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Research Article

Perceptions of health risks of cigarette smoking: A new measure reveals widespread misunderstanding

Affiliation Department of Communication, Stanford University, Stanford, California, United States of America

Affiliation Graduate School of Business, Stanford University, Stanford, California, United States of America

* E-mail: [email protected]

Affiliations Department of Political Science, Vanderbilt University, Nashville, Tennessee, United States of America, Hoover Institution, Stanford University, Stanford, California, United States of America

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Affiliation U.S. Department of Treasury, Washington, D.C., United States of America

Affiliation LinChiat Chang Consulting, LLC, San Francisco, California, United States of America

Affiliation Department of Communication Studies, University of Michigan, Ann Arbor, Michigan, United States of America

Affiliation GfK Custom Research North America, New York City, New York, United States of America

  • Jon A. Krosnick, 
  • Neil Malhotra, 
  • Cecilia Hyunjung Mo, 
  • Eduardo F. Bruera, 
  • LinChiat Chang, 
  • Josh Pasek, 
  • Randall K. Thomas


  • Published: August 14, 2017
  • Reader Comments

15 Feb 2019: Krosnick JA, Malhotra N, Mo CH, Bruera EF, Chang L, et al. (2019) Correction: Perceptions of health risks of cigarette smoking: A new measure reveals widespread misunderstanding. PLOS ONE 14(2): e0212705. View correction

Table 1

Most Americans recognize that smoking causes serious diseases, yet many Americans continue to smoke. One possible explanation for this paradox is that perhaps Americans do not accurately perceive the extent to which smoking increases the probability of adverse health outcomes. This paper examines the accuracy of Americans’ perceptions of the absolute risk, attributable risk, and relative risk of lung cancer, and assesses which of these beliefs drive Americans’ smoking behavior. Using data from three national surveys, statistical analyses were performed by comparing means, medians, and distributions, and by employing Generalized Additive Models. Perceptions of relative risk were associated as expected with smoking onset and smoking cessation, whereas perceptions of absolute risk and attributable risk were not. Additionally, the relation of relative risk with smoking status was stronger among people who held their risk perceptions with more certainty. Most current smokers, former smokers, and never-smokers considerably underestimated the relative risk of smoking. If, as this paper suggests, people naturally think about the health consequences of smoking in terms of relative risk, smoking rates might be reduced if public understanding of the relative risks of smoking were more accurate and people held those beliefs with more confidence.

Citation: Krosnick JA, Malhotra N, Mo CH, Bruera EF, Chang L, Pasek J, et al. (2017) Perceptions of health risks of cigarette smoking: A new measure reveals widespread misunderstanding. PLoS ONE 12(8): e0182063.

Editor: Raymond Niaura, Legacy, Schroeder Institute for Tobacco Research and Policy Studies, UNITED STATES

Received: May 7, 2016; Accepted: June 20, 2017; Published: August 14, 2017

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Data Availability: Data are available at: , doi: 10.7910/DVN/JP2JHH .

Funding: LC and RKT have commercial affiliations with LinChiat Chang Consulting and GfK Custom Research North America, respectively. These companies provided support in the form of salaries for authors LC and RKT, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.

Competing interests: LC and RKT have commercial affiliations with LinChiat Chang Consulting and GfK Custom Research North America, respectively. This does not alter our adherence to PLOS ONE policies on sharing data and materials.


Despite a constant flow of messages reminding Americans of the health risks of cigarette smoking, and despite a steady decline in the proportion of Americans who smoke during the last 50 years, more than 20% of Americans continue to smoke regularly today [ 1 ]. This paper explores whether the continued prevalence of smoking may, in part, stem from a failure to acknowledge these risks. At first blush, this assertion may seem patently implausible; much research indicates that increasingly large proportions of Americans recognize the various dangers of smoking, and some studies even suggest that most Americans overestimate the proportion of smokers who suffer from certain smoking-related ailments [ 2 ]. Nonetheless, it is possible that people underestimate the magnitude of some of the health risks caused by smoking. Because individuals seem to base their decisions about whether to smoke on how they believe the act of smoking changes the risk of contracting specific diseases, correcting any underestimation of risk may yield future reductions in smoking onset and increases in cessation [ 3 ]. To explore these possibilities, we conducted three studies of national samples of American adults documenting risk perceptions and their relations to smoking behavior.

Challenges in the study of risk perception

One way to gauge the accuracy of people’s perceptions of the health dangers of smoking is to focus simply on the list of maladies that become more likely as a result of smoking. This list includes various cancers, heart diseases, respiratory diseases, premature death, and more [ 4 , 5 ]. By asking representative national samples of American adults to identify which diseases and medical conditions on a provided list are linked with smoking, researchers have illuminated three interesting patterns. First, since the 1950s, the proportion of Americans who failed to identify any health risks of smoking dropped consistently [ 6 ]. Second, according to Gallup [ 7 ], a sizable proportion of Americans still fails to recognize a link between smoking and some related ailments (see S1 Fig ). Other contemporary surveys support these same conclusions [ 8 – 10 ]. The proportion of American adults who associate smoking with a particular ailment varies considerably across ailments, from a high of 81% who report a link between smoking and cancer to single-digit proportions who identify links with asthma, hypertension, bronchitis, and stroke [ 11 ]. Thus, even today, Americans apparently underestimate the breadth of the danger.

A more refined way to gauge the accuracy of perceptions is to focus on the amount of increased risk of each malady that results from smoking. According to epidemiological studies, each of these increases is a function of many attributes, including age of smoking onset, number of years of regular smoking, number of cigarettes consumed per day, and more [ 4 , 5 ]. Therefore, actual risks must be expressed as variables that are functions of such factors, and perceptions of these risks must be ascertained specifying such factors.

Furthermore, even holding constant age of onset, length of smoking, and dosage, a smoking-related risk can be perceived in three different ways: (1) absolute risk (i.e., “what is the chance that a person will get lung cancer if he/she smokes?”), (2) attributable risk (i.e., “how much does smoking raise the chances that a person will get lung cancer compared to not smoking?”), and (3) relative risk (i.e., “how much more likely is a person to get lung cancer if he/she smokes?”) [ 12 , 13 ]. Mausner and Bahn [ 14 ] provide a thorough review of how epidemiologists calculate and use each of these different measures of risk. Assessing Americans’ perceptions of all three seems most sensible in order to determine whether people tend to perceive all types of risk accurately, overestimate all types of risk, underestimate all types of risk, or overestimate some while underestimating others. Attributable fraction is another measure of risk perceptions, but we do not investigate this measure in this study [ 15 ].

One way to think about the goal of such an investigation is to identify any ways in which people underestimate risk, so that public health education campaigns can correct this misunderstanding. But it could turn out that people underestimate one particular type of risk (e.g., absolute risk) and yet do not use that particular perception of risk in their decision-making about whether to start or stop smoking. Therefore, efforts to correct the public’s misunderstanding would not translate into changes in smoking behavior. So to draw out implications of measurements of perceived risk, we need evidence indicating which perceptions may be behaviorally consequential.

The research described in this paper set out to do so by gauging perceptions of absolute risk, attributable risk, and relative risk with a focus specifically on lung cancer. And we explored which of these risk perceptions might drive smoking onset and cessation. We focus on lung cancer specifically rather than all health risks associated with smoking following Viscusi’s seminal work on smoking-related risks [ 2 ]. While the share of American adults who associate smoking with a particular health malady varies across maladies [ 11 ], an assessment of which type of risk perception—absolute risk, attributable risk, and relative risk—impacts Americans’ smoking behavior the most should not be sensitive to the health malady of interest. In other words, if perceptions of relative risk of lung cancer affects smoking behavior more than perceptions of absolute and attributable risk of lung cancer, then perceptions of relative risk of another disease should similarly be most effective at driving smoking behavior.

Prior studies of perceptions of the magnitude of risk

A number of past studies have attempted to measure perceptions of the magnitude of the risk of smoking in representative samples of American adults, but their methodologies entailed a series of limitations, as we outline next. It is worth noting that this paper focuses on the U.S. and therefore does not discuss the many interesting studies of smoking-related risk perceptions that have been done in countries other than the U.S [ 16 – 18 ].

We also do not discuss studies that examined people’s perceptions of their own personal smoking-related risks (e.g., Boney-McCoy et al. [ 19 ]; Strecher et al. [ 20 ]) because our focus is on Americans’ perceptions of the risk of smoking to people in general. Many studies have produced interesting results involving people’s perceptions of their own personal risks of smoking-related health problems (e.g., [ 19 , 21 – 27 ]). However, according to Gigerenzer [ 28 ], people naturally think about the population rather than personal chance, and perceptions of personal risk likely mediate the relationship between general risk and behavior.

Because this paper is focused on the beliefs of adults, we also do not discuss the findings of many interesting studies of youth. For example, Romer and Jamieson [ 29 ] asked questions similar to Viscusi’s [ 2 ] of a national sample of 14- and 15-year-olds: “Out of every 100 cigarette smokers, how many do you think will: (a) get lung cancer because they smoke? (b) have heart problems, like a heart attack, because they smoke? (c) die from a smoking-related illness?” Their results mirror Viscusi’s [ 2 ]: on average; respondents said 61.4% of smokers would develop lung cancer, much higher than the true rate. Likewise, a representative sample of 20–22 year olds said 52.6% on average. Many other studies have explored the beliefs of children and adolescents as well [ 21 , 30 – 37 ].

Some past studies have asked people to describe their perceptions of the magnitude of a smoking-related risk of some malady by asking people to select a point on a rating scale with a small number of verbally labeled response options. For example, Weinstein et al. [ 27 ] asked “How likely do you think it is that (the average male cigarette smoker/the average female cigarette smoker/you) will develop lung cancer in the future?” and offered a 5-point scale ranging from “very low” to “very high.” Similarly, Romer and Jamieson [ 29 ] asked respondents “In your opinion, is smoking very risky for a person’s health, somewhat risky, only a little risky, or not risky at all?” It is not clear whether “somewhat risky” or “very risky” is an overestimate or underestimate of risk. In other words, measures that assess perceptions of smoking’s dangers on these non-numeric subjective probability scales do not permit assessing the degree to which magnitudes of perceived risk reflect true numeric risk levels.

Other studies have measured perceptions of risks quantitatively but did not specify the population of people being described or the dosage of smoking being addressed. For example, in a survey conducted by Audits & Surveys Worldwide, respondents were asked, “Among 100 cigarette smokers, how many of them do you think will get lung cancer because they smoke?” [ 2 ]. The characteristics of a smoker are important contextual considerations with regards to actual health risks a given smoker faces. The probabilities of various smoking-related ailments differ for occasional and daily smokers and depend on the age of a smoker as well as the duration of smoking. Because this type of question does not specify what population is to be described or how much smoking was done for how long, it is impossible to gauge the accuracy of responses by comparing them with the results of epidemiological studies, which show risk to vary across populations and age, smoking duration, and dosage. Some scholarly work has begun to remedy this issue, specifying the exact quantity of cigarettes smoked per day [ 38 ].

Another potential limitation of the Audits & Surveys question is the phrase “because they smoke.” This phrase was presumably meant to lead respondents to estimate the number of lung cancer cases completely attributable to smoking. As Slovic [ 36 ] observed, this phrase can be interpreted in various different ways. Specifically, people may believe that smoking, along with other factors, enhances the chances of contracting lung cancer, leading them to respond that smoking is partially responsible for some lung cancer cases. This, too, makes it difficult to identify the appropriate true rate of smoking-induced lung cancer cases to which to compare risk perceptions.

Finally, the notions of “subadditivity” and “the focus of judgment effect” point to another potential problem with the Audits & Surveys question [ 39 – 41 ]. The question, “Among 100 cigarette smokers, how many of them do you think will get lung cancer because they smoke?” focuses respondents’ attention on just one possible outcome of smoking: getting lung cancer. This approach typically leads to overestimation of the probability of the event in question. Asking respondents instead to report the number of smokers who will not get lung cancer would focus attention on that outcome instead, probably leading to overstatement of that probability. So the sum of the average answers to these two forms of the question would most likely total more than 100. A more desirable measurement approach would overcome the bias induced by arbitrarily asking about only one outcome (e.g., either getting lung cancer or not getting lung cancer).

The present research

To overcome the limitations of past studies, we conducted three surveys measuring Americans’ beliefs about smoking-related health risks in different ways. To gauge perceived risk, we asked two questions: one about the risk to nonsmokers, and the other about the risk to smokers. This approach is advantageous if a researcher wants to measure perceptions of attributable risk or relative risk, because (1) subadditivity is likely to bias both reports upward, so subtracting or dividing one judgment from or by the other will minimize the impact of overestimation, (2) answers to these questions can be used to generate assessments of perceived absolute risk, attributable risk, and relative risk, and (3) this approach employs the principle of decomposition, which enhances the accuracy of measures of people’s beliefs [ 15 ]. It is worth noting one limitation of our research is the fact that we only ask about lung cancer, and do not consider other health risks linked with smoking. However, most likely people’s perceptions of risk across multiple disease categories would be positively correlated. Consequently, our general conclusions about lung cancer would likely be similar if respondents were forced to consider multiple disease categories.

In decomposition, a single, global judgment is broken down into a series of sub-judgments, each of which a respondent must make in the process of generating the global judgment. Here, in order to gauge people’s perceptions of relative risk, we could ask, “how many more times likely is a smoker to get lung cancer than a nonsmoker?” To answer the global question, a respondent must estimate both the likelihood a nonsmoker will get lung cancer and estimate the likelihood that a smoker will get lung cancer, and then mentally compute the ratio of the probabilities. Because respondents can accidentally make a computational error when executing that last step, surveyors can more accurately measure people’s beliefs by asking directly about the sub-judgments, leaving the researcher to compute the ratio. The same logic applies to the measurement of perceived attributable risk (see S1 Appendix for a discussion of measuring probabilities and numeracy).

When measuring perceptions of the lung cancer risks of nonsmokers and smokers, we expressed specifically a volume of smoking and at what age it began, so we could more accurately gauge the extent to which people overestimated or under-estimated the health risks of smoking. And rather than asking survey respondents to report probabilities, we asked them to report frequencies, since a variety of studies suggest that people think more naturally in terms of frequencies [ 42 , 43 ].

We compared the three risk perception measures (absolute, attributable, and relative risk) in terms of their associations with cessation among a sample of current and former smokers. We also compared the risk perception measures in terms of their associations with the desire to quit among current smokers. Although previous studies have found positive and significant correlations between risk perceptions and the desire to quit, none of these studies compared different risk perception measures to one another or analyzed numerical risk estimates [ 27 , 44 , 45 ].

Such associations can occur for at least two reasons. First, beliefs about the health risks of smoking may be instigators of smoking cessation (for a review of this literature, see S2 Appendix ). Second, people may adjust their beliefs about smoking’s health risks in order to rationalize their status as a smoker or a non-smoker [ 46 – 48 ]. If perceptions of health risks are motivators of smoking cessation and/or if quitting smoking induces people to inflate their risk perceptions, then perceived risk should be lower among people who currently smoke than among people who have quit. That is, the higher a person’s perceived risk, the more likely he or she is to have quit. Likewise, the higher a current smoker’s perception of risk, the more motivated he or she should be to quit smoking. Therefore, the more strongly a risk perception measure is associated with whether a person has quit smoking and a smoker’s desire to quit, the more likely that risk perception is to capture the way people naturally think about risk in this arena.

Many possible patterns of risk perception types could be found in a population. The most heterogeneous pattern would be one in which one-third of people think in terms of absolute risk, while another one-third of people think in terms of attributable risk, and the remaining people think in terms of relative risk. The most homogeneous case would be one in which everyone thinks in terms of just one of these risk perceptions to make behavioral choices regarding smoking. Our analyses explored the extent of use of each of the three risk perception measures.

We also explored whether people who felt more certain about risk perceptions manifested stronger relations of those perceptions with cessation and desire to quit. Psychological research on attitude strength suggests that people hold beliefs and attitudes with varying degrees of certainty, and beliefs held with more certainty are more likely to shape thinking and action [ 49 ]. Therefore, we explored whether any of the risk perceptions were more strongly related to cessation among people who held their risk perceptions with more certainty.

Three studies

Our three studies explored five main questions: (1) How many people overestimate and underestimate absolute risk, attributable risk, and relative risk of lung cancer due to smoking? (2) How strongly are perceived absolute risk, attributable risk, and relative risk related to quitting? (3) How strongly are perceived absolute risk, attributable risk, and relative risk related to desire to quit among current smokers? (4) Are the relations between risk perceptions and quitting strongest among respondents who are most certain about their risk perceptions? (5) How strongly are perceived absolute risk, attributable risk, and relative risk related to having initiated smoking?

Study 1 was a random digit dial telephone survey of a nationally representative sample of American adults who were current or former smokers, conducted in 2000 by Schulman, Ronca, and Bucuvalas, Inc. (hereafter SRBI). Study 2 was a 2006 survey of a national non-representative sample of current and former smokers who volunteered to complete Internet surveys for Harris Interactive in exchange for points that could be redeemed for gifts. Study 3 was a 2009 survey of a nationally representative sample of all Americans, including people who had never smoked, via the Face-to-Face Recruited Internet Survey Platform (the FFRISP; see S3 Appendix for descriptions of the methodologies of the three studies, and see S4 Appendix for the demographic characteristics of the three samples).

The telephone survey respondents who were current or former smokers were asked:

(1) “Next, I'd like to turn to a different topic: what you personally think about the effect of cigarette smoking on people's health. I'm going to read these next two questions very slowly to let you think about each part of them, and I can repeat each question as many times as you like before you answer, so you can be sure they are clear to you. First, if we were to randomly choose one thousand American adults who never smoked cigarettes at all during their lives, how many of those one thousand people do you think would get lung cancer sometime during their lives?” (2) “And if we were to randomly choose one thousand American adults who each smoked one pack of cigarettes a day every day for 20 years starting when they were 20 years old, how many of those one thousand people do you think would get lung cancer sometime during their lives?” (3) “You said that smokers are [more likely/as likely/less likely] to get lung cancer than nonsmokers. How certain are you about this? Extremely certain, very certain, moderately certain, slightly certain, or not certain at all?”

We ask respondents to assess the prospect of lung cancer incidence generally like Viscusi [ 2 ]. We emphasized “personally” so that people would feel comfortable providing their own best guess of a fact, specifically general population risk of contracting lung cancer. This wording is designed to avoid the question seeming like a “quiz” (or their guess of what a public health authority might say), but rather their personal assessment of risk. For the two Internet surveys, the wording was adapted for self-administration. In all three studies, the response choices for the last question were presented in descending order for a randomly chosen half of the respondents and in ascending order for the other half. By implementing the same internally valid research design three separate times, it is possible to assess whether our findings are replicable.

Each of the three studies discussed above were deemed as suitable for exempt IRB review status by Stanford University’s review board, as no identifying information on the respondents was retained, and disclosure of answers to the survey questions would not place the respondents at risk. Informed consent for Study 1 was provided verbally given that Study 1 was a telephone survey. Written informed consent was provided for both Study 2 and Study 3, and Stanford’s IRB approved use of oral consent in Study 1 and written consent in Study 2 and 3.

Actual risk

We used data reported by Peto et al. [ 50 ] to compute the actual absolute risk, attributable risk, and relative risk of contracting lung cancer for one-pack-a-day smokers who started smoking at age 20 and smoked for 20 years. To do so, we divided the absolute risk of mortality due to lung cancer among these smokers (about 3%) by the absolute risk of mortality due to lung cancer among non-smokers (about 0.4%, yielding a relative risk of about 7). Although Peto et al. [ 50 ] examined mortality instead of incidence, the probability of dying from lung cancer conditional on developing lung cancer is 74.4% within a thirteen-year period according to Marcus et al. [ 51 ], and even higher among smokers [ 52 ]. If relative risk is higher, then our results understate the proportion of Americans who underestimate this relative risk. According to these figures, the attributable risk of lung cancer due to smoking is then about 3% (3% minus 0.4%, rounds to 3%). It is worth noting that although one might imagine that it is difficult to estimate risk rates because of complex functional forms, interactions of smoking with other risk factors, cohort effects, and other complications, research suggests that in fact, risk rates are largely robust to some potential complexities [ 53 – 55 ].

Perceived risk

In Study 1, the mean of current and former smokers’ perceptions of absolute risk of lung cancer among smokers was 48% (i.e., 480.1 smokers out of 1,000 smokers would get lung cancer); the median was 50% (see columns 1 and 2 of Table 1 ). 10.3% of respondents perceived absolute risks between 0% and 5.0%, and the remaining respondents gave answers above 5.0%. 99.5% of respondents overestimated absolute risk, only about 0.3% estimated it correctly (by giving an answer of 30), and 0.2% underestimated it (by giving an answer less than 30).


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As expected, the mean and median perceived absolute risk of nonsmokers getting lung cancer were less: 22% and 10%, respectively. Thirty-six percent of respondents gave answers between 0% and 5.0%. Thus, most people vastly overestimated this absolute risk.

Only 5.2% of respondents thought smokers were less likely to get lung cancer than nonsmokers (a belief revealed by attributable risks less than 0; see columns 1 and 2 of Table 2 ). Attributable risk was calculated by subtracting each respondent’s answer to the question about nonsmokers from his or her answer to the question about smokers. 9.6% of respondents thought smokers and nonsmokers were equally likely to contract lung cancer, reporting an attributable risk of 0. A large majority, 85.2% of respondents, reported that smokers were more likely than nonsmokers to contract lung cancer. 76.1% overestimated attributable risk by reporting figures greater than 4%. The mean perceived attributable risk was about 27%, and the median was 20%.


In contrast, a large majority of respondents (74.6%) underestimated relative risk, because they reported perceptions that implied a relative risk less than 7 (see columns 1 and 2 of Table 3 ). Relative risk was computed by dividing each respondent's answer to the question about 1,000 smokers by his or her answer to the question about 1,000 nonsmokers. Because this quantity is undefined for respondents who said none of the 1,000 nonsmokers would get lung cancer (because the denominator would be zero), 1 was added to these respondents’ answers to the questions about smokers and nonsmokers to allow the relative risk quantity to be defined for all respondents. Note that re-computing all analyses reported below treating these people as having missing data on the relative risk measure had negligible impact on the reported results. 54.6% of the respondents could be said to have vastly underestimated relative risk, because their reports implied a value less than 3. Only about 1.5% of respondents perceived relative risk approximately correctly (e.g., 7), and only 23.9% of respondents overestimated relative risk. 5.2% of respondents perceived a relative risk of less than 1, meaning they thought smokers developed lung cancer less often than nonsmokers, and 9.6% of the sample perceived a relative risk of 1.0, meaning they thought smokers and nonsmokers were equally likely to develop lung cancer. Mean perceived relative risk was 26.7, much higher than the true value, and the median was 2.5, lower than the true value. Thus, relative risk tells a very different story about the prevalent errors in risk perceptions than does attributable risk: most people overestimated the latter, whereas most people underestimated the former.


Compared to the representative sample of current and formers smokers interviewed in Study 1, Study 2’s non-probability sample of current and former smokers reported: (1) lower perceived absolute risk of lung cancer among nonsmokers and smokers (e.g., 49.5% and 25.7%, respectively, gave answers between 0 and 50 out of 1,000 who would get lung cancer, compared to 36.0% and 10.3% in Study 1; see seventh and eighth columns in Table 1 ); (2) lower perceived attributable risk (e.g., 50.9% had a value of 99 or less, compared to 30.7% of the Study 1 respondents; see the eighth column of Table 2 ); and (3) lower perceived relative risk (e.g., 59.5% had values of 2.99 or less, as compared with 54.6% of the Study 1 respondents; see the eighth column of Table 3 ).

Using all three risk measures, Study 3’s representative sample of current and former smokers perceived less risk than the Study 1’s respondents did 9 years earlier. Study 3’s current and former smokers reported lower absolute risk among nonsmokers (mean = 11.9%, median = 5%) than did the Study 1 respondents (mean = 21.5%, median = 10%; see columns nine and one, respectively, of Table 1 ). Study 3’s current and former smokers perceived lower absolute risk for smokers than did the Study 1 respondents (means = 33.1% vs. 48.0%; medians = 30.0% vs. 50.0%; see columns ten and two, respectively, of Table 1 ). And Study 3’s current and former smokers perceived lower attributable risk of smoking than did the Study 1 respondents (means = 21.1% vs. 26.7%; medians = 11.5% vs. 20.0%; see columns nine and one, respectively, of Table 2 ) and lower relative risk than did the Study 1 respondents (means = 12.9 vs. 26.7; medians = 2.5 vs. 2.5; see columns 9 and 1, respectively, of Table 3 ).

Study 3 suggests that the perceived risk of lung cancer may have declined among current and former smokers between 2000 and 2009. That is, the two representative sample surveys indicated that respondents’ assessments of the absolute risk of lung cancer for both smokers and non-smokers became notably more accurate during this period.

Comparing risk measures

Which of these measures is an appropriate focus for claims about public risk perceptions and their accuracy? One way to answer this question is to determine which of these risk perceptions drives people’s decisions about whether or not to smoke. Many possible patterns of risk perception use are possible in any population. The most heterogeneous pattern would be one in which some people decide whether to smoke or quit based upon their perceptions of the attributable risk, while others make this decision with reference to perceptions of relative risk, and still others make their decisions based on perceptions of absolute risk, with the three groups being of roughly equal size. The most homogeneous case is that in which everyone uses just one of these risk perceptions to make their behavioral choices regarding smoking. By gauging which risk perceptions have how much impact for how many people, we can begin to understand whether smoking behavior overall in a population is driven mostly by perceptions that overestimate risk, mostly by perceptions that underestimate risk, or by a mixture of perceptions that sometimes overestimate and other times underestimate.

The data of all three studies allowed us to explore whether perceptions of attributable risk, relative risk, and absolute risk inspire people to quit smoking by comparing current and former smokers. If perceptions of health risks are indeed a principal motivator of smoking cessation, then perceived risk should be lower among people who currently smoke than among people who used to smoke but have quit. In other words, the higher a person’s perceived risk, the more likely he or she should be to have quit smoking. Based upon this assumption, the better a risk perception measure predicts whether a person has quit smoking, the more likely that risk perception is to have driven quitting decisions.

To adjudicate whether absolute risk, attributable risk, or relative risk drove people’s decisions to quit, we estimated the parameters of generalized additive models (GAMs) comparing current smokers to former smokers by using a Gaussian link function predicting a binary variable representing whether a respondent was a current or former smoker using the various measures of perceived risk and the weights for unequal probability of selection and demographic post-stratification (see S5 Appendix for more details on GAMs). GAMs are especially useful for estimating models containing two highly correlated predictors (as we have here) because relaxing the assumption of linearity prevents model misspecification, allowing for better isolation of the unique relations of different risk perceptions with other variables.

Using this flexible approach, we first estimated a model in which relative and attributable risk predicted quitting (more precisely, having quit). It might seem appealing to estimate GAMs predicting quitting using all three measures, but non-independence among the three measures of perceived risk makes that impossible. When examining Study 1’s data, we see that perceptions of relative risk were sensibly correlated with diminished chances of remaining a smoker (see the top-left panel of S2 Fig ). The dark line in the figure represents the estimated relation, and the two light lines demark the bounds of the 95% confidence interval around the estimates. The small vertical lines at the bottom of the figure (called “rugmarks”) indicate whether one or more respondents provided a data point at each point along the x-axis. Increasing perceived relative risk was associated with decreased log-odds of remaining a smoker. Movement from the 25 th percentile to the 75 th percentile (weighted) of relative risk increased the probability of quitting by 13.8 percentage points (see the first row of the first column of Table 4 ).


In contrast, over the range of the bulk of the data (where the majority of the rugmarks on the x-axis are located), the relation between attributable risk and quitting was fairly flat (see bottom-left panel of S2 Fig ). Movement across the interquartile range of attributable risk increased the probability of quitting negligibly, by only 0.3% (see second row of the first column of Table 4 ).

To more formally gauge and compare these relations, we estimated a set of nested GAMs. First, we estimated a model predicting quitting using only attributable risk and then observed the improvement in goodness of fit of the model when we added relative risk as a predictor. A likelihood ratio (hereafter LR) test comparing the log likelihood of the two-variable model to the nested one-variable model indicated that the addition of the extra variable resulted in a significantly better fit (p=.03), meaning that relative risk was a reliable unique predictor of quitting (see third row of the first column of Table 4 ). Next, we estimated a model predicting quitting using only relative risk and then estimated the improvement in goodness of fit when attributable risk was added as a predictor. This addition did not improve the model’s fit significantly (p=.64; see fourth row of the first column of Table 4 ). Thus, relative risk perceptions appear to have been related to decisions to quit smoking, whereas perceptions of attributable risk were not.

To explore whether absolute risk outperforms relative risk, we estimated a GAM in which quitting was predicted by both measures. As shown in the right panels of S2 Fig , relative risk was again sensibly related to quitting (with probability of remaining a smoker declining smoothly as perceived risk increased), whereas absolute risk was not. Again, adding relative risk to a model fitted with only absolute risk improved the fit significantly (p=.002), whereas adding absolute risk to a model with relative risk did not yield a significant improvement in fit (p=.15; see rows seven and eight of the first column of Table 4 ). Movement across the interquartile range of absolute risk was associated with a 10.5% decrease in the chances of quitting, whereas movement across the interquartile range of relative risk was associated with a sizable and more reasonable 15.2% increase in the likelihood of quitting (see rows five and six of the first column of Table 4 ). As shown in columns two and three of Table 4 (as well as S3 and S4 Figs), these same results were replicated in Studies 2 and 3.

There may be an illusion hidden in these results. When people are asked to report a probability but do not know the answer, they sometimes answer “50,” meaning “fifty-fifty” or “I don’t know,” rather than meaning a 50% chance [ 56 ]. To explore the impact of this potential source of measurement error on our conclusions, we re-estimated the logistic GAM by: (1) dropping the respondents who answered “500” to the question about nonsmokers or to the question about smokers; (2) replacing the 500s with values generated by multiple imputation; and (3) replacing the 500s with answers obtained by a follow-up probe. The results supported the above conclusions even more strongly (for details of these approaches and results, see S6 Appendix ).

Next, we explored whether certainty moderated the associations of risk perceptions with quitting behavior. In Study 1, as expected, the correlation of relative risk with quitting was significantly stronger among high certainty respondents (people who were extremely certain, 27% of the sample) than among lower certainty respondents. Among the high certainty respondents, the probability of quitting increased over the interquartile range of relative risk by 23.7 percentage points (p=.008), a much larger increase than among the low certainty respondents, whose positive change was just 10.5 percentage points (p=.054). Accounting for certainty significantly improved the goodness of fit of the model (p=.03).

Likewise, in Study 2, the positive relation between perceived relative risk and quitting was significantly stronger among high certainty respondents than among low certainty respondents (p=.009). Among the high certainty respondents (18% of the sample), movement across the interquartile range of relative risk increased the probability of quitting by 44.1% (p<.001), whereas movement across this interquartile range in the low certainty group was associated with an increase in quitting probability of only 13.6% (p<.001). Accounting for certainty significantly improved the goodness of fit of the model (p=.009).

In Study 3, among high certainty individuals (30.5% of the sample), movement across the interquartile range of relative risk was associated with an increased probability of quitting smoking of 15.8% (p=.06), whereas movement across this interquartile range in the low certainty group was associated with an increase in quitting probability of 11.1% (p=.03). Accounting for certainty again significantly improved the goodness of fit of the model (p=.03).

Desire to quit.

Next, we examined whether current smokers’ risk perceptions were associated with their desire to quit. While a desire to quit does not automatically translate to smoking cessation, a strong desire to quit is predictive of subsequent quitting behavior, and is a necessary condition for quitting [ 57 ]. In Study 1, adding relative risk to a GAM model predicting desire to quit among current smokers with attributable risk caused a marginally non-significant improvement in fit (p=.09; see the third row of column four in Table 4 ). Movement from the 25 th to the 75 th percentile of relative risk raised the probability of wanting to quit by 17.0% (see the first row of column four in Table 4 ). But adding attributable risk to a model predicting desire to quit with relative risk did not improve fit significantly (p=.27; see row four of column four in Table 4 ). Movement across the interquartile range of attributable risk slightly lowered desire to quit by 1.1% (see row two of column four in Table 4 ). Likewise, adding relative risk to a model including absolute risk yielded a significant improvement in fit (p=.046; see row seven of column four in Table 4 ). Movement across the interquartile range of relative risk increased desire to quit by 13.9% (see row five in Table 4 ). But adding absolute risk to a model including relative risk marginally significantly decreased desire to quit (interquartile range movement = 15.6%, p=.06; see rows six and eight of column four in Table 4 ). The data from Studies 2 and 3 yielded similar results (see columns five and six of Table 4 ). This further supports the contention that people think in terms of relative risk perceptions.

Smoking onset.

We observed the expected results when we used the three measures in Study 3 to explore whether perceived risk was greater among people who ever smoked than among people who never smoked. Comparing the distributions in the ninth and tenth columns in Table 1 with the distributions in the last two columns of the table, we see that: (1) both groups had similar expectations for the proportion of nonsmokers who would get lung cancer (mean = 11% for people who never smoked vs. 12% for people who ever smoked), but (2) the expected proportion of smokers who would get lung cancer was higher among people who had never smoked (mean = 43.3%) than among people who ever smoked (mean = 33.1%).

Also as expected, people who never smoked perceived higher attributable risk of smoking than did people who ever smoked (see the last two columns in Table 2 ): (1) 3.9% thought that smokers were less likely to contract lung cancer than nonsmokers (attributable risk of less than 0); (2) 6.3% thought that smokers and nonsmokers were equally likely to get lung cancer (attributable risk of 0); and (3) 89.7% thought that smokers were more likely to contract lung cancer than nonsmokers. Respondents who never smoked thought smokers were 32 percentage points more likely than nonsmokers to get lung cancer, on average (see columns 11 and 12 of Table 2 ). Thus, these individuals perceived a higher attributable risk than did current and former smokers (21.1 percentage points; see column nine of Table 2 ). Likewise, respondents who never smoked also perceived higher relative risk than did current and former smokers (compare the last two columns of Table 3 with the ninth and tenth columns of that table).

As expected, perceptions of relative risk were strongly associated with status as a never smoker vs. a current smoker in GAMs (see the left panels of S5 Fig ). Adding relative risk to a model predicting current smoking with attributable risk considerably improved fit (p<.001), whereas adding attributable risk to a model with relative risk did not significantly improve fit (p=.57). Movement across the interquartile range of relative risk yielded a 22.7 percentage point decrease in the likelihood that respondents were smokers. Movement across the interquartile range of attributable risk yielded a decrease in the probability of being a smoker of only 0.7 percentage points.

Likewise, adding relative risk to a model with only absolute risk improved fit significantly (p<.001), whereas adding absolute risk to a model including relative risk was associated with only a marginally significant improvement in fit (p=.07). Movement across the interquartile range of relative risk (when controlling for absolute risk) was associated with a 22.3 percentage point decrease in the probability of ever having smoked (see the right panels of S5 Fig ). In contrast, movement across the interquartile range of absolute risk (when controlling for relative risk) produced only an 8.5 percentage point decrease in the likelihood of ever having smoked.

Summary and implications

Taken together, this evidence suggests that while Americans have overestimated the absolute risk and risk difference of lung cancer associated with cigarette smoking, Americans have generally underestimated the relative risk. Furthermore, this evidence suggests that people may think more about smoking health risks in terms of relative risk than in terms of absolute risk or risk difference. The relations we saw here may result from the influence of health risk beliefs on decisions to quit smoking, decisions to start smoking, and regret about smoking, or these relations may occur because people rationalize their smoking status by adjusting their risk perceptions, or from some other process. Having seen here that these are possibilities, we look forward to future research exploring them to characterize the basis for the relations we observed.

Communication of risk has been a difficult task for medical professionals, and our findings encourage consideration of a different approach to communicating health risks than has been typical on American cigarette packages and in other prominent health communications [ 58 , 59 ]. There are a large number of studies that show that the design of and warnings on cigarette packs can influence perceptions of the risks of smoking [ 60 – 68 ]. However, much constructive work can perhaps still be done by informing individuals about how much smoking increases their health risks. If the findings reported here are correct in suggesting that people use perceptions of relative risk when deciding whether to quit smoking, and if relative risk is indeed underestimated by most current and former smokers, corrective steps in this regard might be consequential. More specifically, if public health efforts are initiated in the future to encourage Americans to more accurately recognize the magnitudes of relative risks for various undesirable health outcomes of cigarette consumption, this may well lead to a reduction in the nation’s smoking rate and a consequent reduction in smoking-related morbidity and mortality. This may be why quantitative information about relative risk on cigarette packages in Australia (e.g., “Tobacco smoking causes more than four times the number of deaths caused by car accidents.”) appears to have been effective in encouraging smoking cessation [ 69 ].

Future research could explore these possibilities with experiments gauging the effects of different ways of describing risks on cigarette packages and other health communication mediums like television advertisements, poster campaigns, and doctor-patient communication [ 70 ]. Our findings suggest that when conducting such experiments, it may be desirable to attempt to alter people’s perceptions of relative risk in order to most directly address people’s natural approach to thinking about health risks in this arena. Perceptions of relative risk might be changed best by making such direct statements. But it may also be that such perceptions can be changed even more effectively by inducing affective reactions or in other non-quantitative ways, while simultaneously maximizing trust in the source of the information [ 71 , 72 ]. It is important to bear in mind that even successful efforts to change risk perceptions may not produce changes in behavior, so it will be important for future investigations to assess whether risk perception changes are translated into action [ 73 ].

In addition to their applied value, the findings reported here are interesting in basic psychological terms. By distinguishing between absolute, attributable, and relative risk, the present findings encourage future study with such measures to understand how people make many types of risky decisions and, more generally, how people trade off probabilities when making choices. And many important questions remain regarding risk perceptions involving smoking, such as how people arrive at their perceptions of relative, attributable, and absolute risk, and when and why some people use one measure rather than another to make behavioral decisions. Future studies of these sorts of issues seem merited, both in the smoking and other domains.

Resonance with other findings

Various findings reported here resonate with findings of some past studies. For example, Viscusi [ 2 ] and Borland [ 69 ] found that people overestimated the absolute risk of smoking. Khwaja et al. [ 74 ] found that both smokers and non-smokers overestimated their risks of dying from all sorts of causes [ 69 ]. When Weinstein et al. [ 27 ] asked respondents to assess the relative risk of smoking (“Would you say the average smoker has about the same lung cancer risk as a nonsmoker, a little higher lung cancer risk than a nonsmoker, twice the nonsmoker’s risk, five times the nonsmoker’s risk, or ten times the nonsmoker’s risk?”), smokers offered underestimates.

Boney-McCoy et al. [ 19 ] found that current smokers perceived the absolute risk of smoking to be significantly lower than that perceived by former smokers. This is consistent with the evidence reported here that when considered alone, absolute risk perceptions are related to quitting in the same way. However, when controlling for relative risk, the relation of quitting to absolute risk perceptions was close to zero in the present data.

Antoñanzas et al. [ 75 ] found distributions of Spaniards’ perceptions of attributable and relative risk (regarding the impact of cigarette smoking on lung cancer and heart disease) very similar to those reported here. Viscusi et al. [ 76 ] found that each of these risk perceptions predicted Spaniards’ status as a smoker or nonsmoker when considered alone, and relative risk was a considerably stronger predictor than attributable risk, though Viscusi et al. [ 76 ] did not assess the predictive abilities of perceived attributable risk and relative risk in a single regression equation.

The present evidence that people seem to think in terms of relative risk rather than attributable or absolute risk resonates with research on effective ways to communicate risks to patients [ 77 , 78 ]. For example, Malenka et al. [ 13 ] asked respondents to imagine they had a disease and could choose to take one of two medications—one described in terms of its impact on relative risk (“reduces risk of dying by 80%”) and the other (statistically equivalent) described in terms of impact on attributable risk (“can prevent 8 deaths per 100 people”). Most respondents preferred the medication described in terms of relative risk, perhaps because this portrayal resonated with people’s natural way of thinking about medication benefits found that relative risk information had more impact than did attributable risk information [ 79 – 83 ]. These findings contrast with Saitz’s [ 84 ] and Gigerenzer et al.’s [ 85 ] speculations that people will respond as well or better to attributable risk information (presented as two absolute risks) than to relative risk information, a finding challenged by our data as well.

A preference for thinking about health risks in terms of relative risk is also apparent in news media stories. In one study, 83% of such stories reported benefits of medications in terms of relative risk only, 2% reported benefits in terms of attributable risk only, and 15% reported benefits in terms of both indicators [ 86 ]. Similarly, medical journal articles tend to focus on reports of relative risk rather than attributable risk [ 87 ].

Other directions for further research

Future research might gain more insight into people’s natural ways of thinking about health risks by asking people to describe the health risks of smoking with whatever language they wish. With enough probing, open-ended data gathering might reveal whether people naturally use language evoking absolute risk, attributable risks, or relative risk levels, or a non-numeric representation, and such evidence is worthwhile to collect in future research [ 37 , 88 ]. Future work should also incorporate how much life is lost when calculating risk (see Viscusi [ 38 ] for a discussion of how this might affect an understanding of these results).

Generalizing beyond lung cancer

The focus of the analyses reported here has been people’s perceptions of the risk of getting lung cancer due to smoking. Because lung cancer is one of the best-known health risks of smoking [ 11 ], Americans may be less likely to underestimate the relative risk of lung cancer than of other diseases that are known to be caused by smoking. If we had asked survey questions about heart disease, oral cancers, or stroke instead of lung cancer, the prevalence of underestimation of relative risk may have been even greater than was observed for lung cancer. Correcting these misunderstandings may decrease the expected smoking rate even more. Future studies can explore these possibilities.

Implications regarding other domains of risk perception.

Differentiating perceived relative risk from perceived attributable risk may be useful in other health domains as well. For example, Meltzer and Egleston [ 89 ] reported that patients with diabetes vastly overestimated their own absolute risk of experiencing various complications. But perhaps their perceptions of relative risk are more accurate.

Implications for health education.

Psychological research on health counseling communication has revealed errors in people’s understanding of risk information [ 90 – 92 ]. However, educational efforts can present risk rates in various different ways, and some presentation approaches can cause misunderstandings [ 93 , 92 ]. The present evidence bolsters the conclusions of some past studies suggesting that future research may be most successful when presenting relative risk information to yield better quality decisions [ 94 – 99 ].

Supporting information

S1 fig. proportions of americans who failed to assert that smoking is dangerous to human health: gallup organization surveys..

S2 Fig. Generalized Additive Models predicting the probability of being a current smoker: SRBI Survey (n = 456).

S3 Fig. Generalized Additive Models predicting the probability of being a current smoker: Harris Interactive Survey (n = 795).

S4 Fig. Generalized Additive Models predicting the probability of being a current smoker vs. former smoker: FFRISP (n = 471).

S5 Fig. Generalized Additive Models predicting the probability of being a current smoker vs. never smoker: FFRISP (n = 714).

S1 Appendix. Measuring risk.

S2 Appendix. Literature on the relation of health risk perceptions with quitting smoking.

S3 Appendix. Survey methodology.

S4 Appendix. Demographics of current and former smokers in the SRBI Survey, current and former smokers in the Harris Interactive Survey, all individuals in the FFRISP Survey, and the nation’s population.

S5 Appendix. GAMs.

S6 Appendix. Exploring responses of 500.

S7 Appendix. References for supporting information.


The first survey described in this paper was funded by Empire Blue Cross/Blue Shield of New York. The third data set described was collected via the Face-to-Face Recruited Internet Survey Platform (FFRISP), funded by NSF Grant 0619956, Jon A. Krosnick, Principal Investigator. The authors thank Geoffrey Fong and Paul Slovic for very helpful suggestions. The authors acknowledge the excellent research assistance of Virginia Lovison. Jon Krosnick is University Fellow at Resources for the Future.

Author Contributions

  • Conceptualization: JAK LC.
  • Data curation: JAK NM CHM LC JP RKT.
  • Formal analysis: NM CHM LC JP.
  • Funding acquisition: JAK RKT.
  • Investigation: JAK LC RKT.
  • Methodology: NM LC JP.
  • Project administration: JAK NM CHM.
  • Resources: JAK RKT.
  • Software: NM CHM LC JP RKT.
  • Supervision: JAK.
  • Validation: NM CHM JP.
  • Visualization: NM CHM LC JP.
  • Writing – original draft: JAK NM CHM EFB JP.
  • Writing – review & editing: JAK NM CHM EFB JP.
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Tobacco, Nicotine, and E-Cigarettes Research Report What research is being done on tobacco use?

New scientific developments can improve our understanding of nicotine addiction and spur the development of better prevention and treatment strategies.

Genetics and Epigenetics

An estimated 50-75 percent of the risk for nicotine addiction is attributable to genetic factors. 221  A cluster of genes (CHRNA5-CHRNA3-CHRNB4) on chromosome 15 that encode the α5, α3, and β4 protein subunits that make up the brain receptor for nicotine 221–223 are particularly implicated in nicotine dependence and smoking among people of European descent. Variation in the CHRNA5 gene influences the effectiveness of combination NRT, but not varenicline. 224 Other research has identified genes that influence nicotine metabolism and therefore, the number of cigarettes smoked, 225 responsiveness to medication, 204,205 and chances of successfully quitting. 226 For example, the therapeutic response to varenicline is associated with variants for the CHRNB2, CHRNA5, and CHRNA4 genes, while bupropion-related cessation is linked with variation in genes that affect nicotine metabolism. 227

Smoking can also lead to persistent changes in gene expression (epigenetic changes), which may contribute to associated medical consequences over the long term, even following cessation. 228 Epigenetic changes may serve as a potential biomarker for prenatal tobacco smoke exposure. Researchers found tobacco-specific changes at 26 sites on the epigenome, and this pattern predicted prenatal exposure with 81 percent accuracy. 229 A large scale meta-analysis of data on epigenetic changes associated with prenatal exposure to cigarette smoke also identified many epigenetic changes that persisted into later childhood. 230 More research is needed to understand the long-term health impacts of these changes.


Cutting-edge neuroimaging technologies have identified brain changes associated with nicotine dependence and smoking. Using functional magnetic resonance imaging (fMRI), scientists can visualize smokers’ brains as they respond to cigarette-associated cues that can trigger craving and relapse. 231 Such research may lead to a biomarker for relapse risk and for monitoring treatment progress, as well as point to regions of the brain involved in the development of nicotine addiction. 29

A neuroimaging technology called default-mode or resting-state fMRI (rs-fMRI) reveals intrinsic brain activity when people are alert but not performing a particular task. Using this technique, researchers are examining the neurobiological profile associated with withdrawal and how nicotine impacts cognition. 232 Comparisons between smokers and nonsmokers suggest that chronic nicotine may weaken connectivity within brain circuits involved in planning, paying attention, and behavioral control—possibly contributing to difficulty with quitting. 233 fMRI studies also reveal the impact of smoking cessation medications on the brain—particularly how they modulate the activity of different brain regions to alleviate withdrawal symptoms and reduce smoking. A review of these studies suggested that NRT enhances cognition during withdrawal by modulating activity in default-network regions, but may not affect neural circuits associated with nicotine addiction. 234

Some imaging techniques allow researchers to visualize neurotransmitters and their receptors, further informing our understanding of nicotine addiction and its treatment. 27 Using these techniques, researchers have established that smoking increases the number of brain receptors for nicotine. Individuals who show greater receptor upregulation are less likely to stop smoking. 28 Combining neuroimaging and genetics may yield particularly useful information for improving and tailoring treatment. For example, nonsmoking adolescents with a particular variant in the CHRNA5-CHRNA3-CHRNB4 gene cluster (which is associated with nicotine dependence and smoking) showed reduced brain activity in response to reward in the striatum as well as the orbitofrontal and anterior cingulate cortex. This finding suggests that genetics can influence how the brain processes rewards which may influence vulnerability to nicotine dependence. 235 Neuroimaging genetics also shows that other genes, including ones that influence dopamine neurotransmission, influence reward sensitivity and risk for addiction to nicotine. 236

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  • Published: 21 January 2021

The effects of tobacco control policies on global smoking prevalence

  • Luisa S. Flor   ORCID: 1 ,
  • Marissa B. Reitsma 1 ,
  • Vinay Gupta 1 ,
  • Marie Ng   ORCID: 2 &
  • Emmanuela Gakidou   ORCID: 1  

Nature Medicine volume  27 ,  pages 239–243 ( 2021 ) Cite this article

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Substantial global effort has been devoted to curtailing the tobacco epidemic over the past two decades, especially after the adoption of the Framework Convention on Tobacco Control 1 by the World Health Organization in 2003. In 2015, in recognition of the burden resulting from tobacco use, strengthened tobacco control was included as a global development target in the 2030 Agenda for Sustainable Development 2 . Here we show that comprehensive tobacco control policies—including smoking bans, health warnings, advertising bans and tobacco taxes—are effective in reducing smoking prevalence; amplified positive effects are seen when these policies are implemented simultaneously within a given country. We find that if all 155 countries included in our counterfactual analysis had adopted smoking bans, health warnings and advertising bans at the strictest level and raised cigarette prices to at least 7.73 international dollars in 2009, there would have been about 100 million fewer smokers in the world in 2017. These findings highlight the urgent need for countries to move toward an accelerated implementation of a set of strong tobacco control practices, thus curbing the burden of smoking-attributable diseases and deaths.

Decades after its ill effects on human health were first documented, tobacco smoking remains one of the major global drivers of premature death and disability. In 2017, smoking was responsible for 7.1 (95% uncertainty interval (UI), 6.8–7.4) million deaths worldwide and 7.3% (95% UI, 6.8%–7.8%) of total disability-adjusted life years 3 . In addition to the health impacts, economic harms resulting from lost productivity and increased healthcare expenditures are also well-documented negative effects of tobacco use 4 , 5 . These consequences highlight the importance of strengthening tobacco control, a critical and timely step as countries work toward the 2030 Sustainable Development Goals 2 .

In 2003, the World Health Organization (WHO) led the development of the Framework Convention on Tobacco Control (FCTC), the first global health treaty intended to bolster tobacco use curtailment efforts among signatory member states 1 . Later, in 2008, to assist the implementation of tobacco control policies by countries, the WHO introduced the MPOWER package, an acronym representing six evidence-based control measures (Table 1 ) (ref. 6 ). While accelerated adoption of some of these demand reduction policies was observed among FCTC parties in the past decade 7 , many challenges remain to further decrease population-level tobacco use. Given the differing stages of the tobacco epidemic and tobacco control across countries, consolidating the evidence base on the effectiveness of policies in reducing smoking is necessary as countries plan on how to do better. In this study, we evaluated the association between varying levels of tobacco control measures and age- and sex-specific smoking prevalence using data from 175 countries and highlighted missed opportunities to decrease smoking rates by predicting the global smoking prevalence under alternative unrealized policy scenarios.

Despite the enhanced global commitment to control tobacco use, the pace of progress in reducing smoking prevalence has been heterogeneous across geographies, development status, sex and age 8 ; in 2017, there were still 1.1 billion smokers across the 195 countries and territories assessed by the Global Burden of Diseases, Injuries, and Risk Factors Study. Global smoking prevalence in 2017 among men and women aged 15 and older, 15–29 years, 30–49 years and 50 years and older are shown in Extended Data Figs. 1 , 2 , 3 and 4 , respectively. We found that, between 2009 and 2017, current smoking prevalence declined by 7.7% for men (36.3% (95% UI, 35.9–36.6%) to 33.5% (95% UI, 32.9–34.1%)) and by 15.2% for women globally (7.9% (95% UI, 7.8–8.1%) to 6.7% (95% UI, 6.5–6.9%)). The highest relative decreases were observed among men and women aged 15–29 years, at 10% and 20%, respectively. Conversely, prevalence decreased less intensively for those aged over 50, at 2% for men and 9.5% for women. While some countries have shown an important reduction in smoking prevalence between 2009 and 2017, such as Brazil, suggesting sustained progress in tobacco control, a handful of countries and territories have shown considerable increases in smoking rates among men (for example, Albania) and women (for example, Portugal) over this time period.

In an effort to counteract the harmful lifelong consequences of smoking, countries have, overall, implemented stronger demand reduction measures after the FCTC ratification. To assess national-level legislation quality, the WHO attributes a score to each of the MPOWER measures that ranges from 1 to 4 for the monitoring component (M) and 1–5 for the other components. A score of 1 represents no known data, while scores 2–5 characterize the overall strength of each measure, from the lowest level of achievement (weakest policy) to the highest level of achievement (strongest policy) 6 . Between 2008 and 2016, although very little progress was made in treatment provision (O) 7 , 9 , the share of the total population covered by best practice (score = 5) P, W and E measures increased (Fig. 1 ). Notably, however, a massive portion of the global population is still not covered by comprehensive laws. As an example, less than 15% of the global population is protected by strongly regulated tobacco advertising (E) and the number of people (2.1 billion) living in countries where none or very limited smoke-free policies (P) are in place (score = 2) is still nearly twice as high as the population (1.1 billion) living in locations with national bans on smoking in all public places (score = 5).

figure 1

To assess national-level legislation quality, the WHO attributes a score to each MPOWER component that ranges from 1 to 5 for smoke-free (P), health warning (W) and advertising (E) policies. A score of 1 represents no known data or no recent data, while scores 2–5 characterize the overall strength of each policy, from 2 representing the lowest level of achievement (weakest policy), to 5 representing the highest level of achievement (strongest policy).

Source data

In terms of fiscal policies (R), the population-weighted average price, adjusted for inflation, of a pack of cigarettes across 175 countries with available data increased from I$3.10 (where I$ represents international dollars) in 2008 to I$5.38 in 2016. However, from an economic perspective, for prices to affect purchasing decisions, they need to be evaluated relative to income. The relative income price (RIP) of cigarettes is a measure of affordability that reflects, in this study, what proportion of the country-specific per capita gross domestic product (GDP) is needed to purchase half a pack of cigarettes a day for a year. Over time, cigarettes have become less affordable (RIP 2016 > RIP 2008) in about 75% of the analyzed countries, with relatively more affordable cigarettes concentrated across high-income countries.

Our adjusted analysis indicates that greater levels of achievement on key measures across the P, W and E policy categories and higher RIP values were significantly associated with reduced smoking prevalence from 2009 to 2017 (Table 2 ). Among men aged 15 and older, each 1-unit increment in achievement scores for smoking bans (P) was independently associated with a 1.1% (95% UI, −1.7 to −0.5, P  < 0.0001) decrease in smoking prevalence. Similarly, an increase of 1 point in W and E scores was associated with a decrease in prevalence of 2.1% (95% UI, −2.7 to −1.6, P  < 0.0001) and 1.9% (95% UI, −2.6 to −1.1, P  < 0.0001), respectively. Furthermore, a 10 percentage point increase in RIP was associated with a 9% (95% UI, −12.6 to −5.0, P  < 0.0001) decrease in overall smoking prevalence. Results were similar for men from other age ranges.

Among women, the magnitude of effect of different policy indicators varied across age groups. For those aged over 15, each 1-point increment in W and E scores was independently associated with an average reduction in prevalence of 3.6% (95% UI, −4.5 to −2.9, P  < 0.0001) and 1.9% (95% UI, −2.9 to −1.8, P  = 0.002), respectively, and these findings were similar across age groups. Smoking ban (P) scores were not associated with reduced prevalence among women aged 15–29 years or over 50 years. However, a 1-unit increase in P scores was associated with a 1.3% (95% UI, −2.3 to −0.2, P  = 0.016) decline in prevalence among women aged 30–49 years. Lastly, while a 10 percentage point increase in RIP lowered women smoking prevalence by 6% overall (95% UI, −10.0 to −2.0, P = 0.014), this finding was not statistically significant when examining reductions in prevalence among those aged 50 and older (Table 2 ).

If tobacco control had remained at the level it was in 2008 for all 155 countries (with non-missing policy indicators for both 2008 and 2016; Methods ) included in the counterfactual analysis, we estimate that smoking prevalence would have been even higher than the observed 2017 rates, with 23 million more male smokers and 8 million more female smokers (age ≥ 15) worldwide (Table 3 ). Out of the counterfactual scenarios explored, the greatest progress in reducing smoking prevalence would have been observed if a combination of higher prices—resulting in reduced affordability levels—and strictest P, W and E laws had been implemented by all countries, leading to lower smoking rates among men and women from all age groups and approximately 100 million fewer smokers across all countries (Table 3 ). Under this policy scenario, the greatest relative decrease in prevalence would have been seen among those aged 15–29 for both sexes, resulting in 26.6 and 6.5 million fewer young male and female smokers worldwide in 2017, respectively.

Our findings reaffirm that a wide spectrum of tobacco demand reduction policies has been effective in reducing smoking prevalence globally; however, it also indicates that even though much progress has been achieved, there is considerable room for improvement and efforts need to be strengthened and accelerated to achieve additional gains in global health. A growing body of research points to the effectiveness of tobacco control measures 10 , 11 , 12 ; however, this study covers the largest number of countries and years so far and reveals that the observed impact has varied by type of control policy and across sexes and age groups. In high-income countries, stronger tobacco control efforts are also associated with higher cessation ratios (that is, the ratio of former smokers divided by the number of ever-smokers (current and former smokers)) and decreases in cigarette consumption 13 , 14 .

Specifically, our results suggest that men are, in general, more responsive to tobacco control interventions compared to women. Notably, with prevalence rates for women being considerably low in many locations, variations over time are more difficult to detect; thus, attributing causes to changes in outcome can be challenging. Yet, there is already evidence that certain elements of tobacco control policies that play a role in reducing overall smoking can have limited impact among girls and women, particularly those of low socioeconomic status 15 . Possible explanations include the different value judgments attached to smoking among women with respect to maintaining social relationships, improving body image and hastening weight control 16 .

Tax and price increases are recognized as the most impactful tobacco control policy among the suite of options under the MPOWER framework 10 , 14 , 17 , particularly among adolescents and young adults 18 . Previous work has also demonstrated that women are less sensitive than men to cigarette tax increases in the USA 19 . Irrespective of these demographic differences, effective tax policy is underutilized and only six countries—Argentina, Chile, Cuba, Egypt, Palau and San Marino—had adopted cigarette taxes that corresponded to the WHO-prescribed level of 70% of the price of a full pack by 2017 (ref. 20 ). Cigarettes also remain highly affordable in many countries, particularly among high-income nations, an indication that affordability-based prescriptions to countries, instead of isolated taxes and prices reforms, are possibly more useful as a tobacco control target. In addition, banning sales of single cigarettes, restricting legal cross-border shopping and fighting illicit trade are required so that countries can fully experience the positive effect of strengthened fiscal policies.

Smoke-free policies, which restrict the opportunities to smoke and decrease the social acceptability of smoking 17 , also affect population groups differently. In general, women are less likely to smoke in public places, whereas men might be more frequently influenced by smoking bans in bars, restaurants, clubs and workplaces across the globe due to higher workforce participation rates 16 . In addition to leading to reduced overall smoking rates, as indicated in this study, implementing complete smoking bans (that is, all public places completely smoke-free) at a faster pace can also play an important role in minimizing the burden of smoking-attributable diseases and deaths among nonsmokers. In 2017 alone, 2.18% (95% UI, 1.8–2.7%) of all deaths were attributable to secondhand smoke globally, with the majority of the burden concentrated among women and children 21 .

Warning individuals about the harms of tobacco use increases knowledge about the health risks of smoking and promotes changes in smoking-related behaviors, while full advertising and promotion bans—implemented by less than 20% of countries in 2017 (ref. 20 )—are associated with decreased tobacco consumption and smoking initiation rates, particularly among youth 17 , 22 , 23 . Large and rotating pictorial graphic warnings are the most effective in attracting smokers’ attention but are lacking in countries with high numbers of smokers, such as China and the USA 20 . Adding best practice health warnings to unbranded packages seems to be an effective way of informing about the negative effects of smoking while also eliminating the tobacco industry’s marketing efforts of using cigarette packages to make these products more appealing, especially for women and young people who are now the prime targets of tobacco companies 24 , 25 .

While it is clear that strong implementation and enforcement are crucial to accelerating progress in reducing smoking and its burden globally, our heterogeneous results by type of policy and demographics highlight the challenges of a one-size-fits-all approach in terms of tobacco control. The differences identified illustrate the need to consider the stages 26 of the smoking epidemics among men and women and the state of tobacco control in each country to identify the most pressing needs and evaluate the way ahead. Smoking patterns are also influenced by economic, cultural and political determinants; thus, future efforts in assessing the effectiveness of tobacco control policies under these different circumstances are of value. As tobacco control measures have been more widely implemented, tobacco industry forces have expanded and threaten to delay or reverse global progress 27 . Therefore, closing loopholes through accelerated universal adoption of the comprehensive set of interventions included in MPOWER, guaranteeing that no one is left unprotected, is an urgent requirement as efforts toward achieving the Sustainable Development Goals by 2030 are intensified.

This was an ecological time series analysis that aimed to estimate the effect of four key demand reduction measures on smoking rates across 175 countries. Country-year-specific achievement scores for P, W and E measures and an affordability metric measured by RIP—to capture the impact of fiscal policy (R)—were included as predictors in the model. Although the WHO also calls for monitoring (M) and tobacco cessation (O) interventions, these were not evaluated. Monitoring tobacco use is not considered a demand reduction measure, while very little progress has been made in treatment provision over the last decade 7 , 9 . Further information on research design is available in the Life Sciences Reporting Summary linked to this paper.

Smoking outcome data

The dependent variable is represented by country-specific, age-standardized estimates of current tobacco smoking prevalence, defined as individuals who currently use any smoked tobacco product on a daily or occasional basis. Complete time series estimates of smoking prevalence from 2009 to 2017 for men and women aged 15–29, 30–49, 50 years and older and 15 years and older, were taken from the Global Burden of Disease (GBD) 2017 study.

The GBD is a scientific effort to quantify the comparative magnitude of health loss due to diseases, injuries and risk factors by age, sex and geography for specific points in time. While full details on the estimation process for smoking prevalence have been published elsewhere, we briefly describe the main analytical steps in this article 3 . First, 2,870 nationally representative surveys meeting the inclusion criteria were systematically identified and extracted. Since case definitions vary between surveys, for example, some surveys only ask about daily smoking as opposed to current smoking that includes both daily and occasional smokers, the extracted data were adjusted to the reference case definition using a linear regression fit on surveys reporting multiple case definitions. Next, for surveys with only tabulated data available, nonstandard age groups and data reported as both sexes combined were split using observed age and sex patterns. These preprocessing steps ensured that all data used in the modeling were comparable. Finally, spatiotemporal Gaussian process regression, a three-step modeling process used extensively in the GBD to estimate risk factor exposure, was used to estimate a complete time series for every country, age and sex. In the first step, estimates of tobacco consumption from supply-side data are incorporated to guide general levels and trends in prevalence estimates. In the second step, patterns observed in locations, age groups and years with smoking prevalence data are synthesized to improve the first-step estimates. This step is particularly important for countries and time periods with limited or no available prevalence data. The third step incorporates and quantifies uncertainty from sampling error, non-sampling error and the preprocessing data adjustments. For this analysis, the final age-specific estimates were age-standardized using the standard population based on GBD population estimates. Age standardization, while less important for the narrower age groups, ensured that the estimated effects of policies were not due to differences in population structure, either within or between countries.

Using GBD-modeled data is a strength of the study since nearly 3,000 surveys inform estimates and countries are not required to have complete survey coverage between 2009 and 2017 to be included in the analysis. Yet, it is important to note that these estimates have limitations. For example, in countries where a prevalence survey was not conducted after the enactment of a policy, modeled estimates may not reflect changes in prevalence resulting from that policy. Nonetheless, the prevalence estimates from the GBD used in this study are similar to those presented in the latest WHO report 28 , indicating the validity and consistency of said estimates.


Summary indicators of country-specific achievements for each MPOWER measure are released by the WHO every two years and date back to 2007. Data from different iterations of the WHO Report on the Global Tobacco Epidemic (2008 6 , 2009 29 , 2011 30 , 2013 31 , 2015 32 and 2017 20 ) were downloaded from the WHO Tobacco Free Initiative website ( ). To assess the quality of national-level legislation, the WHO attributes a score to each MPOWER component that ranges from 1 to 4 for the monitoring (M) dimension and 1–5 for the other dimensions. A score of 1 represents no known data or no recent data, while scores 2–5 characterize the overall strength of each policy, from the lowest level of achievement (weakest policy) to the highest (strongest policy).

Specifically, smoke-free legislation (P) is assessed to determine whether smoke-free laws provide for a complete indoor smoke-free environment at all times in each of the respective places: healthcare facilities; educational facilities other than universities; universities; government facilities; indoor offices and workplaces not considered in any other category; restaurants or facilities that serve mostly food; cafes, pubs and bars or facilities that serve mostly beverages; and public transport. Achievement scores are then based on the number of places where indoor smoking is completely prohibited. Regarding health warning policies (W), the size of the warnings on both the front and back of the cigarette pack are averaged to calculate the percentage of the total pack surface area covered by the warning. This information is combined with seven best practice warning characteristics to construct policy scores for the W dimension. Finally, countries achievements in banning tobacco advertising, promotion and sponsorship (E) are assessed based on whether bans cover the following types of direct and indirect advertising: (1) direct: national television and radio; local magazines and newspapers; billboards and outdoor advertising; and point of sale (indoors); (2) indirect: free distribution of tobacco products in the mail or through other means; promotional discounts; nontobacco products identified with tobacco brand names; brand names of nontobacco products used or tobacco products; appearance of tobacco brands or products in television and/or films; and sponsorship.

P, W and E achievement scores, ranging from 2 to 5, were included as predictors into the model. The goal was to not only capture the effect of adopting policies at its highest levels but also assess the reduction in prevalence that could be achieved if countries moved into the expected direction in terms of implementing stronger measures over time. Additionally, having P, W and E scores separately, and not combined into a composite score, enabled us to capture the independent effect of different types of policies.

Although compliance is a critical factor in understanding policy effectiveness, the achievement scores incorporated in our main analysis reflect the adoption of legislation rather than degree of enforcement, representing a limitation of these indicators.

Prices in I$ for a 20-cigarette pack of the most sold brand in each of the 175 countries were also sourced from the WHO Tobacco Free Initiative website for all available years (2008, 2010, 2012, 2014 and 2016). I$ standardize prices across countries and also adjust for inflation across time. This information was used to construct an affordability metric that captures the impact of cigarette prices on smoking prevalence, considering the income level of each country.

More specifically, the RIP, calculated as the percentage of per capita GDP required to purchase one half pack of cigarettes a day over the course of a year, was computed for each available country and year. Per capita GDP estimates were drawn from the Institute for Health Metrics and Evaluation; the estimation process is detailed elsewhere 33 .

Given that the price data used in the analysis refer to the most sold brand of cigarettes only, it does not reflect the full range of prices of different types of tobacco products available in each location. This might particularly affect our power in detecting a strong effect in countries where other forms of tobacco are more popular.

Statistical analysis

Sex- and age-specific logit-transformed prevalence estimates from 2009 to 2017 were matched to one-year lagged achievement scores and RIP values using country and year identifiers 34 . The final sample consisted of 175 countries and was constrained to locations and years with non-missing indicators. A multiple linear mixed effects model fitted by restricted maximum likelihood was used to assess the independent effect of P, W and E scores and RIP values on the rates of current smoking. Specifically, a country random intercept and a country random slope on RIP were included to account for geographical heterogeneity and within-country correlation. The regression model takes the following general form:

where y c,t is the prevalence of current smoking in each country ( c ) and year ( t ), β 0 is the intercept for the model and β p , β w , β e and β r are the fixed effects for each of the policy predictors. \(\mathrm{P}_{c,\,t - 1},\,\mathrm{W}_{c,\,t - 1},\,\mathrm{E}_{c,\,t - 1}\) are the P, W and E scores and R c , t −1 is the RIP value for country c in year t  − 1. Finally, α c is the random intercept for country ( c ), while δ c represent the random slope for the country ( c ) to which the RIP value (R t − 1 ) belongs. Variance inflation factor values were calculated for all the predictor parameters to check for multicollinearity; the values found were low (<2) 35 . Bivariate models were also run and are shown in Extended Data Fig. 5 . The one-year lag introduced into the model may have led to an underestimation of effect sizes, particularly as many MPOWER policies require a greater period of time to be implemented effectively. However, due to the limited time range of our data (spanning eight years in total), introducing a longer lag period would have resulted in the loss of additional data points, thus further limiting our statistical power in detecting relevant associations between policies and smoking prevalence.

In addition to a joint model for smokers from both sexes, separate regressions were fitted for men and women and the four age groups (15–29, 30–49, ≥50 and ≥15 years old). To assess the validity of the mixed effects analyses, likelihood ratio tests comparing the models with random effects to the null models with only fixed effects were performed. Linear mixed models were fitted by maximum likelihood and t -tests used Satterthwaite approximations to degrees of freedom. P values were considered statistically significant if <0.05. All analyses were executed with RStudio v.1.1.383 using the lmer function in the R package lme4 v.1.1-21 (ref. 36 ).

A series of additional models to examine the impact of tobacco control policies were developed as part of this study. In each model, cigarette affordability (RIP) and a different set of policy metrics was used to capture the implementation, quality and compliance of tobacco control legislation. In models 1 and 2, we replaced the achievements scores by the proportion of P, W and E measures adopted by each country out of all possible measures reported by the WHO. In model 3, we used P and E (direct and indirect measures separately) compliance scores provided by the WHO to represent actual legislation implementation. Finally, an interaction term for compliance and achievement to capture the combined effect of legislation quality and performance was added to model 4. Results for men and women by age group for each of the additional models are presented in the Supplemental Information (Supplementary Tables 1–4 ).

The main model described in this study was chosen because it includes a larger number of country-year observations ( n  = 823) when compared to models including compliance scores and because it is more directly interpretable.

Counterfactual analysis

To further explore and quantify the impact of tobacco control policies on current smoking prevalence, we simulated what smoking prevalence across all countries would have been achieved in 2017 under 4 alternative policy scenarios: (1) if achievement scores and RIP remained at the level they were at in 2008; (2) if all countries had implemented each of P, W and E component at the highest level (score = 5); (3) if the price of a cigarette pack was I$7.73 or higher, a price that represents the 90th percentile of observed prices across all countries and years; and (4) if countries had implemented the P, W and E components at the highest level and higher cigarette prices. To keep our results consistent across scenarios, we restricted our analysis to 155 countries with non-missing policy-related indicators for both 2008 and 2016.

Random effects were used in model fitting but not in this prediction. Simulated prevalence rates were calculated by multiplying the estimated marginal effect of each policy by the alternative values proposed in each of the counterfactual scenarios for each country-year. The global population-weighted average was computed for status quo and counterfactual scenarios using population data sourced from the Institute for Health Metrics and Evaluation. Using the predicted prevalence rates and population data, the additional reduction in the number of current smokers in 2017 was also computed. Since models were ran using age-standardized prevalence, the number of smokers was proportionally redistributed across age groups using the sex-specific numbers from the age group 15 and older as an envelope.

The UIs for predicted estimates were based on a computation of the results of each of the 1,000 draws (unbiased random samples) taken from the uncertainty distribution of each of the estimated coefficients; the lower bound of the 95% UI for the final quantity of interest is the 2.5 percentile of the distribution and the upper bound is the 97.5 percentile of the distribution.

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The dataset generated and analyzed during the current study is publicly available at ( ). The dataset contains all data necessary to interpret, replicate and build on the methods or findings reported in the article. Tobacco control policy data that support the findings of this study are released every two years as part of the WHO’s Global Report on Tobacco Control; these data are also directly accessible at . Source data are provided with this paper.

Code availability

All code used for these analyses is available at and .

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The study was funded by Bloomberg Philanthropies (grant 47386, Initiative to Reduce Tobacco Use). We thank the support of the Tobacco Metrics Team Advisory Group, which provided valuable comments and suggestions over several iterations of this manuscript. We also thank the Tobacco Free Initiative team at the WHO and the Campaign for Tobacco-Free Kids for making the tobacco control legislation data available and providing clarifications when necessary. We thank A. Tapp, E. Mullany and J. Whisnant for assisting in the management and execution of this study. We thank the team who worked in a previous iteration of this project, especially A. Reynolds, C. Margono, E. Dansereau, K. Bolt, M. Subart and X. Dai. Lastly, we thank all GBD 2017 Tobacco collaborators for their valuable work in providing feedback to our smoking prevalence estimates throughout the GBD 2017 cycle.

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Luisa S. Flor, Marissa B. Reitsma, Vinay Gupta & Emmanuela Gakidou

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L.S.F., M.N. and E.G. conceptualized the study and designed the analytical framework. M.B.R. and V.G. provided input on data, results and interpretation. L.S.F. and E.G. wrote the first draft of the manuscript. All authors read and approved the final version of the manuscript.

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Extended data

Extended data fig. 1 prevalence of current smoking for men (a) and women (b) aged 15 years and older (age-standardized) in 2017..

Age-standardized smoking prevalence (%) estimates from the 2017 Global Burden of Disease Study for men (a) and women (b) aged 15 years and older for 195 countries. Smoking is defined as current use of any type of smoked tobacco product. Details on the estimation process can be found in the Methods section and elsewhere 3 .

Extended Data Fig. 2 Prevalence of current smoking for men (a) and women (b) aged 15 to 29 years old (age-standardized) in 2017.

Age-standardized smoking prevalence (%) estimates from the 2017 Global Burden of Disease Study for men (a) and women (b) aged 15–29 years old for 195 countries. Smoking is defined as current use of any type of smoked tobacco product. Details on the estimation process can be found in the Methods section and elsewhere 3 .

Extended Data Fig. 3 Prevalence of current smoking for men (a) and women (b) aged 30 to 49 years old (age-standardized) in 2017.

Age-standardized smoking prevalence (%) estimates from the 2017 Global Burden of Disease Study for men (a) and women (b) aged 30–49 years old for 195 countries. Smoking is defined as current use of any type of smoked tobacco product. Details on the estimation process can be found in the Methods section and elsewhere 3 .

Extended Data Fig. 4 Prevalence of current smoking for men (a) and women (b) aged 50 years and older (age-standardized) in 2017.

Age-standardized smoking prevalence (%) estimates from the 2017 Global Burden of Disease Study for men (a) and women (b) aged 50 years and older for 195 countries. Smoking is defined as current use of any type of smoked tobacco product. Details on the estimation process can be found in the Methods section and elsewhere 3 .

Extended Data Fig. 5 Percentage changes in current smoking prevalence based on fixed effect coefficients from bivariate mixed effect linear regression models, by policy component, sex and age group.

Bivariate models examined the unadjusted association between smoke-free (P), health warnings (W), and advertising (E) achievement scores, and cigarette’s affordability (RIP) and current smoking prevalence, from 2009 to 2017, across 175 countries (n = 823 country-years). Linear mixed models were fit by maximum likelihood and t-tests used Satterthwaite approximations to degrees of freedom. P values were considered statistically significant if lower than 0.05.

Supplementary information

Supplementary information.

Supplementary Tables 1–4: additional models results.

Source Data Fig. 1

Input data for Fig. 1 replication.

Source Data Extended Data Fig. 1

Input data for Extended Data 1 replication.

Source Data Extended Data Fig. 2

Input data for Extended Data 2 replication.

Source Data Extended Data Fig. 3

Input data for Extended Data 3 replication.

Source Data Extended Data Fig. 4

Input data for Extended Data 4 replication.

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Flor, L.S., Reitsma, M.B., Gupta, V. et al. The effects of tobacco control policies on global smoking prevalence. Nat Med 27 , 239–243 (2021).

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Clinical Effects of Cigarette Smoking: Epidemiologic Impact and Review of Pharmacotherapy Options

Ifeanyichukwu o. onor.

1 Xavier University of Louisiana College of Pharmacy, 1 Drexel Drive, New Orleans, LA 70125, USA; moc.liamg@21irosa (D.L.S.); ude.alux@ailliwrs (S.R.W.); moc.oohay@sttebnnad (D.B.); ude.alux@lohgroba (A.B.); ude.alux@1sirrahm (M.B.H.); moc.liamg@sneradbt (T.B.D.); moc.liamtoh@yalcedrahs (S.D.C.)

2 Department of Medicine, School of Medicine, Louisiana State University Health Sciences Center New Orleans, 1542 Tulane Avenue, New Orleans, LA 70112, USA

Daniel L. Stirling

Shandrika r. williams, daniel bediako, amne borghol, martha b. harris, tiernisha b. darensburg, sharde d. clay, samuel c. okpechi.

3 Stanley S. Scott Cancer Center, School of Medicine, Louisiana State University Health Sciences Center New Orleans, 1700 Tulane Avenue, New Orleans, LA 70112, USA; moc.liamg@ihcepkoleumas

Daniel F. Sarpong

4 Center for Minority Health and Health Disparities Research and Education, Xavier University of Louisiana College of Pharmacy, 1 Drexel Drive, New Orleans, LA 70125, USA; ude.alux@gnoprasd

Cigarette smoking—a crucial modifiable risk factor for organ system diseases and cancer—remains prevalent in the United States and globally. In this literature review, we aim to summarize the epidemiology of cigarette smoking and tobacco use in the United States, pharmacology of nicotine—the active constituent of tobacco, and health consequence of cigarette smoking. This article also reviews behavioral and pharmacologic interventions for cigarette smokers and provides cost estimates for approved pharmacologic interventions in the United States. A literature search was conducted on Google Scholar, EBSCOhost, ClinicalKey, and PubMed databases using the following headings in combination or separately: cigarette smoking, tobacco smoking, epidemiology in the United States, health consequences of cigarette smoking, pharmacologic therapy for cigarette smoking, and non-pharmacologic therapy for cigarette smoking. This review found that efficacious non-pharmacologic interventions and pharmacologic therapy are available for cessation of cigarette smoking. Given the availability of efficacious interventions for cigarette smoking cessation, concerted efforts should be made by healthcare providers and public health professionals to promote smoking cessation as a valuable approach for reducing non-smokers’ exposure to environmental tobacco smoke.

1. Introduction

Tobacco use, in any form, can be described as a behavioral process which elicits psychological and physiologic addictive mood among users. Nicotine, the active ingredient in tobacco, is highly addictive, resulting in sustained tobacco use. Tobacco use is divided into combustible and noncombustible tobacco products. Combustible tobacco products include: cigarettes, cigars, cigarillos, small cigars, water pipes (hookah), and pipes. Noncombustible tobacco products include electronic cigarettes and tobacco formulations developed for chewing, dipping, or snuffing.

According to the 2013–2014 National Adult Tobacco Survey (NATS), the United States’ national prevalence for current tobacco product use was 21.3% in adults aged ≥18 years [ 1 ]. Distribution of tobacco product use include: 17% for cigarettes, 1.8% for cigars/cigarillos/filtered little cigars, 0.3% for pipes, 0.6% for water pipes/hookah, 3.3% for electronic cigarettes, and 2.5% for smokeless tobacco [ 1 ]. These trends sharply contrast with tobacco use in the 1800s—a period which saw predominant use of chewing tobacco and pipe tobacco because there was no mass manufacturing of cigarettes. These less popular methods of tobacco use, while still unhealthy, theoretically were associated with fewer cancers and tobacco related deaths. Now, with its unique design and accessibility, cigarette smoking has become the choice of tobacco use among many youth and adults globally. Cigarettes are designed to allow deep inhalation of smoke into the lungs, delivering high levels of nicotine to the brain within 10–20 s of inhalation [ 2 ]. This rapid rise in nicotine levels makes cigarette smoking the most reinforcing and dependence-producing form of tobacco use [ 2 ]. The epidemiologic impact and adverse health effects of cigarette smoking are significant. Reducing the prevalence of cigarette smoking and the resultant smoking-induced disease is imperative.

This article reviews the epidemiology of cigarette smoking in the United States, pharmacology of nicotine, and health impact of cigarette smoking alongside behavioral and pharmacological interventions available for smoking cessation in the United States. We performed a literature search on Google Scholar, EBSCOhost, ClinicalKey, and PubMed databases using the following keywords in combination or separately: cigarette smoking, tobacco smoking, epidemiology in the United States, health consequences of cigarette smoking, pharmacologic therapy for cigarette smoking, and non-pharmacologic therapy for cigarette smoking. We reviewed and included literature that provided the most relevant and up-to-date information on our search terms. Excluding the epidemiology data, which focused on the United States, our literature search was inclusive of literature without any geographic constraint. The aim of this review is to provide accessible information on the clinical effects of cigarette smoking, interventions available for cigarette smoking cessation, and the cost estimates for U.S. Food and Drug Administration (FDA)—approved pharmacotherapy options for cigarette smoking.

2. Epidemiology of Cigarette (Tobacco) Smoking in the United States

Although cigarette smoking is the most commonly used form of tobacco in the U.S., the prevalence of cigarette smoking amongst adults has been declining in recent years. According to the 2015 National Health Interview Survey (NHIS), the percentage of adults aged ≥18 years who smoked cigarettes was 15.1% in 2015, a decrease from 20.9% in 2005 [ 3 ]. This general trend of decline in tobacco smoking in the United States has also been observed globally [ 4 ]. The World Health Organization (WHO) reports that among adults over 15 years, the global rate of smoking declined from 23.5% in 2007 to 20.7 in 2015, reflecting a 2.8% smoking rate reduction [ 4 ]. Although there has been a decline in the prevalence of smoking globally, the number of people smoking worldwide has remained at 1.1 billion from 2007 to 2015 because of population growth [ 4 ]. Several factors linked to declines in the prevalence of smoking include population-based interventions such as raising tobacco taxes, tobacco price increases, anti-tobacco mass media campaigns, comprehensive smoke-free laws, enhanced access to help quitting tobacco use, and implementation of governmental regulations of tobacco products [ 1 , 4 , 5 ]. Of all these factors, WHO reports that raising tobacco taxes has been the single most effective way to reduce tobacco use [ 4 , 5 ].

The result of the NHIS also highlights several disparities in the prevalence of cigarette smoking [ 3 ]. Cigarette (tobacco) smoking is more prevalent among adult males than adult females [ 3 ]. The prevalence of cigarette smoking in 2015 was 16.7% among adult males and 13.6% among adult females [ 3 ]. Prevalence was highest among adults aged 25–44 years (14.8%) and lowest among persons aged ≥65 years [ 3 ]. Racial and ethnic differences also exist. The prevalence was highest amongst American Indian/Alaska Natives (21.9%), and lowest among Asians (7.0%) [ 3 ]. When examining education level, prevalence was variable. It was highest among those with a General Education Development Certificate (GED) (34.1%) and lowest among those with a graduate degree (3.6%) [ 3 ]. When examining socioeconomic status, prevalence was highest among persons living below poverty level (26.1%) and lowest among persons living at or above poverty level (13.9%) [ 3 ].

A history of substance abuse disorders and mental illness increases cigarette smoking [ 6 , 7 ]. Cooperman et al. reported a high prevalence of cigarette smoking (80%) among opiate dependent smokers on methadone treatment and Santhosh et al. disclosed a 2013 report which showed that although patients with mental illness and substance abuse disorders made up 24.8% of adults in the United States, they consumed nearly 40% of all cigarettes [ 6 , 7 ]. Additional data from the National Surveys on Drug Use and Health corroborate the strong association among cigarette use, mental illness, and substance abuse across gender and age [ 7 ].

Cigarette (tobacco) smoking is not only common among adults, but is also common among youth. With the current trends of monetary investment into the tobacco industry, smoking poses a bigger threat to the younger population in American society. According to the Executive Summary of the U.S. Surgeon General Office report in 2012, everyday 3800 youth under the age of 18 start smoking [ 8 ]. Most adult smokers, 88%, smoked their first cigarette before the age of 18 [ 8 ]. According to the National Survey on Drug Use and Health 2012, the mean age of smoking initiation was 15.3 years and less than 1.5% of cigarette smokers began smoking in adulthood (after 26 years of age) [ 9 ]. Although cigarette smoking most often begins during youth and young adulthood, the use of cigarettes among this population has been declining in recent years. Among high school students, 9.3% reported current cigarette smoking in 2015, a decrease from 15.8% in 2011 [ 10 ]. Among middle school students, 2.3% reported current cigarette smoking in 2015, a decrease from 4.3% in 2011 [ 10 ]. While the use of cigarettes among youth has declined, the use of electronic cigarettes in this population is increasing. Electronic cigarettes are currently the most commonly used form of tobacco among middle and high school students. In 2015, 16% of high school students reported current electronic cigarette use, an increase from 1.5% in 2011 [ 10 ]. Among middle school students, 5.3% reported current electronic cigarette use, an increase from 0.6% in 2011 [ 10 ]. These trends in cigarette and electronic cigarette use highlight the importance of targeting smoking prevention efforts at youth and young adults.

Electronic cigarettes (e-cigarettes) are rapidly increasing in popularity [ 11 ]. There was an increase in e-cigarette use from 1.9% in 2012–2013 to 3.3% in 2013–2014 according to the National Adult Tobacco Survey [ 1 ]. Young adults between 18–24 years account for the highest prevalence of use of newly emerging products, including e-cigarettes and water pipes/hookahs [ 1 ]. E-cigarettes use a battery-powered heating device to deliver nicotine via a vapor that is drawn into the mouth, upper airways and possibly lungs [ 11 ]. The device uses a battery-powered heating element activated by suction or manually to heat a nicotine solution and transform it into vapor [ 11 ]. In a study by D’Ruiz et al., they observed that e-cigarettes had blood plasma nicotine levels lower than that of conventional tobacco cigarettes, yet the reduction in craving was comparable between e-cigarettes and conventional tobacco cigarettes [ 12 ]. E-cigarettes usually contain nicotine dissolved in a solution made up of propylene glycol and/or glycerin, and flavorings [ 12 , 13 , 14 ]. Other toxic substances such as formaldehyde and acrolein may be present in very low levels in e-cigarettes compared to conventional cigarettes [ 14 ]. Although the use of e-cigarette is soaring, several review articles evaluating studies of e-cigarettes have concluded that the short- and long-term effects of e-cigarettes are limited or lacking [ 13 , 14 ]. Even with limited data on the health effects of e-cigarettes, in 2016, the U.S. Food and Drug Administration (FDA)—under authority granted to it by the Congress under the Family Smoking Prevention and Tobacco Control Act of 2009—took a historic step to protect America’s youth from the harmful effects of using e-cigarettes by extending its regulatory authority over the manufacturing, distribution, and marketing of e-cigarettes [ 15 ].

Although current gaps exist in scientific evidence on the spectrum of health effects of e-cigarettes, we know that compared with older adults, brain of youth and young adults is more vulnerable to the negative consequences of nicotine exposure [ 15 ]. These effects include addiction, priming for use of other addictive substances, reduced impulse control, deficits in attention and cognition, and mood disorders [ 15 ]. The U.S. Surgeon General in his report on “E-Cigarette Use Among Youth and Young Adults: A Report of the Surgeon General” raised awareness on the exponential growth of youth and young adults who are using e-cigarettes and encourages concerted societal effort to prevent and reduce the use of e-cigarettes by youth and young adults in order to prevent the well documented harmful effects of nicotine use-which is more pronounced in the development of adolescent brain [ 15 ].

The CDC also discusses the potential for harm and benefit associated with e-cigarette use [ 16 ]. E-cigarettes can cause harm to the public, which is more notable if used by defined populations (youth, young adults, pregnant women). Some of the harms include increased risk for using nicotine and/or other tobacco products, leading former smokers to relapse to nicotine and/or tobacco product use, delay smoking cessation among current smokers, exposure to second-hand aerosol, and nicotine poisoning [ 16 ]. E-cigarette use also contributes to environmental tobacco smoke and may mimic the effects of passive (second-hand) smoking seen with use of conventional cigarettes [ 17 ]. Potential benefit of e-cigarette is that it can help us transition our society to little or no combustible tobacco use [ 16 ]. There is also emerging data suggesting that e-cigarettes may facilitate smoking cessation but further research is needed to compare the effectiveness and safety of e-cigarettes compared to other nicotine replacement therapies [ 13 , 14 ]. Consistent with the U.S. FDA regulatory oversight and the U.S. Surgeon General report, it may be prudent to investigate further the health effects of e-cigarettes prior to widespread advocacy favoring its use as a replacement for combustible tobacco use, given that the public health effects of e-cigarettes are yet to be fully understood.

3. Pharmacology of Nicotine

Nicotine (C 10 H 14 N 2 )—see Figure 1 —is a plant alkaloid found in the tobacco plant and is the principal constituent of tobacco responsible for its addictive character [ 18 , 19 ]. Nicotine acts as a ganglionic nicotinic cholinergic agonist in the autonomic ganglia, brain, spinal cord, neuromuscular junctions and adrenal medulla [ 18 , 20 , 21 ]. Nicotine has dose-dependent pharmacological effects and has both stimulant and depressant action [ 20 , 22 ].

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The chemical structure of nicotine [ 23 ].

The effects of nicotine on the central nervous system (CNS) and its peripheral stimulating effects are mediated through the release of several neurotransmitters, including acetylcholine, beta-endorphin, dopamine, norepinephrine, serotonin, and adrenocorticotropic hormone (ACTH) [ 18 ]. Notable stimulant effects of nicotine stimulant activities include peripheral vasoconstriction, elevated blood pressure, tachycardia, increased cardiac output, and enhanced mental alertness and cognitive function [ 18 , 20 , 22 ]. Depressant effects of nicotine include muscle relaxation and anxiety reduction [ 20 , 22 ]. At higher doses, nicotine stimulates the “reward” center in the limbic system of the brain [ 20 ].

Nicotine use produces a feeling of pleasure and relaxation [ 20 ]. In dependent smokers, the urge to smoke cigarettes correlates with a low blood nicotine level, as though smoking were a means to achieve certain nicotine level, reap the rewarding feeling associated with nicotine and avoid withdrawals [ 22 ]. Repetitive exposure to nicotine leads to neuroadaptation and building of tolerance to nicotine’s initial effects [ 20 ]. Accumulation of nicotine in the body leads to a more substantial withdrawal reaction if cessation is attempted [ 20 ]. Common withdrawal symptoms include anxiety, difficulty concentrating, irritability, and strong cravings for tobacco [ 20 ]. Onset of these withdrawal symptoms occurs within 24 h and can last for days, weeks, or longer [ 20 ]. Nicotine replacement therapies neither achieve the peak levels seen with cigarettes nor produce the same magnitude of subjective effects of cigarette smoking, they do, however, suppress the symptoms of nicotine withdrawal [ 22 ].

Nicotine from cigarette is carried on inhaled tar particles into the lungs where a large alveolar surface area allows rapid absorption into the pulmonary circulation [ 21 ]. Nicotine is well distributed with a volume of distribution of about 2.6 L/kg [ 21 ]. It undergoes primarily hepatic (80–90%) metabolism—with the remainder of the metabolism taking place in the lungs and kidney—to inactive metabolite: cotinine. Nicotine has a half-life of 1–4 h and about 2–35% is excreted unchanged in the urine [ 21 ].

4. Health Effects of Cigarette (Tobacco) Smoking

Annually, more than 400,000 individuals die prematurely in the United States from cigarette use; this represents almost one of every five deaths in the United States [ 9 ]. Approximately 40% of cigarette smokers will die prematurely due to cigarette smoking unless they are able to quit [ 9 ]. Between 1965 and 2014, over 20 million Americans died either from chronic conditions caused by smoking or exposure to secondhand smoke, complications caused by smoking during pregnancy, or smoking related fires in residential buildings [ 9 ]. Table 1 outlines the common causes of smoking-related deaths between 1965 and 2014 [ 9 ].

Premature deaths caused by smoking and exposure to secondhand smoke, 1965–2014 [ 9 ].

Cigarette smoking affects the human body in myriad ways, causing the development of chronic diseases and cancers. Figure 2 categorizes common health effects of tobacco smoking. The health effects are seen not only in smokers, but also individuals exposed to secondhand smoke. The impact of cigarette smoking on health depends on the duration of smoking over years and the exposure to cigarette (tobacco) smoke. The mechanism by which cigarette (tobacco) smoke causes adverse health outcomes involves multiple complex steps resulting from the exposure to free radicals from the components of tobacco smoke, leading to increased oxidative stress, inflammation, and DNA damage [ 9 ]. The chemical toxins in tobacco smoke are transferred from the lungs to the blood stream, where it is transported to nearly every part of the human body.

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Effects of tobacco smoking [ 25 ]. (AA) Aortic aneurysm; (CHD) Coronary heart disease; (PVD) Peripheral Vascular Disease; (COPD) Chronic obstruction pulmonary disease.

4.1. Cancer

Smoking is currently the largest preventable cause of cancer-related deaths, accounting for approximately 30% of cancer related deaths [ 24 ]. Carcinogens in cigarette smoke bind to human DNA, resulting in DNA damage and gene mutations. These genetic changes lead to uncontrolled cell growth and inhibit normal mechanisms that restrain cell growth and spread, resulting in cancer. A causal relationship has been established between cigarette (tobacco) smoking and lung cancer, the leading cause of cancer-related deaths in the U.S. There is also a causal relationship between cigarette smoking and cancers of the head, neck, liver, bladder, cervix, esophagus, colon, and rectum [ 9 ]. The evidence is insufficient to conclude that there is a causal relationship between smoking and cancers of the breast and prostate, however there is an increased risk of dying from cancer in smokers with breast, prostate, and other cancers [ 9 ].

4.2. Cardiovascular Diseases

There is a causal relationship between cigarette smoking and cardiovascular events. Major mechanisms underlying smoking-induced cardiovascular disease include endothelial dysfunction, prothrombotic effects, inflammation, altered lipid metabolism, increased demand for myocardial oxygen and blood, decreased supply of myocardial blood and oxygen, and insulin resistance [ 9 ]. Cigarette smoking and exposure to second hand smoke are major causes of coronary heart disease, stroke, aortic aneurysm, and peripheral arterial disease [ 25 ]. Cigarette smoking and secondhand smoking are also a major cause of death due to CVD. Annually, 194,000 deaths from cardiovascular disease in the U.S. are smoking-related [ 9 ].

4.3. Respiratory Diseases

Cigarette (tobacco) smoking is also associated with the development of chronic pulmonary diseases. In fact, cigarette smoking is the primary cause of COPD in the U.S. [ 26 , 27 ]. Some of the mechanisms involved are loss of cilia in the lungs, mucus gland hyperplasia, and overall inflammation resulting in the abnormal functioning of the lungs as well as injury. Cigarette smoking may exacerbate asthma in adults. Underlying mechanisms may include chronic airway inflammation, impaired mucociliary clearance, increased bronchial hyperresponsiveness, increased development of T helper cell 2 (Th2) pathways relative to Th1 pathways, increased production of IgE, and greater allergic sensitization [ 9 , 25 ]. Smoking also increases the risk of developing tuberculosis and dying from tuberculosis [ 9 ].

4.4. Reproductive Effects

Maternal cigarette (tobacco) smoking causes several reproductive abnormalities. Carbon monoxide in cigarette smoke binds to hemoglobin, depriving the fetus of oxygen, ultimately resulting in low birth weight [ 25 ]. Other toxins in tobacco smoke including nicotine, cadmium, lead, mercury, and polycyclic aromatic hydrocarbons, have been found to cause sudden infant death syndrome, premature births, and decreased fertility in women [ 9 , 24 ]. More recent evidence indicates a causal relationship between maternal cigarette smoking and orofacial clefts and ectopic pregnancies [ 9 ]. A causal relationship between smoking and erectile dysfunction in men has also been established [ 9 ].

4.5. Additional Effects

Smoking impairs immune function, resulting in an increased risk of pulmonary infections and rheumatoid arthritis [ 9 ]. It also affects the gastrointestinal tract, increasing the risk of peptic ulcer disease. There is also increased risk of hip fractures and low bone mineral density in postmenopausal women who smoke. Additionally, smokers with diabetes have a higher risk of developing complications, including nephropathy, blindness, peripheral neuropathy, and amputations [ 25 ]. Recent evidence indicates that the risk of developing type 2 diabetes is 30–40% higher in smokers that nonsmokers [ 9 ]. Passive (second-hand) smoking has also been linked with negative health consequences such as low-birth rate in offspring of mothers exposed to second-hand smoke, sudden infant death syndrome, and type 2 diabetes mellitus [ 28 ].

5. Non-Pharmacologic Treatment of Cigarette (Tobacco) Smoking

About 70% of cigarette smokers visit a physician each year [ 29 ]. Even more smokers visit pharmacists, dentists, nurses, and other healthcare professionals. Clinicians are, therefore, in an excellent position to identify smokers. It is recommended that tobacco use of every patient treated in a healthcare setting be assessed and documented at every visit [ 29 ]. Identifying smokers in the healthcare setting offers a good opportunity for clinicians to recognize and guide effective interventions for smoking cessation [ 30 ]. Patients who begin any major behavioral or lifestyle change go through successive stages of change. To plan an effective intervention, it is important to understand these major stages of change [ 26 ]. Intervention strategies should target the individual’s current stage of change, with an initial objective of moving the individual to the next stage and an overall goal of moving the individual to the maintenance stage. Table 2 below reviews the stages of change.

Stages of behavior change [ 26 ].

A simple five-step algorithm called the 5 A’s (Ask, Advise, Assess, Assist, Arrange) can be used by clinicians to offer a brief counseling intervention in the primary care setting [ 29 ]. The 5 A’s are concisely described in Table 3 . Some of the myriad reasons that patients may be unwilling to quit are as follows. They may be unaware of the harmful effects of tobacco or do not understand the benefits of quitting. They may not have the financial resources to facilitate the smoking cessation process. Also, they may have fears or concerns about quitting, or may be demoralized because of failed quit attempts. Patients in this category or others who are unwilling to quit may respond to brief motivational interventions that are based on principles of Motivational Interviewing (MI) [ 29 ]. The 5 R’s of smoking cessation summarize the areas that should be addressed in Motivational Interviewing (MI). The 5 R’s are described in Table 4 .

The “5A’s” model for treating tobacco use and dependence [ 29 ].

Enhancing motivation to quit tobacco—The “5 R’s” [ 29 ].

Specific non-pharmacologic interventions for smoking cessation can be categorized into three approaches: clinical approaches, public health approaches, and alternative approaches. Clinical approaches to smoking cessation include self-help programs, telephone counseling, cognitive-behavioral approaches such as individual and group counseling, healthcare provider interventions, and exercise programs. Public health approaches include community-level interventions, workplace interventions, multimedia interventions, and public policy changes [ 31 ]. Alternative approaches include acupuncture, aversive therapy, and hypnosis. These various interventions are briefly discussed in Table 5 . It is also important to understand barriers to smoking cessation and effectively address these barriers using motivational intervention technique. A systematic review by Twyman et al. reported common barriers to smoking cessation [ 32 ]. The review found that smoking for stress management, lack of support from health and other service providers, and the high prevalence and acceptability of smoking in vulnerable communities were three consistent barriers to smoking cessation common to six-select vulnerable groups (low socioeconomic status, indigenous, mental illness and substance abuse, homeless; prisoners; and at-risk youth) [ 32 ]. Knowledge of the barriers to smoking cessation and implementation of methods to address these barriers are imperative in helping patients quit smoking.

Non-pharmacologic interventions for smoking cessation [ 31 , 33 , 34 , 35 ].

6. Pharmacologic Treatment of Cigarette (Tobacco) Smoking

All patients who are trying to quit smoking should be offered pharmacologic intervention except when these medications are contraindicated or in certain populations where there is insufficient evidence of effectiveness (e.g., pregnancy, adolescence, light smokers) [ 29 ]. Pharmacologic therapy should be used in addition to behavioral support for smoking cessation [ 36 ]. There are seven FDA approved medications for smoking cessation: transdermal nicotine patch, nicotine gum, nicotine lozenge, nicotine inhaler, nicotine nasal spray, bupropion sustained-release (SR), and varenicline. These medications should be considered first line therapy according to the U.S. Public Health Service guidelines [ 29 ]. First line agents are summarized in Table 6 . Patients who do not respond to any first line medications or who have contraindications to first line agents may be prescribed second line agents. Second line agents include clonidine and nortriptyline. Second line agents are not FDA approved for smoking cessation but have demonstrated some effectiveness in treating tobacco use [ 37 ]. Combination therapy of pharmacologic agents is often used in patients who have failed to achieve cessation with monotherapy. Combination therapy involves adding short acting nicotine replacement therapy (nicotine gum, lozenge, inhaler, or nasal spray) to longer acting agents, such as the nicotine patch or bupropion SR [ 37 ]. Table 7 includes the wholesale acquisition cost of the FDA approved smoking cessation therapy for consideration by providers and patients. Clinicians tasked with selecting appropriate pharmacologic therapy for smoking cessation should consider using the first line agents prior to considering the second line therapy, except when there are contraindications to first line agents or when patients did not respond to first line therapy. Clinicians should also consider other factors such as cost, adverse effect profile, and route of medication delivery. The goal of therapy should be to administer an affordable agent with proven efficacy and good tolerability profile. Selecting a medication formulation that helps patients to achieve medication adherence is also desirable.

Pharmacologic agents for smoking cessation [ 29 , 30 , 37 , 38 ].

Smoking cessation medications and cost [ 54 ].

a Dosage reduction may be needed for hepatic or renal impairment. b Appropriate WAC for 30 days’ treatment at the maximum usual maintenance dosage. WAC = wholesaler acquisition cost, or manufacturer’s published price to wholesalers. WAC represents a published catalogue or list price and may not represent an actual transactional price. Source: Red Book Online ® System (electronic version). Truven Health Analytics, Greenwood Village, Colorado, USA. Available at: (cited: 10/10/2016). c Same price for all dosages. d See specific label for instructions for dose titration. e Cost for 28 transdermal patches. f One spray per nostril. Maximum of 40 doses/day should not be used for >3 months. g Cost of four 10-mL bottles. h Cost of 168 10-mg cartridges; each cartridge delivers 4 mg of nicotine. i Not FDA-approved for this indication. j Only the generic 150 mg SR tablets are FDA-approved for this indication. k Initial dosage is 150 mg once/day for 3 days. l Initial dosage is 0.5 mg once/day for 3 days, then bid for 4–7 days.

6.1. Nicotine Replacement Therapy (NRT)

Five nicotine replacement therapy (NRT) products are approved by the U.S. Food and Drug Administration for tobacco dependence treatment: nicotine gum, nicotine lozenge, nicotine nasal spray, nicotine inhaler, and the transdermal nicotine patch [ 38 ]. The nicotine inhaler and nasal spray are prescription drugs in the U.S., whereas the nicotine gum, lozenge and patch are available over the counter. NRTs work to reduce severity and duration of withdrawal symptoms by partially replacing nicotine obtained by tobacco use. A 2008 meta-analysis of 69 clinical trials found that all five nicotine replacement products are superior to placebo, approximately doubling abstinence rates [ 39 ]. A Cochrane Review of 150 trials also found that all forms of nicotine replacement therapy (inhaler, oral tablets/lozenges, gum, patch, and nasal spray) increased rates of quitting smoking by 50–70% [ 40 ]. A study enrolling 504 patients found that all forms of NRT evaluated (gum, patch, nasal spray, and inhaler) produced similar quit rates and were equally effective at reducing the frequency, duration, and severity of urges to smoke [ 41 ]. NRT is generally well tolerated with mild adverse effects. The three most commonly reported adverse effects of NRT in observational studies were headache, nausea and vomiting, and other gastrointestinal symptoms [ 38 , 42 ]. Adverse effects of NRT are generally formulation specific, depending on the delivery system used [ 38 , 42 ]. NRT must be used with caution in patients with known cardiovascular conditions, but have generally found to be safe in patients with these conditions [ 38 , 42 ]. All products are pregnancy category D with the exception of the nicotine gum (category C), although the benefit of replacement therapy may outweigh the risks [ 37 , 43 ].

6.2. Bupropion Sustained-Release (SR)

Bupropion is the first non-nicotine agent to demonstrate efficacy in the treatment of tobacco dependence [ 37 ]. Bupropion sustained-release is FDA approved for smoking cessation and is regarded as a first line therapy by the U.S. Public Health Service guideline [ 29 ]. Bupropion is an inhibitor of dopamine and norepinephrine reuptake, but its mechanism of action in smoking cessation is not well understood [ 37 ]. A systematic review of 44 clinical trials, published in 2014, found that sole therapy with bupropion significantly increased long-term (≥6 months) smoking abstinence (RR = 1.62; 95% CI, 1.49–1.76) [ 44 ]. The most common adverse effects with bupropion, when used for smoking cessation, are insomnia, which occurs in about 30–40% of patients, and dry mouth, which occurs in 10% of patients [ 37 ]. A more serious side effect is seizure, which can occur because bupropion reduces the seizure threshold. Two large studies reported seizure incidence of 0.1% [ 37 ]. Bupropion has a boxed warning for development of neuropsychiatric symptoms ranging from agitation to suicidal ideation and behavior in patients using this medication [ 45 ]. In 2009, the FDA issued an alert to healthcare professionals reporting that cases of neuropsychiatric symptoms have occurred in patients without pre-existing psychiatric illness and have worsened in patients with pre-existing psychiatric illness [ 45 ]. The FDA recommends close monitoring of neuropsychiatric symptoms in patients receiving Bupropion and to stop Bupropion therapy when necessary and to monitor patient closely until neuropsychiatric symptoms resolve [ 45 ]. Bupropion is pregnancy category C and has been shown to be safe and effective in patients with known cardiovascular conditions [ 46 , 47 ].

6.3. Varenicline

This is a first line agent for smoking cessation. Varenicline is a partial agonist specific for the neuronal nicotinic acetylcholine receptor subtype α 4 β 2 . As a partial agonist, it binds to and produces partial stimulation of the nicotinic receptor, thereby reducing the symptoms of nicotine withdrawal [ 37 ]. Varenicline also stimulates dopamine turnover, which provides relief from nicotine cravings and withdrawal symptoms that can occur when a patient is trying to quit [ 37 ]. A 2008 meta-analysis found that varenicline increased the odds of quitting three times than that of placebo (OR 3.1, 95% CI 2.5–3.8) and produced a quit rate of 33 percent at six month follow-up [ 29 ]. In a systematic review of 39 clinical trials comparing nicotine partial agonists, varenicline significantly increased smoking abstinence at 6 months or longer compared to placebo (RR = 2.24; 95% CI, 2.06–2.34) or bupropion (RR = 1.39; 95% CI, 1.25–1.54). [ 48 ] A 2008 meta-analysis also found varenicline to be superior to placebo (OR 2.55; 95% CI, 1.99–3.24) and bupropion (OR 2.18, 95% CI, 1.09–4.08) [ 39 ]. Varenicline is generally well tolerated, with the most common adverse events being nausea, insomnia, and headache [ 37 ]. Varenicline can be used in patients who have concurrent CVD, but with caution. It should be noted that in 2011 the FDA published a warning, based on data from a clinical trial of smokers with CVD which stated that “cardiovascular adverse events were infrequent overall, however, certain events, including heart attack, were reported more frequently in patients treated with Chantix ® (Varenicline) than in patients treated with placebo” [ 49 , 50 ]. Varenicline is pregnancy category C and, like bupropion, carries a black box warning for increased risk of behavior change, agitation, depressed mood, and suicidal ideation and behavior [ 49 , 50 , 51 ].

6.4. Clonidine

Clonidine should be used as a second line agent when primary therapies are found to be ineffective. Clonidine is only FDA approved for hypertension but has shown to be efficacious in smoking cessation. Clonidine is a α 2 -adrenergic agonist, whose effect in smoking is thought to be based on its ability to counteract CNS features of nicotine withdrawal, including craving and anxiety. Results from a Cochrane Review article found that clonidine approximately doubled the rate of abstinence compared to placebo (OR, 1.89; 95% CI, 1.30–2.74) [ 52 ]. Clonidine is limited by its adverse effect profile, which includes postural hypotension, extreme drowsiness, fatigue, and dry mouth [ 37 ].

6.5. Nortriptyline

Nortriptyline should be used as a second line agent when primary therapies are found to be ineffective. Nortriptyline is a tricyclic antidepressant, whose effects in smoking cessation are not well understood. A meta-analysis review of 6 randomized clinical trials indicated that nortriptyline treatment doubles the odds of smoking cessation, with an OR for abstinence of 2.1 (95% CI, 1.5–3.1) [ 53 ]. The most common side effects of nortriptyline are related to its anticholinergic effects, including dry mouth, constipation, and sedation [ 37 ].

7. Conclusions

Although its prevalence has declined in recent years, cigarette smoking remains the most common method of tobacco use. The adverse health effects associated with cigarette smoking are numerous; thus, continual efforts to reduce the prevalence of cigarette smoking are imperative. Current trends on cigarette smoking highlight the importance of smoking prevention and smoking cessation initiatives that target youth. Promotion of smoking cessation can be a strong public health approach for reducing non-smokers’ environmental exposure to environmental tobacco smoke. Treating tobacco dependence should include both behavioral and pharmacologic interventions. First line agents for smoking cessation include bupropion SR, varenicline, and nicotine replacement therapies.

One of the goals of Healthy People 2020 is to “reduce the illness, disability, and death related to tobacco use and secondhand smoke exposure” [ 55 ]. Twenty-one national objectives, related to tobacco use, are outlined to achieve this goal. Recommended strategies for achieving this goal include: increasing the cost of tobacco products; fully funding tobacco control programs; banning smoking in public places; anti-tobacco media campaigns, particularly those targeted towards youth; community, school, and college anti-tobacco programs; encouraging and assisting tobacco users to quit; expanding insurance coverage of smoking cessation agents; and expanding state quit line capacity [ 55 , 56 ].


This manuscript was partially supported by the Grant from National Institutes of Health (NIH), Department of Health and Human Services (DHHS); 5 S21 MD 000100-12 from the National Institute on Minority Health and Health Disparities (NIMHD).

Author Contributions

IfeanyiChukwu O. Onor and Daniel L. Stirling conceived the project. IfeanyiChukwu O. Onor, Daniel L. Stirling, Shandrika R. Williams, Daniel Bediako, Amne Borghol, Martha B. Harris, Tiernisha B. Darensburg, and Sharde D. Clay performed literature search and drafted sections of the manuscript. Samuel C. Okpechi and Daniel F. Sarpong contributed to the manuscript draft and provided critical revision of the manuscript. All authors have approved the submitted version.

Conflicts of Interest

The authors declare no conflict of interest.

Harmful health effects of cigarette smoking


  • 1 Department of Biochemistry, Meharry Medical College, Nashville, TN 37208, USA. [email protected]
  • PMID: 14619966
  • DOI: 10.1023/a:1026024829294

This is a comprehensive review on the harmful health effects of cigarette smoking. Tobacco smoking is a reprehensible habit that has spread all over the world as an epidemic. It reduces the life expectancy among smokers. It increases overall medical costs and contributes to the loss of productivity during the life span. Smoking has been shown to be linked with various neurological, cardiovascular, and pulmonary diseases. Cigarette smoke not only affects the smokers but also contributes to the health problems of the non-smokers. Exposure to environmental tobacco smoke contributes to health problems in children and is a significant risk factor for asthma. Cigarette smoke contains several carcinogens that alter biochemical defense systems leading to lung cancer.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, U.S. Gov't, P.H.S.
  • Asthma / etiology*
  • Cardiovascular Diseases / etiology*
  • Lung Neoplasms / etiology*
  • Nervous System Diseases / etiology*
  • Smoking / adverse effects*
  • Tobacco Smoke Pollution / adverse effects
  • Tobacco Smoke Pollution

Grant support

  • 2S06 GM08037/GM/NIGMS NIH HHS/United States
  • Open Access
  • Published: 30 July 2021

Impact of tobacco and/or nicotine products on health and functioning: a scoping review and findings from the preparatory phase of the development of a new self-report measure

  • Esther F. Afolalu   ORCID: 1 ,
  • Erica Spies 1 ,
  • Agnes Bacso 1 ,
  • Emilie Clerc 1 ,
  • Linda Abetz-Webb 2 ,
  • Sophie Gallot 1 &
  • Christelle Chrea 1  

Harm Reduction Journal volume  18 , Article number:  79 ( 2021 ) Cite this article

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Measuring self-reported experience of health and functioning is important for understanding the changes in the health status of individuals switching from cigarettes to less harmful tobacco and/or nicotine products (TNP) or reduced-risk products (RRP) and for supporting tobacco harm reduction strategies.

This paper presents insights from three research activities from the preparatory phase of the development of a new self-report health and functioning measure. A scoping literature review was conducted to identify the positive and negative impact of TNP use on health and functioning. Focus groups ( n  = 29) on risk perception and individual interviews ( n  = 40) on perceived dependence in people who use TNPs were reanalyzed in the context of health and functioning, and expert opinion was gathered from five key opinion leaders and five technical consultants.

Triangulating the findings of the review of 97 articles, qualitative input from people who use TNPs, and expert feedback helped generate a preliminary conceptual framework including health and functioning and conceptually-related domains impacted by TNP use. Domains related to the future health and functioning measurement model include physical health signs and symptoms, general physical appearance, functioning (physical, sexual, cognitive, emotional, and social), and general health perceptions.


This preliminary conceptual framework can inform future research on development and validation of new measures for assessment of overall health and functioning impact of TNPs from the consumers’ perspective.

As a leading cause of preventable morbidity and mortality worldwide, smoking remains a major public health problem. Compared with those who do not smoke, people who smoke are significantly more likely to develop heart diseases, lung cancer, chronic obstructive pulmonary disease (COPD), and other diseases [ 1 , 2 ]. It is well established that the best way to avoid the health risks associated with smoking is for people to never start and for those who smoke to quit [ 1 , 3 ]. Tobacco harm reduction is one way to alleviate the health risk for individuals who choose not to quit smoking [ 4 ], by providing less harmful tobacco and/or nicotine products (TNP), such as reduced-risk products (RRP) (used here to refer to products that present, are likely to present, or have the potential to present, less risk of harm to people who smoke and switch to these products versus continued smoking) or modified risk tobacco products (MRTP).

Several smokeless tobacco products and a heated tobacco product were recently authorized for marketing with modified risk claims through the United States (US) Food and Drug Administration (FDA) MRTP pathway [ 5 ]. The guidance on MRTP applications [ 6 ] specifies that health outcomes should be assessed during premarket evaluation and postmarket surveillance of modified risk TNPs such as these. These health outcomes comprise not only objective clinical and biological measures but also self-reported outcomes [ 6 , 7 ]. Studies and reports have recently started providing evidence on the health impact of new TNPs [ 8 ]. For instance, recent papers have investigated the effects of e-cigarettes and heated tobacco products on cardiopulmonary outcomes [ 9 , 10 , 11 , 12 , 13 , 14 ]. However, the papers have mainly focused on clinical measurements, such as spirometry and other lung function tests; consumer perception is rarely explored or the focus of the research. Measuring self-reported experience is important for understanding the changes in the health status of individuals switching from cigarettes to RRPs and is a key component of tobacco harm reduction strategies [ 7 ]. Self-reported ratings of RRP effectiveness or adverse events might differ from clinical measures and provide another perspective as useful as the clinicians. In addition, consumer perception of positive changes in health status, functioning and other behavioral outcomes will also subsequently influence use behaviors and switching to RRPs rather than continuing smoking.

Self-perceived health status is a complex concept to define and measure, particularly within the context of TNP use [ 15 ]. While generic health status measures, such as the Medical Outcomes Study 36-item Short-Form Health Survey (SF-36), have been used to evaluate the health status of people who smoke [ 16 , 17 ], comparisons have mainly been made between those who currently smoke, those who used to smoke, and those who never smoked [ 18 , 19 ]. Results from these studies strongly suggest that, in healthy populations, existing generic measures are not sensitive enough to detect change over time when stopping or switching from cigarettes to other TNPs, owing to high ceiling effects [ 20 ]. While a few smoking-specific quality of life measures have been developed, these measures have not been widely implemented or standardized [ 15 , 17 , 21 , 22 ], and the application of these smoking-specific measures to different TNP use across the risk continuum is scarce [ 20 ].

As part of the A ssessment of B ehavioral OU tcomes related to T obacco and Nicotine Products (ABOUT™) Toolbox initiative [ 23 ], the present project aims at developing a new self-report measure (ABOUT™— Health and Functioning ) to address the current gap and assess the impact of TNPs on health and functioning (including health status, functional status and other health-related quality of life constructs). This paper presents insights from three research activities [ 24 , 25 ] from the preparatory phase of development of the measure—that is, a scoping literature review, reanalysis of consumer focus groups/interviews, and expert opinion. These three activities serve as background research to support the development of a preliminary conceptual framework of health and functioning associated with the use of TNPs.

Scoping literature review

The purpose of the review was to address two main questions among individuals who use TNPs:

What are the positive and negative health and functioning impacts of TNP use?

What concepts are evaluated by measures used to assess the positive and negative impacts of TNP use?

Given the nature and breadth of the research questions and the number of potentially relevant publications, a scoping literature review was used as it provides a means of identifying the literature and mapping the concepts and evidence on a topic by using an informative and iterative research process [ 26 ]. The scoping review involved a PubMed search (August 2018) and application of Sciome’s rapid Evidence Mapping (rEM) [ 27 ], followed by additional manual screening and review. rEM is a proprietary methodology developed by Sciome ( ) to rapidly summarize and produce a quantitative representation of the available body of scientific evidence in a particular area. The study by Lam et al. demonstrated a proof-of-concept application of the rEM methodology [ 27 ]. The PubMed search terms targeted qualitative and quantitative research among people who use TNPs (Table 1 ). This was supplemented by a second, parallel step of manually identifying relevant literature through other known sources. Table 2 describes the general inclusion and exclusion criteria that were applied to the scoping literature review.

After the initial rEM exercise, two reviewers (EC, SG) further manually screened the titles and abstracts of the articles identified through the automated rEM exercise against the inclusion and exclusion criteria. Finally, the selected publications underwent a full screening by two reviewers (VL and DF) for determining their relevance to the research questions for data extraction and one of the co-authors (LA-W) cross-checked the screening and resolved differences in opinion among the reviewers.

The World Health Organization (WHO) International Classification of Functioning, Disability and Health (ICF) [ 28 ] framework and the revised Wilson and Cleary [ 29 , 30 ] model were used as a guide to broadly inform categories for data extraction from the literature on TNP use and health and functioning. These established models enable the conceptualization and description of health status and functioning (the combination of which is often referred to as health-related quality of life) [ 31 , 32 ], and related outcomes and determinants. To complement and refine this and to ensure relevance to those who use TNPs, the data extracted from the literature was also grouped and labeled based on the contents of the literature reviewed.

The elements extracted from the selected papers were as follows:

Author, citation details, and publication type

Objectives and/or research questions

Sample type, size, and principle demographics

Type(s) of TNP and definitions of levels of consumption

Methodology, questionnaires, and statistical methods used

Main results

Results grouped in broad categories: Health Signs and Symptoms; General Health Perceptions; Quality of Life, Health-Related Quality of Life, and Functional Status; Individual Characteristics; Environmental and Social Characteristics; Biomarkers and Biological Endpoints.

Reanalysis of focus groups/in-depth interviews

The objective of the secondary analyses of existing qualitative data in people who use TNPs was to inform the drafting of the initial conceptual framework, as well as interview guides for planned concept elicitation qualitative studies to identify concepts and develop items to detect what is relevant to measure in this context. Two sets of qualitative data containing information related to health and functioning were reanalyzed and participants had consented for their data to be used in future studies. The first was from 29 focus groups (total number of participants n  = 229) that were originally designed to discuss perceived risk, appeal, and intent to use TNPs [ 33 , 34 ]. The focus groups—stratified by smoking status—were conducted in the United States (US; n  = 12), Japan ( n  = 4), Italy ( n  = 4), and the United Kingdom (UK; n  = 9) between December 2012 and August 2013. The second dataset included 40 in-depth interviews conducted in North Carolina, USA, with people who use TNPs, to discuss issues centered on perceived dependence on TNPs [ 35 ]. While 21 interviewees were people who were poly-TNPs users, 19 were people who were exclusive users of one of the following types of TNPs: cigarettes ( n  = 5), smokeless tobacco ( n  = 5), e-cigarettes ( n  = 5), or another type of TNP (pipes, waterpipes, or nicotine replacement therapy [NRT] products; n  = 4). These interviews were conducted in August 2017. The demographics of both data sets are presented in Table 3 . For reanalyzing the data, an initial codebook guided by the literature review data extraction categories was developed; however, new codes were created to complement these categories based on the thematic content analysis of the transcripts. The qualitative analysis software Quirkos [ 36 ] was used for the reanalysis.

Expert panel review

An expert panel consisting of five key opinion leaders (KOL) and five technical consultants was convened in August 28, 2018, in Neuchâtel, Switzerland. The KOLs were subject matter experts in the fields of nicotine and smoking cessation ( n  = 1), Patients Reported Outcomes (PRO) evaluation and scale development ( n  = 3), and health economics ( n  = 1). The consultants were experts on nicotine dependence ( n  = 1), psychometric validation ( n  = 2), market research ( n  = 1), and PRO development and validation ( n  = 1). The meeting followed an agenda and semi-structured discussion guide to facilitate conversations. First, the panel was presented with the principles underlying the tobacco harm reduction assessment strategy [ 4 ]. This session was followed by an open elicitation phase, during which two experienced moderators asked the panel to identify and discuss concepts related to health and functioning in people who use TNPs that different stakeholders might find important. Then, the panel was asked to review and respond to the concepts identified in the literature review and in the qualitative research reanalysis. These findings were discussed in depth to arrive at a consolidated preliminary conceptual framework. Each concept was presented, and the experts were asked to rank and agree on concepts to be included and how the concepts should be grouped by domains in the framework. In generating the framework, the project team and expert panel considered the themes and concepts identified under each of the categories from the scoping literature review, specific concepts from the secondary analyses of the qualitative data, and the expert panel meeting. The authors then synthesized and re-organized concepts emerging from the different preparatory phase activities under main health and functioning and conceptually-related domains. The participants also provided their input on the best strategies for planned qualitative studies to inform and support the development and validity of the proposed health and functioning measure.

The literature search identified 4761 articles. Figure  1 (flow diagram) depicts the results of the search and screening process. Titles and abstracts were screened by the rEM exercise until the machine learning algorithms predicted 97.7% relevant references; thus, 707 abstracts were not screened. After applying the inclusion/exclusion criteria to the remaining 4,054 abstracts, 281 were identified as part of the rEM exercise. After additional manual screening and review of the abstracts and articles against the inclusion/exclusion criteria, 90 full-text articles were included for data extraction [ 20 , 37 – 125 ]. Seven additional full-text articles were also included on the basis of a manual search [ 126 , 127 , 128 , 129 , 130 , 131 , 132 ]. Findings are summarized in Table 4 and a detailed description and data extracted from all the articles from the literature review is presented in Additional File 1 .

figure 1

Flow diagram Sciome’s rapid Evidence Mapping (rEM) and manual screening processes of the scoping literature review

Fifty-six publications (56/97; 58%) presented data related to health signs and symptoms . These are grouped under five core areas: mental health and cognitive functioning (28/97; 29%); pain and physical trauma (6/97; 6%); respiratory, cardiovascular and inflammatory conditions (5/97; 5%); “other” health conditions , which included insomnia, liver disease, eye health, and hearing loss (5/97; 5%); and oral health (4/97; 4%). There were also eight publications related to the effects of smoking cessation on health signs and symptoms, mostly benefits of cessation but also including perceived dependence, addiction, and withdrawal symptoms (8/97; 8%). Overall, the burden and impact of cigarette smoking on both physical and mental health symptoms was negative and generally worse among people who smoke relative to those who do not smoke. On the other hand, quitting smoking was accompanied by improvements in general physical health and psychological wellbeing. However, in spite of the positive impact of quitting smoking, loss of moments of pleasure, struggle to manage stress, the social aspects of smoking, and withdrawal symptoms were seen as barriers to quitting.

The general health perceptions of various adults who use TNPs were reported in 18 of the 97 articles (18%), with 9 of them detailing the general health perceptions related to cigarettes and 9 being related to e-cigarettes and other TNPs. Perceptions were determined by questionnaires and focus groups for evaluating the health impacts, fear of diseases, harm to others and self, social impacts (both positive [e.g., inclusion and looking “cool”] and negative [e.g., stigma and exclusion]), and other reasons for taking up or considering/attempting smoking cessation.

Quality of life, health-related quality of life, and functional status was studied in 9 of the 97 included articles (9%). These studies mostly demonstrated with generic and specific QoL, HRQoL, or functional status questionnaires that cigarette smoking was associated with a worse quality of life and that smoking cessation often resulted in an improved quality of life. However, in some cases, the use of TNPs also reportedly enabled individuals to manage their levels of anxiety and improve some aspects of social engagement and functional status.

Individual, environmental and social characteristics were found to influence the decision to smoke and/or consider or attempt to quit smoking or switching to other TNPs, as reported in 8 (8%) and 11 (11%) of 97 publications, respectively. Some key characteristics and determinants of smoking behavior included low socioeconomic status, male sex, living alone, family, and close social environment, societal stigma, and local regulations.

Finally, 12 of the 97 publications (12%) were related to studies on biomarkers and biological endpoints in people who use TNPs and showed that smoking cigarettes negatively influenced cardiovascular, respiratory, oral, renal, stress, metabolic, and inflammatory-related biomarkers and physiological assessments.

The themes from this reanalysis are summarized below and organized on the basis of the narrative of the participants of their experiences.

Perceived negative impact of smoking

Other than health, the biggest and most salient reported negative impact of smoking was the perceived lack of control related to addiction and emotional health and wellbeing. Participants reported feeling that cigarette smoking was running their lives or “holding them hostage.” They reported that this perceived lack of a sense of control or willpower often led to feelings of weakness or a feeling that they were a “slave” to cigarettes. Many respondents reported smoking even when they did not necessarily want to and experiencing feelings of obsession and craving.

Perceived lack of control and addiction were also related to the activities of the participants throughout the day. People who smoke often reported altering their activities to smoke because of patterns of behavior or routine and the experienced need for a smoke. They reported that the “need for a smoke” sensation would cause them to leave work or social events early, not attend events if smoking was not allowed, interrupt what they were doing to smoke, and get up in the middle of the night.

Fear of withdrawal symptoms, with a strong emphasis on mental/emotional health, was also prominent among reported negative impacts of smoking. This fear was often reported as limiting the willingness of individuals to try to quit smoking or facilitating a return to prior smoking behavior. Individuals reported fearing the following symptoms they associated with withdrawal: mood swings and irritability, violent or aggressive behavior, inability to concentrate, anxiety, anger, and weight gain.

Perceived benefits of smoking

Several perceived benefits were identified that keep individuals smoking or using cigarettes. These included perceptions of enhanced cognitive functioning, relaxation, a way to take a break, use as a coping strategy, a social function, a weight management tool, the perception that it feels good, and being part of one’s identity. It is also important to note that the perceived benefits of smoking often outweighed the risks and the feeling of lack of control in the participant discussions. Even people who used to smoke noted they missed the relaxation and breaks they associated with smoking.

Recognition of symptoms/diseases related to smoking

Table 5 summarizes the negative symptoms and diseases related to smoking recognized by participants in both the focus groups and interviews. These were mostly related to six main body systems (cardiovascular, digestive, oral, neurological, reproductive, and respiratory).

Impacts on physical functioning

The participants noted how smoking impacts their physical functioning. In particular, they noted how their exercise capacity during running, playing sports, walking upstairs, and general physical activity was diminished. They also reported reduced stamina and endurance, decreased physical strength, and feeling tired more easily.

Effects on emotional health

The participants also described how smoking impacts their emotional health and wellbeing. People who smoke reported feelings of shame, guilt, weakness, and a lack of control or powerlessness. They also reported feelings of depression and anxiety associated with worry about health risks. Furthermore, the participants indicated that they experienced a fear of going to places where they could not smoke, being a bad role model for their children, and (in case of people who used to smoke) going back to smoking.

Positive and negative social impacts

Smoking was perceived to have both negative and positive impacts on the social lives of participants. Smoking impacted life negatively when it was not allowed in certain environments, such as in homes, at work, and in cars and airplanes. Stigma was also associated with smoking in an environment where peers and family members do not smoke, but it was also seen as a source of group identity within social networks that had a higher prevalence of smoking behaviors. Participants reported that smoking had some positive impacts on their social interaction, because it facilitated work breaks and increased communication with peers.

Reasons people decided to try to quit

Throughout the focus groups and interviews, individuals identified several reasons why they tried to quit smoking. These included: health, diagnosis of cancer (self, family, or friend), gum disease, pregnancy, hospital stay, worry that it will “kill me,” dislike of taste or odor, social reasons, change in surroundings (fewer smoking spaces), and price.

Reasons people do not like alternatives to cigarettes

The participants’ reasons for not liking alternatives to cigarettes (i.e., less harmful TNPs/RRPs) included perceptions that the alternatives did not work (i.e., the participants still had cravings and experienced withdrawal symptoms), made them feel or get ill (nausea and vomiting), were not “the same” as cigarettes in terms of the ritual, taste, or “feeling,” or were inconvenient/too big to carry.

The conclusions of the expert panel widely supported the findings of the literature review and the input from the reanalyzed focus groups and interviews. Some of the experts working in field of tobacco and nicotine provided additional insights based on their extensive experience with people who use TNPs; they highlighted the importance of the enjoyment of smoking for people who find it difficult to quit, the positive immediate benefits of quitting, and the smoking-related biomarkers that might be on a causal pathway between switching and changes in health and functioning status.

The following main areas were discussed and agreed during the meeting: (1) utility of use, referring to the perceived satisfaction and enjoyment of smoking (e.g., craving relief, weight control, and social affiliation); (2) signs and symptoms of withdrawal (e.g., anxiety, depression, and anger) and the positive immediate physical health effects of quitting smoking (e.g., better general and oral hygiene, less coughing, and improved exercise capacity); (3) functioning, including cognitive, physical, sexual, social, emotional, and role functioning; (4) worry associated with smoking and smoking-related diseases; (5) general health perceptions and quality of life; (6) association with smoking-related biomarkers that could be on the causal pathway between switching and changes in health and functioning; and (7) TNP use patterns and maintenance of switching to RRPs.

Generation of the preliminary conceptual framework

Triangulation of the findings from the literature review, qualitative input from people who use TNPs, and expert panel feedback helped generate a preliminary descriptive conceptual framework that includes the health and functioning and conceptually-related domains impacted by TNP use (Fig.  2 ).

figure 2

Health and functioning conceptual framework related to tobacco and/or nicotine product use from the preparatory phase research findings

Four domains related to the future health and functioning measurement model for TNP use are indicated in grey rectangular boxes and include (moving down from proximal to distal parameters) physical health symptoms (e.g., oral and respiratory symptoms), general physical condition (e.g., appearance and hygiene), functioning (physical, sexual, cognitive, emotional, and social functioning), and general health perceptions, which will be the most distal measure of health and functioning. The preparatory phase research also identified six conceptually-related domains (in dashed rectangular boxes), which are not direct indicators of health status but might influence the impact of TNP use on health and functioning. These include attitudinal variables (worry about the health risks of using TNPs and perceived dependence/fear of withdrawal symptoms associated with quitting smoking), and utilitarian ones (perceived appeal, satisfaction, and benefits of TNP use). In addition, personal factors (e.g., sociodemographic) and environmental factors (e.g., peer/family influence, policies and regulations and sociocultural context) are also reflected in the conceptual framework as indirect indicators of health and functioning.

The framework further indicates that specific behavioral indicators (i.e., TNP use patterns over time) might influence any impact of TNP use on health and functioning. Whilst other causal and reciprocal relationships and hierarchies might exist within the domains, these are not explicitly characterized in this initial draft of the framework and will have to be tested with further empirical data. Finally, identified biomarkers of potential harm (in italics and dashed box) are also integrated in this conceptual framework as part of the conceptually-related domains, because they are on a causal pathway between TNP use and changes in health and functioning [ 133 , 134 ]. Biomarkers are not part of the measurement model that will be considered for a new self-report measure; however, because they are the most proximal parameters to health and functioning, they will be assessed independently as appropriate endpoints by objective clinical or biological analyses.

Triangulation of published literature, reanalysis of qualitative data, and expert opinion helped develop the presented preliminary conceptual framework as the foundation for a new measure to assess the impact of TNPs on self-reported health and functioning. This is essential for identifying relevant concepts and understanding what is important to measure in people who use TNPs. The findings reveal the importance of not only the perceived impacts of TNP use on physical health and physical functioning, but also on aspects of mental health and social interactions and functioning, and general perceptions of health and health-related quality of life.

For the literature review, the WHO ICF [ 28 ] and Wilson and Cleary model [ 29 , 30 ] served as useful guides to develop categories for data abstraction. The scoping literature review yielded 97 articles on TNP use and the relationship to health, perceptions of health, social and individual functioning, and quality of life. Overall, most studies had focused on the known negative effects of cigarette smoking (e.g., mental, respiratory, and oral health) and the rationale and motivation to quit smoking. The WHO ICF and Wilson and Clearly models were not always sufficient for identifying the breadth of relevant concepts, especially from the perspective of TNP use. Development of new codes for the reanalysis of existing qualitative data allowed for the development, extension, and exploration of the topic and provided valuable insights reported in the qualitative data reanalysis, such as the perceived benefits/satisfaction from cigarette smoking, and the rationale for quitting smoking or switching to an RRP. The findings show how this manner of secondary analysis can be valuable in health-related fields where the topic is broad and an existing body of knowledge can contribute by offering a different perspective [ 135 ].

The presentation of the preliminary conceptual framework from this preparatory phase is specific to TNP use and marks a slight departure from the established norms and characterization of the variables typically observed in existing generic health and functioning and health-related quality of life models, such as the WHO ICF and Wilson and Clearly models. Notably, specific hypothesized relationships and the hierarchy between domains are not explicitly characterized in the current draft of the framework. The framework provided an exploratory representation of the current findings to reflect a measurement instrument in people who use TNPs that would ideally be able to assess and demonstrate improvements in self-reported health and functioning status, stability of perceived positive aspects of using TNPs, and no worsening in key areas of physical and emotional health and functioning upon switching to RRPs. Nevertheless, the framework could still undergo further refinement to support the development and validation of a new measure and to further characterize and test the relationships and hierarchies between domains.

This work is not without limitations. For the scoping literature review, among the reviewed articles, not many reported on the use of e-cigarettes and other alternative tobacco or nicotine-delivery devices, because most studies had focused exclusively on cigarettes. It is possible that concepts associated with health and functioning that are relevant to other TNPs were not identified. This is most likely the consequence of the large number of publications related to cigarette use. Some concepts might also have been missed, given the large evidence base on health and functioning-related themes and concepts. However, this was also not a systematic literature search; a scoping review is generally broader than a systematic review in terms of the former having a less-defined research question, broader inclusion and exclusion criteria, and no systematic appraisal of study quality [ 26 ]. Nevertheless, the present scoping review methodology provides a lens on the overall evidence base, and regular updates on the search—specifically related to RRPs and novel TNPs and their health and functioning impacts—could be considered for fully understanding the evolving state of the art in this context. The reanalysis of existing qualitative data also has limitations related to data fit and completeness of preexisting data [ 136 ]. The insights collected from these reanalyzed studies were originally for a different purpose several years prior to the present research, and this might not completely and accurately reflect the objectives of the new project.

Considering the findings of the current research, the development of a health and functioning measure can continue to follow the FDA’s Guidance on PRO measures. As specified within the guideline, gaining input directly from the intended use populations through concept elicitation is a critical activity for ensuring content validity during the development of any new self-reported measure [ 137 ]. Continuous engagement with an expert panel can also support the refinement of the conceptual framework as well as the development of the draft and final measure.

The goal of this research was to identify from varied research activities key concepts and aspects of health and functioning and related changes associated with the use of TNPs. The resulting preliminary conceptual framework provides the basis for informing future research to further understand health and functioning concepts important to measure in individual who switch to RRPs and to develop a new self-report measure to assess this from the consumers’ perspective.

Availability of data and materials

All data generated or analyzed during this study are included in this published article and its supplementary information files.


Assessment of Behavioral OUtcomes related to Tobacco and Nicotine Products Toolbox

Chronic obstructive pulmonary disease

Food and Drug Administration

Health-related quality of life

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We thank the team at Sciome LLC for their assistance and contribution to the literature review. We thank Vivienne Law and David Floyd for their contributions to the literature review, reanalysis of qualitative data, and assistance with review of the draft manuscript. We thank Catherine Acquadro for her review of the draft manuscript. We also thank John Ware, Jed Rose, Ashley Slagle, Donald Patrick, Karl Fagerström, Stefan Cano, and Thomas Salzberger for their input and review.

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EA, ES and CC performed conceptualization. EA, ES and LA-W performed methodology. EA, ES, SG, EC and LA-W were involved in the investigation. EA and ES were involved in writing—original draft. EA, EC, LA-W and CC were involved in writing—review & editing. EA performed visualization. ES and CC performed supervision. AB, EC and SG were involved in data curation. AB and EC were involved in project administration. LA-W performed formal analysis. CC was involved in funding acquisition. All authors read and approved the final manuscript.

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Esther F. Afolalu, Emilie Clerc, and Christelle Chrea are employees of Philip Morris International. Agnes Bacso, Erica Spies, and Sophie Gallot completed the work during prior employment with Philip Morris International. Linda Abetz-Webb is a consultant for Philip Morris International.

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Afolalu, E.F., Spies, E., Bacso, A. et al. Impact of tobacco and/or nicotine products on health and functioning: a scoping review and findings from the preparatory phase of the development of a new self-report measure. Harm Reduct J 18 , 79 (2021).

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This is a comprehensive review on the harmful health effects of cigarette smoking. Tobacco smoking is a reprehensible habit that has spread all over the world as an epidemic. It reduces the life expectancy among smokers. It increases overall medical costs and contributes to the loss of productivity during the life span. Smoking has been shown to be linked with various neurological, cardiovascular, and pulmonary diseases. Cigarette smoke not only affects the smokers but also contributes to the health problems of the non-smokers. Exposure to environmental tobacco smoke contributes to health problems in children and is a significant risk factor for asthma. Cigarette smoke contains several carcinogens that alter biochemical defense systems leading to lung cancer.

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Das, S.K. Harmful health effects of cigarette smoking. Mol Cell Biochem 253 , 159–165 (2003).

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Health Effects of Cigarette Smoking

Smoking and death, smoking and increased health risks, smoking and cardiovascular disease, smoking and respiratory disease, smoking and cancer, smoking and other health risks, quitting and reduced risks.

Cigarette smoking harms nearly every organ of the body, causes many diseases, and reduces the health of smokers in general. 1,2

Quitting smoking lowers your risk for smoking-related diseases and can add years to your life. 1,2

Cigarette smoking is the leading cause of preventable death in the United States. 1

  • Cigarette smoking causes more than 480,000 deaths each year in the United States. This is nearly one in five deaths. 1,2,3
  • Human immunodeficiency virus (HIV)
  • Illegal drug use
  • Alcohol use
  • Motor vehicle injuries
  • Firearm-related incidents
  • More than 10 times as many U.S. citizens have died prematurely from cigarette smoking than have died in all the wars fought by the United States. 1
  • Smoking causes about 90% (or 9 out of 10) of all lung cancer deaths. 1,2  More women die from lung cancer each year than from breast cancer. 5
  • Smoking causes about 80% (or 8 out of 10) of all deaths from chronic obstructive pulmonary disease (COPD). 1
  • Cigarette smoking increases risk for death from all causes in men and women. 1
  • The risk of dying from cigarette smoking has increased over the last 50 years in the U.S. 1

Smokers are more likely than nonsmokers to develop heart disease, stroke, and lung cancer. 1

  • For coronary heart disease by 2 to 4 times 1,6
  • For stroke by 2 to 4 times 1
  • Of men developing lung cancer by 25 times 1
  • Of women developing lung cancer by 25.7 times 1
  • Smoking causes diminished overall health, increased absenteeism from work, and increased health care utilization and cost. 1

Smokers are at greater risk for diseases that affect the heart and blood vessels (cardiovascular disease). 1,2

  • Smoking causes stroke and coronary heart disease, which are among the leading causes of death in the United States. 1,3
  • Even people who smoke fewer than five cigarettes a day can have early signs of cardiovascular disease. 1
  • Smoking damages blood vessels and can make them thicken and grow narrower. This makes your heart beat faster and your blood pressure go up. Clots can also form. 1,2
  • A clot blocks the blood flow to part of your brain;
  • A blood vessel in or around your brain bursts. 1,2
  • Blockages caused by smoking can also reduce blood flow to your legs and skin. 1,2

Smoking can cause lung disease by damaging your airways and the small air sacs (alveoli) found in your lungs. 1,2

  • Lung diseases caused by smoking include COPD, which includes emphysema and chronic bronchitis. 1,2
  • Cigarette smoking causes most cases of lung cancer. 1,2
  • If you have asthma, tobacco smoke can trigger an attack or make an attack worse. 1,2
  • Smokers are 12 to 13 times more likely to die from COPD than nonsmokers. 1

Smoking can cause cancer almost anywhere in your body: 1,2

  • Blood (acute myeloid leukemia)
  • Colon and rectum (colorectal)
  • Kidney and ureter
  • Oropharynx (includes parts of the throat, tongue, soft palate, and the tonsils)
  • Trachea, bronchus, and lung

Smoking also increases the risk of dying from cancer and other diseases in cancer patients and survivors. 1

If nobody smoked, one of every three cancer deaths in the United States would not happen. 1,2

Smoking harms nearly every organ of the body and affects a person’s overall health. 1,2

  • Preterm (early) delivery
  • Stillbirth (death of the baby before birth)
  • Low birth weight
  • Sudden infant death syndrome (known as SIDS or crib death)
  • Ectopic pregnancy
  • Orofacial clefts in infants
  • Smoking can also affect men’s sperm, which can reduce fertility and also increase risks for birth defects and miscarriage. 2
  • Women past childbearing years who smoke have weaker bones than women who never smoked. They are also at greater risk for broken bones.
  • Smoking affects the health of your teeth and gums and can cause tooth loss. 1
  • Smoking can increase your risk for cataracts (clouding of the eye’s lens that makes it hard for you to see). It can also cause age-related macular degeneration (AMD). AMD is damage to a small spot near the center of the retina, the part of the eye needed for central vision. 1
  • Smoking is a cause of type 2 diabetes mellitus and can make it harder to control. The risk of developing diabetes is 30–40% higher for active smokers than nonsmokers. 1,2
  • Smoking causes general adverse effects on the body, including inflammation and decreased immune function. 1
  • Smoking is a cause of rheumatoid arthritis. 1
  • Quitting smoking is one of the most important actions people can take to improve their health. This is true regardless of their age or how long they have been smoking. Visit the Benefits of Quitting  page for more information about how quitting smoking can improve your health.
  • U.S. Department of Health and Human Services. The Health Consequences of Smoking—50 Years of Progress: A Report of the Surgeon General . Atlanta: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2014 [accessed 2017 Apr 20].
  • U.S. Department of Health and Human Services. How Tobacco Smoke Causes Disease: What It Means to You . Atlanta: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2010 [accessed 2017 Apr 20].
  • Centers for Disease Control and Prevention. QuickStats: Number of Deaths from 10 Leading Causes—National Vital Statistics System, United States, 2010 . Morbidity and Mortality Weekly Report 2013:62(08);155. [accessed 2017 Apr 20].
  • Mokdad AH, Marks JS, Stroup DF, Gerberding JL. Actual Causes of Death in the United States . JAMA: Journal of the American Medical Association 2004;291(10):1238–45 [cited 2017 Apr 20].
  • U.S. Department of Health and Human Services. Women and Smoking: A Report of the Surgeon General . Rockville (MD): U.S. Department of Health and Human Services, Public Health Service, Office of the Surgeon General, 2001 [accessed 2017 Apr 20].
  • U.S. Department of Health and Human Services. Reducing the Health Consequences of Smoking: 25 Years of Progress. A Report of the Surgeon General . Rockville (MD): U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 1989 [accessed 2017 Apr 20].

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  • Published: 18 May 2021

An updated overview of e-cigarette impact on human health

  • Patrice Marques   ORCID: 1 , 2 ,
  • Laura Piqueras   ORCID: 1 , 2 , 3 &
  • Maria-Jesus Sanz   ORCID: 1 , 2 , 3  

Respiratory Research volume  22 , Article number:  151 ( 2021 ) Cite this article

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The electronic cigarette ( e-cigarette ), for many considered as a safe alternative to conventional cigarettes, has revolutionised the tobacco industry in the last decades. In e-cigarettes , tobacco combustion is replaced by e-liquid heating, leading some manufacturers to propose that e-cigarettes have less harmful respiratory effects than tobacco consumption. Other innovative features such as the adjustment of nicotine content and the choice of pleasant flavours have won over many users. Nevertheless, the safety of e-cigarette consumption and its potential as a smoking cessation method remain controversial due to limited evidence. Moreover, it has been reported that the heating process itself can lead to the formation of new decomposition compounds of questionable toxicity. Numerous in vivo and in vitro studies have been performed to better understand the impact of these new inhalable compounds on human health. Results of toxicological analyses suggest that e-cigarettes can be safer than conventional cigarettes, although harmful effects from short-term e-cigarette use have been described. Worryingly, the potential long-term effects of e-cigarette consumption have been scarcely investigated. In this review, we take stock of the main findings in this field and their consequences for human health including coronavirus disease 2019 (COVID-19).

Electronic nicotine dispensing systems (ENDS), commonly known as electronic cigarettes or e-cigarettes , have been popularly considered a less harmful alternative to conventional cigarette smoking since they first appeared on the market more than a decade ago. E-cigarettes are electronic devices, essentially consisting of a cartridge, filled with an e-liquid, a heating element/atomiser necessary to heat the e-liquid to create a vapour that can be inhaled through a mouthpiece, and a rechargeable battery (Fig.  1 ) [ 1 , 2 ]. Both the electronic devices and the different e-liquids are easily available in shops or online stores.

figure 1

Effect of the heating process on aerosol composition. Main harmful effects documented. Several compounds detected in e-cigarette aerosols are not present in e-liquid s and the device material also seems to contribute to the presence of metal and silicate particles in the aerosols. The heating conditions especially on humectants, flavourings and the low-quality material used have been identified as the generator of the new compounds in aerosols. Some compounds generated from humectants (propylene glycol and glycerol) and flavourings, have been associated with clear airways impact, inflammation, impairment of cardiovascular function and toxicity. In addition, some of them are carcinogens or potential carcinogens

The e-liquid typically contains humectants and flavourings, with or without nicotine; once vapourised by the atomiser, the aerosol (vapour) provides a sensation similar to tobacco smoking, but purportedly without harmful effects [ 3 ]. However, it has been reported that the heating process can lead to the generation of new decomposition compounds that may be hazardous [ 4 , 5 ]. The levels of nicotine, which is the key addictive component of tobacco, can also vary between the commercially available e-liquids, and even nicotine-free options are available. For this particular reason, e-cigarettes are often viewed as a smoking cessation tool, given that those with nicotine can prevent smoking craving, yet this idea has not been fully demonstrated [ 2 , 6 , 7 ].

Because e-cigarettes are combustion-free, and because most of the damaging and well-known effects of tobacco are derived from this reaction, there is a common and widely spread assumption that e-cigarette consumption or “vaping” is safer than conventional cigarette smoking. However, are they risk-free? Is there sufficient toxicological data on all the components employed in e-liquids ? Do we really know the composition of the inhaled vapour during the heating process and its impact on health? Can e-cigarettes be used to curb tobacco use? Do their consumption impact on coronavirus disease 2019 (COVID-19)? In the present review, we have attempted to clarify these questions based on the existing scientific literature, and we have compiled new insights related with the toxicity derived from the use of these devices.

Effect of e-cigarette vapour versus conventional cigarette exposure: in vivo and in vitro effects

Numerous studies have been performed to evaluate the safety/toxicity of e-cigarette use both in vivo and in in vitro cell culture.

One of the first studies in humans involved the analysis of 9 volunteers that consumed e-cigarettes , with or without nicotine, in a ventilated room for 2 h [ 8 ]. Pollutants in indoor air, exhaled nitric oxide (NO) and urinary metabolite profiles were analysed. The results of this acute experiment revealed that e-cigarettes are not emission-free, and ultrafine particles formed from propylene glycol (PG) could be detected in the lungs. The study also suggested that the presence of nicotine in e-cigarettes increased the levels of NO exhaled from consumers and provoked marked airway inflammation; however, no differences were found in the levels of exhaled carbon monoxide (CO), an oxidative stress marker, before and after e-cigarette consumption [ 8 ]. A more recent human study detected significantly higher levels of metabolites of hazardous compounds including benzene, ethylene oxide, acrylonitrile, acrolein and acrylamide in the urine of adolescent dual users ( e-cigarettes and conventional tobacco consumers) than in adolescent e-cigarette -only users (Table 1 ) [ 9 ]. Moreover, the urine levels of metabolites of acrylonitrile, acrolein, propylene oxide, acrylamide and crotonaldehyde, all of which are detrimental for human health, were significantly higher in e-cigarette -only users than in non-smoker controls, reaching up to twice the registered values of those from non-smoker subjects (Table 1 ) [ 9 ]. In line with these observations, dysregulation of lung homeostasis has been documented in non-smokers subjected to acute inhalation of e-cigarette aerosols [ 10 ].

Little is known about the effect of vaping on the immune system. Interestingly, both traditional and e-cigarette consumption by non-smokers was found to provoke short-term effects on platelet function, increasing platelet activation (levels of soluble CD40 ligand and the adhesion molecule P-selectin) and platelet aggregation, although to a lesser extent with e-cigarettes [ 11 ]. As found with platelets, the exposure of neutrophils to e-cigarette aerosol resulted in increased CD11b and CD66b expression being both markers of neutrophil activation [ 12 ]. Additionally, increased oxidative stress, vascular endothelial damage, impaired endothelial function, and changes in vascular tone have all been reported in different human studies on vaping [ 13 , 14 , 15 , 16 , 17 ]. In this context, it is widely accepted that platelet and leukocyte activation as well as endothelial dysfunction are associated with atherogenesis and cardiovascular morbidity [ 18 , 19 ]. In line with these observations the potential association of daily e-cigarettes consumption and the increased risk of myocardial infarction remains controversial but benefits may occur when switching from tobacco to chronic e-cigarette use in blood pressure regulation, endothelial function and vascular stiffness (reviewed in [ 20 ]). Nevertheless, whether or not e-cigarette vaping has cardiovascular consequences requires further investigation.

More recently, in August 2019, the US Centers for Disease Control and Prevention (CDC) declared an outbreak of the e-cigarette or vaping product use-associated lung injury (EVALI) which caused several deaths in young population (reviewed in [ 20 ]). Indeed, computed tomography (CT scan) revealed local inflammation that impaired gas exchange caused by aerosolised oils from e-cigarettes [ 21 ]. However, most of the reported cases of lung injury were associated with use of e-cigarettes for tetrahydrocannabinol (THC) consumption as well as vitamin E additives [ 20 ] and not necessarily attributable to other e-cigarette components.

On the other hand, in a comparative study of mice subjected to either lab air, e-cigarette aerosol or cigarette smoke (CS) for 3 days (6 h-exposure per day), those exposed to e-cigarette aerosols showed significant increases in interleukin (IL)-6 but normal lung parenchyma with no evidence of apoptotic activity or elevations in IL-1β or tumour necrosis factor-α (TNFα) [ 22 ]. By contrast, animals exposed to CS showed lung inflammatory cell infiltration and elevations in inflammatory marker expression such as IL-6, IL-1β and TNFα [ 22 ]. Beyond airway disease, exposure to aerosols from e-liquids with or without nicotine has also been also associated with neurotoxicity in an early-life murine model [ 23 ].

Results from in vitro studies are in general agreement with the limited number of in vivo studies. For example, in an analysis using primary human umbilical vein endothelial cells (HUVEC) exposed to 11 commercially-available vapours, 5 were found to be acutely cytotoxic, and only 3 of those contained nicotine [ 24 ]. In addition, 5 of the 11 vapours tested (including 4 that were cytotoxic) reduced HUVEC proliferation and one of them increased the production of intracellular reactive oxygen species (ROS) [ 24 ]. Three of the most cytotoxic vapours—with effects similar to those of conventional high-nicotine CS extracts—also caused comparable morphological changes [ 24 ]. Endothelial cell migration is an important mechanism of vascular repair than can be disrupted in smokers due to endothelial dysfunction [ 25 , 26 ]. In a comparative study of CS and e-cigarette aerosols, Taylor et al . found that exposure of HUVEC to e-cigarette aqueous extracts for 20 h did not affect migration in a scratch wound assay [ 27 ], whereas equivalent cells exposed to CS extract showed a significant inhibition in migration that was concentration dependent [ 27 ].

In cultured human airway epithelial cells, both e-cigarette aerosol and CS extract induced IL-8/CXCL8 (neutrophil chemoattractant) release [ 28 ]. In contrast, while CS extract reduced epithelial barrier integrity (determined by the translocation of dextran from the apical to the basolateral side of the cell layer), e-cigarette aerosol did not, suggesting that only CS extract negatively affected host defence [ 28 ]. Moreover, Higham et al . also found that e-cigarette aerosol caused IL-8/CXCL8 and matrix metallopeptidase 9 (MMP-9) release together with enhanced activity of elastase from neutrophils [ 12 ] which might facilitate neutrophil migration to the site of inflammation [ 12 ].

In a comparative study, repeated exposure of human gingival fibroblasts to CS condensate or to nicotine-rich or nicotine-free e-vapour condensates led to alterations in morphology, suppression of proliferation and induction of apoptosis, with changes in all three parameters greater in cells exposed to CS condensate [ 29 ]. Likewise, both e-cigarette aerosol and CS extract increased cell death in adenocarcinomic human alveolar basal epithelial cells (A549 cells), and again the effect was more damaging with CS extract than with e-cigarette aerosol (detrimental effects found at 2 mg/mL of CS extract vs. 64 mg/mL of e-cigarette extract) [ 22 ], which is in agreement with another study examining battery output voltage and cytotoxicity [ 30 ].

All this evidence would suggest that e-cigarettes are potentially less harmful than conventional cigarettes (Fig.  2 ) [ 11 , 14 , 22 , 24 , 27 , 28 , 29 ]. Importantly, however, most of these studies have investigated only short-term effects [ 10 , 14 , 15 , 22 , 27 , 28 , 29 , 31 , 32 ], and the long-term effects of e-cigarette consumption on human health are still unclear and require further study.

figure 2

Comparison of the degree of harmful effects documented from e-cigarette and conventional cigarette consumption. Human studies, in vivo mice exposure and in vitro studies. All of these effects from e-cigarettes were documented to be lower than those exerted by conventional cigarettes, which may suggest that e-cigarette consumption could be a safer option than conventional tobacco smoking but not a clear safe choice

Consequences of nicotine content

Beyond flavour, one of the major issues in the e-liquid market is the range of nicotine content available. Depending on the manufacturer, the concentration of this alkaloid can be presented as low , medium or high , or expressed as mg/mL or as a percentage (% v/v). The concentrations range from 0 (0%, nicotine-free option) to 20 mg/mL (2.0%)—the maximum nicotine threshold according to directive 2014/40/EU of the European Parliament and the European Union Council [ 33 , 34 ]. Despite this normative, however, some commercial e-liquids have nicotine concentrations close to 54 mg/mL [ 35 ], much higher than the limits established by the European Union.

The mislabelling of nicotine content in e-liquids has been previously addressed [ 8 , 34 ]. For instance, gas chromatography with a flame ionisation detector (GC-FID) revealed inconsistencies in the nicotine content with respect to the manufacturer´s declaration (average of 22 ± 0.8 mg/mL vs. 18 mg/mL) [ 8 ], which equates to a content ~ 22% higher than that indicated in the product label. Of note, several studies have detected nicotine in those e-liquids labelled as nicotine-free [ 5 , 35 , 36 ]. One study detected the presence of nicotine (0.11–6.90 mg/mL) in 5 of 23 nicotine-free labelled e-liquids by nuclear magnetic resonance spectroscopy [ 35 ], and another study found nicotine (average 8.9 mg/mL) in 13.6% (17/125) of the nicotine-free e-liquids as analysed by high performance liquid chromatography (HPLC) [ 36 ]. Among the 17 samples tested in this latter study 14 were identified to be counterfeit or suspected counterfeit. A third study detected nicotine in 7 of 10 nicotine-free refills, although the concentrations were lower than those identified in the previous analyses (0.1–15 µg/mL) [ 5 ]. Not only is there evidence of mislabelling of nicotine content among refills labelled as nicotine-free, but there also seems to be a history of poor labelling accuracy in nicotine-containing e-liquids [ 37 , 38 ].

A comparison of the serum levels of nicotine from e-cigarette or conventional cigarette consumption has been recently reported [ 39 ]. Participants took one vape from an e-cigarette , with at least 12 mg/mL of nicotine, or inhaled a conventional cigarette, every 20 s for 10 min. Blood samples were collected 1, 2, 4, 6, 8, 10, 12 and 15 min after the first puff, and nicotine serum levels were measured by liquid chromatography-mass spectrometry (LC–MS). The results revealed higher serum levels of nicotine in the conventional CS group than in the e-cigarette group (25.9 ± 16.7 ng/mL vs. 11.5 ± 9.8 ng/mL). However, e-cigarettes containing 20 mg/mL of nicotine are more equivalent to normal cigarettes, based on the delivery of approximately 1 mg of nicotine every 5 min [ 40 ].

In this line, a study compared the acute impact of CS vs. e-cigarette vaping with equivalent nicotine content in healthy smokers and non-smokers. Both increased markers of oxidative stress and decreased NO bioavailability, flow-mediated dilation, and vitamin E levels showing no significant differences between tobacco and e-cigarette exposure (reviewed in [ 20 ]). Inasmuch, short-term e-cigarette use in healthy smokers resulted in marked impairment of endothelial function and an increase in arterial stiffness (reviewed in [ 20 ]). Similar effects on endothelial dysfunction and arterial stiffness were found in animals when they were exposed to e-cigarette vapor either for several days or chronically (reviewed in [ 20 ]). In contrast, other studies found acute microvascular endothelial dysfunction, increased oxidative stress and arterial stiffness in smokers after exposure to e-cigarettes with nicotine, but not after e-cigarettes without nicotine (reviewed in [ 20 ]). In women smokers, a study found a significant difference in stiffness after smoking just one tobacco cigarette, but not after use of e-cigarettes (reviewed in [ 20 ]).

It is well known that nicotine is extremely addictive and has a multitude of harmful effects. Nicotine has significant biologic activity and adversely affects several physiological systems including the cardiovascular, respiratory, immunological and reproductive systems, and can also compromise lung and kidney function [ 41 ]. Recently, a sub-chronic whole-body exposure of e-liquid (2 h/day, 5 days/week, 30 days) containing PG alone or PG with nicotine (25 mg/mL) to wild type (WT) animals or knockout (KO) mice in α7 nicotinic acetylcholine receptor (nAChRα7-KO) revealed a partly nAChRα7-dependent lung inflammation [ 42 ]. While sub-chronic exposure to PG/nicotine promote nAChRα7-dependent increased levels of different cytokines and chemokines in the bronchoalveolar lavage fluid (BALF) such as IL-1α, IL-2, IL-9, interferon γ (IFNγ), granulocyte-macrophage colony-stimulating factor (GM-CSF), monocyte chemoattractant protein-1 (MCP-1/CCL2) and regulated on activation, normal T cell expressed and secreted (RANTES/CCL5), the enhanced levels of IL-1β, IL-5 and TNFα were nAChRα7 independent. In general, most of the cytokines detected in BALF were significantly increased in WT mice exposed to PG with nicotine compared to PG alone or air control [ 42 ]. Some of these effects were found to be through nicotine activation of NF-κB signalling albeit in females but not in males. In addition, PG with nicotine caused increased macrophage and CD4 + /CD8 + T-lymphocytes cell counts in BALF compared to air control, but these effects were ameliorated when animals were sub-chronically exposed to PG alone [ 42 ].

Of note, another study indicated that although RANTES/CCL5 and CCR1 mRNA were upregulated in flavour/nicotine-containing e-cigarette users, vaping flavour and nicotine-less e-cigarettes did not significantly dysregulate cytokine and inflammasome activation [ 43 ].

In addition to its toxicological effects on foetus development, nicotine can disrupt brain development in adolescents and young adults [ 44 , 45 , 46 ]. Several studies have also suggested that nicotine is potentially carcinogenic (reviewed in [ 41 ]), but more work is needed to prove its carcinogenicity independently of the combustion products of tobacco [ 47 ]. In this latter regard, no differences were encountered in the frequency of tumour appearance in rats subjected to long-term (2 years) inhalation of nicotine when compared with control rats [ 48 ]. Despite the lack of carcinogenicity evidence, it has been reported that nicotine promotes tumour cell survival by decreasing apoptosis and increasing proliferation [ 49 ], indicating that it may work as a “tumour enhancer”. In a very recent study, chronic administration of nicotine to mice (1 mg/kg every 3 days for a 60-day period) enhanced brain metastasis by skewing the polarity of M2 microglia, which increases metastatic tumour growth [ 50 ]. Assuming that a conventional cigarette contains 0.172–1.702 mg of nicotine [ 51 ], the daily nicotine dose administered to these animals corresponds to 40–400 cigarettes for a 70 kg-adult, which is a dose of an extremely heavy smoker. We would argue that further studies with chronic administration of low doses of nicotine are required to clearly evaluate its impact on carcinogenicity.

In the aforementioned study exposing human gingival fibroblasts to CS condensate or to nicotine-rich or nicotine-free e-vapour condensates [ 29 ], the detrimental effects were greater in cells exposed to nicotine-rich condensate than to nicotine-free condensate, suggesting that the possible injurious effects of nicotine should be considered when purchasing e-refills . It is also noteworthy that among the 3 most cytotoxic vapours for HUVEC evaluated in the Putzhammer et al . study, 2 were nicotine-free, which suggests that nicotine is not the only hazardous component in e-cigarettes [ 24 ] .

The lethal dose of nicotine for an adult is estimated at 30–60 mg [ 52 ]. Given that nicotine easily diffuses from the dermis to the bloodstream, acute nicotine exposure by e-liquid spilling (5 mL of a 20 mg/mL nicotine-containing refill is equivalent to 100 mg of nicotine) can easily be toxic or even deadly [ 8 ]. Thus, devices with rechargeable refills are another issue of concern with e-cigarettes , especially when e-liquids are not sold in child-safe containers, increasing the risk of spilling, swallowing or breathing.

These data overall indicate that the harmful effects of nicotine should not be underestimated. Despite the established regulations, some inaccuracies in nicotine content labelling remain in different brands of e-liquids . Consequently, stricter regulation and a higher quality control in the e-liquid industry are required.

Effect of humectants and their heating-related products

In this particular aspect, again the composition of the e-liquid varies significantly among different commercial brands [ 4 , 35 ]. The most common and major components of e-liquids are PG or 1,2-propanediol, and glycerol or glycerine (propane-1,2,3-triol). Both types of compounds are used as humectants to prevent the e-liquid from drying out [ 2 , 53 ] and are classified by the Food and Drug Administration (FDA) as “Generally Recognised as Safe” [ 54 ]. In fact, they are widely used as alimentary and pharmaceutical products [ 2 ]. In an analysis of 54 commercially available e-liquids , PG and glycerol were detected in almost all samples at concentrations ranging from 0.4% to 98% (average 57%) and from 0.3% to 95% (average 37%), respectively [ 35 ].

With regards to toxicity, little is known about the effects of humectants when they are heated and chronically inhaled. Studies have indicated that PG can induce respiratory irritation and increase the probability of asthma development [ 55 , 56 ], and both PG and glycerol from e-cigarettes might reach concentrations sufficiently high to potentially cause irritation of the airways [ 57 ]. Indeed, the latter study established that one e-cigarette puff results in a PG exposure of 430–603 mg/m 3 , which is higher than the levels reported to cause airway irritation (average 309 mg/m 3 ) based on a human study [ 55 ]. The same study established that one e-cigarette puff results in a glycerol exposure of 348–495 mg/m 3 [ 57 ], which is close to the levels reported to cause airway irritation in rats (662 mg/m 3 ) [ 58 ].

Airway epithelial injury induced by acute vaping of PG and glycerol aerosols (50:50 vol/vol), with or without nicotine, has been reported in two randomised clinical trials in young tobacco smokers [ 32 ]. In vitro, aerosols from glycerol only-containing refills showed cytotoxicity in A549 and human embryonic stem cells, even at a low battery output voltage [ 59 ]. PG was also found to affect early neurodevelopment in a zebrafish model [ 60 ]. Another important issue is that, under heating conditions PG can produce acetaldehyde or formaldehyde (119.2 or 143.7 ng/puff at 20 W, respectively, on average), while glycerol can also generate acrolein (53.0, 1000.0 or 5.9 ng/puff at 20 W, respectively, on average), all carbonyls with a well-documented toxicity [ 61 ]. Although, assuming 15 puffs per e-cigarette unit, carbonyls produced by PG or glycerol heating would be below the maximum levels found in a conventional cigarette combustion (Table 2 ) [ 51 , 62 ]. Nevertheless, further studies are required to properly test the deleterious effects of all these compounds at physiological doses resembling those to which individuals are chronically exposed.

Although PG and glycerol are the major components of e-liquids other components have been detected. When the aerosols of 4 commercially available e-liquids chosen from a top 10 list of “ Best E-Cigarettes of 2014” , were analysed by gas chromatography-mass spectrometry (GC–MS) after heating, numerous compounds were detected, with nearly half of them not previously identified [ 4 ], thus suggesting that the heating process per se generates new compounds of unknown consequence. Of note, the analysis identified formaldehyde, acetaldehyde and acrolein [ 4 ], 3 carbonyl compounds with known high toxicity [ 63 , 64 , 65 , 66 , 67 ]. While no information was given regarding formaldehyde and acetaldehyde concentrations, the authors calculated that one puff could result in an acrolein exposure of 0.003–0.015 μg/mL [ 4 ]. Assuming 40 mL per puff and 15 puffs per e-cigarette unit (according to several manufacturers) [ 4 ], each e-cigarette unit would generate approximately 1.8–9 μg of acrolein, which is less than the levels of acrolein emitted by a conventional tobacco cigarette (18.3–98.2 μg) [ 51 ]. However, given that e-cigarette units of vaping are not well established, users may puff intermittently throughout the whole day. Thus, assuming 400 to 500 puffs per cartridge, users could be exposed to up to 300 μg of acrolein.

In a similar study, acrolein was found in 11 of 12 aerosols tested, with a similar content range (approximately 0.07–4.19 μg per e-cigarette unit) [ 68 ]. In the same study, both formaldehyde and acetaldehyde were detected in all of the aerosols tested, with contents of 0.2–5.61 μg and 0.11–1.36 μg, respectively, per e-cigarette unit [ 68 ]. It is important to point out that the levels of these toxic products in e-cigarette aerosols are significantly lower than those found in CS: 9 times lower for formaldehyde, 450 times lower for acetaldehyde and 15 times lower for acrolein (Table 2 ) [ 62 , 68 ].

Other compounds that have been detected in aerosols include acetamide, a potential human carcinogen [ 5 ], and some aldehydes [ 69 ], although their levels were minimal. Interestingly, the existence of harmful concentrations of diethylene glycol, a known cytotoxic agent, in e-liquid aerosols is contentious with some studies detecting its presence [ 4 , 68 , 70 , 71 , 72 ], and others finding low subtoxic concentrations [ 73 , 74 ]. Similar observations were reported for the content ethylene glycol. In this regard, either it was detected at concentrations that did not exceed the authorised limit [ 73 ], or it was absent from the aerosols produced [ 4 , 71 , 72 ]. Only one study revealed its presence at high concentration in a very low number of samples [ 5 ]. Nevertheless, its presence above 1 mg/g is not allowed by the FDA [ 73 ]. Figure  1 lists the main compounds detected in aerosols derived from humectant heating and their potential damaging effects. It would seem that future studies should analyse the possible toxic effects of humectants and related products at concentrations similar to those that e-cigarette vapers are exposed to reach conclusive results.

Impact of flavouring compounds

The range of e-liquid flavours available to consumers is extensive and is used to attract both current smokers and new e-cigarette users, which is a growing public health concern [ 6 ]. In fact, over 5 million middle- and high-school students were current users of e-cigarettes in 2019 [ 75 ], and appealing flavours have been identified as the primary reason for e-cigarette consumption in 81% of young users [ 76 ]. Since 2016, the FDA regulates the flavours used in the e-cigarette market and has recently published an enforcement policy on unauthorised flavours, including fruit and mint flavours, which are more appealing to young users [ 77 ]. However, the long-term effects of all flavour chemicals used by this industry (which are more than 15,000) remain unknown and they are not usually included in the product label [ 78 ]. Furthermore, there is no safety guarantee since they may harbour potential toxic or irritating properties [ 5 ].

With regards to the multitude of available flavours, some have demonstrated cytotoxicity [ 59 , 79 ]. Bahl et al. evaluated the toxicity of 36 different e-liquids and 29 different flavours on human embryonic stem cells, mouse neural stem cells and human pulmonary fibroblasts using a metabolic activity assay. In general, those e-liquids that were bubblegum-, butterscotch- and caramel-flavoured did not show any overt cytotoxicity even at the highest dose tested. By contrast, those e-liquids with Freedom Smoke Menthol Arctic and Global Smoke Caramel flavours had marked cytotoxic effects on pulmonary fibroblasts and those with Cinnamon Ceylon flavour were the most cytotoxic in all cell lines [ 79 ]. A further study from the same group [ 80 ] revealed that high cytotoxicity is a recurrent feature of cinnamon-flavoured e-liquids. In this line, results from GC–MS and HPLC analyses indicated that cinnamaldehyde (CAD) and 2-methoxycinnamaldehyde, but not dipropylene glycol or vanillin, were mainly responsible for the high cytotoxicity of cinnamon-flavoured e-liquids [ 80 ]. Other flavouring-related compounds that are associated with respiratory complications [ 81 , 82 , 83 ], such as diacetyl, 2,3-pentanedione or acetoin, were found in 47 out of 51 aerosols of flavoured e-liquids tested [ 84 ] . Allen et al . calculated an average of 239 μg of diacetyl per cartridge [ 84 ]. Assuming again 400 puffs per cartridge and 40 mL per puff, is it is possible to estimate an average of 0.015 ppm of diacetyl per puff, which could compromise normal lung function in the long-term [ 85 ].

The cytotoxic and pro-inflammatory effects of different e-cigarette flavouring chemicals were also tested on two human monocytic cell lines—mono mac 6 (MM6) and U937 [ 86 ]. Among the flavouring chemicals tested, CAD was found to be the most toxic and O-vanillin and pentanedione also showed significant cytotoxicity; by contrast, acetoin, diacetyl, maltol, and coumarin did not show any toxicity at the concentrations assayed (10–1000 µM). Of interest, a higher toxicity was evident when combinations of different flavours or mixed equal proportions of e-liquids from 10 differently flavoured e-liquids were tested, suggesting that vaping a single flavour is less toxic than inhaling mixed flavours [ 86 ]. Also, all the tested flavours produced significant levels of ROS in a cell-free ROS production assay. Finally, diacetyl, pentanedione, O-vanillin, maltol, coumarin, and CAD induced significant IL-8 secretion from MM6 and U937 monocytes [ 86 ]. It should be borne in mind, however, that the concentrations assayed were in the supra-physiological range and it is likely that, once inhaled, these concentrations are not reached in the airway space. Indeed, one of the limitations of the study was that human cells are not exposed to e-liquids per se, but rather to the aerosols where the concentrations are lower [ 86 ]. In this line, the maximum concentration tested (1000 µM) would correspond to approximately 80 to 150 ppm, which is far higher than the levels found in aerosols of some of these compounds [ 84 ]. Moreover, on a day-to-day basis, lungs of e-cigarette users are not constantly exposed to these chemicals for 24 h at these concentrations. Similar limitations were found when five of seven flavourings were found to cause cytotoxicity in human bronchial epithelial cells [ 87 ].

Recently, a commonly commercialized crème brûlée -flavoured aerosol was found to contain high concentrations of benzoic acid (86.9 μg/puff), a well-established respiratory irritant [ 88 ]. When human lung epithelial cells (BEAS-2B and H292) were exposed to this aerosol for 1 h, a marked cytotoxicity was observed in BEAS-2B but not in H292 cells, 24 h later. However, increased ROS production was registered in H292 cells [ 88 ].

Therefore, to fully understand the effects of these compounds, it is relevant the cell cultures selected for performing these assays, as well as the use of in vivo models that mimic the real-life situation of chronic e-cigarette vapers to clarify their impact on human health.

The e-cigarette device

While the bulk of studies related to the impact of e-cigarette use on human health has focused on the e-liquid components and the resulting aerosols produced after heating, a few studies have addressed the material of the electronic device and its potential consequences—specifically, the potential presence of metals such as copper, nickel or silver particles in e-liquids and aerosols originating from the filaments and wires and the atomiser [ 89 , 90 , 91 ].

Other important components in the aerosols include silicate particles from the fiberglass wicks or silicone [ 89 , 90 , 91 ]. Many of these products are known to cause abnormalities in respiratory function and respiratory diseases [ 89 , 90 , 91 ], but more in-depth studies are required. Interestingly, the battery output voltage also seems to have an impact on the cytotoxicity of the aerosol vapours, with e-liquids from a higher battery output voltage showing more toxicity to A549 cells [ 30 ].

A recent study compared the acute effects of e-cigarette vapor (with PG/vegetable glycerine plus tobacco flavouring but without nicotine) generated from stainless‐steel atomizer (SS) heating element or from a nickel‐chromium alloy (NC) [ 92 ]. Some rats received a single e-cigarette exposure for 2 h from a NC heating element (60 or 70 W); other rats received a similar exposure of e-cigarette vapor using a SS heating element for the same period of time (60 or 70 W) and, a final group of animals were exposed for 2 h to air. Neither the air‐exposed rats nor those exposed to e-cigarette vapor using SS heating elements developed respiratory distress. In contrast, 80% of the rats exposed to e-cigarette vapor using NC heating units developed clinical acute respiratory distress when a 70‐W power setting was employed. Thus, suggesting that operating units at higher than recommended settings can cause adverse effects. Nevertheless, there is no doubt that the deleterious effects of battery output voltage are not comparable to those exerted by CS extracts [ 30 ] (Figs.  1 and 2 ).

E-cigarettes as a smoking cessation tool

CS contains a large number of substances—about 7000 different constituents in total, with sizes ranging from atoms to particulate matter, and with many hundreds likely responsible for the harmful effects of this habit [ 93 ]. Given that tobacco is being substituted in great part by e-cigarettes with different chemical compositions, manufacturers claim that e -cigarette will not cause lung diseases such as lung cancer, chronic obstructive pulmonary disease, or cardiovascular disorders often associated with conventional cigarette consumption [ 3 , 94 ]. However, the World Health Organisation suggests that e-cigarettes cannot be considered as a viable method to quit smoking, due to a lack of evidence [ 7 , 95 ]. Indeed, the results of studies addressing the use of e-cigarettes as a smoking cessation tool remain controversial [ 96 , 97 , 98 , 99 , 100 ]. Moreover, both FDA and CDC are actively investigating the incidence of severe respiratory symptoms associated with the use of vaping products [ 77 ]. Because many e-liquids contain nicotine, which is well known for its powerful addictive properties [ 41 ], e-cigarette users can easily switch to conventional cigarette smoking, avoiding smoking cessation. Nevertheless, the possibility of vaping nicotine-free e-cigarettes has led to the branding of these devices as smoking cessation tools [ 2 , 6 , 7 ].

In a recently published randomised trial of 886 subjects who were willing to quit smoking [ 100 ], the abstinence rate was found to be twice as high in the e-cigarette group than in the nicotine-replacement group (18.0% vs. 9.9%) after 1 year. Of note, the abstinence rate found in the nicotine-replacement group was lower than what is usually expected with this therapy. Nevertheless, the incidence of throat and mouth irritation was higher in the e-cigarette group than in the nicotine-replacement group (65.3% vs. 51.2%, respectively). Also, the participant adherence to the treatment after 1-year abstinence was significantly higher in the e-cigarette group (80%) than in nicotine-replacement products group (9%) [ 100 ].

On the other hand, it is estimated that COPD could become the third leading cause of death in 2030 [ 101 ]. Given that COPD is generally associated with smoking habits (approximately 15 to 20% of smokers develop COPD) [ 101 ], smoking cessation is imperative among COPD smokers. Published data revealed a clear reduction of conventional cigarette consumption in COPD smokers that switched to e-cigarettes [ 101 ]. Indeed, a significant reduction in exacerbations was observed and, consequently, the ability to perform physical activities was improved when data was compared with those non-vapers COPD smokers. Nevertheless, a longer follow-up of these COPD patients is required to find out whether they have quitted conventional smoking or even vaping, since the final goal under these circumstances is to quit both habits.

Based on the current literature, it seems that several factors have led to the success of e-cigarette use as a smoking cessation tool. First, some e-cigarette flavours positively affect smoking cessation outcomes among smokers [ 102 ]. Second, e-cigarettes have been described to improve smoking cessation rate only among highly-dependent smokers and not among conventional smokers, suggesting that the individual degree of nicotine dependence plays an important role in this process [ 97 ]. Third, the general belief of their relative harmfulness to consumers' health compared with conventional combustible tobacco [ 103 ]. And finally, the exposure to point-of-sale marketing of e-cigarette has also been identified to affect the smoking cessation success [ 96 ].

Implication of e-cigarette consumption in COVID-19 time

Different reports have pointed out that smokers and vapers are more vulnerable to SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) infections or more prone to adverse outcomes if they suffer COVID-19 [ 104 ]. However, while a systematic review indicated that cigarette smoking is probably associated with enhanced damage from COVID-19, a meta-analysis did not, yet the latter had several limitations due to the small sample sizes [ 105 ].

Interestingly, most of these reports linking COVID-19 harmful effects with smoking or vaping, are based on their capability of increasing the expression of angiotensin-converting enzyme 2 (ACE2) in the lung. It is well known that ACE2 is the gate for SARS-CoV-2 entrance to the airways [ 106 ] and it is mainly expressed in type 2 alveolar epithelial cells and alveolar macrophages [ 107 ]. To date, most of the studies in this field indicate that current smokers have higher expression of ACE2 in the airways (reviewed by [ 108 ]) than healthy non-smokers [ 109 , 110 ]. However, while a recent report indicated that e-cigarette vaping also caused nicotine-dependent ACE2 up-regulation [ 42 ], others have revealed that neither acute inhalation of e-cigarette vapour nor e-cigarette users had increased lung ACE2 expression regardless nicotine presence in the e-liquid [ 43 , 110 ].

In regard to these contentions, current knowledge suggests that increased ACE2 expression is not necessarily linked to enhanced susceptibility to SARS-CoV-2 infection and adverse outcome. Indeed, elderly population express lower levels of ACE2 than young people and SARS-CoV-2/ACE2 interaction further decreases ACE2 expression. In fact, most of the deaths provoked by COVID-19 took place in people over 60 years old of age [ 111 ]. Therefore, it is plausible that the increased susceptibility to disease progression and the subsequent fatal outcome in this population is related to poor angiotensin 1-7 (Ang-1-7) generation, the main peptide generated by ACE2, and probably to their inaccessibility to its anti-inflammatory effects. Furthermore, it seems that all the efforts towards increasing ACE2 expression may result in a better resolution of the pneumonic process associated to this pandemic disease.

Nevertheless, additional complications associated to COVID-19 are increased thrombotic events and cytokine storm. In the lungs, e-cigarette consumption has been correlated to toxicity, oxidative stress, and inflammatory response [ 32 , 112 ]. More recently, a study revealed that while the use of nicotine/flavour-containing e-cigarettes led to significant cytokine dysregulation and potential inflammasome activation, none of these effects were detected in non-flavoured and non-nicotine-containing e-cigarettes [ 43 ]. Therefore, taken together these observations, e-cigarette use may still be a potent risk factor for severe COVID-19 development depending on the flavour and nicotine content.

In summary, it seems that either smoking or nicotine vaping may adversely impact on COVID-19 outcome. However, additional follow up studies are required in COVID-19 pandemic to clarify the effect of e-cigarette use on lung and cardiovascular complications derived from SARS-CoV-2 infection.


The harmful effects of CS and their deleterious consequences are both well recognised and widely investigated. However, and based on the studies carried out so far, it seems that e-cigarette consumption is less toxic than tobacco smoking. This does not necessarily mean, however, that e-cigarettes are free from hazardous effects. Indeed, studies investigating their long-term effects on human health are urgently required. In this regard, the main additional studies needed in this field are summarized in Table 3 .

The composition of e-liquids requires stricter regulation, as they can be easily bought online and many incidences of mislabelling have been detected, which can seriously affect consumers’ health. Beyond their unknown long-term effects on human health, the extended list of appealing flavours available seems to attract new “never-smokers”, which is especially worrying among young users. Additionally, there is still a lack of evidence of e-cigarette consumption as a smoking cessation method. Indeed, e-cigarettes containing nicotine may relieve the craving for smoking, but not the conventional cigarette smoking habit.

Interestingly, there is a strong difference of opinion on e-cigarettes between countries. Whereas countries such as Brazil, Uruguay and India have banned the sale of e-cigarettes , others such as the United Kingdom support this device to quit smoking. The increasing number of adolescent users and reported deaths in the United States prompted the government to ban the sale of flavoured e-cigarettes in 2020. The difference in opinion worldwide may be due to different restrictions imposed. For example, while no more than 20 ng/mL of nicotine is allowed in the EU, e-liquids with 59 mg/dL are currently available in the United States. Nevertheless, despite the national restrictions, users can easily access foreign or even counterfeit products online.

In regard to COVID-19 pandemic, the actual literature suggests that nicotine vaping may display adverse outcomes. Therefore, follow up studies are necessary to clarify the impact of e-cigarette consumption on human health in SARS-CoV-2 infection.

In conclusion, e-cigarettes could be a good alternative to conventional tobacco cigarettes, with less side effects; however, a stricter sale control, a proper regulation of the industry including flavour restriction, as well as further toxicological studies, including their chronic effects, are warranted.

Availability of data and materials

Not applicable.


Angiotensin-converting enzyme 2

Angiotensin 1-7

Bronchoalveolar lavage fluid


US Centers for Disease Control and Prevention

Carbon monoxide

Chronic obstructive pulmonary disease

Coronavirus disease 2019

Cigarette smoke

Electronic nicotine dispensing systems

e-cigarette or vaping product use-associated lung injury

Food and Drug Administration

Gas chromatography with a flame ionisation detector

Gas chromatography-mass spectrometry

Granulocyte–macrophage colony-stimulating factor

High performance liquid chromatography

Human umbilical vein endothelial cells


Interferon γ

Liquid chromatography-mass spectrometry

Monocyte chemoattractant protein-1

Matrix metallopeptidase 9

α7 Nicotinic acetylcholine receptor

Nickel‐chromium alloy

Nitric oxide

Propylene glycol

Regulated on activation, normal T cell expressed and secreted

Reactive oxygen species

Severe acute respiratory syndrome coronavirus 2

Stainless‐steel atomizer


Tumour necrosis factor-α

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The authors gratefully acknowledge Dr. Cruz González, Pulmonologist at University Clinic Hospital of Valencia (Valencia, Spain) for her thoughtful suggestions and support.

This work was supported by the Spanish Ministry of Science and Innovation [Grant Number SAF2017-89714-R]; Carlos III Health Institute [Grant Numbers PIE15/00013, PI18/00209]; Generalitat Valenciana [Grant Number PROMETEO/2019/032, Gent T CDEI-04/20-A and AICO/2019/250], and the European Regional Development Fund.

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Marques, P., Piqueras, L. & Sanz, MJ. An updated overview of e-cigarette impact on human health. Respir Res 22 , 151 (2021).

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harmful effects of smoking research paper

Smoking and Its Negative Effects on Human Beings Research Paper

Smoking is one of the most common negative habits that people indulge in. Many health experts have warned that smoking is unhealthy and dangerous to the human health. This essay will discuss the negative effects of smoking on human beings.

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Smoking cigarette is addictive that is why many smokers have difficulties in giving up the habit. Cigarettes are produced of tobacco with a large percent of other additives, which account for the largest number of preventable deaths in the world. People who smoke commonly face different health problems, which are caused by tobacco consumption. Therefore, smoking has negative health consequences for smokers and people who live with them and become passive smokers as a result.

The WHO and other health organisations have sensitised people on the dangers of smoking. There are many health conditions which smokers are likely to suffer from (Pampel 61). Their bodies absorb harmful toxins which cigarettes contain which are dangerous to their health.

Smoking is a major health risk which results in heart attacks, strokes, bronchitis, and other respiratory diseases. The accumulation of tobacco and other toxins in the respiratory tract of a smoker makes a person suffer from respiratory health conditions.

Smokers, therefore, are likely to incur huge medical bills when they seek for treatment for these diseases. Many governments spend a lot of money on treating smoking related diseases, which increases the cost of healthcare. Pampel argues that smokers can succumb to such illnesses unless they stop smoking (64).

Tobacco consumption causes dental problems which are difficult to reverse. Smokers are likely to have bad breath, stained teeth and smelly gums. Toxic elements, which cigarettes contain, for instance, tar, have dangerous impacts on human health. These substances cause smokers to have poor dents and even lose their teeth (Peate 362).

Smokers are likely to suffer emotionally and psychologically because poor health and unattractive appearance, caused, for example, by stained or broken teeth, make a person lose his/her own self-esteem. Smokers are likely to be shunned by people close to them because of fetid breath, bad body odour and poor outward appearance. Therefore, people need to be made aware of dental and other health problems they are likely to experience as a result of smoking.

Tobacco consumption causes a lot of deaths in developing countries. These countries have weak laws which do not effectively regulate cigarette selling and consumption. Advertisement implicit messages encourage the young to become smokers. Tobacco advertising in many developed countries has been prohibited. However, some third world countries still allow tobacco advertising, which encourages more people to acquire this bad habit.

The images of sophistication, bravery and glamour which are carried by tobacco adverts easily persuade the young to become smokers. Peate reveals that tobacco companies target adolescents and women to increase their sales (363). These people are easily influenced by what they see in the media. People who begin smoking at early age are likely to be addicted for a longer period than those who develop the habit at mature age (Cox).

Smokers are exposed to various carcinogens in cigarettes. These carcinogens cause cancer and negatively affect human health. Lung, throat, brain, bladder, cervical cancer as well as other forms are caused by smoking. The symptoms are often detected at the time when the smoker’s health condition is already chronic.

Cancer is one of the leading causes of death world wide. A significant number of cancer patients have a history of smoking and tobacco consumption (Peate 365). If people get exposed to exhaled smoke, they are likely to be affected by it. They breathe in toxic components of the exhaled smoke that deposit in their lungs and other respiratory organs. These people can suffer from respiratory illnesses as well.

Women, who smoke during pregnancy, are likely to expose their unborn babies to toxic substances contained in cigarettes. The tar that is present in cigarettes is likely to be embedded in the DNA of a mother, who may pass it on to the child in her womb. These toxic components inhibit the normal growth of a baby in the fetus, which results in death and still births. Cox reveals that if the pregnancy proceeds to full term, the delivered child can have severe brain disorders.

Such children are very slow at learning because their cognitive functions are impaired. Female smokers are likely to become infertile or their reproductive abilities are limited. Nicotine restricts the ability of the female reproductive system to generate estrogen. Many physiological and reproductive functions in women depend on estrogen.

Nicotine is a substance found in cigarettes which is very addictive. People who try to give up smoking experience severe withdrawal symptoms, which restrict their ability to function effectively. They are likely to experience several episodes of depression.

This is because their bodies are used to the intake of nicotine and have difficulties in performing its functions without it (Cox). Nicotine stimulates the human mind just like any other drug, which increases the risk of high blood pressure in a smoker. From the above mentioned, it is easy to conclude that smoking has negative effects on people’s health.

Works Cited

Cox, Jack. “The Lesser Known Harmful Effects of Smoking.” The Register , 21 Nov. 2012. Orange Country Register News . Web. < >.

Pampel, Fred C. Tobacco Industry and Smoking . New York: Infobase Publishing, 2009. Print.

Peate, Ian. “The Effects of Smoking on the Reproductive Health of Men”. British Journal of Nursing 14.7 (2005): 362–366. Print.

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  • Volume 11, Issue 6
  • Awareness regarding the adverse effect of tobacco among adults in India: findings from secondary data analysis of Global Adult Tobacco Survey
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  • Ankita Kankaria ,
  • Soumya Swaroop Sahoo ,
  • Madhur Verma
  • Department of Community & Family Medicine , All India Institute of Medical Sciences Bathinda , Bathinda , Punjab , India
  • Correspondence to Dr Madhur Verma; drmadhurverma{at}

Objective To quantify the extent of awareness regarding the harmful effects of tobacco among the users (both smoked and smokeless) and non-users in India, and explore the determinants of comprehensive knowledge among the participants of the Global Adult Tobacco Survey (GATS), India.

Design Cross-sectional study.

Setting and participants The nationally representative GATS I (2009–2010) included 69 296 participants using a multistage sampling method, while GATS II (2015–2016) interviewed 74 037 respondents aged >15 years using a similar sampling method from all the states and union territories in India.

Primary and secondary outcome measures Comprehensive score were derived from nine items that explored awareness regarding the adverse effects of tobacco use among both users and non-users of tobacco in GATS II. Secondary outcome included predictors of awareness regarding adverse effects of tobacco and changes in the awareness compared with the previous round of the survey.

Results About 60.2%, 57.5% and 66.5% of the smokers, smokeless tobacco (SLT) users and non-users were aware of the adverse effects of tobacco, respectively. The awareness depicted significant age, gender, marital status, education status, urban–rural, wealth and regional disparities (p<0.05). Intention to quit tobacco use also varied significantly with awareness. Among smokers, awareness was high in those residing in eastern India and the poorest participants. Among SLT users, awareness was more among male participants, those who were poorest and lived in western India. Among non-users, awareness was more among middle-aged, more educated, rich participants of west India. Compared with GATS I, an increase in awareness was observed in GATS II across gender, age groups, residential areas and geographical regions in India.

Conclusions Comprehensive awareness of tobacco’s harmful effects is far from desirable among Indian users. We recommend further customised health promotion campaigns to counter the regional disparities, adopt a gender-neutral approach and target adolescents.

  • health policy
  • epidemiology
  • public health
  • substance misuse

Data availability statement

Data are available in a public, open access repository. Data are available in the public domain from the Global Tobacco Surveillance System Data (GTSS Data) maintained by the Centers for Disease Control and Prevention and freely available to all researchers.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: .

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Strengths and limitations of this study

One of the very first comprehensive assessments of the awareness regarding the adverse effects of tobacco from the second round of the Global Adult Tobacco Survey India (2016–2017).

We estimated the predictors of adequate awareness through a weighted analysis that highlighted feasible, actionable points.

We have analysed large and nationally representative data on tobacco use from India.

Appropriate sampling during the survey makes the results generalisable, and other lower middle-income countries can adopt recommendations.

Lack of a uniform tool to assess awareness across two rounds of the survey was the critical limitation of the study that can lead to underestimation of the changes in the understanding across two rounds of the survey.


Tobacco is the single most preventable cause of premature death and risk factor for major non-communicable diseases. 1 Tobacco usage causes more than 8 million deaths per year, with 7 million of these attributable to direct tobacco use. Moreover, the fact that 80% of the world’s 1.3 billion tobacco users live in low/middle-income countries (LMICs) is a matter of concern. 2

There has been a sustained increase in tobacco consumption in LMICs, which are on the target of tobacco manufacturers for newer markets. 3 4 The tobacco epidemic is steadily on the rise, affecting LMICs due to a lack of awareness in the population, insufficient health infrastructure and weak regulatory interventions. 5 India is home to 275 million tobacco users and is second only to China in tobacco products. 6 In India, tobacco is responsible for one-tenth (1 million) of all the deaths each year and a significant burden of cancer cases (45% of male’s cancer and 20% of female’s cancer). 7 8 India displays a diverse pattern of tobacco consumption, in smoked form as cigarettes and bidis and smokeless forms like khaini, pan and gutkha. In India, almost 50% of the users use smokeless tobacco (SLT), followed by smoking and dual-use. 9 SLT use, constituting tobacco products consumed without burning through the mouth or nose, is widely prevalent in India, accounting for 74% of the global burden of SLT. 10

Despite sustained efforts by the government, tobacco consumption is still a growing public health concern. Tobacco is not merely a sociocultural problem but multifaceted with economic, biomedical and geopolitical aspects in many parts of India. Also, a matter of concern is the increasing use of tobacco products in adolescents, young adults and women, particularly more active age groups. In this context, tobacco control policies need to be implemented with interventions adapted to the local environment. The WHO also recommends surveillance, research and an informal approach that promotes the exchange of information and knowledge to increase awareness within the broad framework for addressing tobacco dependence. 11 According to the Global Adult Tobacco Survey (GATS) 2016–2017, in India, 92% of adults believe that smoking causes severe illnesses, and 96% believe that SLT can cause serious illness. 12 The government of India launched the National Tobacco Control Program in 2007–2008. One of the programme’s primary objectives was public awareness/mass media campaigns for awareness building and behaviour change. Despite sustained efforts, awareness has not been on expected trends. Furthermore, studies from other LMICs have demonstrated that an increase in levels of awareness has not continually transformed into desirable quit rates. This disconnect between awareness and quit rates would provide new targets for devising more focused public health education campaigns. 13 14 In India, with varied demographics of tobacco use, monitoring, raising awareness, and realising tobacco control policy achievements are instrumental in halting the tobacco epidemic.

Since enforcing legislation alone cannot bring the desired changes, we need to see the population response and behaviour change towards this public health problem. The legislations need to be supplemented by adequate awareness, health education and communication at the population level. Awareness forms an integral part of health literacy and the first step for behaviour modification. It provides a dual benefit of motivating users to contemplate and quit smoking and dissuading non-smokers from adopting this habit. 15 16 Dissemination of facts regarding the harmful effects of tobacco has been recognised as an essential tool in this context. Also, it encourages people to adhere to tobacco legislation, smoking bans and understand the perceived threats of first and secondhand smoke on their lives, family and the community. Studies in LMICs using the GATS framework have outlined that lower levels of education, rural population and current smokers were likely to be less aware of the harmful effects of tobacco and secondhand smoke. 17 Similar findings have been documented in population-based surveys in Vietnam and Mongolia. 18 19 In India, existing literature suggests that gender, age, ethnicity, education, income and smoking status are associated with awareness of the harmful effects of smoking. 20–22 As awareness contributes to a more considerable extent to behaviour modification and tobacco cessation practices, it needs to be studied in entirety concerning socioeconomic and regional distributions to get a clear view on this aspect. Within this context, we examined the awareness regarding the harmful effects of tobacco among the users (both smoked and smokeless) and non-users based on the secondary data analysis of GATS 2016–2017 and compared it with GATS 2009–2010. Our primary objective was to study the awareness regarding harmful effects of tobacco among the users (smokers and SLT users) and non-users. The secondary objectives were to examine the factors affecting the awareness among these groups and compare the quantum of change in awareness regarding harmful effects of tobacco between the two rounds of GATS India (GATS I and GATS II) among users and non-users of tobacco.


In this study, we used nationally representative data of GATS-I (2009–2010) and GATS-II (2016–2017) in India. 12 23 GATS is a cross-sectional household-based survey conducted among a population aged 15 years and above, using a global standardised methodology to collect tobacco-related information. 12 23 The survey gathers information regarding the respondents’ background characteristics, tobacco use (smoking and smokeless) patterns, cessation, secondhand smoke exposure, economics, media, knowledge, attitudes and perceptions of tobacco use.

We have used GATS-I figures as the baseline to quantify the change in the awareness regarding harmful effects in the recent GATS-II survey data. The GATS-II survey included 74 037 participants, and the response rate was 99.9%. The dependent variable was awareness regarding the harmful effects of smoking and SLT. The GATS-I survey included 69 296 participants, and the response rate was 96.8%.

Operational definitions

We defined our variables as per the publicly available codebooks for GATS I and II. 24 25

Tobacco smokers: The information regarding tobacco users was obtained from the following questions ‘Do you currently smoke tobacco?’ Those who smoked ‘Daily’ or ‘Less than daily’ were considered tobacco smokers.

SLT users: The information regarding SLT users was obtained from the following questions ‘Do you currently use smokeless tobacco?’. Those who smoked Daily or Less than daily were considered tobacco smokers.

Non-users: Those who said not all for both questions mentioned above were considered as non-users.

Awareness regarding the harmful effect of tobacco: For GATS 2017, this information was obtained from the following questions:

‘Based on what you know or believe, does smoking tobacco cause serious illness?’

‘Based on what you know or believe, does smoking tobacco cause stroke?’

‘Based on what you know or believe, does smoking tobacco cause heart attack?’

‘Based on what you know or believe, does smoking tobacco cause lung cancer?’

‘Based on what you know or believe, does smoking tobacco cause chronic cough?’

‘Based on what you know or believe, using smokeless tobacco cause serious illness?’

‘Based on what you know or believe, use of smokeless tobacco cause oral cancer?’

‘Based on what you know or believe, use of smokeless tobacco cause dental disease?’

‘Based on what you know or believe, does using smokeless tobacco during pregnancy cause harm to a fetus?’

In comparison to GATS 2017, only five questions (out of nine items mentioned above) on awareness regarding the harmful effect of tobacco were included in the GATS 2010. For comparison between GATS I and GATS II, the following five common variables were included in the analysis:

‘Based on what you know or believe, does smoking tobacco cause serious illness?’,

‘Based on what you know or believe, does smoking tobacco cause stroke?’,

‘Based on what you know or believe, does smoking tobacco cause heart attack?’,

‘Based on what you know or believe, using smokeless tobacco cause serious illness?’ Those who responded ‘Yes’ to all the questions were considered as aware. Responses other than Yes were viewed as being unaware and included ‘No’ and ‘Don’t Know’.

Ethical consideration

This being a secondary data analysis, we applied for an expedited waiver from the institutional ethics board. The manuscript was prepared following the Strengthening the Reporting of Observational Studies in Epidemiology guidelines ( online supplemental file 1 ).

Supplemental material

Data analysis.

All the estimates in this article are based on the weighted sample, and the numbers are unweighted. Complex sample analysis of the data was carried out using SPSS for Windows V.17.0, Released 2008 (SPSS) after taking stratification, clustering and sampling weights into account. The background characteristics of the study participants were presented as point estimates (%) with 95% CI. The awareness of tobacco’s harmful effects was assessed among smokers, SLT users and non-users. We performed bivariate analysis to determine the statistical significance across selected study variables. Logistic regression analysis was done to identify the predictors of awareness regarding the harmful effects of tobacco use among the respondents. All the significant independent variables on bivariate logistic regression (unadjusted OR: p<0.2) were used to build the final multiple logistic regression model to highlight the predictor variables (adjusted OR: p<0.05) that were associated with awareness. The independent variables were age, gender, marital status, residence, education, region and wealth index. For calculating the wealth index, a score between 0 and 10 was calculated from 10 household assets. These scores were divided into five parts based on their distribution, and households were categorised. The relative proportional changes in the awareness between the two rounds of GATS India were calculated ( Relative Change = (GATS II – GATS I)/GATS I * 100% ) to depict the trends among the tobacco users as compared with the non-users. For calculating the difference, we used only the common questions (five items) to both the rounds.

Background characteristics of the study participants

In GATS-I, there were 69 296 participants, and among them, 14% (11 596) were smokers, 25.9% (16 812) were SLT users, and 64.9% (44 967) were non-tobacco users. In GATS 2017, of the 74 037 participants, 10.7% (9499) were smokers, 24.1% (15 235) were SLT users and 78.6% (52 180) were non-users of tobacco. The background characteristics of tobacco users and non-users from GATS-I and II are presented in table 1 .

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Characteristics of tobacco users (smoked and smokeless) and non-users who participated in the two rounds of the Global Adults Tobacco Survey (GATS) India.

Awareness regarding the adverse effects of tobacco among the participants of the GATS II

Table 2 depicts the awareness regarding the harmful effects of tobacco in three groups—smokers, SLT users and non-tobacco users as per the GATS-II. About 60.2% (5826) smokers and 57.5% (8933) SLT users were aware of tobacco’s adverse effects. High levels of awareness (p<0.05) was observed among male participants, and those who were married, young (15–29 years), educated beyond secondary schools, urban residents, in the third percentile of wealth index and who had positive intentions to quit tobacco use. The only difference was observed in regions where smokers from eastern India were more aware, and awareness was high among SLT users from western India. Among non-users, 66.5% (35 818) were aware of the harmful effects of smoking. The awareness was high (p<0.05) among men, unmarried, aged 30–44 years, educated beyond secondary schools, urban residents, residing in North India and the first percentile of wealth index.

Awareness regarding adverse effects due to tobacco use among the participants of the Global Adults Tobacco Survey (India)-round II (2016–2017) (N=74 025)

Factors affecting the awareness regarding the adverse effects of tobacco among GATS II participants

Among the smokers, SLT users and non-users, bivariate analysis depicted higher odds of having awareness regarding the harmful effects of using tobacco among men, educated beyond secondary school, residing in western India and belonging to low wealth index compared with their respective reference groups. Additionally, awareness was high among smokers and non-users who were unmarried and aged 30–44 years, whereas, among SLT users, awareness was more among those who were married and aged 45–59 years.

Subsequently, multiple logistic regression depicted higher chances of having awareness among participants who were educated beyond secondary school, belong to the third percentile in wealth index, and intend to quit tobacco use in the last 12 months. Additionally, after adjustment, awareness among smokers was high in those residing in eastern India and belonging to the wealth index’s first percentile. Among SLT users, awareness was more among male participants, those who belong to the first percentile of wealth index and those residing in western India ( table 3 ). Among non-users, after adjustment, awareness was more among participants aged 30–44 years, educated beyond secondary school, residing in western India and who belong to the fourth percentile of wealth index.

Bivariate and multivariate analysis showing factors affecting the awareness regarding the adverse effects of tobacco among smokers and smokeless tobacco users from the second round of the Global Adult Tobacco Survey (India)

Changes in the awareness regarding adverse effects of tobacco between two rounds of GATS

We estimated the changes in awareness patterns about tobacco use over the years by comparing it across two surveys of the GATS India ( table 4 ). They were asked regarding their awareness of the harmful effects of tobacco. During GATS-I, 38% of smokers (4930) and 38.9% (6961) of SLT users were aware of tobacco’s adverse effects ( figure 1 ). We observed increased awareness during the second round of GATS-II from the GATS-I among both smokers and SLT users. The increase in awareness was observed across gender, different age groups, residential areas and regions in India. Among smokers, an expansion beyond 50% was observed among men, across all ages, both in the rural and urban areas and in Northern, Southern and Western India. Among SLT users, an increase beyond 50% was observed in ≥45 years, in rural residents and western India. Among non-users, more than 50% increase in awareness was observed only among participants residing in west India.

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Awareness among the smokers and smokeless tobacco users regarding specific illnesses caused by tobacco consumption in two rounds of Global Adults Tobacco Survey (GATS) (India).

Awareness regarding adverse effects due to smoking or smokeless tobacco: a comparison between the two rounds of Global Adults Tobacco Survey (GATS) (India)

This study explored the awareness of tobacco’s harmful effects among its current users and non-users in India using a nationally representative survey dataset. Our study observed that 60% and 57% of the current users of smoked and SLT were aware of its harmful effects, respectively. This was lower than the knowledge levels observed in the non-smokers. This can be attributed to concerted efforts made by the government through public health campaigns. However, awareness about adverse effects has increased in GATS-II compared with round I; there is still a need for consistent efforts to improve it further. Also, the difference in awareness levels of tobacco users and non-users highlights the challenges of the one-size-fits-all approach in terms of awareness generation.

We observed significant disparities in awareness across different sociodemographic characteristics. Our study depicted better awareness among the men regarding the harmful effects of smoked and SLT compared with women. This can be due to the higher use of tobacco among men and their peers. Men have easy access to shops that sell tobacco products, and warnings on these cigarette packets/SLT pouches increase awareness. 26 On the other hand, advertisements over television in movie halls help create awareness across genders and a wider age group. However, previous studies have demonstrated country-specific variations in harmful perception by gender. 20–22 27 In a study by Gupta and Kumar, 91.5% of men and 88.5% of women were aware that smoking causes serious illness. 28 Nevertheless, this disparity is a severe cause of concern and highlights the need to improve awareness about the harmful effect of smoking through a gender-neutral approach. Women usually perceive the risk of dying from smoking significantly higher than men and can influence the smoking behaviours of the men in their family. 29 One of the strategies can be the inclusion of women and family in social media advertisement or broadcasts. Awareness decreased with an increase in the age of the respondents. The reason can be their age-old belief that tobacco usage is not a serious issue as they have seen people around them without any health issue or being chronic tobacco users make them less receptive to any advice that concerns quitting. Previous studies have also depicted a decrease in intention to quit with increasing age. 30 31 Hence, they should be informed that quitting tobacco at any age leads to immediate health benefits, such as reduced stroke risks, cardiovascular disease and tobacco-related cancers. 31

Awareness was directly related to the number of years spent in school and was low among illiterate respondents. Previous studies corroborate the causal association between education levels and the smoking status of the individual. 32–34 Therefore, we require prevention strategies that focus on the formative years of life and modify the factors that influence tobacco usage (smoke and smokeless) in the later stages. Like other studies, low awareness regarding the adverse effects of tobacco was observed in respondents belonging to the lowest wealth quintile. 16 27 30

A relative increase in awareness was observed among rural residents in GATS-II, but it remained lower than that of the urban area. The rural population is the most vulnerable, and more inclination toward tobacco consumption and quit attempts are less likely to be successful. This could be mainly due to reduced community support for quitting and less motivation to quit and stabilise addictive behaviour with socio-cultural traditions. Most of the time, they do not complete pharmaceutical and behavioural intervention for tobacco quitting because of the lack of self-motivation or medical avenues for quitting. This is further compounded by poor access to drug deaddiction-centres and a lack of stringent implementation of tobacco control measures in rural areas. 5 To overcome the rural–urban disparities and give an impetus to the awareness campaign in rural areas, the Panchayati Raj institutions can provide a platform for awareness campaigns with active participation by local bodies like the Sarpanch and village elderly. Furthermore, the primary healthcare centres can function as the first point of contact for advice and treatment of tobacco addiction by providing specific pharmacotherapeutic and psychosocial interventions to help quit for those seeking help. We should adopt a continuum of care to reduce tobacco use among people who intend to quit. 35

Awareness is pivotal to behaviour change and is the cornerstone of tobacco control initiatives. We observed higher awareness among those who had positive intentions to quit tobacco usage. Adequate awareness determines the efficacy of and access to cessation initiatives, quitting rates, compliance with the antitobacco legislation being implemented across the countries. 36 Therefore, the present study underlines the urgent need to improve knowledge on the dangers of active and passive smoking among socially disadvantaged populations. Policymakers can use this information for developing media and educational and interventional campaigns for specific population subgroups.

There are specific strengths and limitations of the study. The major strength was using the scoring system to assess the comprehensive awareness among users and non-users of tobacco. It is because we expect that inaccurate awareness of tobacco’s harmful effects influences users and non-users alike. We were able to track the trends in awareness levels over the two rounds of GATS, which can help us understand the effectiveness of various health promotion activities initiated during the period between at population levels only. However, the cross-sectional nature of data collection couldn’t assess the temporality between intention to quit tobacco and awareness. This study was limited only to adult smokers over 15 years old, so responses might not explain the point of generation of awareness of possible health effects of tobacco use. Also, as there was a difference in the number of questions included in the survey to assess awareness about the harmful effect of tobacco, and hence for comparison of awareness, only common variables were studied.

There are specific policy implications of this investigation. Global research has reiterated that there is no safe form of tobacco, including SLT, which contains at least 70 harmful chemicals that cause cancer. 37 Even with such demonstrable and proven adverse effects, India’s widespread use is not surprising because of the addictive properties of tobacco. Those who get addicted and dependent require motivation and sustained efforts to get rid of this habit. Hence, a high level of awareness and health education is needed to encourage people to give up this habit and, more importantly, discourage others from falling into its addictive trap.

Conclusions and recommendations

Our study findings suggest that awareness regarding the harmful effects of tobacco is lacking among the users and is less than the non-users. It further highlights the need to include strategies for deeper penetration of health promotion activities and bringing the desired behaviour change. Future research can focus on assessing the effectiveness of various health promotion activities and comparing them between tobacco users and non-users and among different sociodemographic variables. We recommend that these campaigns be customised to counter the regional disparities and adopt a gender-neutral approach. Community-based approaches involving stakeholders like village elderly, healthcare frontline workers and allied community workers leading to the inclusion of the bottom of the pyramid will help in practical and widespread Dissemination. Awareness activities should be started during adolescence as it is a critical period to adopt a healthy lifestyle and be aware of the harmful effects of tobacco. Lastly, a public health approach that integrates with the existing sociocultural milieu and a supportive environment can be emphasised on from a policy point of view.

Ethics statements

Patient consent for publication.

Not required.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

  • Data supplement 1

Twitter @drmadhurverma

Contributors AK: conceptualised the study, acquisition of data, developed analytical framework, analysed the data, interpreted the results, wrote the first draft of the manuscript. SSS: interpreted local policy implications of the results and reviewed, approved the early and advanced drafts of the manuscript. MV led the manuscript preparation and the submission process, developed an analytical framework, interpreted the results, gave critical inputs on multiple draft of the manuscript and revised the manuscript.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

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

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Harmful Effects Of Smoking Research Paper To Use For Practical Writing Help

Type of paper: Research Paper

Topic: Smoking , Health , Tobacco , Cigarettes , Tobacco Use , Medicine , Risk , Dentistry

Words: 1000

Published: 03/30/2023


The utilization of cigarettes is a major public health issue and is considered a harmful and highly addictive habit. Many different people smoke for various reasons, including to relieve stress because tobacco use can give the user pleasure and calmness. Smoking also harms the people around the smoker as they become passive smokers. Hundreds of thousands of people are diagnosed daily with the effects of smoking, for example, cancer, cardiovascular diseases, and deteriorated dental hygiene. Tobacco use is an expensive affair, usually costing the average smoker about $5 a day, $35 a week, $140 a month, and approximately $1, 825 every year. For every dollar spent on cigarettes, another extra dollar is lost in mitigation efforts, healthcare, work absenteeism, and funeral expenses. Each year, there is more than $25 billion spent on tobacco use and consequential effects. According to the CDC, smoking results in more than 480,000 deaths each year in the USA, more than 80% of all Chronic Obstructive Pulmonary Diseases, and more than 90% of all lung cancers in both men and women ("CDC - Fact Sheet - Health Effects of Cigarette Smoking - Smoking & Tobacco Use"). For direct smokers, smoking compromises the immune system and results in respiratory and autoimmune infections such as rheumatoid arthritis and Crohn’s disease. It has also been linked to a 30%-40% higher risk of developing type two diabetes, with the risk being higher in people that smoke more cigarettes than individual smokes. Studies have shown a relationship between tobacco use and osteoporosis, which is a condition that causes bones to weaken and susceptible to fracturing. The risk is higher in women smokers because it lowers estrogen levels in the body and subsequently induces early menopause. The chemicals present in tobacco harm the blood cells and disrupt the normal functioning of the heart. Such damages increase the risk of atherosclerosis (peripheral arterial disease) that clogs the arteries, aneurysms, which refers to bulging blood vessels, and cardiovascular diseases that include hypertension, coronary heart diseases, and heart attacks. Additionally, smoking can cause a stroke because it results in the sudden death of brain cells due to blood clots or bleeding. Every cigarette smoked causes COPD, pneumonia, asthma, tuberculosis, chronic bronchitis, and emphysema. The chemicals contained in cigarettes are more than 4,000 and about 70 of them are responsible for causing cancer in the lungs, esophagus, trachea, oral cavity, stomach, bladder, larynx, colon, rectum, uterine cervix, pancreas, and kidney. These chemicals can also cause leukemia ("Health Effects of Smoking"). Other effects of smoking include age-related muscle degeneration, cataract, and optic nerve damage, all of which can result in temporary to permanent blindness. It is also responsible for yellow and smelly teeth in tobacco users. Smoking causes the degeneration of gums and tiny cells that protect the teeth and mouth from diseases and infection. Inhalation of tobacco kills these cells and consequently teeth infection. Infected teeth and gums start to rot and can form a bad odor, which harms people around when one speaks. Teeth cavities are painful and cause pain and loss of teeth. Teeth become stained with a yellow or brown coating and the effect cannot be reversed unless by expensive procedures of de-colorization of teeth by a dentist. Cigarettes harm people near them when they breathe passive smoke from smokers. The risks of passive smoking are excessively dangerous because second hand smoke burning off the end of a cigarette is unfiltered and contains a lot of toxins. Involuntary smoking stiffens the aorta as much as the smoking of a cigarette. Half an hour exposure can result in blood clots and increased build up of fat deposits in the blood vessels; thus, increasing the risk of heart attacks and stroke. Between two and four hours exposure, involuntary smoking can cause irregular heartbeats, also known as arrhythmia, and can also trigger fatal cardiac events and heart attacks. Other effects include eye and nasal irritation and increased risks of sinus and respiratory infections. Children of smoking parents report increased respiratory infections and lower lung functionalities compared to the children of nonsmokers. Environmental tobacco smoke is dirtier and causes air pollution, for example, restaurants that allow smoking may be up to six times more polluted than a busy highway. There is a 34% higher risk of developing lung cancer due to second-hand smoke, 30% higher chance of developing heart conditions, and over 3,000 deaths for nonsmokers above 35 years of age. For pregnant women and their children, passive smoke infects the fetus and reduces the oxygen amount in the unborn baby. Involuntary smoking can also cause miscarriages, development of learning issues in children, and ears and lung infections ("Health Dangers of Smoking for Nonsmokers"). In an attempt to control the effects of tobacco use, manufacturers have been mandated to put a warning sign on each packet, primarily to control the use of cigarettes among pregnant women. We live in a smoking society in which tobacco products are easily accessible to everyone and anyone. Once an individual develops the addiction, he or she is in dire danger of serious diseases and complications within the body and immune system. There is a slogan that states that “if you haven’t smoked, don’t start. If you do smoke, quit. Don’t be a loser.” This slogan is popular in society and is mainly used to discourage smoking and prevent regrets of preventable death and disease among the victims. Tobacco use is one of the greatest epidemic in human history and is ultimately known as a primary cause of death in the world. At any cost, one should avoid the dangerous habit of smoking because of the serious risks involved.

Works Cited

"CDC - Fact Sheet - Health Effects of Cigarette Smoking - Smoking & Tobacco Use". Smoking and Tobacco Use. n.d. Web. 25 May 2016. "Health Dangers of Smoking for Nonsmokers". n.d. Web. 25 May 2016. "Health Effects of Smoking". Be Tobacco n.d. Web. 25 May 2016.


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