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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 ).

figure 1

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 .

figure 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.

figure 3

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.

figure 4

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 ).

Doll, R. & Hill, A. B. Smoking and carcinoma of the lung. Br. Med. J. 2 , 739–748 (1950).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Di Cicco, M. E., Ragazzo, V. & Jacinto, T. Mortality in relation to smoking: the British Doctors Study. Breathe 12 , 275–276 (2016).

Article   PubMed   PubMed Central   Google Scholar  

World Health Organization. WHO Framework Convention on Tobacco Control 36 (WHO, 2003).

Dai, X., Gakidou, E. & Lopez, A. D. Evolution of the global smoking epidemic over the past half century: strengthening the evidence base for policy action. Tob. Control 31 , 129–137 (2022).

Article   PubMed   Google Scholar  

Dikshit, R. P. & Kanhere, S. Tobacco habits and risk of lung, oropharyngeal and oral cavity cancer: a population-based case-control study in Bhopal, India. Int. J. Epidemiol. 29 , 609–614 (2000).

Article   CAS   PubMed   Google Scholar  

Liaw, K. M. & Chen, C. J. Mortality attributable to cigarette smoking in Taiwan: a 12-year follow-up study. Tob. Control 7 , 141–148 (1998).

Gandini, S. et al. Tobacco smoking and cancer: a meta-analysis. Int. J. Cancer 122 , 155–164 (2008).

Deng, X., Yuan, C. & Chang, D. Interactions between single nucleotide polymorphism of SERPINA1 gene and smoking in association with COPD: a case–control study. Int. J. Chron. Obstruct. Pulmon. Dis. 12 , 259–265 (2017).

Leem, A. Y., Park, B., Kim, Y. S., Jung, J. Y. & Won, S. Incidence and risk of chronic obstructive pulmonary disease in a Korean community-based cohort. Int. J. Chron. Obstruct. Pulmon. Dis. 13 , 509–517 (2018).

Forey, B. A., Thornton, A. J. & Lee, P. N. Systematic review with meta-analysis of the epidemiological evidence relating smoking to COPD, chronic bronchitis and emphysema. BMC Pulmon. Med. 11 , 36 (2011).

Article   Google Scholar  

Tan, J. et al. Smoking, blood pressure, and cardiovascular disease mortality in a large cohort of Chinese men with 15 years follow-up. Int. J. Environ. Res. Public Health 15 , E1026 (2018).

Doll, R., Peto, R., Boreham, J. & Sutherland, I. Mortality in relation to smoking: 50 years’ observations on male British doctors. Br. Med. J. 328 , 1519 (2004).

Huxley, R. R. & Woodward, M. Cigarette smoking as a risk factor for coronary heart disease in women compared with men: a systematic review and meta-analysis of prospective cohort studies. Lancet 378 , 1297–1305 (2011).

Hbejan, K. Smoking effect on ischemic heart disease in young patients. Heart Views 12 , 1–6 (2011).

Chao, H. et al. A meta-analysis of active smoking and risk of meningioma. Tob. Induc. Dis. 19 , 34 (2021).

Shi, H., Shao, X. & Hong, Y. Association between cigarette smoking and the susceptibility of acute myeloid leukemia: a systematic review and meta-analysis. Eur. Rev. Med Pharm. Sci. 23 , 10049–10057 (2019).

CAS   Google Scholar  

Macacu, A., Autier, P., Boniol, M. & Boyle, P. Active and passive smoking and risk of breast cancer: a meta-analysis. Breast Cancer Res. Treat. 154 , 213–224 (2015).

Pujades-Rodriguez, M. et al. Heterogeneous associations between smoking and a wide range of initial presentations of cardiovascular disease in 1 937 360 people in England: lifetime risks and implications for risk prediction. Int. J. Epidemiol. 44 , 129–141 (2015).

Kanazir, M. et al. Risk factors for hepatocellular carcinoma: a case-control study in Belgrade (Serbia). Tumori 96 , 911–917 (2010).

Pytynia, K. B. et al. Matched-pair analysis of survival of never smokers and ever smokers with squamous cell carcinoma of the head and neck. J. Clin. Oncol. 22 , 3981–3988 (2004).

Barengo, N. C., Antikainen, R., Harald, K. & Jousilahti, P. Smoking and cancer, cardiovascular and total mortality among older adults: the Finrisk Study. Prev. Med. Rep. 14 , 100875 (2019).

Guo, Y. et al. Modifiable risk factors for cognitive impairment in Parkinson’s disease: a systematic review and meta-analysis of prospective cohort studies. Mov. Disord. 34 , 876–883 (2019).

Aune, D., Vatten, L. J. & Boffetta, P. Tobacco smoking and the risk of gallbladder disease. Eur. J. Epidemiol. 31 , 643–653 (2016).

Qin, L., Deng, H.-Y., Chen, S.-J. & Wei, W. Relationship between cigarette smoking and risk of chronic myeloid leukaemia: a meta-analysis of epidemiological studies. Hematology 22 , 193–200 (2017).

Petrick, J. L. et al. Tobacco, alcohol use and risk of hepatocellular carcinoma and intrahepatic cholangiocarcinoma: the Liver Cancer Pooling Project. Br. J. Cancer 118 , 1005–1012 (2018).

United States Department of Health, Education and Welfare. Smoking and Health. Report of the Advisory Committee on Smoking and Health to the Surgeon General of the United States Public Health Service https://www.cdc.gov/tobacco/data_statistics/sgr/index.htm (US DHEW, 1964).

United States Public Health Service Office of the Surgeon General & National Center for Chronic Disease Prevention and Health Promotion (US) Office on Smoking and Health. Smoking Cessation: A Report of the Surgeon General . (US Department of Health and Human Services, 2020).

Zheng, P., Barber, R., Sorensen, R. J. D., Murray, C. J. L. & Aravkin, A. Y. Trimmed constrained mixed effects models: formulations and algorithms. J. Comput. Graph Stat. 30 , 544–556 (2021).

Zheng, P. et al. The Burden of Proof studies: assessing the evidence of risk. Nat. Med. in press (2022).

Reitsma, M. B. et al. Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019. Lancet 397 , 2337–2360 (2021).

Moher, D., Liberati, A., Tetzlaff, J. & Altman, D. G. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Br. Med. J. 339 , b2535 (2009).

Liu, Z. Y., He, X. Z. & Chapman, R. S. Smoking and other risk factors for lung cancer in Xuanwei, China. Int. J. Epidemiol. 20 , 26–31 (1991).

Brownson, R. C., Reif, J. S., Keefe, T. J., Ferguson, S. W. & Pritzl, J. A. Risk factors for adenocarcinoma of the lung. Am. J. Epidemiol. 125 , 25–34 (1987).

Marugame, T. et al. Lung cancer death rates by smoking status: comparison of the Three-Prefecture Cohort study in Japan to the Cancer Prevention Study II in the USA. Cancer Sci. 96 , 120–126 (2005).

Dosemeci, M., Gokmen, I., Unsal, M., Hayes, R. B. & Blair, A. Tobacco, alcohol use, and risks of laryngeal and lung cancer by subsite and histologic type in Turkey. Cancer Causes Control 8 , 729–737 (1997).

Freedman, N. D. et al. Impact of changing US cigarette smoking patterns on incident cancer: risks of 20 smoking-related cancers among the women and men of the NIH-AARP cohort. Int. J. Epidemiol. 45 , 846–856 (2016).

Bae, J.-M. et al. Lung cancer incidence by smoking status in Korean men: 16 years of observations in the Seoul Male Cancer Cohort study. J. Korean Med. Sci. 28 , 636–637 (2013).

Everatt, R., Kuzmickienė, I., Virvičiūtė, D. & Tamošiūnas, A. Cigarette smoking, educational level and total and site-specific cancer: a cohort study in men in Lithuania. Eur. J. Cancer Prev. 23 , 579–586 (2014).

Nordlund, L. A., Carstensen, J. M. & Pershagen, G. Are male and female smokers at equal risk of smoking-related cancer: evidence from a Swedish prospective study. Scand. J. Public Health 27 , 56–62 (1999).

Siemiatycki, J., Krewski, D., Franco, E. & Kaiserman, M. Associations between cigarette smoking and each of 21 types of cancer: a multi-site case–control study. Int. J. Epidemiol. 24 , 504–514 (1995).

Chyou, P. H., Nomura, A. M. & Stemmermann, G. N. A prospective study of the attributable risk of cancer due to cigarette smoking. Am. J. Public Health 82 , 37–40 (1992).

Potter, J. D., Sellers, T. A., Folsom, A. R. & McGovern, P. G. Alcohol, beer, and lung cancer in postmenopausal women. The Iowa Women’s Health Study. Ann. Epidemiol. 2 , 587–595 (1992).

Chyou, P. H., Nomura, A. M., Stemmermann, G. N. & Kato, I. Lung cancer: a prospective study of smoking, occupation, and nutrient intake. Arch. Environ. Health 48 , 69–72 (1993).

Pesch, B. et al. Cigarette smoking and lung cancer–relative risk estimates for the major histological types from a pooled analysis of case–control studies. Int. J. Cancer 131 , 1210–1219 (2012).

Jöckel, K. H. et al. Occupational and environmental hazards associated with lung cancer. Int. J. Epidemiol. 21 , 202–213 (1992).

Jöckel, K. H., Ahrens, W., Jahn, I., Pohlabeln, H. & Bolm-Audorff, U. Occupational risk factors for lung cancer: a case-control study in West Germany. Int. J. Epidemiol. 27 , 549–560 (1998).

Lei, Y. X., Cai, W. C., Chen, Y. Z. & Du, Y. X. Some lifestyle factors in human lung cancer: a case-control study of 792 lung cancer cases. Lung Cancer 14 , S121–S136 (1996).

Pawlega, J., Rachtan, J. & Dyba, T. Evaluation of certain risk factors for lung cancer in Cracow (Poland)—a case–control study. Acta Oncol. 36 , 471–476 (1997).

Mao, Y. et al. Socioeconomic status and lung cancer risk in Canada. Int. J. Epidemiol. 30 , 809–817 (2001).

Barbone, F., Bovenzi, M., Cavallieri, F. & Stanta, G. Cigarette smoking and histologic type of lung cancer in men. Chest 112 , 1474–1479 (1997).

Matos, E., Vilensky, M., Boffetta, P. & Kogevinas, M. Lung cancer and smoking: a case–control study in Buenos Aires, Argentina. Lung Cancer 21 , 155–163 (1998).

Simonato, L. et al. Lung cancer and cigarette smoking in Europe: an update of risk estimates and an assessment of inter-country heterogeneity. Int. J. Cancer 91 , 876–887 (2001).

Risch, H. A. et al. Are female smokers at higher risk for lung cancer than male smokers? A case–control analysis by histologic type. Am. J. Epidemiol. 138 , 281–293 (1993).

Sankaranarayanan, R. et al. A case–control study of diet and lung cancer in Kerala, south India. Int. J. Cancer 58 , 644–649 (1994).

Band, P. R. et al. Identification of occupational cancer risks in British Columbia. Part I: methodology, descriptive results, and analysis of cancer risks, by cigarette smoking categories of 15,463 incident cancer cases. J. Occup. Environ. Med. 41 , 224–232 (1999).

Becher, H., Jöckel, K. H., Timm, J., Wichmann, H. E. & Drescher, K. Smoking cessation and nonsmoking intervals: effect of different smoking patterns on lung cancer risk. Cancer Causes Control 2 , 381–387 (1991).

Brockmöller, J., Kerb, R., Drakoulis, N., Nitz, M. & Roots, I. Genotype and phenotype of glutathione S-transferase class mu isoenzymes mu and psi in lung cancer patients and controls. Cancer Res. 53 , 1004–1011 (1993).

PubMed   Google Scholar  

Vena, J. E., Byers, T. E., Cookfair, D. & Swanson, M. Occupation and lung cancer risk. An analysis by histologic subtypes. Cancer 56 , 910–917 (1985).

Cascorbi, I. et al. Homozygous rapid arylamine N -acetyltransferase (NAT2) genotype as a susceptibility factor for lung cancer. Cancer Res. 56 , 3961–3966 (1996).

CAS   PubMed   Google Scholar  

Chiazze, L., Watkins, D. K. & Fryar, C. A case–control study of malignant and non-malignant respiratory disease among employees of a fiberglass manufacturing facility. Br. J. Ind. Med 49 , 326–331 (1992).

CAS   PubMed   PubMed Central   Google Scholar  

Ando, M. et al. Attributable and absolute risk of lung cancer death by smoking status: findings from the Japan Collaborative Cohort Study. Int. J. Cancer 105 , 249–254 (2003).

De Matteis, S. et al. Are women who smoke at higher risk for lung cancer than men who smoke? Am. J. Epidemiol. 177 , 601–612 (2013).

He, Y. et al. Changes in smoking behavior and subsequent mortality risk during a 35-year follow-up of a cohort in Xi’an, China. Am. J. Epidemiol. 179 , 1060–1070 (2014).

Nishino, Y. et al. Cancer incidence profiles in the Miyagi Cohort Study. J. Epidemiol. 14 , S7–S11 (2004).

Papadopoulos, A. et al. Cigarette smoking and lung cancer in women: results of the French ICARE case–control study. Lung Cancer 74 , 369–377 (2011).

Shimazu, T. et al. Alcohol and risk of lung cancer among Japanese men: data from a large-scale population-based cohort study, the JPHC study. Cancer Causes Control 19 , 1095–1102 (2008).

Tindle, H. A. et al. Lifetime smoking history and risk of lung cancer: results from the Framingham Heart Study. J. Natl Cancer Inst. 110 , 1201–1207 (2018).

PubMed   PubMed Central   Google Scholar  

Yong, L. C. et al. Intake of vitamins E, C, and A and risk of lung cancer. The NHANES I epidemiologic followup study. First National Health and Nutrition Examination Survey. Am. J. Epidemiol. 146 , 231–243 (1997).

Hansen, M. S. et al. Sex differences in risk of smoking-associated lung cancer: results from a cohort of 600,000 Norwegians. Am. J. Epidemiol. 187 , 971–981 (2018).

Boffetta, P. et al. Tobacco smoking as a risk factor of bronchioloalveolar carcinoma of the lung: pooled analysis of seven case-control studies in the International Lung Cancer Consortium (ILCCO). Cancer Causes Control 22 , 73–79 (2011).

Yun, Y. D. et al. Hazard ratio of smoking on lung cancer in Korea according to histological type and gender. Lung 194 , 281–289 (2016).

Suzuki, I. et al. Risk factors for lung cancer in Rio de Janeiro, Brazil: a case–control study. Lung Cancer 11 , 179–190 (1994).

De Stefani, E., Deneo-Pellegrini, H., Carzoglio, J. C., Ronco, A. & Mendilaharsu, M. Dietary nitrosodimethylamine and the risk of lung cancer: a case–control study from Uruguay. Cancer Epidemiol. Biomark. Prev. 5 , 679–682 (1996).

Google Scholar  

Kreuzer, M. et al. Risk factors for lung cancer in young adults. Am. J. Epidemiol. 147 , 1028–1037 (1998).

Armadans-Gil, L., Vaqué-Rafart, J., Rosselló, J., Olona, M. & Alseda, M. Cigarette smoking and male lung cancer risk with special regard to type of tobacco. Int. J. Epidemiol. 28 , 614–619 (1999).

Kubík, A. K., Zatloukal, P., Tomásek, L. & Petruzelka, L. Lung cancer risk among Czech women: a case–control study. Prev. Med. 34 , 436–444 (2002).

Rachtan, J. Smoking, passive smoking and lung cancer cell types among women in Poland. Lung Cancer 35 , 129–136 (2002).

Thun, M. J. et al. 50-year trends in smoking-related mortality in the United States. N. Engl. J. Med. 368 , 351–364 (2013).

Zatloukal, P., Kubík, A., Pauk, N., Tomásek, L. & Petruzelka, L. Adenocarcinoma of the lung among women: risk associated with smoking, prior lung disease, diet and menstrual and pregnancy history. Lung Cancer 41 , 283–293 (2003).

Hansen, M. S., Licaj, I., Braaten, T., Lund, E. & Gram, I. T. The fraction of lung cancer attributable to smoking in the Norwegian Women and Cancer (NOWAC) Study. Br. J. Cancer 124 , 658–662 (2021).

Zhang, P. et al. Association of smoking and polygenic risk with the incidence of lung cancer: a prospective cohort study. Br. J. Cancer 126 , 1637–1646 (2022).

Weber, M. F. et al. Cancer incidence and cancer death in relation to tobacco smoking in a population-based Australian cohort study. Int. J. Cancer 149 , 1076–1088 (2021).

Guo, L.-W. et al. A risk prediction model for selecting high-risk population for computed tomography lung cancer screening in China. Lung Cancer 163 , 27–34 (2022).

Mezzoiuso, A. G., Odone, A., Signorelli, C. & Russo, A. G. Association between smoking and cancers among women: results from the FRiCaM multisite cohort study. J. Cancer 12 , 3136–3144 (2021).

Hawrysz, I., Wadolowska, L., Slowinska, M. A., Czerwinska, A. & Golota, J. J. Adherence to prudent and mediterranean dietary patterns is inversely associated with lung cancer in moderate but not heavy male Polish smokers: a case–control study. Nutrients 12 , E3788 (2020).

Huang, C.-C., Lai, C.-Y., Tsai, C.-H., Wang, J.-Y. & Wong, R.-H. Combined effects of cigarette smoking, DNA methyltransferase 3B genetic polymorphism, and DNA damage on lung cancer. BMC Cancer 21 , 1066 (2021).

Viner, B., Barberio, A. M., Haig, T. R., Friedenreich, C. M. & Brenner, D. R. The individual and combined effects of alcohol consumption and cigarette smoking on site-specific cancer risk in a prospective cohort of 26,607 adults: results from Alberta’s Tomorrow Project. Cancer Causes Control 30 , 1313–1326 (2019).

Park, E. Y., Lim, M. K., Park, E., Oh, J.-K. & Lee, D.-H. Relationship between urinary 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol and lung cancer risk in the general population: a community-based prospective cohort study. Front. Oncol. 11 , 611674 (2021).

De Stefani, E., Deneo-Pellegrini, H., Mendilaharsu, M., Carzoglio, J. C. & Ronco, A. Dietary fat and lung cancer: a case–control study in Uruguay. Cancer Causes Control 8 , 913–921 (1997).

Wünsch-Filho, V., Moncau, J. E., Mirabelli, D. & Boffetta, P. Occupational risk factors of lung cancer in São Paulo, Brazil. Scand. J. Work Environ. Health 24 , 118–124 (1998).

Hu, J. et al. A case-control study of diet and lung cancer in northeast China. Int. J. Cancer 71 , 924–931 (1997).

Jia, G., Wen, W., Massion, P. P., Shu, X.-O. & Zheng, W. Incorporating both genetic and tobacco smoking data to identify high-risk smokers for lung cancer screening. Carcinogenesis 42 , 874–879 (2021).

Rusmaully, J. et al. Risk of lung cancer among women in relation to lifetime history of tobacco smoking: a population-based case–control study in France (the WELCA study). BMC Cancer 21 , 711 (2021).

Jin, K. et al. Tobacco smoking modifies the association between hormonal factors and lung cancer occurrence among post-menopausal Chinese women. Transl. Oncol. 12 , 819–827 (2019).

Tse, L. A., Wang, F., Wong, M. C.-S., Au, J. S.-K. & Yu, I. T.-S. Risk assessment and prediction for lung cancer among Hong Kong Chinese men. BMC Cancer 22 , 585 (2022).

Huang, C.-C. et al. Joint effects of cigarette smoking and green tea consumption with miR-29b and DNMT3b mRNA expression in the development of lung cancer. Genes 13 , 836 (2022).

Hosseini, M. et al. Environmental risk factors for lung cancer in Iran: a case–control study. Int. J. Epidemiol. 38 , 989–996 (2009).

Naghibzadeh-Tahami, A. et al. Is opium use associated with an increased risk of lung cancer? A case–control study. BMC Cancer 20 , 807 (2020).

Shimatani, K., Ito, H., Matsuo, K., Tajima, K. & Takezaki, T. Cumulative cigarette tar exposure and lung cancer risk among Japanese smokers. Jpn J. Clin. Oncol. 50 , 1009–1017 (2020).

Lai, C.-Y. et al. Genetic polymorphism of catechol- O -methyltransferase modulates the association of green tea consumption and lung cancer. Eur. J. Cancer Prev. 28 , 316–322 (2019).

Schwartz, A. G. et al. Hormone use, reproductive history, and risk of lung cancer: the Women’s Health Initiative studies. J. Thorac. Oncol. 10 , 1004–1013 (2015).

Kreuzer, M., Gerken, M., Heinrich, J., Kreienbrock, L. & Wichmann, H.-E. Hormonal factors and risk of lung cancer among women? Int. J. Epidemiol. 32 , 263–271 (2003).

Sreeja, L. et al. Possible risk modification by CYP1A1, GSTM1 and GSTT1 gene polymorphisms in lung cancer susceptibility in a South Indian population. J. Hum. Genet. 50 , 618–627 (2005).

Siemiatycki, J. et al. Are the apparent effects of cigarette smoking on lung and bladder cancers due to uncontrolled confounding by occupational exposures? Epidemiology 5 , 57–65 (1994).

Chan-Yeung, M. et al. Risk factors associated with lung cancer in Hong Kong. Lung Cancer 40 , 131–140 (2003).

Lawania, S., Singh, N., Behera, D. & Sharma, S. Xeroderma pigmentosum complementation group D polymorphism toward lung cancer susceptibility survival and response in patients treated with platinum chemotherapy. Future Oncol. 13 , 2645–2665 (2017).

De Stefani, E. et al. Mate drinking and risk of lung cancer in males: a case-control study from Uruguay. Cancer Epidemiol. Biomark. Prev. 5 , 515–519 (1996).

Pérez-Padilla, R. et al. Exposure to biomass smoke and chronic airway disease in Mexican women. A case-control study. Am. J. Respir. Crit. Care Med. 154 , 701–706 (1996).

Zhang, X.-R. et al. Glucosamine use, smoking and risk of incident chronic obstructive pulmonary disease: a large prospective cohort study. Br. J. Nutr . https://doi.org/10.1017/S000711452100372X (2021).

Johannessen, A., Omenaas, E., Bakke, P. & Gulsvik, A. Incidence of GOLD-defined chronic obstructive pulmonary disease in a general adult population. Int. J. Tuberc. Lung Dis. 9 , 926–932 (2005).

Fox, J. Life-style and mortality: a large-scale census-based cohort study in Japan. J. Epidemiol. Community Health 45 , 173 (1991).

Article   PubMed Central   Google Scholar  

Thomson, B. et al. Low-intensity daily smoking and cause-specific mortality in Mexico: prospective study of 150 000 adults. Int. J. Epidemiol. 50 , 955–964 (2021).

van Durme, Y. M. T. A. et al. Prevalence, incidence, and lifetime risk for the development of COPD in the elderly: the Rotterdam study. Chest 135 , 368–377 (2009).

Li, L. et al. SERPINE2 rs16865421 polymorphism is associated with a lower risk of chronic obstructive pulmonary disease in the Uygur population: a case–control study. J. Gene Med. 21 , e3106 (2019).

Ganbold, C. et al. The cumulative effect of gene-gene interactions between GSTM1 , CHRNA3 , CHRNA5 and SOD3 gene polymorphisms combined with smoking on COPD risk. Int. J. Chron. Obstruct. Pulmon. Dis. 16 , 2857–2868 (2021).

Omori, H. et al. Twelve-year cumulative incidence of airflow obstruction among Japanese males. Intern. Med. 50 , 1537–1544 (2011).

Manson, J. E., Ajani, U. A., Liu, S., Nathan, D. M. & Hennekens, C. H. A prospective study of cigarette smoking and the incidence of diabetes mellitus among US male physicians. Am. J. Med. 109 , 538–542 (2000).

Lv, J. et al. Adherence to a healthy lifestyle and the risk of type 2 diabetes in Chinese adults. Int. J. Epidemiol. 46 , 1410–1420 (2017).

Waki, K. et al. Alcohol consumption and other risk factors for self-reported diabetes among middle-aged Japanese: a population-based prospective study in the JPHC study cohort I. Diabet. Med. 22 , 323–331 (2005).

Meisinger, C., Döring, A., Thorand, B. & Löwel, H. Association of cigarette smoking and tar and nicotine intake with development of type 2 diabetes mellitus in men and women from the general population: the MONICA/KORA Augsburg Cohort Study. Diabetologia 49 , 1770–1776 (2006).

Huh, Y. et al. Association of smoking status with the risk of type 2 diabetes among young adults: a nationwide cohort study in South Korea. Nicotine Tob. Res. 24 , 1234–1240 (2022).

Sawada, S. S., Lee, I.-M., Muto, T., Matuszaki, K. & Blair, S. N. Cardiorespiratory fitness and the incidence of type 2 diabetes: prospective study of Japanese men. Diabetes Care 26 , 2918–2922 (2003).

Will, J. C., Galuska, D. A., Ford, E. S., Mokdad, A. & Calle, E. E. Cigarette smoking and diabetes mellitus: evidence of a positive association from a large prospective cohort study. Int. J. Epidemiol. 30 , 540–546 (2001).

Nakanishi, N., Nakamura, K., Matsuo, Y., Suzuki, K. & Tatara, K. Cigarette smoking and risk for impaired fasting glucose and type 2 diabetes in middle-aged Japanese men. Ann. Intern. Med. 133 , 183–191 (2000).

Sairenchi, T. et al. Cigarette smoking and risk of type 2 diabetes mellitus among middle-aged and elderly Japanese men and women. Am. J. Epidemiol. 160 , 158–162 (2004).

Hou, X. et al. Cigarette smoking is associated with a lower prevalence of newly diagnosed diabetes screened by OGTT than non-smoking in Chinese men with normal weight. PLoS ONE 11 , e0149234 (2016).

Hu, F. B. et al. Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. N. Engl. J. Med. 345 , 790–797 (2001).

Teratani, T. et al. Dose-response relationship between tobacco or alcohol consumption and the development of diabetes mellitus in Japanese male workers. Drug Alcohol Depend. 125 , 276–282 (2012).

Kawakami, N., Takatsuka, N., Shimizu, H. & Ishibashi, H. Effects of smoking on the incidence of non-insulin-dependent diabetes mellitus. Replication and extension in a Japanese cohort of male employees. Am. J. Epidemiol. 145 , 103–109 (1997).

Patja, K. et al. Effects of smoking, obesity and physical activity on the risk of type 2 diabetes in middle-aged Finnish men and women. J. Intern. Med. 258 , 356–362 (2005).

White, W. B. et al. High-intensity cigarette smoking is associated with incident diabetes mellitus in Black adults: the Jackson Heart Study. J. Am. Heart Assoc. 7 , e007413 (2018).

Uchimoto, S. et al. Impact of cigarette smoking on the incidence of Type 2 diabetes mellitus in middle-aged Japanese men: the Osaka Health Survey. Diabet. Med . 16 , 951–955 (1999).

Rimm, E. B., Chan, J., Stampfer, M. J., Colditz, G. A. & Willett, W. C. Prospective study of cigarette smoking, alcohol use, and the risk of diabetes in men. Br. Med. J. 310 , 555–559 (1995).

Article   CAS   Google Scholar  

Hilawe, E. H. et al. Smoking and diabetes: is the association mediated by adiponectin, leptin, or C-reactive protein? J. Epidemiol. 25 , 99–109 (2015).

InterAct, Consortium et al. Smoking and long-term risk of type 2 diabetes: the EPIC-InterAct study in European populations. Diabetes Care 37 , 3164–3171 (2014).

Jee, S. H., Foong, A. W., Hur, N. W. & Samet, J. M. Smoking and risk for diabetes incidence and mortality in Korean men and women. Diabetes Care 33 , 2567–2572 (2010).

Rasouli, B. et al. Smoking and the risk of LADA: results from a Swedish population-based case-control study. Diabetes Care 39 , 794–800 (2016).

Wannamethee, S. G., Shaper, A. G. & Perry, I. J., British Regional Heart Study. Smoking as a modifiable risk factor for type 2 diabetes in middle-aged men. Diabetes Care 24 , 1590–1595 (2001).

Radzeviciene, L. & Ostrauskas, R. Smoking habits and type 2 diabetes mellitus in women. Women Health 58 , 884–897 (2018).

Carlsson, S., Midthjell, K. & Grill, V., Nord-Trøndelag Study. Smoking is associated with an increased risk of type 2 diabetes but a decreased risk of autoimmune diabetes in adults: an 11-year follow-up of incidence of diabetes in the Nord-Trøndelag study. Diabetologia 47 , 1953–1956 (2004).

Akter, S. et al. Smoking, smoking cessation, and the risk of type 2 diabetes among Japanese adults: Japan Epidemiology Collaboration on Occupational Health Study. PLoS ONE 10 , e0132166 (2015).

Pirie, K. et al. The 21st century hazards of smoking and benefits of stopping: a prospective study of one million women in the UK. Lancet 381 , 133–141 (2013).

Park, C.-H. et al. [The effect of smoking status upon occurrence of impaired fasting glucose or type 2 diabetes in Korean men]. J. Prev. Med. Public Health 41 , 249–254 (2008).

Doi, Y. et al. Two risk score models for predicting incident Type 2 diabetes in Japan. Diabet. Med. 29 , 107–114 (2012).

van den Brandt, P. A. A possible dual effect of cigarette smoking on the risk of postmenopausal breast cancer. Eur. J. Epidemiol. 32 , 683–690 (2017).

Dossus, L. et al. Active and passive cigarette smoking and breast cancer risk: results from the EPIC cohort. Int. J. Cancer 134 , 1871–1888 (2014).

Kawai, M., Malone, K. E., Tang, M.-T. C. & Li, C. I. Active smoking and the risk of estrogen receptor-positive and triple-negative breast cancer among women ages 20 to 44 years. Cancer 120 , 1026–1034 (2014).

Reynolds, P. et al. Active smoking, household passive smoking, and breast cancer: evidence from the California Teachers Study. J. Natl Cancer Inst. 96 , 29–37 (2004).

Ellingjord-Dale, M. et al. Alcohol, physical activity, smoking, and breast cancer subtypes in a large, nested case-control study from the Norwegian Breast Cancer Screening Program. Cancer Epidemiol. Biomark. Prev. 26 , 1736–1744 (2017).

Arthur, R. et al. Association between lifestyle, menstrual/reproductive history, and histological factors and risk of breast cancer in women biopsied for benign breast disease. Breast Cancer Res. Treat. 165 , 623–631 (2017).

Luo, J. et al. Association of active and passive smoking with risk of breast cancer among postmenopausal women: a prospective cohort study. Br. Med. J. 342 , d1016 (2011).

White, A. J., D’Aloisio, A. A., Nichols, H. B., DeRoo, L. A. & Sandler, D. P. Breast cancer and exposure to tobacco smoke during potential windows of susceptibility. Cancer Causes Control 28 , 667–675 (2017).

Gram, I. T. et al. Breast cancer risk among women who start smoking as teenagers. Cancer Epidemiol. Biomark. Prev. 14 , 61–66 (2005).

Gammon, M. D. et al. Cigarette smoking and breast cancer risk among young women (United States). Cancer Causes Control 9 , 583–590 (1998).

Magnusson, C., Wedrén, S. & Rosenberg, L. U. Cigarette smoking and breast cancer risk: a population-based study in Sweden. Br. J. Cancer 97 , 1287–1290 (2007).

Chu, S. Y. et al. Cigarette smoking and the risk of breast cancer. Am. J. Epidemiol. 131 , 244–253 (1990).

Lemogne, C. et al. Depression and the risk of cancer: a 15-year follow-up study of the GAZEL cohort. Am. J. Epidemiol. 178 , 1712–1720 (2013).

Morabia, A., Bernstein, M., Héritier, S. & Khatchatrian, N. Relation of breast cancer with passive and active exposure to tobacco smoke. Am. J. Epidemiol. 143 , 918–928 (1996).

Conlon, M. S. C., Johnson, K. C., Bewick, M. A., Lafrenie, R. M. & Donner, A. Smoking (active and passive), N -acetyltransferase 2, and risk of breast cancer. Cancer Epidemiol. 34 , 142–149 (2010).

Ozasa, K., Japan Collaborative Cohort Study for Evaluation of Cancer. Smoking and mortality in the Japan Collaborative Cohort Study for Evaluation of Cancer (JACC). Asian Pac. J. Cancer Prev. 8 , 89–96 (2007).

Jones, M. E., Schoemaker, M. J., Wright, L. B., Ashworth, A. & Swerdlow, A. J. Smoking and risk of breast cancer in the Generations Study cohort. Breast Cancer Res. 19 , 118 (2017).

Bjerkaas, E. et al. Smoking duration before first childbirth: an emerging risk factor for breast cancer? Results from 302,865 Norwegian women. Cancer Causes Control 24 , 1347–1356 (2013).

Gram, I. T., Little, M. A., Lund, E. & Braaten, T. The fraction of breast cancer attributable to smoking: the Norwegian women and cancer study 1991–2012. Br. J. Cancer 115 , 616–623 (2016).

Li, C. I., Malone, K. E. & Daling, J. R. The relationship between various measures of cigarette smoking and risk of breast cancer among older women 65–79 years of age (United States). Cancer Causes Control 16 , 975–985 (2005).

Xue, F., Willett, W. C., Rosner, B. A., Hankinson, S. E. & Michels, K. B. Cigarette smoking and the incidence of breast cancer. Arch. Intern. Med. 171 , 125–133 (2011).

Parker, A. S., Cerhan, J. R., Putnam, S. D., Cantor, K. P. & Lynch, C. F. A cohort study of farming and risk of prostate cancer in Iowa. Epidemiology 10 , 452–455 (1999).

Sawada, N. et al. Alcohol and smoking and subsequent risk of prostate cancer in Japanese men: the Japan Public Health Center-based prospective study. Int. J. Cancer 134 , 971–978 (2014).

Hiatt, R. A., Armstrong, M. A., Klatsky, A. L. & Sidney, S. Alcohol consumption, smoking, and other risk factors and prostate cancer in a large health plan cohort in California (United States). Cancer Causes Control 5 , 66–72 (1994).

Cerhan, J. R. et al. Association of smoking, body mass, and physical activity with risk of prostate cancer in the Iowa 65+ Rural Health Study (United States). Cancer Causes Control 8 , 229–238 (1997).

Watters, J. L., Park, Y., Hollenbeck, A., Schatzkin, A. & Albanes, D. Cigarette smoking and prostate cancer in a prospective US cohort study. Cancer Epidemiol. Biomark. Prev. 18 , 2427–2435 (2009).

Butler, L. M., Wang, R., Wong, A. S., Koh, W.-P. & Yu, M. C. Cigarette smoking and risk of prostate cancer among Singapore Chinese. Cancer Causes Control 20 , 1967–1974 (2009).

Lotufo, P. A., Lee, I. M., Ajani, U. A., Hennekens, C. H. & Manson, J. E. Cigarette smoking and risk of prostate cancer in the physicians’ health study (United States). Int. J. Cancer 87 , 141–144 (2000).

Hsing, A. W. et al. Diet, tobacco use, and fatal prostate cancer: results from the Lutheran Brotherhood Cohort Study. Cancer Res. 50 , 6836–6840 (1990).

Veierød, M. B., Laake, P. & Thelle, D. S. Dietary fat intake and risk of prostate cancer: a prospective study of 25,708 Norwegian men. Int. J. Cancer 73 , 634–638 (1997).

Meyer, J., Rohrmann, S., Bopp, M. & Faeh, D. & Swiss National Cohort Study Group. Impact of smoking and excess body weight on overall and site-specific cancer mortality risk. Cancer Epidemiol. Biomark. Prev . 24 , 1516–1522 (2015).

Putnam, S. D. et al. Lifestyle and anthropometric risk factors for prostate cancer in a cohort of Iowa men. Ann. Epidemiol. 10 , 361–369 (2000).

Taghizadeh, N., Vonk, J. M. & Boezen, H. M. Lifetime smoking history and cause-specific mortality in a cohort study with 43 years of follow-up. PLoS ONE 11 , e0153310 (2016).

Park, S.-Y. et al. Racial/ethnic differences in lifestyle-related factors and prostate cancer risk: the Multiethnic Cohort Study. Cancer Causes Control 26 , 1507–1515 (2015).

Nomura, A. M., Lee, J., Stemmermann, G. N. & Combs, G. F. Serum selenium and subsequent risk of prostate cancer. Cancer Epidemiol. Biomark. Prev. 9 , 883–887 (2000).

Rodriguez, C., Tatham, L. M., Thun, M. J., Calle, E. E. & Heath, C. W. Smoking and fatal prostate cancer in a large cohort of adult men. Am. J. Epidemiol. 145 , 466–475 (1997).

Rohrmann, S. et al. Smoking and risk of fatal prostate cancer in a prospective U.S. study. Urology 69 , 721–725 (2007).

Giovannucci, E. et al. Smoking and risk of total and fatal prostate cancer in United States health professionals. Cancer Epidemiol. Biomark. Prev. 8 , 277–282 (1999).

Rohrmann, S. et al. Smoking and the risk of prostate cancer in the European Prospective Investigation into Cancer and Nutrition. Br. J. Cancer 108 , 708–714 (2013).

Lund Nilsen, T. I., Johnsen, R. & Vatten, L. J. Socio-economic and lifestyle factors associated with the risk of prostate cancer. Br. J. Cancer 82 , 1358–1363 (2000).

Hsing, A. W., McLaughlin, J. K., Hrubec, Z., Blot, W. J. & Fraumeni, J. F. Tobacco use and prostate cancer: 26-year follow-up of US veterans. Am. J. Epidemiol. 133 , 437–441 (1991).

Murray, C. J. L. et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396 , 1223–1249 (2020).

Bero, L. A. & Jadad, A. R. How consumers and policymakers can use systematic reviews for decision making. Ann. Intern. Med. 127 , 37–42 (1997).

Centers for Disease Control and Prevention (CDC). Cigarette smoking among adults and trends in smoking cessation—United States, 2008. MMWR Morb. Mortal. Wkly Rep. 58 , 1227–1232 (2009).

Prochaska, J. O. & Goldstein, M. G. Process of smoking cessation: implications for clinicians. Clin. Chest Med. 12 , 727–735 (1991).

Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Br. Med. J. 372 , n71 (2021).

Stevens, G. A. et al. Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement. Lancet 388 , e19–e23 (2016).

BMJ Best Practice. What is GRADE? https://bestpractice.bmj.com/info/us/toolkit/learn-ebm/what-is-grade (BMJ, 2021).

The GRADE Working Group. GRADE handbook . https://gdt.gradepro.org/app/handbook/handbook.html (The GRADE Working Group, 2013).

Efron, B., Hastie, T., Johnstone, I. & Tibshirani, R. Least angle regression. Ann. Stat. 32 , 407–499 (2004).

Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B Stat. Methodol. 58 , 267–288 (1996).

von Hippel, P. T. The heterogeneity statistic I2 can be biased in small meta-analyses. BMC Med. Res. Methodol. 15 , 35 (2015).

Kontopantelis, E., Springate, D. A. & Reeves, D. A re-analysis of the Cochrane Library data: the dangers of unobserved heterogeneity in meta-analyses. PLoS ONE 8 , e69930 (2013).

Biggerstaff, B. J. & Tweedie, R. L. Incorporating variability in estimates of heterogeneity in the random effects model in meta-analysis. Stat. Med. 16 , 753–768 (1997).

Egger, M., Smith, G. D., Schneider, M. & Minder, C. Bias in meta-analysis detected by a simple, graphical test. Br. Med. J. 315 , 629–634 (1997).

Lee, P. N., Forey, B. A. & Coombs, K. J. Systematic review with meta-analysis of the epidemiological evidence in the 1900s relating smoking to lung cancer. BMC Cancer 12 , 385 (2012).

Rücker, G., Carpenter, J. R. & Schwarzer, G. Detecting and adjusting for small-study effects in meta-analysis. Biometr. J. 53 , 351–368 (2011).

Wu, Z.-J., Zhao, P., Liu, B. & Yuan, Z.-C. Effect of cigarette smoking on risk of hip fracture in men: a meta-analysis of 14 prospective cohort studies. PLoS ONE 11 , e0168990 (2016).

Thun, M. J. et al. in Cigarette Smoking Behaviour in the United States: changes in cigarette-related disease risks and their implication for prevention and control (eds Burns, D.M. et al.) Tobacco Control Monograph No. 8 Ch. 4 (National Cancer Institute, 1997).

Tolstrup, J. S. et al. Smoking and risk of coronary heart disease in younger, middle-aged, and older adults. Am. J. Public Health 104 , 96–102 (2014).

Jonas, M. A., Oates, J. A., Ockene, J. K. & Hennekens, C. H. Statement on smoking and cardiovascular disease for health care professionals. American Heart Association. Circulation 86 , 1664–1669 (1992).

Khan, S. S. et al. Cigarette smoking and competing risks for fatal and nonfatal cardiovascular disease subtypes across the life course. J. Am. Heart Assoc. 10 , e021751 (2021).

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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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Xiaochen Dai, Gabriela F. Gil, Marissa B. Reitsma, Noah S. Ahmad, Jason A. Anderson, Catherine Bisignano, Sinclair Carr, Rachel Feldman, Simon I. Hay, Jiawei He, Vincent Iannucci, Hilary R. Lawlor, Matthew J. Malloy, Laurie B. Marczak, Susan A. McLaughlin, Larissa Morikawa, Erin C. Mullany, Sneha I. Nicholson, Erin M. O’Connell, Chukwuma Okereke, Reed J. D. Sorensen, Joanna Whisnant, Aleksandr Y. Aravkin, Peng Zheng, Christopher J. L. Murray & Emmanuela Gakidou

Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA

Xiaochen Dai, Simon I. Hay, Jiawei He, Peng Zheng, Christopher J. L. Murray & Emmanuela Gakidou

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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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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
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  • 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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Open Access

Peer-reviewed

Research Article

The Health Effects of Passive Smoking: An Overview of Systematic Reviews Based on Observational Epidemiological Evidence

Affiliation School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

* E-mail: [email protected]

  • Shiyi Cao, 
  • Chen Yang, 
  • Yong Gan, 

PLOS

  • Published: October 6, 2015
  • https://doi.org/10.1371/journal.pone.0139907
  • Reader Comments

Fig 1

We aim to systematically summarize the available epidemiological evidence to identify the impact of environmental tobacco smoke on health.

A systematic literature search of PubMed, Embase, Web of Science, and Scopus for meta-analyses was conducted through January 2015. We included systematic reviews that investigated the association between passive smoking and certain diseases. Quantitative outcomes of association between passive smoking and the risk of certain diseases were summarized.

Sixteen meta-analyses covering 130 cohort studies, 159 case-control studies, and 161 cross-sectional studies and involving 25 diseases or health problems were reviewed. Passive smoking appears not to be significantly associated with eight diseases or health problems, but significantly elevates the risk for eleven specific diseases or health problems, including invasive meningococcal disease in children (OR 2.18; 95% CI 1.63–2.92), cervical cancer (OR 1.73; 95% CI 1.35–2.21), Neisseria meningitidis carriage (OR 1.68; 95% CI 1.19–2.36), Streptococcus pneumoniae carriage (OR 1.66; 95% CI 1.33–2.07), lower respiratory infections in infancy (OR 1.42; 95% CI 1.33–1.51), food allergy (OR 1.43; 95% CI 1.12–1.83), and so on.

Conclusions

Our overview of systematic reviews of observational epidemiological evidence suggests that passive smoking is significantly associated with an increasing risk of many diseases or health problems, especially diseases in children and cancers.

Citation: Cao S, Yang C, Gan Y, Lu Z (2015) The Health Effects of Passive Smoking: An Overview of Systematic Reviews Based on Observational Epidemiological Evidence. PLoS ONE 10(10): e0139907. https://doi.org/10.1371/journal.pone.0139907

Editor: Yan Li, Shanghai Institute of Hypertension, CHINA

Received: April 23, 2015; Accepted: September 19, 2015; Published: October 6, 2015

Copyright: © 2015 Cao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

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

Funding: The authors have no support or funding to report.

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

Introduction

Smoking is a major public health problem worldwide. There have been thousands of studies investigating the impact of active smoking on health, and the overall toxic effects of active smoking are generally recognized [ 1 ]. In comparison, the effects of passive smoking on health are not fully understood. Existing studies suggest that passive smoking and active smoking might equally increase the risk of certain diseases, such as female breast cancer [ 2 ], allergic rhinitis, allergic dermatitis, and food allergy [ 3 ]. As early as 1928, Schonherr suspected that inhalation of husbands’ smoke could cause lung cancer among non-smoking wives [ 4 ]. Since then a substantial body of research about environmental tobacco smoke and health has appeared [ 5 ]. But the impact of passive smoking on health remains largely inconclusive and has not been systematically summarized.

Due to the relative small health risks associated with exposure to passive smoking, investigation of this issue requires large study sizes. Difficulties in measuring passive smoking and controlling various confounding factors further add to the uncertainty in any investigation of the effects of passive smoking. Consequently, a meta-analysis, pooling together individual original studies quantitatively, has played an important part in establishing the evidence about the health effects of passive smoking [ 5 ]. Since Zmirou evaluated the respiratory risk of passive smoking by a meta-analysis in the early 1990s, many meta-analyses of observational epidemiological studies have been published to identify the impact of passive smoking on health.

Recognizing that the evidence is accumulating constantly worldwide, we conducted an overview of systematic reviews that have summarized the evidence from observational epidemiological studies on the health effects of passive smoking.

No protocol exists for this overview of systematic reviews.

Data for this research was acquired from previously published papers. Written consent and ethical approval were not required.

Literature search strategy

We attempted to conduct this overview of systematic reviews in accordance with the rationale and guideline recommended by Cochrane handbook 5.1.0 [ 6 ] ( S1 Checklist ). A systematic literature search of PubMed, Embase, Web of Science, and Scopus was conducted in January 2015 using the following search terms with no restrictions: passive smoking, secondhand smoking, environmental tobacco smoke, involuntary smoking, and tobacco smoke pollution. The reference lists of the retrieved articles were also reviewed. We did not contact authors of the primary studies for additional information.

Selection of relevant systematic reviews

Systematic reviews meeting the following criteria were regarded as eligible: (1) the design was meta-analysis, (2) passive smoking was an exposure variable and the outcome was the incidence of certain diseases or health problems, (3) the included original studies were cross-sectional, case-control, or/and cohort study design, (4) the literature search was international or worldwide, and (5) the pooled relative risk (RR) or odds ratio (OR) and the corresponding 95% confidence interval (CI) of specific diseases relating to exposure to passive smoking were reported or could be calculated from the data provided. Systematic reviews in which all included original studies were conducted in one country or region were excluded. We also excluded the meta-analyses that investigated the association between maternal smoking in pregnancy and the health risk of offspring. All potential meta-analyses were independently screened by two authors (SC and CY), who reviewed the titles or/abstracts first and then conducted a full-text assessment. Disagreements between the two reviewers were resolved through discussion with the third investigator (ZL).

Data extraction

The following information was extracted from the studies by two investigators (SC and CY): first author, publication year, country, number and design of the included original studies, and main quantitative estimates of the association of interest.

Quality appraisal

We appraised all the included meta-analyses using the Assessment of Multiple Systematic Reviews (AMSTAR) standard, an 11-item assessment tool designed to appraise the methodological quality of systematic reviews [ 7 ]. The maximum score is 11, and 0–4, 5–8, and 9–11 respectively indicates low, moderate, and high quality [ 8 ]. Disagreements on assessment scores were resolved by discussion among the authors.

Synthesis of the evidence

There may be more than one meta-analysis published regarding the association between passive smoking and risk of a specific disease. We only included the latest meta-analysis and excluded all the previous ones. For each included meta-analysis, we summarized the number and design of the included original studies, the main quantitative estimates of association of interest, heterogeneity between original studies, and so on. In any included meta-analyses, when estimates of association between passive smoking and certain diseases were reported separately for subgroups, we combined the results of the subgroups and calculated common estimates using a fixed-effects model if appropriate.

Literature search

Fig 1 shows the process of study identification and inclusion. Initially, we retrieved 2,079 articles from Pubmed, Emabse, Web of Science, and Scopus. After 1,105 duplicates were excluded, 974 articles were screened through titles and abstracts, of which 858 were excluded mainly because they were original studies or irrelevant reviews. After full-text review of the remaining 116 articles, 100 were further excluded because they did not report the outcomes of interest or their findings were already updated by newer systematic reviews. Finally, 16 meta-analyses were included [ 3 , 9 – 23 ].

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https://doi.org/10.1371/journal.pone.0139907.g001

Characteristics and quality of the included systematic reviews

The main characteristics of the sixteen meta-analyses were summarized in Table 1 . These meta-analyses covered a total of 130 cohort studies, 159 case-control studies, and 161 cross-sectional studies. They were published between 1998 and 2014. The quality scores of these meta-analyses appraised using AMSTAR ranged from 3 to 10. The numbers of meta-analyses with high quality, middle quality, and low quality were 5, 9, and 2 respectively (see Table 2 ).

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https://doi.org/10.1371/journal.pone.0139907.t001

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https://doi.org/10.1371/journal.pone.0139907.t002

The Main Health Consequences of Passive Smoking

Fig 2 shows the integrated results on the impact of passive smoking on specific diseases. The included 16 meta-analyses covered 25 diseases or health problems. There was statistically significant positive relationship between exposure environmental tobacco smoke and the risk of eleven diseases, especially invasive meningococcal disease in children (OR 2.18; 95% CI 1.63–2.92) and other three diseases or health problems with a 1.5 to 2.0-fold increase in the risk: cervical cancer (OR 1.73; 95% CI 1.35–2.21), Neisseria meningitidis carriage (OR 1.68; 95% CI 1.19–2.36), and Streptococcus pneumoniae carriage (OR 1.66; 95% CI 1.33–2.07). The increase in the risk of other seven diseases associated with exposure to passive smoking was statistically significant but small in impact size (OR was less than 1.5): lower respiratory infections in infancy (OR 1.42; 95% CI 1.33–1.51), food allergy (OR 1.43; 95% CI 1.12–1.83), childhood asthma (OR 1.32; 95% CI 1.23–1.42), lung cancer (OR 1.27; 95% CI 1.17–1.37), stroke (OR 1.25; 95% CI 1.12–1.38), allergic rhinitis (OR 1.09; 95% CI 1.04–1.14), and allergic dermatitis (OR 1.07; 95% CI 1.03–1.12). Of these 25 diseases or health problems, eight diseases were not found to be significantly associated with passive smoking. They were invasive Haemophilus influenzae type B (Hib) disease, invasive pneumococcal disease, Crohn's disease, pancreatic cancer, ulcerative colitis, breast cancer, bladder cancer, and pharyngeal carriage for Hib. In addition, the effects of passive smoking on increased risk of coronary heart disease, tuberculosis, diabetes, and middle ear disease in children (recurrent otitis media, middle ear effusion, and glue ear) were not conclusive, because the number of included studies was small or the quality of the corresponding meta-analysis was low.

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https://doi.org/10.1371/journal.pone.0139907.g002

Passive smoking and cancer risk

We investigated the association of passive smoking with the risk of lung cancer, cervical cancer, pancreatic cancer, breast cancer, and bladder cancer. Based on 55 observational studies (7 cohort studies, 25 population-based case-control studies and 23 non-population-based case-control studies), passive smoking were found to be associated with the increased risk of lung cancer (OR 1.27; 95% CI 1.17 to 1.37). The ORs for lung cancer in North America, Asia, and Europe were similar [ 19 ]. 11 case-control studies, involving 3,230 cases and 2,982 controls, suggested a positive relationship between passive smoking and cervical cancer (OR 1.73; 95% CI 1.35–2.21) [ 15 ]. Pancreatic cancer [ 21 ], breast cancer [ 13 ], and bladder cancer were not found to be associated with passive smoking.

Passive smoking and allergic diseases

A meta-analysis of observational studies published in PLOS Medicine systematically reviewed the effects of exposure to environmental smoke on allergic diseases [ 3 ]. The pooled ORs of 63 studies for allergic rhinitis, 58 studies for allergic dermatitis, and 6 studies for food allergies were 1.07 (95% CI 1.03–1.12), 1.09 (95% CI 1.04–1.14), and 1.43 (95% CI 1.12–1.83) respectively. Another meta-analysis investigated the association between passive smoking and the risk of physician-diagnosed childhood asthma [ 9 ], and suggested that there was consistent evidence of a modest positive association between them (OR 1.32; 95% CI: 1.23–1.42).

Passive smoking and pediatric invasive bacterial disease and bacterial carriage

Passive smoking was also thought to be associated with pediatric invasive bacterial disease and bacterial carriage. A meta-analysis involving 30 case-control studies for invasive bacterial disease and 12 cross-sectional studies for bacterial carriage indicated that the risk of invasive meningococcal disease, pharyngeal carriage for Neisseria, meningitidies and Streptococcus pneumoniae were significantly associated with passive smoking, and the ORs were 2.18, 95% CI 1.63 to 2.92), 1.68 (95% CI, 1.19–2.36), and 1.66 (95% CI 1.33–2.07), respectively. The risk of invasive pneumococcal disease, invasive Hib disease, and pharyngeal carriage for Hib were not found to be related to exposure to environmental smoke.

The health effects of environmental tobacco smoke are attracting more and more attention worldwide. Increasing numbers of original studies and meta-analyses are being published focusing on this important issue. In the present overview of systematic reviews based on sixteen systematic reviews involving 450 original observational studies, we found that passive smoking could significantly increase the risk of eleven diseases, especially invasive meningococcal disease in children, cervical cancer, Neisseria meningitidis carriage, and Streptococcus. pneumoniae carriage, but not associated with other eight diseases. Cancers were one of the most common investigated health outcomes associated with passive smoking. We found that exposure to environmental tobacco smoke could increase the risk of lung cancer and cervical cancer, but was not the risk of pancreatic cancer, breast cancer, or bladder cancer. It appears that passive smoking could increase the risk of some diseases among children, especially bacterial infections (e.g., lower respiratory infections in infancy, middle ear disease in children, invasive meningococcal disease in children, allergic diseases in children, and childhood asthma).

Previously, there were some reviews focusing on the health effects of exposure to environmental tobacco smoke. But they were qualitative or only involved children or limited to several diseases [ 24 – 26 ]. We used a systematic overview to summarize the quantitative estimates of the associations between passive smoking and various diseases based on all latest available meta-analyses. It should be noted that, in the present overview, we excluded meta-analyses evaluating the effects of smoking during pregnancy on fetus or offspring health, because the effects was obviously different from the health effects of active smoking or conventional passive smoking in the general population.

The quality of included original studies influences the reliability of the results and conclusions of the corresponding meta-analysis; similarly, the validity of the results of an overview of systematic reviews depends on the quality of the included systematic reviews. We used AMSTAR protocol, an internationally recognized assessment tool, to appraise the methodological quality of all included meta-analyses, and found that there were two meta-analyses with low quality. Accordingly, the conclusions drawn based on these two meta-analyses involving middle ear disease in children and coronary heart disease need to be interpreted with caution.

The evidence level of meta-analyses partly depends on the number and the design type of included original studies. Although there was no consensus about the minimum number of original studies included in meta-analysis, but more caution is needed when an association is assessed based on a small number of original studies. In our overview, we found a significant positive association between passive smoking and tuberculosis (OR 4.01; 95% CI 2.54–6.34), but it was only based on 4 case-control studies. More studies should be conducted to further assess the relationship between them. Similarly, the effect of passive smoking on diabetes was based on 6 cohort studies (OR 1.21; 95% CI 1.07–1.38), and more original studies are also needed.

There were several strengths in our research. Firstly, we followed the primary rationale and method of Cochrane overviews of reviews [ 6 ] to summarize the health consequences of certain exposure. Overview of systematic reviews is primarily intended to summarize multiple reviews addressing the effects of two or more potential interventions for a single condition or health problem. Up to now, most of overviews have been conducted to evaluate the effects of several interventions [ 27 , 28 ], and very few overviews have addressed the effects of a single exposure factor on multiple diseases or health problems based on observational studies. Our present overview expands the application of overviews of systematic reviews. Additionally, our study provides robust and comprehensive scientific information for smoking ban in public places and for educational pamphlets about passive smoking.

Some limitations in our overview should be noted. Firstly, we only included systematic reviews but not original studies. The associations of passive smoking with some diseases might have been investigated by original studies but not synthesized by meta-analyses and, therefore, were not summarized in this overview. Secondly, the mechanism on the health effects of passive smoking was not be examined since our study only intended to summarize relevant observational epidemiological evidence.

In summary, our overview of systematic reviews of up-to-date epidemiological evidence suggests that passive smoking is significantly associated with an increasing risk of many diseases and health problems, especially diseases in children and cancers. This study provides comprehensive population-based evidence about toxic effect of exposure to environmental tobacco smoke and should benefit developing health promotion strategies of smoking control. Stricter regulations against cigarette smoking should be formulated and implemented, because smoking harms not only own health but also the health of neighboring people.

Supporting Information

S1 prisma checklist..

https://doi.org/10.1371/journal.pone.0139907.s001

Acknowledgments

Access to data.

All the data in this review are from publicly published papers, and we take responsibility for the integrity of the data and the accuracy of the data analysis.

Author Contributions

Conceived and designed the experiments: ZL. Performed the experiments: SC CY. Analyzed the data: SC YG. Contributed reagents/materials/analysis tools: CY. Wrote the paper: SC.

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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.

U.S. Department of Health and Human Services. Preventing tobacco use among youth and young adults: a report of the surgeon general. Atlanta: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, Coordinating Center for Health Promotion, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2012.

Google Scholar  

Centers for Disease Control and Prevention (CDC). Smoking and tobacco use. 2016. http://www.cdc.gov/tobacco/data_statistics/ . Accessed 16 Aug 2016.

Pierce JP, White MM, Messer K. Changing age-specific patterns of cigarette consumption in the United States, 1992–2002: association with smoke-free homes and state-level tobacco control activity. Nicotine Tob Res. 2009;11:171–7.

Article   PubMed   PubMed Central   Google Scholar  

Schane RE, Ling PM, Glanz SA. Health effects of light and intermittent smoking: a review. Circulation. 2010;121:1518–22.

Peto R, Darby S, Deo H, Silcocks P, Whitley E, Doll R. Smoking, smoking cessation, and lung cancer in the U.K. since 1950: combination of national statistics with two case-control studies. BMJ. 2000;321:323–9.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Bruvold WH. A meta-analysis of adolescent smoking prevention programs. Am J Public Health. 1993;83:872–80.

Pentz MA, Dwyer JH, MacKinnon DP, Flay BR, Hansen WB, Wang EY, Johnson CA. A multicommunity trial for primary prevention of adolescent drug abuse: effects on drug use prevalence. JAMA. 1989;261:3259–66.

Article   CAS   PubMed   Google Scholar  

Tobler NS, Roona MR, Ochshom P, Marshall DG, Streke AV, Stackpole KM. School-based adolescent drug prevention programs: 1998 meta-analysis. J Prim Prev. 2000;20:275–336.

Article   Google Scholar  

Ling PM, Glantz SA. Why and how the tobacco industry sells cigarettes to young adults: evidence from industry documents. Am J Public Health. 2002;92:908–16.

National Center for Education Statistics (NCES). Fast facts. 2016. http://nces.ed.gov/fastfacts/display.asp?id=98 . Accessed 16 Aug 2016.

American College Health Association. Position statement on tobacco on college and university campuses. 2011. https://www.acha.org/documents/resources/guidelines/ACHA_Position_Statement_on_Tobacco_Nov2011.pdf . Retrieved November 20, 2016.

Wechsler H, Kelly K, Seibring M, Kuo M, Rigotti NA. College smoking policies and smoking cessation programs: results of a survey of college health center directors. J Am Coll Health. 2001;49:205–12.

Patterson F, Lerman C, Kaufmann VG, Neuner GA, Audrain-McGovern J. Cigarette smoking practices among American college students: review and future directions. J Am Coll Health. 2004;52:203–12.

Article   PubMed   Google Scholar  

Wolfson M, McCoy TP, Sutfin EL. College students’ exposure to secondhand smoke. Nicotine Tob Res. 2009;11:977–84.

Hahn EJ, Rayens MK, Ridner SL, Butler KM, Zhang M, Staten RR. Smoke-free laws and smoking and drinking among college students. J Community Health. 2010;35:503–11.

Fallin A, Roditis M, Glantz SA. Association of campus tobacco policies with secondhand smoke exposure, intention to smoke on campus, and attitudes about outdoor smoking restrictions. Am J Public Health. 2015;105:1098–100.

Fallin A, Johnson AO, Riker C, Cohen E, Rayens MK, Hahn EJ. An intervention to increase compliance with a tobacco-free university policy. Am J Health Promot. 2013;27:162–9.

Ickes MJ, Hahn EJ, McCann M, Kercmar S. Tobacco‐free take action: increasing policy adherence on a college campus. World Med Health Policy. 2013;5:47–56.

Ickes MJ, Gokun Y, Rayens MK, Hahn EJ. Comparing two observational measures to evaluate compliance with tobacco-free campus policy. Health Promot Pract. 2015;16:210–7.

Wilson LM, Tang EA, Chander G, et al. Impact of tobacco control interventions on smoking initiation, cessation, and prevalence: a systematic review. J Environ Public Health. 2012. doi: 10.1155/2012/961724 .

Fichtenberg CM, Glanz SA. Effect of smoke-free workplaces on smoking behavior: systematic review. BMJ. 2002;325:188–95.

Frazer K, Callinan JE, McHugh J, van Baarsel S, Clarke A, Doherty K, Kelleher C. Legislative smoking bans for reducing harms from secondhand smoke exposure, smoking prevalence and tobacco consumption (review). Cochrane Database Syst Rev. 2016. doi: 10.1002/14651858.CD005992.pub3 .

Americans for Nonsmokers’ Rights. 2016. http://www.no-smoke.org/ . Accessed 20 Nov 2016.

Lee JG, Goldstein AO, Kramer KD, Steiner J, Ezzell MM, Shah V. Statewide diffusion of 100% tobacco-free college and university policies. Tob Control. 2010;19:311–7.

Miller KD, Yu D, Lee JG, Ranney LM, Simons DJ, Goldstein AO. Impact of the adoption of tobacco-free campus policies on student enrollment at colleges and universities, North Carolina, 2001–2010. J Am Coll Health. 2015;63:230–6.

Garg T, Fradkin N, Moskowitz JM. Adoption of an outdoor residential hall smoking policy in a California public university: a case study. J Am Coll Health. 2011;59:769–71.

Ickes MJ, Rayens MK, Wiggins AT, Hahn EJ. A tobacco-free campus ambassador program and policy compliance. J Am Coll Health. 2015;63:126–33.

Russette HC, Harris KJ, Schuldberg D, Green L. Policy compliance of smokers on a tobacco-free university campus. J Am Coll Health. 2014;62:110–6.

Gerson M, Allard JL, Towvim LG. Impact of smoke-free residence hall policies: the views of administrators at 3 state universities. J Am Coll Health. 2005;54:157–65.

Mamudu HM, Veeranki SP, He Y, Dadkar S, Boone E. University personnel’s attitudes and behaviors toward the first tobacco-free campus policy in Tennessee. J Community Health. 2012;37:855–64.

Borders TF, Xu KT, Bacchi D, Cohen L, SoRelle-Miner D. College campus smoking policies and programs and students’ smoking behaviors. BMC Public Health 2005;5: doi: 10.1186/1471-2458-5-74 .

Harris KJ, Stearns JN, Kovach RG, Harrar SW. Enforcing an outdoor smoking ban on a college campus: effects of a multicomponent approach. J Am Coll Health. 2009;58:121–6.

Braverman MT, Hoogesteger LA, Johnson JA. Predictors of support among students, faculty and staff for a smoke-free university campus. Prev Med. 2015;71:114–20.

Fallin A, Murrey M, Johnson AO, Riker CA, Rayens MK, Hahn EJ. Measuring compliance with tobacco-free campus policy. J Am Coll Health. 2012;60:496–504.

Lee JG, Ranney LM, Goldstein AO. Cigarette butts near building entrances: what is the impact of smoke-free college campus policies? Tob Control. 2011;22:107–12.

Lechner WV, Meier E, Miller MB, Wiener JL, Fils-Aime Y. Changes in smoking prevalence, attitudes, and beliefs over 4 years following a campus-wide anti-tobacco intervention. J Am Coll Health. 2012;60:505–11.

Seo DC, Macy JT, Torabi MR, Middlestadt SE. The effect of a smoke-free campus policy on college students’ smoking behaviors and attitudes. Prev Med. 2011;53:347–52.

Hahn EJ, Fallin A, Darville A, Kercsmar SE, McCann M, Record RA. The three Ts of adopting tobacco-free policies on college campuses. Nurs Clin North Am. 2012;47:109–17.

The National Tobacco Free College Campus Initiative. 2012. http://tobaccofreecampus.org/ . Retrieved November 20.

Butler KM, Rayens MK, Hahn EJ, Adkins SM, Staten RR. Smoke‐free policy and alcohol use among undergraduate college students. Public Health Nurs. 2012;29:256–65.

Lochbihler SL, Miller DA, Etcheverry PE. Extending animal models to explore social rewards associated with designated smoking areas on college campuses. J Am Coll Health. 2014;62:145–52.

Gorber SC, Tremblay MS. Self-report and direct measures of health: bias and implications. In: Shepherd RJ, Tudor-Locke C, editors. The objective monitoring of physical activity: contributions of accelerometry to epidemiology, exercise science and rehabilitation. Switzerland: Springer International Publishing; 2016. p. 369–76.

Chapter   Google Scholar  

U.S. Census Bureau. United States Census 2010. 2010. http://www.census.gov/ . Accessed 3 Jun 2013.

Pokhrel P, Herzog TA, Muranaka N, Fagan P. Young adult e-cigarette users’ reasons for liking and not liking e-cigarettes: a qualitative study. Psychol Health. 2015;30:1450–69.

Pokhrel P, Little MA, Herzog TA. Current methods in health behavior research among US community college students: a review of the literature. Eval Health Prof. 2014;37:178–202.

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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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Smoking-related psychosocial beliefs and justifications among smokers in India: Findings from Tobacco Control Policy (TCP) India Surveys

  • Anupreet K. Sidhu 1 ,
  • Mangesh S. Pednekar 2 ,
  • Geoffrey T. Fong 3 , 4 , 5 ,
  • Prakash C. Gupta 2 ,
  • Anne C. K. Quah 3 ,
  • Jennifer Unger 6 ,
  • Steve Sussman 6 ,
  • Neeraj Sood 7 ,
  • Heather Wipfli 6 &
  • Thomas Valente 6  

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Previous research in high-income countries (HICs) has shown that smokers reduce their cognitive dissonance through two types of justifications over time: risk minimizing and functional beliefs. To date, however, the relationship between these justifications and smoking behaviors over time has limited evidence from low- and middle-income countries. This study examines these of justifications and their relation to quitting behavior and intentions among smoking tobacco users in India.

The data are from the Tobacco Control Policy (TCP) India Survey, a prospective cohort of nationally representative sample of tobacco users. The respondents include smoked tobacco (cigarettes and bidi) users ( n  = 1112) who participated in both Wave 1 (W1; 2010–2011) and Wave 2 (W2; 2012–2013) surveys. Key measures include questions about psychosocial beliefs such as functional beliefs (e.g., smoking calms you down when you are stressed or upset) and risk-minimizing beliefs (e.g., the medical evidence that smoking is harmful is exaggerated) and quitting behavior and intentions at Wave 2.

Of the 1112 smokers at W1, 78 (7.0%) had quit and 86 (7.8%) had intentions to quit at W2. Compared to W1, there was a significant increase in functional beliefs at W2 among smokers who transitioned to mixed use (using both smoking and smokeless tobacco) and a significant decrease among those who quit. At W2, smokers who quit held significantly lower levels of functional beliefs, than continuing smokers, and mixed users ((M = 2.96, 3.30, and 3.93, respectively, p  < .05). In contrast, risk-minimizing beliefs did not change significantly between the two waves. Additionally, higher income and lower functional beliefs were significant predictors of quitting behavior at W2.

These results suggest that smokers in India exhibit similar patterns of dissonance reduction as reported in studies from HICs: smokers who quit reduced their smoking justifications in the form of functional beliefs, not risk-minimizing beliefs. Smokers’ beliefs change in concordance with their smoking behavior and functional beliefs tend to play a significant role as compared to risk-minimizing beliefs. Tobacco control messaging and interventions can be framed to target these functional beliefs to facilitate quitting.

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In India, as in the vast majority of countries, tobacco use is a major public health concern. Despite widespread knowledge of tobacco use risks and harm, staggering numbers of smokers continue this deadly behavior. When people continue to smoke despite knowing the harms of smoking, it creates cognitive dissonance which is an aversive emotional state that leads to motivation to reduce that dissonance [ 1 ]. Because smoking is so addictive, reducing dissonance by changing behavior does not happen very often; therefore, smokers may resort to dissonance reduction by changing one’s dissonant beliefs [ 2 ].

Social psychological research demonstrates that because quitting is very difficult [ 3 , 4 ], smokers may generate beliefs to justify their smoking [ 5 ]. Adult smokers who continue to use tobacco despite knowledge of harmful effects of smoking engage in dissonance reduction using justifications for continuing smoking. These justifications are also referred to as rationalizations, disengagement beliefs, or self-exempting beliefs [ 6 , 7 , 8 ]. These justifications have been characterized in a number of ways; some beliefs act as a shield for smokers, providing false reassurances, and enabling avoidance of thinking deeply about quitting [ 9 ]. Two types of beliefs are functional beliefs , which serve to highlight the perceived benefits of smoking, such as increased concentration, stress reduction, and risk-minimizing beliefs , which justify smoking by undermining the harms and negative health consequences of smoking [ 6 , 9 , 10 , 11 , 12 , 13 , 14 ]. Multiple cross-sectional studies and longitudinal studies have found that high endorsements of pro-smoking beliefs are associated with lower quit intentions among smokers [ 4 , 5 , 6 , 9 , 10 , 15 , 16 ]. Additionally, previous research has shown associations between price promotions and functional beliefs in some HICs [ 17 ].

The vast majority of research on the interplay between smoking and dissonance reducing beliefs has been conducted in high-income Western countries. There are, however, some studies that have been conducted in Asian countries. An analysis of predictors of intentions to quit among smokers in Korea found no significant association of risk-minimizing beliefs (termed as self-exempting beliefs in the study) with intentions to quit smoking [ 18 ]. Higher smoking rationalizations were associated with lower intentions to quit among male smokers in China [ 19 ]. Another study from Southeast Asia found higher prevalence of rationalization (“You’ve got to die of something, so why not enjoy yourself and smoke”) among Malaysian smokers compared to Thai smokers, which may discourage cessation efforts in Malaysia with lower levels of intentions to quit [ 4 ]. The patterns of rationalizations and the association between regret and rationalization were different between Thailand and Malaysia; therefore, it is important to analyze smoking rationalizations and justifications in different countries to understand the belief systems and design counter-tobacco messaging accordingly.

There has been important research conducted on the role of functional and risk-minimizing beliefs to sustain smoking [ 6 , 10 ]. These have been shown to be important as justifications for continued smoking over time, and they tend to reduce when a smoker quits and bounce back when a quitter relapses. This is indicative of the use of these beliefs to reduce the strong level of cognitive dissonance that arises when a smoker continues to smoke in the face of the knowledge that smoking is dangerous. In fact, a study by Fotuhi et al. [ 5 ] assessed if smokers adjusted their beliefs in patterns consistent with Cognitive Dissonance Theory [ 1 ] while determining the magnitude of belief change among smokers accompanying behavior change. The study found that smokers tend to rationalize their smoking behaviors and those beliefs change systematically with their smoking status.

Most studies that have examined cognitive dissonance and dissonance reduction among smokers have been conducted in high-income countries (HICs). Though some of these studies are from low- and middle-income countries, these studies were largely cross-sectional, which limits the ability to assess causal relationships between beliefs and quitting. This study aims to investigate the smoking related beliefs and their association with quitting behaviors among smokers in India, and to understand how the evidence stacks up in relation to what we know from HICs. The proposed study is designed to address the following aims: 1) To examine the pattern of functional and risk-minimizing beliefs (justifications) among smokers in India. 2) To assess changes in and associations of smoking justifications with quitting intentions and behavior over time. This study is among the first to examine the predictive value of two kinds of beliefs: functional beliefs and risk-minimizing beliefs and how they may predict future quitting among smokers in India.

This study is a part of the larger Tobacco Control Policy (TCP) India Survey, a prospective cohort study of adult tobacco users (aged 15 +) and non-users from 4 Indian states: Bihar, Madhya Pradesh (MP), Maharashtra, and West Bengal (WB). Within each state, one major city represented an urban area and a surrounding area within 50 km outside the city represented a rural area. At Wave 1, the survey employed a stratified multistage cluster sampling design and was conducted between August 2010 and October 2011. Wave 2 was conducted in October 2011 to September 2013. The survey protocol and questionnaires were first developed in English followed by translation into the dominant languages of each state (Hindi in Bihar and MP, Marathi in Maharashtra, and Bengali in WB). At the end, respondents were debriefed, remunerated, and thanked for their time [ 20 , 21 ]. Additional details on the construction of survey weights, household enumeration, selection criteria and response rates are available in TCP India Technical Reports [ 22 , 23 ].

Study sample

Data for this study were drawn from the TCP India Survey comprising 8940 participants and only baseline smoked tobacco users who participated at both waves of data collection were selected for analysis. Of the 8940 participants sampled at Wave 1, 1255 were smoked tobacco users. Of those 1255 smokers, 1112 were followed up at Wave 2 and reported their tobacco use status. The analytical sample of smokers had an 88.6% retention rate at Wave 2.

Socio-demographic variables

Socio-demographic variables measured were age, sex, highest level of educational attainment, monthly household income and urban residence. Education was categorized into low, moderate, and high. Low education included illiterate, primary or middle school education; moderate included secondary school or Industrial Training Institute courses; and high included those who completed college and higher education. Similarly, income level was divided into low, moderate, and high. Low-income category included those earning less than 5000 INR per month; moderate income had those earning between 5000 and 15,000 INR per month and high income included those earning more than 15,000 INR.

Smoking tobacco user

A smoker was defined as anyone who said yes to either of the following questions: “Do you currently smoke cigarettes at least once a month?” or “Do you currently smoke bidis at least once a month?” (Yes/No/Don’t Know).

Tobacco use variables

The tobacco use variables were the use frequency, intention to quit smoking, and quit status.

Use frequency

The cigarette and bidi smoking frequency were measured by two different questions asking: “On average, how often do you smoke cigarettes?” and “On average, how often do you smoke bidis?” The response categories – “Less than once a week/Once a week/Twice a week/3–5 times a week/Every day or almost every day More than once a day” – were combined and reported as daily smoker (Every day or almost every day/More than once a day), less than daily smoker (Once a week/Twice a week/3–5 times a week), and less than weekly smoker (Less than once a week) for cigarette and bidi users separately.

Intention to quit

Intention to quit was measured by asking “Are you planning to quit smoking…” and the response categories were: “Within the next month/Within the next 6 months/Sometime in the future, beyond 6 months/Not planning to quit/Refused/Don’t know.” The responses were recoded as a dichotomous variable with any plans to quit as 1 or Yes and “Not planning to quit/Refused/Don’t know” as 0 or No.

At Wave 2, all smokers from Wave 1 were asked whether they were still smoking. Those who indicated that they had completely quit smoking were categorized as 1 (having quit) and those who continued smoking or transitioned to mixed use were coded as 0 (continuing smoking).

  • Psychosocial beliefs

Functional beliefs were assessed using three statements: (F1) You enjoy smoking too much to give it up, (F2) Smoking calms you down when you are stressed or upset, and (F3) Smoking is an important part of your life. Risk-minimizing beliefs were assessed using the following three statements: (R1) The medical evidence that smoking is harmful is exaggerated, (R2) Everybody has got to die of something, so why not enjoy yourself and smoke, and (R3) Smoking is no more risky than lots of other things that people do. These psychosocial beliefs were measured on a five-point Likert-scale ranging from Strongly agree [ 5 ] to Strongly disagree [ 1 ]. These beliefs were also dichotomized for frequency analysis where Strongly agree/Agree were coded as 1 (having a belief) and the Neither agree nor disagree/Disagree/Strongly Disagree were coded as 0 (NOT having a belief).

Data analysis

Analyses were conducted using STATA/SE 17. Univariate statistics were used to categorize the sample and bivariate statistics such as paired t-tests were conducted to analyze the difference between justifications and smoking status between two waves. Multivariable logistic regression was used to examine the association between quitting at Wave 2 and functional as well as risk-minimizing beliefs. Two separate models were run to account for these beliefs at Wave 1 and Wave 2 separately. Additionally, models assessing the mean scores of functional and risk-minimizing beliefs were followed by models that analyze each belief item individually. Similar models were also run for the “planning to quit” outcome at Wave 2. Weights were calculated to adjust for disproportionate sampling respondents in subgroups and longitudinal sampling weights were used for regression analysis. The models also included the covariates: age, sex, education, and income.

Sample characteristics were calculated using unweighted data and are reported in Table 1 . The analytic sample comprised of smokers who responded to both waves of the TCP Survey ( n  = 1112). Of these exclusive smokers at Wave 1, 962 reported still smoking at Wave 2, 36 initiated mixed use, 36 switched to smokeless tobacco use, and 78 respondents quit smoking. Mixed use indicates use of a smoked as well as a smokeless tobacco product. At baseline, mean age was 44 years (SD = 14.17), 97% were male, 66% were aged 25–54 years, and 67% resided in urban areas (Table 1 ). Overall, 55% of the sample reported having low education level and 82% reported low or moderate income.

The functional beliefs were held by 44% to 66% of the respondents (F1 = 58%, F2 = 66%, and F3 = 44%) and risk-minimizing beliefs were held by 10% to 45% (R1 = 10%, R2 = 22%, and R3 = 45%) respondents at Wave 1. At Wave 2, the participants holding functional beliefs ranged from 44 to 61% (F1 = 60%, F2 = 61%, and F3 = 44%) and risk-minimizing beliefs were 14% to 42% (R1 = 14%, R2 = 23%, and R3 = 42%). As evident, the functional beliefs “you enjoy smoking tobacco too much to give it up” and “smoking tobacco calms you down when you are stressed or upset” were held by most respondents (about 60%). Overall, the risk-minimizing beliefs were held by far fewer respondents with least number of people (10%) believing that “the medical evidence that smoking is harmful is exaggerated” and about 40% believing that “smoking is no more risky than lots of other things that people do”.

The mean scores of the beliefs at baseline (with higher scores representing greater agreement) were higher for functional beliefs (F1 = 3.4 (SD = 1.1), F2 = 3.6 (SD = 1.1), and F3 = 3.0 (SD = 1.3) when compared to risk-minimizing beliefs (R1 = 1.9 (SD = 1.0), R2 = 2.4 (SD = 1.1), and R3 = 3.0 (SD = 1.2) (see Table 2 ). These mean scores did not change significantly for continued smoked tobacco users between the two waves. However, the mean functional belief “you enjoy smoking tobacco too much to give it up” increased significantly for those who transitioned from smoking at Wave 1 into mixed tobacco use at Wave 2 from 3.37 to 4.13 ( p  = 0.004). Smokers at Wave 1 who quit at follow-up had a significant decline in the individual functional beliefs at Wave 2 ( p  < 0.05). The risk-minimizing belief “smoking is no more risky than lots of other things that people do” also declined significantly among those who quit at Wave 2 ( p  = 0.01). There was no significant change in risk-minimizing beliefs among those who transitioned from smoking at Wave 1 to mixed tobacco use at Wave 2. These results show that smokers are more likely to adjust their beliefs according to their changing smoking status, though some beliefs alter more significantly than others (see Fig.  1 ).

figure 1

Functional and risk-minimizing beliefs of smokers at Wave 1 and continued smokers, mixed tobacco users and quitter at Wave 2

Weighted logistic regression was conducted to assess the association of covariates, functional and risk-minimizing beliefs for two key outcomes: quitting and intentions to quit at Wave 2. The beliefs at both waves were analyzed in separate models (Table 3 ) and the individual belief items at both waves were analyzed as well (Table 4 ). Among the covariates, odds of quitting were three times for those in the high-income category when compared to the low-income group ( p  < 0.05). The functional beliefs at Wave 2 were negatively significantly associated with quitting at Wave 2 (OR = 0.63, SE = 0.10, p  = 0.01). Among the functional beliefs, decline in beliefs “smoking tobacco calms you down when you are stressed or upset” (OR = 0.70, SE = 0.10, p  = 0.02) and “smoking is an important part of your life” (OR = 0.72, SE = 0.09, p  = 0.01) were significantly associated with quitting at Wave 2. For those who did not quit at Wave 2 but expressed intentions to quit in the future, there was a marginally significant association with risk-minimizing beliefs at Wave 1 (OR = 1.39, SE = 0.22, p  = 0.04), primarily driven by the belief that “smoking tobacco is no more risky than lots of other things people do” (OR = 1.45, SE = 0.19, p  = 0.007). The individual beliefs associated with intentions to quit were the functional beliefs (W2) “enjoy smoking tobacco too much to give it up” (OR = 0.63, SE = 0.08, p  = 0.002) and “smoking is an important part of your life” (OR = 1.38, SE = 0.17, p  = 0.02, and risk-minimizing belief (W2) “smoking tobacco is no more risky than lots of other things people do” (OR = 0.82, SE = 0.07, p  = 0.03).

The main aim of this study was to analyze the functional and risk-minimizing beliefs and their associations with quitting behavior and intentions at Wave 2 among exclusively smoking tobacco users at baseline. Our findings show that the pattern of belief change, particularly among functional beliefs is consistent with dissonance reduction and evidence from previous studies. These beliefs stay consistent over time among continued smokers but become stronger among those who transition to mixed tobacco use at Wave 2 and become weaker among those who quit. The results, among this population show a greater magnitude of change among functional, but not risk-minimizing beliefs overall.

The psychosocial beliefs assessed were functional beliefs which reinforce the role of smoking in one’s life and risk-minimizing beliefs which tend to reduce the perception of harm caused by tobacco use. Overall, there was a greater percentage of respondents who agreed with functional beliefs at both waves. The functional beliefs “you enjoy smoking too much to give it up” and “smoking calms you down when you are stressed or upset” were held by about 60% respondents at both waves with 44% agreeing that “smoking is an important part of your life”. As Fotuhi et al. (2013) concluded, functional beliefs may be less susceptible to encounter resistance as they are not easy to challenge using counterarguments and rationale. In comparison, the risk-minimizing beliefs were held by fewer smokers with highest agreement (44%) for “smoking is no more risky than lots of other things people do” at both waves. About 22% respondents held the belief that “everybody has got to die of something so, why not enjoy yourself and smoke” and the least supported belief was “medical evidence that smoking is harmful is exaggerated” held by 10% smokers. These beliefs are considered “weak beliefs” as they might be susceptible to being easily changed [ 16 , 24 ]. An overall lower agreement with risk-minimizing justifications is a positive sign overall and bolsters the support for policies (such as graphic warning labels) and education campaigns to highlight harms of smoking in India.

Among the smokers that quit successfully at Wave 2, there was no change in the risk-minimizing beliefs as they were quite low to begin with. There was, however, a reduction in functional beliefs among those who quit. The levels of functional beliefs were similar at baseline for smokers but significantly changed as they transitioned to mixed tobacco use or quitting at follow-up; the levels increased among mixed tobacco users and declined among quitters, in concordance with their smoking behaviors. Regression analysis shows a significant negative association of functional beliefs at Wave 2 with quitting smoking at Wave 2; these beliefs at Wave 1, however, had no significant association with quitting at Wave 2. Therefore, those who quit are more likely to express reduction in functional beliefs over time. Evidence suggests that functional beliefs play a crucial role in early periods of quitting, wherein highly dependent smokers and those holding strong functional beliefs are at greater risk of relapse [ 25 ]. Future cessation efforts and tobacco control campaigns can target these beliefs to inoculate smokers against tobacco marketing that highlights the functional aspects of smoking (concentration, calmness, weight loss etc.) and boost self-efficacy in quitting overall.

Risk-minimizing beliefs at Wave 1 were marginally significantly associated with intentions to quit at Wave 2, driven by the belief “smoking tobacco is no more risky than lots of other things people do”. However, this association was positive which seems counterintuitive. Beliefs at Wave 2, that were negatively associated with intentions to quit were F1 (You enjoy smoking too much to give it up) and R3 (Smoking is no more risky than lots of other things that people do) whereas F2 (Smoking calms you down when you are stressed or upset) was positively associated. Given the incoherent patterns of these associations with intentions to quit, it is worthy of further investigation.

The association between health beliefs and smoking behavior may differ based on sociocultural factors and norms [ 16 , 26 ]. These findings, particularly ones tracking patterns of beliefs and their association with quitting at Wave 2, highlight the key beliefs that drive smoking behaviors and provide evidence from a low-middle income country context. It adds to the larger literature in tobacco research that seeks to determine if these phenomena are culturally universal and whether these associations differ by countries. The study analyzing the association between smoking rationalizations and intention to quit smoking from China, utilized a smoking rationalization scale developed specifically from a population-based sample of Chinese male smokers within the socio-cultural context [ 19 ]. Since these beliefs are driven by culture, tobacco marketing efforts, and regulatory environments, more research is needed to develop and evaluate the reliability of smoking belief measures in different contexts.

These findings should be interpreted in the light of a few limitations. First, the data is self-reported at two different time points which may be subject to recall and/or social desirability bias. Second, the smoking tobacco sub-sample selected for this study comprised cigarette and bidi users at Wave 1. It is possible that the beliefs held by users of either product are distinct which make them prefer a filtered cigarette over the unfiltered bidis. This could be investigated in subsequent studies alongside assessment of beliefs among smokeless tobacco users and vulnerable groups. Future studies focusing on different forms of tobacco use and populations (such as rural vs urban) can aid in addressing tobacco use related disparities. This study utilized two waves of data from the cohort of smokers which provides more information than cross-sectional data, but future waves of data may illuminate patterns of beliefs among those who continued smoking, relapsed quitters, or those who successfully quit. Lastly, we assessed the patterns and associations of two key types of beliefs based on prominent tobacco literature, but there is a wide array of psychosocial beliefs surrounding tobacco use that may be worthwhile to analyze in different cultural contexts, even if they were not found to be influential in some countries.

Conclusion and implications

The study advances our understanding of the role that self-exempting beliefs and justifications play in smoking tobacco use and cessation, demonstrating that in the vastly different cultural context of India, strategies (whether conscious or not) to reduce dissonance among smokers may be quite similar to those among smokers in high-income countries. In both India and Western countries, these beliefs seem to play an integral role in dissonance reduction and undergo shifts with one’s own tobacco consumption behavior. A broader understanding of these belief patterns, especially in different regulatory and cultural contexts can be influential in developing effective tobacco control programs and policies.

Availability of data and materials

The data are jointly owned by a third party in each country that collaborates with the International Tobacco Control Policy Evaluation (ITC) Project. Data from the ITC Project are available to approved researchers 2 years after the date of issuance of cleaned data sets by the ITC Data Management Centre. Researchers interested in using ITC data are required to apply for approval by submitting an International Tobacco Control Data Repository (ITCDR) request application and subsequently to sign an ITCDR Data Usage Agreement. The criteria for data usage approval and the contents of the Data Usage Agreement are described online ( http://www.itcproject.org ). The authors of this paper obtained the data following this procedure. This is to confirm that others would be able to access these data in the same manner as the authors. The authors did not have any special access privileges that others would not have. The data that support the findings of this study are available from the ITC Project, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors ([email protected]) upon reasonable request and with permission of the ITC Project team.

Abbreviations

High Income Countries

Low- and Middle- Income Countries

Software for Statistics and Data Science developed by StataCorp

Tobacco Control Policy India Survey

Festinger L. A theory of cognitive dissonance. California: Stanford university press; 1957.

Fong GT, Hammond D, Laux FL, Zanna MP, Cummings KM, Borland R, et al. The near-universal experience of regret among smokers in four countries: findings from the International Tobacco Control Policy Evaluation Survey. Nicotine Tob Res. 2004;6(Suppl_3):S341-51.

Article   Google Scholar  

Hyland A, Li Q, Bauer JE, Giovino GA, Steger C, Cummings KM. Predictors of cessation in a cohort of current and former smokers followed over 13 years. Nicotine Tob Res. 2004;6(Suppl_3):S363-9.

Lee WB, Fong GT, Zanna MP, Omar M, Sirirassamee B, Borland R. Regret and rationalization among smokers in Thailand and Malaysia: findings from the International Tobacco Control Southeast Asia Survey. Health Psychol Off J Div Health Psychol Am Psychol Assoc. 2009;28(4):457–64.

Google Scholar  

Fotuhi O, Fong GT, Zanna MP, Borland R, Yong HH, Cummings KM. Patterns of cognitive dissonance-reducing beliefs among smokers: a longitudinal analysis from the International Tobacco Control (ITC) Four Country Survey. Tob Control. 2013;22(1):52–8.

Chapman S, Wong WL, Smith W. Self-exempting beliefs about smoking and health: differences between smokers and ex-smokers. Am J Public Health. 1993;83(2):215–9.

Article   CAS   Google Scholar  

Kleinjan M, van den Eijnden RJJM, Engels RCME. Adolescents’ rationalizations to continue smoking: the role of disengagement beliefs and nicotine dependence in smoking cessation. Addict Behav. 2009;34(5):440–5.

McMaster C, Lee C. Cognitive dissonance in tobacco smokers. Addict Behav. 1991;16(5):349–53.

Oakes W, Chapman S, Borland R, Balmford J, Trotter L. “Bulletproof skeptics in life’s jungle”: which self-exempting beliefs about smoking most predict lack of progression towards quitting? Prev Med. 2004;39(4):776–82.

Borland R, Yong HH, Balmford J, Fong GT, Zanna MP, Hastings G. Do risk-minimizing beliefs about smoking inhibit quitting? Findings from the International Tobacco Control (ITC) Four-Country Survey. Prev Med. 2009;49(2–3):219–23.

Croog SH, Richards NP. Health beliefs and smoking patterns in heart patients and their wives: a longitudinal study. Am J Public Health. 1977;67(10):921–30.

Dillard AJ, McCaul KD, Klein WMP. Unrealistic Optimism in Smokers: Implications for Smoking Myth Endorsement and Self-Protective Motivation. J Health Commun. 2006;11(sup001):93–102.

Weinstein ND. The precaution adoption process. Health Psychol. 1988;7(4):355.

Weinstein ND, Marcus SE, Moser RP. Smokers’ unrealistic optimism about their risk. Tob Control. 2005;14(1):55.

Yong HH, Borland R. Functional beliefs about smoking and quitting activity among adult smokers in four countries: findings from the International Tobacco Control Four-Country Survey. Health Psychol Off J Div Health Psychol Am Psychol Assoc. 2008;27(3S):S216-223.

Jiraniramai S, Jiraporncharoen W, Pinyopornpanish K, Jakkaew N, Wongpakaran T, Angkurawaranon C. Functional beliefs and risk minimizing beliefs among Thai healthcare workers in Maharaj Nakorn Chiang Mai hospital: its association with intention to quit tobacco and alcohol. Subst Abuse Treat Prev Policy. 2017;12(1):1–11.

El-Toukhy S, Choi K, Hitchman SC, Bansal-Travers M, Thrasher JF, Yong HH, et al. Banning tobacco price promotions, smoking-related beliefs and behaviour: findings from the International Tobacco Control Four Country (ITC 4C) Survey. Tob Control. 2018;27(3):310–8.

Myung SK, Seo HG, Cheong YS, Park S, Lee WB, Fong GT. Association of sociodemographic factors, smoking-related beliefs, and smoking restrictions with intention to quit smoking in Korean adults: findings from the ITC Korea Survey. J Epidemiol. 2012;22(1):21–7.

Huang X, Fu W, Zhang H, Li H, Li X, Yang Y, et al. Why are male Chinese smokers unwilling to quit? A multicentre cross-sectional study on smoking rationalisation and intention to quit. BMJ Open. 2019;9(2):e025285.

Dhumal GG, Pednekar MS, Gupta PC, Sansone GC, Quah ACK, Bansal-Travers M, et al. Quit history, intentions to quit, and reasons for considering quitting among tobacco users in India: findings from the Tobacco Control Policy Evaluation India Wave 1 Survey. Indian J Cancer. 2014;51(Suppl 1):S39-45.

PubMed   PubMed Central   Google Scholar  

Gravely S, Fong GT, Driezen P, Xu S, Quah ACK, Sansone G, et al. An examination of the effectiveness of health warning labels on smokeless tobacco products in four states in India: findings from the TCP India cohort survey. BMC Public Health. 2016;16(1):1246.

ITC Project. TCP India Wave 1 (2010–2012) Technical Report. [Internet]. University of Waterloo, Waterloo, Ontario, Canada; Healis-Sekhsaria Institute for Public Health, Navi Mumbai, India.; 2013 Jul. Available from: http://www.itcproject.org/technical-report/?country=India

ITC Project. TCP India Wave 2 (2012–2013) Technical Report. [Internet]. University of Waterloo, Waterloo, Ontario, Canada; Healis-Sekhsaria Institute for Public Health, Navi Mumbai, India.; 2016 Mar [cited 1 Apr 2019]. Available from: https://www.itcproject.org/technical-report/?country=India

Dijkstra A. Disengagement beliefs in smokers: Do they influence the effects of a tailored persuasive message advocating smoking cessation? Psychol Health. 2009;24(7):791–804.

Yong H, Borland R, Cummings KM, Partos T. Do predictors of smoking relapse change as a function of duration of abstinence? Findings from the United States, Canada. United Kingdom and Australia Addiction. 2018;113(7):1295–304.

PubMed   Google Scholar  

Yong HH, Borland R, Siahpush M. Quitting-related beliefs, intentions, and motivations of older smokers in four countries: findings from the International Tobacco Control Policy Evaluation Survey. Addict Behav. 2005;30(4):777–88.

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Acknowledgements

The authors would like to acknowledge and thank all those that contributed to the TCP India Project: all study investigators, the project managers at University of Waterloo, Canada; the TCP India Research Team at Healis-Sekhsaria Institute for Public Health, Navi Mumbai, India, the state collaborators of the TCP India Survey and their field teams for their dedicated efforts in collecting data: Bihar--School of Preventive Oncology; West Bengal--Cancer Foundation of India; Madhya Pradesh--Madhya Pradesh Voluntary Health Association; and Maharashtra--Healis-Sekhsaria Institute for Public Health.

The Waves 1 and 2 TCP India Surveys were supported by grants from the US National Cancer Institute (P50 CA111236, P01 CA138389) and the Canadian Institute of Health Research (MOP-79551, MOP-115016). Additional support to GTF was provided by the Canadian Institutes of Health Research (FDN-148477), a Senior Investigator Award from the Ontario Institute for Cancer Research, and the Canadian Cancer Society O. Harold Warwick Prize. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Tobacco Center of Regulatory Science and Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Anupreet K. Sidhu

Healis-Sekhsaria Institute for Public Health, Mumbai, India

Mangesh S. Pednekar & Prakash C. Gupta

Department of Psychology, University of Waterloo, Waterloo, ON, Canada

Geoffrey T. Fong & Anne C. K. Quah

School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada

Geoffrey T. Fong

Ontario Institute for Cancer Research, Toronto, ON, Canada

Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

Jennifer Unger, Steve Sussman, Heather Wipfli & Thomas Valente

Sol Price School of Public Policy and Schaeffer Center, University of Southern California, Los Angeles, CA, USA

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AKS: Conceptualization, Validation, Formal analysis, Methodology, Visualization, Writing – Original draft preparation. MSP: Funding acquisition, Methodology, Project Administration, Investigation, Writing – Review and editing. GTF: Conceptualization, Funding acquisition, Methodology, Investigation, Writing – Review and editing. PCG: Funding acquisition, Resources, Methodology, Investigation, Writing – Review and editing. ACKQ: Project Administration, Investigation, Writing – Review and editing. JU: Conceptualization, Writing – Review and editing . SS: Conceptualization, Writing – Reviewing and editing. NS: Conceptualization, Writing – Review and editing. HW: Conceptualization, Writing – Review and editing. TV: Conceptualization, Formal analysis, Methodology, Resources, Writing – Original draft, Writing – Review and editing, Supervision. The author(s) read and approved the final manuscript.

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Correspondence to Anupreet K. Sidhu .

Ethics declarations

Ethics approval and consent to participate.

All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all subjects and/or their legal guardian(s). The survey protocols and all materials, including the TCP India survey questionnaires, were cleared for ethics by Office of Research Ethics, University of Waterloo, Canada (ORE#15722) and the Healis Sekhsaria Institute for Public Health International Research Board, India (IRB00007340). The use of secondary data from this project was approved by the Institutional Review Board (IRB) at University of Southern California (#HS-18–00666).

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GTF has served as an expert witness or a consultant for governments defending their country’s policies or regulations in litigation and has served as a paid expert consultants to the Ministry of Health of Singapore in reviewing the evidence on plain/standardised packaging.

All other authors declare no competing interests.

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Sidhu, A.K., Pednekar, M.S., Fong, G.T. et al. Smoking-related psychosocial beliefs and justifications among smokers in India: Findings from Tobacco Control Policy (TCP) India Surveys. BMC Public Health 22 , 1738 (2022). https://doi.org/10.1186/s12889-022-14112-w

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Received : 21 January 2022

Accepted : 25 August 2022

Published : 13 September 2022

DOI : https://doi.org/10.1186/s12889-022-14112-w

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Cause and Effects of Smoking Cigarettes, Essay Example

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Smoking cigarettes has historically been a leisurely and highly popular social activity that a litany of people turn to as a way to assuage daily stress, lose weight, and feel socially accepted in a constantly evolving social world. Tobacco, the main ingredient in cigarettes, has high levels of nicotine, which is a highly addictive ingredient that makes it hard for people to quit smoking if nicotine is ingested on a quotidian basis (Woolbright, 1994, p. 337). According to the CDC (2014), cigarette smoking causes over 480,000 deaths annually in the United States alone, which translates into one out of every five people extirpating due to the ingestion of tobacco. A preventable cause of death, cigarette smoking kills more persons than accidents caused due motor vehicle accidents, alcohol consumption, illegal drug use, deaths involving firearms, and the HIV/AIDS virus altogether (Center For Disease Control and Prevention, 2014). Women who smoke tobacco disproportionately suffer from even more health problems as it directly harms not only their reproductive health but also their mortality and morbidity rates of their progeny or future children (American Lung Association, n.d.). People should not smoke because it not only spawns negative health effects but also because it is not economically useful. If people stopped smoking, many lives would be both indirectly and directly saved from premature and preventative deaths as a result.

Doctors and other medical experts pinpoint the various health hazards caused by smoking, especially to the statistics pertaining to the nexus between smoking cigarettes and premature death, in order to convince people to quit smoking. In the past five decades, the risk of premature death in both female and male smokers has profoundly increased (Centers for Disease Control and Prevention, 2014). According to the CDC (2014), smoking cigarettes causes a handful of diseases because it adversely impacts almost all bodily organs and detracts from the general health of enthusiastic smokers. The risk of developing coronary heart disease (COPD), various cardiovascular maladies, and stroke–the leading cause of death in the United States alone–increases two to four times as much due to the damage it spawns to blood vessels because tobacco narrows and thickens them. These ramifications cause rapid heartbeat, which results in higher blood pressure levels which renders smokers vulnerable to blood clots. If blood clots prevent blood from reaching the heart, people put themselves  at risk for heart attack due to the fact that the heart does not get enough oxygen and thus kills the heart muscle. In addition, blood clots can also cause a stroke because they can hinder blood flow to the brain. Shockingly, quitting smoking even after just one year drastically enhances an individual’s risk of incurring poor cardiovascular health. Moreover, smoking is directly connected to various respiratory diseases due to the fact that it harms both airways and alveoli, or the minute air vacs, that are in the lungs. Chronic Obstructive Pulmonary Disease (COPD), emphysema, and bronchitis are common forms of lung disease that chronic smokers often develop. In addition, medical experts correlate cigarette smoking with a litany of cancers, which have been pinpointed as the primary cause of lung cancer in individuals who smoke for a protracted period of time. Smoking cigarettes can also spawn various other types of cancer, including cancer in the stomach, liver, kidneys, bladders, pancreas, and oropharynx. Smoking not only puts smokers at risk for these often fatal types of cancer but also to those around smokes as a result of second-hand smoking. Second-hand smoke, according to the CDC (2014), causes an estimated 34,000 deaths per year in non-smokers because they too develop various cardiovascular diseases while an estimated 8,000 persons prematurely dying as a result of stroke (CDC, 2014). They also are put at risk for developing lung cancer by approximately thirty percent, and their risk for heart attack is also amplified. Physicians estimate that if nobody smoked cigarettes around the world, an estimated one out of every three deaths caused by cancer would not manifest (1).

More poignantly, smoking cigarettes negatively impacts women’s reproductive health, and children who are exposed to cigarette smoke suffer from often fatal effects. Many studies have analyzed and outlined the negative ramifications of maternal smoking on both the mother and the baby and/or infant ( Hofhuis, de Jongste, & Merkus, 2003 & Woolbright, 1994). Many states require documentation on birth certificates of maternal tobacco consumption (Woolbright, 1994). Despite the Surgeon General’s stern warning that maternal smoking has been linked to fetal injury, premature birth, and/or low birth rate, 15-37% of pregnant women still smoke cigarettes while pregnant (Hofhuis, de Jongste, & Merkus, 2003). Mothers who smoke also frequently participate in other high-risk behaviors that also negatively impacts the health of their progeny. Additionally, factors including marital and socio-economic status in addition education level affect the outcome of pregnancies due to increased vulnerability to cigarette smoking (Woolbright, 1994, p. 330). Low birth weight is the main impact of maternal smoking, although the existing literature pinpoints infant death and premature birth as major ramifications of it as well. Infant exposure to tobacco after they are born puts him or her at risk of premature death if they develop respiratory diseases in addition to Sudden Infant Death Syndrome (Woolbright, 1994). Hofhuis, de Jongste, and Merkus (2003) assessed how smoking cigarettes during pregnancy in addition to passive smoking thereafter affects both the mortality and morbidity rates in children. Statistics show that other obstetric complications directly linked to smoking, including spontaneous abortions, premature rupture of membranes, ectopic pregnancies, and complications related to the placenta. Smoking also stunts the lung growth that fetuses need in utero, which results in the child suffering from weakened lungs after birth while also exponentially increases the child’s chance of suffering from asthma and a vast array of other crippling  respiratory diseases. In addition, it stunts brain development and detracts from the child’s mental acuity.

Health Effects of Cigarette Smoking. (2014, February 6).  Centers for Disease Control and Prevention . Retrieved November 21, 2015 from http://www.cdc.gov/tobacco/data_statistics/fact_sheets/health_effects/effects_ cig_smoking/

American Lung Association. (n.d.). Women and tobacco use.  American Lung Association . Retrieved November 21, 2015 from http://www.lung.org/stop- smoking/about-smoking/facts- figures/women-and-tobacco-use.html

Ault, R. W., Jr., R. E., Jackson, J. D., Saba, R. S., & Saurman, D. S. (1991). Smoking and Absenteeism. Applied Economics ,  23 , 743-754.

Hodgson TA. Cigarette Smoking and Lifetime Medical Expenditures.  Millbank Q  1992, 70, 81-125.

Hofhuis, W., de Jongste, J. C., & Merkus, P. J. (2003). Adverse Health Effects of Prenatal and Postnatal Tobacco Smoke Exposure on Children.  Arch Dis Child ,  88 , 1086-1090.

Woolbright, L. A. (1994). The effects of maternal smoking on infant health. Population Research and Policy Review ,  13 (3), 327-339.

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Essay on Smoking

500 words essay on  smoking.

One of the most common problems we are facing in today’s world which is killing people is smoking. A lot of people pick up this habit because of stress , personal issues and more. In fact, some even begin showing it off. When someone smokes a cigarette, they not only hurt themselves but everyone around them. It has many ill-effects on the human body which we will go through in the essay on smoking.

essay on smoking

Ill-Effects of Smoking

Tobacco can have a disastrous impact on our health. Nonetheless, people consume it daily for a long period of time till it’s too late. Nearly one billion people in the whole world smoke. It is a shocking figure as that 1 billion puts millions of people at risk along with themselves.

Cigarettes have a major impact on the lungs. Around a third of all cancer cases happen due to smoking. For instance, it can affect breathing and causes shortness of breath and coughing. Further, it also increases the risk of respiratory tract infection which ultimately reduces the quality of life.

In addition to these serious health consequences, smoking impacts the well-being of a person as well. It alters the sense of smell and taste. Further, it also reduces the ability to perform physical exercises.

It also hampers your physical appearances like giving yellow teeth and aged skin. You also get a greater risk of depression or anxiety . Smoking also affects our relationship with our family, friends and colleagues.

Most importantly, it is also an expensive habit. In other words, it entails heavy financial costs. Even though some people don’t have money to get by, they waste it on cigarettes because of their addiction.

How to Quit Smoking?

There are many ways through which one can quit smoking. The first one is preparing for the day when you will quit. It is not easy to quit a habit abruptly, so set a date to give yourself time to prepare mentally.

Further, you can also use NRTs for your nicotine dependence. They can reduce your craving and withdrawal symptoms. NRTs like skin patches, chewing gums, lozenges, nasal spray and inhalers can help greatly.

Moreover, you can also consider non-nicotine medications. They require a prescription so it is essential to talk to your doctor to get access to it. Most importantly, seek behavioural support. To tackle your dependence on nicotine, it is essential to get counselling services, self-materials or more to get through this phase.

One can also try alternative therapies if they want to try them. There is no harm in trying as long as you are determined to quit smoking. For instance, filters, smoking deterrents, e-cigarettes, acupuncture, cold laser therapy, yoga and more can work for some people.

Always remember that you cannot quit smoking instantly as it will be bad for you as well. Try cutting down on it and then slowly and steadily give it up altogether.

Get the huge list of more than 500 Essay Topics and Ideas

Conclusion of the Essay on Smoking

Thus, if anyone is a slave to cigarettes, it is essential for them to understand that it is never too late to stop smoking. With the help and a good action plan, anyone can quit it for good. Moreover, the benefits will be evident within a few days of quitting.

FAQ of Essay on Smoking

Question 1: What are the effects of smoking?

Answer 1: Smoking has major effects like cancer, heart disease, stroke, lung diseases, diabetes, and more. It also increases the risk for tuberculosis, certain eye diseases, and problems with the immune system .

Question 2: Why should we avoid smoking?

Answer 2: We must avoid smoking as it can lengthen your life expectancy. Moreover, by not smoking, you decrease your risk of disease which includes lung cancer, throat cancer, heart disease, high blood pressure, and more.

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Causes and Effects of Smoking in Public Essay

Introduction, core problems.

Smoking in public places has been banned in most parts of the world. Some people smoke publicly simply because this is their personal lifestyles and individual choice on which they should not be victimized. However the right to smoke as one pleases could greatly be countered by another person’s lifestyle of the right to breathing smokeless air. Recent researches have shown that those who do not smoke are at risk of being exposed to the same carcinogens which are cancer causing as those who are active smokers and they smoke in public settings.

The environmental tobacco smoke is known to contain more than 4000 chemicals and at least 40 carcinogens that are known. 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. This is because there are very harmful effects that follow on the smokers themselves and to those who inhale the second hand smoke. One of the major effects for smoking in public places is that it causes a higher risk of cancer, emphysema heart diseases and other acute and chronic diseases. Cigarette smoking is known to increase the aggregation of blood platelets or the clotting of blood. It also damages the endolithium a layer of cells in the blood vessels. Due to public smoking, the second hand smoke has been a triggering factor for the heart attacks and there have been an increased number of heart attack hospitalizations and even deaths from the smoke effects. For the smokers and the non smokers who inhale the fumes they are at a greater risk of developing heart diseases especially if one has high blood pressure. Another effect of smoking in the public places is that there are increased risks of fire break outs in the areas that have any explosive hazards or even where there is handling of flammable materials. Similarly when smokers smoke publicly they increase the risk of contamination in places where pharmaceuticals and foods are manufactured and prepared for human consumption.

On the other hand smokers litter around without considering the environmental effects and this causes the environment to be hazardous. Public smoking also affects the air quality in public establishments where some respirable suspended particles are released thus enhancing air pollution and also increasing the toxin exposure to human beings. Public smoking on the other hand has made many businesses to suffer directly or indirectly due to the loss of customers especially in establishments like hotels which encourage smoking in their premises. Public smoking similarly affects people from vulnerable groups such as the children, the pregnant women and also the disabled who are unable to choose their environments.

There is a more serious concern that the banning of a smoking in public places may lead to an increased rate of smoking in the homes and this could be more hazardous especially when there is the presence of small children. Passive smoke contains very strong sensitizers and irritants and many children as well as adults the suffer a lot of irritation and other acute effects when they are exposed to secondhand smoke.In addition to this there is increasing evidence that an individuals exposure to passive smoke can affect the cardiovascular system. (Scollo, 2003).

Smoking publicly has negative effects on the health of those who work in the public places especially the bars workers. On the contrary smoking in public places brings a sense of belonging and identity to those who smoke since they can easily identify with other public smokers in the public setting as they share similar habits. Public smoking needs to be controlled so as to reduce the negative effects that come as a result of the exposure of individuals to the fumes. (The New York Times, 2003).

Scollo, M. (2003): Review of the quality of studies on the economic effects of smoke-free policies on the hospitality industry. Tobacco Control, pgs 13-20.

The New York Times (2003): Bars and Restaurants Thrive Amid Smoking Ban, New York Times Archives.

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Impact of Smoking Status and Nicotine Dependence on Academic Performance of Health Sciences Students

Jaber s alqahtani.

1 Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, 34313, Saudi Arabia

Abdulelah M Aldhahir

2 Respiratory Therapy Department, Faculty of Applied Medical Sciences, Jazan University, Jazan, 45142, Saudi Arabia

Zaid Alanazi

3 Family Medicine Department, Northern Area Armed Forces Hospital (NAAFH), Hafar Al Batin, Saudi Arabia

Emad Zahi Alsulami

4 Family Medicine Department, Armed Forces Hospital in King Abdulaziz Airbase, Dhahran, Saudi Arabia

Mujahid A Alsulaimani

5 Basic Medical Unit, Prince Sultan Military College of Health Sciences, Dammam, 34313, Saudi Arabia

Abdullah A Alqarni

6 Department of Respiratory Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

Abdullah S Alqahtani

Ayadh yahya alayadi, musallam alnasser, ibrahim a aldraiwiesh, saeed m alghamdi.

7 Respiratory Care Program, College of Applied Medical Sciences, Umm Al-Qura University, Makkah, 24382, Saudi Arabia

Hussam M Almarkhan

Abdullah s alsulayyim.

8 National Heart and Lung Institute, Imperial College London, London, SW7 2BX, UK

Saad M AlRabeeah

Mohammed d alahmari.

Smoking behavior has been associated with poor academic performance among adult students worldwide. However, the detrimental effect of nicotine dependence on several students’ academic achievement indicators is still unclear. This study aims to assess the impact of smoking status and nicotine dependence on grade point average (GPA), absenteeism rate and academic warnings among undergraduate health sciences students in Saudi Arabia.

A validated cross-sectional survey was conducted, in which, participants responded to questions evaluated cigarette consumption, urge to consume and dependency, learning performance, days of absentees, and academic warnings.

A total of 501 students from different health specialties have completed the survey. Of whom, 66% were male, 95% ranging between the age of 18–30 years old, and 81% reported no health issues or chronic diseases. Current smokers estimated to be 30% of the respondents, of which 36% revealed smoking history of 2–3 years. The prevalence of nicotine dependency (high to extremely high) was 50%. Overall, smokers had significantly lower GPA, higher absenteeism rate, and higher number of academic warnings when compared to nonsmokers ( p <0.001). Heavy smokers demonstrated significantly less GPA (p=0.036), higher days of absences (p=0.017), and more academic warnings (p=0.021) compared to light smokers. The linear regression model indicated a significant association between smoking history (increased pack-per-year) and poor GPA (p=0.01) and increased number of academic warning last semester (p=0.01), while increased cigarette consumption was substantially linked with higher academic warnings (p=0.002), lower GPA (p=0.01), and higher absenteeism rate for last semester (p=0.01).

Smoking status and nicotine dependence were predictive of worsening academic performance, including lower GPA, higher absenteeism rate and academic warnings. In addition, there is a substantial and unfavorable dose–response association between smoking history and cigarette consumption with impaired academic performance indicators.

Introduction

Tobacco smoking is one of the greatest threats to public health and is defined as any habitual use of the tobacco plant leaf. 1 The use of tobacco is divided into combustible and non-combustible forms. Combustible tobacco products include cigarettes, cigars and water pipes, while electronic cigarettes and tobacco formulations developed for chewing or snuffing are classified as non-combustible tobacco products. 2 Cigarette smoking, the most common form of tobacco use worldwide, is the leading cause of preventable death and illness, as it contains many harmful chemicals, one of which is nicotine. 3

Despite the efforts that have been made to control nicotine-containing tobacco, the prevalence of cigarette smoking globally among adults and adolescents worldwide, and especially in Saudi Arabia, remains high. It has been shown that cigarette smoking is prevalent among adolescents in Arar (41%), 4 Jeddah (37%) 5 and Hail (20%), Saudi Arabia. 6 Cigarette smoking has also been reported to be prevalent among Saudi medical students at Qassim University (6%), 7 at King Fahad Medical City in Riyadh (18%), 8 as well as among Saudi dental students at King Abdulaziz University (25%). 9 More importantly, studies suggest that cigarette smoking can influence students’ academic performance. 10–13

Nicotine is the main addictive component in cigarettes. Nicotine dependence or addiction, commonly assessed using the Fagerstrom Tolerance Questionnaire, 14 can lead to both physiological and psychological effects. We have previously demonstrated that both extracts of cigarette smoke and e-cigarettes that contain nicotine can cause an imbalance between vasodilators and vasoconstrictors and induce inflammatory mediators in human pulmonary artery and airway cells, 15 , 16 respectively. This may eventually lead to several pulmonary diseases, including chronic obstructive pulmonary disease and pulmonary hypertension. These observations suggest that nicotine dependence can increase a person’s risk of experiencing smoking-related morbidity and all-cause mortality.

Poor academic performance among college students can lead to a low cumulative grade point average (GPA), excessive absences or tardiness and an increased number of academic warning letters received. Previous studies have suggested an association between smoking behaviour and academic achievement among students. For instance, it has been demonstrated that cigarette smoking among both Saudi secondary school and medical students is associated with poor academic performance, 12 , 13 suggesting that students who smoke are likely to achieve less academically. In support of these findings, it has also been reported that smoking is also negatively associated with academic performance among Norwegian adolescents 11 and undergraduates at a public university in Islamabad, Pakistan. 10

Although previous studies have suggested a high prevalence of cigarette smoking among medical students, the prevalence of cigarette smoking and nicotine dependence among other allied health sciences students (such as Anesthesia Technology, Biomedical Technology, Clinical Laboratory Sciences, Emergency Medical Services, Health Information Management, Respiratory Care, and Nursing professions) have not been assessed before in Saudi Arabia. In addition, the association between nicotine dependence as a result of cigarette smoking and academic performance indicators among health sciences students in the country is largely unknown. Therefore, this study aims to assess the impact of smoking status and nicotine dependence on GPA, absenteeism and academic warnings among undergraduate health sciences students in Saudi Arabia. We hypothesize that smoking has detrimental effects on health sciences students’ academic performance.

Study Overview

This research was carried out at the Prince Sultan Military College of Health Sciences between March 2022 and July 2022. Institutional Review Board approval for the study was obtained from Prince Sultan Military College of Health Sciences (Ref. IRB-2022-RC-029). All respondents gave informed consent to participate in this research, and the study was in compliance with the Helsinki Declaration.

Design and Tools

A validated survey: Fagerström Test for Nicotine Dependence (FTND) was utilized to investigate the influence of smoking status and nicotine dependency on GPA, absenteeism, and academic warnings among Saudi undergraduate health sciences students. 14 We employed a non-probability convenience sampling strategy. To reach the target groups, the research team distributed the survey at the break time for each specialty. Those who were absent were also approached during the next classes’ break time in order to reach additional students.

Inclusion and Exclusion Criteria

The inclusion criteria were being a student with a major in health science at the Prince Sultan Military College of Health Sciences. Health Sciences majors at the college were Anesthesia Technology, Biomedical Technology, Clinical Laboratory Sciences, Emergency Medical Services, Dental & Oral Health Care, Health Information Management, Respiratory Care, and Nursing professions). Students who majored in fields other than these or who refused to participate in the study were excluded.

Data Collection

We utilized Google forms to offer a self-administered questionnaire that took 10 minutes to complete. There are two elements to the survey: the socio-demographic sheet and the FTND questionnaire. Self-reported gender, age, specialty, current study year, smoking history, GPA, days of absentees, and academic warnings are included in the demographic section. The FTND is a standard tool for measuring the severity of physical nicotine addiction, providing an ordinal measure of nicotine dependency in relation to cigarette smoking. 14 It includes six measures that assess cigarette consumption, urge to consume, and dependency. The final score ranges from 0 to 10, depending on the total of the elements. More severe nicotine addiction is indicated by a higher overall FTND score. A current smoker was defined as someone who has smoked at least 100 cigarettes in their lifetime and is still smoking at the time of data collection. While a former smoker was defined as someone who had previously smoked at least 100 cigarettes but was no longer smoking at the time of data collection. In addition, a “light smoker” is a smoker who smokes between one and ten cigarettes per day; while ‘heavy’ smoker was defined as a smoker who reported smoking more than 30 cigarettes each day. Academic warning was defined as a GPA below 2 on a scale of 5 or failing in more than one course.

Power Calculation

The Prince Sultan Military College of Health Sciences is home to almost 1258 students majoring in different allied health sciences professions. Taking into consideration the total number of students and assuming a 50% response distribution, a 5% margin of error, and a 95% confidence interval, the minimum needed sample size was 295.

Statistical Analysis

The data was automatically captured by the hosting platform and then exported to an Excel file. The characteristics of respondents were analyzed using descriptive analysis (ie, absolute values and proportions). To compare groups (non-smokers versus smokers), we performed Chi square tests. We performed multivariate linear regression analysis to investigate which characteristics were associated to the dependent variables: smoking history and nicotine dependence. We included GPA, days absent and academic warnings as our independent variables as well as gender and socioeconomic status in these models. Those variables with no significant results in the univariate analysis were excluded from the model. Multicollinearity has been considered and verified with our regression models using the indicator of multicollinearity: variance inflation factor (VIF). VIF <3, indicates low correlation among variables under ideal conditions and can be added to the regression model. SPSS version 28 was used to analyze the findings (IBM Corp. Armonk, New York, USA). A P value of <0.05 was used to determine statistical significance.

Demographic Characteristics

A total of 501 students completely answered the survey. More than half of the participants were male (66%), and 473 (95%) of them were between the ages of 18 to 30. The students’ area of specialization and academic level among all respondents was fairly distributed ( Table 1 ). The majority of the students surveyed 430 (86%) were unemployed full-time students, about half of them had limited income 215 (43%).

Demographics Data and Characteristics of the Respondents (n=501)

The population mean for the Body Mass Index (BMI) was 25.14 where more than half of the participants 272 (54%) had healthy weight body mass index based on their self-reported height and weight. In addition, 407 (81%) of the participants reported no health issues or chronic diseases. Diabetes 25 (5%), asthma 20 (4%) were the highest prevalent comorbidities followed by depression 14 (3%) and Stomach disease 14 (3%).

Academic Performance, Smoking History and Nicotine Dependence Indicators

Half of the participants 248 (49.5%) reported a grade point average (GPA) ranging from 3.75 to 4.49 ( Table 2 ). On the other hand, more than a third of participants 164 (33%) were reported absent during last semester for 1 to 2 days and about the third 131 (26%) with full attendance. Almost three-fifths of the participants 307 (61%) did not receive any academic warnings, while 123 (25%) received one academic warning.

Academic Performance, Smoking History and Nicotine Dependence Indicators (n=501)

More than half of the participants 275 (55%) never smoked, while 152 (30%) were currently smokers. As many as 32 (43%) of the former smokers smoked for more than five years, while 55 (36%) of the current smokers smoked for two to three years. 50% of current smokers had high to extremely high nicotine dependency on the Fagerstrom test Table 2 ).

Academic Performance Between Non-Smokers versus Smokers

Table 3 shows the academic performance for non-smokers and smokers, in which significant differences were observed regarding the GPA, absences days and the number of academic warnings. Smokers had lower GPA, more absent days and higher number of academic warnings.

Non-Smokers versus Smokers Academic Performance (n=501)

Cigarettes Consumption Effect on Academic Performance

We also found a significant relationship between cigarettes consumption and academic performance where we noted that higher cigarettes smoked per a day impacted the GPA, absent days and number of academic warnings. None of the heavy smokers (smoked more than 30 cigarettes per a day) had GPA of 4.50–5.00. It was a significant difference when compared to light smokers (10 or less cigarettes per a day) that 24% of them had GPA of 4.50–5.00 ( Figure 1 ). These percentages of higher academic achievement were significantly lower in smokers than non-smokers, in which 40% of non-smokers reported GPA of 4.50–5.00, P-value <0.001, Chisquare value = 19.25 ( Table 3 ).

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GPA by cigarettes smoked per day P-value: 0.036.

We found a significant increase of days of absences when we compare smoker students and non-smokers (P-value < 0.001, Chisquare value = 32.87). That led to study the amount of cigarettes consumption in relation to days of absences. Figure 2 shows the significant relation between days of absences and cigarettes smoked per a day (P-value: 0.017). Almost 50% of students who smoked 21 to 30 had 3–4 absences days last semester. Heavy smokers stand out by 40% (5–6 absences days) and 20% of them had more than 6 absent days. Compared to non-smokers, smokers received more academic warnings, p value <0.001, Chisquare= 26.76. Heavy smokers were the highest to have academic warnings, in which 40% had two academic warnings and 30% had three academic warnings ( Figure 3 ).

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Days of absences by cigarettes smoked per day P-value: 0.017.

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Academic warnings by cigarettes smoked per day P-value: 0.021.

Association Between Smoking History, Cigarette Consumption and Academic Performance

We did liner regression to assess the association between smoking history and other factors including gender, socioeconomic status, GPA, absent days and number of academic warnings. Gender and socioeconomic status were not statistically significant, p value >0.05. We found significant associations between GPA and academic warning, which higher pack per year was significantly associated with both poorer GPA and increased academic warning (see Table 4 ). Moreover, the total cigarette consumption was also associated with GPA, days absent last semester and academic warning ( Table 4 ).

Association of Smoking History and Cigarette Consumption with All the Academic Performance Parameters

This is the first study conducted in Saudi Arabia to look at the effects of smoking history and nicotine dependence on academic performance, absenteeism, and academic warning among health science students. The findings showed that smoking was prevalent at 30%, with 50% of current smokers classified as having high to extremely high nicotine dependency. In addition, we observed that smoking history had a detrimental impact on students’ academic performance as measured by GPA, absence days, and the number of academic warnings. We also identified a substantial association between cigarette consumption and students’ academic performance, noting that the quantity of cigarettes smoked each day negatively influenced GPA, absence days, and the number of academic warnings.

Smoking is a major risk for morbidity and mortality. 17 The prevalence of smoking is relatively high among Saudi adolescents. It affects the academic achievement of students, by decreasing attentiveness, cognitive, and memory functions. 18 In this study, we found that the prevalence of smoking is 30%. This finding is supported by previous studies demonstrating that smoking prevalence among Saudi secondary school students is 40.8% in Arar 4 and 37% in Jeddah, Saudi Arabia. 5 Inconsistent with our findings, lower prevalence rate (12.4%) has been reported among medical students at Jazan University, Saudi Arabia. 13 The discrepancies may be due to the differences in sample size (the sample size used in the other study is 354) and geographical locations of the students studied, 13 considering the fact that the smoking prevalence is found to differ by the location of residence. 19 Another possible explanation is that, in the current study, we determined the smoking prevalence among health science students while in the other study only medical students were studied. 13

We also found a significant relationship between cigarettes consumption and poor academic performance. This finding is in agreement with a study conducted in 1960s that found smokers among adolescents have lower grade compared with a nonsmoker. 20 Another study also showed a negative correlation between smoking and academic performance among school students. 21 In addition, our findings are supported by several studies showing that increased prevalence of smoking and nicotine dependence and that smoking is associated with lower academic achievement in African Americans, 22 and European students. 23 Interestingly, a cohort study confirmed that smoking in youth is associated with lower education attainment. 24 Our novel findings are further strengthened by a study conducted in Saudi medical students at Jazan University showing that inverse proportion between the prevalence of smoking and students GPA. 13 Moreover, all types of nicotine-containing products (including e-cigarettes) are found to be associated with poor academic achievement. 25 However, one study found that the low academic achievement depends on multifactorial things and smoking may be one of these factors. The socioeconomic factor is also an important contributor. 26 Our study was in parallel to other studies that found an association between smoking and absenteeism among European adolescent school 27 and among workers, 28 but none of these conducted on health science students. According to the best of our knowledge, this is the first study conducted to measure the impact of smoking on undergrad health academic performance, class attendance, and academic warning.

Research and Practice Implications

Evidence of smoking’s deleterious effects on college students majoring in health fields is provided in this research. There needs to be a long-term cohort research to determine whether or not smoking has a causal influence on students’ academic success. As a result, it is crucial to evaluate the impact of smoking on long-term professional success and potential scientific accomplishments (such a graduate degree) in comparison to a nonsmoking group. Furthermore, further research is required to evaluate the association between smoking and drug abuse.

Practically, the findings of this study would help increase public health awareness of the detrimental effects of smoking on academic performance that may have far-reaching consequences on a person’s future career and life trajectory. Psychoeducation is a powerful therapeutic technique, particularly for tobacco users. This technique would help students understand how smoking affects their health and academic performance, empowering them to change. This information may lead to a positive impact on the number of smokers who decide to give up their smoking habit. As a consequence, the burden of chronic obstructive pulmonary disease in Saudi Arabia would be reduced, 29 resulting in lower morbidity and death rates.

Limitations

Because it is a cross-sectional research, reverse causality biases are a potential issue. Multifactorial influences on students’ academic performance mean that confounding might impact the findings. Therefore, our findings should be taken with caution.

We found that smoking status negatively affects students’ grades, attendance, and academic warning. It is interesting to note that compared to non-smokers, students who smoke tend to do worse academically. We showed a strong and negative dose–response association between cigarette consumption and negative academic outcomes such as lower GPA, lower attendance rate, and worse grades.

The authors report no conflicts of interest in this work.

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Even Photoshop Can’t Erase Royals’ Latest P.R. Blemish

A Mother’s Day photo was meant to douse speculation about the Princess of Wales’ health. It did the opposite — and threatened to undermine trust in the royal family.

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Mark Landler

By Mark Landler

Reporting from London

If a picture is worth a thousand words, then a digitally altered picture of an absent British princess is apparently worth a million.

That seemed to be the lesson after another day of internet-breaking rumors and conspiracy theories swirling around Catherine, Princess of Wales, who apologized on Monday for having doctored a photograph of herself with her three children that circulated on news sites and social media on Sunday.

It was the first official photo of Catherine since before she underwent abdominal surgery two months ago — a cheerful Mother’s Day snapshot, taken by her husband, Prince William, at home. But if it was meant to douse weeks of speculation about Catherine’s well-being, it had precisely the opposite effect.

Now the British royal family faces a storm of questions about how it communicates with the press and public, whether Catherine manipulated other family photos she released in previous years, and whether she felt driven to retouch this photo to disguise the impact of her illness.

It adds up to a fresh tempest for a royal family that has lurched from one self-created crisis to another. Unlike previous episodes, this involves one of the family’s most popular members, a commoner-turned-future queen. It also reflects a social media celebrity culture driven in part by the family itself, one that is worlds away from the intrusive paparazzi pictures that used to cause royals, including a younger Kate Middleton, chagrin.

“Like so many millennial celebrities, the Princess of Wales has built a successful public image by sharing with her audience a carefully curated version of her personal life,” said Ed Owens, a royal historian who has studied the relationship between the monarchy and the media. The manipulated photograph, he said, is damaging because, for the public, it “brings into question the authenticity” of Catherine’s home life.

Authenticity is the least of it: the mystery surrounding Catherine’s illness and prolonged recovery, out of the public eye, has spawned wild rumors about her physical and mental health, her whereabouts, and her relationship with William.

The Princess of Wales holding red roses and speaking with a small group of people taking photographs.

The discovery that the photo was altered prompted several international news agencies to issue advisories — including one from The Associated Press that was ominously called a “kill notification” — urging news organizations to remove the image from their websites and scrub it from any social media.

Mr. Owens called the incident a “debacle.”

“At a time when there is much speculation about Catherine’s health, as well as rumors swelling online about her and Prince William’s private lives,” he said, “the events of the last two days have done nothing to dispel questions and concerns.”

Kensington Palace, where Catherine and William have their offices, declined to release an unedited copy of the photograph on Monday, which left amateur visual detectives to continue scouring the image for signs of alteration in the poses of the princess and her three children, George, Charlotte, and Louis.

The A.P. said its examination yielded evidence that there was “an inconsistency in the alignment of Princess Charlotte’s left hand.” The image has a range of clear visual inconsistencies that suggest it was doctored. A part of a sleeve on Charlotte’s cardigan is missing, a zipper on Catherine’s jacket and her hair is misaligned, and a pattern in her hair seems clearly artificial.

Samora Bennett-Gager, an expert in photo retouching, identified multiple signs of image manipulation. The edges of Charlotte’s legs, he said, were unnaturally soft, suggesting that the background around them had been shifted. Catherine’s hand on the waist of her youngest son, Louis, is blurry, which he said could indicate that the image was taken from a separate frame of the shoot.

Taken together, Mr. Bennett-Gager said, the changes suggested that the photo was a composite drawn from multiple images rather than a single image smoothed out with a Photoshop program. A spokesman for Catherine declined to comment on her proficiency in photo editing.

Even before Catherine’s apology, the web exploded with memes of “undoctored” photos. One showed a bored-looking Catherine smoking with a group of children. Another, which the creator said was meant to “confirm she is absolutely fine and recovering well,” showed the princess splashing down a water slide.

Beyond the mockery, the royal family faces a lingering credibility gap. Catherine has been an avid photographer for years, capturing members of the royal family in candid situations: Queen Camilla with a basket of flowers; Prince George with his great-grandfather, Prince Philip, on a horse-drawn buggy.

The palace has released many of these photos, and they are routinely published on the front pages of British papers (The Times of London splashed the Mother’s Day picture over three columns). A former palace official predicted that the news media would now examine the earlier photographs to see if they, too, had been altered.

That would put Kensington Palace in the tricky position of having to defend one of its most effective communicators against a potentially wide-ranging problem, and one over which the communications staff has little control. After a deluge of inquires about the photograph, the palace left it to Catherine to explain what happened. She was contrite, but presented herself as just another frustrated shutterbug with access to Photoshop.

“Like many amateur photographers, I do occasionally experiment with editing,” she wrote on social media. “I wanted to express my apologies for any confusion the family photograph we shared yesterday caused.”

Catherine’s use of social media sets her apart from older members of the royal family, who rely on the traditional news media to present themselves. When King Charles III taped a video message to mark Commonwealth Day, for example, Buckingham Palace hired a professional camera crew that was paid for by British broadcasters, a standard arrangement for royal addresses.

When Charles left the hospital after being treated for an enlarged prostate, he and Queen Camilla walked in front of a phalanx of cameras, smiling and waving as they made their way to their limousine.

Catherine was not seen entering or leaving the hospital for her surgery, nor were her children photographed visiting her. That may reflect the gravity of her health problems, royal watchers said, but it also reflects the determination of William and Catherine to erect a zone of privacy around their personal lives.

William, royal experts said, is also driven by a desire not to repeat the experience of his mother, Diana, who was killed in a car crash in Paris in 1997 after a high-speed pursuit by photographers. Catherine, too, has been victimized by paparazzi, winning damages from a French court in 2017 after a celebrity magazine published revealing shots of her on vacation in France.

Last week, grainy photos of Catherine riding in a car with her mother surfaced on the American celebrity gossip site TMZ. British newspapers reported the existence of the photos but did not publish them out of deference to the palace’s appeal that she be allowed to recuperate in privacy.

Catherine and William are not the only members of their royal generation who have sought to exercise control over their image. Prince Harry and his wife, Meghan, posted photos of themselves on Instagram, even using their account to announce their withdrawal from royal duties in 2020.

Catherine’s embrace of social media to circulate her pictures is a way of reclaiming her life from the long lenses of the paparazzi. But the uproar over the Mother’s Day photo shows that this strategy comes with its own risks, not least that a family portrait has added to the very misinformation about her that it was calculated to counteract.

On Monday afternoon, Catherine found herself back in traditional royal mode. She was photographed, fleetingly, in the back of a car with William as he left Windsor Castle for a Commonwealth Day service at Westminster Abbey. Kensington Palace said she was on her way to a private appointment.

Gaia Tripoli and Lauren Leatherby contributed reporting.

Mark Landler is the London bureau chief of The Times, covering the United Kingdom, as well as American foreign policy in Europe, Asia and the Middle East. He has been a journalist for more than three decades. More about Mark Landler

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