research paper for smoking

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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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Megan E. Piper

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Christie D. Fowler

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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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Systematic review of changed smoking behaviour, smoking cessation and psychological states of smokers according to cigarette type during the COVID-19 pandemic

Affiliations.

• 1 Graduate School of Public Health, Seoul National University, Gwanak-gu, Seoul, Republic of Korea.
• 2 College of health science, Dankook University, Chungnam, Republic of Korea [email protected].
• PMID: 35701057
• PMCID: PMC9198387
• DOI: 10.1136/bmjopen-2021-055179

Objectives: Although the global COVID-19 pandemic has increased interest in research involving high-risk smokers, studies examining changed smoking behaviours, cessation intentions and associated psychological states among smokers are still scarce. This study aimed to systematically review the literature related to this subject.

Design: A systematic review of published articles on cigarettes and COVID-19 -related topics DATA SOURCES: Our search was conducted in January 2021. We used the keywords COVID-19, cigarettes, electronic cigarettes (e-cigarettes) and psychological factors in PubMed and ScienceDirect and found papers published between January and December 2020.

Data selection: We included articles in full text, written in English, and that surveyed adults. The topics included smoking behaviour, smoking cessation, psychological state of smokers and COVID-19-related topics.

Data extraction and synthesis: Papers of low quality, based on quality assessment, were excluded. Thirteen papers were related to smoking behaviour, nine papers were related to smoking cessation and four papers were related to psychological states of smokers.

Results: Owing to the COVID-19 lockdown, cigarette users were habituated to purchasing large quantities of cigarettes in advance. Additionally, cigarette-only users increased their attempts and willingness to quit smoking, compared with e-cigarette-only users.

Conclusions: Owing to the COVID-19 outbreak, the intention to quit smoking was different among smokers, according to cigarette type (cigarette-only users, e-cigarette-only users and dual users). With the ongoing COVID-19 pandemic, policies and campaigns to increase smoking cessation intentions and attempts to quit smoking among smokers at high risk of COVID-19 should be implemented. Additionally, e-cigarette-only users with poor health-seeking behaviour require interventions to increase the intention to quit smoking.

Keywords: COVID-19; mental health; public health.

© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.

Publication types

• Systematic Review
• Research Support, Non-U.S. Gov't
• COVID-19* / epidemiology
• Communicable Disease Control
• Electronic Nicotine Delivery Systems*
• Smoking / epidemiology
• Smoking / psychology
• Smoking Cessation* / psychology
• Tobacco Products*

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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 ).

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

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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Register for the oss 25th anniversary event, no, eating french fries is not the same as smoking cigarettes.

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This article was first published in  The Montreal Gazette.

I had never heard of psychiatrist Dr. Paul Saladino, which is somewhat surprising because he is quite frisky in the duck pond. His TikTok videos in which he tries to convince his legions of followers that dietary fibre is unnecessary, that drinking beer leads to “man boobs,” that LDL cholesterol does not increase the risk of heart disease, that oatmeal is toxic and the key to health is eating red meat, are laughable.

Saladino’s pseudoscientific rants were brought to my attention by a former student who now teaches science in Germany. He was asked by one of his students about a video in which Saladino claims that eating a serving of McDonald’s fries is equivalent to smoking a pack of 25 cigarettes.

The stimulus for this video seems to be a paper that Saladino read but was unable to properly digest. It discussed similarities between the chemical content of french fries and tobacco smoke and noted that a serving of fries can contain some carcinogenic aldehydes in amounts comparable with that found in the smoke from 25 cigarettes. In no way did the authors suggest that the risks were comparable.

Let’s note right away that there is a big difference between inhaling or ingesting a substance. Inhalation leads to direct entry into the bloodstream, while the digestive tract contains numerous enzymes that metabolize food components.

Next, tobacco smoke contains thousands of compounds, with 62 of these listed by the International Agency for Research on Cancer (IARC) as carcinogenic to humans. The most significant carcinogens in tobacco smoke are not aldehydes, but N-nitrosamines, polyaromatic hydrocarbons, aromatic amines, 1,3-butadiene, benzene and ethylene oxide. While there is no question that carcinogenic aldehydes such as crotonaldehyde can form when fats are heated, the total number of carcinogens that invade a body from a pack of cigarettes are far, far greater than from a serving of french fries.

Of course, the only way to compare the health impact of a daily serving of fries with smoking a pack a day would be to run a long-term study comparing two groups of subjects with the only difference between them being smoking or eating french fries. Clearly, this is impossible to do, but if it were carried out, I would wager that the smoker group would have a far higher incidence of cancer than the french fry group.

Fearmongering has become an industry, and Saladino is a head honcho in this arena. The usual technique is to pick a scientific study that finds some risk and then exaggerate it without taking into account type and extent of exposure. Pesticides, fluoride, oxalates, gluten, lectins and vaccines have all been unrealistically portrayed as villains. This is not to say that there are no legitimate chemical risks. We live in a very complex world, with some 160 million known chemicals, both natural and synthetic. There certainly are issues with some of these. Perfluoroalkyl substances (PFAS), bisphenol A and phthalates are present in just about everyone’s bloodstream and may indeed be causing some serious mischief.

One way or another, we are in contact with thousands of chemicals on a regular basis, and teasing out individual effects is not possible. While french fries may indeed contain some carcinogens, it does not automatically follow that eating them causes cancer. As a classic analogy, coffee contains carcinogens such as furfural, caffeic acid and styrene, but we know that coffee doesn’t cause cancer.

None of this is to say that I am willing to absolve french fries from all blame. Excessive consumption of fried foods is a problem, and not only because of the extra calories provided by the fat. When fats are heated, particularly polyunsaturated seed oils, they form a slew of potentially carcinogenic compounds.

And then there is the issue of the “Maillard reaction,” named after Louis Camille Maillard, physician turned chemist, who in 1912 described the reaction between sugars and amino acids that produces a variety of “melanoidins” responsible for the browning of toast, doughnuts and french fries. In fried potatoes, glucose and the amino acid asparagine undergo a Maillard reaction to yield acrylamide, classified by IARC as a “probable human carcinogen.”

Although associations cannot prove a cause-and-effect relationship, a study by the highly reputable Fred Hutchinson Cancer Research Center in Seattle compared about 1,500 prostate cancer patients with the same number of controls and found that regular consumption, at least once a week, of fried chicken, fried fish, doughnuts or french fries increases the risk of developing the disease.

While carcinogens in fried foods cannot be totally eliminated, they can be significantly reduced. The secret is to do your “frying” in an “air fryer.” These devices have taken kitchens by storm, including mine. Basically, they are small convection ovens in which a current passing through an element heats air that is then circulated by a fan. The basket in which the food is placed has openings to ensure heating from all sides, so covering these with parchment paper or aluminum foil in pursuit of cleanliness is counterproductive.

Although the temperature to which the air is heated, about 180-190 degrees C, is comparable with the temperature of frying oil, air is far less efficient at transferring heat to food. While deep frying takes only five or six minutes, air frying can take three times as long. However, since no oil is being used, there is no worry about its carcinogenic breakdown products. Furthermore, hot air penetrates the food less effectively than hot oil, so the inside of the food doesn’t get as hot, which means significantly less acrylamide formation.

As far as crispiness goes, that is determined by the moisture content at the food’s surface. When food is placed in a deep fryer, the immediate bubbling seen is because of steam released from its surface. Hot air does not heat the surface quite as well, but still well enough to drive out moisture and produce crispiness. In the case of fries, this can be improved by first coating the potatoes with a thin layer of oil. If you really want to reduce oil-degradation products, the best choice is avocado oil because of its extremely high smoke point. I won’t say that my “air fries” are comparable with the best double-fried restaurant version, but they are very acceptable. And healthier.

Remember that the claim of french fries being as dangerous as smoking comes from someone who thinks that lamb testicles and raw liver are healthy, and cruciferous vegetables like broccoli, Brussels sprouts, chard and kale are “bulls–t.” These, Saladino says, should be avoided because “once chewed they produce sulforaphane, which is toxic to humans.”

Actually, sulforaphane has been shown to be an anti-carcinogen. So go for your broccoli and kale. If it is taste and crispiness you are after, put them in the air fryer. As far as Saladino’s TikTok videos go, after watching a bunch of them with their confusing message, I am led to conclude that this psychiatrist needs a psychiatrist.

@JoeSchwarcz

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