cigarette smoking thesis

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• Published: 10 October 2022

Health effects associated with smoking: a Burden of Proof study

• Xiaochen Dai   ORCID: orcid.org/0000-0002-0289-7814 1 , 2 ,
• Gabriela F. Gil 1 ,
• Marissa B. Reitsma 1 ,
• Noah S. Ahmad 1 ,
• Jason A. Anderson 1 ,
• Catherine Bisignano 1 ,
• Sinclair Carr 1 ,
• Rachel Feldman 1 ,
• Simon I. Hay   ORCID: orcid.org/0000-0002-0611-7272 1 , 2 ,
• Jiawei He 1 , 2 ,
• Vincent Iannucci 1 ,
• Hilary R. Lawlor 1 ,
• Matthew J. Malloy 1 ,
• Laurie B. Marczak 1 ,
• Susan A. McLaughlin 1 ,
• Larissa Morikawa   ORCID: orcid.org/0000-0001-9749-8033 1 ,
• Erin C. Mullany 1 ,
• Sneha I. Nicholson 1 ,
• Erin M. O’Connell 1 ,
• Chukwuma Okereke 1 ,
• Reed J. D. Sorensen 1 ,
• Joanna Whisnant 1 ,
• Aleksandr Y. Aravkin 1 , 3 ,
• Peng Zheng 1 , 2 ,
• Christopher J. L. Murray   ORCID: orcid.org/0000-0002-4930-9450 1 , 2 &
• Emmanuela Gakidou   ORCID: orcid.org/0000-0002-8992-591X 1 , 2  

Nature Medicine volume  28 ,  pages 2045–2055 ( 2022 ) Cite this article

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• Risk factors

Matters Arising to this article was published on 14 April 2023

As a leading behavioral risk factor for numerous health outcomes, smoking is a major ongoing public health challenge. Although evidence on the health effects of smoking has been widely reported, few attempts have evaluated the dose–response relationship between smoking and a diverse range of health outcomes systematically and comprehensively. In the present study, we re-estimated the dose–response relationships between current smoking and 36 health outcomes by conducting systematic reviews up to 31 May 2022, employing a meta-analytic method that incorporates between-study heterogeneity into estimates of uncertainty. Among the 36 selected outcomes, 8 had strong-to-very-strong evidence of an association with smoking, 21 had weak-to-moderate evidence of association and 7 had no evidence of association. By overcoming many of the limitations of traditional meta-analyses, our approach provides comprehensive, up-to-date and easy-to-use estimates of the evidence on the health effects of smoking. These estimates provide important information for tobacco control advocates, policy makers, researchers, physicians, smokers and the public.

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Among both the public and the health experts, smoking is recognized as a major behavioral risk factor with a leading attributable health burden worldwide. The health risks of smoking were clearly outlined in a canonical study of disease rates (including lung cancer) and smoking habits in British doctors in 1950 and have been further elaborated in detail over the following seven decades 1 , 2 . In 2005, evidence of the health consequences of smoking galvanized the adoption of the first World Health Organization (WHO) treaty, the Framework Convention on Tobacco Control, in an attempt to drive reductions in global tobacco use and second-hand smoke exposure 3 . However, as of 2020, an estimated 1.18 billion individuals globally were current smokers and 7 million deaths and 177 million disability-adjusted life-years were attributed to smoking, reflecting a persistent public health challenge 4 . Quantifying the relationship between smoking and various important health outcomes—in particular, highlighting any significant dose–response relationships—is crucial to understanding the attributable health risk experienced by these individuals and informing responsive public policy.

Existing literature on the relationship between smoking and specific health outcomes is prolific, including meta-analyses, cohort studies and case–control studies analyzing the risk of outcomes such as lung cancer 5 , 6 , 7 , chronic obstructive pulmonary disease (COPD) 8 , 9 , 10 and ischemic heart disease 11 , 12 , 13 , 14 due to smoking. There are few if any attempts, however, to systematically and comprehensively evaluate the landscape of evidence on smoking risk across a diverse range of health outcomes, with most current research focusing on risk or attributable burden of smoking for a specific condition 7 , 15 , thereby missing the opportunity to provide a comprehensive picture of the health risk experienced by smokers. Furthermore, although evidence surrounding specific health outcomes, such as lung cancer, has generated widespread consensus, findings about the attributable risk of other outcomes are much more heterogeneous and inconclusive 16 , 17 , 18 . These studies also vary in their risk definitions, with many comparing dichotomous exposure measures of ever smokers versus nonsmokers 19 , 20 . Others examine the distinct risks of current smokers and former smokers compared with never smokers 21 , 22 , 23 . Among the studies that do analyze dose–response relationships, there is large variation in the units and dose categories used in reporting their findings (for example, the use of pack-years or cigarettes per day) 24 , 25 , which complicates the comparability and consolidation of evidence. This, in turn, can obscure data that could inform personal health choices, public health practices and policy measures. Guidance on the health risks of smoking, such as the Surgeon General’s Reports on smoking 26 , 27 , is often based on experts’ evaluation of heterogenous evidence, which, although extremely useful and well suited to carefully consider nuances in the evidence, is fundamentally subjective.

The present study, as part of the Global Burden of Diseases, Risk Factors, and Injuries Study (GBD) 2020, re-estimated the continuous dose–response relationships (the mean risk functions and associated uncertainty estimates) between current smoking and 36 health outcomes (Supplementary Table 1 ) by identifying input studies using a systematic review approach and employing a meta-analytic method 28 . The 36 health outcomes that were selected based on existing evidence of a relationship included 16 cancers (lung cancer, esophageal cancer, stomach cancer, leukemia, liver cancer, laryngeal cancer, breast cancer, cervical cancer, colorectal cancer, lip and oral cavity cancer, nasopharyngeal cancer, other pharynx cancer (excluding nasopharynx cancer), pancreatic cancer, bladder cancer, kidney cancer and prostate cancer), 5 cardiovascular diseases (CVDs: ischemic heart disease, stroke, atrial fibrillation and flutter, aortic aneurysm and peripheral artery disease) and 15 other diseases (COPD, lower respiratory tract infections, tuberculosis, asthma, type 2 diabetes, Alzheimer’s disease and related dementias, Parkinson’s disease, multiple sclerosis, cataracts, gallbladder diseases, low back pain, peptic ulcer disease, rheumatoid arthritis, macular degeneration and fractures). Definitions of the outcomes are described in Supplementary Table 1 . We conducted a separate systematic review for each risk–outcome pair with the exception of cancers, which were done together in a single systematic review. This approach allowed us to systematically identify all relevant studies indexed in PubMed up to 31 May 2022, and we extracted relevant data on risk of smoking, including study characteristics, following a pre-specified template (Supplementary Table 2 ). The meta-analytic tool overcomes many of the limitations of traditional meta-analyses by incorporating between-study heterogeneity into the uncertainty of risk estimates, accounting for small numbers of studies, relaxing the assumption of log(linearity) applied to the risk functions, handling differences in exposure ranges between comparison groups, and systematically testing and adjusting for bias due to study designs and characteristics. We then estimated the burden-of-proof risk function (BPRF) for each risk–outcome pair, as proposed by Zheng et al. 29 ; the BPRF is a conservative risk function defined as the 5th quantile curve (for harmful risks) that reflects the smallest harmful effect at each level of exposure consistent with the available evidence. Given all available data for each outcome, the risk of smoking is at least as harmful as the BPRF indicates.

We used the BPRF for each risk–outcome pair to calculate risk–outcome scores (ROSs) and categorize the strength of evidence for the association between smoking and each health outcome using a star rating from 1 to 5. The interpretation of the star ratings is as follows: 1 star (*) indicates no evidence of association; 2 stars (**) correspond to a 0–15% increase in risk across average range of exposures for harmful risks; 3 stars (***) represent a 15–50% increase in risk; 4 stars (****) refer to >50–85% increase in risk; and 5 stars (*****) equal >85% increase in risk. The thresholds for each star rating were developed in consultation with collaborators and other stakeholders.

The increasing disease burden attributable to current smoking, particularly in low- and middle-income countries 4 , demonstrates the relevance of the present study, which quantifies the strength of the evidence using an objective, quantitative, comprehensive and comparative framework. Findings from the present study can be used to support policy makers in making informed smoking recommendations and regulations focusing on the associations for which the evidence is strongest (that is, the 4- and 5-star associations). However, associations with a lower star rating cannot be ignored, especially when the outcome has high prevalence or severity. A summary of the main findings, limitations and policy implications of the study is presented in Table 1 .

We evaluated the mean risk functions and the BPRFs for 36 health outcomes that are associated with current smoking 30 (Table 2 ). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines 31 for each of our systematic reviews, we identified studies reporting relative risk (RR) of incidence or mortality from each of the 36 selected outcomes for smokers compared with nonsmokers. We reviewed 21,108 records, which were identified to have been published between 1 May 2018 and 31 May 2022; this represents the most recent time period since the last systematic review of the available evidence for the GBD at the time of publication. The meta-analyses reported in the present study for each of the 36 health outcomes are based on evidence from a total of 793 studies published between 1970 and 2022 (Extended Data Fig. 1 – 5 and Supplementary Information 1.5 show the PRISMA diagrams for each outcome). Only prospective cohort and case–control studies were included for estimating dose–response risk curves, but cross-sectional studies were also included for estimating the age pattern of smoking risk on cardiovascular and circulatory disease (CVD) outcomes. Details on each, including the study’s design, data sources, number of participants, length of follow-up, confounders adjusted for in the input data and bias covariates included in the dose–response risk model, can be found in Supplementary Information 2 and 3 . The theoretical minimum risk exposure level used for current smoking was never smoking or zero 30 .

Five-star associations

When the most conservative interpretation of the evidence, that is, the BPRF, suggests that the average exposure (15th–85th percentiles of exposure) of smoking increases the risk of a health outcome by >85% (that is, ROS > 0.62), smoking and that outcome are categorized as a 5-star pair. Among the 36 outcomes, there are 5 that have a 5-star association with current smoking: laryngeal cancer (375% increase in risk based on the BPRF, 1.56 ROS), aortic aneurysm (150%, 0.92), peripheral artery disease (137%, 0.86), lung cancer (107%, 0.73) and other pharynx cancer (excluding nasopharynx cancer) (92%, 0.65).

Results for all 5-star risk–outcome pairs are available in Table 2 and Supplementary Information 4.1 . In the present study, we provide detailed results for one example 5-star association: current smoking and lung cancer. We extracted 371 observations from 25 prospective cohort studies and 53 case–control studies across 25 locations (Supplementary Table 3 ) 5 , 6 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 . Exposure ranged from 1 pack-year to >112 pack-years, with the 85th percentile of exposure being 50.88 pack-years (Fig. 1a ).

a , The log(RR) function. b , RR function. c , A modified funnel plot showing the residuals (relative to 0) on the x axis and the estimated s.d. that includes reported s.d. and between-study heterogeneity on the y axis.

We found a very strong and significant harmful relationship between pack-years of current smoking and the RR of lung cancer (Fig. 1b ). The mean RR of lung cancer at 20 pack-years of smoking was 5.11 (95% uncertainty interval (UI) inclusive of between-study heterogeneity = 1.84–14.99). At 50.88 pack-years (85th percentile of exposure), the mean RR of lung cancer was 13.42 (2.63–74.59). See Table 2 for mean RRs at other exposure levels. The BPRF, which represents the most conservative interpretation of the evidence (Fig. 1a ), suggests that smoking in the 15th–85th percentiles of exposure increases the risk of lung cancer by an average of 107%, yielding an ROS of 0.73.

The relationship between pack-years of current smoking and RR of lung cancer is nonlinear, with diminishing impact of further pack-years of smoking, particularly for middle-to-high exposure levels (Fig. 1b ). To reduce the effect of bias, we adjusted observations that did not account for more than five confounders, including age and sex, because they were the significant bias covariates identified by the bias covariate selection algorithm 29 (Supplementary Table 7 ). The reported RRs across studies were very heterogeneous. Our meta-analytic method, which accounts for the reported uncertainty in both the data and between-study heterogeneity, fit the data and covered the estimated residuals well (Fig. 1c ). After trimming 10% of outliers, we still detected publication bias in the results for lung cancer. See Supplementary Tables 4 and 7 for study bias characteristics and selected bias covariates, Supplementary Fig. 5 for results without 10% trimming and Supplementary Table 8 for observed RR data and alternative exposures across studies for the remaining 5-star pairs.

Four-star associations

When the BPRF suggests that the average exposure of smoking increases the risk of a health outcome by 50–85% (that is, ROS > 0.41–0.62), smoking is categorized as having a 4-star association with that outcome. We identified three outcomes with a 4-star association with smoking: COPD (72% increase in risk based on the BPRF, 0.54 ROS), lower respiratory tract infection (54%, 0.43) and pancreatic cancer (52%, 0.42).

In the present study, we provide detailed results for one example 4-star association: current smoking and COPD. We extracted 51 observations from 11 prospective cohort studies and 4 case–control studies across 36 locations (Supplementary Table 3 ) 6 , 8 , 9 , 10 , 78 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 . Exposure ranged from 1 pack-year to 100 pack-years, with the 85th percentile of exposure in the exposed group being 49.75 pack-years.

We found a strong and significant harmful relationship between pack-years of current smoking and RR of COPD (Fig. 2b ). The mean RR of COPD at 20 pack-years was 3.17 (1.60–6.55; Table 2 reports RRs at other exposure levels). At the 85th percentile of exposure, the mean RR of COPD was 6.01 (2.08–18.58). The BPRF suggests that average smoking exposure raises the risk of COPD by an average of 72%, yielding an ROS of 0.54. The results for the other health outcomes that have an association with smoking rated as 4 stars are shown in Table 2 and Supplementary Information 4.2 .

a , The log(RR) function. b , RR function. c , A modified funnel plot showing the residuals (relative to 0) on th e x axis and the estimated s.d. that includes the reported s.d. and between-study heterogeneity on the y axis.

The relationship between smoking and COPD is nonlinear, with diminishing impact of further pack-years of current smoking on risk of COPD, particularly for middle-to-high exposure levels (Fig. 2a ). To reduce the effect of bias, we adjusted observations that did not account for age and sex and/or were generated for individuals aged >65 years 116 , because they were the two significant bias covariates identified by the bias covariate selection algorithm (Supplementary Table 7 ). There was large heterogeneity in the reported RRs across studies, and our meta-analytic method fit the data and covered the estimated residuals well (Fig. 2b ). Although we trimmed 10% of outliers, publication bias was still detected in the results for COPD. See Supplementary Tables 4 and 7 for study bias characteristics and selected bias covariates, Supplementary Fig. 5 for results without 10% trimming and Supplementary Table 8 for reported RR data and alternative exposures across studies for the remaining health outcomes that have a 4-star association with smoking.

Three-star associations

When the BPRF suggests that the average exposure of smoking increases the risk of a health outcome by 15–50% (or, when protective, decreases the risk of an outcome by 13–34%; that is, ROS >0.14–0.41), the association between smoking and that outcome is categorized as having a 3-star rating. We identified 15 outcomes with a 3-star association: bladder cancer (40% increase in risk, 0.34 ROS); tuberculosis (31%, 0.27); esophageal cancer (29%, 0.26); cervical cancer, multiple sclerosis and rheumatoid arthritis (each 23–24%, 0.21); lower back pain (22%, 0.20); ischemic heart disease (20%, 0.19); peptic ulcer and macular degeneration (each 19–20%, 0.18); Parkinson's disease (protective risk, 15% decrease in risk, 0.16); and stomach cancer, stroke, type 2 diabetes and cataracts (each 15–17%, 0.14–0.16).

We present the findings on smoking and type 2 diabetes as an example of a 3-star risk association. We extracted 102 observations from 24 prospective cohort studies and 4 case–control studies across 15 locations (Supplementary Table 3 ) 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 . The exposure ranged from 1 cigarette to 60 cigarettes smoked per day, with the 85th percentile of exposure in the exposed group being 26.25 cigarettes smoked per day.

We found a moderate and significant harmful relationship between cigarettes smoked per day and the RR of type 2 diabetes (Fig. 3b ). The mean RR of type 2 diabetes at 20 cigarettes smoked per day was 1.49 (1.18–1.90; see Table 2 for other exposure levels). At the 85th percentile of exposure, the mean RR of type 2 diabetes was 1.54 (1.20–2.01). The BPRF suggests that average smoking exposure raises the risk of type 2 diabetes by an average of 16%, yielding an ROS of 0.15. See Table 2 and Supplementary Information 4.3 for results for the additional health outcomes with an association with smoking rated as 3 stars.

a , The log(RR) function. b , RR function. c , A modified funnel plot showing the residuals (relative to 0) on the x axis and the estimated s.d. that includes the reported s.d. and between-study heterogeneity on the y axis.

The relationship between smoking and type 2 diabetes is nonlinear, particularly for high exposure levels where the mean risk curve becomes flat (Fig. 3a ). We adjusted observations that were generated in subpopulations, because it was the only significant bias covariate identified by the bias covariate selection algorithm (Supplementary Table 7 ). There was moderate heterogeneity in the observed RR data across studies and our meta-analytic method fit the data and covered the estimated residuals extremely well (Fig. 3b,c ). After trimming 10% of outliers, we still detected publication bias in the results for type 2 diabetes. See Supplementary Tables 4 and 7 for study bias characteristics and selected bias covariates, Supplementary Fig. 5 for results without 10% trimming and Supplementary Table 8 for observed RR data and alternative exposures across studies for the remaining 3-star pairs.

Two-star associations

When the BPRF suggests that the average exposure of smoking increases the risk of an outcome by 0–15% (that is, ROS 0.0–0.14), the association between smoking and that outcome is categorized as a 2-star rating. We identified six 2-star outcomes: nasopharyngeal cancer (14% increase in risk, 0.13 ROS); Alzheimer’s and other dementia (10%, 0.09); gallbladder diseases and atrial fibrillation and flutter (each 6%, 0.06); lip and oral cavity cancer (5%, 0.05); and breast cancer (4%, 0.04).

We present the findings on smoking and breast cancer as an example of a 2-star association. We extracted 93 observations from 14 prospective cohort studies and 9 case–control studies across 14 locations (Supplementary Table 3 ) 84 , 87 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 . The exposure ranged from 1 cigarette to >76 cigarettes smoked per day, with the 85th percentile of exposure in the exposed group being 34.10 cigarettes smoked per day.

We found a weak but significant relationship between pack-years of current smoking and RR of breast cancer (Extended Data Fig. 6 ). The mean RR of breast cancer at 20 pack-years was 1.17 (1.04–1.31; Table 2 reports other exposure levels). The BPRF suggests that average smoking exposure raises the risk of breast cancer by an average of 4%, yielding an ROS of 0.04. See Table 2 and Supplementary Information 4.4 for results on the additional health outcomes for which the association with smoking has been categorized as 2 stars.

The relationship between smoking and breast cancer is nonlinear, particularly for high exposure levels where the mean risk curve becomes flat (Extended Data Fig. 6a ). To reduce the effect of bias, we adjusted observations that were generated in subpopulations, because it was the only significant bias covariate identified by the bias covariate selection algorithm (Supplementary Table 7 ). There was heterogeneity in the reported RRs across studies, but our meta-analytic method fit the data and covered the estimated residuals (Extended Data Fig. 6b ). After trimming 10% of outliers, we did not detect publication bias in the results for breast cancer. See Supplementary Tables 4 and 7 for study bias characteristics and selected bias covariates, Supplementary Fig. 5 for results without 10% trimming and Supplementary Table 8 for observed RR data and alternative exposures across studies for the remaining 2-star pairs.

One-star associations

When average exposure to smoking does not significantly increase (or decrease) the risk of an outcome, once between-study heterogeneity and other sources of uncertainty are accounted for (that is, ROS < 0), the association between smoking and that outcome is categorized as 1 star, indicating that there is not sufficient evidence for the effect of smoking on the outcome to reject the null (that is, there may be no association). There were seven outcomes with an association with smoking that rated as 1 star: colorectal and kidney cancer (each –0.01 ROS); leukemia (−0.04); fractures (−0.05); prostate cancer (−0.06); liver cancer (−0.32); and asthma (−0.64).

We use smoking and prostate cancer as examples of a 1-star association. We extracted 78 observations from 21 prospective cohort studies and 1 nested case–control study across 15 locations (Supplementary Table 3 ) 157 , 160 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 . The exposure among the exposed group ranged from 1 cigarette to 90 cigarettes smoked per day, with the 85th percentile of exposure in the exposed group being 29.73 cigarettes smoked per day.

Based on our conservative interpretation of the data, we did not find a significant relationship between cigarettes smoked per day and the RR of prostate cancer (Fig. 4B ). The exposure-averaged BPRF for prostate cancer was 0.94, which was opposite null from the full range of mean RRs, such as 1.16 (0.89–1.53) at 20 cigarettes smoked per day. The corresponding ROS was −0.06, which is consistent with no evidence of an association between smoking and increased risk of prostate cancer. See Table 2 and Supplementary Information 4.5 for results for the additional outcomes that have a 1-star association with smoking.

The relationship between smoking and prostate cancer is nonlinear, particularly for middle-to-high exposure levels where the mean risk curve becomes flat (Fig. 4a ). We did not adjust for any bias covariate because no significant bias covariates were selected by the algorithm (Supplementary Table 7 ). The RRs reported across studies were very heterogeneous, but our meta-analytic method fit the data and covered the estimated residuals well (Fig. 4b,c ). The ROS associated with the BPRF is −0.05, suggesting that the most conservative interpretation of all evidence, after accounting for between-study heterogeneity, indicates an inconclusive relationship between smoking exposure and the risk of prostate cancer. After trimming 10% of outliers, we still detected publication bias in the results for prostate cancer, which warrants further studies using sample populations. See Supplementary Tables 4 and 7 for study bias characteristics and selected bias covariates, Supplementary Fig. 5 for results without 10% trimming and Supplementary Table 8 for observed RR data and alternative exposures across studies for the remaining 1-star pairs.

Age-specific dose–response risk for CVD outcomes

We produced age-specific dose–response risk curves for the five selected CVD outcomes ( Methods ). The ROS associated with each smoking–CVD pair was calculated based on the reference risk curve estimated using all risk data regardless of age information. Estimation of the BPRF, calculation of the associated ROS and star rating of the smoking–CVD pairs follow the same rules as the other non-CVD smoking–outcome pairs (Table 1 and Supplementary Figs. 2 – 4 ). Once we had estimated the reference dose–response risk curve for each CVD outcome, we determined the age group of the reference risk curve. The reference age group is 55–59 years for all CVD outcomes, except for peripheral artery disease, the reference age group for which is 60–64 years. We then estimated the age pattern of smoking on all CVD outcomes (Supplementary Fig. 2 ) and calculated age attenuation factors of the risk for each age group by comparing the risk of each age group with that of the reference age group, using the estimated age pattern (Supplementary Fig. 3 ). Last, we applied the draws of age attenuation factors of each age group to the dose–response risk curve for the reference age group to produce the age group-specific dose–response risk curves for each CVD outcome (Supplementary Fig. 4 ).

Using our burden-of-proof meta-analytic methods, we re-estimated the dose–response risk of smoking on 36 health outcomes that had previously been demonstrated to be associated with smoking 30 , 186 . Using these methods, which account for both the reported uncertainty of the data and the between-study heterogeneity, we found that 29 of the 36 smoking–outcome pairs are supported by evidence that suggests a significant dose–response relationship between smoking and the given outcome (28 with a harmful association and 1 with a protective association). Conversely, after accounting for between-study heterogeneity, the available evidence of smoking risk on seven outcomes (that is, colon and rectum cancer, kidney cancer, leukemia, prostate cancer, fractures, liver cancer and asthma) was insufficient to reject the null or draw definitive conclusions on their relationship to smoking. Among the 29 outcomes that have evidence supporting a significant relationship to smoking, 8 had strong-to-very-strong evidence of a relationship, meaning that, given all the available data on smoking risk, we estimate that average exposure to smoking increases the risk of those outcomes by >50% (4- and 5-star outcomes). The currently available evidence for the remaining 21 outcomes with a significant association with current smoking was weak to moderate, indicating that smoking increases the risk of those outcomes by at least >0–50% (2- and 3-star associations).

Even under our conservative interpretation of the data, smoking is irrefutably harmful to human health, with the greatest increases in risk occurring for laryngeal cancer, aortic aneurysm, peripheral artery disease, lung cancer and other pharynx cancer (excluding nasopharynx cancer), which collectively represent large causes of death and ill-health. The magnitude of and evidence for the associations between smoking and its leading health outcomes are among the highest currently analyzed in the burden-of-proof framework 29 . The star ratings assigned to each smoking–outcome pair offer policy makers a way of categorizing and comparing the evidence for a relationship between smoking and its potential health outcomes ( https://vizhub.healthdata.org/burden-of-proof ). We found that, for seven outcomes in our analysis, there was insufficient or inconsistent evidence to demonstrate a significant association with smoking. This is a key finding because it demonstrates the need for more high-quality data for these particular outcomes; availability of more data should improve the strength of evidence for whether or not there is an association between smoking and these health outcomes.

Our systematic review approach and meta-analytic methods have numerous benefits over existing systematic reviews and meta-analyses on the same topic that use traditional random effects models. First, our approach relaxes the log(linear) assumption, using a spline ensemble to estimate the risk 29 . Second, our approach allows variable reference groups and exposure ranges, allowing for more accurate estimates regardless of whether or not the underlying relative risk is log(linear). Furthermore, it can detect outliers in the data automatically. Finally, it quantifies uncertainty due to between-study heterogeneity while accounting for small numbers of studies, minimizing the risk that conclusions will be drawn based on spurious findings.

We believe that the results for the association between smoking and each of the 36 health outcomes generated by the present study, including the mean risk function, BPRF, ROS, average excess risk and star rating, could be useful to a range of stakeholders. Policy makers can formulate their decisions on smoking control priorities and resource allocation based on the magnitude of the effect and the consistency of the evidence relating smoking to each of the 36 outcomes, as represented by the ROS and star rating for each smoking–outcome association 187 . Physicians and public health practitioners can use the estimates of average increased risk and the star rating to educate patients and the general public about the risk of smoking and to promote smoking cessation 188 . Researchers can use the estimated mean risk function or BPRF to obtain the risk of an outcome at a given smoking exposure level, as well as uncertainty surrounding that estimate of risk. The results can also be used in the estimation of risk-attributable burden, that is, the deaths and disability-adjusted life-years due to each outcome that are attributable to smoking 30 , 186 . For the general public, these results could help them to better understand the risk of smoking and manage their health 189 .

Although our meta-analysis was comprehensive and carefully conducted, there are limitations to acknowledge. First, the bias covariates used, although carefully extracted and evaluated, were based on observable study characteristics and thus may not fully capture unobserved characteristics such as study quality or context, which might be major sources of bias. Second, if multiple risk estimates with different adjustment levels were reported in a given study, we included only the fully adjusted risk estimate and modeled the adjustment level according to the number of covariates adjusted for (rather than which covariates were adjusted for) and whether a standard adjustment for age and sex had been applied. This approach limited our ability to make full use of all available risk estimates in the literature. Third, although we evaluated the potential for publication bias in the data, we did not test for other forms of bias such as when studies are more consistent with each other than expected by chance 29 . Fourth, our analysis assumes that the relationships between smoking and health outcomes are similar across geographical regions and over time. We do not have sufficient evidence to quantify how the relationships may have evolved over time because the composition of smoking products has also changed over time. Perhaps some of the heterogeneity of the effect sizes in published studies reflects this; however, this cannot be discerned with the currently available information.

In the future, we plan to include crude and partially adjusted risk estimates in our analyses to fully incorporate all available risk estimates, to model the adjusted covariates in a more comprehensive way by mapping the adjusted covariates across all studies comprehensively and systematically, and to develop methods to evaluate additional forms of potential bias. We plan to update our results on a regular basis to provide timely and up-to-date evidence to stakeholders.

To conclude, we have re-estimated the dose–response risk of smoking on 36 health outcomes while synthesizing all the available evidence up to 31 May 2022. We found that, even after factoring in the heterogeneity between studies and other sources of uncertainty, smoking has a strong-to-very-strong association with a range of health outcomes and confirmed that smoking is irrefutably highly harmful to human health. We found that, due to small numbers of studies, inconsistency in the data, small effect sizes or a combination of these reasons, seven outcomes for which some previous research had found an association with smoking did not—under our meta-analytic framework and conservative approach to interpreting the data—have evidence of an association. Our estimates of the evidence for risk of smoking on 36 selected health outcomes have the potential to inform the many stakeholders of smoking control, including policy makers, researchers, public health professionals, physicians, smokers and the general public.

For the present study, we used a meta-analytic tool, MR-BRT (metaregression—Bayesian, regularized, trimmed), to estimate the dose–response risk curves of the risk of a health outcome across the range of current smoking levels along with uncertainty estimates 28 . Compared with traditional meta-analysis using linear mixed effect models, MR-BRT relaxes the assumption of a log(linear) relationship between exposure and risk, incorporates between-study heterogeneity into the uncertainty of risk estimates, handles estimates reported across different exposure categories, automatically identifies and trims outliers, and systematically tests and adjusts for bias due to study designs and characteristics. The meta-analytic methods employed by the present study followed the six main steps proposed by Zheng et al. 28 , 29 , namely: (1) enacting a systematic review approach and data extraction following a pre-specified and standardized protocol; (2) estimating the shape of the relationship between exposure and RR; (3) evaluating and adjusting for systematic bias as a function of study characteristics and risk estimation; (4) quantifying between-study heterogeneity while adjusting for within-study correlation and the number of studies; (5) evaluating potential publication or reporting biases; and (6) estimating the mean risk function and the BPRF, calculating the ROS and categorizing smoking–outcome pairs using a star-rating scheme from 1 to 5.

The estimates for our primary indicators of this work—mean RRs across a range of exposures, BRPFs, ROSs and star ratings for each risk–outcome pair—are not specific to or disaggregated by specific populations. We did not estimate RRs separately for different locations, sexes (although the RR of prostate cancer was estimated only for males and of cervical and breast cancer only for females) or age groups (although this analysis was applied to disease endpoints in adults aged ≥30 years only and, as detailed below, age-specific estimates were produced for the five CVD outcomes).

The present study complies with the PRISMA guidelines 190 (Supplementary Tables 9 and 10 and Supplementary Information 1.5 ) and Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) recommendations 191 (Supplementary Table 11 ). The study was approved by the University of Washington Institutional Review Board (study no. 9060). The systematic review approach was not registered.

Selecting health outcomes

In the present study, current smoking is defined as the current use of any smoked tobacco product on a daily or occasional basis. Health outcomes were initially selected using the World Cancer Research Fund criteria for convincing or probable evidence as described in Murray et al. 186 . The 36 health outcomes that were selected based on existing evidence of a relationship included 16 cancers (lung cancer, esophageal cancer, stomach cancer, leukemia, liver cancer, laryngeal cancer, breast cancer, cervical cancer, colorectal cancer, lip and oral cavity cancer, nasopharyngeal cancer, other pharynx cancer (excluding nasopharynx cancer), pancreatic cancer, bladder cancer, kidney cancer and prostate cancer), 5 CVDs (ischemic heart disease, stroke, atrial fibrillation and flutter, aortic aneurysm and peripheral artery disease) and 15 other diseases (COPD, lower respiratory tract infections, tuberculosis, asthma, type 2 diabetes, Alzheimer’s disease and related dementias, Parkinson’s disease, multiple sclerosis, cataracts, gallbladder diseases, low back pain, peptic ulcer disease, rheumatoid arthritis, macular degeneration and fracture). Definitions of the outcomes are described in Supplementary Table 1 .

Step 1: systematic review approach to literature search and data extraction

Informed by the systematic review approach we took for the GBD 2019 (ref. 30 ), for the present study we identified input studies in the literature using a systematic review approach for all 36 smoking–outcome pairs using updated search strings to identify all relevant studies indexed in PubMed up to 31 May 2022 and extracted data on smoking risk estimates. Briefly, the studies that were extracted represented several types of study design (for example, cohort and case–control studies), measured exposure in several different ways and varied in their choice of reference categories (where some compared current smokers with never smokers, whereas others compared current smokers with nonsmokers or former smokers). All these study characteristics were catalogued systematically and taken into consideration during the modeling part of the analysis.

In addition, for CVD outcomes, we also estimated the age pattern of risk associated with smoking. We applied a systematic review of literature approach for smoking risk for the five CVD outcomes. We developed a search string to search for studies reporting any association between binary smoking status (that is, current, former and ever smokers) and the five CVD outcomes from 1 January 1970 to 31 May 2022, and included only studies reporting age-specific risk (RR, odds ratio (OR), hazard ratio (HR)) of smoking status. The inclusion criteria and results of the systematic review approach are reported in accordance with PRISMA guidelines 31 . Details for each outcome on the search string used in the systematic review approach, refined inclusion and exclusion criteria, data extraction template and PRISMA diagram are given in Supplementary Information 1 . Title and/or abstract screening, full text screening and data extraction were conducted by 14 members of the research team and extracted data underwent manual quality assurance by the research team to verify accuracy.

Selecting exposure categories

Cumulative exposure in pack-years was the measure of exposure used for COPD and all cancer outcomes except for prostate cancer, to reflect the risk of both duration and intensity of current smoking on these outcomes. For prostate cancer, CVDs and all the other outcomes except for fractures, we used cigarette-equivalents smoked per day as the exposure for current smoking, because smoking intensity is generally thought to be more important than duration for these outcomes. For fractures, we used binary exposure, because there were few studies examining intensity or duration of smoking on fractures. The smoking–outcome pairs and the corresponding exposures are summarized in Supplementary Table 4 and are congruent with the GBD 2019 (refs. 30 , 186 ).

Steps 2–5: modeling dose–response RR of smoking on the selected health outcomes

Of the six steps proposed by Zheng et al. 29 , steps 2–5 cover the process of modeling dose–response risk curves. In step 2, we estimated the shape (or the ‘signal’) of the dose–response risk curves, integrating over different exposure ranges. To relax the log(linear) assumption usually applied to continuous dose–response risk and make the estimates robust to the placement of spline knots, we used an ensemble spline approach to fit the functional form of the dose–response relationship. The final ensemble model was a weighted combination of 50 models with random knot placement, with the weight of each model proportional to measures of model fit and total variation. To avoid the influence of extreme data and reduce publication bias, we trimmed 10% of data for each outcome as outliers. We also applied a monotonicity constraint to ensure that the mean risk curves were nondecreasing (or nonincreasing in the case of Parkinson’s disease).

In step 3, following the GRADE approach 192 , 193 , we quantified risk of bias across six domains, namely, representativeness of the study population, exposure, outcome, reverse causation, control for confounding and selection bias. Details about the bias covariates are provided in Supplementary Table 4 . We systematically tested for the effect of bias covariates using metaregression, selected significant bias covariates using the Lasso approach 194 , 195 and adjusted for the selected bias covariates in the final risk curve.

In step 4, we quantified between-study heterogeneity accounting for within-study correlation, uncertainty of the heterogeneity, as well as small number of studies. Specifically, we used a random intercept in the mixed-effects model to account for the within-study correlation and used a study-specific random slope with respect to the ‘signal’ to capture between-study heterogeneity. As between-study heterogeneity can be underestimated or even zero when the number of studies is small 196 , 197 , we used Fisher’s information matrix to estimate the uncertainty of the heterogeneity 198 and incorporated that uncertainty into the final results.

In step 5, in addition to generating funnel plots and visually inspecting for asymmetry (Figs. 1c , 2c , 3c and 4c and Extended Data Fig. 6c ) to identify potential publication bias, we also statistically tested for potential publication or reporting bias using Egger’s regression 199 . We flagged potential publication bias in the data but did not correct for it, which is in line with the general literature 10 , 200 , 201 . Full details about the modeling process have been published elsewhere 29 and model specifications for each outcome are in Supplementary Table 6 .

Step 6: estimating the mean risk function and the BPRF

In the final step, step 6, the metaregression model inclusive of the selected bias covariates from step 3 (for example, the highest adjustment level) was used to predict the mean risk function and its 95% UI, which incorporated the uncertainty of the mean effect, between-study heterogeneity and the uncertainty in the heterogeneity estimate accounting for small numbers of studies. Specifically, 1,000 draws were created for each 0.1 level of doses from 0 pack-years to 100 pack-years or cigarette-equivalents smoked per day using the Bayesian metaregression model. The mean of the 1,000 draws was used to estimate the mean risk at each exposure level, and the 25th and 95th draws were used to estimate the 95% UIs for the mean risk at each exposure level.

The BPRF 29 is a conservative estimate of risk function consistent with the available evidence, correcting for both between-study heterogeneity and systemic biases related to study characteristics. The BPRF is defined as either the 5th (if harmful) or 95th (if protective) quantile curve closest to the line of log(RR) of 0, which defines the null (Figs. 1a , 2b , 3a and 4a ). The BPRF represents the smallest harmful (or protective) effect of smoking on the corresponding outcome at each level of exposure that is consistent with the available evidence. A BPRF opposite null from the mean risk function indicates that insufficient evidence is available to reject null, that is, that there may not be an association between risk and outcome. Likewise, the further the BPRF is from null on the same side of null as the mean risk function, the higher the magnitude and evidence for the relationship. The BPRF can be interpreted as indicating that, even accounting for between-study heterogeneity and its uncertainty, the log(RR) across the studied smoking range is at least as high as the BPRF (or at least as low as the BPRF for a protective risk).

To quantify the strength of the evidence, we calculated the ROS for each smoking–outcome association as the signed value of the log(BPRF) averaged between the 15th and 85th percentiles of observed exposure levels for each outcome. The ROS is a single summary of the effect of smoking on the outcome, with higher positive ROSs corresponding to stronger and more consistent evidence and a higher average effect size of smoking and a negative ROS, suggesting that, based on the available evidence, there is no significant effect of smoking on the outcome after accounting for between-study heterogeneity.

For ease of communication, we further classified each smoking–outcome association into a star rating from 1 to 5. Briefly, 1-star associations have an ROS <0, indicating that there is insufficient evidence to find a significant association between smoking and the selected outcome. We divided the positive ROSs into ranges 0.0–0.14 (2-star), >0.14–0.41 (3-star), >0.41–0.62 (4-star) and >0.62 (5-star). These categories correspond to excess risk ranges for harmful risks of 0–15%, >15–50%, >50–85% and >85%. For protective risks, the ranges of exposure-averaged decreases in risk by star rating are 0–13% (2 stars), >13–34% (3 stars), >34–46% (4 stars) and >46% (5 stars).

Among the 36 smoking–outcome pairs analyzed, smoking fracture was the only binary risk–outcome pair, which was due to limited data on the dose–response risk of smoking on fracture 202 . The estimation of binary risk was simplified because the RR was merely a comparison between current smokers and nonsmokers or never smokers. The concept of ROS for continuous risk can naturally extend to binary risk because the BPRF is still defined as the 5th percentile of the effect size accounting for data uncertainty and between-study heterogeneity. However, binary ROSs must be divided by 2 to make them comparable with continuous ROSs, which were calculated by averaging the risk over the range between the 15th and the 85th percentiles of observed exposure levels. Full details about estimating mean risk functions, BPRFs and ROSs for both continuous and binary risk–outcome pairs can be found elsewhere 29 .

Estimating the age-specific risk function for CVD outcomes

For non-CVD outcomes, we assumed that the risk function was the same for all ages and all sexes, except for breast, cervical and prostate cancer, which were assumed to apply only to females or males, respectively. As the risk of smoking on CVD outcomes is known to attenuate with increasing age 203 , 204 , 205 , 206 , we adopted a four-step approach for GBD 2020 to produce age-specific dose–response risk curves for CVD outcomes.

First, we estimated the reference dose–response risk of smoking for each CVD outcome using dose-specific RR data for each outcome regardless of the age group information. This step was identical to that implemented for the other non-CVD outcomes. Once we had generated the reference curve, we determined the age group associated with it by calculating the weighted mean age across all dose-specific RR data (weighted by the reciprocal of the s.e.m. of each datum). For example, if the weighted mean age of all dose-specific RR data was 56.5, we estimated the age group associated with the reference risk curve to be aged 55–59 years. For cohort studies, the age range associated with the RR estimate was calculated as a mean age at baseline plus the mean/median years of follow-up (if only the maximum years of follow-up were reported, we would halve this value and add it to the mean age at baseline). For case–control studies, the age range associated with the OR estimate was simply the reported mean age at baseline (if mean age was not reported, we used the midpoint of the age range instead).

In the third step, we extracted age group-specific RR data and relevant bias covariates from the studies identified in our systematic review approach of age-specific smoking risk on CVD outcomes, and used MR-BRT to model the age pattern of excess risk (that is, RR-1) of smoking on CVD outcomes with age group-specific excess RR data for all CVD outcomes. We modeled the age pattern of smoking risk on CVDs following the same steps we implemented for modeling dose–response risk curves. In the final model, we included a spline on age, random slope on age by study and the bias covariate encoding exposure definition (that is, current, former and ever smokers), which was picked by the variable selection algorithm 28 , 29 . When predicting the age pattern of the excess risk of smoking on CVD outcomes using the fitted model, we did not include between-study heterogeneity to reduce uncertainty in the prediction.

In the fourth step, we calculated the age attenuation factors of excess risk compared with the reference age group for each CVD outcome as the ratio of the estimated excess risk for each age group to the excess risk for the reference age group. We performed the calculation at the draw level to obtain 1,000 draws of the age attenuation factors for each age group. Once we had estimated the age attenuation factors, we carried out the last step, which consisted of adjusting the risk curve for the reference age group from step 1 using equation (1) to produce the age group-specific risk curves for each CVD outcome:

We implemented the age adjustment at the draw level so that the uncertainty of the age attenuation factors could be naturally incorporated into the final adjusted age-specific RR curves. A PRISMA diagram detailing the systematic review approach, a description of the studies included and the full details about the methods are in Supplementary Information 1.5 and 5.2 .

Estimating the theoretical minimum risk exposure level

The theoretical minimum risk exposure level for smoking was 0, that is, no individuals in the population are current or former smokers.

Model validation

The validity of the meta-analytic tool has been extensively evaluated by Zheng and colleagues using simulation experiments 28 , 29 . For the present study, we conducted two additional sensitivity analyses to examine how the shape of the risk curves was impacted by applying a monotonicity constraint and trimming 10% of data. We present the results of these sensitivity analyses in Supplementary Information 6 . In addition to the sensitivity analyses, the dose–response risk estimates were also validated by plotting the mean risk function along with its 95% UI against both the extracted dose-specific RR data from the studies included and our previous dose–response risk estimates from the GBD 2019 (ref. 30 ). The mean risk functions along with the 95% UIs were validated based on data fit and the level, shape and plausibility of the dose–response risk curves. All curves were validated by all authors and reviewed by an external expert panel, comprising professors with relevant experience from universities including Johns Hopkins University, Karolinska Institute and University of Barcelona; senior scientists working in relevant departments at the WHO and the Center for Disease Control and Prevention (CDC) and directors of nongovernmental organizations such as the Campaign for Tobacco-Free Kids.

Statistical analysis

Analyses were carried out using R v.3.6.3, Python v.3.8 and Stata v.16.

Statistics and reproducibility

The study was a secondary analysis of existing data involving systematic reviews and meta-analyses. No statistical method was used to predetermine sample size. As the study did not involve primary data collection, randomization and blinding, data exclusions were not relevant to the present study, and, as such, no data were excluded and we performed no randomization or blinding. We have made our data and code available to foster reproducibility.

Reporting summary

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

Data availability

The findings from the present study are supported by data available in the published literature. Data sources and citations for each risk–outcome pair can be downloaded using the ‘download’ button on each risk curve page currently available at https://vizhub.healthdata.org/burden-of-proof . Study characteristics and citations for all input data used in the analyses are also provided in Supplementary Table 3 , and Supplementary Table 2 provides a template of the data collection form.

Code availability

All code used for these analyses is publicly available online ( https://github.com/ihmeuw-msca/burden-of-proof ).

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Acknowledgements

Research reported in this publication was supported by the Bill & Melinda Gates Foundation and Bloomberg Philanthropies. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. The study funders had no role in study design, data collection, data analysis, data interpretation, writing of the final report or the decision to publish.

We thank the Tobacco Metrics Team Advisory Group for their valuable input and review of the work. The members of the Advisory Group are: P. Allebeck, R. Chandora, J. Drope, M. Eriksen, E. Fernández, H. Gouda, R. Kennedy, D. McGoldrick, L. Pan, K. Schotte, E. Sebrie, J. Soriano, M. Tynan and K. Welding.

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X.D., S.I.H., S.A.M., E.C.M., E.M.O., C.J.L.M. and E.G. managed the estimation or publications process. X.D. and G.F.G. wrote the first draft of the manuscript. X.D. and P.Z. had primary responsibility for applying analytical methods to produce estimates. X.D., G.F.G., N.S.A., J.A.A., S.C., R.F., V.I., M.J.M., L.M., S.I.N., C.O., M.B.R. and J.W. had primary responsibility for seeking, cataloguing, extracting or cleaning data, and for designing or coding figures and tables. X.D., G.F.G., M.B.R., N.S.A., H.R.L., C.O. and J.W. provided data or critical feedback on data sources. X.D., J.H., R.J.D.S., A.Y.A., P.Z., C.J.L.M. and E.G. developed methods or computational machinery. X.D., G.F.G., M.B.R., S.I.H., J.H., R.J.D.S., A.Y.A., P.Z., C.J.L.M. and E.G. provided critical feedback on methods or results. X.D., G.F.G., M.B.R., C.B., S.I.H., L.B.M., S.A.M., A.Y.A. and E.G. drafted the work or revised it critically for important intellectual content. X.D., S.I.H., L.B.M., E.C.M., E.M.O. and E.G. managed the overall research enterprise.

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Extended data

Extended data fig. 1 prisma 2020 flow diagram for an updated systematic review of the smoking and tracheal, bronchus, and lung cancer risk-outcome pair..

The PRISMA flow diagram of an updated systematic review on the relationship between smoking and lung cancer conducted on PubMed to update historical review from previous cycles of the Global Burden of Disease Study. Template is from: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71. For more information, visit: http://www.prisma-statement.org/ .

Extended Data Fig. 2 PRISMA 2020 flow diagram for an updated systematic review of the Smoking and Chronic obstructive pulmonary disease risk-outcome pair.

The PRISMA flow diagram of an updated systematic review on the relationship between smoking and chronic obstructive pulmonary disease conducted on PubMed to update historical review from previous cycles of the Global Burden of Disease Study. Template is from: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71. For more information, visit: http://www.prisma-statement.org/ .

Extended Data Fig. 3 PRISMA 2020 flow diagram for an updated systematic review of the Smoking and Diabetes mellitus type 2 risk- outcome pair.

The PRISMA flow diagram of an updated systematic review on the relationship between smoking and type 2 diabetes conducted on PubMed to update historical review from previous cycles of the Global Burden of Disease Study. Template is from: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71. For more information, visit: http://www.prisma-statement.org/ .

Extended Data Fig. 4 PRISMA 2020 flow diagram for an updated systematic review of the Smoking and Breast cancer risk-outcome pair.

The PRISMA flow diagram of an updated systematic review on the relationship between smoking and breast cancer conducted on PubMed to update historical review from previous cycles of the Global Burden of Disease Study. Template is from: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71. For more information, visit: http://www.prisma-statement.org/ .

Extended Data Fig. 5 PRISMA 2020 flow diagram for an updated systematic review of the Smoking and Prostate cancer risk-outcome pair.

The PRISMA flow diagram of an updated systematic review on the relationship between smoking and prostate cancer conducted on PubMed to update historical review from previous cycles of the Global Burden of Disease Study. Template is from: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71. For more information, visit: http://www.prisma-statement.org/ .

Extended Data Fig. 6 Smoking and Breast Cancer.

a , log-relative risk function. b , relative risk function. c , A modified funnel plot showing the residuals (relative to 0) on the x-axis and the estimated standard deviation (SD) that includes reported SD and between-study heterogeneity on the y-axis.

Supplementary information

Supplementary information.

Supplementary Information 1: Data source identification and assessment. Supplementary Information 2: Data inputs. Supplementary Information 3: Study quality and bias assessment. Supplementary Information 4: The dose–response RR curves and their 95% UIs for all smoking–outcome pairs. Supplementary Information 5: Supplementary methods. Supplementary Information 6: Sensitivity analysis. Supplementary Information 7: Binary smoking–outcome pair. Supplementary Information 8: Risk curve details. Supplementary Information 9: GATHER and PRISMA checklists.

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Dai, X., Gil, G.F., Reitsma, M.B. et al. Health effects associated with smoking: a Burden of Proof study. Nat Med 28 , 2045–2055 (2022). https://doi.org/10.1038/s41591-022-01978-x

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• Published: 01 February 2017

College anti-smoking policies and student smoking behavior: a review of the literature

• Brooke L. Bennett 1 ,
• Melodi Deiner 1 &
• Pallav Pokhrel 1  

Tobacco Induced Diseases volume  15 , Article number:  11 ( 2017 ) Cite this article

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Currently, most college campuses across the U.S. in some way address on-campus cigarette smoking, mainly through policies that restrict smoking on campus premises. However, it is not well understood whether college-level anti-smoking policies help reduce cigarette smoking among students. In addition, little is known about policies that may have an impact on student smoking behavior. This study attempted to address these issues through a literature review.

A systematic literature review was performed. To identify relevant studies, the following online databases were searched using specific keywords: Ovid MEDLINE, PsycINFO, PubMed, and Google Scholar. Studies that met the exclusion and inclusion criteria were selected for review. Studies were not excluded based on the type of anti-smoking policy studied.

Total 11 studies were included in the review. The majority of the studies (54.5%) were cross-sectional in design, 18% were longitudinal, and the rest involved counting cigarette butts or smokers. Most studies represented more women than men and more Whites than individuals of other ethnic/racial groups. The majority (54.5%) of the studies evaluated 100% smoke-free or tobacco-free campus policies. Other types of policies studied included the use of partial smoking restriction and integration of preventive education and/or smoking cessation programs into college-level policies. As far as the role of campus smoking policies on reducing student smoking behavior is concerned, the results of the cross-sectional studies were mixed. However, the results of the two longitudinal studies reviewed were promising in that policies were found to significantly reduce smoking behavior and pro-smoking attitudes over time.

More longitudinal studies are needed to better understand the role of college anti-smoking policies on student smoking behavior. Current data indicate that stricter, more comprehensive policies, and policies that incorporate prevention and cessation programming, produce better results in terms of reducing smoking behavior.

Tobacco use, especially cigarette smoking, continues to remain a leading preventable cause of mortality in the United States (U.S.). Across different age-groups, young adults (18–29 year olds) tend to show the highest prevalence of cigarette smoking [ 1 ]. For example, past-30-day prevalence of cigarette smoking among 18–24 year olds is 17%, whereas the prevalence is approximately 9% among high school students [ 2 ]. Although most smokers initiate cigarette smoking in adolescence, young adulthood is the period during which experimenters transition into regular use and develop nicotine dependence [ 1 ]. Young adulthood is also the period that facilitates continued intermittent or occasional smoking [ 3 ], neither of which is safe. In addition to the possibility that intermittent smokers may show escalation in nicotine dependence, intermittent smoking exposes individuals to carcinogens and induces adverse physiological consequences [ 4 ].

Research [ 5 ] shows that smokers who quit smoking before the age of 30 almost eliminate the risk of mortality due to smoking-induced causes. Thus smoking prevention and cessation efforts that target young adults are of importance. Traditionally, tobacco-related primary prevention efforts have mostly focused on adolescents [ 6 ] and have utilized mass media as well as school and community settings [ 7 , 8 ]. This is only natural given that most smoking initiation occurs in adolescence. However, primary and secondary prevention efforts focusing on young adults have been less common. This is particularly of concern because tobacco industry is known to market tobacco products strategically to promote tobacco use among young adults by integrating tobacco use into activities and places that are relevant to young adults [ 9 ].

As more and more young adults attend college [ 10 ], college campuses provide a great setting for primary and secondary smoking prevention as well as smoking cessation efforts targeting young adults. According to the American College Health Association [ 11 ], approximately 29% U.S. college students report lifetime cigarette smoking and 12% report past-30-day smoking. Currently, most college campuses across the U.S. in some way address on-campus cigarette smoking, mainly through policies that restrict smoking [ 12 , 13 ]. One of the main reasons why such policies are considered important is the concern about students’ exposure to secondhand tobacco smoke [ 14 ]. Therefore, at their most rudimentary forms, such policies tend to be extensions of local- or state-level policies restricting smoking in public places [ 15 ]. However, some colleges may take a more comprehensive approach, by integrating, for example, smoke-free policies with anti-smoking campaigns and college-sponsored cessation services [ 16 ]. Further, some colleges may implement plans to enhance enforcement of and compliance to the smoke-free policies [ 17 – 19 ].

At present, there are a number of questions related to college-level anti-smoking policies that need to be examined carefully in order to scientifically inform how colleges can be better utilized to promote smoking prevention and cessation among young adults. Besides the degree of variation in anti-smoking policies, there are questions about students’ compliance with such policies and whether such policies have influence on students’ attitudes and behavior related to cigarette smoking. Past reviews of the studies on the effects of tobacco control policies in general (e.g., not specific to college populations) [ 20 – 22 ] emphasize the need for a review such as the current study. Wilson et al. [ 20 ] found that interventions involving smoke-free public places, mostly restaurants/bars and workplaces, showed a moderate to low effect in terms of reducing smoking prevalence and promoting smoking cessation. The review included three longitudinal studies, none of which showed that the policies had an effect on smoking cessation. Fichtenberg & Glanz [ 21 ] focused on smoke-free workplaces and found that the effects of such policies seemed to depend on their strength. That is, 100% smoke-free policies were found to reduce cigarette consumption and smoking prevalence twice as much as partial smoke-free policies that allowed smoking in certain areas. In a recent exhaustive review, Frazer et al. [ 22 ] found that although national restrictions on smoking in public places may improve cardiovascular health outcomes and reduce smoking-related mortality, their effects on smoking behavior appear inconsistent. There are reasons why college anti-smoking policies may be more effective than policies focused on restaurant/bars or even workplaces. For example, students tend to spend the majority of their time on campus premises. In fact, in the case of 4-year colleges, a large number of students live on or around campus premises. Strong anti-smoking policies may deter students from smoking by making, for example, smoking very inconvenient. However, the current state of research on college anti-smoking policies and student smoking behavior is not well documented.

The purpose of the current study is to systematically review quantitative studies that have investigated the impact of college-level anti-smoking policies on students’ attitudes towards tobacco smoking and smoking behavior. In the process, we intend to highlight the types of research designs used across studies, the types of college and student participants represented across studies, and the studies’ major findings. A point to note is that this review’s focus is on anti-smoking policies and cigarette smoking. Although the review does assess tobacco-free policies in general, our assumption at the outset has been that most studies in the area have had a focus on smoke-free policies and smoking behavior because of the emphasis on secondhand smoke exposure. Smoke-free and tobacco-free policies are different in that smoke-free policies have traditionally targeted smoking only whereas tobacco-free policies that have targeted tobacco use of any kind, including smokeless tobacco [ 23 ]. Both types of policy could be easily extended to incorporate new tobacco products such as the electronic nicotine delivery devices, commonly known as e-cigarettes. Given that e-cigarettes are a relatively new phenomenon in the process of being regulated, we assumed that the studies eligible for the current review might not have addressed e-cigarette use, although if addressed by the studies reviewed, we were open to addressing e-cigarettes and e-cigarette use or vaping in the current review.

Study selection

We searched Ovid MEDLINE (1990 to June, 2016), PubMed (1990 to June, 2016), PsycINFO (1990 to 2013), and Google Scholar databases to identify U.S.-based peer-reviewed studies that examined the effects of college anti-smoking policies on young adults’ smoking behavior. Searches were conducted by crossing keywords “college” and “university” separately with “policy/policies” and “smoking”, “tobacco”, “school tobacco”, “smoke-free” “smoking ban,” and “tobacco free.” Article relevance was first determined by scanning the titles and abstracts of the articles generated from the initial search. Every quantitative study that dealt with college smoking policy was selected for the next round of appraisal, during which, the first and the last authors independently read the full texts of the articles to vet them for selection. Studies were selected for inclusion in the review if they met the following criteria: studies 1) were conducted in the U.S. college campuses, including 2- and 4-year colleges and universities; 2) were focused on young adults (18–25 year olds); 3) focused on implementation of college-level smoking policies; 4) were quantitative in methodology (e.g., case studies and studies based on focus groups and interviews were excluded); and 5) directly (e.g., self-report) or indirectly (e.g., counting cigarette butts on premises) assessed the cigarette smoking behavior. References and bibliographies of the articles that met the inclusion criteria were also carefully examined to locate additional, potentially eligible studies.

Selected studies were reviewed independently by the first and the last authors in terms of study objectives, study design (i.e., cross-sectional or longitudinal), data collection methods, participant characteristics, U.S. region where the study was conducted, college type (e.g., 2- year vs. 4-year), policies examined and the main study findings. The review results independently compiled by the two authors were compared and aggregated after differences were sorted out and a consensus was reached.

Study characteristics

Figure  1 depicts the path to the final set of articles selected for review. Initial searches across databases resulted in total 71 titles and abstracts related to college smoking policies. Of these, 49 were deemed ineligible at the first phase of evaluation. The remaining 22 articles were evaluated further, of which, 11 were excluded eventually. Two studies [ 24 , 25 ] were excluded because these studies did not assess students’ tobacco use behavior. One study [ 26 ] was excluded because it was not quantitative. Five studies [ 17 – 19 , 27 , 28 ] were excluded because the studies focused on compliance to existing smoking policies and did not assess the impact of policies on behavior. One study [ 15 ] was excluded because although it studied college students, the smoking policies examined were county-wide rather than college-level. Two studies [ 29 , 30 ] were excluded because their samples consisted of college personnel rather than students. Thus, a total of 11 studies were included in the current review.

Chart depicting selection of the final set of articles reviewed

Table  1 summarizes the selected studies in terms of research purpose, study design, subjects, type of college, region, policies and findings. The majority of the studies were conducted in the Midwestern ( n  = 3; 27.3%) or Southeastern United States ( n  = 3; 27.3%). Other regions represented across studies were Southern ( n  = 2; 18.1%), Northwestern ( n  = 2; 18.1%), and Western United States ( n  = 1; 9.1%). Six studies (54.5%) included predominantly White participants (i.e., greater than 70%), and 2 studies (18%) included predominantly female participants. Nationally, women and Whites comprise 56% and 59% of the U.S. college student demographics, respectively [ 10 ]. Two studies (18.1%) assessed smoking behavior indirectly by counting cigarette butts on college premises, counting the number of individuals smoking cigarettes in campus smoking “hotspots,” or counting the number of smokers who utilized smoking cessation services. Across studies, the sample size ranged between N  = 36 and N  = 13,041. The mean and median sample sizes across studies were 3102 (SD = 4138) and 1309, respectively. Participants tended to range between 18 and 30 years in age. The majority of the studies ( n  = 6; 54.4%) were cross-sectional in design. Only 2 (18%) of the studies were longitudinal. The majority of the studies were conducted at 4-year colleges ( n  = 10; 90.9%). Only 1 study was conducted at a 2-year college ( n  = 1; 9.1%).

Three studies (27%) focused on tobacco-free policies and 3 studies (27%) on smoke-free policies. Three studies ( n  = 3; 27.3%) compared the associations of differing policies on smoking behavior. One study [ 31 ] examined the relative impacts of policies utilizing preventive education, smoking cessation programs, and designated smoking areas or partial smoking restriction. Another study [ 32 ] implemented an intervention to increase adherence to a partial smoking policy (i.e., smoking ban within 25 ft of buildings). The intervention involved increasing anti-tobacco signage, moving receptacles, marking the ground, and distributing reinforcements and reminder cards.

Anti-smoking policies and students’ smoking behavior

Table  1 lists the types of anti-smoking policies examined across studies and the corresponding findings. Major findings are as follows:

Partial smoking restriction

Borders et al. [ 31 ] compared colleges that utilized partial smoking restriction by providing “designated smoking areas” to curb smoking with college-level policies that incorporated preventive education and with those that provided smoking cessation courses only. Results indicated that the presence of preventive education was associated with lower odds of past-30-day smoking whereas the presence of designated smoking areas only or smoking cessation programs only was associated with higher odds of past-30-day smoking. Fallin et al. [ 16 ] found that college campuses with designated smoking areas tended to show higher prevalence of smoking, compared with campuses that enforced smoke-free and tobacco-free policies. Braverman et al.’s [ 33 ] findings indicate that enforcing smoke-free policies tends to reduce secondhand exposure close to college buildings but may increase smoking behavior on the campus periphery.

Smoke- and tobacco-free campuses

Fallin et al. [ 16 ] found that compared with policies that relied on partial smoking restriction, tobacco-free policies were associated with reduced self-reported exposure to secondhand smoke as well as students’ lower self-reported intentions to smoke cigarettes in the future. Studies [ 34 , 35 ] consistently observed fewer cigarette butts or smokers in campuses under smoke-free policies compared with campuses without smoke-free policies. Prevalence of cigarette butts was likely to be inversely related to policy strength [ 35 ]. A study that monitored smokers’ behavioral compliance to smoke-free policies [ 32 ] indicated that interventions to promote compliance, such as use of signage, are likely to be effective in improving compliance and reducing student smoking in areas were the policy is enforced.

Lechner et al. [ 36 ] conducted assessments at a single college campus before and after a tobacco-free policy went into implementation. The policy, which also involved making smoking cessation services available campus-wide, was found to reduce proportions of high- and low-frequency smokers, pro-smoking attitudes (i.e., weight loss expectancy), and exposure to second-hand tobacco smoke [ 36 ]. The study did not find an effect on smoking prevalence. Seo et al. [ 37 ] followed a similar design where a policy intervention was evaluated based on pretest and posttest surveys. However, this study [ 37 ] included a “control” campus where similar assessments as in the “treatment” campus were conducted but no intervention was implemented. The study found that compared with the control campus, the campus that implemented smoke-free policies showed an overall decrease in smoking prevalence.

Other policies

Borders et al. [ 31 ] did not find policies governing the sales and distribution of cigarettes on campus to be associated with smoking behavior. Hahn et al. [ 38 ] found that college smoking policies that integrate smoking cessation services may increase the use of such services as well as promote smoking cessation. This study kept track of students who utilized the smoking cessation service offered by a college after the policy offering such a service was enacted. Sixteen months after the policy was first implemented, smokers who utilized the service were surveyed. Based the results it was estimated that approximately 9% of them had quit smoking.

To our knowledge, this is the first study to systematically review studies examining the effects of anti-smoking policies on smoking behaviors among U.S. college students. We found that such studies are severely limited. Only 11 studies met the inclusion criteria in the present review, although the review appeared to encompass all policies aimed at smoking behavior on college campuses. Thus, this review stresses the need for increased smoking policy and smoking behavior research on college campuses.

Rigorous evaluation of existing college anti-tobacco policies are needed to refine and improve the policies so that national-level efforts to reduce tobacco use among young adults are realized. Key initiatives at the national level have recognized the importance of mobilizing college campuses in the fight against tobacco use. For example, in September 2012 several national leaders involved in tobacco control efforts, in collaboration with the ACHA, came together to launch the Tobacco-Free College Campus Initiative (TFCCI) [ 39 ]. The TFCCI aims to promote and support the use of college-level anti-tobacco policies as a means to change pro-tobacco social norms on campuses, discourage tobacco use, protect non-smokers from second-hand exposure to tobacco smoke and promote smoking cessation. The ACHA’s position statement [ 11 ] regarding college tobacco control recommends a no tobacco use policy aimed towards achieving a 100% indoor and outdoor campus-wide tobacco-free environment.

We found that the majority of studies on smoking policies were cross-sectional in nature. Researchers relied upon students to report their smoking behavior or their observations of other students’ smoking behavior after a smoke-free or tobacco-free policy had been implemented. It is difficult to draw conclusions about an anti-smoking policy’s ability to change smoking behavior without knowing the smoking behavior prior to policy implementation. This domain of research would benefit from additional longitudinal studies. Ideally, research studies should collect data before the policy is implemented, immediately after, and at follow-up time points.

We found inconsistencies in the measurement of smoking behavior across studies. Two studies [ 34 , 35 ] counted cigarette butts, one study [ 38 ] counted people seeking tobacco dependence treatment, one study [ 32 ] counted smokers violating policy, and seven studies [ 16 , 31 , 36 , 37 , 40 , 41 ] relied upon self-report of smoking behavior. Another study [ 33 ] used survey methods to obtain participants’ response on other students’ smoking behavior. Counting cigarette butts has been validated as an effective measure of smoking behavior [ 19 ], especially when validating compliance to an anti-smoking policy, and self-report measures are commonly used in public health research [ 42 ]. Despite the validity and feasibility of these measures, the lack of a consistent measurement tool makes comparing effectiveness of anti-smoking policies on smoking behaviors across campuses difficult. Research in this domain would benefit from a consistently used measurement of smoking behaviors.

Although the reviewed studies represented diverse U.S. regions, the majority of the research was set in the Southeastern and Midwestern United States; Northeastern and Southwestern regions were not represented. Only one of the reviewed studies reported a sample that contained less than 50% White participants. Across studies, the minority group most represented was Asian American; but only one of the reviewed studies [ 16 ] included 20% or more Asian Americans. Relatively few studies included or reported Hispanic participants, although Hispanics are the largest minority group in the United States [ 43 ]. None of the reviewed studies included 20% or more Black participants. Only three studies [ 33 , 36 , 37 ] included American Indian/Alaska Natives and in only one of those studies [ 32 ] was the proportion greater than one percent. Only two studies [ 33 , 37 ] included Pacific Islanders, and in both the proportion was less than one percent. Clearly, more research is needed on minority populations, specifically Black, Hispanic, Native Hawaiian/Pacific Islander, American Indian/Alaska Native students and the subgroups commonly subsumed under these ethnic/racial categories. The U.S. college student demography is ethnically/racially diverse [ 10 ], comprising 59% Whites. The remaining 44% include various minority groups. Thus, for research on U.S. college students across the nation, studies with more ethnically/racially diverse student samples are needed.

The review findings were helpful in elucidating the types of tobacco policies being implemented on college campuses and their effects on the smoking behavior of U.S. college students. Mainly, three types of smoking policies were studied: smoke-free policies, tobacco-free policies and policies that enforced partial smoking restriction, including prohibition of smoking within 20–25 ft of all buildings and providing designated smoking areas. Indeed, campus-wide indoor and outdoor tobacco-free policy is considered a gold-standard for college campus tobacco control policy [ 11 ]. But only one study [ 16 ] compared tobacco-free and smoke-free policies. Other policies such as governing the sale and distribution of tobacco products, preventive education programs, and smoking cessations programs were also studied, but to a lesser extent. In general, interventions regarding the implementation of smoking policies on college campuses were difficult to find in the existing literature.

The combined results of the studies reviewed suggest that stricter smoking policies are more successful in reducing the smoking behavior of students. Tobacco-free and smoke-free policies were linked with reduced smoking frequency [ 16 , 36 , 37 ], reduced exposure to second-hand smoke [ 16 , 36 ], and a reduction in pro-smoking attitudes [ 36 ]. Implementation of a campus-wide tobacco-free or smoke-free policy combined with access to smoking cessation services was also associated with increased quit attempts [ 38 , 40 ] and treatment seeking behaviors [ 38 ]. It appears that 100% smoke-free policies are not only successful in reducing smoking rates, but also have strong support from students and staff members alike [ 33 ]. These results remained consistent when compared to less comprehensive tobacco control policies, which was evidenced by student report and the number of cigarette butts found on campus [ 34 , 35 ].

There was one important consistent exception to the general success of anti-smoking policies: designated smoking areas. All three studies which included designated smoking areas [ 16 , 31 , 41 ] found that designated smoking areas were associated with higher rates of smoking compared with smoke-free or tobacco-free policies. Designated smoking areas were also associated with the highest rates of recent smoking [ 16 ]. Lochbihler, Miller, and Etcheverry [ 41 ] proposed that students using the designated areas were more likely to experience positive effects of social interaction while smoking. They found that social interaction while smoking on campus significantly increased the perceived rewards associated with smoking and the frequency of visits to designated smoking areas [ 41 ].

None of the studies included in this review addressed new and emerging tobacco products such as e-cigarettes. This is understandable given that the surge in e-cigarette use is relatively new and in general there have only been a few studies examining the effects of anti-smoking policies on student smoking behavior, which has been the focus of this review. However, going forward, it will be crucial for studies to examine how campus policies are going to handle e-cigarette use, including the enforcement of on-campus anti-smoking policies given the new challenges posed by e-cigarette use [ 44 ]. For example, e-cigarette use is highly visible, the smell of the e-cigarette vapor does not linger in the air for long and e-cigarette consumption does not result in something similar to cigarette butts. These characteristics are likely to make the monitoring of policy compliance more difficult. Moreover, because of the general perception among e-cigarette users that e-cigarette use is safer than cigarette smoking, compared with cigarette smokers smoking cigarettes, e-cigarette users might be more likely to use e-cigarettes in public places. The fact that the TFCCI strongly recommends the inclusion of e-cigarettes in college tobacco-free policies [ 39 ] bodes well for the future of college health.

The current study has certain limitations. It is possible that this review might have missed a very small number of eligible studies. We believe that the literature searches we completed were thorough. However, new studies are regularly being published and the possibility that a new, eligible study may have been published after we completed our searches cannot be ignored. In addition, we may not have tapped eligible studies that were in press during our searches. If indeed a few eligible studies were not included in our review, the non-inclusion may have biased our results somewhat, although it is difficult for us to speculate the nature of such a bias. Hence, we recommend that similar studies need to be conducted in the future to periodically review the literature. Second, non-peer-reviewed articles or book chapters were excluded from this review. Despite the potential relevance of non-peer-reviewed materials, the choice was made to limit the inclusion in order to maintain scientific rigor of the review. However, it is possible that some data pertinent to the review might have been overlooked because of this, thus increasing the possibility of introducing a bias to the current findings. Third, this study focused on anti-smoking policies. Although we used “tobacco free” as search terms, “smoking” dominated our search strategies. Thus our results are more pertinent to cigarette smoking than other tobacco products and may not generalize to the latter. Lastly, in order to be as inclusive as possible, we reviewed three studies [ 32 , 35 , 38 ] that focused on more on compliance to anti-smoking policy than on the effect of policy on student smoking behavior. The findings of these studies may not be comprehensive in regard to student smoking behavior, even though they are indicative of the success of the policies under examination.

Conclusions

Despite limitations, this study is significant for increasing the understanding of smoking policies on U.S. college campuses and their effects on the smoking behavior of college students. We found that research on smoking policies on U.S. college campuses is very limited and is an area in need of additional research contribution. Within existing research, the majority used samples that were primarily White females. More diverse samples are needed. Future research should also report the full racial/ethnic characteristics of their samples in order to identify where representation may be lacking. Future research would benefit from longitudinal and interventional studies of the implementation of smoking policies. The majority of current research is cross-sectional, which does not provide the needed data in order to make causal statements about anti-smoking policies. Lastly, existing research was primarily conducted at 4-year colleges or universities. Future research would benefit from broadening the target campuses to include community colleges and trade schools. Community colleges provide a rich and unique opportunity to collect data on a population that is often older and more racial diverse than a typical 4-year college sample [ 45 ]. Also, there is at present a need to understand through research how evidence-based implementation and compliance strategies can be utilized to ensure policy success. A strong policy on paper does not often translate into a strong policy in action. Thus, comparing policies on the strength of written documents alone is not enough; policies need to be compared on the extent to which they are enforced as well as the impact they have on student behavior.

This review may be of particular interest to college or universities in the process of making their own anti-smoking policies. The combined results of the existing studies on the impact of anti-smoking policies on smoking behaviors among U.S. college students can help colleges and universities make informed decisions. The existing research suggests that stricter policies produce better results for smoking behavior reduction and with smoking continuing to remain a leading preventable cause of mortality in the U.S. across age-groups [ 1 ], college and university policy makers should take note. Young adults (18–25 year olds) show the highest prevalence of cigarette smoking [ 1 ], which places colleges and universities in the unique position to potentially intervene through restrictive anti-smoking policies on campus.

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This research was supported by National Cancer Institute (NCI) grant 1R01CA202277-01.

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Bennett, B.L., Deiner, M. & Pokhrel, P. College anti-smoking policies and student smoking behavior: a review of the literature. Tob. Induced Dis. 15 , 11 (2017). https://doi.org/10.1186/s12971-017-0117-z

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Prevalence and determinants of cigarette smoking among college students: a cross-sectional study in Douala, Cameroon

• Bertrand Hugo Mbatchou Ngahane 1 , 2 ,
• Huguette Atangana Ekobo 2 &
• Christopher Kuaban 3  

Archives of Public Health volume  73 , Article number:  47 ( 2015 ) Cite this article

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Tobacco is the most important avoidable risk for non communicable diseases. While tobacco consumption is stable or declining in developed countries, it is increasing in the developing world with a rate of 3.4 % per annum. The objective of this study was to estimate the prevalence and factors associated with cigarette smoking among college students.

A cross-sectional study was conducted from December 2012 to April 2013 in secondary schools in Douala, Cameroon. A self-administered questionnaire was used to collect sociodemographic data, smoking behavior and peer smoking among college students. Logistic regression analyses was employed to identify factors associated with cigarette smoking.

Of a total of 2623 students included, 1579 (60.2 %) were female. The mean age of participants was 19.2 ± 2.53 years. The prevalence of current smoking was 11.2 % [95 % confidence interval (CI) 10 – 12], with 20 % in males and 5.3 % in females. Cigarette smoking was with significantly associated with friends smoking [Odds ratio (OR) 6.66; 95 % CI 4.69 – 9.45)], male gender (OR 3.61; 95 % CI 2.52 – 5.16), increase in age (OR 1.10; 1.03 – 1.17), parental smoking 1.69 (1.04 – 2.76), and attending general education (OR 1.85; 1.23 – 2.78).

Conclusions

Cigarette smoking constitutes a significant health hazard in college students in Douala. Youth population and especially male students should be continuously targeted by preventive measures and sensitization campaigns against tobacco use. Parents should be aware on the influence of their smoking behavior on initiation of smoking in their children and should be encouraged to quit smoking.

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Tobacco smoking remains a serious threat to global health, killing nearly 6 million people each year and causing excessive health-care costs and lost productivity [ 1 ]. About 80 % of the more than one billion smokers worldwide live in low- and middle-income countries, where the burden of tobacco-related illness and death is heaviest [ 1 ]. While tobacco consumption is stable or declining in developed countries, it is increasing in the developing world with a rate of 3.4 % per annum [ 2 ]. A recent study on projection of tobacco use predicted a worsening of tobacco epidemics in countries of Africa and eastern Mediterranean where health system are fragile [ 3 ]. Youth and women are the main targets of tobacco industries in these countries as they are developing economically [ 4 ]. Tobacco is the most important avoidable risk for non communicable diseases (NCDs) such as cancers, chronic lung disease, diabetes and cardiovascular diseases [ 1 ]. With the increasing prevalence of smoking in developing world over the years, NCDs will double the burden of infective and non-infective diseases [ 2 ].

The Global Youth Tobacco Survey (GYTS) which was designed by the Center for Disease Control and Prevention and the World Health Organization estimated the worldwide burden of tobacco use among youth [ 5 ]. The results of this survey which included school children from 131 countries showed a global prevalence of 8.9 % for current smoking students. This prevalence was highest in the WHO Region of the Americas (17.5 %) and the WHO European Region (17.9 %) and less than 10 % in the four other WHO regions [ 5 ]. In Ethiopia, the GYTS collaborative group reported 4.5 % of prevalence of smoking in males and 1 % in females aged 13–15 years [ 6 ]. In Cameroon, a country without any tobacco control legislation, the prevalence of smoking is relatively low [ 7 ]. The GYTS reported a cigarette smoking prevalence of 5.7 % among college students aged 13–15 years [ 8 ]. It has been shown that children who start smoking during a younger age are more likely to smoke as adults than individuals who begin at older ages [ 9 ]. Therefore, though it is important to determine the burden of tobacco use in this age group, it is also necessary to investigate its associated factors in order to design efficient preventive programs against tobacco consumption. Only few studies have investigated the factors associated with tobacco use among school adolescents in sub-Saharan Africa [ 10 – 12 ] and we didn’t find any related study in Cameroon. We therefore aimed in this study to estimate the prevalence and associated factors of cigarette smoking in college students.

Study design and setting

A cross-sectional study was conducted in secondary schools in Douala from 1 st December 2012 to April 30, 2013. Douala is the economic capital and the largest biggest city of Cameroon. It is also the headquarters of the Littoral region and the population was estimated at 1907479 inhabitants in 2005 [ 13 ]. In Cameroon, secondary education which follows the 6 years of primary education has a 7 years duration and is divided into 2 cycles: the 1 st cycle which comprises the 4 first years of studies and the 3 last years of the secondary education constitute the 2 nd cycle [ 14 ]. Students are allowed to choose technical and vocational education or general education. They can register either in a private or public school.

Participants and sampling

We conveniently choose to include students attending the second cycle of secondary education. Of a total of 300 secondary schools in the city of Douala, 85 of them were excluded because they had only the first 4 years of secondary education. We finally had 215 eligible schools. Eligible students were those present in class during our visit in schools. Non consenting students were excluded.

We used a multistage probability proportional to size stratified sampling procedure with the school being the primary sampling unit. In the first stage, the strata were defined according to the type of education (general vs technical and vocational) and the status (public vs private). We thus had 4 strata: public general (20 schools), private general (118 schools), public technical and vocational (6 schools) and private technical and vocational (71 schools). These schools were randomly selected in proportion to their size, giving respectively 3, 20, 1 and 12 schools for the above strata. In the second stage, 2 classes were randomly selected in each school and finally, in each class, all students were invited to participate in the study.

The study was approved by the Cameroon’s National Ethics Committee. In addition, we got administrative authorizations from the Ministry of Secondary Education and the school authorities. Parents of students aged less than 18 years were also informed about the scope of the study and gave a verbal consent before the recruitment of their children in the study.

Data collection

The selected students were given a self-administered anonymous questionnaire in their classroom during breaks. Teachers were asked to leave the classroom during the survey administration and the data collection was supervised by a trained investigator who was present in the class. He collected the forms as soon as they were filled by the students.

The questionnaire assessed the sociodemographic data (age, gender, year of education), number of classes repeated, smoking behavior (smoking status, reason of smoking, smoking during the last 30 days), smoking status of family members and friends, knowledge of tobacco hazards. For the latter, participants were asked to give a yes or no response for the question if they knew some negative effect of tobacco health, without giving the details of this hazards. The Fagerström score of nicotine dependence was accessed in smokers. A score smaller than 3 indicates low dependence while a score between 3 and 6 indicates moderate dependence and a score between 7 and 10 reflects a high nicotine dependence [ 15 ].

The dependent variable in this study was the smoking status. Were considered as current smokers in this study, students smoking at least one cigarette per month. Daily smokers were those smoking at least one cigarette per day, weekly smokers were those smoking 1 to 6 cigarettes per week, ,while occasional smokers or monthly smokers were those smoking 1 to 3 cigarettes per month. The exposure variables were: age, gender, type of education (general vs technical), school status (private vs public), number of classes repeated, parental smoking, peer smoking and knowledge of health effect of smoking.

Statistical analysis

Data were entered and analyzed using IBM SPSS statistics Version 20 (Armonk, NY: IBM Corp). Descriptive statistics included frequencies and proportions for categorical data and means with standard deviations (SD) for continuous data. Logistic regression analysis was used to assess the factors associated with smoking. A univariate analysis was firstly performed to estimate crude odds ratios and their 95 % confidence intervals (95 % CI). Variables found to be significantly associated in univariate analysis were considered in the multivariable models using a stepwise backward elimination procedure. A p -value less than 0.05 was considered to be statistically significant.

General characteristics of participants

Of a total of 3170 students who completed the survey, exploitable data were available for 2623 (82.7 %) respondents. Of them, 1579 (60.2 %) were female and 1044 (39.2 %) were male. The mean age of the participants was 19.2 ± 2.53 years (range 12 – 29). Students aged between 15 and 19 years were the most represented (55.7 %). The mean number of classes repeated by students was 1.28 ± 0.97. The other baseline characteristics of participants are showed in Table  1 .

Smoking status of participants

One third of the respondents (33.5 %) had ever smoked a cigarette while the prevalence of current cigarette smoking was 11.2 % (95 % CI 10 – 12) with 8.4 % being regular smokers and 2.8 % occasional smokers. The male prevalence (20 %) of cigarette smoking was significantly higher than that of females (5.3 %) ( P  = 0.000). The mean age at starting smoking of cigarettes was 14.6 ± 3.8 (range 6 – 24) and 270 (10.3 %) smoked at least one cigarette in the last 30 days prior the study. Among the 293 cigarette smokers, the Fagerström score for nicotine dependence was high for 18.4 % of them, moderate for 46.4 % and low for 35.2 %. The main reasons of starting smoking were curiosity (92.1 %), pleasure (91.8 %) and stressful situations (90.4 %). Table  2 shows the other attitudes of smoking students.

Risk factors of cigarette smoking

The univariate analysis revealed that male sex, age, attending general education, number of classes repeated, parental smoking, family smoking and friends smoking were associated with current smoking status (Table  3 ). After adjusting these variables to each other in the multivariate analysis, the most important risk factors for cigarette smoking was friends smoking (OR 6.66; 95 % CI 4.69 – 9.45). It was followed by male sex (OR 3.61; 95 % CI 2.52 – 5.16). An increase in age was also associated with smoking as well as parental smoking and attending a general education system (Table  4 ).

In this survey on smoking habits of college students in Cameroon, we found that the prevalence of cigarette smoking among college students was 11.2 %. The main predictors of cigarette smoking were having friends who smoke, male sex, age, parental smoking and attending general education.

Whatever the case this prevalence is higher than 5.7 % found in GYTS survey in Cameroon in 2008 [ 8 ]. May be the prevalence of smoking is increasing with the years, but we should notice that the GYTS study involved adolescents of 13 to 15 years while our study included college students with an elder age. It has been demonstrated that the prevalence of smoking increases with age among youths [ 16 – 18 ]. Our prevalence is similar to that of a study conducted in Ethiopia among college students aged 15 to 25 years which showed 12.2 % of smokers [ 12 ].

Having smoking friends in this study was the most important independent factor associated with cigarette smoking. Reports from different regions of the world found similar results [ 17 , 19 – 22 ]. Evidence from two longitudinal studies conducted in the United States showed that non-smoking adolescents who have friends who smoke are more likely to start smoking in the future than those without any smoking friends [ 23 , 24 ]. We also found that parental smoking was associated with smoking among college students. The critical influence of parental smoking on adolescent’s smoking behavior was demonstrated by Bricker et al. in a cohort study involving five thousand families [ 25 ]. This finding is consistent with the results of other studies carried out in developing countries as well as in industrialized countries [ 26 ]. In fact, children are more likely to reproduce the behaviors and attitudes of their parents who are considered by them as models. Secondly, as demonstrated by Scragg et al., parents who smoke are more likely to allow smoking in the house [ 27 ]. Students living with another family member who smoke had a twofold risk of being smokers than those living with non-smokers. Similar results were recently found by Shadid and Hossain in Jordan [ 28 ].

In this study, the influence of smoking of peers on the smoking status of students was greater than that of parental smoking. Similar results were reported in previous studies in sub-Saharan Africa [ 6 , 29 ]. On contrary, although a study conducted in 27 European countries reported parental smoking and smoking of peers as factors associated with smoking initiation, the effects of these two factors were similar [ 30 ].

Male sex was strongly associated with cigarette smoking. The same results have been found in other African countries [ 12 , 22 , 26 ] and in studies carried out in middle East and in South East Asia [ 26 , 28 , 31 ]. On the contrary, in developed countries, the disparity between male and female prevalence is smaller [ 32 ]. These data show the difference in social-cultural habits of different parts of the world. However, data concerning smoking prevalence in women in Africa may be underreported, especially in areas where smoking of women is not culturally accepted and is socially regarded as a pejorative behavior [ 26 ].

We detected an association between age and cigarette smoking in the present study. An increase in age was increasing the odds of smoking. Although this association has not been revealed by most of the studies, it was reported by some studies [ 12 , 16 , 18 , 33 ]. One possible explanation for this finding is that during adolescence, the self-affirmation of adolescents and their risk behavior increase with increasing age. Consequently, at the late adolescence, there is a high risk of smoking [ 34 , 35 ].

Attending general secondary education was another factor associated with cigarette smoking. The type of education as factor associated with smoking among students has not been well studied. However, our result is similar to that of Mohammad in Iran [ 36 ] but contrary to that of Nowicka-Sauer et al. in Poland who identified technical high school as a predictor of tobacco smoking [ 37 ]. Further studies are needed to investigate the role of technical or general education in the initiation of smoking among students.

The strengths of this study are the large sample of students and the sampling methods which participate to the accuracy of our results. This is confirmed by the narrowed confidence intervals that we obtained in the multivariate regression analysis.

Meanwhile, there are some limitations in this study. First, the prevalence of smoking may have been underestimated by negative responses from students who smoke secretly. Meanwhile, 17.3 % of students were excluded in this study because of they didn’t answer to key questions. The prevalence of smoking might have been increased or decreased if they had responded properly to these questions. In addition, some factors such as psychosocial factors, the influence of media were not assessed.

In conclusion, although the prevalence of smoking among college students in Douala is low, it may increase if there is no efficient action against tobacco use in Cameroon. Male sex, parental and peer smoking are the main predictors of smoking among youths. There is a need to design and implement effective preventive measures against tobacco use. In addition to college students, smoking families should be targeted by these programs.

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Acknowledgements

The authors thank the Pan African Thoracic Society MECOR course staff for their contribution during the preparation of the manuscript.

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Bertrand Hugo Mbatchou Ngahane

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Bertrand Hugo Mbatchou Ngahane & Huguette Atangana Ekobo

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MNBH conceived the study, analyzed the data and drafted the manuscript. AEH collected the data and KC revised the study protocol and the manuscript. All authors read and approved the final manuscript

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Mbatchou Ngahane, B.H., Atangana Ekobo, H. & Kuaban, C. Prevalence and determinants of cigarette smoking among college students: a cross-sectional study in Douala, Cameroon. Arch Public Health 73 , 47 (2015). https://doi.org/10.1186/s13690-015-0100-1

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DOI : https://doi.org/10.1186/s13690-015-0100-1

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Original research

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

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

Beverley M Essue

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

Prabhat Jha

Associated data.

bmjopen-2021-058324supp001.pdf

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

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

Interrupted time series analysis, adjusted for cigarette tax levels.

Four Canadian provinces, UK and Australia.

Participants

Entire population of smokers in each country.

Interventions

The year that vaping was widely introduced in each country.

Primary and secondary outcome measures

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

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

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

Strengths and limitations of this study

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

Introduction

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

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

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

Choice of jurisdictions

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

Measure of e-cigarette use and cigarette smoking

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

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

Data sources

Prevalence of current e-cigarette use.

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

Cigarette consumption

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

Smoking prevalence

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

Tobacco tax/cigarette price

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

Statistical analysis

Its analysis.

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

Supplementary data

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

Sensitivity analysis

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

Patient and public involvement

Patients or the public were not involved in this study.

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

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

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

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

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

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

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

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

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

Values in bold are statistically significant at 95% CI.

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

BC, British Columbia.

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

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

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

AHS, Australian National Health Survey.

Less restrictive vaping environment (+)

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

Environment with somewhat restrictive regulations on vaping (++)

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

More restrictive vaping environment (+++)

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

Most restrictive vaping environment (++++)

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

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

Smoking prevalence among young adults

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

Somewhat restrictive vaping environment (++)

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

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

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

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

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

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

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

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

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

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

Supplementary Material

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

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

Competing interests: None declared.

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

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

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

Data availability statement

Ethics statements, patient consent for publication.

Not applicable.

235 Smoking Essay Topics & Examples

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• Smoking Cigarette Should Be Banned Ban on tobacco smoking has resulted to a decline in the number of smokers as the world is sensitized on the consequences incurred on 31st May.
• Causes and Effects of Smoking Some people continue smoking as a result of the psychological addiction that is associated with nicotine that is present in cigarettes.
• Smoking: Effects, Reasons and Solutions This presentation provides harmful health effects of smoking, reasons for smoking, and solutions to smoking. Combination therapy that engages the drug Zyban, the concurrent using of NRT and counseling of smokers under smoking cessation program […]
• On Why One Should Stop Smoking Thesis and preview: today I am privileged to have your audience and I intend to talk to you about the effects of smoking, and also I propose to give a talk on how to solve […]
• Advertisements on the Effect of Smoking Do not Smoke” the campaign was meant to discourage the act of smoking among the youngsters, and to encourage them to think beyond and see the repercussions of smoking.
• “Thank You For Smoking” by Jason Reitman Film Analysis Despite the fact that by the end of the film the character changes his job, his nature remains the same: he believes himself to be born to talk and convince people.
• Smoking Cessation Programs Through the Wheel of Community Organizing The first step of the wheel is to listen to the community’s members and trying to understand their needs. After the organizer and the person receiving treatment make the connection, they need to understand how […]
• Smoking and Its Negative Effects on Human Beings Therefore, people need to be made aware of dental and other health problems they are likely to experience as a result of smoking.
• Hookah Smoking and Its Risks The third component of a hookah is the hose. This is located at the bottom of the hookah and acts as a base.
• Causes and Effects of Smoking in Public The research has further indicated that the carcinogens are in higher concentrations in the second hand smoke rather than in the mainstream smoke which makes it more harmful for people to smoke publicly.
• Summary of “Smokers Get a Raw Deal” by Stanley Scott Lafayette explains that people who make laws and influence other people to exercise these laws are obviously at the top of the ladder and should be able to understand the difference between the harm sugar […]
• Ban Smoking in Cars Out of this need, several regulations have been put in place to ensure children’s safety in vehicles is guaranteed; thus, protection from second-hand smoke is an obvious measure that is directed towards the overall safety […]
• Aspects of Anti-Smoking Advertising Thus, it is safe to say that the authors’ main and intended audience is the creators of anti-smoking public health advertisements.
• Smoking Among Teenagers as Highlighted in Articles The use of tobacco through smoking is a trend among adolescents and teenagers with the number of young people who involve themselves in smoking is growing each day.
• Teenage Smoking and Solution to This Problem Overall, the attempts made by anti-smoking campaigners hardly yield any results, because they mostly focus on harmfulness of tobacco smoking and the publics’ awareness of the problem, itself, but they do not eradicate the underlying […]
• Smoking and Its Effect on the Brain Since the output of the brain is behavior and thoughts, dysfunction of the brain may result in highly complex behavioral symptoms. The work of neurons is to transmit information and coordinate messengers in the brain […]
• Smoking Cessation and Health Promotion Plan Patients addicted to tobacco are one of the major concerns of up-to-date medicine as constant nicotine intake leads to various disorders and worsens the health state and life quality of the users.
• How Smoking Cigarettes Effects Your Health Cigarette smoking largely aggravates the condition of the heart and the lung. In addition, the presence of nicotine makes the blood to be sticky and thick leading to damage to the lining of the blood […]
• Virginia Slims’ Impact on Female Smokers’ Number Considering this, through the investigation of Philip Morris’ mission which it pursued during the launch of the Virginia Slims campaign in 1968-1970 and the main regulatory actions undertaken by the Congress during this period, the […]
• Smoking Culture in Society Smoking culture refers to the practice of smoking tobacco by people in the society for the sheer satisfaction and delight it offers.
• Should Cigarettes Be Banned? Essay Banning cigarette smoking would be of great benefit to the young people. Banning of cigarette smoking would therefore reduce stress levels in people.
• Smoking and Cancer in the United States In this research study, data on tobacco smoking and cancer prevalence in the United States was used to determine whether cancer in the United States is related to tobacco smoking tobacco.
• Smoking Ban and UK’s Beer Industry However, there is an intricate type of relationship between the UK beer sector, the smoking ban, and the authorities that one can only understand by going through the study in detail The history of smoking […]
• Health Promotion for Smokers The purpose of this paper is to show the negative health complications that stem from tobacco use, more specifically coronary heart disease, and how the health belief model can help healthcare professionals emphasize the importance […]
• Gender-Based Assessment of Cigarette Smoking Harm Thus, the following hypothesis is tested: Women are more likely than men to believe that smoking is more harmful to health.
• Hazards of Smoking and Benefits of Cessation Prabhat Jha is the author of the article “The Hazards of Smoking and the Benefits of Cessation,” published in a not-for-profit scientific journal, eLife, in 2020.
• The Impact of Warning Labels on Cigarette Smoking The regulations requiring tobacco companies to include warning labels are founded on the need to reduce nicotine intake, limit cigarette dependence, and mitigate the adverse effects associated with addiction to smoking.
• Psilocybin as a Smoking Addiction Remedy Additionally, the biotech company hopes to seek approval from FDA for psilocybin-based therapy treatment as a cigarette smoking addiction long-term remedy.
• Tobacco Smoking: The Health Outcomes Tobacco smoke passing through the upper respiratory tract irritates the membrane of the nasopharynx, and other organism parts, generating copious separation of mucus and saliva.
• Investing Savings from Quitting Smoking: A Financial Analysis The progression of interest is approximately $50 per year, and if we assume n equal to 45 using the formula of the first n-terms of the arithmetic progression, then it comes out to about 105 […]
• Smoking as a Community Issue: The Influence of Smoking A review of the literature shows the use of tobacco declined between 1980 and 2012, but the number of people using tobacco in the world is increasing because of the rise in the global population.
• Smoking Public Education Campaign Assessment The major influence of the real cost campaign was to prevent the initiation of smoking among the youth and prevent the prevalence of lifelong smokers.
• Smoking Cessation Therapy: Effectiveness of Electronic Cigarettes Based on the practical experiments, the changes in the patients’ vascular health using nicotine and electronic cigarettes are improved within one-month time period. The usage only of electronic cigarettes is efficient compared to when people […]
• Quitting Smoking and Related Health Benefits The regeneration of the lungs will begin: the process will touch the cells called acini, from which the mucous membrane is built. Therefore, quitting the habit of smoking a person can radically change his life […]
• Smoking and Stress Among Veterans The topic is significant to explore because of the misconception that smoking can alleviate the emotional burden of stress and anxiety when in reality, it has an exacerbating effect on emotional stress.
• Smoking as a Predictor of Underachievement By comparing two groups smoking and non-smoking adolescents through a parametric t-test, it is possible to examine this assumption and draw conclusions based on the resulting p-value.
• Smoking and the Pandemic in West Virginia In this case, the use of the income variable is an additional facet of the hypothesis described, allowing us to evaluate whether there is any divergence in trends between the rich and the poor.
• Anti-Smoking Policy in Australia and the US The anti-smoking policy is to discourage people from smoking through various means and promotion of a healthy lifestyle, as well as to prevent the spread of the desire to smoke.
• Smoking Prevalence in Bankstown, Australia The secondary objective of the project was to gather and analyze a sufficient amount of auxiliary scholarly sources on smoking cessation initiatives and smoking prevalence in Australia.
• Drug Addiction in Teenagers: Smoking and Other Lifestyles In the first part of this assignment, the health problem of drug addiction was considered among teens and the most vulnerable group was established.
• Anti-Smoking Communication Campaign’s Analysis Defining the target audience for an anti-smoking campaign is complicated by the different layers of adherence to the issue of the general audience of young adults.
• Smoking as a Risk Factor for Lung Cancer Lung cancer is one of the most frequent types of the condition, and with the low recovery rates. If the problem is detected early and the malignant cells are contained to a small region, surgery […]
• Smoking Cessation Project Implementation In addition, the review will include the strengths and weaknesses of the evidence presented in the literature while identifying gaps and limitations.
• Maternal and Infant Health: Smoking Prevention Strategies It is known that many women know the dangers of smoking when pregnant and they always try to quit smoking to protect the lives of themselves and the child.
• A Peer Intervention Program to Reduce Smoking Rates Among LGBTQ Therefore, the presumed results of the project are its introduction into the health care system, which will promote a healthy lifestyle and diminish the level of smoking among LGBTQ people in the SESLHD.
• Tackling Teenage Smoking in Community The study of the problem should be comprehensive and should not be limited by the medical aspect of the issue. The study of the psychological factor is aimed at identifying the behavioral characteristics of smoking […]
• Peer Pressure and Smoking Influence on Teenagers The study results indicate that teenagers understand the health and social implications of smoking, but peer pressure contributes to the activity’s uptake.
• Smoking: Benefits or Harms? Hundreds of smokers every day are looking for a way to get rid of the noose, which is a yoke around the neck, a cigarette.
• The Culture of Smoking Changed in Poland In the 1980-90s, Poland faced the challenge of being a country with the highest rates of smoking, associated lung cancer, and premature mortality in the world.
• The Stop Smoking Movement Analysis The paper discusses the ideology, objective, characteristics, context, special techniques, organization culture, target audience, media strategies, audience reaction, counter-propaganda and the effectiveness of the “Stop Smoking” Movement.”The Stop Smoking” campaign is a prevalent example of […]
• Health Promotion Plan: Smokers in Mississippi The main strategies of the training session are to reduce the number of smokers in Mississippi, conduct a training program on the dangers of smoking and work with tobacco producers.
• Smoking Health Problem Assessment The effects of smoking correlate starkly with the symptoms and diseases in the nursing practice, working as evidence of the smoking’s impact on human health.
• Integration of Smoking Cessation Into Daily Nursing Practice Generally, smoking cessation refers to a process structured to help a person to discontinue inhaling smoked substances. It can also be referred to as quitting smoking.
• E-Cigarettes and Smoking Cessation Many people argue that e-cigarettes do not produce secondhand smoke. They believe that the e-fluids contained in such cigarettes produce vapor and not smoke.
• Introducing Smoking Cessation Program: 5 A’s Intervention Plan The second problem arises in an attempt to solve the issue of the lack of counseling in the unit by referring patients to the outpatient counseling center post-hospital discharge to continue the cessation program.
• Outdoor Smoking Ban in Public Areas of the Community These statistics have contributed to the widespread efforts to educate the public regarding the need to quit smoking. However, most of the chronic smokers ignore the ramifications of the habit despite the deterioration of their […]
• Nicotine Replacement Therapy for Adult Smokers With a Psychiatric Disorder The qualitative research methodology underlines the issue of the lack of relevant findings in the field of nicotine replacement therapy in people and the necessity of treatment, especially in the early stages of implementation.
• Smoking and Drinking: Age Factor in the US As smoking and drinking behavior were both strongly related to age, it could be the case that the observed relationship is due to the fact that older pupils were more likely to smoke and drink […]
• Poland’s Smoking Culture From Nursing Perspective Per Kinder, the nation’s status as one of Europe’s largest tobacco producers and the overall increase in smoking across the developing nations of Central and Eastern Europe caused its massive tobacco consumption issues.
• Smoking Cessation Clinic Analysis The main aim of this project is to establish a smoking cessation clinic that will guide smoker through the process of quitting smoking.
• Cigarette Smoking Among Teenagers in the Baltimore Community, Maryland The paper uses the Baltimore community in Maryland as the area to focus the event of creating awareness of cigarette smoking among the teens of this community.
• Advocating for Smoking Cessation: Health Professional Role Health professionals can contribute significantly to tobacco control in Australia and the health of the community by providing opportunities for smoking patients to quit smoking.
• Lifestyle Management While Quitting Smoking Realistically, not all of the set goals can be achieved; this is due to laxity in implementing them and the associated difficulty in letting go of the past lifestyle.
• Smoking in the Actuality The current use of aggressive marketing and advertising strategies has continued to support the smoking of e-cigarettes. The study has also indicated that “the use of such e-cigarettes may contribute to the normalization of smoking”.
• Analysis of the Family Smoking Prevention and Tobacco Control Act The law ensures that the FDA has the power to tackle issues of interest to the public such as the use of tobacco by minors.
• “50-Year Trends in Smoking-Related Mortality in the United States” by Thun et al. Thun is affiliated with the American Cancer Society, but his research interests cover several areas. Carter is affiliated with the American Cancer Society, Epidemiology Research Program.
• Pulmonology: Emphysema Caused by Smoking The further development of emphysema in CH can lead to such complications caused by described pathological processes as pneumothorax that is associated with the air surrounding the lungs.
• Smoking and Lung Cancer Among African Americans Primarily, the research paper provides insight on the significance of the issue to the African Americans and the community health nurses.
• Health Promotion and Smoking Cessation I will also complete a wide range of activities in an attempt to support the agency’s goals. As well, new studies will be conducted in order to support the proposed programs.
• Maternal Mental Health and Prenatal Smoking It was important to determine the variables that may lead to postpartum relapse or a relapse during the period of pregnancy. It is important to note that the findings are also consistent with the popular […]
• Nursing Interventions for Smoking Cessation For instance, the authors are able to recognize the need to classify the level of intensity in respect to the intervention that is employed by nurses towards smoking cessation.
• Marketing Plan: Creating a Smoking Cessation Program for Newton Healthcare Center The fourth objective is to integrate a smoking cessation program that covers the diagnosis of smoking, counseling of smokers, and patient care system to help the smokers quit their smoking habits. The comprehensive healthcare needs […]
• Smoking Among the Youth Population Between 12-25 Years I will use the theory to strengthen the group’s beliefs and ideas about smoking. I will inform the group about the relationship between smoking and human health.
• Risks of Smoking Cigarettes Among Preteens Despite the good news that the number of preteen smokers has been significantly reducing since the 1990s, there is still much to be done as the effects of smoking are increasingly building an unhealthy population […]
• Public Health Education: Anti-smoking Project The workshop initiative aimed to achieve the following objectives: To assess the issues related to smoking and tobacco use. To enhance the health advantages of clean air spaces.
• Healthy People Program: Smoking Issue in Wisconsin That is why to respond to the program’s effective realization, it is important to discuss the particular features of the target population in the definite community of Wisconsin; to focus on the community-based response to […]
• Health Campaign: Smoking in the USA and How to Reduce It That is why, the government is oriented to complete such objectives associated with the tobacco use within the nation as the reduction of tobacco use by adults and adolescents, reduction of initiation of tobacco use […]
• Smoking Differentials Across Social Classes The author inferred her affirmations from the participant’s words and therefore came to the right conclusion; that low income workers had the least justification for smoking and therefore took on a passive approach to their […]
• Cigarette Smoking Side Effects Nicotine is a highly venomous and addictive substance absorbed through the mucous membrane in the mouth as well as alveoli in the lungs.
• Long-Term Effects of Smoking The difference between passive smoking and active smoking lies in the fact that, the former involves the exposure of people to environmental tobacco smoke while the latter involves people who smoke directly.
• Smoking Cessation Program Evaluation in Dubai The most important program of this campaign is the Quit and Win campaign, which is a unique idea, launched by the DHCC and is in the form of an open contest.
• Preterm Birth and Maternal Smoking in Pregnancy The major finding of the discussed research is that both preterm birth and maternal smoking during pregnancy contribute, although independently, to the aortic narrowing of adolescents.
• Enforcement of Michigan’s Non-Smoking Law This paper is aimed at identifying a plan and strategy for the enforcement of the Michigan non-smoking law that has recently been signed by the governor of this state.
• Smoking Cessation for Patients With Cardio Disorders It highlights the key role of nurses in the success of such programs and the importance of their awareness and initiative in determining prognosis.
• Legalizing Electronic Vaping as the Means of Curbing the Rates of Smoking However, due to significantly less harmful effects that vaping produces on health and physical development, I can be considered a legitimate solution to reducing the levels of smoking, which is why it needs to be […]
• Drinking, Smoking, and Violence in Queer Community Consequently, the inequality and discrimination against LGBTQ + students in high school harm their mental, emotional, and physical health due to the high level of stress and abuse of various substances that it causes.
• Self-Efficacy and Smoking Urges in Homeless Individuals Pinsker et al.point out that the levels of self-efficacy and the severity of smoking urges change significantly during the smoking cessation treatment.
• “Cigarette Smoking: An Overview” by Ellen Bailey and Nancy Sprague The authors of the article mentioned above have presented a fair argument about the effects of cigarette smoking and debate on banning the production and use of tobacco in America.
• “The Smoking Plant” Project: Artist Statement It is the case when the art is used to pass the important message to the observer. The live cigarette may symbolize the smokers while the plant is used to denote those who do not […]
• Dangers of Smoking While Pregnant In this respect, T-test results show that mean birthweight of baby of the non-smoking mother is 3647 grams, while the birthweight of smoking mother is 3373 grams. Results show that gestation value and smoking habit […]
• The Cultural Differences of the Tobacco Smoking The Middle East culture is connected to the hookah, the Native American cultures use pipes, and the Canadian culture is linked to cigarettes.
• Ban on Smoking in Enclosed Public Places in Scotland The theory of externality explains the benefit or cost incurred by a third party who was not a party to the reasoning behind the benefit or cost. This will also lead to offer of a […]
• Alcohol and Smoking Abuse: Negative Physical and Mental Effects The following is a range of effects of heavy alcohol intake as shown by Lacoste, they include: Neuropsychiatric or neurological impairment, cardiovascular, disease, liver disease, and neoplasm that is malevolent.
• Smoking Prohibition: Local Issues, Personal Views This is due to the weakening of blood vessels in the penis. For example, death rate due to smoking is higher in Kentucky than in other parts of the country.
• Smoking During Pregnancy Issues Three things to be learned from the research are the impact of smoking on a woman, possible dangers and complications and the importance of smoking cessation interventions.
• The Smoking Problem: Mortality, Control, and Prevention The article presents smoking as one of the central problems for many countries throughout the world; the most shocking are the figures related to smoking rate among students. Summary: The article is dedicated to the […]
• Tobacco Smoking: Bootleggers and Baptists Legislation or Regulation The issue is based on the fact that tobacco smoking also reduces the quality of life and ruins the body in numerous ways.
• Smoking: Causes and Effects Considering the peculiarities of a habit and of a disease, smoking can be considered as a habit rather than a disease.
• Smoking Behavior Under Clinical Observation The physiological aspect that influences smokers and is perceived as the immediate effect of smoking can be summarized as follows: Within ten seconds of the first inhalation, nicotine, a potent alkaloid, passes into the bloodstream, […]
• Smoking Causes and Plausible Arguments In writing on the cause and effect of smoking we will examine the issue from the point of view of temporal precedence, covariation of the cause and effect and the explanations in regard to no […]
• Smoking and Its Effects on Human Body The investigators explain the effects of smoking on the breath as follows: the rapid pulse rate of smokers decreases the stroke volume during rest since the venous return is not affected and the ventricles lose […]
• Post Smoking Cessation Weight Gain The aim of this paper is to present, in brief, the correlation between smoking cessation and weigh gain from biological and psychological viewpoints.
• Marketing a Smoking Cessation Program In the case of the smoking cessation program, the target group is made up of smokers who can be further subdivided into segments such as heavy, medium, and light smokers.
• Smoking Cessation for Ages 15-30 The Encyclopedia of Surgery defines the term “Smoking Cessation” as an effort to “quit smoking” or “withdrawal from smoking”. I aim to discuss the importance of the issue by highlighting the most recent statistics as […]
• Smoking Qualitative Research: Critical Analysis Qualitative research allows researchers to explore a wide array of dimensions of the social world, including the texture and weave of everyday life, the understandings, experiences and imaginings of our research participants, the way that […]
• Motivational Interviewing as a Smoking Cessation Intervention for Patients With Cancer The dependent variable is the cessation of smoking in 3 months of the interventions. The study is based on the author’s belief that cessation of smoking influences cancer-treated patients by improving the efficacy of treatment.
• Factors Affecting the Success in Quitting Smoking of Smokers in West Perth, WA Australia Causing a wide array of diseases, health smoking is the second cause of death in the world. In Australia, the problem of smoking is extremely burning due to the high rates of diseases and deaths […]
• Media Effects on Teen Smoking But that is not how an adult human brain works, let alone the young and impressionable minds of teenagers, usually the ads targeted at the youth always play upon elements that are familiar and appealing […]
• “Passive Smoking Greater Health Hazard: Nimhans” by Stephen David The article focuses on analyzing the findings of the study and compares them to the reactions to the ban on public smoking.
• Partnership in Working About Smoking and Tobacco Use The study related to smoking and tobacco use, which is one of the problematic areas in terms of the health of the population.
• Cigar Smoking and Relation to Disease The article “Effect of cigar smoking on the risk of cardiovascular disease, chronic obstructive pulmonary disease and cancer in Men” by Iribarren et al.is a longitudinal study of cigar smokers and the impact of cigar […]
• Quitting Smoking: Motivation and Brain As these are some of the observed motivations for smoking, quitting smoking is actually very easy in the sense that you just have to set your mind on quitting smoking.
• Health Effects of Tobacco Smoking in Hispanic Men The Health Effects of Tobacco Smoking can be attributed to active tobacco smoking rather than inhalation of tobacco smoke from environment and passive smoking.
• Smoking in Adolescents: A New Threat to the Society Of the newer concerns about the risks of smoking and the increase in its prevalence, the most disturbing is the increase in the incidences of smoking among the adolescents around the world.
• The Importance of Nurses in Smoking-Cessation Programs When a patient is admitted to the hospital, the nursing staff has the best opportunity to assist them in quitting in part because of the inability to smoke in the hospital combined with the educational […]
• Smoking and Youth Culture in Germany The report also assailed the Federal Government for siding the interest of the cigarette industry instead of the health of the citizens.
• New Jersey Legislation on Smoking The advantages and disadvantages of the legislation were discussed in this case because of the complexity of the topic at hand as well as the potential effects of the solution on the sphere of public […]
• Environmental Health: Tabaco Smoking and an Increased Concentration of Carbon Monoxide The small size of the town, which is around 225000 people, is one of the reasons for high statistics in diseases of heart rate.
• Advanced Pharmacology: Birth Control for Smokers The rationale for IUD is the possibility to control birth without the partner’s participation and the necessity to visit a doctor just once for the device to be implanted.
• Legislation Reform of Public Smoking Therefore, the benefit of the bill is that the health hazard will be decreased using banning smoking in public parks and beaches.
• Female Smokers Study: Inferential Statistics Article The article “Differential Effects of a Body Image Exposure Session on Smoking Urge between Physically Active and Sedentary Female Smokers” deepens the behavioral mechanisms that correlate urge to smoke, body image, and physical activity among […]
• Smoking Bans: Protecting the Public and the Children of Smokers The purpose of the article is to show why smoking bans aim at protecting the public and the children of smokers.
• Clinical Effects of Cigarette Smoking Smoking is a practice that should be avoided or controlled rigorously since it is a risk factor for diseases such as cancer, affects the health outcomes of direct and passive cigarette users, children, and pregnant […]
• Public Health and Smoking Prevention Smoking among adults over 18 years old is a public health issue that requires intervention due to statistical evidence of its effects over the past decades.
• Smoking in the US: Statistics and Healthcare Costs According to the Centers for Disease Control and Prevention, tobacco smoking is the greatest preventable cause of death in the US.
• Smoking Should Be Banned Internationally The questions refer to the knowledge concerning the consequences of smoking and the opinions on smoking bans. 80 % of respondents agree that smoking is among the leading causes of death and 63, 3 % […]
• Microeconomics: Cigarette Taxes and Public Smoking Ban The problem of passive smoking will be minimized when the number of smokers decreases. It is agreeable that the meager incomes of such families will be used to purchase cigarettes.
• Tobacco Debates in “Thank You for Smoking” The advantage of Nick’s strategy is that it offers the consumer a role model to follow: if smoking is considered to be ‘cool’, more people, especially young ones, will try to become ‘cool’ using cigarettes.
• Alcohol and Smoking Impact on Cancer Risk The research question is to determine the quantity of the impact that different levels of alcohol ingestion combined with smoking behavioral patterns make on men and women in terms of the risks of cancer.
• Teenagers Motivated to Smoking While the rest of the factors also matter much in the process of shaping the habit of smoking, it is the necessity to mimic the company members, the leader, or any other authority that defines […]
• Indoor Smoking Restriction Effects at the Workplace Regrettably, they have neglected research on the effect of the legislation on the employees and employers. In this research, the target population will be the employees and employers of various companies.
• Hypnotherapy Session for Smoking Cessation When I reached the age of sixty, I realized that I no longer wanted to be a smoker who was unable to take control of one’s lifestyle.
• Stopping Tobacco Smoking: Lifestyle Management Plan In addition, to set objective goals, I have learned that undertaking my plan with reference to the modifying behaviour is essential for the achievement of the intended goals. The main intention of the plan is […]
• Smoking Epidemiology Among High School Students In this way, with the help of a cross-sectional study, professionals can minimalize the risk of students being afraid to reveal the fact that they smoke. In this way, the number of students who smoke […]
• Social Marketing: The Truth Anti-Smoking Campaign The agreement of November 1998 between 46 states, five territories of the United States, the District of Columbia, and representatives of the tobacco industry gave start to the introduction of the Truth campaign.
• Vancouver Coastal Health Smoking Cessation Program The present paper provides an evaluation of the Vancouver Coastal Health smoking cessation program from the viewpoint of the social cognitive theory and the theory of planned behavior.
• Smoking Experience and Hidden Dangers When my best college friend Jane started smoking, my eyes opened on the complex nature of the problem and on the multiple negative effects of smoking both on the smoker and on the surrounding society.
• South Illinois University’s Smoking Ban Benefits The purpose of this letter is to assess the possible benefits of the plan and provide an analysis of the costs and consequences of the smoking ban introduction.
• Smoking Cessation in Patients With COPD The strategy of assessing these papers to determine their usefulness in EBP should include these characteristics, the overall quality of the findings, and their applicability in a particular situation. The following article is a study […]
• Smoking Bans: Preventive Measures There have been several public smoking bans that have proved to be promising since the issue of smoking prohibits smoking in all public places. This means it is a way of reducing the exposure to […]
• Ban Smoking Near the Child: Issues of Morality The decision to ban smoking near the child on father’s request is one of the demonstrative examples. The father’s appeal to the Supreme Court of California with the requirement to prohibit his ex-wife from smoking […]
• The Smoking Ban: Arguments Comparison The first argument against banning smoking employs the idea that smoking in specially designated areas cannot do harm to the health of non-smokers as the latter are supposed to avoid these areas.
• Smoking Cessation and Patient Education in Nursing Pack-years are the concept that is used to determine the health risks of a smoking patient. The most important step in the management plan is to determine a date when the man should quit smoking.
• Philip Morris Company’s Smoking Prevention Activity Philip Morris admits the existence of scientific proof that smoking leads to lung cancer in addition to other severe illnesses even after years of disputing such findings from health professionals.
• Tobacco Smoking and Its Dangers Sufficient evidence also indicates that smoking is correlated with alcohol use and that it is capable of affecting one’s mental state to the point of heightening the risks of development of disorders.
• Cigarette Smoking and Parkinson’s Disease Risk Therefore, given the knowledge that cigarette smoking protects against the disease, it is necessary to determine the validity of these observations by finding the precise relationship between nicotine and PD.
• Tuberculosis Statistics Among Cigarette Smokers The proposal outlines the statistical applications of one-way ANOVA, the study participants, the variables, study methods, expected results and biases, and the practical significance of the expected results.
• Smoking Habit, Its Causes and Effects Smoking is one of the factors that are considered the leading causes of several health problems in the current society. Smoking is a habit that may be easy to start, but getting out of this […]
• Status of Smoking around the World Economic factors and level of education have contributed a lot to the shift of balance in the status of smoking in the world.
• Redwood Associates Company’s Smoking Ethical Issues Although employees are expected to know what morally they are supposed to undertake at their work place, it is the responsibility of the management and generally the Redwood’s hiring authority to give direction to its […]
• Smokers’ Campaign: Finding a Home for Ciggy Butts When carrying out the campaign, it is important to know what the situation on the ground is to be able to address the root cause of the problem facing the population.
• Mobile Applications to Quit Smoking A critical insight that can be gleaned from the said report is that one of the major factors linked to failure is the fact that smokers were unable to quit the habit on their own […]
• Behavior Modification Technique: Smoking Cessation Some of its advantages include: its mode of application is in a way similar to the act of smoking and it has very few side effects.
• Quitting Smoking: Strategies and Consequences Thus, for the world to realize a common positive improvement in population health, people must know the consequences of smoking not only for the smoker but also the society. The first step towards quitting smoking […]
• Effects of Thought Suppression on Smoking Behavior In the article under analysis called I suppress, Therefore I smoke: Effects of Thought Suppression on Smoking Behavior, the authors dedicate their study to the evaluation of human behavior as well as the influence of […]
• Suppressing Smoking Behavior and Its Effects The researchers observed that during the first and the second weeks of the suppressed behavior, the participants successfully managed to reduce their intake of cigarettes.
• Smoking Cessation Methods
• Understanding Advertising: Second-Hand Smoking
• People Should Quit Smoking
• Importance of Quitting Smoking
• Cigarette Smoking in Public Places
• Ban of Tobacco Smoking in Jamaica
• Anti-Smoking Campaign in Canada
• Electronic Cigarettes: Could They Help University Students Give Smoking Up?
• The Change of my Smoking Behavior
• Psychosocial Smoking Rehabilitation
• The Program on Smoking Cessation for Employees
• Tips From Former Smokers (Campaign)
• Combating Smoking: Taxation Policies vs. Education Policies
• The Program to Quit Smoking
• Possible Smoking Policies in Florida
• Smoking Ban in the State of Florida
• Core Functions of Public Health in the Context of Smoking and Heart Disease
• Smoking: Pathophysiological Effects
• Putting Out the Fires: Will Higher Taxes Reduce the Onset of Youth Smoking?
• Smoking Bans in US
• Smoking as Activity Enhancer: Schizophrenia and Gender
• Health Care Costs for Smokers
• Medical Coverage for Smoking Related Diseases
• Exposure to mass media proliferate smoking
• The Realm of reality: Smoking
• Ethical Problem of Smoking
• The Rate of Smoking Among HIV Positive Cases.
• Studying the Government’s Anti-Smoking Measures
• Smoking Should Be Banned In the United States
• Effectiveness of Cognitive Behavioral Theory on Smoking Cessation
• Effectiveness of the Cognitive Behavioral Therapy for Smoking Cessation
• Wayco Company’s Non-smoking Policy
• Adverse Aspects of Smoking
• Negative Impacts of Smoking on Individuals and Society
• Dealing With the Increase in the Number of Smokers Between Ages 17 and 45
• Cannabis Smoking in Canada
• Smoking Ban in the United States of America
• Dangers of Smoking Campaign
• Smoking Ban in New York
• Smoking and Adolescents
• Trends in Smoking Prevalence by Race/Ethnicity
• Business Ethics: Smoking Issue
• Should Smoking Tobacco Be Classified As an Illegal Drug?
• Where Does the Path to Smoking Addiction Start?
• Public Health Communication: Quit Smoking
• Are Estimated Peer Effects on Smoking Robust?
• Are There Safe Smoking and Tobacco Options?
• What Are the Health Risks of Smoking?
• Does Cigarette Smoking Affect Body Weight?
• Does Cigarette Smuggling Prop Up Smoking Rates?
• What Foods Help You Quit Smoking?
• How Can People Relax Without Smoking?
• Does Education Affect Smoking Behaviors?
• Is Vaping Worse Than Smoking?
• Do Movies Affect Teen Smoking?
• What Is Worse: Drinking or Smoking?
• Does Smoking Affect Breathing Capacity?
• Does Smoking Cause Lung Cancer?
• Does Having More Children Increase the Likelihood of Parental Smoking?
• Does Smoking Cigarettes Relieve Stress?
• Does Time Preference Affect Smoking Behavior?
• How Does Smoking Affect Cardiovascular Endurance?
• How Hypnosis Can Help You Quit Smoking?
• How Does Smoking Affect Brain?
• How Nicotine Affects Your Quit Smoking Victory?
• How Does Secondhand Smoking Affect Us?
• Why Is Smoking Addictive?
• How Smoking Bans Are Bad for Business?
• Why Smoking Should Not Be Permitted in Restaurants?
• Why Public Smoking Should Be Banned?
• Why Has Cigarette Smoking Become So Prominent Within the American Culture?
• What Makes Smoking and Computers Similar?
• Does Smoking Affect Schooling?
• What Effects Can Cigarette Smoking Have on the Respiratory System?
• What Are the Most Prevalent Dangers of Smoking and Drinking?
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1. IvyPanda . "235 Smoking Essay Topics & Examples." February 29, 2024. https://ivypanda.com/essays/topic/smoking-essay-examples/.

Bibliography

IvyPanda . "235 Smoking Essay Topics & Examples." February 29, 2024. https://ivypanda.com/essays/topic/smoking-essay-examples/.

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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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