Epidemiology
Cigarette smoking is the principal cause of premature death and disability in the United States. In 2016 about 480,000 deaths in the United States were caused by cigarette smoking. According to the International Agency for Research on Cancer, tobacco smoking is causally linked to at least 13 different types of neoplastic disease. However, despite education about the health hazards of smoking and other tobacco control efforts, many smokers continue to encounter extreme difficulty quitting and staying tobacco-free long-term.
With the advent of electronic cigarettes, nicotine addiction has now taken on a new form outside of the traditional route of cigarette smoking. E-cigarettes have been seen both as a public health benefit and a potential danger. E-cigarette use is now more common than combustible nicotine products among youth. This raises the interesting question of what harm does nicotine addiction do apart from the largely known risks of cancer, respiratory, and cardiovascular disease due to the smoke in combustible tobacco? The answer to this question affects millions of people in the United States and around the world. According to the Centers for Disease Control and Prevention (CDC), 16% of US adults have ever used e-cigarettes as of 2016 and about 4% are current users, the highest use is in the age 18–24 group; however, the relatively good news is that most of that age group seem to be using non-nicotine content e-cigarettes.
The latest annual National Survey on Drug Use and Health (NSDUH) in 2017 covered ≈70,000 noninstitutionalized US residents 12 years of age or older and reported that tobacco use in past month has declined in recent years, from the highest rate of 42% in 1965 to the lowest reported rate of 23.5% in 2016. Surveys with different methodologies and definitions of smoking have produced varying rates of smoking prevalence. The latest CDC report from 2017 estimates that 14% of the US population were current smokers (10.5% are daily smokers); it also reported that smoking is more prevalent in men (16.7%) than in women (13.6%), in those with less than a high school diploma (24.2%), and in those under the poverty level (26.1%). In another epidemiological study, the latest data from the 2012 National Health Interview Survey (NHIS) reported that 18% of the US population age 18 years or older were current smokers (21% of men and 16% of women). Smoking rates were substantially higher among individuals with less than a high school education. What is interesting is that, according to CDC, 55% of current smokers made at least one quit attempt of at least 24 hours in the previous year and 70% are interested in quitting.
The latest University of Michigan Monitoring the Future survey from 2018 found a continued decrease in lifetime prevalence of cigarette smoking to historic lows among 8th, 10th, and 12th graders with 9.4%, 15.9%, and 26.6%, respectively. This survey also reported a continuing decline in smoking in the last 30 days, with 1.9%, 5.0%, and 7.6%, respectively. Although the trend is downward, the above numbers highlight the magnitude of the problem with smoking and nicotine dependence, with past 30-day use trailing only cannabis and alcohol in terms of past 30-day prevalence. Adolescents from different background or life trajectories have very different smoking behaviors, 21.9% of those who do not plan to complete a 4-year college are smokers versus 8.9% of those who plan to do 4 years. Furthermore, 15% of Caucasians were smokers during the past 30 days, versus 6.9% of African Americans.
The evolving trend of e-cigarettes has shown rapid growth in recent years. Levels of nicotine vaping in the past year increased dramatically in 2018. In 10th and 12th grades, the annual increases are the largest ever recorded for any substance in the 44 years that MTF has tracked adolescent drug use. From 2017 to 2018 nicotine vaping increased by 3.4, 8.9, and 10.9 percentage points in 8th, 10th, and 12th grades. These increases resulted in yielded prevalence levels of 11%, 25%, and 30%, respectively. Although e-cigarettes have been touted as a potential smoking cessation tool, the evidence is that the majority of young e-cigarette users are not using them to quit, but are using them for motives such as novelty. The percent of 12th grade students who reported use of nicotine in the past 30 days significantly increased to 28.5% in 2018 from 23.7% in 2017. Nicotine use is indicated by any use of cigarettes, large cigars, flavored or regular small cigars, hookah, smokeless tobacco, or a vaping device with nicotine. This increase was driven entirely by vaping. Use of each of the other tobacco products was slightly down in 2018, although none of these decreases were statistically significant. Having flavors that are enjoyable and acceptable to young people is leading to greater youth acceptance. In 2018, of youth who use vaporized e-cigarettes, 13.5% did so because it looks cool, 22% use because of boredom, and 20.7% use to relieve tension and relax, whereas only 9.6% say they are using e-cigarettes to help them quit regular cigarettes. On the other hand, 53.6% say they vape because they want to experiment and 38.4% because they think e-cigarettes taste good.
In 2016, among baseline e-cigarette users, conversion to combustible tobacco smoking was much greater in a longitudinal school-based assessment study. Whereas it is almost certain that much of this overlap represents correlated liabilities to both combustible and e-cigarette use, it shows that the e-cigarettes did not have much of a protective effect and that this group is at high-risk of conversion. New aspects of e-cigarette use are constantly being elucidated, and the practice of “dripping,” where the liquid is dripped directly onto the heating element to increase flavor and throat-hit, has been reported recently. The use of flavors in e-cigarettes continues to be a topic of controversy, and it could be associated with its own health consequences.
Hookah, water pipe, shisha, or narghile’ smoking is an increasingly prevalent method of using tobacco. Although it is commonly thought to be safer for the lungs, this is not borne-out by the scientific research. In addition to the higher level of carbon monoxide (CO), the charcoal used to heat the tobacco discharges carbon nanoparticles that can impair the respiratory system, and filtering through water is not effective to eliminate CO, carbon, or other particles and toxins. Furthermore, the hit of the tobacco from these devices can be substantial, and lead to a greater dose of nicotine with greater cardiac effects. In youth, the hookah/water pipe may be associated with different gateway effects on the later use of other substances, but this will need to be investigated further. The amount of tobacco consumed in the session may be quite large, and water pipe smoking exposes people to risks of smoking not seen with other products, such as acute carbon monoxide poisoning.
The number of young smokers needs to be closely watched because tobacco prevention is preferred over treatment due to the difficulty of treating an established nicotine dependence. The difficulty in overcoming nicotine dependence is illustrated by the poor success rates among smokers who try to quit. The majority of smokers (∼70%) report an interest in quitting, and around 55% have attempted to quit in the previous year. However, only ∼7% of smokers are abstinent at 1 month after their quit date and fewer than 2% are abstinent 1 year after quitting, including those who receive assistance in smoking cessation. It is worth noting that the difficulty in maintaining abstinence is strongly related to affective and cognitive dysfunction, which may persist in some smokers for some time after the initial cessation, as well as postcessation cigarette cravings. The health consequences associated with smoking tobacco are substantial and life-threatening ( Fig. 23.1 ). Smoking is the primary causal factor for 30% of all cancer deaths and 80% of deaths related to chronic obstructive pulmonary disease. According to the CDC, cigarette smoking or exposure to tobacco smoke resulted in 443,000 premature deaths and 5.1 million years of potential life lost from 2000 to 2004. The three leading causes of smoking-attributable deaths were lung cancer, ischemic heart disease, and chronic obstructive pulmonary disease. In addition, an estimated 776 infant deaths attributed to smoking during pregnancy occurred annually from 2000 to 2004. Sadly, despite the fact that cigarette use has declined substantially since the 1960s, the number of smoking-related deaths has remained relatively unchanged.
Biological, Behavioral, and Cognitive Aspects of Nicotine Dependence
The Reward Pathway
Among the more than 9000 components of tobacco smoke, about 70 are known carcinogens. The most studied component of tobacco smoke is nicotine, which is the major psychoactive ingredient in tobacco smoke and the component most associated with tobacco dependence. Like many drugs associated with abuse and dependence, nicotine ingestion stimulates a rapid increase in dopamine in the nucleus accumbens and the ventral tegmental area, typically within 10 seconds of smoking a cigarette. Under normal circumstances, the nucleus accumbens and ventral tegmental area are activated by food, social affiliation, and sexual activity, all of which are linked to survival ( Fig. 23.2A ).
The key component of the reward pathway within the mesocorticolimbic system is the neurotransmitter dopamine, the pathways of which project from the nucleus accumbens and ventral tegmental area to the prefrontal cortex, the amygdala, and the olfactory tubercle ( Fig. 23.2B ). At the same time that dopamine is released from the ventral tegmental area, a signal is sent to the amygdala, which stamps-in the positive associations of pleasure with the environmental cues that were presented at the time, as well as the delivery of the reinforcing dopamine in the accumbens shell. This means that previously neutral stimuli, such as a brand of cigarette, a package, a vaporizing device, or even a painful burn on the throat (termed, “throat hit”) are now associated with reinforcement. Repeat appearances of cues such as the cigarette, the logo, or even the locations in which one has smoked will trigger the craving and expectation of reward—and when the anticipatory firing of dopamine is not met with actual delivery, it leads to frustration and continued or intensified craving.
Although dopamine appears to be the final common neurotransmitter of this pathway, other neurotransmitter systems such as γ-aminobutyric acid, glutamate, cholinergics, and anticholinergic are believed to be involved in the activation of the reward pathway and the sustainability of substance sue.
Nicotine affects the reward pathway by more than one mechanism; for example, in animal studies, dopamine antagonists or the destruction of dopaminergic neurons in the nucleus accumbens results in a decrease of nicotine self-administration in laboratory animals. Nicotinic acetylcholine receptors (nAChRs) are a subtype of cholinergic receptors present throughout the central nervous system (CNS) and exert varying effects (excitatory, inhibitory, or modulatory) depending on their location in the brain. In turn, nAChRs have an impact on the activity of several neurotransmitters, including dopamine, norepinephrine, serotonin, glutamate, and γ-aminobutyric acid, and of endogenous opioid peptides. Prior research has focused primarily on dopamine as a main determinant of nicotine and other drug addictions, as well as the effect of nicotine on the nucleus accumbens and the similarity of that to other addictive drugs. However, the cholinergic mechanism is also obviously an important determinant and the role of glutamate is ubiquitous to any CNS process ( Fig. 23. 2C ). The endogenous opioid, or endorphin, system is also involved in nicotine dependence, and naloxone can precipitate withdrawal in nicotine-dependent individuals. Most recently the emphasis is shifting to include most if not all the other major neurotransmitter systems in the brain. Finally, cannabinoid-1 (CB 1 ) receptors also seem to be involved in nicotine dependence and the activation of dopaminergic neurons in the mesocorticolimbic system, highlighting once more the importance of broadening the horizon and scope of our research efforts to include other systems in addition to dopamine and the reward pathway, and other downstream effects of nicotine addiction.
Neuronal Adaptation
Most if not all substances of abuse and dependence initially produce desirable and pleasant effects. However, not everyone who uses these substances goes on to chronically use them, and not all long-term substance abusers become dependent or addicted to them. Genetic, environmental, and cultural factors may all interact to predispose some individuals to substance use and subsequent dependence/addiction.
The pleasurable sensation produced by reward pathway activation is associated with acute use of the substance, whereas repeated administration of nicotine over months or years is likely to lead to increased tolerance and withdrawal in the absence of nicotine. Tolerance and withdrawal are the physiological hallmarks of dependence, and they may be reflecting the neuroadaptive effects occurring within the brain. Of interest, the chronic use of drugs appears to cause a generalized decrease in dopaminergic neurotransmission, likely in response to the intermittent yet repetitive increases in dopamine-inducing presynaptic downregulation of dopamine as a compensatory mechanism for supraphysiological levels of signaling ( Fig. 23.3 ). Using and withdrawing from drugs also increase the levels of corticotropin-releasing factor (CRF), which is associated with the activation of central stress pathways. In vivo animal studies utilizing microdialysis during withdrawal from ethanol, cocaine, nicotine, or tetrahydrocannabinol showed an increase in extracellular CRF. Of interest, the direct injection of a CRF antagonist into the amygdala reversed some of the symptoms of withdrawal (i.e., anxiogenic behaviors).
Two neuroadaptive models have been used to explain how changes in reward function are associated with the development of substance dependence: sensitization and counter adaptation. The sensitization model postulates that there is an increased desire for the drug without a corresponding increase in pleasure, and often with a decrease in pleasure, following intermittent but administration of a drug. This is in contrast to the pharmacological tolerance to a substance, which occurs after continuous exposure to a drug. Sensitization can be thought of as the increase in wanting a drug after intermittent use and can facilitate the transition from occasional use to chronic use and tolerance. This phenomenon is well described and summarized in a paper by Nora Volkow, titled: “Decreased Reward Sensitivity and Increased Expectation Sensitivity Conspire to Overwhelm the Brain’s Control Circuit.”
The counteradaptation model postulates that the initial positive feelings of reward resulting from the use of a drug are followed by an opposing rather than synchronous development of tolerance that is manifested by the appearance of withdrawal associated with the lack of the substance. The positive rewarding effects diminish gradually with sustained use, whereas tolerance for the effect of a drug takes longer to dissipate after stopping the use; a cycle of escalating drug use may follow after each cessation and consequent withdrawal. When the neurotransmitter system of the reward pathway is overactivated through escalating drug use, the system may not be able to maintain an increasingly pleasurable response to the drug. The individual is motivated to escalate the amount of use, to compensate for not delivering the reward that was expected based-on previous experience. This is evidenced in microdialysis experiments that have documented decreases in dopaminergic and serotonergic transmission in the nucleus accumbens after chronic and escalating use. Increase in CRF and concomitant decrease in neuropeptide Y during substance withdrawal (including nicotine) are associated with increases in anxiety. In turn, during substance withdrawal, activation of norepinephrine pathways stimulates additional CRF, possibly resulting in an amplification of arousal and stress, and even neurotoxic effects if this amplification of arousal and stress are long-lasting.
Of course, the two previous models are not mutually exclusive. One aspect of withdrawal that expands the concept of negative reinforcement is the effects of withdrawal on the brain’s prefrontal control circuit, and the depletion of inhibitory capacity. The state of withdrawal alters the ability of the individual to resist drug-related opportunities, and cigarettes are no exception. The brain needs to have sufficient “energy” to inhibit drug cravings, and this has been a point of practical wisdom in mutual help groups, which warn that individuals who are tired are at greater risk of relapse.
Other models of nicotine addiction have been proposed, based on mechanisms associated with cognitive control and reinforcement learning, particularly the negative reinforcement associated with the reduction in negative affect that may follow smoking after a period of abstinence (withdrawal). These models are discussed in detail later in this chapter.
Cognitive Impairment
Although much of the focus of previous research has been related to nicotine’s effects on reward processes and mesolimbic dopamine neurotransmission, a growing body of literature suggests that nicotine’s noradrenergic and dopaminergic effects on attention, information processing, and affective regulation may be of considerable importance in understanding the maintenance of a nicotine use disorder. Neurological deficits common to attention and substance use disorders, such as impaired performance, lack of motivation, decreased working memory, and impaired executive function, have been well documented in both children and adults who have these disorders. Current lines of investigation suggest that overlapping interrelated brain areas are responsible for explaining the attentional and executive impairments common to the two disorders. The involvement of two areas, in particular the prefrontal cortex and anterior cingulate cortex, highlights the commonalities between drug dependence and attentional disorders, including nicotine and neurophysiological deficits related to cognitive dysfunction. It also may be that nicotine causes a distinct problem with decision-making, as evidenced by smokers’ performance on the Iowa Gambling Task. Stopping smoking can partially reverse these deficits. Longitudinal studies will be needed to determine how much of the decision-making deficit was preexisting (and predisposed the individual to substance use), and how much was an effect of the drug. Likewise, it is important to see if group differences between current and former smokers are due to acute drug-effects, or represent a different set of neurobiological characteristics in those who have been able to quit and those who have not.
The prefrontal cortex regulates goal-directed behavior, thought, and affect by using working memory to provide representational knowledge about past or future events and integrating this information into a plan for action or to exercise inhibitory control over inappropriate actions or thoughts. In attentional/cognitive disorders these processes are impaired and manifested in symptoms that involve poor attention, planning, impulse control, and monitoring of one’s behavior. Disentangling the direct relationship of these processes to a substance use disorder is difficult, because these individuals also have neurochemical deficits in the mesolimbic dopamine system. Studies indicate that the right prefrontal cortex in humans is particularly important in the inhibition of activity (i.e., Stop or Go-No Go tasks). The orbital and ventral prefrontal cortex may also have a similar inhibitory effect in the affective domain, thus permitting appropriate social behaviors. In attention-deficit/hyperactivity disorder (ADHD), for example, the anterior cingulate cortex has been implicated in the regulation of the motivational aspects of attention as well as in the regulation of response selection and inhibition. Thus researchers have begun to characterize ADHD as a disorder with deficits in inhibitory processes involving frontal cortical structures. Notably, there is a significant relationship between a history of ADHD and smoking. If a person must mentally manipulate information and make a response, the anterior cingulate cortex (with its connections to the prefrontal cortex) becomes active. This area become particularly active in tasks where inhibitory control or divided attention are necessary.
The importance of the inhibitory role of these structures in drug dependence has also been highlighted by several researchers. Drug-addicted individuals, including smokers, continue to use drugs even when faced with negative consequences and diminished reward, suggesting an apparent loss of control. The failure to regulate (i.e., inhibit) this drive points to a dysfunction within the prefrontal cortex and related areas including the anterior cingulate and orbitofrontal cortices. These prefrontal dysfunctions may themselves be caused by deficits in the limbic structures, or exacerbated by them. As shown in Fig. 23.3 , the resulting persistence of the behavior is not necessarily due to continued reinforcement by the drug (mesolimbic dopamine) but rather to the enhanced saliency of the drug and drug cues that have been firmly established (learned) in memory during the acquisition of dependence. During maintenance of substance use disorders, these super-salient drug-related cues, including the effects of priming doses from the first amount of administration, overcome the inhibitory control of the prefrontal cortex that might normally inhibit the response to take the drug due to perceived costs (consequences) with decreasing hedonistic properties. The expectation of reward is maintained by the brain as it releases significant dopamine when approaching an opportunity to use a substance, even if the actual hedonic enjoyment of that substance has decreased with the development of tolerance. This causes the individual to escalate their use, and sometimes attempt to recreate the level of stimulation that they have previously experienced, and will forever remember that state when presented with cues to use (often termed “euphoric recall”). The development of tolerance occurs over time and leads to the individual needing to consume an increasing amount of the drug, particularly because the effects of the first use of the drug remain the ones that are expected and predicted by the user.
Preclinical studies suggest that the impairment in prefrontal cortex function may be related to significant dendritic branching and spine density resulting from repeated drug administration, thus amplifying the signal of salient events. Moreover, abstinence from the drug significantly reduces the efficiency of the prefrontal cortex to process information in working memory, thereby interfering with its regulatory function. Such effects might be mediated by the negative affect associated with nicotine withdrawal, and when present, reduce the probability that a smoker may exercise an appropriate coping response and increase the probability of relapse. There is electroencephalography (EEG) evidence supporting persistent frontal lobe dysfunction among smokers using tasks related to working memory (P300). Neuhaus and colleagues found a hypoactivation of the anterior cingulate, orbitofrontal, and prefrontal cortices among both current and former smokers compared to never smokers, suggesting that the dysfunctional activation patterns found in smokers may not completely remit after quitting; a fact that may increase their vulnerability to relapse. It remains to be seen whether this is an effect of smoking, or a preexisting risk factor that led these individuals to be more likely to smoke.
A model by Curtin, Baker, and colleagues attempts to address the conditions under which cognitive control mechanisms affect the processing of motivationally relevant information (i.e., smoking cues) and the execution of situationally appropriate behavior. The model holds that once dependence is established, drug use motivation is frequently driven by implicit processes that are largely automatic and outside of the user’s awareness (contextual cueing involving the hippocampus and amygdala, and discrete cueing involving the basolateral amygdala). These implicit processes are developed and maintained by negative and positive reinforcement learning.
In the case of negative reinforcement, internal states associated with negative affect or drug withdrawal can engage motivational systems and drug use behavior in an attempt to ameliorate these aversive states. This involves such processes as the central amygdala cascade occurring with withdrawal, and the release of CRF, which has direct effects on brain structures and also involves engagement of the wider hypothalamus-pituitary-adrenal (HPA) axis. Activation of the habenula, which occurs when there is a reward prediction error, also further dampens the ventral tegmental area (VTA) to positive stimuli and facilitates inhibitory avoidance. In addition, there is evidence that the endorphin (endogenous opioid) system mediates certain aspects of nicotine dependence.
With positive reinforcement, environmental cues and positive mood states previously associated with rewarding drug effects can increase approach motivation. The model postulates that these learned associations trigger subcortical, “bottom-up” processes that can influence drug-seeking behavior implicitly by engaging appetitive or avoidance motivational systems. Thus the drug user may frequently engage in drug-use behaviors for reasons that are outside of conscious awareness, and each use will reinforce the strength of the circuit.
Although the model proposed by Curtin et al. holds that drug sensitization is largely maintained by the implicit influence of learned associations on motivation, the authors also speculate about circumstances in which drug use comes under explicit or cognitive control. Cognitive control can be defined as the effortful application of attentional resources to meaningful information and tasks. Cognitive control is crucial to learning as it is activated when an organism encounters unexpected outcomes, unfavorable outcomes, or response errors. In this model, cognitive control is important because it is elicited during response conflict, which can occur when the user attempts to regulate the craving and drug-seeking behaviors that result from exposure to conditioned cues. Ultimately, cognitive control is what allows a drug user to engage in less well-learned alternatives to drug-seeking behavior when drug craving and approach motivation is activated. However, it is during instances of response conflict and engagement of cognitive control mechanisms that drug craving will be most acutely experienced by the drug user. If there are clear processing deficits engendered in the management of response conflict (also pertinent to error monitoring in the anterior cingulate cortex), then behavioral resistance to the increased craving is also diminished.
Development of Nicotine Dependence Risk, and the Effect on Other Substance Use Disorders
Effect on Comorbid Substance Use
All substances use, share, and activate the same underlying neuronal pathways of reward and reinforcement; nicotine may be strengthening the circuits involved in the maintenance of all-drug taking behaviors. Such that these circuits are primed to carry-out addictive behaviors for substances that the nicotine users subsequently experiment with and use regularly, the brain effects of nicotine use mean that individuals are often craving when they are in withdrawal, which can begin within hours of cessation of use. It is unknown what effect this may have on alcohol consumption or other drug use, and traditionally clinical recommendations have often proceeded from the assumption that it will be too difficult for individuals to simultaneously quit nicotine and other substances of abuse as that might incur increased risk of relapse to the other substance. However, the data continue to accumulate against that concept. It seems that quitting smoking (tobacco use) while in treatment either has no impact or, in some studies, has a favorable impact on the ability to quit and stay quit from other substances. Therefore it is important to systematically assess for tobacco use disorder while people come in for treatment of other substance use disorders and offer them assistance to quit.
Because nicotine is one of the earliest drugs to be used, it is important to assess whether use by youth has a gateway effect toward other substances of abuse. For “gateway effect,” the order of the substance may not be as important, but rather that the use of one substance leads to greater vulnerability for the subsequent use and addiction to other substances. The tobacco gateway seems to open quickly for urban adolescent smokers, who demonstrate a very high rate of concomitant substance use in a recent study. The uptake of cannabis seems to be much greater for youth with preexisting nicotine dependence, with the route of administration being an important variable, as smoking had greater risks than chewing tobacco. Furthermore, there is strong evidence that the relationship between cannabis and nicotine is bidirectional, with cannabis use being one of the strongest predictors of the subsequent onset of daily nicotine use after controlling for other variables.
The effects of nicotine cessation on other substances of abuse have been studied, even in high-risk groups like adolescents diagnosed with ADHD and substance use disorders. Still the cessation of nicotine did not increase the rate of relapse to other substances. Furthermore, the addition of smoking cessation to an established cannabis intervention did not adversely affect quit rates. In recent years there has seemed to be an increase in the number of adolescents and young adults who are moving away from tobacco and nicotine dependence to use other substances, which ultimately could make the costs to society even greater. Conversely, not ceasing the use of other substances does increase the uptake of nicotine use.
Nicotine and Negative Affect
Relative to some other substances of abuse (e.g., cocaine), negative reinforcement plays a more powerful role in the maintenance of tobacco use disorders. Negative reinforcement is the process of strengthening a behavior by having an aversive state removed after the behavior is performed, such as the smoking of a cigarette to avoid nicotine withdrawal. One of the most fundamental aspects of nicotine dependence involves its neuroregulatory function on mood. The negative emotions involve decreased experience of reward, increased perception of threat, increased activation of what Panskepp termed the “rage system,” and increased sensations of tension (possibly with relative disinhibition of locomotor activity). The experience of frustrated nonreward recruits more motivation and energy for reward seeking, and increases the probability of reward attainment. To resist this frustration, an individual would need to have a good level of inhibition and an ability to tolerate negative affect.
The relationship between negative affect and the ability to sustain cessation of smoking behavior plays a prominent role in theories of nicotine dependence. It has been theorized that individuals addicted to a substance learn to detect internal cues that negative affect is approaching as drug levels fall within the body. To prevent the onset of these negative feelings, the addicted person self-administers the drug, although often this process proceeds without conscious awareness. The longer the individual is without the drug, the more likely these negative feelings are to enter conscious awareness, providing direct reinforcement that taking the drug relieves negative affect ( Fig. 23.4 ). This relationship has driven the development of new pharmacological and behavioral approaches to treatment. The experience of negative affect is a significant contributor to the risk of relapse, and negative affect reduction is cited by many smokers as an important reason to smoke. This is, of course, a fool’s errand, as the negative affect is not truly relieved, but simply postponed. Newer approaches such as the ecological momentary assessment are helpful tools for tobacco researchers to look at the day-to-day, moment-to-moment correlates of smoking that could lead to the targeting of specific moments and tailoring strategies to forestall the resumption of tobacco consumption. Improving the understanding of the psychobiological and genetic mechanisms associated with the modulation of mood by nicotine will help us better understand the mechanisms of nicotine dependence and the relationship between these mechanisms and treatment success (see Fig. 23.4 ).
The term “negative affect” refers to a composite index of many negative mood states, including feelings of depression, dysphoria, irritability, and nervousness, and is usually measured by Likert-type scales such as the Positive and Negative Affect Scale (PANAS), Profile of Mood States (POMS), or other similar adjective checklists. Research on the relationship between negative affect and smoking behavior has included evaluation of the effects of a past history of major depression, which may serve as a marker for vulnerability to future depressed mood, and evaluation of the effects of precessation and postcessation negative affect.
Indeed, the presence of negative affect following cessation has been found to characterize over 50% of all smoking lapses, with 19% of all lapses occurring under conditions of extreme negative mood. Negative affect appears to be the component of nicotine withdrawal that most profoundly influences relapse and the trajectory of nicotine withdrawal symptoms. The expectation that nicotine will produce desirable emotional consequences has also been shown to inversely predict cessation success. In addition to postcessation negative affect, precessation levels of negative affect, have been shown to predict cessation outcome. Negative affect following a quit attempt has been related to treatment failure and relapse across a variety of treatment modalities.
When a smoker quits using tobacco, the above biological, cognitive, and behavioral aspects of dependence may increase the risk of relapse. However, many factors are associated with an increased risk for relapse after quitting smoking, including the availability of cigarettes, an increase in psychological stressors, and a triggering of conditioning factors (cues). Visual cues can be seeing people smoking or going to a location where one used to smoke or obtain cigarettes. Such factors may trigger the enduring adaptational changes that occurred in the brain during the period of nicotine consumption and subsequent addiction. The amygdala is very slow to forget, if it ever does, positively reinforced cues, in a term called incubation of drug craving.
Genetics
Longitudinal twin studies have shown that the genetic risk for all substance use is shared and there is substantial genetic contribution, although there can be a substantial reduction in heritability in rural environments, where the cultural factors are more important. There is some evidence of a specific genetic risk factor for nicotine dependence that does not significantly overlap with other substance use risk. There is a noteworthy distinction between the factors and processes leading to the initiation of a drug and the persistence of its use, and these factors are particularly important for a drug with as widespread exposure as nicotine. The persistence of nicotine dependence displays some difference in the genetics of initiation and persistence. Genetic studies have been performed to take into consideration a very large number of single nucleotide polymorphisms (SNPs); we see that a substantial portion of variance in cigarette use can be explained by genetic effects that also mediate behavioral disinhibition. Some of these genes could be involved in many different pathways, and it will take time to see if there are groups of genetic markers involved in similar or overlapping processes. Currently, there is no genetic test that can precisely determine an individual’s risk of developing a tobacco use disorder.
Heritability
Recent family, twin, and molecular genetic studies provide compelling evidence of a role for genetic factors governing smoking initiation, continuation, and cessation, with estimated heritability rates ranging from 47%–76% for initiation and 62% for persistence. a
a References 60, 192, 194, 245, 264, 282.
The concordance rates for smoking, not smoking, and quitting are higher for monozygotic than for dizygotic twins, and the concordance rates for smoking in 108 pairs of identical twins reared apart (where 82 smoked) was 75.9%. Although monozygotic concordance rates for initiation and continuation of smoking were both above 80% in another study, the weighted correlation in liability to lifetime regular smoking was 0.80 in monozygotic twins, and 0.53 in dizygotic twins, suggesting that the heritability of liability to regular smoking is likely 50% to 60%. A meta-analysis of data from six studies revealed an estimated heritability rate of 0.6 for males and 0.48 for females for smoking persistence. For the maintenance of dependent smoking behavior, the percent genetic contribution seems to be about 70%. Three linkage studies of smoking behavior suggest that alleles that influence smoking behavior occur in only a small proportion of families.Genome-Wide Association Studies of Nicotine Dependence
Relatively recent genome-wide association studies related to nicotine dependence have been published. Uhl et al. used 520,000 SNPs using a DNA pooling approach. They prepared pools of DNA from nicotine-dependent European-American smoking cessation trial participants and control individuals. Because in the DNA pooling technique individual genotypes are not available, they compared genotypes from the entire group of nicotine-dependent research participants to genotypes from European-American research volunteers free from any substantial lifetime use of any addictive substance. They performed analyses using smokers versus nonsmokers and successful versus nonsuccessful quitters and identified several genes of interest.
A study by Berrettini and colleagues examined nicotine dependence using genome-wide association data from proprietary databases (GlaxoSmithKline) established to study cardiovascular and other common diseases. In this study, nicotine dependence was studied using a single indicator: cigarettes per day where cases were defined as smokers consuming >25 cigarettes per day and controls were noted as consuming <5 cigarettes per day. Their initial analysis identified a significant relationship ( P = .0006) between a SNP in the cholinergic receptor nicotinic alpha 3 subunit (CHRNA3) region, rs6495308, although the P -value fell below the 10 -7 expected for genome-wide analysis. Nevertheless in a replication sample, another SNP in this same region, rs1317286, did meet the expected P -value for a relationship with cigarettes per day.
Bierut et al. performed a genome-wide association on 1050 nicotine-dependent cases and 879 nondependent smokers. This was a two-stage study in which DNA pooling was used in the first stage of analyses and 31,960 SNPs were selected and genotyped in the nicotine-dependent cases and nondependent controls. They identified 35 SNPs with P -values less than 10 -6 ; however, none of these SNPs maintained significance after correcting for multiple testing. However, this study did identify several candidate genes. In a follow-up study, Bierut used data from The Collaborative Study on the Genetics of Alcoholism (COGA) and contrasted smokers who consumed over 20 cigarettes per day with those who smoked >100 cigarettes in their lifetime but never more than 10 cigarettes per day. The results showed the nonsynonymous coding SNP of the CHRNA5 gene, rs16969968 ( P = 0.007), was associated with habitual smoking. Other SNPs in this region that were highly correlated with rs16969968 included rs2036527, rs17486278, rs1051730, and rs17487223 ( r 2 > 0.79). A second independent finding noted by these authors in this gene cluster, was an association with rs578776, for which a low correlation with rs16969968 ( r 2 < 0.15) was observed.
Three recent genome-wide association studies have identified gene variants in a region on the long arm of chromosome 15 (15q24/15q25.1) as significant contributors to the risk of lung cancer, as well as nicotine dependence. The region of interest encompasses the nicotinic acetylcholine receptor subunit genes CHRNA3 , CHRNA5 , and CHRNB4 , and involves several SNPs in strong linkage disequilibrium with each other. These include rs10517309,145,294 and rs8034191. In the case control study by Amos and in a further analysis of these and other data by Spitz and colleagues, a significant relationship was noted for gene variants in this region (the A variant for rs1051730 in this analysis) associated with lung cancer, nicotine dependence, and smoking quantity indices in cases and controls, as well as earlier age of smoking initiation and time to first cigarette in controls. A nonsignificant trend was also noted for an inverse relationship between the adverse allele and duration of cessation. Thorgeirsson and colleagues also found a significant relationship between risk of lung cancer and peripheral artery disease and the T variant (TT TG GG) of rs1051730. A significant association was also found between the adverse allele likelihood of being a former smoker, and as with the previous study, associations were also noted between the minor allele and smoking quantity, Fagerström Test for Cigarette Dependence scores, and symptoms of nicotine dependence from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV TR). Of interest, the genome-wide association study by Hung and colleagues also noted a significant risk of lung cancer and the variant alleles of rs1051730 and rs8034191, but unlike the other two genome-wide association studies for lung cancer, these authors did not note an association with nicotine dependence, a finding which is at variance with several recent studies of nicotine dependence not involving cancer patients. For example, the rs1051730 SNP is in strong linkage disequilibrium (correlated) with the CHRNA5 SNP, rs16969968, for which the A variant has been shown to increase the risk of nicotine dependence in the studies noted earlier. This SNP also has an r 2 of .18 and .90 with rs6495308 and rs1317286, respectively, which are two SNPs in this CHRNA3-A5 region that have been shown to predict cigarettes per day in heavy smokers in the genome-wide association study by Berrettini and colleagues. Furthermore, in the study of Thorgeirsson et al., the relationship of rs1051730 with FTCD scores or symptoms on the DSM-IV, was at a level similar to that observed in a candidate gene study using low frequency smokers as controls.
Candidate Gene Studies for Nicotine Dependence
An examination of the literature in this area shows that over 60 unique genes have been noted in candidate gene studies of nicotine dependence. Several reviews have been published in this area, b
b References 62, 198, 201, 202, 222–224.
with most concluding that small sample size and replicability pose significant issues in interpreting these results. In addition, the limited characterization of the phenotype (i.e., simple classification as a smoker or not) may further restrict the information that can be obtained from these studies. Most of the candidate genes studied to date fall into two categories: Nicotine metabolism and CNS receptor or neurotransmitter function. An example of the metabolism is the cytochrome P450 (CYP)2A polymorphisms have also been associated with different risks for tobacco use disorders. The receptor and neurotransmitter studies have included all the major SNPs that have been researched in the smoking literature related to dopamine pathways and nicotinic receptors—for example dopamine receptor D2 ( DRD2 ), dopamine ( DOPA ), ankyrin repeat and kinase domain containing 1 ( ANKK1 ), dopamine transporter ( DAT ), catechol-O-methyltransferase ( COMT ), cholinergic receptor nicotinic alpha 4 subunit ( CHRNA4 ), and cholinergic receptor nicotinic beta 2 subunit ( CHRNB2 ) (see Blomqvist et al., Huang et al., Hutchison et al., Saccone et al., and Zhang et al. ).Genome-Wide Studies Predicting Nicotine Cessation Treatment Outcome
Uhl and colleagues recently conducted a genome-wide association study examining successful versus unsuccessful quitters across three clinical trials: one used nicotine replacement therapy (NRT), and results from this sample were also previously published by this group ; and two used bupropion, in mixed racial samples. The combined sample for all three trials totaled 540 individuals, with individual trial samples 266 and 150 for the two bupropion trials and 124 in the NRT trials. This group of investigators used a DNA pooling strategy and Monte Carlo simulation analysis of gene frequencies to identify SNPs that differentiated abstainers and nonabstainers, as well as those that were specific to nicotine replacement therapy or bupropion. In total, they noted several thousand SNPs with nominal significance covering over 100 genes, involved in numerous biological processes ranging from cell adhesion, transcription regulation, intracellular signaling, cell structure, and unknown function. Although intriguing and suggestive of a pharmacogenetic effect, the results from this study are difficult to interpret given the sheer number of hits and the complex biological processes involved. Clearly, much more information is needed, taking a more traditional approach to genome-wide association techniques to examine both predictors of abstinence and pharmacogenetic effects of smoking cessation medications.
Candidate Gene Studies Predicting Treatment Outcome
A handful of candidate studies have examined genetic predictors of NRT and bupropion. Like the candidate gene studies on nicotine dependence, most of these studies have focused on markers in the dopamine pathway, given the importance of the dopaminergic neurotransmission in nicotine reinforcement. For example, several polymorphisms in the D2 receptor gene (DRD2), including the following variants: C957T, -141Cins/del and Taq1A (ANKK1), C32806T, and a variable-number-of-tandem-repeat (VNTR) in the DRD4 (C-521T) have been shown to predict cessation outcome to NRT. Others have identified genes associated with opioid or serotonergic pathways. With the exception of the study by David, most have predicted only end of treatment success. Similarly, many of these same markers (Taq1A, –141Cins/del), and others (COMT, cytochrome P450 2B6 [CYP 2B6], DAT) have been associated with successful treatment by bupropion, as well as another antidepressant, venlafaxine. One recent candidate gene study took a systems approach to identifying SNPs associated with smoking cessation using bupropion. This study involved a population of 217 and 195 smokers receiving bupropion or placebo, respectively. Using a systems-based candidate gene approach this study identified polymorphisms (rs2072661 and rs2072660) within the β 2 nicotinic acetylcholine receptor (CHRNB2), which showed significant association with abstinence rates at end of treatment and at 6-month follow-up in a placebo-controlled trial of bupropion for smoking cessation. The association with the two SNPs was very high (r 2 = 0.96). These effects were independent of treatment but there was some indication that abstinence might be modulated by bupropion. For example, there was a substantial increase in relapse rates for those individuals carrying the minor allele after treatment was discontinued. Subsequent analyses of rs2072661 showed a significant relationship with time to relapse at the 6-month follow-up period and modulation of withdrawal symptoms at the target quit date.
Diagnosis
In 2013, the Fifth Edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) was published, and included several changes to the section now entitled: Substance Related and Addictive Disorders. Tobacco Use Disorder has replaced nicotine dependence and has the same general 11 symptoms like other substance use disorders. The new list of 11 symptoms was basically a combination of the DSM-IV-TR abuse and dependence symptoms with the removal of “legal consequences” from the list in exchange for “craving, or strong desire or urge to use.” Although all seven of the prior dependence symptoms were retained, the newly added symptoms are “recurrent tobacco use in situations in which it is physically hazardous,” continued use despite the social or interpersonal problems caused or exacerbated by tobacco, and recurrent tobacco use resulting in a failure to fulfill major role obligations at work, school, or home. Nicotine is reported to be among the most addictive of substances of use, especially when consumed through smoking tobacco. After prolonged smoking, the user develops nicotine tolerance and exhibits withdrawal symptoms when nicotine is absent; these are two physiological symptoms of dependence (addiction). Furthermore, nicotine may be responsible for other criteria for dependence: loss of control over smoking (e.g., not being able to reduce or stop smoking; or smoking more than intended), compulsive use (e.g., spending more time using the substance or giving up important events to use the substance), and continued smoking despite adverse consequences (e.g., heart attack, emphysema, or cancer). The presence of any two or more of those 11 criteria for at least a year satisfies the definition of tobacco use disorder, previously called dependence and classically known as addiction.
A commonly used scale for assessing nicotine dependence is the Fagerström Test for Nicotine Dependence (or FTCD) ( Table 23.1 ). The FTCD measures physiological dependence (tolerance and withdrawal) reliably well, but does not reliably measure some of the other dimensions of nicotine dependence (especially the behavioral ones). Of particular importance is the first item of the scale (How soon after you wake up in the morning you smoke your first cigarette?), which has been validated recently as a proxy for nicotine dependence and called “heaviness of smoking index” (HIS). Most research studies until now have used the total FTCD score of equal to or greater than 4, as a cutoff as synonymous to physiological dependence to nicotine. The Wisconsin Inventory of Smoking Dependence Motives (WISDM-68) and its shortened version (WISDM-37) are more recently developed multidimensional scales of nicotine dependence. These are usually used in research as they are more comprehensive and detailed than the FTCD and include measures of cognitive enhancement, negative reinforcement, positive reinforcement, automaticity, affiliative attachment, loss of control, behavioral choice/amelioration, craving, cue exposure/associative processes, social/environmental goals, taste/sensory processes, weight control, and tolerance (see Table 23.1 ).
| Questions | Answers | Points |
|---|---|---|
| Within 5 min 6–30 min 31–60 min After 60 min | 3 2 1 0 |
| Yes No | 1 0 |
| The first one in the morning All others | 1 0 |
| 10 or less 11–20 21–30 31 or more | 0 1 2 3 |
| Yes No | 1 0 |
| Yes No | 1 0 |
Smoking and Psychiatric Comorbidities
There is substantial evidence to suggest that smoking is closely linked with several psychiatric comorbidities, suggesting shared biological pathways between nicotine dependence and these psychiatric conditions. For example, current smoking rates among those with no mental illness, lifetime mental illness, and past-month mental illness has been reported as 22.5%, 34.8%, and 41.0%, respectively. Remarkably, smokers with a mental disorder in the past month reportedly consumed 44.3% of all cigarettes smoked in this nationally representative sample. Several studies have demonstrated a strong relationship between alcohol, substance abuse, and other psychiatric disorders and smoking. c
c References 22, 40, 44, 79, 121, 136, 185, 332.
For example, the lifetime prevalence rate of alcohol dependence or drug abuse is estimated at 23%–30% among adult smokers, and this number is higher among the young adult cohort. Among nondependent and dependent current smokers, lifetime rates of mood and anxiety disorders have been reported as 12%–26.7%, and 33.5%–46.5%, respectively. In addition, there is an elevated risk of first onset of major depression, panic disorder, and generalized anxiety disorder among smokers. A significant shared familial risk of depression and smoking has been identified for heavy and nonheavy nicotine-dependent smokers, and a history of major depression has been associated with an increased prevalence of smoking, nicotine dependence, and greater nicotine withdrawal severity. Some studies have found an inverse relationship between major depression history and quitting success, but these findings have not been uniform.Some data have suggested that treatment with sequential fluoxetine, beginning 8 weeks before the target quit rate, is associated with enhancement of quit rate, and significantly reduced negative affect during the withdrawal state. In addition, bipolar disorders have been shown to have a relationship to smoking, with rates nearly as high as those for schizophrenia. As with posttraumatic stress disorder (PTSD), which carries a very strong association with tobacco use.
In the area of neurodevelopmental disorders, odds ratios comparing “ever” with “never” smokers were positively related to the number of ADHD symptoms. Among those reporting regular smoking over their lifetime, an inverse relationship between number of ADHD symptoms and age at onset, and a positive relationship between symptoms and number of cigarettes smoked, has also been observed. The time to first use of cigarette in the morning, the number of cigarettes smoked in a day, and the likelihood of smoking when sick, have all been correlated with ADHD symptoms. In addition, in longitudinal studies of individuals with ADHD, it has been revealed that hyperactive-impulsive symptoms were risk factors for multiple substance use disorder, and inattentive symptoms only predicted nicotine dependence ; the links persisted even after controlling for the presence of conduct disorder. The association with ADHD naturally raised the question of whether treatment with stimulants might affect nicotine dependence risk. In clinical laboratory studies, the acute administration of methylphenidate is associated with greater nicotine use in controls, and in ADHD individuals, but one study showed the effect of increasing cigarette intake to be curiously correlated with decreases in food intake. Clinical trials using osmotic-released methylphenidate led to no clinical improvement or worsening of smoking for ADHD individuals, but had some benefit for nonwhites, and has generally been established to be relatively safe in a substance-using population. A meta-analysis of several studies showed a relationship between treating ADHD symptoms with stimulants and reduced rates of smoking: “effect sizes were larger for studies that used clinical samples, included more women, measured smoking in adolescence rather than adulthood, conceptualized stimulant treatment as consistent over time, and accounted for comorbid conduct disorder.”
Conduct disorder (CD) has also been associated with smoking, “with a dose-dependent effect,” as it has been associated with all other substances of abuse. The general lack of response to aversive information relative to healthy controls (CD individuals are relatively insensitive to punishment) is associated with substance use in this population, and may require alteration in the common approaches to substance abuse treatment in this population.
Eating disorders are linked with tobacco, as the use of nicotine for weight reduction is an important factor in smoking, particularly for women. A drive for thinness has been associated with smoking on a daily basis. Smokers have a greater prevalence of eating pathology and body shape concerns, and among women, bulimia nervosa and binge-eating disorder are associated with higher rates of smoking. Subtyping of the eating disorders appears important, because both the binge-purge types of anorexia and bulimia have been shown to have higher odds-ratios of smoking than the other subtypes. There is concern that smoking cessation could lead to reemergence of anorexia symptoms in remitted anorexics, triggered by the rapid weight-gain with smoking cessation, and caution certainly needs to be exercised in this population, with careful planning around triggers to relapse of both disorders. Smoking status affects the resting energy expenditure in patients with anorexia, and should be considered during refeeding. In smokers, comorbid bulimia and binge-eating disorder have higher rates of depression and alcohol use, and binge-eating and smoking carry an additional risk of panic disorder and PTSD.
The impact of comorbid psychiatric symptoms on the ability to quit using tobacco varies according to the disorder. Certain comorbidities such as ADHD and social anxiety may strongly predict relapse rates and provide opportunities for intervention to improve outcomes by treating the comorbid disorder.
Prevention Science
Prevention science is suggesting that there are weak and inconsistent effects of school policies on tobacco use. However, enforcing the laws regarding tobacco use and its sales to minors can affect the rate of its use. In addition, there are studies showing the use of graphic warning labels that can induce brain responses that effectively change the individual’s motivation to use a substance. Early prevention and interactive education using audiovisual technologies such as the ASPIRE program seem to be successful in lowering the uptake of smoking from 6% to 2% among high school students who participated versus controls. The Good Behavior Game showed decreased rates of regular smoking from ages 19 to 21, and Unplugged has been associated with decreased rates of smoking uptake and slower progression of increased rates of smoking. Unplugged uses a social influence model to decrease the normative perception of peers using tobacco, and increased the refusal skills for tobacco. The Unplugged curriculum may have the critical difference of using teachers to deliver the evidence-based prevention of substance use. The relationship of the teachers to the students helps the message remain salient for the individual.
A public health campaign in the United States has shown some effect, and it is partly responsible for the downtrend in American youth use of combustible tobacco. The decreased frequency of combustible tobacco use in the American population means that there have been demographic and psychological drifts in the makeup of the average smoker. Today’s smokers have become increasingly concentrated among those with psychiatric diagnosis, less wealth, or living in rural areas. Demographic shifts are important to consider when designing prevention efforts. The targeting of peer-crowds (groups of adolescents or youth who share similarities across the nation, for example, alternative, hipster, hip-hop, and country) seems to be a promising way to tailor prevention messages using the cutting-edge techniques (and science) of marketing. The tactic of tobacco prevention using messages tailored to specific peer crowds on social media is a promising intervention being used in the Commonwealth of Virginia.
Increasingly a large proportion of individuals who smoke are diagnosed with a mental illness. In addition to the seriously mentally ill (schizophrenia and bipolar disorder), of particular concern is the correlation of cigarette smoking with depressive symptoms, due to the high prevalence of depression in the general population. This raises an ethical concern with the state and federal government filling tax coffers with the taxation of cigarettes, because the burden of that taxation is increasingly falling on the marginalized and persons living with mental illness. This is not to say that the taxation should not occur because increases in cost greatly decrease the prevalence of smoking, but the shifting burden onto vulnerable users highlights a moral imperative that the funds garnered from the taxation of cigarettes should be reinvested into smoking cessation efforts, in particular for those who are marginalized and/or diagnosed with mental disorders.
For youth, living in a smoking home can be a risk factor independent of having smoking parents. There is limited evidence that family-based interventions can affect the risk of smoking in youth unless they are intensive. Two studies showed that these can be additive beyond the interventions at school, and one study showed that the effects on smoking were comparable to those of the Good Behavior Game. Anti-tobacco mass-media campaigns are likely to be more effective when they are buttressed by the interventions being delivered in the classroom.
Treatment
As smoking becomes less prevalent, treatment may become a more difficult task for individuals who continue to smoke, and especially residual smokers who have failed earlier treatments for nicotine dependence. The current smoker is more likely to be more dependent on nicotine and have low motivation to change, most likely due to prior failed attempts—what has been termed a “hardening effect”—whereby a greater proportion of those with high motivation to change have already ceased tobacco use, leaving a more difficult-to-treat cohort. Other markers of continuing to smoke and unsuccessful quitting (hard core smoker) are lower education and lower socioeconomic status. Newer, tailored, and perhaps more comprehensive approaches may be needed for the more recalcitrant and difficult to quit population.
The US Public Health Service along with the Department of Health and Human Services, and in concert with other public health agencies, has sponsored general guidelines for the treatment of tobacco use and dependence. The first guideline was initially published in 1996 (summarizing 3000 publications), updated in 2000 (adding 2000 publications), and further updated in 2008, when information was added from about 2700 newer publications and had 10 key recommendations ( Table 23.2 ). The latest review in 2015 done by the US preventative task force (a group of independent scientists focus on reviewing the literature for evidence in prevention) assigned the evidence for effectiveness of tobacco treatment as having the highest possible category A, with the standard of care being counseling plus medications.
The overarching goal of these recommendations is that clinicians strongly recommend the use of effective tobacco dependence counseling and medication treatments to their patients who use tobacco, and that health systems, insurers, and purchasers assist clinicians in making such effective treatments available.
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The chance for recovery from nicotine dependence is maximized when a comprehensive biological, psychological, and social (biopsychosocial) assessment is done. Such assessments, which should account for the smoker’s motivation for change, can guide both psychosocial therapy and pharmacological treatment. Pharmacological treatments produce the best results when combined with psychosocial therapy by doubling the odds of quitting smoking of either alone. However, medications are often used alone or with minimal support and they do alleviate some of the effects of nicotine withdrawal, decrease cravings for tobacco use, and decrease the risk of relapse.
Nicotine replacement therapies (or NRTs) and non–nicotine-based medications such as sustained-release bupropion-SR (Zyban or Wellbutrin-SR) and varenicline (Chantix) have been shown to reduce cravings and nicotine withdrawal symptoms when used as aids to quitting smoking. NRTs, Bupropion-SR, and varenicline are first-line therapies for tobacco dependence, whereas nortriptyline (Pamelor) and clonidine (Catapres) are considered second-line ( Table 23.3 ). Although there have been reported mood alterations that occur with varenicline, bupropion and other medications while patients try to quit smoking, multiple studies concluded that the risk is fairly minimal and insignificant, even among people with current or past but stable psychiatric disorders. As a result, in December 2016, the US Food and Drug Administration (FDA) revised the packet insert and removed the black box warning for both varenicline and bupropion, replacing it with an adverse effect precaution. A historic decision, as the FDA has never before removed a black box warning from any medication insert.
| Cessation Agent | Dosage | Label Indication and Use | Availability in United States | RR of Efficacy (95% CI) |
|---|---|---|---|---|
| Nicotine gum | 2 mg and 4 mg | 2 mg ≤25 cig/day and 4 mg ≥25 cig/day; one piece every 1–2 hours for weeks 1–6, one every 2–4 hours for weeks 7–9, and one every 4–8 hours for weeks 10–12 | OTC; traditional, mint, and orange flavors, generic available | 1.49 (1.40–1.60) a |
| Nicotine patch | 21 mg, 14 mg, and 7 mg | ≥10 cig/day: 21 mg for 6 weeks, then 14 mg for 2 weeks; then 7 mg for 2 weeks, ≤10 cig/day: 14 mg for 6 weeks, then 7 mg for 2 weeks | OTC; clear and skin color; generic available | 1.64 (1.53–1.75) a |
| Nicotine nasal spray | 10 mg/mL, 0.5 mg/squirt | 2 squirts (one dose) per hour, minimum 8 doses/day, maximum 40 doses/day; recommended up to 3 months | Prescription only, 100 mg/bottle; no generic | 2.02 (1.49–2.73) a |
| Nicotine oral inhaler | 10 mg/cartridge, 4 mg delivered | 6–16 cartridges/day up to 12 weeks, then gradual reduction for another 12 weeks; usually individualized | Prescription only, 168 cartridges/box; no generic | 1.90 (1.36–2.67) a |
| Nicotine lozenges | 2 mg and 4 mg | If first cig is ≤30 minutes after waking, use 4-mg lozenge; if ≥30 minutes, use 2-mg lozenge; use one every 1–2 hours for 6 weeks, then one every 2–4 hours for 3 weeks, then one every 4–8 hours for 3 weeks; minimum 8 lozenges/day, maximum 20 lozenges/day | OTC; mint and cherry flavors; no generic | 1.52 (1.32–1.74) a |
| Bupropion-SR | 100 mg and 150 mg | 150 mg every morning for 3 days, then 150 mg twice daily; recommended for 3 months | Prescription available; generic available | 1.94 (1.72–2.19) b |
| Varenicline | 0.5 mg and 1 mg | 0.5 mg every morning for 3 days, then 0.5 mg twice daily for 4 days, then 1 mg twice daily up to 3 months; if successful may extend another 3 months | Prescription only; no generic | 2.24 (2.06–2.43) c |
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