Skip to main content
Tobacco Use Insights logoLink to Tobacco Use Insights
. 2024 May 13;17:1179173X241253962. doi: 10.1177/1179173X241253962

Smoking Habits and Nicotine Dependence Among the General Lebanese Population Before and During Both the Economic Crisis and COVID-19 Pandemic

Nina Rossa Haddad 1,*, Charbel B Aoun 1,2,*, Abdo Mghames 1,*, Mustafa Saleh 1, Mirna N Chahine 1,3,4,
PMCID: PMC11092306  PMID: 38746596

Abstract

Objective

Smoking habits have widely changed over time; however, they remain a well-known fashion that risks people’s health. In addition, nicotine addiction depends on the interplay between several factors. Our study aimed to understand the smoking habits and nicotine dependence in the Lebanese population before (September 2019) and during (June 2020) the economic crisis and COVID-19 pandemic.

Methods

This observational cross-sectional survey-based study included 1560 Lebanese individuals aged between 13 and 75 years old from June till October 2020. Data collection was performed through an electronic survey including patients’ demographics and validated instruments to assess addiction to nicotine (CAGE, Four C’s, Fagerström test, and Smoker’s profile scores).

Results

Out of 1560 participants, 794 (50.9%) were males. The mean age was 26.5 ± 11.69 years, and 67.8% were aged between 18 and 25 years old. We found that 865 (55.4%) participants were smokers. In addition, smoking cigarettes or vaping, significantly increased between September 2019 and June 2020. Our smoker group showed a high CAGE positivity (P < .001), marked compulsion (P < .001), and a considerable lack of self-control to surcease smoking (P < .001). Furthermore, the nicotine dependence score (NDS) increased with age (B = .166) and decreased with higher educational levels (B = −.219).

Conclusion

During the economic crisis and the COVID-19 pandemic, the Lebanese population showed an increased prevalence of smoking, a high level of CAGE positivity, strong compulsion, and a significant lack of self-control when it came to quitting smoking. This strongly entails public health measures for smoking cessation through national awareness campaigns.

Keywords: smoking habits, Lebanon, nicotine dependence, COVID-19, economic crisis

Introduction

Smoking habits are widely recognized as harmful practices that endanger people’s health. Chronic smoking has been linked not only with cardiovascular events and respiratory injuries 1 but also with substance addiction, a major cause of disability and premature death. 2 People usually face difficulties when it comes to quitting smoking due to the continuous activation of their reward system by nicotine and the severity of the withdrawal symptoms experienced during abstinence. 3 Nicotine addiction depends on the interplay between pharmacologic properties, genetics, learned or conditioned factors, and environmental factors. 4

The most prevalent smoking methods include tobacco, waterpipe, vaping, and electronic cigarettes. Understanding smoking habits revolve around divergent parameters such as gender, 5 age group, 6 socioeconomic status, 7 country of residence, 8 psychiatric, 9 psychological, 10 and environmental factors. 11 For instance, an American study showed that smoking traditional cigarettes has decreased over the previous 40 years, while electronic cigarettes, hookahs, and vaping are rapidly increasing among adolescents. 6 Moreover, a Brazilian study reported a higher prevalence of tobacco use among lower socioeconomic classes. 7 The 2008 financial crisis in the United States also resulted in an increase in the number of smokers, with a substantially higher prevalence of smoking among unemployed individuals. 12 Since October 2019, Lebanon has witnessed an unprecedented socioeconomic crisis and financial collapse. In Lebanon, a recent study showed that e-cigarettes are considered as healthier substitutes for cigarettes and waterpipes, considering them as viable aids for quitting smoking. However, due to the recent economic downturn, e-cigarettes have become unaffordable. 13 In addition, the COVID-19 pandemic has aggravated the economic and social crisis in Lebanon. To the best of our knowledge and compared with the aforementioned literature, no previous comprehensive studies have explored the interplay between the recent economic crisis, the COVID-19 pandemic, and nicotine dependence in Lebanon, in a single cohort study. Therefore, the general objective of our study was to understand the smoking habits (cigarettes, electronic cigarettes/vaping, waterpipe) among the general Lebanese population following a biphasic pattern: before (September 2019) and during (June 2020) the economic crisis and COVID-19 pandemic. Specifically, we identified differences between the demographics and habits of smokers vs non-smokers. We also determined the type of tobacco smokers and estimated their nicotine dependence and the factors affecting it.

Methods

Study design and participants

An observational cross-sectional survey-based study was conducted between June and October 2020. The sample included 1560 participants. The target was to collect 400 participants distributed evenly from all Lebanese governorates by using the Slovin’s formula. All individuals -smokers, and non-smokers- aged between 13 and 75 years old were included in this study. Non-Lebanese citizens were excluded from our study.

Tools and procedure of data collection

Tools

Data collection was performed through a validated questionnaire including standard instruments to assess the addiction to nicotine (Four C’s test and CAGE questionnaire for smoking),14-16 and to score the intensity of the reasons that people smoke (Smoker’s profile scores through the Fagerström test). 15

In this study, participants were categorized as never-smokers, ex-smokers, and smokers, with the latter subgroup, further divided into light (smoked fewer than 11 cigarettes per day), moderate (smoked more than 10 but fewer than 21 cigarettes per day), and heavy smokers (smoked more than 20 cigarettes per day). 17

The smoking behavior, dependence, and nicotine addiction were assessed using four tests: the 4 C’s test, Fagerström test, smokers’ profile test, and CAGE questionnaire for smoking, modified from the familiar CAGE questionnaire for alcoholism. These assessments were used to compare the addiction levels of daily and occasional smokers before and during the economic crisis and COVID-19 pandemic. 16 The Fagerström Test, a standard tool used to measure physical nicotine dependence, was scored based on yes or no items (0-1) and multiple-choice items (0-3). The total score ranged from 0 to 10, with a Nicotine Dependence Score (NDS) of 1-2 considered as low dependence, 3-4 considered as low to moderate dependence, 5-7 considered as moderate dependence, and a score higher than 8 indicating a higher level of physical nicotine dependence, thus requiring nicotine replacement therapy as part of the patient’s care plan to manage withdrawal symptoms. The CAGE questionnaire was also utilized to screen patients for addictive disorders, specifically smoking behavior. The questions used were revised from the original questionnaire for alcoholism. It is a four-question screening tool that helps in the diagnosis of addictive disorders; it is based on simple yes or no questions that assess the will to cut down on substance use, annoyance by criticism, guilty feeling and eye-opener (which assesses the need for substance use first thing in the morning). Lastly, psychiatrists, psychotherapists, social workers, and addiction 18 counselors have used the Diagnostic and Statistical Manual of Mental Disorders, 4th ed. (DSM-IV) criteria to diagnose substance dependence, including nicotine addiction. These criteria can be grouped into four categories in the Four C’s test: Compulsion (irresistible urge for substance use), Control (complete loss of control over drug use), Cutting down (withdrawal symptoms), and Consequences (use despite awareness of the negative consequences); those four factors must be present to separate addiction from neurologic disorders. This is a simple approach to determine if someone is dealing with a substance use disorder.

In addition, the questionnaire, written in English and Arabic languages, included questions on participants’ socio-economic and demographic characteristics (eg, age, gender, marital status, educational level, medical status, and money spent), their smoking status, profile, and habits (cigarettes, electronic cigarettes/vaping, waterpipe) with additional information on knowledge towards smoking cessation.

Procedure of data collection

The study survey was spread through google forms on social media (WhatsApp, Facebook, Twitter…). Only a third of our participants filled out the form by themselves. However, most of our participants (62%) were directly interviewed via face-to-face interviews or over phone calls. This survey was also made available in Arabic using the inverted method of Fortin where it was first translated from English to Arabic, and then the Arabic version was retranslated to English by a healthcare professional/translator to compare the agreement between both surveys. A pre-test was carried out with ten people who were not part of the sample to validate the understanding and clarity of the questionnaire items. 14

Statistical analysis

A descriptive analysis was enrolled, and the variables were presented as per their type. The categorical variables were presented as frequency and proportions (for example gender, smoking). The continuous variables were presented as the frequency, mean, median and standard deviation (for example age). Bivariate analysis was enrolled to test the differences between smokers and non-smokers in the function of the demographic and habits characteristics. In addition, bivariate analysis was conducted to test the differences between daily smokers and occasional smokers in the function of the demographic and habits characteristics and to test differences between daily smokers and occasional smokers in the function of the nicotine dependence tests (CAGE, four Cs, and smokers’ profile). Tests used in bivariate settings were chi-square, fisher, and student T-tests. Finally, a linear regression was conducted in order to assess factors affecting Nicotine Dependence Score (NDS). P-value <.05 was considered statistically significant. IBM SPSS version 25 was used for statistical analysis.

Ethical considerations

Prior to proceeding with our study, the Institutional Review Board of Al Hayat Hospital approved our study (Reference Number: ETC-15-2020). Patients were consented orally by the interviewer who carefully explained the background, objectives, risks, and advantages of the study, and clearly informed them that they have the right to withdraw at any time. By consenting, participants agreed that they provided credible information and that they voluntarily agreed to participate in our study. The informed consent was prepared in English and Arabic. Data was transcribed and then destroyed.

Results

Demographic and lifestyle characteristics of the study population

Out of 1560 participants, 794 (50.9%) were males and 766 (49.1%) were females (Table 1). The mean age was 26.5 ± 11.69 years, and 67.8% were aged between 18 and 25 years old. Among the 1560 participants, 80.5% were single and 17.1% were married. Participants were distributed among all the Lebanese governorates: Mount Lebanon (29.7%), Beirut (27.6%), South (11.7%), North (7.7%), Bekaa (7.5%), Nabatieh (7.1%), Akkar (4.6%), and Baalbek-Hermel (4.2%). More than half of the participants (59.7%) had a University/Technical level education, and 33% had a medical/healthcare-related education.

Table 1.

Demographic Characteristics According to Smoking Status (Smoker versus Non-smoker).

Variable Smoking Status P-Value
Smoker (N = 822) Non-smoker (N = 738)
N % N %
Gender <.001*
 Male 505 61.40 289 39.20
 Female 317 38.60 449 60.80
Age <.001¥
 Mean (SD) 29.12 (13.09) 23.62 (9.07)
 Min – Max 15 - 76 13 – 66
Age <.001*
 <18 years 13 1.60 70 9.50
 18 - 25 years 511 62.20 546 74.00
 26 - 34 years 111 13.50 55 7.50
 35 - 44 years 39 4.70 20 2.70
 45 - 54 years 89 10.80 27 3.70
 >54 years 59 7.20 20 2.70
Marital status <.001*
 Single 605 73.60 651 88.20
 Married 186 22.60 81 11.00
 Divorced 20 2.40 6 .80
Widow/widower 11 1.30 0 .00
Living location <.001*
 Beirut governorate 267 32.50 163 22.10
 Bekaa governorate 48 5.80 69 9.30
 Mount Lebanon governorate 235 28.60 228 30.90
 North governorate 56 6.80 64 8.70
 South governorate 103 12.50 79 10.70
 Baalbek-Hermel governorate 22 2.70 43 5.80
 Akkar governorate 24 2.90 48 6.50
 Nabatiyeh governorate 67 8.20 44 6.00
Educational level <.001*
 I did not attend school 2 .20 0 .00
 Elementary school 65 7.90 15 2.00
 High school 154 18.70 179 24.30
 University/Technical institute 511 62.20 420 56.90
 Post education (Masters, PhD) 90 10.90 124 16.80
Medical/healthcare-related education <.001*
 Yes 205 24.90 310 42.00
 No 617 75.10 428 58.00

*Chi Square test ¥ Student t-test.

In addition, more than 57% of the participants stated minimal physical exercise and more than 88% were coffee consumers. Moreover, between September 2019 and June 2020, 71.7%, 66.2%, and 64.5% of the participants stated no changes in coffee consumption, health ratings, and the frequency of physical exercise, respectively.

Smoking habits and status

Our population included 39.8% of daily smokers, 12.9% of occasional smokers, 44.6% of non-smokers, and 2.8% of ex-smokers (Figure 1). Out of the ex-smokers’ group, 79.1% were tobacco cigarette smokers and 44.2% were tobacco waterpipe (nargileh) smokers. Among the ex-smokers, 44.2% have stopped smoking within the previous 2 years. Within this subset of ex-smokers, 79.1% were able to quit smoking without assistance, whereas 7% transitioned to using vaping or e-cigarette products. Moreover, the top three reasons being a non-smoker, were that most participants believed that smoking is harmful to their health (78.4%), they didn’t like the taste/odor (55.5%), or that it is harmful to their surroundings (37.7%).

Figure 1.

Figure 1.

Distribution of participants’ smoking status.

Statistically significant differences were shown between smokers and non-smokers in the function of demographic characteristics (Table 1). Most smokers were males (61.4%) compared to 39.2% in the non-smokers group (P < .001). Smokers had higher age (mean = 29.1 ± 13.1) compared to non-smokers (mean = 23.6 ± 9.1) (P < .001). In addition, 42% of the non-smokers were healthcare workers compared to 24.9% of the smokers (P < .001).

Statistically significant differences were also shown between daily smokers and occasional smokers in the function of demographic characteristics. The male gender was more prevalent in daily smokers (68.1%) vs occasional smokers (40.8%) (P < .001). Daily smokers had higher age (mean = 30.05 ± 13.8) compared to occasional smokers (mean = 26.25 ± 10.2) (P < .001). Concerning the educational level, occasional smokers were highly educated (63.7% university or technical level and 17.4% post-education (Master or Ph.D.)) compared to daily smokers (P < .001). In addition, 38.3% of occasional smokers were healthcare workers compared to 20.6% of daily smokers (P < .001).

Smoking consumption and nicotine addiction and dependence

Participants reported a statistically significant increase in smoking consumption from September 2019 till June 2020. The mean number of cigarettes consumed in September 2019 was 1.49 ± 1.16 and increased to 1.62 ± 1.21 in June 2020 (P < .001). Within the light smokers’ group, 57.69% had a low NDS in 2019 compared to none having a high NDS (P < .001). Within the heavy smokers’ group, 4.03% had a low NDS in 2019 compared to 38.93% having a high NDS (P < .001). Within the light smokers’ group, 65.29% had a low NDS in 2020 compared to none having a high NDS (P < .001). Within the heavy smokers’ group, 2.96% had a low NDS in 2020 compared to 42.01% having a high NDS (P < .001).

Additionally, the CAGE questionnaire demonstrated that out of 822 smokers, 45% felt a need to cut down or control their smoking, but had difficulty doing so and 48.2% had ever smoked within half an hour of waking up (Eye-opener). Furthermore, the Four C’s test showed that 59.2% of the 822 smokers participants had ever smoked more than they intend to and 19.8% had ever neglected a responsibility because they were smoking. Also, 99.1% knew that smoking hurts their body and expect to live a mean of 58.20 (±24.17) years if they continue smoking compared to a mean of 63.69 (±23.69) years if they were able to quit smoking today and never start again.

Moreover, the Smokers’ Profile is a useful tool to screen for addictive disorders and to score the intensity of the following four reasons that people smoke. First, the stress relief profile in smokers included 4 items and showed that out of 822 smokers, 76.3% thought about smoking when they went frustrated or angry and 67.5% smoked to calm down if they went upset or scared. Second, the conditioned responses profile in smokers included 4 items and demonstrated that out of 822 smokers, 46.2% always smoked while they were driving a car or drinking a cup of coffee and 71.9% smoked automatically if they were with someone who was smoking. Third, the relief of withdrawal symptoms profile in smokers included 4 items and proved that out of 822 smokers, 25.9% got irritable if they had to go more than 2 hours without smoking and 39.2% got irritable, moody, or had trouble concentrating during the first few days after they stopped. Fourth, the elevation of depressed mood profile in smokers included 2 items and demonstrated that out of 822 smokers, 27.7% liked to smoke and do not consider quitting compared to 47.3% who liked to smoke but know they need to quit.

Fagerström test was utilized to assess the Nicotine Dependence Score among participants. Table 2 presents the results of the Fagerström test, highlighting the responses indicating the most severe nicotine dependence among participants. Notably, a significant proportion of participants reported smoking within the first five minutes after waking up (12.4%), finding it difficult to refrain from smoking in restricted locations such as churches or hospitals (24.1%), rating the first cigarette of the day as highly important (41.7%), smoking more than 60 cigarettes per day (10.9%), smoking more heavily during the first few hours after waking up (23.8%), and smoking even when they were ill (28.5%).

Table 2.

Fagerström Test Between Daily and Occasional Smokers.

Frequency Percent
How soon after you wake up do you smoke?
 Within 5 minutes 102 12.4
 5 to 30 minutes 199 24.2
 31 to 60 minutes 107 13.0
 After 60 minutes 414 50.4
Do you find it difficult not to smoke in places where you shouldn’t, such as in church or school, in a movie, at the library, on a bus, in court or in a hospital?
 Yes 198 24.1
 No 624 75.9
Which cigarette would you most hate to give up; which cigarette do you treasure the most?
 The first one in the morning 343 41.7
 Any other one 479 58.3
How many cigarettes do you smoke each day?
 0 155 18.9
 10 or fewer 265 32.2
 11 to 20 233 28.3
 21 to 30 79 9.6
 31 or more 90 10.9
Do you smoke more during the first few hours after waking up than during the rest of the day?
 Yes 196 23.8
 No 626 76.2
Do you still smoke if you are so sick that you are in bed most of the day, or if you have a cold or the flu and have trouble breathing?
 Yes 234 28.5
 No 588 71.5

Analysis of smoking status in smokers

Concerning the CAGE (Questionnaire Modified for Smoking Behavior) items, statistically significant differences were shown between daily and occasional smokers in the function of the 4 CAGE items. Out of 621 daily smokers, 55.2% felt a need to cut down or control their smoking but had difficulty doing so compared to 13.4% of occasional smokers (P < .001). Out of 621 daily smokers, 43.5% felt guilty about their smoking or about something they did while smoking, compared to 28.9% of occasional smokers (P < .001).

Concerning the Four Cs’ test items, statistically significant differences were shown between daily and occasional smokers in the function of the Four Cs’ test items. Daily smokers were more nicotine dependent compared to occasional smokers. Out of 621 daily smokers, 69.7% had ever smoked more than they intend to, compared to 26.9% of occasional smokers (P < .001). Out of 621 daily smokers, 53 % had any of the following symptoms when they went for a while without smoking (agitation, difficulty concentrating, irritability, mood swings) whereas 83.1% of occasional smokers had never felt the cited symptoms (P < .001).

Concerning the Fagerström test, statistically significant differences were shown between daily and occasional smokers in the function of the Fagerström items. Daily smokers were more nicotine dependent compared to occasional smokers wherein mean NDS (P < .001) was much higher in daily smokers (3.68 ± 2.74 over 10) compared to occasional smokers (.36 ± .94 over 10) (Figure 2).

Figure 2.

Figure 2.

Nicotine dependence score between daily and occasional smokers.

Standardized linear regression results showed that 2 variables were associated with NDS: age (P < .001) and educational level (P < .001) (Table 3). NDS increased with age (B = .166) and decreased with a higher educational level (B = −.219). The model was moderately adequate (Regression sum of squares = 514.95, residual = 4430.20, Mean Square = 171.65, and R = .323).

Table 3.

Linear Regression for Factors Associated With NDS.

Model Unstandardized Coefficients Standardized Coefficients t P-value
B Std. Error Beta
(Constant) 6.444 .759 8.491 .000
Age .034 .009 .166 3.952 .000
Educational level −.802 .152 −.219 −5.289 .000

aDependent Variable: Nicotine Dependence Score.

Discussion

To understand smoking habits among the Lebanese population, we conducted an observational cross-sectional study between June and October 2020, where 1560 participants from all over the country responded to an electronic survey. We found that 865 subjects were smokers. In addition, smoking significantly increased between September 2019 and June 2020. Almost all responders acknowledged that smoking harms their bodies and showed a high CAGE positivity.

Comparison between smokers and non-smokers

The prevalence of smoking in our study reached 55.4%, which was higher than the rate reported in Lebanon in 2018 (42.1%) 19 and higher than the ones reported in some studies done in Taiwan and Germany, on individuals aged more than 40 years old and more than 10 years old respectively20,21 and lower than the rate expressed in a Chinese study. 22 Geographic location is an important factor responsible for these distinct findings, different regulations in different parts of the world might also play an important role in this. Many studies agreed with our findings since our sample of smokers included more males, 20 had higher age and lower educational levels, 23 which could be explained by the fact that older people might be less educated than younger people resulting in a decreased level of awareness about the possible adverse outcomes of smoking. In contrast, several studies showed that the mean age of smokers was higher 20 Click or tap here to enter text.than the mean age of smokers found in our study. Tobacco usage has also been shown to aggravate poverty and is responsible for increased financial distress. 24 Concerning exercise, our results are in agreement with the literature, since smokers are less likely to exercise. 25 This might be due to physiological and behavioral factors, including prioritizing smoking and nicotine intake over other activities (including exercise), health problems (respiratory, cardiovascular, etc…) and motivational factors.25,26 Our study showed also that smokers had a decrease in coffee and caffeinated drinks consumption and an increase in cigarettes/electronic cigarettes consumption between September 2019 and June 2020 most probably related to the economic crisis that Lebanon has been witnessing, which has caused a substantial increase in the prices of cigarettes and caffeinated drinks. We believe that smokers in our population were more likely to buy cigarettes than caffeinated drinks. Our sample was also characterized by poor health quality which was consistent with the findings of previously published papers,26,27 this could be attributed to the deleterious effects of nicotine on the heart, arteries, and lungs that hamper physical activity1,28 leading to poor health quality. A Chinese study, conducted on a large-scale population, found that smokers are more likely to experience sleep disturbances than non-smokers, 29 and Bhujade et al reported poor general and oral health among tobacco users. 30 For instance, The NIH-AARP cohort concluded that lifetime cigarette smoking is a major predictor of death after the age of 70. 31

Comparison between daily and occasional smokers

As reported in previous research, the reasons behind smoking and its frequency varied between cigarettes being a stress relief and an enjoyable habit, peer pressure, social media influence, and desirable image.32,33 Conforming to the literature, daily smoking was associated with lower educational levels34,35 and unfavorable habits.34,36 Daily smokers, compared to occasional smokers, were more likely to spend money since they smoked more than they intend to as shown in our results. This was also confirmed by Tolajamo et al. 37 Moreover, daily smokers found it more difficult to cut out smoking in comparison to occasional smokers.32,37 This could be due to higher levels of nicotine dependence, habitual behaviors and tolerance in daily smokers as compared to occasional smokers. 35

Regarding FTND, it is recognized as a good and simple tool to measure nicotine dependence. 38 A Vietnamese cross-sectional study, conducted solely on men, reported an average FTND score of 4.02, higher than ours (2.97), and an increased score was associated with a longer smoking period and more difficulty to quit smoking, 39 this higher FTND score could be explained by the fact that the Vietnamese participants were all males and daily smokers while our FTND score was calculated for all smokers (daily and occasional). Another study done by Roz et al showed that FTND score was significantly higher among cigarette smokers than electronic cigarette smokers, 40 while Jankowsky et al found the opposite results with 2 times increase in FTND in electronic cigarette smokers compared to traditional smokers. 41 Strikingly, Donny et al reported that nicotine dependence could be absent in moderate and heavy smokers underlining some resistance mechanisms, 42 in contrast to our findings that showed that nicotine dependence is higher in moderate and heavy smokers, which could be explained by the fact that most dependent people consume more cigarettes than the less dependent ones. 43

In conjunction to our findings, Breslau and Fenn (1993) demonstrated that older smokers were likely to report higher nicotine dependence compared to younger individuals. 44 This could be explained by the fact that nicotinic receptors decrease with age, resulting in more cravings and more cigarettes consumption to maintain a high level of nicotine in the blood. 45 However, this correlation was not underlined across several studies. Vink et al 46 and Aryal et al 47 did not find any association between age and nicotine dependence and another study showed that middle age groups (45-64 years old) were more likely nicotine dependent compared to older groups (above 65 years old) and younger groups (below 45 years old). 48 In addition, previous papers demonstrated that low educational level is related to nicotine dependence.49,50 Our data confirmed this finding as well. A possible explanation is that subjects with low educational levels suffer from a low socioeconomically status associated with financial and psychological stress and social disadvantage. In addition, many studies showed that there was a significant effect of smoking condition in reducing stress. 51 Hence, to cope with their more stressful life they smoke (which is showed in our results in the stress relief profile), resulting in nicotine dependence.

Moreover, the four Cs test applied in our study showed a marked compulsion (P < .001). Based on case studies, it has been proposed that the International Classification of Diseases criteria (ICD) criterion of a compulsion to use tobacco is not only essential but also adequate for diagnosing addiction. They also highlighted that the compulsion to use tobacco plays a pivotal role in the underlying pathophysiological processes of tobacco addiction. 52 The compulsion and urge to smoke serve as a more sensitive clinical indicator than the ICD criteria for assessing tobacco dependence since all smokers who have failed to quit smoking consistently exhibit a strong compulsion to smoke. 43 This contradicts the assumption implicit in the ICD that compulsion is neither necessary nor sufficient for a diagnosis.

The results of the CAGE questionnaire and the Four C’s test provide valuable insights into the smoking behaviors and attitudes of the 822 participants. The CAGE questionnaire revealed that a significant proportion of the smokers felt a need to cut down or control their smoking, had difficulty doing so, and had engaged in early morning smoking, which are indicators of nicotine dependence. Additionally, the Four C’s test showed that a substantial number of participants had smoked more than they intended to and had neglected responsibilities because of smoking, further highlighting the challenges associated with controlling their smoking behavior. These results highlight the challenging complex nature of nicotine dependence and the challenges associated with smoking cessation. They also emphasize the importance of tailored and evidence-based smoking cessation interventions to address the specific needs and behaviors of individuals with nicotine dependence. The smoker’s profile results showed a strong presumed association between smoking and stress relief among the participants. Environmental and social cues play a significant role in their smoking habits. The relief of withdrawal symptoms profile demonstrated that a less than half of smokers experienced irritability when unable to smoke for a few hours. These findings highlight the presence of withdrawal symptoms and the challenges associated with smoking cessation among selected participants. Finally, the elevation of depressed mood profile towards quitting, underscores the complex relationship between smoking and mood regulation among the participants and their multifaceted attitude towards smoking cessation.

Our findings highlighted a strong association between age, educational level, and nicotine dependence, contributing to a growing body of literature on the complex interplay between these factors during crises,12,53 suggesting that older individuals and those with lower educational levels may be at higher risk of nicotine dependence. Considering the practical implications of our findings, there is an evident need for targeted public health interventions during crises to address the unique challenges faced by these vulnerable populations. This may include mental health support groups and community-based initiatives to mitigate the impact of crises on smoking behavior, especially in lower-middle income countries.

In terms of study limitations, due to the COVID-19 pandemic and subsequent lockdown, we had to collect data from participants using electronic surveys made available on social media; the surveys were filled in a self-reported manner causing some reporting bias. Certainly, this has limited the ability of the population that was not active on the internet, particularly the elderly, and those with very low socioeconomic and educational status to be included in our study. Indeed, our population was overrepresented by younger, well-educated participants, and healthcare workers, thus favoring selection and sampling bias. Furthermore, some parameters in our study were related to previous behaviors from almost a year ago, giving rise to a recall bias which can affect the accuracy of exposure or outcome data. Since this is a retrospective study, we recognize that there could be missing data that could affect our results due to variables that were not considered to be relevant in advance, which can lead to information bias. This can also be challenging to control for all potential confounding variables. In addition, in our study, the FTND score was calculated for all smokers (including traditional cigarettes, waterpipes, vaping and electronic cigarettes (e-cigs) smoking). However, this test is best used to assess the level of nicotine dependence in cigarette smokers mainly and not commonly suitable for use with other methods of smoking. Finally, due to variations in population density, socioeconomic factors, and other regional characteristics, certain governorates might exhibit unique patterns or trends that are not adequately represented in the overall national data. As a result, the generalized results might overlook specific challenges that are specific to those areas.

Despite these limitations, the findings of this study have contributed significantly to the literature which is facing a paucity of research regarding the impact of the current Lebanese crisis in all its dimensions (social and financial stress) on the smoking habits of the population. We also took on a different aspect of this phenomenon, which is nicotine dependence, because it remains the main problem and the element that will drive individuals into trying other alternatives for smoking cessation. Thus, the essential aim of our study was to discourage its use and as a result, decrease its prevalence in our community to preserve the health of the Lebanese population. Indeed, bans on smoking in public places to be effective need the support of a broad public and a specific subgroup of smokers.

Conclusion

In conclusion, smoking is a tremendous burden on societies worldwide. It is not solely a health problem, but also a social, economic, and psychological issue. Our study found that our smokers had a high level of CAGE positivity, strong compulsion, and a significant lack of self-control when it came to quitting smoking; more than half of smokers attempted to quit, and more than a third had withdrawal symptoms. Our findings suggest that it is time to treat smoking as a disease rather than a risk factor for an illness. Indeed, bans on smoking in public places to be effective need the support of a broad public and a specific subgroup of smokers.

Acknowledgements

We are grateful to Mr. Bachir Atallah for performing the statistical analysis of this study. We also acknowledge Ahed El Abed, Ahmad Lakkis, Ahmad Issawi, Dina Essaily, Ibrahim Jabr, Lea Tahan, Maria Bou Eid, Marie-Belle El Khoury, Michel Boueiz, Michel Nasrallah, Moussa Hojeij, Mustapha Sahili, Raoul Kassis, Rim Chehab, Sarah Ayoub, Solay Farhat, Tia El Kazzi, and Zeinab Hammoud for helping us with our data collection.

Footnotes

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.

References

  • 1.Benowitz NL, Burbank AD. Cardiovascular toxicity of nicotine: implications for electronic cigarette use. Trends Cardiovasc Med. 2016;26:515-523. doi: 10.1016/j.tcm.2016.03.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Benowitz NL. Pharmacology of nicotine: addiction, smoking-induced disease, and therapeutics. Annu Rev Pharmacol Toxicol. 2009;49:57-71. doi: 10.1146/annurev.pharmtox.48.113006.094742 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Alasmari F, Crotty Alexander LE, Hammad AM, Bojanowski CM, Moshensky A, Sari Y. Effects of chronic inhalation of electronic cigarette vapor containing nicotine on neurotransmitters in the frontal cortex and striatum of C57BL/6 mice. Front Pharmacol. 2019;10:885. doi: 10.3389/fphar.2019.00885 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Benowitz NL. Nicotine addiction. N Engl J Med. 2010;362:2295-2303. doi: 10.1056/NEJMra0809890 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Carreira H, Pereira M, Azevedo A, Lunet N. Trends in the prevalence of smoking in Portugal: a systematic review. BMC Publ Health. 2012;12:958. doi: 10.1186/1471-2458-12-958 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Solecki S, Adegite E, Turchi R. Clearing the air: adolescent smoking trends. Curr Opin Pediatr. 2019;31:670-674. doi: 10.1097/MOP.0000000000000810 [DOI] [PubMed] [Google Scholar]
  • 7.Motta JVS, Lima NP, Olinto MTA, Gigante DP. Social mobility and smoking: a systematic review. Ciência Saúde Coletiva. 2015;20:1515-1520. doi: 10.1590/1413-81232015205.01642014 [DOI] [PubMed] [Google Scholar]
  • 8.Ho SY, Chen J, Leung LT. et al. Adolescent smoking in Hong Kong: prevalence, psychosocial correlates, and prevention. J Adolesc Health. 2019;64:S19-S27. doi: 10.1016/j.jadohealth.2019.01.003 [DOI] [PubMed] [Google Scholar]
  • 9.Lam TH, Stewart SM, Ho SY, et al. Depressive symptoms and smoking among Hong Kong Chinese adolescents. Addiction. 2005;100:1003-1011. doi: 10.1111/j.1360-0443.2005.01092.x [DOI] [PubMed] [Google Scholar]
  • 10.Yang G, Ma J, Chen AP, Brown S, Taylor CE, Samet JM. Smoking among adolescents in China: 1998 survey findings. Int J Epidemiol. 2004;33:1103-1110. doi: 10.1093/ije/dyh225 [DOI] [PubMed] [Google Scholar]
  • 11.Leonardi-Bee J, Nderi M, Britton J. Smoking in movies and smoking initiation in adolescents: systematic review and meta-analysis. Addiction. 2016;111:1750-1763. doi: 10.1111/add.13418 [DOI] [PubMed] [Google Scholar]
  • 12.Gallus S, Ghislandi S, Muttarak R. Effects of the economic crisis on smoking prevalence and number of smokers in the USA. Tobac Control. 2015;24(1):82-88. doi: 10.1136/tobaccocontrol-2012-050856 [DOI] [PubMed] [Google Scholar]
  • 13.Mugharbil S, Tleis M, Romani M, Salloum RG, Nakkash R. Understanding determinants of electronic cigarette and heated tobacco product use among young adults in Lebanon: prevention and policy implications. Int J Environ Res Publ Health. 2023;20(5):4273. doi: 10.3390/ijerph20054273. Published 2023 Feb 28 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Talbot N, Fortin M-F, Gagnon J. (2016). Fondements et étapes du processus de recherche : Méthodes quantitatives et qualitatives. 3rd éd. Montréal, Québec: Chenelière Éducation. doi: 10.7202/1042088ar [DOI] [Google Scholar]
  • 15.Rustin TA. Assessing nicotine dependence. Am Fam Physician. 2000;62:579-584 [PubMed] [Google Scholar]
  • 16.Lairson DR, Harrist R, Martin DW, et al. Screening for patients with alcohol problems: severity of patients identified by the CAGE. J Drug Educ. 1992;22:337-352. doi: 10.2190/H8QV-KAYU-QBYH-1LN3 [DOI] [PubMed] [Google Scholar]
  • 17.Pan A, Wang Y, Talaei M, Hu FB, Wu T. Relation of active, passive, and quitting smoking with incident type 2 diabetes: a systematic review and meta-analysis. Lancet Diabetes Endocrinol. 2015;3:958-967. doi: 10.1016/S2213-8587(15)00316-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Guze SB. Diagnostic and statistical manual of mental disorders, (DSM-IV). American Journal of Psychiatry 1995 Aug;152(8):1228-1228. doi: 10.1176/ajp.152.8.1228 [DOI] [Google Scholar]
  • 19.Abdulrahim S, Jawad M. Socioeconomic differences in smoking in Jordan, Lebanon, Syria, and Palestine: a cross-sectional analysis of national surveys. PLoS One. 2018;13:e0189829. doi: 10.1371/journal.pone.0189829 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Chung W-S, Kung P-T, Chang H-Y, Tsai W-C. Demographics and medical disorders associated with smoking: a population-based study. BMC Publ Health. 2020;20:702. doi: 10.1186/s12889-020-08858-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.John U, Hanke M, Meyer C, Schumann A. Gender and age differences among current smokers in a general population survey. BMC Publ Health. 2005;5:57. doi: 10.1186/1471-2458-5-57 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Liu S, Zhang M, Yang L, et al. Prevalence and patterns of tobacco smoking among Chinese adult men and women: findings of the 2010 national smoking survey. J Epidemiol Community Health. 2017;71:154-161. doi: 10.1136/jech-2016-207805 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Kleykamp BA, Heishman SJ. The older smoker. JAMA. 2011;306:876-877. doi: 10.1001/jama.2011.1221 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Efroymson D, Ahmed S, Townsend J, et al. Hungry for tobacco: an analysis of the economic impact of tobacco consumption on the poor in Bangladesh. Tobac Control. 2001;10:212-217. doi: 10.1136/tc.10.3.212 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Heydari G, Hosseini M, Yousefifard M, Asady H, Baikpour M, Barat A. Smoking and physical activity in healthy adults: a cross-sectional study in Tehran. Tanaffos. 2015;14:238-245 [PMC free article] [PubMed] [Google Scholar]
  • 26.Rezaei S, Karami Matin B, Kazemi Karyani A, et al. Impact of smoking on health-related quality of life: a general population survey in west Iran. Asian Pac J Cancer Prev APJCP. 2017;18:3179-3185. doi: 10.22034/APJCP.2017.18.11.3179 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Chang JT, Anic GM, Rostron BL, Tanwar M, Chang CM. Cigarette smoking reduction and health risks: a systematic review and meta-analysis. Nicotine Tob Res. 2021;23:635-642. doi: 10.1093/ntr/ntaa156 [DOI] [PubMed] [Google Scholar]
  • 28.Mishra A, Chaturvedi P, Datta S, Sinukumar S, Joshi P, Garg A. Harmful effects of nicotine. Indian J Med Paediatr Oncol. 2015;36:24-31. doi: 10.4103/0971-5851.151771. Official journal of Indian Society of Medical & Paediatric Oncology [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Liao Y, Xie L, Chen X, et al. Sleep quality in cigarette smokers and nonsmokers: findings from the general population in central China. BMC Publ Health. 2019;19:808. doi: 10.1186/s12889-019-6929-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Bhujade R, Ibrahim T, Wanjpe AK, Chouhan DS. A comparative study to assess general health status and oral health score of tobacco users and nonusers in geriatric population in central India. J Fam Med Prim Care. 2020;9:3387-3391. doi: 10.4103/jfmpc.jfmpc_157_20 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Nash SH, Liao LM, Harris TB, Freedman ND. Cigarette smoking and mortality in adults aged 70 years and older: results from the NIH-AARP cohort. Am J Prev Med. 2017;52:276-283. doi: 10.1016/j.amepre.2016.09.036 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Oksuz E, Mutlu ET, Malhan S. Characteristics of daily and occasional smoking among youths. Public health. 2007;121:349-356. doi: 10.1016/j.puhe.2006.12.007 [DOI] [PubMed] [Google Scholar]
  • 33.Jarvis MJ. Why people smoke. BMJ (Clinical Research ed). 2004;328:277-279. doi: 10.1136/bmj.328.7434.277 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Baron R, Manniën J, de Jonge A, et al. Socio-demographic and lifestyle-related characteristics associated with self-reported any, daily and occasional smoking during pregnancy. PLoS One. 2013;8:e74197. doi: 10.1371/journal.pone.0074197 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Tomioka K, Kurumatani N, Saeki K. The association between education and smoking prevalence, independent of occupation: a nationally representative survey in Japan. J Epidemiol. 2020;30:136-142. doi: 10.2188/jea.JE20180195 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Swan JH, Brooks JM, Amini R, Moore AR, Turner KW. Smoking predicting physical activity in an aging America. J Nutr Health Aging. 2018;22:476-482. doi: 10.1007/s12603-017-0967-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Toljamo T, Hamari A, Nieminen P, Kinnula VL. Young male daily smokers are nicotine dependent and experience several unsuccessful quit attempts. Scand J Prim Health Care. 2012;30:183-188. doi: 10.3109/02813432.2012.704809 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Fidler JA, Shahab L, West R. Strength of urges to smoke as a measure of severity of cigarette dependence: comparison with the Fagerström Test for Nicotine Dependence and its components. Addiction. 2011;106:631-638. doi: 10.1111/j.1360-0443.2010.03226.x [DOI] [PubMed] [Google Scholar]
  • 39.Kõks G, Tran HDT, Ngo NBT, et al. Cross-sectional study to characterise nicotine dependence in central Vietnamese men. Subst Abuse. 2019;13:1178221818822979. doi: 10.1177/1178221818822979 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.González Roz A, Secades Villa R, Weidberg S. Evaluating nicotine dependence levels in e-cigarette users. Adicciones. 2017;29:136-138. doi: 10.20882/adicciones.905 [DOI] [PubMed] [Google Scholar]
  • 41.Jankowski M, Krzystanek M, Zejda JE, et al. E-cigarettes are more addictive than traditional cigarettes-a study in highly educated young people. Int J Environ Res Publ Health. 2019;16. doi: 10.3390/ijerph16132279 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Donny EC, Dierker LC. The absence of DSM-IV nicotine dependence in moderate-to-heavy daily smokers. Drug Alcohol Depend. 2007;89:93-96. doi: 10.1016/j.drugalcdep.2006.11.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.O’Loughlin J, DiFranza J, Tyndale RF. et al. Nicotine-dependence symptoms are associated with smoking frequency in adolescents. Am J Prev Med. 2003;25:219-225. doi: 10.1016/s0749-3797(03)00198-3 [DOI] [PubMed] [Google Scholar]
  • 44.Breslau N, Fenn N, Peterson EL. Early smoking initiation and nicotine dependence in a cohort of young adults. Drug Alcohol Depend. 1993;33:129-137. doi: 10.1016/0376-8716(93)90054-t [DOI] [PubMed] [Google Scholar]
  • 45.Mitsis EM, Cosgrove KP, Staley JK, et al. Age-related decline in nicotinic receptor availability with [(123)I]5-IA-85380 SPECT. Neurobiol Aging. 2009;30:1490-1497. doi: 10.1016/j.neurobiolaging.2007.12.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Vink JM, Willemsen G, Beem AL, Boomsma DI. The Fagerström test for nicotine dependence in a Dutch sample of daily smokers and ex-smokers. Addict Behav. 2005;30:575-579. doi: 10.1016/j.addbeh.2004.05.023 [DOI] [PubMed] [Google Scholar]
  • 47.Aryal UR, Bhatta DN, Shrestha N, Gautam A. Assessment of nicotine dependence among smokers in Nepal: a community based cross-sectional study. Tob Induc Dis. 2015;13:26. doi: 10.1186/s12971-015-0053-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Li H, Zhou Y, Li S, et al. The relationship between nicotine dependence and age among current smokers. Iran J Public Health. 2015;44:495-500 [PMC free article] [PubMed] [Google Scholar]
  • 49.Shahwan S, Abdin E, Shafie S, et al. Prevalence and correlates of smoking and nicotine dependence: results of a nationwide cross-sectional survey among Singapore residents. BMJ Open. 2019;9:e032198. doi: 10.1136/bmjopen-2019-032198 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Yang T, Shiffman S, Rockett IRH, Cui X, Cao R. Nicotine dependence among Chinese city dwellers: a population-based cross-sectional study. Nicotine Tob Res. 2011;13:556-564. doi: 10.1093/ntr/ntr040 [DOI] [PubMed] [Google Scholar]
  • 51.Levin ED, Rose JE, Behm F, Caskey NH. The effects of smoking-related sensory cues on psychological stress. Pharmacol Biochem Behav. 1991;39(2):265-268. doi: 10.1016/0091-3057(91)90177-4 [DOI] [PubMed] [Google Scholar]
  • 52.DiFranza JR, Ursprung WW, Carson A. New insights into the compulsion to use tobacco from an adolescent case-series. J Adolesc. 2010;33(1):209-214. doi: 10.1016/j.adolescence.2009.03.009 [DOI] [PubMed] [Google Scholar]
  • 53.Widome R, Joseph AM, Hammett P, et al. Associations between smoking behaviors and financial stress among low-income smokers. Prev Med Rep. 2015;2:911-915. doi: 10.1016/j.pmedr.2015.10.011. Published 2015 Oct 29 [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Tobacco Use Insights are provided here courtesy of SAGE Publications

RESOURCES