgraz et al., focusing on smokers aged 40 to 70 years, revealed that 39–50% of participants underestimated their risk of developing cardiovascular diseases in the next ten years based on the Framingham Ten-Year Risk Perception Scale and assessed Prospective Cardiovascular Münster (PROCAM) more than anticipated, a lower percentage, 9–12%, underestimated their risk of cardiovascular disease over the same period. Notably, among men, the odds ratio (OR) was 8.16, indicating a higher likelihood of underestimation, and among the elderly, the odds ratio was 1.06, surpassing that of other age groups [45].
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The findings of Ayanian et al.‘s study revealed that only 29% of smokers were identified as being at high risk of heart attack due to smoking, while this figure rose to 40% for the risk of lung cancer. Furthermore, older individuals, those with lower educational attainment, and light smokers (defined as those consuming fewer than 20 cigarettes per day) exhibited a diminished perceived risk of heart attack and lung cancer compared to heavier smokers [46].
The findings of Kaddumukasa et al.‘s study, conducted in a rural area in Uganda, revealed that none of the subjects regarded smoking as a risk factor for stroke [47]. The findings of Dawood et al.‘s study, which involved 386 smokers in Iraq, demonstrated that 66.3% of participants lacked awareness regarding the perceived risk of smoking causing stroke [48].
Overall, smokers appear to have a diminished perceived risk compared to non-smokers which might justify their continuation of smoking. In Campbell et al.‘s study, individuals who smoked reported a lower perceived risk compared to non-smokers [49]. This trend has been observed in other studies as well [50, 51].
Smoking duration
The result of the present study showed that the longer the duration of smoking, the lower the perceived risk of heart attack, stroke, and hypertension. This finding has been confirmed in other studies. For instance, research conducted by Peretti-Watel et al. found a positive correlation between smoking duration and decreased perception of health risks associated with smoking. This means the longer the individuals smoke, the more normalized and less severe they perceive the health consequences of smoking to be. This normalization of risk perception could contribute to prolonged smoking behavior and hinder cessation efforts [52]. Cognitive biases such as optimism bias and the illusion of control also influence smokers’ perception of smoking risks. Studies by Davis et al. and Lee and Kim emphasized the role of cognitive bias in maintaining smokers’ perception of low personal risk, regardless of smoking duration [53, 54, 55].
Age at first use
The result of this study showed that people who started smoking under the age of 20 had a higher perceived risk, while in other studies, contrary to the findings of the present study, the results showed that people who started smoking in their teenage years had a lower perceived risk.The age at which individuals initiate smoking plays a critical role in shaping their perceptions of the harms associated with smoking. Therefore, understanding how smoking age onset influences risk perception is essential for developing effective smoking prevention and cessation interventions, especially among youth.
Some studies proved that individuals who start smoking at a younger age tend to underestimate the harm associated with smoking which is consistent with our results. For instance, a study by Slovic indicated that a high percentage of adolescent smokers don’t assume any health risk from smoking the next cigarette or even smoking regularly for the “first few years” of smoking [56]. This denial of ‘short-term’ risks, coupled with a tendency observed in other studies for young smokers to underestimate the addictive properties of tobacco, indicates that many young people do not understand the risks of smoking cigarettes [56]. Individuals who initiated smoking during adolescence were less likely to perceive themselves at risk of developing smoking-related health conditions compared to those who started smoking later in life [57]. The results of a study conducted by Romer and Jamieson revealed that even when smokers accurately perceived the likelihood of death as a result of smoking, nearly half of young smokers rated the personal risks of smoking as not very risky, and about one-fourth of the smokers who overestimated the mortality risk still rated their risk to be low [58]. Additionally, in this study over 40% of smokers and 25% of nonsmokers underestimated, or did not know, the likelihood of smoking-related death, and over 40%, underestimated the number of years of life lost due to smoking [58]. To reach a better conclusion about these differences, it is necessary to conduct similar studies in Iran because certain cultural and social conditions may have caused this difference.
Study limitations
While yielding significant results, this study is subject to certain limitations that should be considered when generalizing its findings. First, the cross-sectional nature of this study imposes inherent limitations commonly associated with this study design. Second, reliance on self-reporting questionnaires introduces potential biases and limitations inherent in such methods. Third, using a questionnaire with a scoring range of 0–10, as opposed to alternative scales such as qualitative rating scales or a quantitative scale spanning 0-100, may influence the precision of risk perception assessment. Fourth, the study did not assess the potential underlying diseases of participants that might have impacted their perception of risk. Fifth, the failure to examine the history of smoking in the family members (exposure to second-hand smoking) of the study subjects is another limitation of this study. There are also unmeasured variables such as work-related factors (e.g., environmental exposures), level of daily stress, location of residence in terms of air pollution, and lifestyle considerations (e.g., diet, physical activity levels) which are other limitations of the current study. Finally, the study population was relatively young with an average age of 36 and the overall proportion of female to male smokers was significantly different than prior cited literature regarding smoking among Iranian adults. It’s recommended to conduct larger studies in other parts of Iran to recruit older smokers and an appropriate ratio of male to female smokers, to enhance understanding of this phenomenon among different sexes and age groups. The perceived risk of other contributing factors to cardiovascular disease (e.g., air pollution in larger cities such as Tehran) can also be evaluated to assess the overall perceived risk of CVD. The findings of this study underscore a notable limitation in the predictive modeling of perceived risk, with the assessed variables accounting for only approximately 10% of the observed variance. This limited explanatory power likely reflects the absence of robust predictors within the model, as many variables demonstrated only marginal associations with perceived risk. Moreover, it is crucial to acknowledge the potential influence of unmeasured factors, such as social dynamics and cultural contexts, which may play a significant role in shaping risk perceptions but were not captured in the current analysis. Additionally, the inherent complexity of perceived risk, driven by psychological, emotional, and cognitive processes, poses challenges for its quantification. These subjective dimensions highlight the need for more comprehensive and nuanced modeling approaches in future research to better account for the multifaceted nature of risk perception.
Conclusion
The results of this study indicate a relatively low perceived risk of participants. Study findings have implications for informing public education messaging on cigarette smoking risks, especially related to CVD. Therefore, it is important to develop interventions to educate the public about the harms of smoking in general and its role in causing cardiovascular disease, especially in the long term. Our findings could help health policymakers identify strategies to enhance public health knowledge to increase the perceived risk of cigarette smoking and reduce its consumption. Additionally, our findings highlight new targets for future educational efforts and health warnings that are specific to the perceived risk of CVD among cigarette smokers. To reach a more comprehensive conclusion, longitudinal studies with larger sample sizes can be undertaken in other parts of Iran. Public health professionals and policymakers should put more emphasis on preventive programs on the health harms of smoking, especially among adolescents and youth.
Acknowledgements
The authors express their gratitude to the participants of this study.
Declarations
Ethics approval and consent to participate
All procedures were conducted following the Declaration of Helsinki (revised in 2013). The local ethics committee of Affiliated Golestan University of Medical Sciences approved all experimental protocols (Ethical number: IR.GOUMS.REC.1401.251). Informed consent was obtained from all subjects and/or their legal guardian(s).
Consent for publication
Not Applicable.
Competing interests
The authors declare no competing interests.
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