smoking health effects essay

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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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Matters Arising to this article was published on 14 April 2023

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

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

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

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

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

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

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

Five-star associations

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

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

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

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

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

Four-star associations

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

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

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

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

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

Three-star associations

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

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

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

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

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

Two-star associations

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

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

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

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

One-star associations

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

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

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

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

Age-specific dose–response risk for CVD outcomes

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

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

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

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

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

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

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

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

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

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

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

Selecting health outcomes

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

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

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

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

Selecting exposure categories

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

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

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

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

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

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

Step 6: estimating the mean risk function and the BPRF

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

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

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

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

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

Estimating the age-specific risk function for CVD outcomes

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

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

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

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

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

Estimating the theoretical minimum risk exposure level

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

Model validation

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

Statistical analysis

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

Statistics and reproducibility

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

Reporting summary

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

Data availability

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

Code availability

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

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Acknowledgements

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

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

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

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Extended data

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

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

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

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

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

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

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

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

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

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

Extended Data Fig. 6 Smoking and Breast Cancer.

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

Supplementary information

Supplementary information.

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

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

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Received : 11 April 2022

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Published : 10 October 2022

Issue Date : October 2022

DOI : https://doi.org/10.1038/s41591-022-01978-x

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Reply to: Concerns about the Burden of Proof studies

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Nature Medicine (2023)

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Health Effects of Smoking

Smoking is the number one cause of preventable disease and death worldwide. Smoking-related diseases claim more than 480,000 lives in the U.S. each year. Smoking costs the U.S. at least $289 billion each year, including at least $151 billion in lost productivity and $130 billion in direct healthcare expenditures. 1

Key Facts about Smoking

• Cigarette smoke contains more than 7,000 chemicals, at least 69 of which are known to cause cancer. 2 Smoking is directly responsible for approximately 90 percent of lung cancer deaths and approximately 80 percent of deaths caused by chronic obstructive pulmonary disease (COPD), including emphysema and chronic bronchitis. 1
• Among adults who have ever smoked daily, 78% had smoked their first cigarette by the time they were 18 years of age, and 94% had by age 21. 3
• Among current smokers, 73% of their diagnosed smoking-related conditions are chronic lung diseases. Even among smokers who have quit, chronic lung disease still accounts for 50% of smoking-related conditions. 4
• Smoking harms nearly every organ in the body, and is a main cause of lung cancer and COPD. It also is a cause of coronary heart disease, stroke and a host of other cancers and diseases. 1 See more of the health effects caused by smoking.

Smoking Rates among Adults & Youth

• In 2017, an estimated 34.3 million, or 14.0% of adults 18 years of age and older were current cigarette smokers. 5
• Men tend to smoke more than women. In 2017, 15.8% of men currently smoked cigarettes daily compared to 12.2% of women. 5 
• Prevalence of current cigarette smoking in 2017 was highest among American Indians/Alaska Natives (24.6%), non-Hispanic whites (15.3%) and non-Hispanic blacks (15.1%), and was lowest among Hispanics (9.9%) and Asian-Americans (7.0%). 5
• In 2017, 7.6 % of high school students and 2.1% of middle school students were current cigarette users. 6

Facts about Quitting Smoking

• Nicotine is the chemical in cigarettes that causes addiction. Smokers not only become physically addicted to nicotine; they also link smoking with many social activities, making smoking an extremely difficult addiction to break. 7
• In 2017, an estimated 55.2 million adults were former smokers. Of the 34.3 million current adult smokers, 48.4% stopped smoking for a day or more in the preceding year because they were trying to quit smoking completely. 5
• Quitting smoking for good often requires multiple attempts. Using counseling or medication alone increases the chance of a quit attempt being successful; the combination of both is even more effective. 8
• There are seven medications approved by the U.S. Food and Drug Administration to aid in quitting smoking. Nicotine patches, nicotine gum and nicotine lozenges are available over the counter, and a nicotine nasal spray and inhaler are currently available by prescription. Bupropion SR (Zyban®) and varenicline (Chantix®) are non-nicotine pills. 8
• Individual, group and telephone counseling are effective. Telephone quitline counseling is available in all 50 states and is effective for many different groups of smokers. 8

Learn about the American Lung Association’s programs to help you or a loved one quit smoking , and join our advocacy efforts to reduce tobacco use and exposure to secondhand smoke. Visit Lung.org or call the Lung HelpLine at 1-800-LUNGUSA (1-800-586-4872).

The Health Effects of Smoking

U.S. Department of Health and Human Services. The Health Consequences of Smoking - 50 Years of Progress: A Report of the Surgeon General. 2014.

U.S. Department of Health and Human Services. How Tobacco Smoke Causes Disease: The Biology and Behavioral Basis for Smoking-Attributable Disease A Report of the Surgeon General. 2010.

Substance Abuse and Mental Health Services Administration. National Survey on Drug Use and Health, 2017. Analysis by the American Lung Association Epidemiology and Statistics Unit using SPSS software.

U.S. Department of Health and Human Services. The Health Consequences of Smoking: A Report of the Surgeon General, 2004.

Centers for Disease Control and Prevention. National Center for Health Statistics. National Health Interview Survey, 2015. Analysis performed by the American Lung Association Epidemiology and Statistics Unit using SPSS software.

Centers for Disease Control and Prevention. National Youth Tobacco Survey, 2017. Analysis by the American Lung Association Epidemiology and Statistics Unit using SPSS software.

National Institute on Drug Abuse. Tobacco/Nicotine Research Report: Is Nicotine Addictive? January 2018.

Fiore M, Jaen C, Baker T, et al. Treating Tobacco Use and Dependence: 2008 Update. Clinical Practice Guideline. Vol 35. Rockville, MD; 2008.

Page last updated: May 31, 2023

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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.

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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Benefits of Quitting

• Health Benefits of Quitting Smoking
• Cardiovascular Health Benefits of Quitting Smoking
• Respiratory Health Benefits of Quitting Smoking
• Cancer-Related Health Benefits of Quitting Smoking
• Reproductive Health Benefits of Quitting Smoking
• Health Benefits of Quitting Smoking Over Time
• Additional Resources

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. 1

Quitting smoking 1 :

• improves health status and enhances quality of life.
• reduces the risk of premature death and can add as much as 10 years to life expectancy.
• reduces the risk for many adverse health effects, including poor reproductive health outcomes, cardiovascular diseases, chronic obstructive pulmonary disease (COPD), and cancer.
• benefits people already diagnosed with coronary heart disease or COPD.
• benefits the health of pregnant women and their fetuses and babies.
• reduces the financial burden that smoking places on people who smoke, healthcare systems, and society.

While quitting earlier in life yields greater health benefits, quitting smoking is beneficial to health at any age. Even people who have smoked for many years or have smoked heavily will benefit from quitting. 1

Quitting smoking is the single best way to protect family members, coworkers, friends, and others from the health risks associated with breathing secondhand smoke. 2

Quitting smoking is one of the most important actions people who smoke can take to reduce their risk for cardiovascular disease.

• reduces the risk of disease and death from cardiovascular disease.
• reduces markers of inflammation and hypercoagulability.
• leads to rapid improvement in high-density lipoprotein cholesterol (HDL-C) levels.
• reduces the development of subclinical atherosclerosis and slows its progression over time.
• reduces the risk of coronary heart disease with risk falling sharply 1-2 years after cessation and then declining more slowly over the longer term.
• reduces the risk of disease and death from stroke with risk approaching that of never smokers after cessation.
• reduces the risk of abdominal aortic aneurysm, with risk reduction increasing with time since cessation.
• may reduce the risk of atrial fibrillation, sudden cardiac death, heart failure, venous thromboembolism, and peripheral arterial disease (PAD).

People already diagnosed with coronary heart disease also benefit from quitting smoking.

Quitting smoking after a diagnosis of coronary heart disease 1 :

• reduces the risk of premature death.
• reduces the risk of death from heart disease,
• reduces the risk of having a first heart attack or another heart attack.

Quitting smoking is one of the most important actions people who smoke can take to reduce their risk for respiratory diseases.

Quitting smoking: 1,2

• reduces the risk of developing COPD.
• among those with COPD, slows the progression of COPD and reduces the loss of lung function over time.
• reduces respiratory symptoms (e.g., cough, sputum production, wheezing).
• reduces respiratory infections (e.g. bronchitis, pneumonia).
• may improve lung function, reduce symptoms, and improve treatment outcomes among persons with asthma.

Quitting smoking is one of the most important actions people who smoke can take to reduce their risk for cancer.

Quitting smoking reduces the risk of 12 different cancers, including 1 :

• acute myeloid leukemia (AML)
• cancer of the lung
• colon and rectum
• mouth and throat (oral cavity and pharynx)
• voice box (larynx)

For cancer survivors, quitting smoking may improve prognosis and reduce risk of premature death.

Quitting smoking is one of the most important actions women who smoke can take for a healthy pregnancy and a healthy baby. The best time for women to quit smoking is before they try to get pregnant. But quitting at any time during pregnancy can benefit mother and baby’s health. 1

Quitting smoking: 1

• before pregnancy or early in pregnancy reduces the risk for a small-for-gestational-age baby.
• during pregnancy reduces the risk of delivering a low birth weight baby.
• early in pregnancy eliminates the adverse effects of smoking on fetal growth.
• before pregnancy or early in pregnancy may reduce the risk of preterm delivery.

Over time, people who quit smoking see many benefits to their health. After quitting, the body begins a series of positive changes that continue for years. Some benefits of quitting smoking occur quickly while others occur over time: 1,2,3,4

Minutes after quitting

• Heart rate drops

24 hours after quitting

• Nicotine level in the blood drops to zero

Several days after quitting 

• Carbon monoxide level in the blood drops to level of someone who does not smoke

1 to 12 months after quitting 

• Coughing and shortness of breath decrease

1 to 2 years after quitting 

• Risk of heart attack drops sharply

3 to 6 years after quitting 

• Added risk of coronary heart disease drops by half

5 to 10 years after quitting 

• Added risk of cancers of the mouth, throat, and voice box drops by half
• Risk of stroke decreases

10 years after quitting 

• Added risk of lung cancer drops by half after 10-15 years
• Risk of cancers of the bladder, esophagus, and kidney decreases

15 years after quitting 

• Risk of coronary heart disease drops to close to that of someone who does not smoke

20 years after quitting 

• Risk of cancers of the mouth, throat, and voice box drops to close to that of someone who does not smoke
• Risk of pancreatic cancer drops to close to that of someone who does not smoke
• Added risk of cervical cancer drops by about half

Reduced risks refer to cessation in comparison to continued smoking.

Over time, people who quit smoking see many benefits to their health. After you smoke your last cigarette, your body begins a series of positive changes that continue for years.

• U.S. Department of Health and Human Services. Smoking Cessation: A Report of the Surgeon General . Atlanta, GA: 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, 2020. [accessed 2020 May 13].
• 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 2020 May 13]
• U.S. Department of Health and Human Services. How Tobacco Smoke Causes Disease: The Biology and Behavioral Basis for Smoking-Attributable Disease 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, 2010 [accessed 2020 May 13].
• U.S. Department of Health and Human Services. The Health Consequences of Smoking: 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, 2004 [accessed 2020 May 13].
• U.S. Department of Health and Human Services. The Health Consequences of Involuntary Exposure to Tobacco Smoke: 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, 2006 [accessed 2020 May 13].

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Persuasive Essay Guide

Persuasive Essay About Smoking

Persuasive Essay About Smoking - Making a Powerful Argument with Examples

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Are you wondering how to write your next persuasive essay about smoking?

Smoking has been one of the most controversial topics in our society for years. It is associated with many health risks and can be seen as a danger to both individuals and communities.

Writing an effective persuasive essay about smoking can help sway public opinion. It can also encourage people to make healthier choices and stop smoking. 

But where do you begin?

In this blog, we’ll provide some examples to get you started. So read on to get inspired!

• 1. What You Need To Know About Persuasive Essay
• 2. Persuasive Essay Examples About Smoking
• 3. Argumentative Essay About Smoking Examples
• 4. Tips for Writing a Persuasive Essay About Smoking

What You Need To Know About Persuasive Essay

A persuasive essay is a type of writing that aims to convince its readers to take a certain stance or action. It often uses logical arguments and evidence to back up its argument in order to persuade readers.

It also utilizes rhetorical techniques such as ethos, pathos, and logos to make the argument more convincing. In other words, persuasive essays use facts and evidence as well as emotion to make their points.

A persuasive essay about smoking would use these techniques to convince its readers about any point about smoking. Check out an example below:

Simple persuasive essay about smoking

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Persuasive Essay Examples About Smoking

Smoking is one of the leading causes of preventable death in the world. It leads to adverse health effects, including lung cancer, heart disease, and damage to the respiratory tract. However, the number of people who smoke cigarettes has been on the rise globally.

A lot has been written on topics related to the effects of smoking. Reading essays about it can help you get an idea of what makes a good persuasive essay.

Here are some sample persuasive essays about smoking that you can use as inspiration for your own writing:

Persuasive speech on smoking outline

Persuasive essay about smoking should be banned

Persuasive essay about smoking pdf

Persuasive essay about smoking cannot relieve stress

Persuasive essay about smoking in public places

Speech about smoking is dangerous

Persuasive Essay About Smoking Introduction

Persuasive Essay About Stop Smoking

Short Persuasive Essay About Smoking

Stop Smoking Persuasive Speech

Check out some more persuasive essay examples on various other topics.

Argumentative Essay About Smoking Examples

An argumentative essay is a type of essay that uses facts and logical arguments to back up a point. It is similar to a persuasive essay but differs in that it utilizes more evidence than emotion.

If you’re looking to write an argumentative essay about smoking, here are some examples to get you started on the arguments of why you should not smoke.

Argumentative essay about smoking pdf

Argumentative essay about smoking in public places

Argumentative essay about smoking introduction

Check out the video below to find useful arguments against smoking:

Tips for Writing a Persuasive Essay About Smoking

You have read some examples of persuasive and argumentative essays about smoking. Now here are some tips that will help you craft a powerful essay on this topic.

Choose a Specific Angle

Select a particular perspective on the issue that you can use to form your argument. When talking about smoking, you can focus on any aspect such as the health risks, economic costs, or environmental impact.

Think about how you want to approach the topic. For instance, you could write about why smoking should be banned. 

Check out the list of persuasive essay topics to help you while you are thinking of an angle to choose!

Research the Facts

Before writing your essay, make sure to research the facts about smoking. This will give you reliable information to use in your arguments and evidence for why people should avoid smoking.

You can find and use credible data and information from reputable sources such as government websites, health organizations, and scientific studies. 

For instance, you should gather facts about health issues and negative effects of tobacco if arguing against smoking. Moreover, you should use and cite sources carefully.

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Make an Outline

The next step is to create an outline for your essay. This will help you organize your thoughts and make sure that all the points in your essay flow together logically.

Your outline should include the introduction, body paragraphs, and conclusion. This will help ensure that your essay has a clear structure and argument.

Use Persuasive Language

When writing your essay, make sure to use persuasive language such as “it is necessary” or “people must be aware”. This will help you convey your message more effectively and emphasize the importance of your point.

Also, don’t forget to use rhetorical devices such as ethos, pathos, and logos to make your arguments more convincing. That is, you should incorporate emotion, personal experience, and logic into your arguments.

Introduce Opposing Arguments

Another important tip when writing a persuasive essay on smoking is to introduce opposing arguments. It will show that you are aware of the counterarguments and can provide evidence to refute them. This will help you strengthen your argument.

By doing this, your essay will come off as more balanced and objective, making it more convincing.

Finish Strong

Finally, make sure to finish your essay with a powerful conclusion. This will help you leave a lasting impression on your readers and reinforce the main points of your argument. You can end by summarizing the key points or giving some advice to the reader.

A powerful conclusion could either include food for thought or a call to action. So be sure to use persuasive language and make your conclusion strong.

To conclude,

By following these tips, you can write an effective and persuasive essay on smoking. Remember to research the facts, make an outline, and use persuasive language.

However, don't stress if you need expert help to write your essay! Our professional essay writing service is here for you!

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Health effects associated with smoking: a Burden of Proof study

Xiaochen dai.

1 Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA USA

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

Gabriela F. Gil

Marissa b. reitsma, noah s. ahmad, jason a. anderson, catherine bisignano, sinclair carr, rachel feldman, simon i. hay, 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.

3 Department of Applied Mathematics, University of Washington, Seattle, WA USA

Christopher J. L. Murray

Emmanuela gakidou, associated data.

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.

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

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.

A meta-analysis using the Burden of proof method reported consistent evidence supporting harmful associations between smoking and 28 different health outcomes.

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 – 7 , chronic obstructive pulmonary disease (COPD) 8 – 10 and ischemic heart disease 11 – 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 – 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 – 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 ​ Table1 1 .

Policy summary

We evaluated the mean risk functions and the BPRFs for 36 health outcomes that are associated with current smoking 30 (Table ​ (Table2). 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. ​ Fig.1 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 .

Strength of the evidence for the relationship between current smoking and the 36 health outcomes analyzed

The ROS represents the signed value of the log(BPRF) averaged across the 15th–85th percentiles of exposure. The BPRF corresponds to the lower (if harmful) or higher (if protective) UI—inclusive of between-study heterogeneity—for each risk–outcome pair’s RR curve. ROSs are directly comparable across outcomes and each risk–outcome pair receives an ROS based on the final formulation of the risk curve. For Parkinson’s disease, the ROS reflects a protective effect of smoking, whereas for the other outcomes it reflects a harmful effect. Negative ROSs indicate that a conservative interpretation of the available evidence suggests that there may be no association between risk and outcome. For ease of interpretation, we have transformed the ROS and BPRF into a star rating (1–5), with a higher rating representing a larger effect and stronger evidence. Average BPRF, which is the exponential ROS for harmful effects (or exponential negative ROS for protective effects), is the conservative exposure-averaged RR consistent with all the available data. Average increased risk, which equates to (average BPRF − 1) × 100% for harmful effects or (1 − average BPRF) × 100% for protective effects, refers to the percentage increase in RR based on a conservative interpretation of the evidence. For harmful risks, this percentage is positive and, for protective risks, negative, indicating the percentage decrease in RR. The average increased risk is not applicable for pairs with negative ROSs. N/A, not available; Pub., Publication; ref., reference.

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/ .

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/ .

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 ​ Table2 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 – 107 . Exposure ranged from 1 pack-year to >112 pack-years, with the 85th percentile of exposure being 50.88 pack-years (Fig. ​ (Fig.1a 1a ).

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

We found a very strong and significant harmful relationship between pack-years of current smoking and the RR of lung cancer (Fig. ​ (Fig.1b). 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 ​ Table2 2 for mean RRs at other exposure levels. The BPRF, which represents the most conservative interpretation of the evidence (Fig. ​ (Fig.1a), 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. ​ (Fig.1b). 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. ​ (Fig.1c). 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 – 10 , 78 , 108 – 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. ​ (Fig.2b). 2b ). The mean RR of COPD at 20 pack-years was 3.17 (1.60–6.55; Table ​ Table2 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 ​ Table2 2 and Supplementary Information 4.2 .

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

The relationship between smoking and COPD is nonlinear, with diminishing impact of further pack-years of current smoking on risk of COPD, particularly for middle-to-high exposure levels (Fig. ​ (Fig.2a). 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. ​ (Fig.2b). 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 – 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. ​ (Fig.3b). 3b ). The mean RR of type 2 diabetes at 20 cigarettes smoked per day was 1.49 (1.18–1.90; see Table ​ Table2 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 ​ Table2 2 and Supplementary Information 4.3 for results for the additional health outcomes with an association with smoking rated as 3 stars.

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

The relationship between smoking and type 2 diabetes is nonlinear, particularly for high exposure levels where the mean risk curve becomes flat (Fig. ​ (Fig.3a). 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 – 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. ​ Fig.6). 6 ). The mean RR of breast cancer at 20 pack-years was 1.17 (1.04–1.31; Table ​ Table2 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 ​ Table2 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.

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.

The relationship between smoking and breast cancer is nonlinear, particularly for high exposure levels where the mean risk curve becomes flat (Extended Data Fig. ​ Fig.6a). 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. ​ Fig.6b). 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 – 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. ​ (Fig.4B). 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 ​ Table2 2 and Supplementary Information 4.5 for results for the additional outcomes that have a 1-star association with smoking.

The relationship between smoking and prostate cancer is nonlinear, particularly for middle-to-high exposure levels where the mean risk curve becomes flat (Fig. ​ (Fig.4a). 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 ​ (Table1 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. ​ (Figs.1c, 1c , ​ ,2c, 2c , ​ ,3c 3c and ​ and4c 4c and Extended Data Fig. ​ Fig.6c) 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. ​ (Figs.1a, 1a , ​ ,2b, 2b , ​ ,3a 3a and ​ and4a). 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 – 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.

Online content

Any methods, additional references, Nature Research reporting summaries, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41591-022-01978-x.

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.

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.

Extended data

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/ .

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/ .

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/ .

Author contributions

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.

Peer review

Peer review information.

Nature Medicine thanks Frederic Sitas and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Jennifer Sargent and Ming Yang, in collaboration with the Nature Medicine team.

Data availability

Code availability, competing interests.

The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

are available for this paper at 10.1038/s41591-022-01978-x.

The online version contains supplementary material available at 10.1038/s41591-022-01978-x.

Home — Essay Samples — Nursing & Health — Nursing — Argumentative Essay On Smoking Cigarettes

Argumentative Essay on Smoking Cigarettes

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Published: Mar 13, 2024

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Health effects of smoking, economic implications, impact on non-smokers, the case for regulation, references:.

• Centers for Disease Control and Prevention. (2020). Smoking & Tobacco Use. Retrieved from https://www.cdc.gov/tobacco/data_statistics/index.htm

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UK smoking ban would have many benefits for public health – but only if it’s effectively implemented

Professor of Public Health, University of Sheffield

Disclosure statement

Andrew Lee has previously received research funding from the National Institute for Health Research. He is a member of the UK Faculty of Public Health and the Royal Society for Public Health, and has previously worked for Public Health England.

University of Sheffield provides funding as a founding partner of The Conversation UK.

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MPs have recently voted to ban anyone in England born after 2009 from buying cigarettes, as part of the government’s plan to achieve a smoke-free generation .

Smoking is the single most important preventable cause of ill health and death globally. In England alone, around 64,000 people in England die each year from a smoking-related disease such as lung cancer, stroke and heart attacks.

There’s also the economic impact of smoking. Every year, around £14 billion is lost due to people being off ill or out of work as a result of smoking-related illnesses. Illnesses linked to smoking also cost the health and social care system around £3 billion per year. And, in 2022-2023, more than 400,000 hospital admissions could be attributed to smoking.

Research shows that most smokers start in their teens and become addicted for life. Many want to quit, but find it exceedingly difficult to do so because of their nicotine addiction. Indeed, one study noted that it may take thirty or more attempts before a smoker is successful in quitting.

Stopping smoking at any age is beneficial. But the effect is greatest if young people are prevented from smoking in the first place.

Public health benefits

Smoking rates in the UK have been falling over the last few decades, in part helped by tobacco control efforts during this time . In 2022, just under 13% of adults (around 6.4 million people) in England smoked cigarettes compared to nearly half the population in 1974 .

Young adults are more likely to be smokers than older adults, partly due to tobacco marketing that targets them. However, it takes many years for the harms of smoking to manifest. Effectively banning young people from ever being able to start smoking cigarettes would have many benefits for public health.

Smoking causes countless preventable diseases. These include nine out of ten cases of chronic obstructive lung disease , seven out of ten cases of lung cancer , early heart disease, stroke, dementia, and diabetes , as well as numerous other cancers and medical conditions – such as stomach ulcers and severe asthma.

These diseases are often lethal . Indeed, smoking causes one in four of all UK cancer deaths, one in three deaths due to lung disease and one in nine circulatory disease deaths.

These harms are not limited only to smokers, either. Non-smokers exposed to secondhand smoke also suffer increased risks of these harms. This is particularly a concern for children, babies and pregnant women . Pregnant women exposed to secondhand smoke are also at greater risk of stillbirth and congenital malformations , low birth weights and cot deaths .

The prevalence of smoking is also greater in poorer communities and is a major contributor to the difference in life expectancy between richer and poorer areas. Smoking-related diseases are more common in areas of deprivation, and lead to more hospital admissions and greater NHS pressures in those areas.

Notably, ill health and deaths due to smoking are avoidable. By reducing the number of people who can smoke cigarettes, this will have considerable public health benefits – both in the short-term and long-term , and for both smokers and non-smokers.

Other considerations

A ban on sales to youths on its own will not be enough to eliminate smoking.

First, if the ban is not enforced or adhered to it won’t be effective. Currently, it’s already illegal to supply cigarettes to young people under the age of eighteen. But one 2019-2020 survey found that a quarter of under-18s who were regular smokers reported getting their cigarettes from shops . Retailers complying with the ban may help reduce underage smoking.

Yet even if most mainstream retailers comply with the ban, there’s also the problem of illegal cigarette sales to young people. In 2021-2022, the illicit market was estimated to make up nearly 18% of all tobacco trade in the UK.

Public support is also needed for the legislation to be effective. While many people may support the government’s ambition to be smokefree by 2030, there will also be many who don’t support the ban. Numerous MPs have even been outspoken in their opposition to the ban – citing concerns that it may encourage an illicit tobacco trade, be difficult to enforce and that other measures could be more effective in preventing young people from smoking.

What’s needed to drive down smoking rates in young people is a comprehensive package of measures . Measures such as teaching young people on the harms of tobacco in schools, mass media campaigns and smoke-free policies in public spaces can all help to prevent the uptake of smoking and change society’s attitudes to its use .

Help for those who want to quit is also needed. Plain packaging of cigarettes, as well as tax increases on tobacco products to raise their prices, are also effective.

Vaping is safer than smoking and is another tool that may help people addicted to nicotine to stop smoking . However, it isn’t a solution to youth smoking rates as vapes are still highly addictive and can cause lung injury . Worryingly, youth vaping is on the rise – with one study finding that 24% of 16-to-19-year-olds in England having vaped in the past month.

The UK’s previous tobacco control efforts between 1998 and 2010 may have prevented 210,000 deaths . These included interventions such as the NHS’s Stop Smoking Services, which was set up to target disadvantage smokers. But around half a million more people will die from smoking by 2030 if action is not taken now. There are many more lives that can be saved.

• Smoking ban

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The unexpected health benefits of Ozempic and Mounjaro

Research is showing that these new weight-loss drugs can help treat conditions from addiction to kidney disease—and may even be contributing to a boom of “Ozempic babies.”

Casey Arnold, who lives in a suburb of Houston, spent years trying to quit smoking. She’d tried nicotine patches. That failed. She tried quitting cold turkey but that made her short tempered. On other occasions the idea of quitting made her so anxious, she smoked more to ease her fears.

By the time she permanently gave up cigarettes in the winter of 2023, at age 55, she’d been smoking for four decades and was up to two packs a day. But this time it was a new type of weight loss drug that helped her quit.

GLP-1, short for glucagon-like peptide 1, is a natural hormone that stimulates the production and release of insulin, slows digestion, curbs appetite, and blunts the brain’s focus on food. GLP-1 agonist drugs, like exanetide, tirzepatide and semaglutide, mimic this hormone. They were originally developed as diabetes treatments, but as more people began taking them, researchers observed these medications are effective for many more conditions than just diabetes and weight loss.

The FDA recently approved semaglutide, the active ingredient of Wegovy, for the treatment of obesity and for reducing the risk of heart attack and stroke in patients with obesity and heart disease . But as the number of people taking these drugs grows, physicians and researchers are learning about unanticipated health benefits for conditions where treatments have been limited, such as addiction, heart failure, and kidney disease.

( Ozempic is a serious drug with serious risks. Here’s what to know. )

Arnold quit smoking while participating in a clinical trial examining the potential of GLP-1 agonists as a treatment for smoking addiction.

“It was totally opposite of when I tried to quit in my previous years,” Arnold says. “I was shocked at how calm I was, compared to how I used to think about quitting.” Instead of anxiety and rage, she felt at peace, and her cravings faded.

“It’s just been an avalanche across the different patient populations,” says Mark Petrie , a cardiologist at the University of Glasgow, whose research focuses on the use of GLP-1 agonists in patients with heart failure. “It’s just good news all around.”

Heart failure with preserved ejection fraction

More than six million Americans are living with heart failure , a condition where the heart progressively loses the ability to pump enough blood to the rest of the body. Of these patients, approximately half have a type known as heart failure with preserved ejection fraction , in which the heart can pump normally but is too stiff to fill up with blood.

In a study published last year , researchers tested semaglutide as a treatment for heart failure with preserved ejection fraction in patients who were not diabetic. The result: patients who received the drug showed fewer symptoms and reported a better quality of life, compared to those who received the placebo. Patients who received the drug had lower levels of C-reactive protein, which is a marker for inflammation.

“This is a big finding,” says James de Lemos, a cardiologist at UT Southwestern Medical Center, in Dallas, Texas, who was not associated with the study. The study was too small to determine if semaglutide can reduce the risk of hospitalization or death but given the stark improvement in patient quality of life, it’s promising.

Although some of these benefits are likely due to weight loss, that’s just part of what makes this treatment effective.

These medications are also cardioprotective and reduce inflammation, which is known to be a driver of heart failure, says Amanda Vest , a cardiologist at the Cleveland Clinic, who specializes in treating patients with heart failure. “We must continue to think more expansively than just about the number on the scale,” Vest says.

For patients with the other major type of heart failure—heart failure with reduced ejection fraction—there is less evidence, so far, that these drugs are effective. More trials are in the works to determine which types of patients will benefit from the use of these medications.

Kidney disease

An estimated 850 million people worldwide are living with chronic kidney disease ,   but there are few effective treatments. Historically, the main strategy has been to stall kidney failure for as long as possible and then move the patient to dialysis or wait for a kidney transplant. But nine out of 10 patients die of complications before reaching that point.

For patients with severe chronic kidney disease, “you are looking at a mortality rate that’s 10 to 20 percent a year,” says Katherine Tuttle , a nephrologist at the University of Washington Medicine. “This is on par with the worst malignancies.”

As a couple of recent studies have shown , the GLP-1 agonist dulaglutide helps patients who suffer from chronic kidney disease and diabetes. In a recent trial looking at the effect of semaglutide on patients with chronic kidney disease and type 2 diabetes, the treatment was so effective at delaying the progression of chronic kidney disease that the clinical trial was stopped early so that all the trial patients could benefit from the drug.

“It’s the only semaglutide trial that was stopped early for efficacy,” says Tuttle, who is on the executive committee for the trial. “To stop a trial early for efficacy, the bar is set really high,” which includes strong enough evidence for its efficacy that it would be no longer considered ethical to continue giving patients the placebo.

( New obesity drugs are coming. Here's how they could change everything. )

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As Tuttle notes, the effects on the kidneys is only partially due to reductions in risk factors such as blood pressure, blood sugar, and weight. Other benefits are likely to result from reduced inflammation.

“They have a profound anti-inflammatory effect,” Tuttle says. “Our field is really under recognizing the importance of inflammation, particularly in kidney damage caused by diabetes.”

Results from the trial will be published later this year.

Effects on fertility

For a growing number of patients on GLP-1 agonists, such as Ozempic or Mounjaro, one surprising side effect has been unexpected pregnancy, which for some patients, has come after years of struggling with infertility. Although more research is needed to explore the link between GLP-1 agonists and pregnancy, it’s become enough of a phenomenon that ‘Ozempic babies’ has become a trending phrase. Meanwhile, experts think there are several factors responsible.

The first factor is the fact that GLP-1 agonists cause a delayed gastric emptying, which can cause oral contraception pills to be absorbed by the body at a slower rate. “These drugs are altering that particular part of the drug absorption phase,” says Archana Sadhu , an endocrinologist at Houston Methodist Hospital, adding that this effect can be particularly prominent during dosage increases. This means that oral birth control may not be as effective.

The second factor is the link between polycystic ovarian syndrome (PCOS)—the leading cause of infertility in women—and insulin resistance.

“Insulin resistance will dysregulate the ovarian cycle,” Sadhu says. Insulin resistance can lead to infertility by disrupting hormones such as estrogen and testosterone, which are related to fertility; and it can affect the release of eggs from the ovaries. When patients start taking GLP-1 agonists, this reduces their insulin resistance, which boosts fertility.

However, the effects of these drugs on pregnancy are still unknown, which means that it’s important for patients to talk with their doctors about any plans for becoming pregnant, as well as strategies for contraception, which may include adding in a second method to augment oral contraceptive pills, or switching to a different method.

Treating addiction

Since Ozempic and Mounjaro have been become more common, patients have been reporting several unexpected side effects, such as a diminished desire to smoke or drink. Although more research is needed, it’s thought that the part of the brain that is responsible for food cravings overlaps with the part of the brain that is responsible for cravings for substances of abuse, says Luba Yammine, an addiction researcher at UTHealth Houston.

For doctors working in the field, earlier versions of these GLP-1 drugs showed tremendous potential as anti-addiction medications.

“We have far fewer medications available” for treating addiction and many patients report difficulties accessing these, says Christian Hendershot, an addiction researcher at the University of North Carolina School of Medicine. The field also receives less research funding compared with other diseases.

For Yammine, she first became interested in studying the effect of GLP-1 agonists on addiction while working in primary care, where she had several patients who were smokers with diabetes. Yammine would counsel her patients on quitting smoking, prescribing nicotine patches or the medication buproprion, to help them quit. But most of the time these strategies failed.

“It’s hard to quit smoking, period,” Yammine says. “The vast majority of smokers want to quit, but even with the use of these therapies, many of them are not successful.”

To help these smokers with their diabetes she would prescribe GLP-1 agonist medications, only to discover when they returned for a follow-up that they had quit smoking. When she asked them what happened, their answer was that suddenly their cravings vanished. “That was a very interesting finding,” Yammine says.

This happened often enough that Yammine decided to explore the impact of these GLP-1 receptor agonists on addiction through a clinical trial.

Yammine and her collaborators led a pilot study , in which 46 percent of the participants who received exanetide, plus nicotine patches and smoking cessation counseling, were able to quit, compared to 26 percent of participants who received nicotine patches, counseling, and a placebo. Yammine and her collaborators are now following up with a larger trial. They are also planning a separate trial with semaglutide.

For the patients in the study who received exanetide, their post-cessation weight was 5.6 pounds lower than those who received the placebo, a side effect that can help offset the weight gain that is often associated with quitting smoking.  

“This weight gain is very problematic,” Yammine says, adding that many patients are either afraid to quit or relapse due to concerns about weight gain, while it can also put them at heightened risk for developing weight-related conditions, such as type 2 diabetes.

For Arnold, who was enrolled in a follow up trial that Yammine is conducting, the months in which she was participating in the trial was characterized both by a calmness surrounding her efforts to quit, as well as minimal weight gain. Since the trial has ended, she’s been able to maintain her efforts to quit smoking, although she gained a little weight. “I don’t have cravings,” Arnold says. “It’s this weight gain that is bothering me.”

Arnold, who works for an HVAC company, would really like to go back on exanetide, but as is the case with so many other patients who have experienced benefits from GLP-1 receptor agonists, she’s finding that it’s too expensive to do so. Just one month’s supply costs about $1,000, and without FDA approval for its use as an anti-addiction drug, most health insurance companies won’t pay for it.

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What to know about Zyn, the tiny nicotine pouch that’s sparked a big health debate

FILE - Containers of Zyn, a Phillip Morris smokeless nicotine pouch, are displayed for sale among other nicotine and tobacco products at a newsstand Friday, Feb. 23, 2024, in New York. The product has been making big headlines, sparking debate about whether new nicotine-based alternatives intended for adults may be catching on with underage teens and adolescents. (AP Photo/Bebeto Matthews, File)

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WASHINGTON (AP) — A tiny Philip Morris product called Zyn has been making big headlines, sparking debate about whether new nicotine-based alternatives intended for adults may be catching on with underage teens and adolescents.

Here’s what to know about Zyn:

WHAT IS ZYN?

Zyn is an oral pouch that contains nicotine powder and flavorings like mint, coffee and citrus. The pouches are the fastest-growing segment of the tobacco industry, which has struggled for decades to replace falling cigarette sales.

Zyn is marketed by Philip Morris International to adult tobacco users. Although it doesn’t contain tobacco, U.S. regulators still treat it as a tobacco product.

Competitors sell similar products. Altria, for instance, sells its own flavored pouches called On.

HOW DO NICOTINE POUCHES WORK?

Users stick them between their lip and gums, where they slowly release low levels of nicotine that are absorbed into the bloodstream. Because pouches generally don’t contain tobacco, there’s no spitting, unlike older products like chew and snuff.

Philip Morris representatives say the nicotine-only formulation is part of Zyn’s appeal.

“People can be reluctant to move into an oral tobacco product if they view it as similar to traditional chewing tobacco,” company spokesman Corey Henry said. “Consumer acceptability is a big part of Zyn.”

IS ZYN HEALTHIER THAN OTHER TOBACCO PRODUCTS?

All tobacco products carry serious health risks. Cigarettes are widely understood as the most harmful, increasing the likelihood of cancer, heart disease and lung problems. Chewing tobacco is linked to mouth cancer, gum disease and tooth loss.

But in the last decade or so, researchers and health regulators have begun to acknowledge different levels of harm among different tobacco products.

In 2019, the Food and Drug Administration said a different oral tobacco product, called snus , contains lower cancer-causing chemicals than cigarettes and could benefit smokers who switch.

Snus are similar to nicotine pouches like Zyn, except that they contain fermented tobacco. Studies from Sweden and other places where they are popular have shown lower rates of lung cancer and related diseases compared with other European countries where smoking is more prevalent.

There’s little research on the long-term effects of nicotine pouches, but many researchers expect they will show similarly low rates of carcinogens and other toxic components.

Still, that doesn’t mean they’re safe. A study last year found Zyn and similar products contain low levels of harmful substances such as ammonia and formaldehyde.

WILL THE FDA AUTHORIZE ZYN FOR ADULT SMOKERS?

Currently FDA officials are letting Zyn stay on the market while they review Philip Morris’ marketing application, which was submitted in 2020.

To win FDA authorization , companies generally must show that their products will reduce disease among adult tobacco users without attracting underage use by teens and adolescents.

IS ZYN POPULAR AMONG YOUNG PEOPLE?

Not according to the latest federal data. Only 1.5% of high school and middle schoolers reported using nicotine pouches when surveyed last year. That’s well below the roughly 10% who used electronic cigarettes.

But anti-tobacco advocates point to worrying signs: videos of young people popping the pouches have racked up millions of views on social media in recent months. A similar surge of online activity preceded the rise of Juul , the sleek e-cigarette widely blamed for triggering a spike in teen vaping in the years before COVID-19.

Concerns about Zyn going viral have sparked debate among health experts, parents and even politicians.

The FDA says it’s monitoring underage use of Zyn and other pouches and will take action, if necessary.

CAN ADULT SMOKERS USE ZYN TO HELP QUIT?

Currently only a handful of products are FDA-approved to help with quitting smoking, including medications, nicotine gums and patches. Some researchers point out that Zyn works similarly to some of those products — gradually delivering nicotine that reduces cravings.

But early research suggests Zyn and other pouches may not be enough to help smokers quit.

Ohio State University researchers recently found it took smokers 30 minutes to an hour to get enough nicotine from Zyn to relieve their cravings. With cigarettes, smokers achieved the same nicotine levels — and relief — in five minutes.

For now, Philip Morris is focused on obtaining FDA authorization to stay on the market, and eventually it has said it could seek a reduced-risk designation similar to snus. But no tobacco company — Philip Morris included — has ever asked the FDA to approve their products to help smokers quit completely.

The Associated Press Health and Science Department receives support from the Howard Hughes Medical Institute’s Science and Educational Media Group. The AP is solely responsible for all content.

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David Wallace-Wells

This is what a miracle drug looks like, and it costs only $5 to make.

By David Wallace-Wells

Opinion Writer

Last year was called the year of Ozempic, though it was also a year of Ozempic backlash and Ozempic shortages , which could persist for years. Even so, we appear very far from a peak for GLP-1 drugs, like Ozempic and Wegovy, which are powered by a molecule called semaglutide, and Mounjaro, which uses its cousin tirzepatide. It seems possible to imagine a future in which almost everyone is taking some variety of GLP-1 drug, and with a pretty good reason to do so.

Probably, you have heard about the game-changing impact of such drugs on obesity, a condition that affects 40 percent of Americans and increases the risk of heart disease, stroke and hundreds of other comorbidities. Patients on Ozempic and Wegovy can lose 15 to 20 percent or more of their weight in a little over a year, and if they stay on the drugs, the weight tends to stay off. That may not sound like a monumental effect, but consider that on average, an obese 210-pound man who loses 20 percent of his body mass generally passes quickly through the overweight stage all the way to a normal weight.

If anything, though, we’ve probably talked too much about cosmetic weight loss and Hollywood vanity — and certainly made too many comparisons to fen-phen, Botox and Viagra. The GLP-1 drugs have been shown to cut risk of heart attacks, strokes and death from coronary disease by 20 percent among overweight and obese patients, presumably through the salubrious effects of weight loss, though the researchers can’t yet say for sure. Semaglutide has been shown to eliminate or reduce the need for insulin among those with recent-onset Type 1 diabetes. In a clinical trial of people with Type 2 diabetes and moderate to severe kidney disease, the drug reduced the risk of kidney disease progression and cut the death rate from cardiovascular and kidney-related causes by 24 percent — such a clear result that the trial was ended early. Semaglutide has reduced fatty liver deposits in patients with H.I.V. and nonalcoholic steatotic liver disease. It has normalized the menstrual cycles of those with polycystic ovary syndrome. (It has also, somewhat mysteriously, seemed to produce a wave of unintended pregnancies among women taking birth control, at least if TikTok videos are to be trusted.)

Studies have shown promise in treating Alzheimer’s and Parkinson’s with GLP-1 drugs, perhaps by regulating insulin levels and reducing inflammation, and the drugs may yet prove useful in treating many other conditions made worse by chronic inflammation. Some studies have found large decreases in the risk of depression and anxiety; others found smaller but still positive effects. There are potential applications for schizophrenia and neurological dysfunction, thanks to the role that insulinlike hormones like GLP-1 play in the development of the central nervous system and the way semaglutide reshapes the brain’s chemical reward system. It seems to bend the curve on alcoholism and drug addiction and curb other addictive behaviors, as well — compulsive shopping and sex addiction, gambling and nail biting, smoking and skin picking. A compulsive nation has stumbled into what looks like a treatment for compulsion and one that happens to protect against some of the country’s biggest killers and curb some of its most pervasive pathologies and inner demons.

Americans love to dream of miracle drugs, but hardly anything ever seems to fill the bill. True, semaglutide has arrived with real questions trailing like bunting: Much of the weight loss is from lean muscle mass, which isn’t ideal, and there are reasons to worry over the possibility of thyroid problems, loss of bone density and sarcopenia, a weakness disorder associated with aging. There are potentially other serious long-term side effects, though millions of Americans have been taking Ozempic for Type 2 diabetes for years without serious issues. (Some of them do report more familiar side effects, like nausea.) The GLP-1 drugs aren’t a permanent fix in a single shot — whether the thing being addressed is body mass index or cardiac risk or the progression of Alzheimer’s — but a permanent disease-management program. They also haven’t exactly cured cancer, although more than a dozen cancers are linked to obesity, and in at least one case, colorectal cancer, there is reason to believe GLP-1 drugs may directly cut the chances of developing the disease.

All that means that semaglutide isn’t exactly a cure-all, in the vernacular sense. But it seems to be about as close as we’ve gotten, even in a time of racing biomedical progress, to that old science-fiction proposition — one pill for almost everything and almost everyone forever.

And pretty soon, it won’t be just one. Technically, Ozempic hasn’t even been approved yet for weight loss, though Wegovy and Mounjaro (under the new brand name Zepbound) have, and there are almost 100 new GLP-1 obesity drugs in various stages of development. Roughly 70 percent of American adults are obese or overweight, and while not everyone who might benefit from GLP-1 drugs is likely to take them, it’s also hard to have confidence in projections that the market will grow only 26 percent annually over the next five years, when over the past five alone, semaglutide use has grown fortyfold. When we talk about GLP-1 drugs as a major breakthrough or even potential solution to obesity, it raises questions about health care access, the social determinants of health and the political determinants of health inequality, the pathologies of the United States and the modern world. (Not to mention the unpredictability of putting so many people on what may need to be lifelong drug regimens.) But it also means, very simply and straightforwardly, that the drug could help a couple of hundred million Americans right now.

At the moment, getting those drugs to those people would be remarkably expensive. A single month’s worth of Ozempic or Wegovy is today priced at around $1,000 or more, which is more than private companies currently pay per employee into employer-based insurance in total, and at present few private insurers cover these drugs for weight loss. A group of researchers recently calculated that at current prices, the cost of providing GLP-1 drugs to all Americans who could benefit from them could grow past $1 trillion annually: more than the full annual cost of Medicare or even than that of the U.S. military.

But miracles don’t have to be this expensive, and in fact, they aren’t elsewhere in the world, where Ozempic costs one-fifth as much as it does here or even less. A month of doses can be manufactured for less than $5 , which means that American customers are paying a 200-fold markup or more, with many of them paying it out of pocket. That suggests one additional way that semaglutide could reshape American health and health care: The price of marginal production has never determined American medication costs, but the sheer magnitude of Ozempic demand may force a belated reckoning with the mess of U.S. drug pricing. Perhaps it will also refocus our approach to health care away from crisis treatments and toward underlying conditions and preventive care, as reformers have advocated for decades.

If this is the beginning of a health revolution, we are still in its early days. We don’t know how many Americans would like to avail themselves of GLP-1 drugs or how many of them will find an unending course sustainable and helpful. (Though we do know that a majority of those who took the medications over the past several years have already stopped.) We don’t know whether the costs will be brought down to manageable levels, for individuals and for insurers, and we don’t know what that might mean for the government’s role in setting drug prices generally. We don’t know how quickly, if at all, obesity rates will fall. We don’t know what medical complications might follow from the sudden uptake of weight-loss meds by a conspicuously obese nation. We don’t even really know everything about how these drugs work or what else they might do.

What we do know is that treatment for obesity has been called for decades a holy grail. All of a sudden, we have several, with many more to come.

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We know how to deter British children from alcohol, say experts, after concerns over WHO report

Study found Great Britain had worst rate of child alcohol consumption in world, but youth drinking is said still to have ‘declined sharply’

In 2000, about 19% of children under 16 in England smoked, according to Action on Smoking and Health. By 2018, this had declined to 5%.

But, according to a major report by the World Health Organization released on Thursday , a third of 11-year-olds and over half of 13-year-olds had drunk alcohol, the highest rate of any country worldwide. Girls were found to be more likely than boys to have drunk at the age of 15.

Although the findings have caused concern, experts say rates of youth drinking have still been sharply declining. Dr Katherine Severi, the chief executive of the Institute of Alcohol Studies, said that, although the findings from the WHO report were troubling, it “should be acknowledged that youth drinking has declined sharply from highs around the year 2000, particularly drinking among boys”.

She added: “We still don’t know exactly what is driving this trend, but research suggests young people are socialising in different ways and better recognise the health harms of alcohol.”

Severi said that the evidence was clear on what could further be done to discourage alcohol consumption, particularly among young people, across the UK.

“We have known for decades how to reduce alcohol harm: reduce its affordability through duty increases and minimum pricing policies, give local areas control over its availability and restrict alcohol marketing,” Severi said. “We know that alcohol marketing leads to children starting to drink from an earlier age and at heavier levels than they would have done.”

The report also found that the proportion of 15-year-old girls in England who had ever smoked had risen from 20% in 2018 to 28% in 2022. But, for boys, the trend was reversed, with 25% of boys having smoked in 2018 in England dropping to just 16% in 2022.

Dr Sabina Hulbert, a senior research fellow at the University of Kent, said that the rise in smoking among young girls in England, but not boys, could be because of the decline of gender stereotypes in modern society.

“What we think is probably leading that gender inversion in trend is that social stereotypes are diminishing, and gender stereotypes are being overcome, meaning that gender equality is much more achieved nowadays,” Hulbert said. “But with that comes the risk of girls wanting to do what boys did, and almost wanting to catch up and to show that they can, and that because boys do it so can we.”

The analysis also found that 40% of girls in England and Scotland had vaped before 15, and did so at a higher rate than countries such as France and Germany. Hans Kluge, the WHO regional director for Europe, said that the rise in vaping among young people could be related to children being exposed to these products online, as well as them being marketed directly to children.

He said: “Considering that the brain continues to develop well into a person’s mid-20s, adolescents need to be protected from the effects of toxic and dangerous products.

“Unfortunately, children today are constantly exposed to targeted online marketing of harmful products, while popular culture, like video games, normalises them.”

Dr Jo Inchley a reader at the University of Glasgow, said the availability of vapes could be part of the rise. She said: “Disposable vapes seem to be fairly readily accessible to young people, and schools are reporting that’s a major issue they’re having to deal with on a day-to-day basis. Young people are telling us that too.

“Having ready access to any kind of substance like that obviously makes it more attractive and available, so that is a big issue.”

• Young people

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235 Smoking Essay Topics & Examples

Looking for smoking essay topics? Being one of the most serious psychological and social issues, smoking is definitely worth writing about.

🏆 Best Smoking Essay Examples & Topic Ideas

🥇 good titles for smoking essay, 👍 best titles for research paper about smoking, ⭐ simple & easy health essay titles, 💡 interesting topics to write about health, ❓ essay questions about smoking.

In your essay about smoking, you might want to focus on its causes and effects or discuss why smoking is a dangerous habit. Other options are to talk about smoking prevention or to concentrate on the reasons why it is so difficult to stop smoking. Here we’ve gathered a range of catchy titles for research papers about smoking together with smoking essay examples. Get inspired with us!

Smoking is a well-known source of harm yet popular regardless, and so smoking essays should cover various aspects of the topic to identify the reasons behind the trend.

You will want to discuss the causes and effects of smoking and how they contributed to the persistent refusal of large parts of the population to abandon the habit, even if they are aware of the dangers of cigarettes. You should provide examples of how one may become addicted to tobacco and give the rationales for smokers.

You should also discuss the various consequences of cigarette use, such as lung cancer, and identify their relationship with the habit. By discussing both sides of the issue, you will be able to write an excellent essay.

Reasons why one may begin smoking, are among the most prominent smoking essay topics. It is not easy to begin to enjoy the habit, as the act of smoke inhalation can be difficult to control due to a lack of experience and unfamiliarity with the concept.

As such, people have to be convinced that the habit deserves consideration by various ideas or influences. The notion that “smoking is cool” among teenagers can contribute to the adoption of the trait, as can peer pressure.

If you can find polls and statistics on the primary factors that lead people to tweet, they will be helpful to your point. Factual data will identify the importance of each cause clearly, although you should be careful about bias.

The harmful effects of tobacco have been researched considerably more, with a large body of medical studies investigating the issue available to anyone.

Lung cancer is the foremost issue in the public mind because of the general worry associated with the condition and its often incurable nature, but smoking can lead to other severe illnesses.

Heart conditions remain a prominent consideration due to their lethal effects, and strokes or asthma deserve significant consideration, as well. Overall, smoking has few to no beneficial health effects but puts the user at risk of a variety of concerns.

As such, people should eventually quit once their health declines, but their refusal to do so deserves a separate investigation and can provide many interesting smoking essay titles.

One of the most prominent reasons why a person would continue smoking despite all the evidence of its dangers and the informational campaigns carried out to inform consumers is nicotine addiction.

The substance is capable of causing dependency, a trait that has led to numerous discussions of the lawfulness of the current state of cigarettes.

It is also among the most dangerous aspects of smoking, a fact you should mention.

Lastly, you can discuss the topics of alternatives to smoking in your smoking essay bodies, such as e-cigarettes, hookahs, and vapes, all of which still contain nicotine and can, therefore, lead to considerable harm. You may also want to discuss safe cigarette avoidance options and their issues.

Here are some additional tips for your essay:

• Dependency is not the sole factor in cigarette consumption, and many make the choice that you should respect consciously.
• Cite the latest medical research titles, as some past claims have been debunked and are no longer valid.
• Mortality is not the sole indicator of the issues associated with smoking, and you should take chronic conditions into consideration.

Find smoking essay samples and other useful paper samples on IvyPanda, where we have a collection of professionally written materials!

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

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