introduction of smoking research paper

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• Published: 24 March 2022

Tobacco and nicotine use

• Bernard Le Foll 1 , 2 ,
• Megan E. Piper 3 , 4 ,
• Christie D. Fowler 5 ,
• Serena Tonstad 6 ,
• Laura Bierut 7 ,
• Lin Lu   ORCID: orcid.org/0000-0003-0742-9072 8 , 9 ,
• Prabhat Jha 10 &
• Wayne D. Hall 11 , 12  

Nature Reviews Disease Primers volume  8 , Article number:  19 ( 2022 ) Cite this article

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• Disease genetics
• Experimental models of disease
• Preventive medicine

Tobacco smoking is a major determinant of preventable morbidity and mortality worldwide. More than a billion people smoke, and without major increases in cessation, at least half will die prematurely from tobacco-related complications. In addition, people who smoke have a significant reduction in their quality of life. Neurobiological findings have identified the mechanisms by which nicotine in tobacco affects the brain reward system and causes addiction. These brain changes contribute to the maintenance of nicotine or tobacco use despite knowledge of its negative consequences, a hallmark of addiction. Effective approaches to screen, prevent and treat tobacco use can be widely implemented to limit tobacco’s effect on individuals and society. The effectiveness of psychosocial and pharmacological interventions in helping people quit smoking has been demonstrated. As the majority of people who smoke ultimately relapse, it is important to enhance the reach of available interventions and to continue to develop novel interventions. These efforts associated with innovative policy regulations (aimed at reducing nicotine content or eliminating tobacco products) have the potential to reduce the prevalence of tobacco and nicotine use and their enormous adverse impact on population health.

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Introduction.

Tobacco is the second most commonly used psychoactive substance worldwide, with more than one billion smokers globally 1 . Although smoking prevalence has reduced in many high-income countries (HICs), tobacco use is still very prevalent in low-income and middle-income countries (LMICs). The majority of smokers are addicted to nicotine delivered by cigarettes (defined as tobacco dependence in the International Classification of Diseases, Tenth Revision (ICD-10) or tobacco use disorder in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5)). As a result of the neuro-adaptations and psychological mechanisms caused by repeated exposure to nicotine delivered rapidly by cigarettes, cessation can also lead to a well-characterized withdrawal syndrome, typically manifesting as irritability, anxiety, low mood, difficulty concentrating, increased appetite, insomnia and restlessness, that contributes to the difficulty in quitting tobacco use 2 , 3 , 4 .

Historically, tobacco was used in some cultures as part of traditional ceremonies, but its use was infrequent and not widely disseminated in the population. However, since the early twentieth century, the use of commercial cigarettes has increased dramatically 5 because of automated manufacturing practices that enable large-scale production of inexpensive products that are heavily promoted by media and advertising. Tobacco use became highly prevalent in the past century and was followed by substantial increases in the prevalence of tobacco-induced diseases decades later 5 . It took decades to establish the relationship between tobacco use and associated health effects 6 , 7 and to discover the addictive role of nicotine in maintaining tobacco smoking 8 , 9 , and also to educate people about these effects. It should be noted that the tobacco industry disputed this evidence to allow continuing tobacco sales 10 . The expansion of public health campaigns to reduce smoking has gradually decreased the use of tobacco in HICs, with marked increases in adult cessation, but less progress has been achieved in LMICs 1 .

Nicotine is the addictive compound in tobacco and is responsible for continued use of tobacco despite harms and a desire to quit, but nicotine is not directly responsible for the harmful effects of using tobacco products (Box  1 ). Other components in tobacco may modulate the addictive potential of tobacco (for example, flavours and non-nicotine compounds) 11 . The major harms related to tobacco use, which are well covered elsewhere 5 , are linked to a multitude of compounds present in tobacco smoke (such as carcinogens, toxicants, particulate matter and carbon monoxide). In adults, adverse health outcomes of tobacco use include cancer in virtually all peripheral organs exposed to tobacco smoke and chronic diseases such as eye disease, periodontal disease, cardiovascular diseases, chronic obstructive pulmonary disease, stroke, diabetes mellitus, rheumatoid arthritis and disorders affecting immune function 5 . Moreover, smoking during pregnancy can increase the risk of adverse reproductive effects, such as ectopic pregnancy, low birthweight and preterm birth 5 . Exposure to secondhand cigarette smoke in children has been linked to sudden infant death syndrome, impaired lung function and respiratory illnesses, in addition to cognitive and behavioural impairments 5 . The long-term developmental effects of nicotine are probably due to structural and functional changes in the brain during this early developmental period 12 , 13 .

Nicotine administered alone in various nicotine replacement formulations (such as patches, gum and lozenges) is safe and effective as an evidence-based smoking cessation aid. Novel forms of nicotine delivery systems have also emerged (called electronic nicotine delivery systems (ENDS) or e-cigarettes), which can potentially reduce the harmful effects of tobacco smoking for those who switch completely from combustible to e-cigarettes 14 , 15 .

This Primer focuses on the determinants of nicotine and tobacco use, and reviews the neurobiology of nicotine effects on the brain reward circuitry and the functioning of brain networks in ways that contribute to the difficulty in stopping smoking. This Primer also discusses how to prevent tobacco use, screen for smoking, and offer people who smoke tobacco psychosocial and pharmacological interventions to assist in quitting. Moreover, this Primer presents emerging pharmacological and novel brain interventions that could improve rates of successful smoking cessation, in addition to public health approaches that could be beneficial.

Box 1 Tobacco products

Conventional tobacco products include combustible products that produce inhaled smoke (most commonly cigarettes, bidis (small domestically manufactured cigarettes used in South Asia) or cigars) and those that deliver nicotine without using combustion (chewing or dipping tobacco and snuff). Newer alternative products that do not involve combustion include nicotine-containing e-cigarettes and heat-not-burn tobacco devices. Although non-combustion and alternative products may constitute a lesser risk than burned ones 14 , 15 , 194 , no form of tobacco is entirely risk-free.

Epidemiology

Prevalence and burden of disease.

The Global Burden of Disease Project (GBDP) estimated that around 1.14 billion people smoked in 2019, worldwide, increasing from just under a billion in 1990 (ref. 1 ). Of note, the prevalence of smoking decreased significantly between 1990 and 2019, but increases in the adult population meant that the total number of global smokers increased. One smoking-associated death occurs for approximately every 0.8–1.1 million cigarettes smoked 16 , suggesting that the estimated worldwide consumption of about 7.4 trillion cigarettes in 2019 has led to around 7 million deaths 1 .

In most populations, smoking prevalence is much higher among groups with lower levels of education or income 17 and among those with mental health disorders and other co-addictions 18 , 19 . Smoking is also more frequent among men than women (Figs  1 – 3 ). Sexual and/or gender minority individuals have disproportionately high rates of smoking and other addictions 17 , 20 . In addition, the prevalence of smoking varies substantially between regions and ethnicities; smoking rates are high in some regions of Asia, such as China and India, but are lower in North America and Australia. Of note, the prevalence of mental health disorders and other co-addictions is higher in individuals who smoke compared with non-smokers 18 , 19 , 21 . For example, the odds of smoking in people with any substance use disorder is more than five times higher than the odds in people without a substance use disorder 19 . Similarly, the odds of smoking in people with any psychiatric disorder is more than three times higher than the odds of smoking in those without a psychiatric diagnosis 22 . In a study in the USA, compared with a population of smokers with no psychiatric diagnosis, subjects with anxiety, depression and phobia showed an approximately twofold higher prevalence of smoking, and subjects with agoraphobia, mania or hypomania, psychosis and antisocial personality or conduct disorders showed at least a threefold higher prevalence of smoking 22 . Comorbid disorders are also associated with higher rates of smoking 22 , 23 .

a | Number of current male smokers aged 15 years or older per country expressed in millions. b | Former male smokers aged 45–59 years per country expressed in millions. c | Former male smokers aged 45–59 years per country expressed as the percentage of smokers who stopped. The data shown are for male smokers for the period 2015–2019 from countries with direct smoking surveys. The prevalence of smoking among males is less variable than among females. Data from ref. 1 .

a | Number of current female smokers aged 15 years or older per country expressed in millions. b | Former female smokers aged 45–59 years per country expressed in millions. c | Former female smokers aged 45–59 years per country expressed as the percentage of smokers who stopped. The data shown are for female smokers for the period 2015–2019 from countries with direct smoking surveys. The prevalence of smoking among females is much lower in East and South Asia than in Latin America or Eastern Europe. Data from ref. 1 .

a | Number of current male and female smokers aged 15 years or older per country expressed in millions. b | Former male and female smokers aged 45–59 years per country expressed in millions. c | Former male and female smokers aged 45–59 years per country expressed as the percentage of smokers who stopped. The data shown are for the period 2015–2019 from countries with direct smoking surveys. Cessation rates are higher in high-income countries, but also notably high in Brazil. Cessation is far less common in South and East Asia and Russia and other Eastern European countries, and also low in South Africa. Data from ref. 1 .

Age at onset

Most smokers start smoking during adolescence, with almost 90% of smokers beginning between 15 and 25 years of age 24 . The prevalence of tobacco smoking among youths substantially declined in multiple HICs between 1990 and 2019 (ref. 25 ). More recently, the widespread uptake of ENDS in some regions such as Canada and the USA has raised concerns about the long-term effects of prolonged nicotine use among adolescents, including the possible notion that ENDS will increase the use of combustible smoking products 25 , 26 (although some studies have not found much aggregate effect at the population level) 27 .

Smoking that commences in early adolescence or young adulthood and persists throughout life has a more severe effect on health than smoking that starts later in life and/or that is not persistent 16 , 28 , 29 . Over 640 million adults under 30 years of age smoke in 22 jurisdictions alone (including 27 countries in the European Union where central efforts to reduce tobacco dependence might be possible) 30 . In those younger than 30 years of age, at least 320 million smoking-related deaths will occur unless they quit smoking 31 . The actual number of smoking-related deaths might be greater than one in two, and perhaps as high as two in three, long-term smokers 5 , 16 , 29 , 32 , 33 . At least half of these deaths are likely to occur in middle age (30–69 years) 16 , 29 , leading to a loss of two or more decades of life. People who smoke can expect to lose an average of at least a decade of life versus otherwise similar non-smokers 16 , 28 , 29 .

Direct epidemiological studies in several countries paired with model-based estimates have estimated that smoking tobacco accounted for 7.7 million deaths globally in 2020, of which 80% were in men and 87% were current smokers 1 . In HICs, the major causes of tobacco deaths are lung cancer, emphysema, heart attack, stroke, cancer of the upper aerodigestive areas and bladder cancer 28 , 29 . In some lower income countries, tuberculosis is an additional important cause of tobacco-related death 29 , 34 , which could be related to, for example, increased prevalence of infection, more severe tuberculosis/mortality and higher prevalence of treatment-resistant tuberculosis in smokers than in non-smokers in low-income countries 35 , 36 .

Despite substantial reductions in the prevalence of smoking, there were 34 million smokers in the USA, 7 million in the UK and 5 million in Canada in 2017 (ref. 16 ), and cigarette smoking remains the largest cause of premature death before 70 years of age in much of Europe and North America 1 , 16 , 28 , 29 . Smoking-associated diseases accounted for around 41 million deaths in the USA, UK and Canada from 1960 to 2020 (ref. 16 ). Moreover, as smoking-associated diseases are more prevalent among groups with lower levels of education and income, smoking accounts for at least half of the difference in overall mortality between these social groups 37 . Any reduction in smoking prevalence reduces the absolute mortality gap between these groups 38 .

Smoking cessation has become common in HICs with good tobacco control interventions. For example, in France, the number of ex-smokers is four times the number of current smokers among those aged 50 years or more 30 . By contrast, smoking cessation in LMICs remains uncommon before smokers develop tobacco-related diseases 39 . Smoking cessation greatly reduces the risks of smoking-related diseases. Indeed, smokers who quit smoking before 40 years of age avoid nearly all the increased mortality risks 31 , 33 . Moreover, individuals who quit smoking by 50 years of age reduce the risk of death from lung cancer by about two-thirds 40 . More modest hazards persist for deaths from lung cancer and emphysema 16 , 28 ; however, the risks among former smokers are an order of magnitude lower than among those who continue to smoke 33 .

Mechanisms/pathophysiology

Nicotine is the main psychoactive agent in tobacco and e-cigarettes. Nicotine acts as an agonist at nicotinic acetylcholine receptors (nAChRs), which are localized throughout the brain and peripheral nervous system 41 . nAChRs are pentameric ion channels that consist of varying combinations of α 2 –α 7 and β 2 –β 4 subunits, and for which acetylcholine (ACh) is the endogenous ligand 42 , 43 , 44 . When activated by nicotine binding, nAChR undergoes a conformational change that opens the internal pore, allowing an influx of sodium and calcium ions 45 . At postsynaptic membranes, nAChR activation can lead to action potential firing and downstream modulation of gene expression through calcium-mediated second messenger systems 46 . nAChRs are also localized to presynaptic membranes, where they modulate neurotransmitter release 47 . nAChRs become desensitized after activation, during which ligand binding will not open the channel 45 .

nAChRs with varying combinations of α-subunits and β-subunits have differences in nicotine binding affinity, efficacy and desensitization rate, and have differential expression depending on the brain region and cell type 48 , 49 , 50 . For instance, at nicotine concentrations found in human smokers, β 2 -containing nAChRs desensitize relatively quickly after activation, whereas α 7 -containing nAChRs have a slower desensitization profile 48 . Chronic nicotine exposure in experimental animal models or in humans induces an increase in cortical expression of α 4 β 2 -containing nAChRs 51 , 52 , 53 , 54 , 55 , but also increases the expression of β 3 and β 4 nAChR subunits in the medial habenula (MHb)–interpeduncular nucleus (IPN) pathway 56 , 57 . It is clear that both the brain localization and the type of nAChR are critical elements in mediating the various effects of nicotine, but other factors such as rate of nicotine delivery may also modulate addictive effects of nicotine 58 .

Neurocircuitry of nicotine addiction

Nicotine has both rewarding effects (such as a ‘buzz’ or ‘high’) and aversive effects (such as nausea and dizziness), with the net outcome dependent on dose and others factors such as interindividual sensitivity and presence of tolerance 59 . Thus, the addictive properties of nicotine involve integration of contrasting signals from multiple brain regions that process reward and aversion (Fig.  4 ).

During initial use, nicotine exerts both reinforcing and aversive effects, which together determine the likelihood of continued use. As the individual transitions to more frequent patterns of chronic use, nicotine induces pharmacodynamic changes in brain circuits, which is thought to lead to a reduction in sensitivity to the aversive properties of the drug. Nicotine is also a powerful reinforcer that leads to the conditioning of secondary cues associated with the drug-taking experience (such as cigarette pack, sensory properties of cigarette smoke and feel of the cigarette in the hand or mouth), which serves to enhance the incentive salience of these environmental factors and drive further drug intake. When the individual enters into states of abstinence (such as daily during sleep at night or during quit attempts), withdrawal symptomology is experienced, which may include irritability, restlessness, learning or memory deficits, difficulty concentrating, anxiety and hunger. These negative affective and cognitive symptoms lead to an intensification of the individual’s preoccupation to obtain and use the tobacco/nicotine product, and subsequently such intense craving can lead to relapse.

The rewarding actions of nicotine have largely been attributed to the mesolimbic pathway, which consists of dopaminergic neurons in the ventral tegmental area (VTA) that project to the nucleus accumbens and prefrontal cortex 60 , 61 , 62 (Fig.  5 ). VTA integrating circuits and projection regions express several nAChR subtypes on dopaminergic, GABAergic, and glutamatergic neurons 63 , 64 . Ultimately, administration of nicotine increases dopamine levels through increased dopaminergic neuron firing in striatal and extrastriatal areas (such as the ventral pallidum) 65 (Fig.  6 ). This effect is involved in reward and is believed to be primarily mediated by the action of nicotine on α 4 -containing and β 2 -containing nAChRs in the VTA 66 , 67 .

Multiple lines of research have demonstrated that nicotine reinforcement is mainly controlled by two brain pathways, which relay predominantly reward-related or aversion-related signals. The rewarding properties of nicotine that promote drug intake involve the mesolimbic dopamine projection from the ventral tegmental area (VTA) to the nucleus accumbens (NAc). By contrast, the aversive properties of nicotine that limit drug intake and mitigate withdrawal symptoms involve the fasciculus retroflexus projection from the medial habenula (MHb) to the interpeduncular nucleus (IPN). Additional brain regions have also been implicated in various aspects of nicotine dependence, such as the prefrontal cortex (PFC), ventral pallidum (VP), nucleus tractus solitarius (NTS) and insula (not shown here for clarity). All of these brain regions are directly or indirectly interconnected as integrative circuits to drive drug-seeking and drug-taking behaviours.

Smokers received brain PET scans with [ 11 C]PHNO, a dopamine D 2/3 PET tracer that has high sensitivity in detecting fluctuations of dopamine. PET scans were performed during abstinence or after smoking a cigarette. Reduced binding potential (BP ND ) was observed after smoking, indicating increased dopamine levels in the ventral striatum and in the area that corresponds to the ventral pallidum. The images show clusters with statistically significant decreases of [ 11 C]PHNO BP ND after smoking a cigarette versus abstinence condition. Those clusters have been superimposed on structural T1 MRI images of the brain. Reprinted from ref. 65 , Springer Nature Limited.

The aversive properties of nicotine are mediated by neurons in the MHb, which project to the IPN. Studies in rodents using genetic knockdown and knockout strategies demonstrated that the α 5 -containing, α 3 -containing and β 4 -containing nAChRs in the MHb–IPN pathway mediate the aversive properties of nicotine that limit drug intake, especially when animals are given the opportunity to consume higher nicotine doses 68 , 69 , 70 , 71 , 72 . In addition to nAChRs, other signalling factors acting on the MHb terminals in the IPN also regulate the actions of nicotine. For instance, under conditions of chronic nicotine exposure or with optogenetic activation of IPN neurons, a subtype of IPN neurons co-expressing Chrna5 (encoding the α 5 nAChR subunit) and Amigo1 (encoding adhesion molecule with immunoglobulin-like domain 1) release nitric oxide from the cell body that retrogradely inhibits MHb axon terminals 70 . In addition, nicotine activates α 5 -containing nAChR-expressing neurons that project from the nucleus tractus solitarius to the IPN, leading to release of glucagon-like peptide-1 that binds to GLP receptors on habenular axon terminals, which subsequently increases IPN neuron activation and decreases nicotine self-administration 73 . Taken together, these findings suggest a dynamic signalling process at MHb axonal terminals in the IPN, which regulates the addictive properties of nicotine and determines the amount of nicotine that is self-administered.

Nicotine withdrawal in animal models can be assessed by examining somatic signs (such as shaking, scratching, head nods and chewing) and affective signs (such as increased anxiety-related behaviours and conditioned place aversion). Interestingly, few nicotine withdrawal somatic signs are found in mice with genetic knockout of the α 2 , α 5 or β 4 nAChR subunits 74 , 75 . By contrast, β 2 nAChR-knockout mice have fewer anxiety-related behaviours during nicotine withdrawal, with no differences in somatic symptoms compared with wild-type mice 74 , 76 .

In addition to the VTA (mediating reward) and the MHb–IPN pathway (mediating aversion), other brain areas are involved in nicotine addiction (Fig.  5 ). In animals, the insular cortex controls nicotine taking and nicotine seeking 77 . Moreover, humans with lesions of the insular cortex can quit smoking easily without relapse 78 . This finding led to the development of a novel therapeutic intervention modulating insula function (see Management, below) 79 , 80 . Various brain areas (shell of nucleus accumbens, basolateral amygdala and prelimbic cortex) expressing cannabinoid CB 1 receptors are also critical in controlling rewarding effects and relapse 81 , 82 . The α 1 -adrenergic receptor expressed in the cortex also control these effects, probably through glutamatergic afferents to the nucleus accumbens 83 .

Individual differences in nicotine addiction risk

Vulnerability to nicotine dependence varies between individuals, and the reasons for these differences are multidimensional. Many social factors (such as education level and income) play a role 84 . Broad psychological and social factors also modulate this risk. For example, peer smoking status, knowledge on effect of tobacco, expectation on social acceptance, exposure to passive smoking modulate the risk of initiating tobacco use 85 , 86 .

Genetic factors have a role in smoking initiation, the development of nicotine addiction and the likelihood of smoking cessation. Indeed, heritability has been estimated to contribute to approximatively half of the variability in nicotine dependence 87 , 88 , 89 , 90 . Important advances in our understanding of such genetic contributions have evolved with large-scale genome-wide association studies of smokers and non-smokers. One of the most striking findings has been that allelic variation in the CHRNA5 – CHRNA3 – CHRNB4 gene cluster, which encodes α 5 , α 3 and β 4 nAChR subunits, correlates with an increased vulnerability for nicotine addiction, indicated by a higher likelihood of becoming dependent on nicotine and smoking a greater number of cigarettes per day 91 , 92 , 93 , 94 , 95 . The most significant effect has been found for a single-nucleotide polymorphism in CHRNA5 (rs16969968), which results in an amino acid change and reduced function of α 5 -containing nAChRs 92 .

Allelic variation in CYP2A6 (encoding the CYP2A6 enzyme, which metabolizes nicotine) has also been associated with differential vulnerability to nicotine dependence 96 , 97 , 98 . CYP2A6 is highly polymorphic, resulting in variable enzymatic activity 96 , 99 , 100 . Individuals with allelic variation that results in slow nicotine metabolism consume less nicotine per day, experience less-severe withdrawal symptoms and are more successful at quitting smoking than individuals with normal or fast metabolism 101 , 102 , 103 , 104 . Moreover, individuals with slow nicotine metabolism have lower dopaminergic receptor expression in the dopamine D2 regions of the associative striatum and sensorimotor striatum in PET studies 105 and take fewer puffs of nicotine-containing cigarettes (compared with de-nicotinized cigarettes) in a forced choice task 106 . Slower nicotine metabolism is thought to increase the duration of action of nicotine, allowing nicotine levels to accumulate over time, therefore enabling lower levels of intake to sustain activation of nAChRs 107 .

Large-scale genetic studies have identified hundreds of other genetic loci that influence smoking initiation, age of smoking initiation, cigarettes smoked per day and successful smoking cessation 108 . The strongest genetic contributions to smoking through the nicotinic receptors and nicotine metabolism are among the strongest genetic contributors to lung cancer 109 . Other genetic variations (such as those related to cannabinoid, dopamine receptors or other neurotransmitters) may affect certain phenotypes related to smoking (such as nicotine preference and cue-reactivity) 110 , 111 , 112 , 113 , 114 , 115 .

Diagnosis, screening and prevention

Screening for cigarette smoking.

Screening for cigarette smoking should happen at every doctor’s visit 116 . In this regard, a simple and direct question about a person’s tobacco use can provide an opportunity to offer information about its potential risks and treatments to assist in quitting. All smokers should be offered assistance in quitting because even low levels of smoking present a significant health risk 33 , 117 , 118 . Smoking status can be assessed by self-categorization or self-reported assessment of smoking behaviour (Table  1 ). In people who smoke, smoking frequency can be assessed 119 and a combined quantity frequency measure such as pack-year history (that is, average number of cigarettes smoked per day multiplied by the number of years, divided by 20), can be used to estimate cumulative risk of adverse health outcomes. The Association for the Treatment of Tobacco Use and Dependence recommends that all electronic health records should document smoking status using the self-report categories listed in Table  1 .

Owing to the advent of e-cigarettes and heat-not-burn products, and the popularity of little cigars in the US that mimic combustible cigarettes, people who use tobacco may use multiple products concurrently 120 , 121 . Thus, screening for other nicotine and tobacco product use is important in clinical practice. The self-categorization approach can also be used to describe the use of these other products.

Traditionally tobacco use has been classified according to whether the smoker meets criteria for nicotine dependence in one of the two main diagnostic classifications: the DSM 122 (tobacco use disorder) and the ICD (tobacco dependence) 123 . The diagnosis of tobacco use disorder according to DSM-5 criteria requires the presence of at least 2 of 11 symptoms that have produced marked clinical impairment or distress within a 12-month period (Box  2 ). Of note, these symptoms are similar for all substance use disorder diagnoses and may not all be relevant to tobacco use disorder (such as failure to complete life roles). In the ICD-10, codes allow the identification of specific tobacco products used (cigarettes, chewing tobacco and other tobacco products).

Dependence can also be assessed as a continuous construct associated with higher levels of use, greater withdrawal and reduced likelihood of quitting. The level of dependence can be assessed with the Fagerström Test for Nicotine Dependence, a short questionnaire comprising six questions 124 (Box  2 ). A score of ≥4 indicates moderate to high dependence. As very limited time may be available in clinical consultations, the Heaviness of Smoking Index (HSI) was developed, which comprises two questions on the number of cigarettes smoked per day and how soon after waking the first cigarette is smoked 125 . The HSI can guide dosing for nicotine replacement therapy (NRT).

Other measures of cigarette dependence have been developed but are not used in the clinical setting, such as the Cigarette Dependence Scale 126 , Hooked on Nicotine Checklist 127 , Nicotine Dependence Syndrome Scale 128 , the Wisconsin Inventory of Smoking Dependence Motives (Brief) 129 and the Penn State Cigarette Dependence Index 130 . However, in practice, these are not often used, as the most important aspect is to screen for smoking and encourage all smokers to quit smoking regardless of their dependence status.

Box 2 DSM-5 criteria for tobacco use disorder and items of the Fagerström Test for nicotine dependence

DSM-5 (ref. 122 )

Taxonomic and diagnostic tool for tobacco use disorder published by the American Psychiatric Association.

A problematic pattern of tobacco use leading to clinically significant impairment or distress as manifested by at least two of the following, occurring within a 12-month period.

Tobacco often used in larger amounts or over a longer period of time than intended

A persistent desire or unsuccessful efforts to reduce or control tobacco use

A great deal of time spent in activities necessary to obtain or use tobacco

Craving, or a strong desire or urge to use tobacco

Recurrent tobacco use resulting in a failure to fulfil major role obligations at work, school or home

Continued tobacco use despite having persistent or recurrent social or interpersonal problems caused or exacerbated by the effects of tobacco (for example, arguments with others about tobacco use)

Important social, occupational or recreational activities given up or reduced because of tobacco use

Recurrent tobacco use in hazardous situations (such as smoking in bed)

Tobacco use continued despite knowledge of having a persistent or recurrent physical or psychological problem that is likely to have been caused or exacerbated by tobacco use

Tolerance, defined by either of the following.

A need for markedly increased amounts of tobacco to achieve the desired effect

A markedly diminished effect with continued use of the same amount of tobacco

Withdrawal, manifesting as either of the following.

Withdrawal syndrome for tobacco

Tobacco (or a closely related substance, such as nicotine) taken to relieve or avoid withdrawal symptoms

Fagerström Test for Nicotine Dependence 124

A standard instrument for assessing the intensity of physical addiction to nicotine.

How soon after you wake up do you smoke your first cigarette?

Within 5 min (scores 3 points)

5 to 30 min (scores 2 points)

31 to 60 min (scores 1 point)

After 60 min (scores 0 points)

Do you find it difficult not to smoke in places where you should not, such as in church or school, in a movie, at the library, on a bus, in court or in a hospital?

Yes (scores 1 point)

No (scores 0 points)

Which cigarette would you most hate to give up; which cigarette do you treasure the most?

The first one in the morning (scores 1 point)

Any other one (scores 0 points)

How many cigarettes do you smoke each day?

10 or fewer (scores 0 points)

11 to 20 (scores 1 point)

21 to 30 (scores 2 points)

31 or more (scores 3 points)

Do you smoke more during the first few hours after waking up than during the rest of the day?

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?

A score of 7–10 points is classified as highly dependent; 4–6 points is classified as moderately dependent; <4 points is classified as minimally dependent.

DSM-5, Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition.

Young people who do not start smoking cigarettes between 15 and 25 years of age have a very low risk of ever smoking 24 , 131 , 132 . This age group provides a critical opportunity to prevent cigarette smoking using effective, evidence-based strategies to prevent smoking initiation and reduce escalation from experimentation to regular use 131 , 132 , 133 , 134 , 135 .

Effective prevention of cigarette uptake requires a comprehensive package of cost-effective policies 134 , 136 , 137 to synergistically reduce the population prevalence of cigarette smoking 131 , 135 . These policies include high rates of tobacco taxation 30 , 134 , 137 , 138 , widespread and rigorously enforced smoke-free policies 139 , bans on tobacco advertising and promotions 140 , use of plain packaging and graphic warnings about the health risks of smoking 135 , 141 , mass media and peer-based education programmes to discourage smoking, and enforcement of laws against the sale of cigarettes to young people below the minimum legal purchase age 131 , 135 . These policies make cigarettes less available and affordable to young people. Moreover, these policies make it more difficult for young people to purchase cigarettes and make smoking a much less socially acceptable practice. Of note, these policies are typically mostly enacted in HICs, which may be related to the declining prevalence of smoking in these countries, compared with the prevalence in LMICs.

Pharmacotherapy

Three evidence-based classes of pharmacotherapy are available for smoking cessation: NRT (using nicotine-based patches, gum, lozenges, mini-lozenges, nasal sprays and inhalers), varenicline (a nAChR partial agonist), and bupropion (a noradrenaline/dopamine reuptake inhibitor that also inhibits nAChR function and is also used as an antidepressant). These FDA-approved and EMA-approved pharmacotherapies are cost-effective smoking cessation treatments that double or triple successful abstinence rates compared with no treatment or placebo controls 116 , 142 .

Combinations of pharmacotherapies are also effective for smoking cessation 116 , 142 . For example, combining NRTs (such as the steady-state nicotine patch and as-needed NRT such as gum or mini-lozenge) is more effective than a single form of NRT 116 , 142 , 143 . Combining NRT and varenicline is the most effective smoking cessation pharmacotherapy 116 , 142 , 143 . Combining FDA-approved pharmacotherapy with behavioural counselling further increases the likelihood of successful cessation 142 . Second-line pharmacotherapies (for example, nortriptyline) have some potential for smoking cessation, but their use is limited due to their tolerability profile.

All smokers should receive pharmacotherapy to help them quit smoking, except those in whom pharmacotherapy has insufficient evidence of effectiveness (among adolescents, smokeless tobacco users, pregnant women or light smokers) or those in whom pharmacotherapy is medically contraindicated 144 . Table  2 provides specific information regarding dosing and duration for each FDA-approved pharmacotherapy. Extended use of pharmacotherapy beyond the standard 12-week regimen after cessation is effective and should be considered 116 . Moreover, preloading pharmacotherapy (that is, initiating cessation medication in advance of a quit attempt), especially with the nicotine patch, is a promising treatment, although further studies are required to confirm efficacy.

Cytisine has been used for smoking cessation in Eastern Europe for a long time and is available in some countries (such as Canada) without prescription 145 . Cytisine is a partial agonist of nAChRs and its structure was the precursor for the development of varenicline 145 . Cytisine is at least as effective as some approved pharmacotherapies for smoking cessation, such as NRT 146 , 147 , 148 , and the role of cytisine in smoking cessation is likely to expand in the future, notably owing to its much lower cost than traditional pharmacotherapies. E-cigarettes also have the potential to be useful as smoking cessation devices 149 , 150 . The 2020 US Surgeon General’s Report concluded that there was insufficient evidence to promote cytisine or e-cigarettes as effective smoking cessation treatments, but in the UK its use is recommended for smoking cessation (see ref. 15 for regularly updated review).

Counselling and behavioural treatments

Psychosocial counselling significantly increases the likelihood of successful cessation, especially when combined with pharmacotherapy. Even a counselling session lasting only 3 minutes can help smokers quit 116 , although the 2008 US Public Health Service guidelines and the Preventive Services Task Force 151 each concluded that more intensive counselling (≥20 min per session) is more effective than less intensive counselling (<20 min per session). Higher smoking cessation rates are obtained by using behavioural change techniques that target associative and self-regulatory processes 152 . In addition, behavioural change techniques that will favour commitment, social reward and identity associated with changed behaviour seems associated with higher success rates 152 . Evidence-based counselling focuses on providing social support during treatment, building skills to cope with withdrawal and cessation, and problem-solving in challenging situations 116 , 153 . Effective counselling can be delivered by diverse providers (such as physicians, nurses, pharmacists, social workers, psychologists and certified tobacco treatment specialists) 116 .

Counselling can be delivered in a variety of modalities. In-person individual and group counselling are effective, as is telephone counselling (quit lines) 142 . Internet and text-based intervention seem to be effective in smoking cessation, especially when they are interactive and tailored to a smoker’s specific circumstances 142 . Over the past several years, the number of smoking cessation smartphone apps has increased, but there the evidence that the use of these apps significantly increases smoking cessation rates is not sufficient.

Contingency management (providing financial incentives for abstinence or engagement in treatment) has shown promising results 154 , 155 but its effects are not sustained once the contingencies are removed 155 , 156 . Other treatments such as hypnosis, acupuncture and laser treatment have not been shown to improve smoking cessation rates compared with placebo treatments 116 . Moreover, no solid evidence supports the use of conventional transcranial magnetic stimulation (TMS) for long-term smoking cessation 157 , 158 .

Although a variety of empirically supported smoking cessation interventions are available, more than two-thirds of adult smokers who made quit attempts in the USA during the past year did not use an evidence-based treatment and the rate is likely to be lower in many other countries 142 . This speaks to the need to increase awareness of, and access to, effective cessation aids among all smokers.

Brain stimulation

The insula (part of the frontal cortex) is a critical brain structure involved in cigarette craving and relapse 78 , 79 . The activity of the insula can be modulated using an innovative approach called deep insula/prefrontal cortex TMS (deep TMS), which is effective in helping people quit smoking 80 , 159 . This approach has now been approved by the FDA as an effective smoking cessation intervention 80 . However, although this intervention was developed and is effective for smoking cessation, the number of people with access to it is limited owing to the limited number of sites equipped and with trained personnel, and the cost of this intervention.

Quality of life

Generic instruments (such as the Short-Form (SF-36) Health Survey) can be used to evaluate quality of life (QOL) in smokers. People who smoke rate their QOL lower than people who do not smoke both before and after they become smokers 160 , 161 . QOL improves when smokers quit 162 . Mental health may also improve on quitting smoking 163 . Moreover, QOL is much poorer in smokers with tobacco-related diseases, such as chronic respiratory diseases and cancers, than in individuals without tobacco-related diseases 161 , 164 . The dimensions of QOL that show the largest decrements in people who smoke are those related to physical health, day-to-day activities and mental health such as depression 160 . Smoking also increases the risk of diabetes mellitus 165 , 166 , which is a major determinant of poor QOL for a wide range of conditions.

The high toll of premature death from cigarette smoking can obscure the fact that many of the diseases that cause these deaths also produce substantial disability in the years before death 1 . Indeed, death in smokers is typically preceded by several years of living with the serious disability and impairment of everyday activities caused by chronic respiratory disease, heart disease and cancer 2 . Smokers’ QOL in these years may also be adversely affected by the adverse effects of the medical treatments that they receive for these smoking-related diseases (such as major surgery and radiotherapy).

Expanding cessation worldwide

The major global challenge is to consider individual and population-based strategies that could increase the substantially low rates of adult cessation in most LMICs and indeed strategies to ensure that even in HICs, cessation continues to increase. In general, the most effective tools recommended by WHO to expand cessation are the same tools that can prevent smoking initiation, notably higher tobacco taxes, bans on advertising and promotion, prominent warning labels or plain packaging, bans on public smoking, and mass media and educational efforts 29 , 167 . The effective use of these policies, particularly taxation, lags behind in most LMICs compared with most HICs, with important exceptions such as Brazil 167 . Access to effective pharmacotherapies and counselling as well as support for co-existing mental health conditions would also be required to accelerate cessation in LMICs. This is particularly important as smokers living in LMICs often have no access to the full range of effective treatment options.

Regulating access to e-cigarettes

How e-cigarettes should be used is debated within the tobacco control field. In some countries (for example, the UK), the use of e-cigarettes as a cigarette smoking cessation aid and as a harm reduction strategy is supported, based on the idea that e-cigarette use will lead to much less exposure to toxic compounds than tobacco use, therefore reducing global harm. In other countries (for example, the USA), there is more concern with preventing the increased use of e-cigarettes by youths that may subsequently lead to smoking 25 , 26 . Regulating e-cigarettes in nuanced ways that enable smokers to access those products whilst preventing their uptake among youths is critical.

Regulating nicotine content in tobacco products

Reducing the nicotine content of cigarettes could potentially produce less addictive products that would allow a gradual reduction in the population prevalence of smoking. Some clinical studies have found no compensatory increase in smoking whilst providing access to low nicotine tobacco 168 . Future regulation may be implemented to gradually decrease the nicotine content of combustible tobacco and other nicotine products 169 , 170 , 171 .

Tobacco end games

Some individuals have proposed getting rid of commercial tobacco products this century or using the major economic disruption arising from the COVID-19 pandemic to accelerate the demise of the tobacco industry 172 , 173 . Some tobacco producers have even proposed this strategy as an internal goal, with the idea of switching to nicotine delivery systems that are less harmful ( Philip Morris International ). Some countries are moving towards such an objective; for example, in New Zealand, the goal that fewer than 5% of New Zealanders will be smokers in 2025 has been set (ref. 174 ). The tobacco end-game approach would overall be the best approach to reduce the burden of tobacco use on society, but it would require coordination of multiple countries and strong public and private consensus on the strategy to avoid a major expansion of the existing illicit market in tobacco products in some countries.

Innovative interventions

The COVID-19 pandemic has shown that large-scale investment in research can lead to rapid development of successful therapeutic interventions. By contrast, smoking cessation has been underfunded compared with the contribution that it makes to the global burden of disease. In addition, there is limited coordination between research teams and most studies are small-scale and often underpowered 79 . It is time to fund an ambitious, coordinated programme of research to test the most promising therapies based on an increased understanding of the neurobiological basis of smoking and nicotine addiction (Table  3 ). Many of those ideas have not yet been tested properly and this could be carried out by a coordinated programme of research at the international level.

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Acknowledgements

B.Le F. is supported by a clinician-scientist award from the Department of Family and Community Medicine at the University of Toronto and the Addiction Psychiatry Chair from the University of Toronto. The funding bodies had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication. The authors thank H. Fu (University of Toronto) for assistance with Figs 1–3.

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Introduction (B.Le F.); Epidemiology (P.J. and W.D.H.); Mechanisms/pathophysiology (C.D.F., L.B., L.L. and B.Le F.); Diagnosis, screening and prevention (P.J., M.E.P., S.T. and B.Le F.); Management (M.E.P., S.T., W.D.H., L.L. and B.Le F.); Quality of life (P.J. and W.D.H.); Outlook (all); Conclusions (all). All authors contributed substantially to the review and editing of the manuscript.

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B.Le F. has obtained funding from Pfizer (GRAND Awards, including salary support) for investigator-initiated projects. B.Le F. has received some in-kind donations of cannabis product from Aurora and medication donation from Pfizer and Bioprojet and was provided a coil for TMS study from Brainsway. B.Le F. has obtained industry funding from Canopy (through research grants handled by CAMH or the University of Toronto), Bioprojet, ACS, Indivior and Alkermes. B.Le F. has received in-kind donations of nabiximols from GW Pharma for past studies funded by CIHR and NIH. B.Le F. has been an advisor to Shinoghi. S.T. has received honoraria from Pfizer the manufacturer of varenicline for lectures and advice. All other authors declare no competing interests.

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Tobacco, Nicotine, and E-Cigarettes Research Report Introduction

In 2014, the Nation marked the 50th anniversary of the first Surgeon General’s Report on Smoking and Health. In 1964, more than 40 percent of the adult population smoked. Once the link between smoking and its medical consequences—including cancers and heart and lung diseases—became a part of the public consciousness, education efforts and public policy changes were enacted to reduce the number of people who smoke. These efforts resulted in substantial declines in smoking rates in the United States—to half the 1964 level. 1

However, rates of cigarette smoking and other tobacco use are still too high, 2 and some populations are disproportionately affected by tobacco’s health consequences. Most notably, people with mental disorders—including substance use disorders—smoke at higher rates than the general population. 3–6 Additionally, people living below the poverty line and those with low educational attainment are more likely to smoke than those in the general population. As tobacco use is the leading preventable cause of mortality in the United States, 1 differential rates of smoking and use of other tobacco products is a significant contributor to health disparities among some of the most vulnerable people in our society.

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Research Article

The impact of peer pressure on cigarette smoking among high school and university students in Ethiopia: A systemic review and meta-analysis

Roles Conceptualization, Data curation, Methodology, Software, Writing – review & editing

* E-mail: [email protected]

Affiliation College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia

Roles Formal analysis, Resources, Supervision

Roles Data curation, Formal analysis, Investigation, Methodology, Validation

Roles Data curation, Formal analysis, Project administration, Software, Supervision

Roles Formal analysis, Visualization, Writing – original draft

Roles Data curation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Department of Nursing, College of Nursing, University of Saskatchewan, Regina, Canada

Roles Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Validation, Visualization

Roles Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization

Roles Data curation, Investigation, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing

Roles Investigation, Project administration, Software, Supervision, Validation, Writing – review & editing

Roles Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Roles Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Roles Methodology, Supervision, Writing – review & editing

Affiliations Colleges of Nursing, University of Saskatchewan, Saskatoon, Canada, School of Life Sciences and Bioengineering, Nelson Mandela African Institute of Science and Technology, Arusha City, Tanzania

Roles Methodology, Software, Supervision, Visualization, Writing – original draft, Writing – review & editing

Affiliations School of Science and Health, Western Sydney University, Penrith, NSW, Australia, Translational Health Research Institute, Western Sydney University, Penrith, NSW, Australia, Discipline of Child and Adolescent Health, Sydney Medical School, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia, Oral Health Services, Sydney Local Health District and Sydney Dental Hospital, NSW Health, Surry Hills, NSW, Australia

• Cheru Tesema Leshargie, 
• Animut Alebel, 
• Getiye Dejenu Kibret, 
• Molla Yigzaw Birhanu, 
• Henok Mulugeta, 
• Patricia Malloy, 
• Fasil Wagnew, 
• Atsede Alle Ewunetie, 
• Daniel Bekele Ketema, 

• Published: October 11, 2019
• https://doi.org/10.1371/journal.pone.0222572
• Reader Comments

Cigarettes and their by-products (i.e., smoke; ash) are a complex, dynamic, and reactive mixture of around 5,000 chemicals. Cigarette smoking potentially harms nearly every organ of the human body, causes innumerable diseases, and impacts the health of smokers and those interacting with the smokers. Smoking brings greater health problems in the long-term like increased risk of stroke and brain damage. For students, peer pressure is one of the key factors contributing to cigarette smoking. Therefore, this systematic review and meta-analysis assessed the impact of peer pressure on cigarette smoking among high school and university students in Ethiopia.

An extensive search of key databases including Cochrane Library, PubMed, Google Scholar, Hinari, Embase and Science Direct was conducted to identify and access articles published on the prevalence of cigarette smoking by high school and university students in Ethiopia. The search period for articles was conducted from 21 st September, 2018 to 25 th December 25, 2018. All necessary data were extracted using a standardized data extraction checklist. Quality and risk of bias of studies were assessed using standardized tools. Heterogeneity between the included studies was assessed using Cochrane Q-test statistic and I 2 test. To estimate the pooled prevalence of cigarette smoking, a random effects model was fitted. The impact of peer pressure on cigarette smoking was determined and was reported in Odds Ratio (OR) with 95% Confidence Interval (CI). Meta-analysis was conducted using Stata software.

From 175 searched articles, 19 studies fulfilled the eligibility criteria and were included in this study. The pooled prevalence of cigarette smoking among Ethiopian high school and university students was 15.9% (95% CI: 12.21, 19.63). Slightly higher prevalence of cigarette smoking was noted among university students [17.35% (95% CI: 13.21, 21.49)] as compared to high school students [12.77% (95% CI: 6.72%, 18.82%)]. The current aggregated meta-analysis revealed that peer pressure had a significant influence on cigarette smoking (OR: 2.68 (95% CI: 2.37, 3.03).

More than one sixth of the high school and university students in Ethiopia smoke cigarette. Students who had peer pressure from their friends were more likely to smoke cigarette. Therefore, school-based intervention programs are needed to reduce the high prevalence of cigarette smoking among students in Ethiopia.

Citation: Leshargie CT, Alebel A, Kibret GD, Birhanu MY, Mulugeta H, Malloy P, et al. (2019) The impact of peer pressure on cigarette smoking among high school and university students in Ethiopia: A systemic review and meta-analysis. PLoS ONE 14(10): e0222572. https://doi.org/10.1371/journal.pone.0222572

Editor: Wisit Cheungpasitporn, University of Mississippi Medical Center, UNITED STATES

Received: March 15, 2019; Accepted: September 3, 2019; Published: October 11, 2019

Copyright: © 2019 Leshargie et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: CI, Confidence Interval; HIV, Human Immune Deficiency Virus; OR, Odd Ration; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; SE, Standard Error; SNNPR, South Nation and Nationalities People of the Region; RR, Relative Risk; WHO, World Health Organization

Introduction

Smoking cigarettes yields a complex, dynamic and reactive mixture of around 5,000 chemicals [ 1 – 3 ]. Globally, it is one of the leading preventable causes of respiratory tract complications, disability, and early deaths related to complications [ 4 – 7 ]. It accounts for six of the eight leading causes of morbidity and mortality [ 5 ]. Essentially, it is a legal drug that kills many of its users when used exactly as intended by manufacturers. Currently, the World Health Organization (WHO) estimates that the use of both smoking and smokeless tobacco account for around 6 million deaths worldwide annually, of which 600,000 deaths were among non-smokers due to exposure to the smoke [ 8 ]. More than 30% of world’s adult population are consumers of tobacco, which leads to a warning that a billion people will die of adverse health effects related to the tobacco epidemic within the 21st century unless effective preventative measures are undertaken [ 3 ].

Smoking affects almost every organ in the human body (such as circulatory, respiratory, gastrointestinal and musculoskeletal systems), increases the risk for several diseases, and reduces the health of smokers in general [ 9 , 10 ]. The key effect of smoking cigarettes is primarily on the lungs with approximately 85% of chronic obstructive pulmonary disease (COPD) and lung cancer and about 33% of other cancers (i.e., esophagus, oral cavity, uterus, stomach, and pancreas) related to smoking [ 9 – 11 ].

Normal adolescent developmental stage is affected by high level of peer pressure that can influence risk-taking behaviors including substance use [ 12 ]. Globally, especially in low- and middle-income countries, an estimated 80% of the one billion adolescent smokers are suffering from tobacco-related morbidity and mortality [ 7 ]. Cigarette smoking negatively influences the physical and mental health of an individual [ 13 ]. This is particularly true for high school and university students who already face major health challenges such as stress [ 14 ]. Smoking is also associated with poor educational performance, high-risk drinking behavior, illegal drug use, and high-risk sexual behaviors [ 14 , 15 ]. Peer pressure is widely recognized as a crucial factor affecting young people's early experimentation with tobacco and their willingness to continue smoking [ 16 ]. Several students attending higher education institutions practice cigarette smoking for several reasons, such as a way to cope with stress [ 17 ]. Factors that contribute to the continued use of tobacco include being male, drinking alcohol, having a friend who drinks alcohol, having a friend who smokes, having family members who smoke and being older in age, to mention some [ 18 ].

In sub-Saharan Africa, the prevalence of smoking is increasing and is projected to continue to increase [ 19 , 20 ]. The current data in the region reveals substantial variation in smoking rates among countries ranging from 1.8% in Zambia to 25.8% in Sierra Leone [ 21 ]. In Ethiopia, cigarette smoking is among one of the most commonly used substances, which leads to addiction [ 22 ]. It has deleterious effects on the health of the young users, significantly reduces academic performance in students and increases risk of contracting HIV and other sexually transmitted diseases. Several primary studies on the prevalence and associated factors of cigarette smoking among high school and university students have been conducted in Ethiopia [ 23 – 37 ]. According to earlier reviews of the literature, prevalence of smoking in Ethiopia ranges from 2.99% in Addis Ababa [ 38 ] to 28.6% in Hawassa and Jima University [ 30 ]. Therefore, this systematic review and meta-analysis aimed to review the pooled prevalence of cigarette smoking among high school and university students in Ethiopia and the impact of peer pressure on cigarette smoking among high school and university students in Ethiopia.

Method and materials

This systematic review is based on the Preferred Reporting Items of Systematic Reviews and Meta-Analysis (PRISMA) checklist guidelines to ensure scientific rigor [ 39 ] ( S1 Table ). Prospective registration of systematic review and meta-analysis promotes transparency, helps reduce potential for bias, and improves review’s credibility. However, this meta-analysis and systematic review was not registered on the prosperous, and we have acknowledged this gap in the limitation section.

This systematic review and meta-analysis reports data from Ethiopia. Ethiopia is located in the north-eastern part of the African continent or what is known as the “Horn of Africa”. The country is divided into nine regional states and two administrative cities [ 40 ] containing a total of 108,386,391 million population with a national density of 94 people per square kilometer, 2019 [ 41 ]. Ethiopia shares land borders with five countries: Sudan , Somalia , Djibouti , Eritrea , and Kenya [ 42 ].

Inclusion and exclusion criteria

Eligibility criteria..

This systematic review and meta-analysis included studies only conducted in Ethiopia that assessed the prevalence of cigarette smoking. Published articles were reviewed and rated for inclusion. Full articles were retrieved if a specific outcome of interest (smoking status) was defined. This review included all observational study designs (cross-sectional studies, case-control studies, and cohort studies). However, case reports or case series, duplicate reports, and inconsistent outcome measures were excluded. Moreover, we excluded articles that were published in a language other than English. Documents that were not accessible after contacting the principal investigator three times by email were also excluded. Articles that reported measures other than Relative Risk (RR) or equivalent values, or from which an Odds Ratio (OR) could not be calculated were also excluded from consideration, The eligibility criteria for each individual article were checked by three authors independently (CT, AA1, and AA2). If there was a disagreement between the two authors, a third person (UGM) resolved the disagreement. All reviewers came together in person and discussed the assessment results.

Information sources

This systematic review and meta-analysis were conducted by considering all the available studies (both published and open grey reports), governmental and other stakeholder annual reports, and national surveys on children and adolescents which have data on cigarette smoking among high school and university students in Ethiopia. An extensive search was done from the following international databases, including Cochrane Library, PubMed, Google Scholar, Hinari , Embase, CINAHL, Web of Science, and Science Direct to access articles conducted on the prevalence of smoking cigarette. The following keywords “prevalence”, ("cigarette smoking" OR ("cigarette"[All Fields] AND "smoking"[All Fields]) OR "cigarette smoking"[All Fields]) AND substance[All Fields]) AND (high[All Fields] AND ("schools"[MeSH Terms] OR "schools"[All Fields] OR "school"[All Fields]) AND ("universities"[MeSH Terms] OR "universities"[All Fields] OR "university"[All Fields])) AND ("students"[MeSH Terms] OR "students"[All Fields]) AND ("Ethiopia"[MeSH Terms] OR "Ethiopia"[All Fields]) were used to obtain published articles. Boolean operators particularly pairing aspects of “OR” or “AND” were used as search terms to separate articles. The search for all articles was conducted from 21 st September, 2018 to 25 th December, 2018 ( S2 Table ).

This systematic review and meta-analysis had two outcomes. The first outcome was the pooled prevalence of cigarette smoking among high school and university students in Ethiopia, which was calculated by dividing the number of smokers to the total students (sample size) multiplied by 100. The second outcome was the impact of peer pressure on cigarette smoking practice. We adjusted the effect size into Odd Ratio (OR) since all the studies were cross sectional and the appropriate effect size estimate for cross sectional design is OR to estimate the impact of peer pressure on cigarette smoking.

Data extraction

The necessary data (primary author, publication year, region, study design, sample size, prevalence of cigarette smoking) were extracted from the eligible articles by two authors (CT, AA and AA1) independently using prepiloted data extraction format prepared in Microsoft ™ Excel spreadsheet ( S3 Table ). Any disagreements between the three reviewers in the review process were discussed with the three reviewer team members (GD, DB and PM) until consensus was reached. Moreover, the data of kappa of agreement during the systematic searches was also used to solve the disagreements among two independent reviewers (CT and AA4). The kappa agreement was interpreted as less than chance agreement if less than 0, slight agreement if 0.01–0.20, fair agreement if 0.21–0.40, moderate agreement if 0.41–0.60, substantial agreement if 0.61–0.80 and moderate agreement if the kappa was 0.81–0.99 [ 43 ].

The four authors (CT, FW, MA and AA1) also independently extracted data on the association of cigarette smoking and peer pressure. If studies did not report OR, RR, or equivalent measures, raw data were screened to determine whether OR could be calculated. When the studies reported both the crude OR/RRs and the adjusted OR/RRs, the adjusted figures were extracted.

Quality assessment of the included studies

We assessed the quality of the included studies according to the Newcastle-Ottawa Scale (NOS) [ 44 ] ( S4 Table ). The NOS has three main domains and uses a star-based grading system with each study scoring a maximum of 10 stars. The first domain focuses on the methodological quality of the study (sample size, response rate, and sampling technique) with the possibility of a five-star grading (1 = poor to 5 = excellent). The second domain of the tool deals with the comparability of the study cases or cohorts, with the possibility of two stars. The last domain deals with the outcomes and statistical analysis of the study with a possibility of three stars. Three authors (MA, UGM, and DB) independently assessed the quality of each included study using the NOS. Any disagreement between the three authors was resolved by requesting other two authors (MY and PP) to independently assess the methodological quality to reach a consensus. Finally, studies with stars of ≥ 7 out of 10 were considered to be of a high quality [ 45 ]. Moreover, we assessed the quality of each included articles using National Institutes of Health (NIH) ( S5 Table ) which is a more detail tool on quality assessment than NOS. The tool has 14 criteria to assess the article independently with a response of “Yes, No and Not Applicable”. Articles with NIH assessment result of 85% and more (that means number of articles with yes divided by total criteria minus not applicable) were considered as good quality.

Risk of bias

For each included study, the risk of bias was assessed independently by two authors (UGM and CT). Risk of bias assessment was carried out using Holly 2012 tool which contain 10 recommended criteria for the internal and external validity tool [ 46 ]. This tool includes: representation of the population, sampling frame, methods of participants’ selection, non-response bias, data collection directly from subjects, acceptability of case definition, reliability and validity of study tools, mode of data collection, length of prevalence period; and appropriateness of numerator and denominator. Each item was classified as low and high risk of bias. Unclear assessment was classified as high risk of bias. The overall score of the risk of bias was then categorized according to the number of high risk item scores for bias per study: low (≤ 2), moderate (3–4), and high (≥ 5) ( S6 Table ).

Statistical data analysis

Standard error for all included studies was computed using the binomial distribution formula. Heterogeneity across studies were assessed by determining the p-values of Cochrane Q-test and I 2 -test statistics [ 47 ]. For meta-analysis result with significant heterogeneity, univariate meta-regression was used to assess the source of heterogeneity across each study. A funnel plot was also used for visual assessment of the publication bias. Asymmetry of the funnel plot is an indicator of potential publication bias. Furthermore, Egger’s test was used to determine if there was significant publication bias, and a p -value less than 0.10 was considered to indicate the presence of significant publication bias [ 48 ]. We selected Egger`s test to assess the publication bias because, the value of Egger`s test is more specific than Begg`s test [ 49 , 50 ]. We conducted the log relative risk to assess the effect of peer pressure on students’ cigarette smoking status. Furthermore, sensitivity analysis using a random effects model was performed to assess the influence of a single study on the pooled prevalence estimates. Subgroup analysis was used to minimize the random variations between the point estimates of the primary study subgroup, and analysis was done based on study settings (i.e., institution). Univariable meta-regression analysis was also conducted with year of publication and the outcome variable. All data manipulation and statistical analysis were performed using Stata ™ software (Version 14; Stata Corp, College Station, TX).

The electronic database search identified a total of 179 published articles. Of these, 121 duplicate articles were removed. Furthermore, 28 articles were removed after reviewing the titles and the abstract as they were not relevant to the focus of the review. Finally, one article was excluded due to inaccessibility of the full text despite three requests to the primary author on data, and 10 articles were excluded after reviewing their full text. Finally, 19 articles met all the prior criteria and were included in this analysis ( Fig 1 ).

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https://doi.org/10.1371/journal.pone.0222572.g001

Overview of the original included articles

All of the 19 articles included in this study were published between 1999 to 2017 in peer-reviewed journals. A total of 16,486 study participants were included in this systematic review and meta-analysis. The smallest sample size was 155 from a study conducted at Bahir Dar University [ 36 ], and the largest sample size was 1,984 in a study conducted in Gondar Medical College, Amhara Region [ 34 ]. All included studies were cross-sectional in design. The characteristics of the studies included in this review are described in ( Table 1 ) .

https://doi.org/10.1371/journal.pone.0222572.t001

Quality assessment result of the included articles

The qualities of individual articles were assessed using different tools; namely NOS and NIH quality assessment tools. Accordingly, NOS assessment result all articles had good quality using the NOS criteria. However, when assessed using NIH quality assessment tool, 1 (5.3%) study [ 36 ] was categorized as poor and the rest [ 11 , 15 , 23 – 35 , 37 , 38 , 51 ] were categorized as good quality ( S5 Table ).

Kappa agreement

Disagreements between the two reviewers during data extraction process were assessed using the Kappa agreement. Therefore, a = 9 and b = 2 represent the number of times the two reviewers agreed while c = 1 and d = 7 represent the number of times the two reviewers disagree. If there are no disagreements, b and c would be zero, and the reviewers agreement (po) is 1, or 100%. If there are no agreements, a and d would be zero, and the reviewers agreement (po) is 0. Interobserver agreement was 68% that indicate a substantial agreement between the two main reviewers who extracted data.

Risk of bias was performed for each included study using the risk of bias assessment tool that includes ten different items [ 46 ]. From the 19 included studies, the risk of bias summary assessment revealed that 94.7% of the included studies had a low risk of bias [ 15 , 23 – 35 , 37 , 38 , 51 ] while only one (5.3%) of the included studies had a moderate risk of bias [ 36 ].

Prevalence of cigarette smoking

The overall pooled prevalence of cigarette smoking in Ethiopia using the 19 studies was 16.31% (95% CI: 12.17, 20.45). A random-effects model was used because of the significant heterogeneity ( I 2 = 98.1%, p-value <0.001) across the studies ( Fig 2 ). Additionally, univariate meta-regression analysis was conducted to identify possible sources of heterogeneity. The different covariates included in the analysis were publication year and sample size. However, none of these variables were found to be statistically significant.

https://doi.org/10.1371/journal.pone.0222572.g002

The existence of publication bias was assured by funnel plot asymmetry. The funnel plot graph indicates that there is a significant variability within the findings of the 19 individual primary articles included in this meta-analysis ( Fig 3 ). The publication bias checked by objective measurement namely Egger’s tests also showed a statistically significant publication bias ( Egger's test p-value = 0 . 001 ). To handle the observed publication bias, we performed the trim and fill analysis, which is a nonparametric methods for estimating the number of missing studies that might exist and helps in reducing and adjusting publication bias in meta-analysis.

https://doi.org/10.1371/journal.pone.0222572.g003

Assessment of heterogeneity

We used I 2 statistics to investigate the presence of variation across the included studies. Accordingly, the result of I 2 statistics using a random effects model revealed a significant heterogeneity across the included studies ((I 2 = 98.1%, p-value <0 . 001 ).

Subgroup analysis

The findings from the subgroup analysis showed that the highest and lowest cigarette smoking was observed among university students 17.35% (95% CI: 12.97, 22.16) and high school students 13.76% (95% CI: 7.24, 20.27), respectively ( Fig 4 ).

https://doi.org/10.1371/journal.pone.0222572.g004

Similarly, the regional subgroup analysis result revealed the pooled prevalence of smoking from highest to lowest was [20.11% (95% CI: 11.39, 28.84)] in Ethio-Somalia and Harari region, [18.96% (95% CI: -0.03, 38.01)] in Tigray region, [17.35% (95% CI: 13.21, 21.49)] in South Nation Nationality and People of Ethiopia (SNNPE), [15.34% (95% CI: 10.84, 19.83)] in Amhara region, [14.98% (95% CI: 7.37, 22.55)] in Oromia region, and [5.9% (95% CI: 0.02, 11.79)] in Addis Ababa region ( Fig 5 ).

https://doi.org/10.1371/journal.pone.0222572.g005

The linear trend of cigarette smoking status of students in Ethiopia

The cumulative univariate meta-analysis on cigarette smoking status among high school and university with the year of 1984–2017 was performed. The result from cumulative univariate meta-analysis showed the trend in prevalence estimates of cigarette smoking status among high school and university over time. The finding revealed that there is more or less constant trend ( Fig 6 ).

https://doi.org/10.1371/journal.pone.0222572.g006

The univariate meta-regression using bubble plot was also performed. The bubble plot figure indicates that the trend was slight increment ( Fig 7 ).

https://doi.org/10.1371/journal.pone.0222572.g007

The effect of peer pressure on cigarette smoking status

Five of the 19 included studies reported the effect of peer pressure on cigarette smoking. From this, three studies [ 11 , 30 , 37 ] showed a positive effect of peer pressure on cigarette smoking, while the other two studies [ 31 , 51 ] showed no relationship between peer pressure and cigarette smoking. However, the aggregated meta-analysis revealed a higher odds of cigarette smoking among students who experienced peer pressure than those who didn’t (OR: 2.68, 95% CI: 2.37, 3.03) ( Fig 8 ).

https://doi.org/10.1371/journal.pone.0222572.g008

Cigarette smoking has major health and social consequences, and it reduces the educational performance of students [ 52 , 53 ]. This systematic review and meta-analysis, therefore, was conducted to assess the pooled prevalence of cigarette smoking and its association with peer pressure among high school and university students in Ethiopia. Accordingly, the pooled prevalence of cigarette smoking among Ethiopian high school and university students was 15.92%. This finding is lower than a study conducted among students in South Africa which reported a prevalence of 16.9% [ 50 ]. Conversely, the current reported pooled prevalence of cigarette smoking was higher than a study conducted among government and private schools and college students in Bengaluru, India (12.8%) [ 54 ] and amongst university students in Iran (13.8%) [ 55 ].

In this review, the pooled prevalence of cigarette smoking was lower than a study finding observed among Kenyan secondary school students (38.6%) and Cameroon university students (93.1%) [ 56 , 57 ]. In addition, our finding was slightly lower than a study conducted among high school students in Shiraz- Iran (19.7%) [ 58 ]. This might be due to the difference between sample size and socio-demographic nature of the two study populations. There is also cultural variation among the study communities. Moreover, the higher prevalence of cigarette smoking in the current study could be due to the dominance of male participants as evidence suggests that males tend towards different types of substance abuse than females [ 59 , 60 ].

Similarly, the current pooled prevalence of cigarette smoking is also lower than a systematic review conducted in Africa [ 50 ] and the Middle East [ 61 ]. This variation might be due to the differences in the study period and sample size between these two studies. In addition, the previous review was conducted only among university students, while the current review included both high school and university students.

The current review also considered subgroup analysis to appreciate the variability or heterogenic characteristics of the included studies. Accordingly, a higher prevalence was observed among university students (17.35%) than high school students (12.77%). This could be because most high school students live with their families which may limit them from cigarette smoking because of parental control. Additionally, in most cases, students during their high school time live with families and that may not encourage smoking cigarette. On the contrary, when they join to the university, almost all students become independent of their family supervision. This independency and pressure from their friends increases the proportion of students who smokes cigarette [ 62 ]. Educational institutions can be a challenging environment and everyone copes with stress in different ways [ 17 ]. Moreover, as students enter to university, they start a new life away from their families in a different and strange environment which can contribute to their behavior or involvement in substance abuse like cigarette smoking [ 55 ]. Evidence also supports that as the level of education increase, the proportion of smoking increases [ 63 , 64 ].

A subgroup analysis by regions of the country also showed a higher prevalence of cigarette smoking among universities in other category (i.e., Harar region, Somalia region and Oromia region). This finding might be due to typical local practices of substances like cigarette and khat in these regions. Therefore, the government, school management, local communities and other concerned bodies need to implement school-based intervention programs in order to reduce the pooled prevalence of cigarette smoking.

Students who felt peer pressure were more likely to smoke cigarette than those who had no peer pressure. This finding was similar to a study conducted in Kenyan students and Shiraz- Iran [ 57 ] where peer pressure was found to have a significant (positive) effect on the likelihood of cigarette smoking [ 56 , 58 ]. Peer group pressure is widely known as a decisive factor which affects the early onset of experimentation with tobacco and the individual’s subsequent willingness to continue smoking [ 16 ]. Similarly, other systematic reviews state the most common factors influencing students’ smoking status was having smoker friends [ 55 , 65 ]. Therefore, the school management needs to implement youth association focusing on counseling and rehabilitation service for to seize students already practicing smoking and also those who are not practicing yet now.

Strengths and limitations of the study

This review has several strengths including: this review focus on the adolescent and young adult populations who are vulnerable to initiating substance use/abuse behaviors. In addition, this review rigorous adherence to the PRISMA checklist which improves its quality for the readers. Moreover, this finding will give an insight into developing a health promotion policy for the country. Whereas, on top of the above strength, this review has the following limitations: This review included studies that were published only in English language which may limit the number of studies that were reported in other languages. Moreover, the other limitation of this review was the risk of self-report bias introduced from the original studies included in the review. On top of these the protocol of this manuscript was not registered online before conducting it.

Conclusions

This systematic review and meta-analysis indicate that the prevalence of cigarette smoking among Ethiopian high school and university students was high. More than one sixth of the high school and university students smoke cigarettes. This higher cigarette smoking proportion of students was influenced by peer pressure. Variations were also observed in the prevalence of cigarette smoking by different regions in the country. Therefore, school-based intervention programs aimed at prevention of cigarette smoking is recommended. In particular, educational programs on how to resist and handle peer pressure are essential to prevent cigarette smoking among high school and university students in Ethiopia.

Supporting information

S1 table. prisma 2009 checklist..

https://doi.org/10.1371/journal.pone.0222572.s001

S2 Table. Searches for databases.

https://doi.org/10.1371/journal.pone.0222572.s002

S3 Table. Data extraction tools Smoke.

https://doi.org/10.1371/journal.pone.0222572.s003

S4 Table. Quality assessments.

https://doi.org/10.1371/journal.pone.0222572.s004

S5 Table. NIH quality assessments.

https://doi.org/10.1371/journal.pone.0222572.s005

S6 Table. Risk of bias for each study.

https://doi.org/10.1371/journal.pone.0222572.s006

Acknowledgments

The authors of this work would like to forward great and deepest gratitude for Debre Markos University for creating convenient environment and internet service. Furthermore, the authors would like also to forward special acknowledgement for authors of primary studies.

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Anthropology of smoking : Introduction

1991, Social Science Medicine

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INTRODUCTION: Disparities in tobacco&#x27;s harm persist. Declines in smoking among the general population have not been experienced to the same extent by vulnerable populations. Innovative strategies are required to diminish disparities in tobacco&#x27;s harm. As novel tools, anthropological concepts and methods may be applied to improve the design and outcomes of tobacco cessation interventions. METHODS: We reviewed over 60 articles published in peer-reviewed journals since 1995 for content on anthropology and smoking cessation. The specific questions framing the review were: (a) &quot;How can lessons learned from anthropological studies of smoking improve the design and effectiveness of smoking cessation interventions?&quot; (b) How can anthropology be applied to diminish disparities in smoking cessation? and (c) How can qualitative methods be used most effectively in smoking cessation intervention research? RESULTS: Three specific disciplinary tools were identified and examined:...

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In this commentary issues are raised relating to the role of ethnicity and ‘culture’ as a context influencing adolescent smoking. A processual rendering of culture is encouraged, as is an appreciation of intraethnic diversity. The question posed is ‘what is cultural about particular patterns, transitions and trajectories of smoking?’ Productive ways of investigating patterns of smoking which attend to class, ethnicity, gender norms, modernity and popular culture are focused upon as an ongoing project subject to both the identity needs of youth and the agenda of the tobacco industry. Promising areas of research are identified, as are the potential contributions of ethnographies of tobacco use.

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smoking , the act of inhaling and exhaling the fumes of burning plant material. A variety of plant materials are smoked, including marijuana and hashish , but the act is most commonly associated with tobacco as smoked in a cigarette , cigar , or pipe . Tobacco contains nicotine , an alkaloid that is addictive and can have both stimulating and tranquilizing psychoactive effects. The smoking of tobacco, long practiced by American Indians , was introduced to Europe by Christopher Columbus and other explorers. Smoking soon spread to other areas and today is widely practiced around the world despite medical, social, and religious arguments against it.

Smoking and health

At the dawn of the 20th century, the most common tobacco products were cigars, pipe tobacco, and chewing tobacco . The mass production of cigarettes was in its infancy, although cigarette smoking was beginning to increase dramatically. According to the ninth edition of the Encyclopædia Britannica (1888), tobacco products were suspected of producing some adverse health effects, yet tobacco was also considered to have medicinal properties. Many scholars and health professionals of the day advocated tobacco’s use for such effects as improved concentration and performance, relief of boredom, and enhanced mood.

By the dawn of the 21st century, in stark contrast, tobacco had become recognized as being highly addictive and one of the world’s most-devastating causes of death and disease. Moreover, because of the rapid increase in smoking in developing countries in the late 20th century, the number of smoking-related deaths per year was projected to rise rapidly in the 21st century. For example, the World Health Organization (WHO) estimated that in the late 1990s there were approximately four million tobacco-caused deaths per year worldwide. This estimate was increased to approximately five million in 2003 and six million in 2011 and was expected to reach eight million per year by 2030. An estimated 80 percent of those deaths were projected to occur in developing countries. Indeed, although tobacco use was declining in many countries of western Europe and North America and in Australia, it continued to increase in countries of Asia, Africa, and South America .

The primary cause of the escalation in the number of deaths and incidents of disease from tobacco is the large increase in cigarette smoking during the 20th century. During that time cigarette smoking grew to account for approximately 80 percent of the world’s tobacco market. Nonetheless, all tobacco products are toxic and addictive. In some regions of the world, the use of smokeless tobacco products is a major health concern.

Tobacco products are manufactured with various additives to preserve the tobacco’s shelf life, alter its burning characteristics, control its moisture content, inhibit the hatching of insect eggs that may be present in the plant material, mask the irritative effects of nicotine, and provide any of a wide array of flavours and aromas. The smoke produced when tobacco and these additives are burned consists of more than 4,000 chemical compounds . Many of these compounds are highly toxic, and they have diverse effects on health.

The primary constituents of tobacco smoke are nicotine , tar (the particulate residue from combustion ), and gases such as carbon dioxide and carbon monoxide . Although nicotine can be poisonous at very high dosages, its toxic effect as a component of tobacco smoke is generally considered modest compared with that of many other toxins in the smoke. The main health effect of nicotine is its addictiveness. Carbon monoxide has profound, immediate health effects. It passes easily from the lungs into the bloodstream, where it binds to hemoglobin , the molecule in red blood cells that is responsible for the transfer of oxygen in the body. Carbon monoxide displaces oxygen on the hemoglobin molecule and is removed only slowly. Therefore, smokers frequently accumulate high levels of carbon monoxide, which starves the body of oxygen and puts an enormous strain on the entire cardiovascular system .

The harmful effects of smoking are not limited to the smoker. The toxic components of tobacco smoke are found not only in the smoke that the smoker inhales but also in environmental tobacco smoke, or secondhand smoke—that is, the smoke exhaled by the smoker (mainstream smoke) and the smoke that rises directly from the smoldering tobacco (sidestream smoke). Nonsmokers who are routinely exposed to environmental tobacco smoke are at increased risk for some of the same diseases that afflict smokers, including lung cancer and cardiovascular disease .

Clean-air laws that prohibit cigarette smoking are becoming widespread. In the 1980s and 1990s, such laws typically required that nonsmoking areas be established in restaurants and workplaces. However, the finding that toxins in environmental smoke could easily diffuse across large spaces led to much stronger bans. Since 2000 many cities, states, and regions worldwide, including New York City in 2003, Scotland in 2006, Nairobi in 2007, and Chicago in 2008, have implemented complete smoking bans in restaurants, taverns , and enclosed workplaces. A ban introduced in 2011 in China , which was home to one-third of the global smoking population , barred smoking in hotels, restaurants, and other indoor public spaces (the ban did not include smoking in workplaces, nor did it specify penalties).

In addition, entire countries have implemented smoking bans in workplaces or restaurants or, in some cases, in all public areas, including Ireland , Norway , and New Zealand in 2004 and France and India in 2008. In 2005 Bhutan became the first country to ban both smoking in public places and the sale of tobacco products.

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Essays About Smoking

Smoking essay, types of essay about smoking.

• Cause and Effect Essay: This type of essay focuses on the causes and effects of smoking. It discusses why people start smoking and the consequences of smoking on both the smoker and those around them.
• Argumentative Essay: This essay type aims to persuade the reader about the negative effects of smoking. It presents an argument and provides supporting evidence to convince the reader that smoking is harmful and should be avoided.
• Persuasive Essay: Similar to an argumentative essay, this type of essay aims to persuade the reader to quit smoking. It presents facts, statistics, and other relevant information to convince the reader to stop smoking.

Smoking Essay Example: Cause and Effect

• Identify the causes of smoking: Start by examining why people start smoking in the first place. Is it peer pressure, addiction, stress, or curiosity? Understanding the reasons why people smoke is crucial in creating an effective cause and effect essay.
• Discuss the effects of smoking: Highlight the impact smoking has on an individual's health and the environment. Discuss the risks associated with smoking, such as lung cancer, heart disease, and respiratory problems, and explain how smoking affects non-smokers through secondhand smoke.
• Use reliable sources: To make your essay more convincing, ensure that you use credible sources to back up your claims. Use scientific studies, government reports, and medical journals to support your arguments.
• Provide statistical evidence: Incorporate statistical data to make your essay more impactful. Use figures to show the number of people who smoke, the effects of smoking on the environment, and the costs associated with smoking.
• Offer solutions: Conclude your essay by suggesting solutions to the problem of smoking. Encourage smokers to quit by outlining the benefits of quitting smoking and offering resources for those who want to quit.

Smoking: Argumentative Essay

• Choose a clear position: The writer should choose a side on the issue of smoking, either for or against it, and be clear in presenting their stance.
• Gather evidence: Research and collect facts and statistics to support the writer's argument. They can find data from reliable sources like scientific journals, government reports, and reputable news organizations.
• Address counterarguments: A good argumentative essay will acknowledge opposing viewpoints and then provide a counterargument to refute them.
• Use persuasive language: The writer should use persuasive language to convince the reader of their position. This includes using rhetorical devices, such as ethos, pathos, and logos, to appeal to the reader's emotions and logic.
• Provide a clear conclusion: The writer should summarize the key points of their argument and reiterate their stance in the conclusion.

Persuasive Essay on Smoking

• Identify your audience and their beliefs about smoking.
• Present compelling evidence to support your argument, such as statistics, research studies, and personal anecdotes.
• Use emotional appeals, such as stories or images that show the negative impact of smoking.
• Address potential counterarguments and refute them effectively.
• Use strong and clear language to persuade the reader to take action.
• When choosing a topic for a smoking persuasive essay, consider a specific aspect of smoking that you would like to persuade the audience to act upon.

Hook Examples for Smoking Essays

Anecdotal hook.

Imagine a teenager taking their first puff of a cigarette, unaware of the lifelong addiction they're about to face. This scenario illustrates the pervasive issue of smoking among young people.

Question Hook

Is the pleasure derived from smoking worth the serious health risks it poses? Dive into the contentious debate over tobacco use and its consequences.

Quotation Hook

"Smoking is a habit that drains your money and kills you slowly, one puff after another." — Unknown. Explore the financial and health impacts of smoking in today's society.

Statistical or Factual Hook

Did you know that smoking is responsible for nearly 8 million deaths worldwide each year? Examine the alarming statistics and data associated with tobacco-related illnesses.

Definition Hook

What exactly is smoking, and what are the various forms it takes? Delve into the definitions of smoking, including cigarettes, cigars, pipes, and emerging alternatives like e-cigarettes.

Rhetorical Question Hook

Can we truly call ourselves a smoke-free generation when new nicotine delivery devices are enticing young people? Investigate the impact of vaping and e-cigarettes on the youth.

Historical Hook

Trace the history of smoking, from its ancient roots to its prevalence in different cultures and societies. Explore how perceptions of smoking have evolved over time.

Contrast Hook

Contrast the images of the suave, cigarette-smoking characters from classic films with the grim reality of tobacco-related diseases and addiction in the modern world.

Narrative Hook

Walk in the shoes of a lifelong smoker as they recount their journey from that first cigarette to a battle with addiction and the quest to quit. Their story reflects the struggles of many.

Shocking Statement Hook

Prepare to uncover the disturbing truth about smoking—how it not only harms the smoker but also affects non-smokers through secondhand smoke exposure. It's an issue that goes beyond personal choice.

Smoking Should Be Banned: a Call for Public Health and Safety

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Identifying the Determinants of Hookah Smoking Among the Youth; A Mixed-Methods Study

• Original Paper
• Published: 14 July 2024

Cite this article

• Tony Jehi   ORCID: orcid.org/0000-0002-7588-9241 1 ,
• Parichart Sabado 1 ,
• Lawrence Beeson 2 ,
• Dania Matta 3 ,
• Patti Herring 3 ,
• Archana Sharma 1 ,
• Kristen Emory 1 &
• Pamela Serban 3  

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Hookahs have been rising in popularity in the United States (U.S.) especially among the youth yet not much research has been carried out to understand the various predictors of hookah use among youth. We have thus conducted a cross-sectional study with a mixed-methods triangulation design to identify the hookah use determinants at different levels of the Social Ecological Model among youth. Participants between the ages of 18–24 years were sampled purposively, between April to November 2023, following a snowballing technique from various communities in Virginia and California, United States. Data were collected via a survey, one-on-one interviews, and focus groups. The study had a total sample size of 20. We found that participants smoked for a median of 5 times in the past 30 days. The main determinants of hookah smoking included the limited knowledge of health effects and addiction, positive attitude, family and peer influence, use as a means to socially connect with others, culture, social acceptability, lack of education at school and work place, access to hookah bars and smoke shops, and lack of strict enforcement of laws to ban smoking of youth. Educational interventions should be implemented by public health authorities to target the youth, their social and communities to provide education on hookah harm and addictiveness and to restrict access to- and the production, distribution, marketing and sales of hookahs.

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The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We would like to acknowledge James Madison University, College of Health and Behavioral Studies, for supporting and funding the study.

The study was funded College of Health and Behavioral Studies teaching and research grant.

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Tony Jehi, Parichart Sabado, Archana Sharma & Kristen Emory

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Dania Matta, Patti Herring & Pamela Serban

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Conceptualization: [Tony Jehi]; Methodology: [Tony Jehi], [Dania Matta], [Lawrence Beeson], [Pamela Serban], [Patti Herring]; Formal analysis and investigation: [Tony Jehi], [Pamela Serban], [Lawrence Beeson], [Patti Herring], [Parichart Sabado]; Writing—original draft preparation: [Tony Jehi], [Parichart Sabado], [Dania Matta], [Pamela Serban]; Writing—review and editing: [Tony Jehi], [Archana Sharma], [Kristen Emory]; Funding acquisition: [Tony Jehi].

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Jehi, T., Sabado, P., Beeson, L. et al. Identifying the Determinants of Hookah Smoking Among the Youth; A Mixed-Methods Study. J Community Health (2024). https://doi.org/10.1007/s10900-024-01374-1

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Original research

Impact of vaping introduction on cigarette smoking in six jurisdictions with varied regulatory approaches to vaping: an interrupted time series analysis, daphne c wu.

1 Centre for Global Health Research, Unity Health Toronto and Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada

Beverley M Essue

2 Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada

Prabhat Jha

Associated data.

bmjopen-2021-058324supp001.pdf

Data used in this study are available in public, open access repositories. Data are available upon reasonable request to the corresponding author. The dataset used in the study is publicly available from the countries’ government website (see the Data sources subsection in the Methods section) or by request to the last author (PJ).

We sought to quantify the impact of vaping introduction on cigarette smoking across settings with varied regulatory approaches to vaping.

Interrupted time series analysis, adjusted for cigarette tax levels.

Four Canadian provinces, UK and Australia.

Participants

Entire population of smokers in each country.

Interventions

The year that vaping was widely introduced in each country.

Primary and secondary outcome measures

The primary outcome is cigarette consumption per adult, and the secondary outcome is smoking prevalence among young adults.

Based on allowable nicotine levels, restrictions on e-cigarette advertising, sales and access, and taxation, the least to most restrictive jurisdictions were, in order, Alberta, Ontario, Quebec and British Columbia (all in Canada), UK and Australia. In most, but not all, settings where higher nicotine content was permitted in vaping products (66 mg/mL), vaping introduction led to a reduction in cigarette consumption per capita (Ontario: p=0.037, Quebec: p=0.007) or in smoking prevalence among young adults (Alberta men, p=0.027; Quebec men, p=0.008; Quebec women, p=0.008). In the UK, where the maximum permitted nicotine content in vaping products was 20 mg/mL, vaping introduction slowed the declining trend in cigarette smoking among men aged 16–24 years (p=0.031) and 25–34 years (p=0.002) but not in cigarette consumption per adult. In Australia, where nicotine was not permitted in e-cigarettes, e-cigarette introduction slowed the declining trend in cigarette consumption per capita and in smoking prevalence among men aged 18–24 years (cigarette consumption: p=0.015, prevalence: p=0.044).

In environments that enable substitution of cigarettes with e-cigarettes, e-cigarette introduction reduces overall cigarette consumption. Thus, to reduce cigarette smoking, policies that encourage adults to substitute cigarette smoking with vaping should be considered.

Strengths and limitations of this study

• This study uses an interrupted time series (ITS) design, which provides credible evidence on the longitudinal effects of interventions where randomisation is not possible.
• We are able to assess e-cigarette introduction in the context of permissible nicotine levels and regulations for their use, which is appropriate when considering substitution effects of vaping on cigarette demand.
• Since our definition of the intervention year is based on the first year when nationally representative surveys included questions on e-cigarette use, there may be a delay in capturing the effect of the intervention, and the ITS results are sensitive to the intervention year.

Introduction

Use of electronic nicotine delivery systems (ENDS) (also called ‘vaping’), particularly electronic cigarettes (e-cigarettes), has increased rapidly in many high-income countries since about 2010, especially among youths and young adults. 1 2 As an e-cigarette contains fewer of the toxic and carcinogenic chemicals that are in a conventional cigarette, e-cigarette use is believed to be less harmful than smoking, but not completely harmless, and the long-term risks of vaping remain unknown. 3 The net effect of e-cigarette use will depend on its harms and if e-cigarettes reduce cigarette smoking (harms for cigarette use are well documented, including a typical loss of a decade of life among lifelong cigarette smokers). 1 4

Numerous studies have found or supported the view that among youths and young adults, vaping acts as a substitute for cigarette smoking. 5–8 However, the degree of substitution also depends on government regulations on vaping such as whether nicotine is permitted in vaping devices, maximum permissible nicotine content, minimum age for purchase and tax on e-cigarettes, as well as the regulatory and taxation environment for cigarettes. To date, to the best of our knowledge, no studies have examined the impact of vaping introduction on cigarette smoking across settings with varied regulatory approaches to vaping.

This study aims to quantify the impact of vaping introduction on cigarette smoking in six high-income jurisdictions that vary in regulatory approaches to vaping: four provinces of Canada and UK and Australia, using interrupted time series (ITS) analysis. We hypothesise that in settings where regulations favour the uptake of vaping (such as higher permissible nicotine level in vaping devices, greater access to e-cigarettes, and low or no tax on e-cigarettes), vaping introduction has led to a faster decline in cigarette smoking based on aggregate sales of legal (non-contraband) cigarettes. Our secondary outcome is smoking prevalence among youths and young adults, stratified by sex.

Choice of jurisdictions

We selected Canada, UK and Australia as jurisdictions that have adopted varied regulatory approaches to vaping based on differential levels of vaping regulations 9 10 and availability of data on e-cigarette use and smoking. In Canada, vaping regulations vary substantially across provinces, hence necessitating a province-specific examination. We selected Alberta, British Columbia (BC), Ontario and Quebec provinces in Canada, as they account for about 85% of Canada’s young adult population (aged between 18 and 34 years) and total cigarette sales. 11 12 For each of the six selected jurisdictions, we examined the regulations on vaping products as they pertain to the maximum permissible nicotine content in the products, minimum age for purchase and sales, marketing, and advertisement of the products. Based on these criteria, we then classified the jurisdictions along the range from ‘less restrictive’ to ‘most restrictive’. Across these settings, regulation of cigarette smoking is fairly similar, with generally high excise taxes on cigarettes (for which we adjust in our analysis); restrictions on tobacco advertising, sales and promotion; and use of prominent health warning labels on cigarette packaging. 13

Measure of e-cigarette use and cigarette smoking

We examined the trends in prevalence of current e-cigarette use or e-cigarette use in the past 30 days, reported by national surveys in Canada, UK and Australia from 2012 (or the year when surveys first collected data on e-cigarette use) to 2019. The survey sources are presented in the Data sources section.

Our primary outcome was annual cigarette consumption per adult, which we defined as individuals aged 18 years and over. Annual cigarette consumption is measured as the number of legal (non-contraband) cigarette sticks sold; in the UK and Australia, where these data were not available, we used the monetary value of cigarettes consumed per adult (at inflation-adjusted price). Out of the total annual cigarette consumption, consumption by youths and young adults, which we defined as individuals aged between 15 years and 30 years, accounted for about 30% across Canadian provinces (authors’ calculation, insufficient data to estimate for the UK and Australia). For cigarette smoking among youths and young adults, we used prevalence of cigarette smoking between the age of 15 years and 30 years (age range varies by country, depending on data availability; see the Smoking prevalence section), stratified by sex. For countries where prevalence of cigarette smoking was not available, we used prevalence of any tobacco smoking, assuming that the majority of tobacco smoking comprises cigarette smoking. 14

Data sources

Prevalence of current e-cigarette use.

In Canada, we obtained prevalence of past 30 days’ e-cigarette use, by province, from the Canadian Tobacco, Alcohol and Drugs Survey, which is the first national survey in Canada that included questions on e-cigarette use in 2013. 15 In the UK, we used prevalence of current e-cigarette use reported by Action on Smoking and Health based on annual surveys carried out online on over 12 000 adults aged 18 years and over in Great Britain. 16 The survey included questions on e-cigarette use for smokers from 2010 and for all adults from 2012. 16 For Australia, we used data from the National Drug Strategy Household Survey (NDSHS), which collects information on alcohol and tobacco consumption and illicit drug use every 2–3 years among Australians aged 14 years and older. 17 The NDSHS began reporting prevalence of e-cigarette use among the general population from 2016.

Cigarette consumption

We estimated the annual cigarette consumption per adult in the Canadian provinces as the number of cigarette sticks consumed per adult, using cigarette sales data from Health Canada 12 and population data from Statistics Canada. 11 For the UK, we used cigarette retail sales value per adult using sales data, expressed as retail value in US dollars of 2018, from Euromonitor. For Australia, we used chain volume (which measures changes in quantity by holding price constant) of cigarettes and other tobacco products per adult expressed in Australian dollars of 2018, estimated by the Australian Bureau of Statistics. 18 The total cigarette consumption in the UK and Australia was then divided by the number of adults aged 18 years and older, estimated in the United Nations World Population Prospects 2019, 19 to obtain cigarette consumption per adult.

Smoking prevalence

For Canada, we obtained prevalence of current cigarette smoking (daily or occasional) by province from the Canadian Community Health Survey. 20 In our study, we used the prevalence of cigarette smoking among individuals aged 18–34 from 2008 to 2018. Smoking prevalence estimates among younger age groups are unreliable due to small sample sizes 20 ; hence, they were not used. For UK, we obtained cigarette smoking prevalence from the Opinions and Lifestyle Survey. 21 Although the Annual Population Survey collects smoking data in the UK, data prior to 2010 are not available. We used cigarette smoking prevalence among those aged 16–24 years and 25–34 years from 2007 to 2019. For Australia, we used prevalence of tobacco smoking among individuals aged 18–24 years and 25–34 years from 2001 to 2017 from the Australian National Health Survey (AHS). 22 The AHS is conducted every 2–3 years and reports prevalence of any tobacco smoking but not cigarette smoking. 22 As cigarette sales comprise about 85% of the overall sales of tobacco products in Australia, 23 we used tobacco smoking prevalence as a proxy for cigarette smoking prevalence.

Tobacco tax/cigarette price

Our ITS model adjusted for tobacco tax or cigarette price as a potential confounder. For Canada, we obtained the annual federal and provincial tobacco tax rates from 2008 to 2018 from the Canadian Cancer Society 24 and Non-Smokers’ Rights Association/Smoking and Health Action Foundation (2018). 24 25 For UK, we used data on the price of a 20-cigarette pack of the most sold brand obtained from the WHO Tobacco taxes and prices database. 26 For Australia, we used cigarette tax rates, as Australian dollar per kilogram of cigarettes, obtained from the Australian Bureau of Statistics. 27 All taxes and prices were adjusted for inflation by converting them to local currency units of 2018. 28 29

Statistical analysis

Its analysis.

We used ITS analysis to examine changes in the secular trend (slope change) in (1) cigarette consumption per adult and (2) smoking prevalence among youths and young adults, stratified by sex, after e-cigarette introduction in the selected settings with differential levels of vaping regulations. Details of the ITS methodology used and choice of intervention year can be found in the online supplemental material .

Supplementary data

As a potential confounder for changes in cigarette consumption and smoking prevalence, we adjusted our model for major tobacco control measures implemented during the period examined in our study: plain packaging for cigarettes which was implemented in the UK in 2017 and in Australia in 2012 (entered as a categorical variable with ‘0’ for the years prior to the implementation and ‘1’ for years after the implementation), 30 and tobacco tax increase using inflation-adjusted tobacco tax or cigarette price, thereby allowing for expected non-linearity in the ITS regression curve. 31 We did not control for smoke-free public places and bans on tobacco advertising, promotion and sponsorship, as they were already enforced before the period of our analysis. Any change in the slope (the rate of change) in cigarette sales or smoking prevalence with p<0.05 was considered statistically significant. All analyses were carried out in Stata V.15.1. 32

Sensitivity analysis

We conducted a sensitivity analysis by (1) using the relative rate of change in cigarette consumption and in smoking prevalence per year as the outcomes and (2) changing the intervention year such that the intervention year is the year prior to the intervention year used in the main analysis. Data were insufficient for carrying out sensitivity analysis by moving the intervention year 1 year ahead of the year used in the main analysis.

Patient and public involvement

Patients or the public were not involved in this study.

Table 1 shows the vaping regulations, in terms of maximum permissible nicotine content, minimum age for purchase and sales, marketing, and advertisement of e-cigarettes, in the six selected jurisdictions. Based on these regulations, the least restrictive to the most restrictive vaping environments are in order: Alberta, Ontario, Quebec, BC, UK and Australia. In Canada, the maximum nicotine level allowed in vaping devices during our study period was 66 mg/mL, which is more than three times the maximum allowed in the UK of 20 mg/mL. 1 10 In Australia, nicotine-containing e-cigarettes were not permitted unless prescribed for therapeutic purposes by a registered medical practitioner. 33 In Canada, UK, and Australia, where e-cigarettes were permitted, sales to persons under 18 years were prohibited, and marketing, advertisement and promotion of e-cigarettes were restricted. 10

Vaping regulations by country and Canadian provinces during the study period 10 16 30 34

E-cigarettes are taxed only in the UK, where as consumer products they are subject to 20% value added tax (VAT), and if they are regulated as medicinal products, the VAT levied is 5%. 10 In contrast to more homogenous regulation across subregions in the UK and Australia, Canadian vaping regulations vary across provinces. 34

Figure 1 shows the trend in prevalence of current e-cigarette use in the six selected jurisdictions for the years for which data were available. Across all study settings, the prevalence of current e-cigarette use was variable over time, but low overall (<7%).

Prevalence of current e-cigarette use in the UK (aged 18+), Canada (aged 15+, by province) and Australia (aged 18+).

The coefficients for the underlying linear time trend and slope change after vaping introduction, and the tax (or price) and plain packaging variables from the ITS analysis of the impact of vaping introduction on cigarette consumption and smoking prevalence in the six selected jurisdictions are shown in tables 2 and 3 , respectively. In the ITS analysis, a slope change represents a change in the trend in smoking after vaping introduction relative to the trend before the introduction which we expect would have been unchanged had there been no e-cigarettes. 35 The trends in cigarette consumption per adult and smoking prevalence among youths and young adults are presented in online supplemental figure S1 . All analyses are adjusted for changes in cigarette price or tax. In most settings, we found a secular decline in cigarette consumption per adult before vaping introduction except in Quebec where it increased modestly between 2008 and 2015 ( table 2 ).

Impact of vaping introduction on cigarette consumption from interrupted time series analysis, after adjusting for cigarette tax/price and plain packaging

Cigarette consumption is measured as number of cigarette sticks sold per adult in Canada, cigarette retail value per adult (2018 US$) in the UK, and cigarette chain volume per adult (2018 $A) in Australia.

The constant terms are 2516.83 for Alberta, 767.52 for Ontario, 1290.18 for Quebec, 918.38 for BC, 500.59 for UK and 1843.70 for Australia.

Values in bold are statistically significant at 95% CI.

*For cigarette tax/price, we used cigarette tax per 200 sticks (in $C) in Canada, price of a 20-cigarette pack of the most sold brand (in British £) for the UK and tax per kilogram of cigarettes (in $A) in Australia. All taxes and prices are inflated to currency units of 2018.

BC, British Columbia.

Impact of vaping introduction on smoking prevalence from interrupted time series analysis, after adjusting for cigarette tax/price and plain packaging

*Among young adults aged 18–34 years from 2008 to 2014 and 20–34 years from 2015 to 2018.

†Cigarette smoking prevalence in Canada and UK, and tobacco smoking prevalence in Australia.

AHS, Australian National Health Survey.

Less restrictive vaping environment (+)

In Alberta, between 2008 and 2011, cigarette consumption per adult declined significantly annually by 27 sticks (95% CI −50 to −4). After the introduction of e-cigarettes in 2012, the rate of decline in cigarette consumption slowed by 34 sticks per year (95% CI −13 to 80) and was not significant. In Ontario, after e-cigarette introduction in 2015, cigarette consumption per adult declined significantly faster during 2015–2018 relative to during 2011–2014 by 90 sticks per year (95% CI −171 to −10).

Environment with somewhat restrictive regulations on vaping (++)

In Quebec, cigarette consumption per adult was increasing significantly during 2011–2014 by 86 sticks per year (95% CI 35 to 138) but declined significantly faster annually after e-cigarette introduction compared with before (−117 sticks per year, 95% CI −172 to −61). In BC, after e-cigarette introduction, cigarette consumption per adult declined faster but was not statistically significant (−7 sticks, 95% CI −2 to 16).

More restrictive vaping environment (+++)

In the UK, between 2007 and 2010, cigarette consumption, in terms of retail sales value per adult, declined by US$9 annually (95% CI −20 to 2) but was not significant. With e-cigarette introduction in 2011, the declining trend in cigarette consumption slowed by US$7 per adult annually (95% CI −2 to 16), although this difference in the rate of decline was not statistically significant.

Most restrictive vaping environment (++++)

In Australia, between 2011 and 2014, cigarette consumption, in terms of chain volume per adult, was declining significantly by $A75 per year (95% CI −$A148 to −$A2). After e-cigarette introduction in 2015, the declining trend significantly slowed during 2015–2018 compared with during 2011–2014 ($A120, 95% CI $A56 to $A184).

In the sensitivity analysis when we examined the impact of vaping introduction on the relative rate of decline in cigarette consumption over time, similar results were found across jurisdictions ( online supplemental table S1 ). In Alberta, BC and the UK, there was insufficient evidence to detect a difference in cigarette consumption patterns before and after e-cigarette introduction. In Ontario and Quebec, the relative rate of decline in cigarette consumption per adult increased significantly after e-cigarette introduction, whereas in Australia, it decreased significantly after e-cigarette introduction. However, in the sensitivity analysis when the intervention is moved back 1 year from the year used in the main analysis, we found insufficient evidence to detect any difference in cigarette consumption per adult across the six jurisdictions ( online supplemental table S2 ).

Smoking prevalence among young adults

In Alberta, after e-cigarette introduction in 2012, the secular declining trend in smoking prevalence among men aged 18–34 years accelerated significantly by 3.21% per year (95% CI −5.74 to −0.69, table 3 ). For young adult women in Alberta and young adult men and women in Ontario, we found insufficient evidence to detect any difference in smoking prevalence before and after e-cigarette introduction. Sensitivity analyses conducted by moving back the intervention year 1 year from the year used in the main analysis showed similar results ( online supplemental table S2 ).

Somewhat restrictive vaping environment (++)

In Quebec, after e-cigarette introduction in 2012, smoking prevalence among young adults declined significantly faster during 2012–2015 relative to during 2008–2011 for both men and women. In BC, the declining trend in smoking prevalence slowed by 0.05% for men (95% CI −3.38% to 3.48%) but accelerated by 0.12% for women (95% CI −2.62% to 2.37%), although the changes in the trend for both are insignificant.

In the UK, after e-cigarette introduction in 2011, the declining trend in smoking prevalence among men aged 16–24 during 2007–2010 slowed significantly by 1.88% per year (95% CI 0.33% to 3.42%) during 2011–2014. Among men aged 25–34 years, smoking prevalence was increasing by 4.28% (95% CI 3.23% to 5.34%) annually between 2007 and 2010. With e-cigarette introduction in 2011, the increasing trend in smoking prevalence increased significantly by 2.07% (95% CI 1.46% to 2.68%) during 2011–2014.

In Australia, after e-cigarette introduction in 2015, compared with those during 2011–2014, smoking prevalence among men aged 18–24 years declined significantly slower annually for men. Sensitivity analysis using the relative rate of change over time as the outcome showed similar results. However, when the intervention year is changed to 2014, we found insufficient evidence to detect a difference in the rate of change in prevalence before and after e-cigarette introduction.

In the sensitivity analysis using the relative rate of change in cigarette consumption and smoking prevalence over time as the outcomes, we found similar results across all six jurisdictions. However, when the intervention year is changed to 1 year prior to the intervention year used in the main analysis, we found insufficient evidence of the impact of e-cigarette introduction on the change in the trend of smoking prevalence among young adults.

This study used ITS to analyse the impact of vaping introduction on cigarette smoking in six jurisdictions with varied approaches to vaping regulations. Across the Canadian provinces of Alberta, Ontario, Quebec and BC, where vaping regulations are less or somewhat restrictive, we found evidence that cigarette smoking (in terms of consumption or prevalence among young adults or both) declined significantly faster following e-cigarette introduction. In the UK, where vaping regulations are more restrictive, and in Australia, where vaping regulations were (and still are) highly restrictive, we found that vaping introduction has slowed the secular declining trends in cigarette smoking. Our findings suggest that, while e-cigarettes may be substitutes for cigarettes, actual substitution depends on the regulatory environment around vaping, such as nicotine content and tax on vaping products in the setting, and supports our hypothesis that in settings where regulations favour the uptake of vaping, vaping introduction had led to a faster decline in cigarette smoking.

Unlike Canada and the UK, where nicotine is permitted in e-cigarettes (although the maximum permissible content varies by country), in Australia, sale of e-cigarettes containing nicotine is banned under the argument that nicotine in vaping products would lead young people who would otherwise not take up cigarette smoking to smoke. 33 36 Our finding that e-cigarette introduction has slowed the declining trends of smoking in Australia, which could be attributed to the nicotine ban in e-cigarettes, falls in line with several studies among adolescents in the USA that found that banning e-cigarette sales is significantly associated with an increase in smoking, 37–39 and supports Lillard’s (2020) model on the economics of nicotine consumption in which nicotine is the primary object that e-cigarette consumers demand. 40 This limits the number of data points post intervention in our analysis, particularly with data from the AHS survey, which is only conducted every 2–3 years. Hence, continued monitoring of both cigarette and e-cigarette use among youths and young adults is needed in order to examine the impact of e-cigarette uptake on smoking more precisely.

Based on our findings from the perspective of tobacco harm reduction, at least in Canada and the UK where e-cigarette use has accelerated the rate of smoking decline among youths and young adults, controlled access to vaping could contribute to further curbing smoking rates in the long run. The net reduction in overall smoking was small—less than 2% vs baseline trends in Canada (data not shown)—consistent with the low level of e-cigarette use. Across Canada and the UK, the total volume of cigarettes consumed in 2017 was 63 billion sticks. Given that every 1.0–1.2 million sticks will eventually cause one death, 1 this means that about 63 000 deaths can be expected eventually, unless there are notable increases in cessation from current levels. Any meaningful reduction in cigarette consumption will reduce the leading cause of adult death in these countries, and the net benefit or harm of vaping must consider offsetting decreases in cigarettes. Imposing differential taxes on ENDS to encourage switching from the most harmful tobacco products (ie, cigarettes) to the least harmful ones could be another strategy. 41 42 Our study also found different impacts of vaping introduction on smoking among men and women. Further studies are needed to examine whether there are differential impacts by socioeconomic status, race and other characteristics.

Our study has several limitations. First, our definition of the intervention period for Canada, UK and Australia, which plays a major role in the ITS model, is based on the first year when national surveys included questions on e-cigarette use in the general population. Hence, there may be a delay in capturing the effect of the intervention, particularly as countries were experiencing significant declines in smoking prevalence in the years preceding the assigned intervention date in the ITS. However, sensitivity analysis using relative rate of decline over time as the outcome found similar results. In addition, based on the first national survey that collected data on e-cigarette use, the prevalence was under 3% across all settings included in this study. Second, in this study, the ITS model assumes that without the introduction of e-cigarettes, the trend in smoking (cigarette consumption and smoking prevalence) would remain unchanged during the postintervention period. However, across the jurisdictions selected for this study, there has been a long-term secular decline in smoking. Hence, the decline in smoking observed preintervention is likely to continue post intervention. Third, our main outcome was legal cigarette consumption measured using legal sales and did not include contraband sales, which account for about 15%–20% of total cigarette sales. 43–45 Fourth, our secondary outcomes, age-specific and sex-specific smoking prevalence among youths and young adults, were obtained from self-reported surveys. Hence, there could be an under-reporting of smoking due to social desirability bias, which might be greater in younger adults. Similarly, the prevalence of e-cigarette use could also be under-reported. Fifth, while examining the impact of vaping introduction on smoking prevalence, we did not account for the impact on smoking intensity and frequency. Additionally, we did not perform a test to examine the relationship between restrictions defined by maximum permissible nicotine content in vaping products to other variables such as minimum age for purchase, and restrictions around marketing of vaping products and the trend in smoking. As of 23 July 2021, Canada lowered the maximum permissible nicotine content in vaping products to 20 mg/mL. 46 Future studies would need to examine the impact of this restriction and restrictions on sales, advertisement and marketing of vaping products on the trend in smoking prevalence to directly establish a link between vaping restrictions and cigarette smoking. Finally, we did not control for vaping regulations which may indirectly impact smoking behaviour.

Despite these limitations, our study showed the impact of vaping introduction on cigarette consumption and smoking prevalence among youths and young adults in four high-income countries that have adopted different approaches to vaping regulation, using ITS while controlling for the secular trends in smoking decline and major tobacco control measures adopted by jurisdictions during the period examined.

This study used ITS analysis to examine the impact of vaping introduction on smoking in six high-income jurisdictions that have adopted varied regulatory approaches to vaping. Our findings showed that in most, but not all, settings where policies enable substitution of cigarettes with e-cigarettes, vaping introduction has accelerated the rate of decline in smoking, whereas in settings that restrict the uptake of e-cigarettes or do not permit the use of nicotine in e-cigarettes, vaping introduction has slowed the secular rate of decline in smoking.

Supplementary Material

Contributors: DCW and PJ designed the study. PJ conceived the study, led the study design as principal investigator, acquired funding for the study, and planned and supervised the study. DCW obtained, cleaned, analysed and interpreted the data, and drafted the paper. DCW, BME and PJ conducted and reported the work in the manuscript, and reviewed, revised and approved the final manuscript. DCW and PJ are guarantors. The corresponding author attests that all listed authors meet the authorship criteria and that no other authors meeting the criteria have been omitted.

Funding: This work was supported by the Canadian Institutes of Health Research Foundation scheme (grant number FDN 154277).

Competing interests: None declared.

Patient and public involvement: Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research.

Provenance and peer review: Not commissioned; externally peer reviewed.

Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Data availability statement

Ethics statements, patient consent for publication.

Not applicable.