smoking effects on health essay

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.

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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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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 2, 2024

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• Open access
• Published: 10 October 2022

Health effects associated with smoking: a Burden of Proof study

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

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

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• Risk factors

Matters Arising to this article was published on 14 April 2023

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

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The Burden of Proof studies: assessing the evidence of risk

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

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

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

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

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Extended data

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

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

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

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

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

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

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

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

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

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

Extended Data Fig. 6 Smoking and Breast Cancer.

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

Supplementary information

Supplementary information.

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

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

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Health Effects of Cigarette Smoking

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Argumentative Essay on Smoking Cigarettes

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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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Office of the Surgeon General (US); Office on Smoking and Health (US). The Health Consequences of Smoking: A Report of the Surgeon General. Atlanta (GA): Centers for Disease Control and Prevention (US); 2004.

The Health Consequences of Smoking: A Report of the Surgeon General.

4 respiratory diseases.

• Introduction

Smoking has adverse health effects on the entire lung—affecting every aspect of lung structure and function—including impairing lung defenses against infection and causing the sustained lung injury that leads to chronic obstructive pulmonary disease ( COPD ). In fact, among the postulated causes of COPD are acute respiratory infections, for which smokers are at an increased risk. This chapter addresses smoking and acute and chronic respiratory diseases other than lung cancer (see Chapter 2 , “Cancer”), and discusses the relevant evidence of the underlying mechanisms. COPD was the focus of the 1984 Surgeon General’s report ( U.S. Department of Health and Human Services [USDHHS] 1984 ), and a number of previous reports have addressed acute respiratory infections, which can range in severity from minor to fatal. This chapter emphasizes acute respiratory illnesses and COPD, which are leading causes of morbidity and mortality in the United States and worldwide.

• Acute Respiratory Illnesses

Acute respiratory illnesses are presumed to have an infection as the predominant underlying cause. Smoking might act to increase the frequency or severity of infections. In this section, acute respiratory infections are examined separately for persons with and without smoking-related chronic obstructive lung diseases (COLDs), because patients with smoking-related diseases have frequent exacerbations of their underlying diseases. Whenever possible, effects of smoking that increase the incidence of disease are distinguished from effects that relate to the severity of the disease.

A MEDLINE search was conducted to identify relevant studies published between 1966 and 2000. To identify studies focusing on the biologic basis of and the evidence linking smoking and acute respiratory infections in persons without COPD , the following Medical Subject Headings ( MeSH ) terms were searched: “respiratory tract infections” and “smoking,” “respiratory tract infections” and “immunology,” “smoking” and “immunology,” “nicotine” and “immunology,” and “smoking” and “respiratory tract infections” and “epidemiology.” To identify studies focusing on smoking and acute respiratory infections accompanied by COPD and asthma, the MeSH term “lung diseases, obstructive” was searched in combination with multiple key words: “antibiotic(s),” “respiratory infection(s),” “respiratory tract infection(s),” “infection(s),” “Tecumseh,” “immunization,” and “immunotherapy.” The MeSH terms “bronchitis” and “asthma” were also searched in conjunction with the above key words. The searches were then repeated substituting the key words “COPD,” “chronic obstructive pulmonary disease,” “asthma,” “chronic bronchitis,” and “acute bronchitis.” The Cochrane database was also searched. All searches included a hand search of bibliographies and authors’ files.

Acute respiratory illnesses are usually divided into those that include the upper respiratory tract (nose and pharynx) and larynx, and those that include the lower respiratory tract (below the larynx). In people with normal immune systems, viruses account for most cases of upper respiratory syndromes ( Gwaltney 1995c ): acute bronchitis ( Gwaltney 1995a ), bronchiolitis ( Hall and Hall 1995 ), and a majority of pneumonia cases ( Marrie et al. 1989 ). Bacteria can cause pharyngitis ( Gwaltney 1995b ) and some pneumonias ( Marrie et al. 1989 ). Cigarette smoke combustion products reportedly increase morbidity and mortality in acute respiratory infections by impairing physical defenses in the respiratory tract, and by impairing cellular and humoral immune responses to microbes ( Donowitz and Mandell 1995 ). Moreover, the effects of smoking can be expected to differ in respiratory infections caused by viruses and in infections caused by bacteria, because each class of microbes stimulates different immune responses specific to the infection ( Mandell et al. 1995 ).

Conclusions of Previous Surgeon General’s Reports

Previous Surgeon General’s reports on smoking and health have noted possible adverse effects of cigarette smoking on acute respiratory infections. The 1979 report ( U.S. Department of Health, Education, and Welfare [USDHEW] 1979 ) cited data from the 1964–1965 Health Interview Survey, which found a higher age-adjusted incidence of self-reported influenza in male and female smokers when compared with non-smokers, and more upper respiratory illnesses (URIs) in female smokers than in female nonsmokers. The 1989 report ( USDHHS 1989a ) identified a number of studies that reported higher mortality ratios for smokers than for nonsmokers suffering from respiratory tuberculosis (the range of ratios was 1.27–5.0 in three studies), and from influenza and pneumonia as one combined category (the range of ratios was 1.4–2.6 in seven studies). The 1990 report focused on the health benefits of smoking cessation, and it comprehensively reviewed evidence suggesting that smoking increased the risk of acute respiratory illnesses ( USDHHS 1990 ).

Providing a more detailed analysis of the smoking-related mortality data presented in the 1989 report, the 1990 report identified exposure-response relationships between mortality from pneumonia and influenza and the number of cigarettes currently smoked, and identified reductions in mortality rates of former smokers in relation to years of not smoking ( USDHHS 1990 ). A review of possible mechanisms related to acute respiratory illnesses documented a variety of effects on host defenses: increases in peripheral blood total leukocyte counts, increases in poly-morphonuclear leukocyte and monocyte counts, decreases in monocyte intracellular killing, decreases in the CD4/CD8 ratio in heavy smokers, decreases in concentrations of serum immunoglobulins (other than IgE), an increase in alveolar macrophage release of superoxide anions, a decrease in microbicidal activity of the macrophages, and a blunted immune response to an influenza vaccination. Although the 1990 report noted that smoking cessation restored many of these impaired defenses, it also found that few epidemiologic studies directly addressed the effects of smoking on acute respiratory morbidity. Conflicting data were observed for nonspecific acute lower respiratory illnesses (LRIs), but findings for increased morbidity from influenza virus infections in smokers were more consistent. The 1994 report ( USDHHS 1994 ), which focused on young people, added little new information.

Biologic Basis

Animal studies.

More than 25 years ago, in vitro exposure of rabbit alveolar macrophages to a water soluble fraction of tobacco smoke was shown to impair the ability of macrophages to kill bacteria ( Green and Carolin 1967 ). An extensive body of data has since accumulated on the effects of exposure to tobacco smoke on immune and cellular function in animal models. However, differences in responses among species to different experimental exposures of tobacco smoke and its products make it difficult to provide a simple, unifying summary of the animal data. Impaired immunoglobulin responses to immunization ( Roszman and Rogers 1973 ) and dose-dependent decreases in responses to T cell and B cell mitogens have been reported for both short-term in vitro ( Roszman et al. 1975 ) and in vivo ( Johnson et al. 1990 ) exposures to tobacco smoke. Johnson and colleagues (1990) provide a comprehensive review of in vivo subchronic exposures in animals ( Table 4.1 ) and of the voluminous relevant animal toxicology literature through 1990. In addition to the general immunologic effects summarized in Table 4.1 , direct effects of tobacco smoke exposure on lung defenses include suppressed functioning of bronchial-associated lymphoid tissue, increased numbers of alveolar macrophages that have a higher than normal metabolic rate, and increased generation of reactive oxygen species precursors during phagocytosis, but without changes in bactericidal capacity (rat alveolar macrophages [summarized in Johnson et al. 1990 ]).

Summary of subchronic exposure to cigarette smoke on immune function in animals.

Studies of the effects of nicotine on the immune function of rodents provide some relevant insights into the effects of tobacco smoke on host responses. Exposing rats to a four-week continuous infusion of nicotine inhibited the increase of intracellular calcium that usually happens when the T cell antigen receptor is blocked ( Sopori et al. 1998 ). The calcium ion plays a role in the early receptor-mediated activation of cells in general ( Sopori and Kozak 1998 ), and this effect of nicotine on calcium fluxes could explain a number of observed nicotine effects on host defenses: (1) suppressed febrile response to turpentine-induced abscesses in mice ( Sopori and Kozak 1998 ), (2) decreased inflammatory response to influenza infections with an increased proliferation of virus in mice ( Sopori and Kozak 1998 ), (3) decreased responses to T cell mitogens in mice ( McAllister-Sistilli et al. 1998 ) (T cell anergy [ Sopori and Kozak 1998 ]), and (4) decreased induction of antibody-forming cells and proliferative response to anti-CD3 antibody in rats ( McAllister-Sistilli et al. 1998 ).

Human Studies

Studies of the effects of tobacco smoke on immune function and host defenses can be broadly grouped as those focusing on markers in peripheral blood, serologic responses to specific antigens, and markers in specimens obtained by bronchoalveolar lavage.

Studies of immune response markers in peripheral blood to acute respiratory infections are summarized in Table 4.2 . However, the interpretive value of many of these studies is limited by insufficient information on the sources and health status of the participants. Of the studies noted in Table 4.2 , only those by Gulsvik and Fagerhol (1979) , Tollerud and colleagues (1989a , b ), Mili and colleagues (1991) , Kurtti and colleagues (1997) , and Sankilampi and colleagues (1997) are based on population samples with clearly defined criteria for classifying the health status of smokers and nonsmokers. Torres and colleagues (1996) also examined population samples in an effort to assess clinical characteristics of COPD patients with community-acquired pneumonia. The remaining studies have small samples, and the sources of the participants are not always clear. Although innumerable studies have observed increased peripheral white blood cell counts in smokers when compared with nonsmokers, the consequences of this increase remain unclear, especially because few data exist on the effects of smoking on peripheral phagocytic and immune-competent cells. Inconsistent findings in studies observing exposure-response relationships based on the amount of smoking may reflect varying definitions of smoking and the small numbers of persons in some of the studies. Even among those studies that were population-based or those that were larger, exposure-response relationships have not been consistently demonstrated ( Gulsvik and Fagerhol 1979 ; Petitti and Kipp 1986 ; Tollerud et al. 1989b ).

Studies on the effects of smoking on markers of human immune function and host defenses, derived from analyses of peripheral blood.

Nasal mucociliary clearance is probably important in the clearing of microorganisms from the nasopharynx. A study of the rate of nasociliary clearance found the rate of clearance to be delayed in smokers (20.8 [standard deviation = 9.3] minutes versus 11.1 [standard deviation = 3.8] minutes in nonsmokers). In this study the beat frequency of the cilia was not affected in smokers, and this finding suggests that the slower clearance is due either to a loss of cilia and/or changes in the viscoelastic properties of nasal mucus caused by cigarette smoke ( Stanley et al. 1986 ). A study of bacterial adherence to buccal cells found that Streptococcus pneumoniae ( S. pneumoniae ) but not Hemophilus influenzae ( H. influenzae ) had an increased adherence in cigarette smokers. Since bacterial adherence to the cell is the first step in the colonization of bacteria, this finding may indicate an important mechanism for enhancing bacterial colonization and infection in smokers ( Piatti et al. 1997 ).

Although smoking generally seems to suppress immune function, the evidence does not suggest particular mechanisms by which smoking might act to increase the risk of an acute infection ( Table 4.2 ). One possible mechanism relates to the effect of cigarette smoke on the enhancement of IgE immunoglobulin responses through effects on interleukin-4 ( IL -4) production by CD4 lymphocytes ( Byron et al. 1994 ). IgE levels tend to be higher in smokers than in nonsmokers, and the age-related decline in serum IgE levels is not seen in smokers ( Burrows et al. 1981 ). Exposure to cigarette smoke also skews immune responses away from a T-helper ( Th ) 1 type response, characterized by the production of interferon γ, IL-2, tumor necrosis factor alpha, and IL-12 that lead to phagocytosis and the destruction of microbial pathogens ( Fearon and Locksley 1996 ; Locksley et al. 1998 ). As a result, smoking may enhance the ability of common respiratory microbial pathogens (e. g ., viruses) both to infect the host and decrease the host’s ability to control the infection.

Studies of markers in bronchoalveolar lavage specimens provide additional insights into how exposure to tobacco smoke could alter host defenses and increase morbidity from acute infections ( Table 4.3 ). Moreover, the differences in marker profiles (e. g ., distribution of CD4 and CD8 T lymphocytes) between peripheral blood and bronchoalveolar lavage data suggest that both systemic and pulmonary responses need to be evaluated to assess the effects of smoking on host defenses against respiratory pathogens. New data from bronchoalveolar lavage studies also suggest that smoking can alter regulation of the cytokine network. The lower production in smokers of the cytokine IL -1 by alveolar macrophages may be responsible for decreased levels of serum immunoglobulins and decreased antibody responses to vaccines because of IL-1’s role in the production of κ light chains in B cells ( Yamaguchi et al. 1989 ). The suppression of regulatory cytokines IL-1 receptor antagonist and IL-6 ( Mikuniya et al. 1999 ), the inhibition of the chemotactic factor inactivator by tobacco smoke, and the increase in numbers of neutrophils in the lung ( Robbins et al. 1990 ; Costabel et al. 1992 ; Repine et al. 1997 ) could contribute to a heightened inflammatory response that increases morbidity and/or mortality from a respiratory infection.

Studies on the effects of smoking on markers of human immune function and host defenses, derived from analyses of bronchoalveolar lavage fluid.

In summary, since the last Surgeon General’s reports to address the topic ( USDHHS 1989a , 1990 ), new evidence has emerged buttressing the biologic basis of how cigarette smoking could increase the risk of and morbidity from acute respiratory infections: (1) animal data on the inhibitory effects of nicotine on T cell receptor stimulation indicate a plausible basis for the decreased mitogenic responses observed in smokers; (2) bronchoalveolar lavage fluid in smokers shows a more pro-inflammatory cytokine profile than in nonsmokers, suggesting that dysregulation of the cytokine network and inhibition of inflammation regulators provide a basis for more severe inflammation in smokers with respiratory infections; and (3) the emergent understanding of the role of Th -1 and Th-2 lymphocyte phenotypes on immune responses to foreign antigens indicates that the capacity of cigarette smoke to skew immune responses to a Th-2 phenotype could play a role in host responses to an infection. These immunologic alterations can be expected to increase the risk of acute infections through various effects on pulmonary airways, including decreased ciliary function and impaired mucociliary clearance ( Janoff et al. 1987 ), and metaplasic changes in the airway epithelium ( Sherman 1992 ) that diminish the capacity of physical clearance mechanisms.

Acute Respiratory Infections in Persons Without Chronic Obstructive Pulmonary Disease

Epidemiologic evidence, influenza infections.

Some of the earliest studies of the effects of cigarette smoking on acute respiratory infections focused on the influenza virus ( Table 4.4 ). Studies have shown an increased incidence of clinical influenza illness and infection in young, healthy smokers when compared with young, healthy nonsmokers ( Finklea et al. 1969 , 1971a ; Kark and Lebiush 1981 ; Kark et al. 1982 ). An attributable risk of 31.2 percent (95 percent confidence interval [ CI ], 16.5–43.1) was reported for clinical influenza in U.S. male military recruits in a closed outbreak environment ( Kark et al. 1982 ). The data for the severity of an illness are less clear, with studies of young, healthy persons providing conflicting results ( Table 4.4 ) ( Finklea et al. 1969 ; Kark et al. 1982 ). The evidence on smoking and influenza-like illnesses in older populations is even more limited. A randomized, placebo-controlled Dutch trial of influenza vaccines in persons aged 60 years and older ( Cruijff et al. 1999 ) did not show an increase in clinical disease among smokers, but did show an increase in asymptomatic (by serology) infections in smokers in the placebo arm of the trial (the odds ratio [ OR ] adjusted for age, gender, and an underlying risk group = 1.61 [95 percent CI, 0.91–2.83]). A study of adults (age distribution not given) from a health maintenance organization in the United States found an increased OR for a physician/nurse practitioner visit for pneumonia/influenza (no distinction made) among smokers of high-tar cigarettes compared with low-tar cigarette smokers ( Table 4.4 ) ( Petitti and Friedman 1985b ). Unfortunately, the study depended on a medical record review of practitioner diagnoses, with no criteria in the report as to how the “pneumonia/influenza” diagnosis was assigned. Without these criteria, it is difficult to interpret the OR of 1.7 (95 percent CI, 1.0–3.0) for the occurrence of illness in smokers of low-tar cigarettes compared with non-smokers, since this analysis was not adjusted for the presence of COPD in the smokers.

Studies on the association between smoking and the occurrence of influenza virus illness and infection.

Whether smokers have an increased risk of infection with influenza viruses in contrast to more often having a clinically recognizable illness remains clouded. A study of healthy U.S. military cadets found evidence of increased asymptomatic infections among smokers in addition to a larger percentage of smokers with high hemagglutination inhibition ( HI ) titers (>1:40) to influenza A ( Finklea et al. 1969 , 1971a ). As a group, however, ill smokers tended to have lower HI titers to influenza A 2 than ill lifetime nonsmokers, after adjusting for the effects of illness and vaccination status. Ill smokers also had higher titers to influenza B but poorer responses to vaccination with influenza B antigen. Overall responses to vaccination with influenza A and B antigens did not differ among various smoking groups and lifetime nonsmokers. However, smokers had a decreased persistence of antibody at a one-year follow-up evaluation. In the Dutch study of persons aged 60 years or older ( Cruijff et al. 1999 ), smoking status was inversely related to the likelihood of a serologic infection among those who were vaccinated—possibly because smokers develop a better immunologic protection after vaccination than nonsmokers—but showed a direct relationship in those who received a placebo ( Table 4.4 ). These findings do not suggest that smokers are less responsive to the beneficial effects of influenza vaccination, at least in the elderly.

Pneumonia and Infections with Pathogens that Infect the Lower Respiratory Tract

Several well-designed and well-executed U.S. population-based studies have provided evidence of a link between cigarette smoking and acute lower respiratory tract infections ( Table 4.5 ). A population-based, case-control study of 205 cases of community-acquired pneumonia ( Almirall et al. 1999a , b ) reported an attributable risk of 23.0 percent (95 percent CI , 3.3–42.7) for a history of ever smoking. An exposure-response relationship based on the number of cigarettes smoked per day was observed in former smokers, who had an adjusted OR close to that of current smokers of 10 to 20 cigarettes per day ( Table 4.5 ). The Centers for Disease Control and Prevention sponsored a case-control study of invasive pneumococcal disease based on a population surveillance system ( Nuorti et al. 2000 ). Although the number of cases for which pneumonia was the underlying source of the invasive disease was not given, pneumonia is likely to have been the main diagnosis in the 216 (out of a total sample of 228) cases in patients with bacteremia. The population attributable risk estimate for smoking was 51 percent (no CIs were given), compared with 14 percent for chronic illnesses. The authors estimated that reducing the prevalence of smoking to 15 percent among persons aged 18 through 64 years would prevent 4,000 cases per year of invasive pneumococcal disease in the United States. Of particular interest in this study was the observation that after 10 years of smoking cessation, the risk of invasive pneumococcal disease reached that of nonsmokers.

Studies on the association between smoking and the occurrence of pneumonia and infection with pathogens that infect the lower respiratory tract.

Serologic evidence of infection with Chlamydia pneumoniae ( C. pneumoniae ) was evaluated in a sample from the European Respiratory Health Survey ( Table 4.5 ) ( Ferrari et al. 2000 ). The adjusted OR as evidence of recent infection (IgG titer >512 or IgM titer >16) with C. pneumoniae in smokers compared with non-smokers was 3.51 (95 percent CI , 1.26–9.67). Finally, a matched, case-control study of community-acquired infections with Legionella pneumophila was carried out with cases derived from a prospective pneumonia surveillance system in the United States ( Table 4.5 ) ( Straus et al. 1996 ). The univariate OR for infection in current smokers compared with nonsmokers was 3.75 (95 percent CI, 2.27–6.17). However, in a multivariable logistic regression model, an effect from current smoking was observed only in those patients with no evidence of an underlying disease (OR = 7.49 [95 percent CI, 3.27–17.17]).

A study of Finnish twins (all zygosities) discordant for smoking reported that male current and former smokers were more likely to have evidence of ongoing infections with C. pneumoniae (IgA titer >40) than their male twins who had never smoked ( Table 4.5 ) ( von Hertzen et al. 1998a , b ). Antigen-specific lymphocyte responses to C. pneumoniae , but not to other Chlamydia antigens, also were decreased in the male smokers ( von Hertzen et al. 1998b ). No effects were observed in female twins. The authors interpreted the lymphocyte data as being consistent with Th -2 skewing of the immune response in males. The gender differences in these responses are not explained.

Data from several different types of studies have suggested a link between smoking and infection with Mycobacterium tuberculosis ( Table 4.5 ). A study of one million deaths from 1988–1990 in 98 urban and rural areas of China estimated that 11.3 percent of deaths from tuberculosis could be attributed to smoking ( Table 4.5 ) ( Liu et al. 1998 ). Exposure-response relationships with the number of cigarettes smoked per day and time since onset of smoking were observed in both urban and rural environments. However, a survey of the occurrence of positive tuberculin skin tests in a large nursing home population in Hong Kong ( Woo et al. 1996 ) failed to find an association with smoking ( Table 4.5 ). In contrast, three case-control studies provided evidence of an association. A nonpopulation-based, case-control study in Spain evaluated smoking as a risk factor for newly diagnosed tuberculosis ( Table 4.5 ) ( Alcaide et al. 1996 ), and found an estimated attributable risk of 48 percent (95 percent CI , 13–69). Moreover, the authors observed a strong exposure-response relationship with the number of cigarettes smoked per day and an additive effect from passive exposure to tobacco smoke. Two other case-control studies in the United States (both in Washington state) demonstrated associations between the duration of smoking and risk for newly diagnosed tuberculosis ( Buskin et al. 1994 ) and skin test conversion ( Anderson et al. 1997 ), but no association with the current number of cigarettes smoked per day ( Table 4.5 ).

Acute Upper and Lower Respiratory Illnesses with and Without Identification of Specific Pathogens

A large number of studies on the incidence of URI and LRI in relation to cigarette smoking were reviewed in the 1990 Surgeon General’s report on smoking and health ( USDHHS 1990 ), some of which are summarized in Table 4.6 . Although not provided in the text of the papers, attributable risk estimates for the effects of smoking ( Rockhill et al. 1998 ) can be calculated for several of the previously reviewed studies ( Table 4.6 ) ( Parnell et al. 1966 ; Finklea et al. 1971b ; Monto et al. 1975 ; Blake et al. 1988 ). Attributable risk estimates of URI for smokers were similar in studies from divergent populations: 31 percent (95 percent CI , 23–39) in student nurses ( Parnell et al. 1966 ) and 22 percent (95 percent CI, 12–30) and 29 percent (95 percent CI, 10–44) in two military trainee populations ( Finklea et al. 1971b ; Blake et al. 1988 ). A similar coherence was found for LRI ( Table 4.6 ) ( Parnell et al. 1966 ; Finklea et al. 1971b ). In the Tecumseh, Michigan, population-based cohort study ( Monto et al. 1975 ), smokers tended to have a higher incidence of serologically determined infections ( Table 4.6 ).

Studies on the association between smoking and the occurrence of acute upper respiratory illness (URI) and lower respiratory illness (LRI), with and without identification of specific pathogens.

Of three studies published since the 1990 report, two supported an association between smoking and acute respiratory illnesses ( Table 4.6 ) ( Cohen et al. 1993 ; Nicholson et al. 1996 ). The third study, which did not support this association ( Jaakkola and Heinonen 1995 ), was based entirely on self-reported illnesses. A study of volunteers who received an intranasal challenge with rhinovirus and coronavirus ( Table 4.6 ) ( Cohen et al. 1993 ) found an adjusted OR for infection in smokers compared with nonsmokers (virus isolation or serologic response at 28 days) of 2.03 (95 percent CI , 1.18–3.70). A prospective study of a community sample of people aged 60 through 90 years ( Nicholson et al. 1996 ) reported an adjusted OR associated with current smoking for complicated LRI of 1.47 (95 percent CI, 1.14–1.90).

Acute Respiratory Infections in Persons with Human Immunodeficiency Virus Infection

Respiratory infections are a main source of morbidity in persons with human immunodeficiency virus ( HIV ) infection. Several studies have evaluated cigarette smoking and risk for incident lower respiratory infections in persons infected with HIV ( Table 4.7 ).

Studies on the association between smoking and the occurrence of acute respiratory infections in persons with human immunodeficiency virus (HIV) infection.

A large observational cohort study with up to four years of follow-up found a CD4-adjusted relative hazard ( RH ) for bacterial pneumonia in HIV -infected current smokers of 1.57 (95 percent CI , 1.14–2.15) ( Table 4.7 ) ( Burns et al. 1996 ). No excess risk from tuberculosis or infection with Pneumocystis carinii ( P. carinii ) was observed. A second cohort study did not find an excess risk of bacterial pneumonia in HIV-infected patients who smoked when compared with infected patients who did not smoke ( Hirschtick et al. 1995 ). However, among HIV-infected patients with a CD4 count below 200/ mm 3 , smokers had an incidence of pneumonia more than three times higher (13.8/100 person-years compared with 4.0 in nonsmokers) ( Table 4.7 ). A cross-sectional study of a variety of infections within the past six months in HIV-positive and HIV-negative women with similar characteristics based on self-reporting documented an OR for pneumonia in smokers of 2.7 (95 percent CI, 1.2–5.9) ( Table 4.7 ) ( Flanigan et al. 1999 ). No other infections were associated with smoking. A study based on a retrospective evaluation of medical records found that the median time from the onset of HIV infection to a clinical infection with P. carinii was significantly shorter in smokers (9 months) than in nonsmokers (16 months) ( Nieman et al. 1993 ). Smoking did not appear to affect the time of onset of acquired immunodeficiency syndrome ( AIDS ) for non- Pneumocystis AIDS-defining conditions.

Evidence Synthesis

Since the publication of the 1990 Surgeon General’s report ( USDHHS 1990 ), the biologic basis for evaluating associations between cigarette smoking and acute respiratory infections has been strengthened, adding to the plausibility of an association of smoking with respiratory infection. Animal studies on the effects of nicotine demonstrate a mechanism for immune suppression. The effects of cigarette smoke on the regulation of the cytokine network and in producing a Th -2 bias in lymphocyte responses to antigens imply that smokers will have an increase in inflammation and a decrease in protective host responses to infections with respiratory pathogens.

A review of the evidence across all of the studies indicates that cigarette smokers, particularly current smokers, have an increased risk for an acute URI or LRI . The findings are generally consistent among studies and some provide evidence for dose-response with amount of smoking. When persons are classified as current or former smokers or lifetime nonsmokers, ORs generally have been above 1.5 for acute respiratory infections in smokers without an underlying illness compared with nonsmokers ( Tables 4.4 through 4.6 ). However, ORs as high as seven have been reported in at least one well-conducted study of Legionella infection ( Straus et al. 1996 ). The few studies that focused on persons with HIV infection documented a similar range of excess infection rates ( Table 4.7 ). When current smokers are classified by the number of cigarettes smoked per day, exposure-response relationships have been found in some studies. The lack of a standardized measure for current smoking makes the comparison of estimates from various studies difficult. Lower tar content of cigarettes is associated with a decrease in the incidence of acute respiratory illnesses ( Petitti and Friedman 1985b ), consistent with the exposure-response relationship observed with the amount smoked each day and with population-based studies showing a decreased incidence in former smokers when compared with current smokers ( Almirall et al. 1999a , b ; Nuorti et al. 2000 ). A range of potential confounding factors has been considered across the studies.

The evidence is less clear as to whether the risk associated with smoking varies for lower versus upper respiratory infections. In studies reporting an excess incidence of lower respiratory infections, infections tended to be in the heaviest smokers. Studies of military populations have produced conflicting results. A single study of persons aged 60 years or older ( Nicholson et al. 1996 ) indicated that smokers were more likely than nonsmokers to have a complicated LRI .

Finally, the available data do not provide a basis for identifying subgroups particularly susceptible to the smoking-induced risks of acute respiratory illnesses. Studies of HIV -infected persons suggest that the incremental incidence of disease is similar to that in non-HIV-infected people. One study did provide evidence that the effects of smoking on acute respiratory illnesses might be greatest in those most severely immunocompromised ( Hirschtick et al. 1995 ).

• The evidence is sufficient to infer a causal relationship between smoking and acute respiratory illnesses, including pneumonia, in persons without underlying smoking-related chronic obstructive lung disease.

Implications

There are numerous studies providing population attributable risk estimates of the effects of smoking on respiratory illness outcomes ( Table 4.8 ). Two of these estimates have limited generalizability because they were based on selected military populations ( Kark and Lebiush 1981 ; Kark et al. 1982 ). The estimate based on a surveillance system of invasive pneumococcal disease ( Nuorti et al. 2000 ) is indirectly useful, because it has to be assumed that in most of the cases studied the disease originated in the respiratory tract. Although this assumption is reasonable given the particular bacterium, no data on this point were given. Nonetheless, the 51 percent estimate indicates a large contribution to disease burden in the populations studied. The remaining estimates in Table 4.8 are the attributable fractions for smokers. Excluding the estimate with CIs including 1, estimates ranged from 19 to 63 percent. Because the various estimates are based on incidence density data as well as on cumulative incidence data, it is not possible to give a unifying interpretation (etiologic or excess fraction) for all of the estimates ( Greenland and Robins 1988 ). However, considering all of these estimates as “excess” cases ( Greenland 1999 ) of acute respiratory illness provides a maximum estimate of the excess burden that smoking imposes on the occurrence of these illnesses. In most cases, the estimated amount of excess cases is greater than 20 percent.

Estimates of attributable risk fractions for smoking and acute respiratory illness (ARI) in persons without chronic obstructive pulmonary disease.

From a public health standpoint, an argument could be made that additional studies on the broad question of smoking and acute respiratory illnesses are not needed. However, studies to assess the economic and social impacts of this association may still be useful, particularly if they establish common definitions of and criteria for acute respiratory conditions and smoking status. Ideally, these studies should provide data detailing current smoking patterns and smoking patterns for the five years before the study. Using open populations in these studies should make estimates of both population and smoking attributable fractions possible. Such studies must be large enough to provide precise estimates of these fractions and to take into account whatever confounders may be relevant. Small studies are not likely to be useful. National studies, such as the National Health and Nutrition Examination Survey, would be an ideal venue for addressing these components.

Finally, in the context of health care services, health care providers need to make all smokers aware of the implications of these data for their health. The effects of smoking on the incidence of acute respiratory diseases should be included in all health care messages to smokers.

Acute Respiratory Infections in Persons with Chronic Obstructive Pulmonary Disease and Asthma

The population-based Tecumseh study was one of the most extensive epidemiologic investigations examining the effects of cigarette smoking on acute respiratory infections in persons with and without chronic lung disease in the United States ( Monto et al. 1975 ; Monto and Ross 1977 , 1978 ). This multiyear study recruited several stratified random samples of families. During a one-year period, people participated in weekly telephone interviews to identify prospectively the occurrence of an acute respiratory illness. Each participant also underwent serial clinical, spirometric, and serologic examinations. Two definitions of an acute respiratory infection were used: self-reported acute respiratory symptoms and serology (a fourfold rise in serum antibody titer to selected respiratory pathogens).

The observed association between current smoking and self-reported acute respiratory infections was addressed in a series of study reports ( Table 4.9 ). The small sample sizes in subgroups resulted in wide CIs, complicating the interpretation of results. However, smoking has been associated with an increased risk for several indexes of illness: acute respiratory infections in healthy men, based on both self-reported and serologic evidence of infection ( Monto et al. 1975 ); serologic evidence of respiratory infections in women with or without chronic bronchitis ( Monto and Ross 1978 ); and acute, self-reported lower respiratory tract infections in men, especially in those with chronic bronchitis ( Monto and Ross 1977 ). However, not all of the analyses found smoking to be associated with a higher risk of acute respiratory infections in persons with chronic bronchitis ( Table 4.9 ).

Studies on the association between smoking and the risk of acute respiratory illness (ARI)— Results from the Tecumseh Study.

In the Tecumseh study, COPD , as indicated by chronic bronchitis or pulmonary function impairment, was itself associated with a greater risk of developing an acute respiratory infection ( Table 4.10 ), although the effects of smoking were stronger and more consistent among men. In men, the risk varied with the number of cigarettes smoked and the presence of chronic bronchitis, with the risk of an acute respiratory illness highest in heavy smokers of more than one pack per day with chronic bronchitis (relative risk [ RR ] = 1.63), followed by moderate smokers of approximately one and one-half packs per day (RR = 1.45), and nonsmokers (RR = 1.16). (The smoking categories were based on the sum of three reports measuring the number of cigarettes smoked per day: none equals zero packs, category 1 equals less than one pack, category 2 equals one to one and one-half packs, and category 3 equals one and one-half packs or more per day; moderate smokers were in the four to six packs category and heavy smokers were in the seven to nine packs category.) This pattern was not apparent in women.

Studies on the association between smoking, chronic obstructive pulmonary disease, and the risk of acute respiratory illness (ARI)—Results from the Tecumseh Study.

Many studies have documented a high prevalence of potentially pathogenic bacteria isolated from the sputum of persons with an exacerbation of COPD ( Tager and Speizer 1975 ; Fagon et al. 1990 ; Murphy and Sethi 1992 ; Monsó et al. 1995 ; Murphy et al. 2000 ; Voelkel and Tuder 2000 ). In most studies, the specific role of current cigarette smoking in acute infections was not examined. Soler and colleagues (1998) used bronchoscopy with a protected specimen brush to examine bacterial infections in 50 patients with severe COPD exacerbations requiring mechanical ventilation. The prevalence of a positive culture for gram-negative bacilli, including Pseudomonas species, was similar in former and current smokers (23 percent versus 32 percent). In contrast, a study of 91 ambulatory patients with an acute exacerbation of COPD demonstrated an association between current smoking and a greater risk for a quantitative sputum culture yielding H. influenzae ( OR = 8.16 [95 percent CI , 1.9–43]) ( Miravitlles et al. 1999 ).

A population-based, cross-sectional study from Norway examined the association between a clinical diagnosis of obstructive lung disease ( COPD or asthma) and serologic evidence of a respiratory viral infection (influenza A and influenza B viruses, para-influenza virus types 1–3, adenovirus, and respiratory syncytial virus [ RSV ]) ( Omenaas et al. 1996 ). The prevalence of a positive serology, indicating recent or past infections, was higher among persons with obstructive lung disease (74 percent) than among those with chronic respiratory symptoms (60 percent) or persons who were asymptomatic (48 percent). Compared with persons without evidence of infections, those with positive serology for RSV and influenza B virus had lower standardized forced expiratory volume in one second ( FEV 1 ) residuals (−0.61 and −0.54, respectively). For these viruses, an exposure-response relationship was observed between viral titers and FEV 1 residuals. The association between a positive RSV serology and FEV 1 residuals was of a greater magnitude in smokers (−0.93) than in former smokers (−0.65) or nonsmokers (−0.48), although the interaction between smoking and RSV infections was not significant. The investigators observed a similar pattern of results for influenza B virus serology (−1.02 among smokers, −0.46 among former smokers, and −0.30 among nonsmokers). Analyses were not carried out to assess the interaction between the joint effect of having obstructive lung disease and smoking, which would directly address the risk posed by smoking for viral infections among persons with COPD. The cross-sectional design precludes determining whether a viral infection reduces lung function or whether decreased lung function increases susceptibility to viral infections.

The impact of smoking on the risk of death from pulmonary infections among persons with COPD was examined in the population-based Copenhagen City Heart Study ( Prescott et al. 1995 ). In the cohort of 13,888 persons followed for 10 to 12 years, 214 persons died from COPD (8 percent of deaths). Of these deaths, 133 occurred in the hospital. Medical records were reviewed for 101 patients to determine whether death was due to a pulmonary infection. Compared with persons who died without pulmonary infections ( n = 51), those who died from a pulmonary infection (n = 38) had similar smoking statuses. Both groups also had similar prevalence rates of current smoking (75 percent of those without pulmonary infection versus 82 percent of those with infection) and current heavy smoking (53 percent for both), and a similar mean duration of smoking (36 years versus 40 years). In a Cox proportional hazard model that controlled for age, gender, and FEV 1 , daily tobacco use was related to the risk of death from a pulmonary infection ( RH = 1.4 per 10 grams of tobacco used; 95 percent CI , 1.04–1.80). When current smokers and lifetime nonsmokers were compared, smoking was not associated with an increased risk. Although a selection bias from examining a subset of COPD deaths cannot be excluded, the data strongly suggest a relationship between current smoking intensity and the risk of death from a pulmonary infection.

A population-based, case-control study demonstrated that cigarette smoking was a strong risk factor for invasive pneumococcal disease ( Nuorti et al. 2000 ). Moreover, both COPD and asthma were associated with a greater risk of pneumococcal infection ( OR = 3.4 [95 percent CI , 1.6–7.0] and OR = 2.5 [95 percent CI, 1.4–4.7]), respectively. In a multivariate analysis that included smoking variables and demographic characteristics, neither disease was associated with a greater risk of pneumococcal infection. Other investigators also found that COPD was associated with a greater risk of pneumococcal pneumonia and bronchitis ( RR = 1.96 [95 percent CI, 1.51–2.56]) ( Simberkoff et al. 1986 ).

A recent report from the Lung Health Study evaluated the effects of the frequency of self-reported nonspecific LRI that resulted in a visit to a physician on the annual rate of change in FEV 1 levels in participants with mild COPD ( Kanner et al. 2001 ). The number of illness episodes was few in this population, averaging about 0.24 per year for the study population as a whole. Illnesses in the year before the study and female gender were the best predictors of subsequent illnesses, but these two variables explained only 8.4 percent of the total variation. However, during the five-year observation period, participants who were continuous smokers had significantly more illnesses than those who had quit smoking for the entire five-year period ( p = 0.0003). Intermittent smokers had illness rates that fell between the continuing smoker and sustained quitter groups. In this study, nonspecific lower respiratory tract illnesses that resulted in a physician visit had an adverse effect on the annual rate of change in lung function only in those who continued to smoke. The illness effect on changes in the FEV 1 was not seen in sustained quitters ( Kanner et al. 2001 ).

Evidence from Antibiotic Trials

The potential etiologic role of smoking in acute respiratory infections among persons with COPD can be assessed indirectly by examining data from clinical trials of the efficacy of antibiotic treatments for acute exacerbations of COPD. If a bacterial infection plays an important causal role in the acute exacerbation of COPD, characterized by increases in coughing, sputum production, wheezing, dyspnea (difficulty breathing and shortness of breath), and/or airflow obstruction, then treatment with appropriate antibiotics should accelerate symptomatic resolution. Current smoking might decrease the efficacy of antibiotic therapy, and past smoking might influence the risk for infections by determining the level of lung function. This section considers the evidence from trials of antibiotics in exacerbations of COPD. These trials are potentially informative as to the role of bacteria in causing these exacerbations and whether current smoking modifies the effects of antibiotics. Furthermore, they offer evidence on the role of bacteria in causing the exacerbations and provide insights into a causal pathway that begins with smoking, is followed by the onset of COPD, and finally leads to an increased risk for a bacterial infection. However, these studies do not address the role of viruses, which cause the majority of acute upper respiratory infections in the general population.

Beginning in 1957, randomized placebo-controlled clinical trials have examined the efficacy of antibiotics in acute exacerbations of chronic bronchitis characterized by coughing, sputum production, wheezing, or dyspnea ( Table 4.11 ). Studies have examined patients hospitalized for acute exacerbations of chronic bronchitis ( Elmes et al. 1965 ; Petersen et al. 1967 ; Pines et al. 1968 , 1972 ; Nicotra et al. 1982 ) and persons treated as outpatients ( Elmes et al. 1957 ; Berry et al. 1960 ; Fear and Edwards 1962 ; Anthonisen et al. 1987 ; Jørgensen et al. 1992 ; Sachs et al. 1995 ). Except for one single-blind study ( Petersen et al. 1967 ), all trials were double-blind. Several trials demonstrated that antibiotic treatments reduced respiratory symptoms ( Elmes et al. 1957 ; Anthonisen et al. 1987 ), physician-assessed clinical severity ( Berry et al. 1960 ; Pines et al. 1968 , 1972 ), work loss ( Elmes et al. 1957 ), and sputum purulence ( Pines et al. 1972 ). Other trials found that antibiotic treatment improved peak expiratory flow rates ( Elmes et al. 1965 ; Anthonisen et al. 1987 ). Conversely, other clinical trials showed no effects of antibiotics on respiratory symptoms ( Fear and Edwards 1962 ; Sachs et al. 1995 ), clinical severity ( Elmes et al. 1965 ; Jørgensen et al. 1992 ), sputum volume or purulence ( Elmes et al. 1965 ; Petersen et al. 1967 ; Nicotra et al. 1982 ), or peak expiratory flow or other pulmonary function testing ( Petersen et al. 1967 ; Pines et al. 1972 ; Nicotra et al. 1982 ; Jørgensen et al. 1992 ; Sachs et al. 1995 ).

Studies on the efficacy of antibiotic treatment in acute exacerbations of chronic obstructive pulmonary disease.

In a randomized controlled trial that has been widely cited, Anthonisen and colleagues (1987) tested three different antibiotic treatments (trimethoprim-sulfamethoxazole, ampicillin, or doxycycline) against a placebo. In contrast to earlier studies, all patients had a clinical diagnosis of COPD and a FEV 1 /forced vital capacity ( FVC ) ratio of less than 70 percent. Nearly all patients had a history of smoking cigarettes (95 percent), with 21 percent indicating current smoking. After two weeks of standard treatments for COPD, patients received an antibiotic or placebo for acute exacerbations characterized by increased dyspnea, sputum volume, and sputum purulence. In the trial, 173 patients had 362 exacerbations. Treatment success, defined as symptom resolution within 21 days, was significantly more apparent in the antibiotic group than in the placebo group (68 percent versus 55 percent of exacerbations). The duration of antibiotic-treated exacerbations was also shorter (averaging 2.2 days less). When the analysis was restricted to first exacerbations, the results were similar. Increases in peak expiratory flow rates were also greater in patients treated with antibiotics.

In the largest clinical trial, Jørgensen and colleagues (1992) randomly assigned 278 general practice patients with acute exacerbations of chronic bronchitis to amoxicillin or a placebo. Smoking history was not reported. Based on blinded physician assessments, there were no differences in clinical outcomes between the amoxicillin (63 percent) or placebo (64 percent) groups after eight days. Although peak expiratory flows improved in all patients, there were no differences between the groups.

These studies are limited by a small sample size and low statistical power, which likely reduced the ability to detect antibiotic efficacy. One study of hospitalized patients included patients with radiographic infiltrates, suggesting pneumonia ( Elmes et al. 1965 ); other studies of inpatients did not explicitly exclude persons with pneumonia ( Petersen et al. 1967 ; Pines et al. 1968 ). Inclusion of patients with pneumonia would likely inflate the apparent efficacy of antibiotics in acute COPD exacerbations. Although most patients with chronic bronchitis have smoked cigarettes, most studies did not report smoking histories ( Elmes et al. 1957 , 1965 ; Berry et al. 1960 ; Fear and Edwards 1962 ; Petersen et al. 1967 ; Pines et al. 1972 ; Nicotra et al. 1982 ; Anthonisen et al. 1987 ; Jørgensen et al. 1992 ). Even if the efficacy of antibiotics were to suggest that smoking plays a causal role in acute bacterial infections, none of the studies separated remote effects from immediate effects of cigarette smoking on the risk of infection. Remote effects of smoking on acute respiratory infections are those mediated through chronic airway obstruction, mucous hyper-secretion, and impaired mucociliary clearance; immediate effects are the alteration of immune and inflammatory functions ( USDHHS 1990 ).

The limitations of low study power were addressed by a meta-analysis that combined 11 of the randomized controlled trials ( Elmes et al. 1957 , 1965 ; Berry et al. 1960 ; Fear and Edwards 1962 ; Petersen et al. 1967 ; Pines et al. 1968 , 1972 ; Nicotra et al. 1982 ; Anthonisen et al. 1987 ; Jørgensen et al. 1992 ; Sachs et al. 1995 ). Because the studies used many different outcome measures, Saint and colleagues (1995) calculated a standardized effect size. The overall summary effect size, which was the difference between mean outcomes in the antibiotic and placebo groups divided by the pooled standard deviation, was 0.22 (95 percent CI , 0.10–0.34), indicating a small benefit from antibiotics. Combining the six trials that measured peak expiratory flow rates yielded a summary improvement of 10.75 liters per minute with antibiotic treatments (95 percent CI, 4.96–16.54 liters per minute).

Observational data also support the efficacy of antibiotics in treating acute exacerbations of COPD . A nonrandomized clinical trial examined the efficacy of cefaclor in 106 outpatients with acute exacerbations of chronic bronchitis ( Cazzola et al. 1991 ). In this trial all patients were current cigarette smokers, and potentially pathogenic bacteria were isolated from the sputum of most participants. On the basis of clinical examinations, the majority of patients were considered to be cured (75.5 percent) or improved (17 percent). There was no significant change in pulmonary function. A major limitation of this trial is the absence of a placebo control group. Taken together with randomized trials, this trial suggests the efficacy of antibiotics for current smokers with acute exacerbations of chronic bronchitis.

A cohort study examined 173 patients who had 362 emergency department visits for acute exacerbations of COPD during an 18-month period ( Adams et al. 2000 ). For patients to be included, the investigators required evidence of airway obstruction verified by pulmonary function testing during the previous three years. Of 1,754 patient visits to the emergency department for an acute COPD exacerbation, 1,392 were excluded. The most common reason for exclusion was no record of recent pulmonary function testing (1,122 visits). Although antibiotics were prescribed preferentially to patients with more severe exacerbations, antibiotic administration was associated with a lower proportion of recurrent emergency department visits during the ensuing 14 days (19 percent versus 32 percent, p <0.001). Active cigarette smoking was associated with a greater risk of relapse ( OR = 4.45 [95 percent CI , 2.09–10.13]), which suggests that smoking may increase the severity of an acute exacerbation. Selection bias, introduced by excluding many emergency department visits by patients without recent pulmonary function testing, limits any conclusions based on this study.

Prevention of COPD Exacerbation. Randomized trials of antibiotic prophylaxis in patients with COPD, conducted mostly in the 1950s and 1960s, provide evidence on cigarette smoking and the risk of respiratory infections in persons with chronic lung disease. If data indicate that antibiotics could prevent exacerbations of COPD, the indication would be that bacterial infection plays a role in COPD exacerbation. Because smoking is the principal cause of COPD, smoking would then have been shown to act on the causal pathway to acute bacterial respiratory infections in this patient group.

Placebo-controlled, randomized clinical trials have tested a variety of antibiotics, including tetracycline, penicillin, sulfonamides, and combination agents ( Table 4.12 ). Preventive treatment with antibiotics was administered for 2 weeks to 20 months, with treatment in most trials lasting 4 to 6 months during the winter months ( McVay and Sprunt 1953 ; Buchanan et al. 1958 ; Cherniack et al. 1959 ; Francis and Spicer 1960 ; Pirdie et al. 1960 ; Davis et al. 1961 , 1965 ; Francis et al. 1961 ; Johnston et al. 1961 , 1969 ; Fear and Edwards 1962 ; Medical Research Council 1966 ; Pines 1967 ; Liippo et al. 1987 ). Only three trials reported smoking status: 79 to 95 percent ever smoked, and 29 to 79 percent were current smokers ( Medical Research Council 1966 ; Johnston et al. 1969 ; Liippo et al. 1987 ).

Studies on the efficacy of antibiotic preventive treatment of persons with chronic obstructive pulmonary disease.

Of the various study outcomes examined, preventive antibiotics have demonstrated the most consistent efficacy in reducing missed workdays among persons with chronic bronchitis ( Table 4.12 ). In two early large-scale, well-conducted clinical trials, Francis and Spicer (1960) and Francis and colleagues (1961) demonstrated that the prophylactic administration of tetracycline decreased the number of lost workdays by about 50 percent. The benefits of penicillin were less clear. A later clinical trial conducted by the Medical Research Council (1966) of Great Britain also suggested that oxytetracycline reduced the duration of missed workdays (22 percent reduction, 95 percent CI , 55 percent reduction to 4 percent increase, but the CI did not exclude a lack of benefit). Smaller or less well-controlled trials suggested that antibiotic prophylaxis reduced lost workdays ( Pirdie et al. 1960 ; Johnston et al. 1961 , 1969 ).

The salutary impact of prophylactic antibiotics on other clinical outcomes has been less consistent. Some clinical trials demonstrated that preventive antibiotics reduced acute exacerbations of chronic bronchitis ( McVay and Sprunt 1953 ; Buchanan et al. 1958 ; Cherniack et al. 1959 ; Davis et al. 1961 ; Pines 1967 ), whereas others showed no benefit ( Francis and Spicer 1960 ; Francis et al. 1961 ; Davis et al. 1965 ; Medical Research Council 1966 ; Johnston et al. 1969 ; Liippo et al. 1987 ). Despite reducing lost workdays, the two early British trials found that antibiotics did not reduce the incidence of symptomatic exacerbation, suggesting an effect mostly on symptom severity or duration ( Francis and Spicer 1960 ; Francis et al. 1961 ). Although patients receiving prophylactic antibiotics may experience subjective ( McVay and Sprunt 1953 ) or clinical improvements as determined by physicians ( Fear and Edwards 1962 ), these benefits were not always observed ( Davis et al. 1961 , 1965 ; Johnston et al. 1961 ). In all trials that examined pulmonary function, antibiotics were not associated with any benefit ( Francis and Spicer 1960 ; Pirdie et al. 1960 ; Davis et al. 1961 , 1965 ; Medical Research Council 1966 ; Johnston et al. 1969 ; Liippo et al. 1987 ). Taken together, the conflicting evidence does not allow for a clear conclusion regarding the efficacy of prophylactic antibiotics in persons with COPD .

Randomized, placebo-controlled clinical trials tested the efficacy of an oral vaccination against formalin-killed H. influenzae bacteria in patients with COPD ( Clancy et al. 1985 , 1990 ; Lehmann et al. 1991 ; Tandon and Gebski 1991 ). The efficacy of vaccinations would support a role for bacterial infections in acute exacerbations of COPD, with smoking acting on the causal pathway. Most persons in these trials reported having ever smoked cigarettes (78 to 91 percent), and fewer indicated current smoking (10 to 73 percent). In an early trial of 50 patients, Clancy and colleagues (1985) reported a tenfold reduction in the cumulative incidence of acute episodes of bronchitis after oral immunizations (6 percent in the placebo group versus 63 percent in the immunized group, RR = 0.10 [95 percent CI , 0.014–0.64]). The same investigators demonstrated in a subsequent controlled trial ( n = 40) a reduction in episodes of acute wheezy bronchitis (30 percent versus 80 percent, RR = 0.38 [95 percent CI, 0.19–0.76]) and a decreased use of antibiotics (25 percent versus 60 percent, RR = 0.42 [95 percent CI, 0.18–0.96]) ( Clancy et al. 1990 ). The study also suggested a reduction in the cumulative incidence of acute bronchitis exacerbations (50 percent versus 80 percent, RR = 0.63 [95 percent CI, 0.38–1.02]). Compared with the placebo group, the group that received oral vaccinations had no reductions in symptom duration or reports of dyspnea, and no improvement in FEV 1 . The RRs and CIs for both studies by Clancy and colleagues (1985 , 1990) were not published; the calculations were based on data available in the papers. A similar trial conducted in the highlands of Papua, New Guinea, enrolled 62 adults with chronic bronchitis ( Lehmann et al. 1991 ). Oral vaccinations were associated with a reduced risk of acute bronchitis (RR for placebo group = 1.92 [95 percent CI, 1.58–2.26]). There was no impact on the risk of pneumonia (RR = 0.66 [95 percent CI, 0.23–1.09]). In a similar study of 64 persons with chronic bronchitis, an oral vaccination was associated with a reduced risk of acute lower respiratory tract infections ( OR = 0.4 [95 percent CI, 0.2–0.9]) and improved general well-being assessed by a visual analog scale (median score 5.0 versus 2.5) ( Tandon and Gebski 1991 ).

Large-scale randomized controlled trials also have examined the efficacy of an oral vaccination with OM-85 BV, an antigenic extract of eight microorganisms commonly found in the respiratory tract that has been subjected to alkaline lysis. These agents are thought to activate lung macrophages and enhance antigen presentation to T lymphocytes ( Collet et al. 1997 ). For the following studies, the RRs and CIs were calculated based on data available in the papers. In a study by Orcel and colleagues (1994) , 354 adults aged 65 years or older with chronic bronchitis were randomly selected to receive OM-85 BV or a placebo. Of these patients, 51 percent had ever smoked and 25 percent were current smokers. Among the 290 patients analyzed, the cumulative incidence of acute lower respiratory tract infections was lower in the active treatment group (35 percent versus 52 percent, RR = 0.67 [95 percent CI , 0.51–0.88]). More recently, Collet and colleagues (1997) conducted a multicenter trial that enrolled patients with COPD , a history of heavy smoking (20 or more pack-years 1 ), and airway obstruction ( FEV 1 less than 70 percent predicted). There was no difference in the cumulative incidence of acute symptomatic exacerbation between the placebo group and the treatment group (44.5 percent versus 43.7 percent, RR = 1.02 [95 percent CI, 0.81–1.28]). The risk of hospitalization for a respiratory problem was lower in the treatment group (16.2 percent versus 23.2 percent, RR = 0.70 [95 percent CI, 0.46–1.06]). Moreover, the average duration of hospitalization for a respiratory problem was lower in the oral vaccination group (1.5 versus 3.4 days per person). The treatment had no impact on FEV 1 levels, which declined 5.5 mL in the treatment group and 7.5 mL in the placebo group, or on a health-related quality-of-life index (health status questionnaire SF-36 physical and mental component summary scores and eight subscales). Although the evidence is mixed, the oral vaccination trials suggest that bacterial infections play a role in COPD exacerbations and that smoking, as the major cause of COPD, acts on the causal pathway to acute infections.

Antibiotics and Acute Bronchitis. Clinical trials assessing the efficacy of antibiotic treatments for acute bronchitis also indirectly addressed the role of smoking in acute respiratory infections among persons with chronic lung disease ( Howie and Clark 1970 ; Stott and West 1976 ; Franks and Gleiner 1984 ; Williamson 1984 ; Brickfield et al. 1986 ; Dunlay et al. 1987 ; Scherl et al. 1987 ; Hueston 1994 ; Verheij et al. 1994 ; King et al. 1996 ). Although these clinical trials excluded persons with overt COPD , the prevalence of current smoking among patients was substantial (32 to 55 percent). In three trials, at least 50 percent of patients indicated current smoking ( Howie and Clark 1970 ; Franks and Gleiner 1984 ; Hueston 1994 ). Other reviews have established the strong association between current smoking and a decrement in pulmonary function ( USDHHS 1990 ; see “Chronic Respiratory Diseases” later in this chapter). Epidemiologic studies also indicate a higher risk of acute bronchitis in persons with COPD ( Monto and Ross 1977 , 1978 ). As a consequence, these clinical trials of acute bronchitis likely included persons with smoking-related airway obstruction.

Taken together, these randomized, double-blind, controlled clinical trials suggest that antibiotic treatments provide a small clinical benefit compared with a placebo ( Howie and Clark 1970 ; Stott and West 1976 ; Franks and Gleiner 1984 ; Williamson 1984 ; Brickfield et al. 1986 ; Dunlay et al. 1987 ; Scherl et al. 1987 ; Hueston 1994 ; Verheij et al. 1994 ; King et al. 1996 ). A meta-analysis of these clinical trials indicated that antibiotic treatments were associated with a duration of cough and sputum production that was one-half day shorter ( Bent et al. 1999 ). The efficacy of antibiotics supports a causal role of bacterial infections in acute bronchitis.

Of the five clinical trials that used current smoking status to stratify analyses of clinical outcomes ( Franks and Gleiner 1984 ; Brickfield et al. 1986 ; Dunlay et al. 1987 ; Verheij et al. 1994 ; King et al. 1996 ), all but one found no evidence of an effect modification from smoking ( Brickfield et al. 1986 ). All of the studies found a similar salutary effect from antibiotics on the duration of respiratory symptoms in both smokers and nonsmokers ( Franks and Gleiner 1984 ; Brickfield et al. 1986 ; Dunlay et al. 1987 ; Verheij et al. 1994 ; King et al. 1996 ). In a randomized, placebo-controlled trial of erythromycin for acute bronchitis involving 50 patients from a family practice clinic, antibiotics appeared to attenuate the duration of coughing and sputum production only among non-smokers ( Brickfield et al. 1986 ). Although these studies are limited by low power for stratified analysis, the overall evidence suggests no difference in antibiotic efficacy between smokers or nonsmokers.

These findings suggest that the incidence of bacterial infection as a cause of acute bronchitis is similar in smokers and nonsmokers. As a consequence, these studies provide indirect evidence that current smoking does not cause acute bacterial bronchitis in persons who, on average, are likely to have decreased pulmonary function. A major limitation of these studies is the absence of any evaluation of viral respiratory infections.

Although previous Surgeon General’s reports have examined the effects of smoking on acute respiratory infections ( USDHHS 1990 , 1994 ), the impact of smoking on persons with a preexisting chronic lung disease was not previously reviewed. The preponderance of evidence presented in this section implicates smoking as a cause of acute respiratory infections among persons with COPD . The Tecumseh study indicated that COPD predisposes smokers to a greater risk of acute respiratory infections, and more recent data confirm that COPD is strongly associated with the development of invasive pneumococcal disease ( Nuorti et al. 2000 ). Although the epidemiologic data are not consistent across studies and study outcomes (i.e., self-reported acute respiratory infection, serologic evidence, pulmonary function decrement, and death from respiratory infection), controlled clinical trials have established the efficacy of antibiotics in treating acute COPD exacerbations. Clinical trials of antibiotics as a prophylaxis against acute infections yielded conflicting results and did not clearly establish efficacy in persons with COPD. The evidence did not clearly establish efficacy in persons with COPD, or whether smoking increases the frequency of acute bacterial bronchitis or modifies the effects of antibiotics in persons with reduced lung function. The oral vaccination trials indicated a reduction in the risk of acute infections. However, none of these studies explicitly evaluated the interaction between COPD and smoking, which would directly address the specific effects of smoking on acute respiratory infections in persons with chronic lung diseases.

Taken together, the epidemiologic and clinical trial evidence indicates that smoking probably acts on the causal pathway to an acute respiratory infection in persons with COPD . However, studies did not clearly separate the risk from remote effects of cigarette smoking (mediated by chronic airway obstruction and its attendant complications) from the immediate effects (through the alteration of immune or inflammatory functions). In vitro and in vivo studies support a biologic basis for the immediate adverse impact of smoking on acute respiratory infections.

The data also support an exposure-response relationship between smoking intensity and the risk of chronic bronchitis ( Monto and Ross 1978 ) and the risk of self-reported acute lower respiratory tract infections among persons with chronic bronchitis ( Monto and Ross 1978 ). For other outcome measures, exposure-response relationships have not been clearly demonstrated ( Monto and Ross 1977 ). One investigation demonstrated an association between smoking intensity and the risk of death from an infection among persons with COPD ( Prescott et al. 1995 ).

The evidence supports the causal role of cigarette smoking in acute asthma exacerbations, and acute respiratory viral infections are an important cause of asthma exacerbations. As a consequence, smoking may precipitate an exacerbation by promoting a viral infection. However, evidence does not directly address this possible mechanism, and further research is needed to clarify the precise impact of smoking on acute asthma.

• Conclusions
• The evidence is suggestive but not sufficient to infer a causal relationship between smoking and acute respiratory infections among persons with preexisting chronic obstructive pulmonary disease.
• In persons with asthma, the evidence is inadequate to infer the presence or absence of a causal relationship between smoking and acute asthma exacerbation.

Both COPD and asthma are chronic respiratory conditions associated with substantial morbidity, activity limitation, and economic costs. Although sufficient data exist to infer a causal relationship between smoking and an increased risk for acute respiratory infections in persons without chronic respiratory diseases, effects in persons with chronic lung diseases are less clearly established. Further research should specifically evaluate the impact of current smoking status on acute respiratory infections among persons with COPD and asthma. Particularly in persons with COPD, the effects of past and current smoking should be evaluated both separately and together. The effects of current and past smoking intensity also should be examined.

Conclusive data confirming the health care costs of smoking-related respiratory infections would place the problem in a larger public health context. Clinical practice guidelines could then incorporate more precise information about the potential benefits of smoking cessation.

• Chronic Respiratory Diseases

Chronic respiratory diseases are a heterogeneous group of disorders that affect mainly the conducting airways and alveoli, two main components of the respiratory system. A major function of the airways is to conduct air to the alveoli, also known as the lung parenchyma, where gas exchange occurs. There, oxygen is taken up by red blood cells, and carbon dioxide is removed from the bloodstream. In addition, the airways provide defenses against inhaled particles and other agents that impact the airway walls.

Past reports of the Surgeon General on active cigarette smoking and chronic respiratory diseases have emphasized respiratory symptoms, lung function, and COPD . Key conclusions of those reports relevant to these topics are summarized in Table 4.13 . Although these topics continue to be important public health concerns and are updated in this review, this report also addresses other chronic respiratory diseases including diseases of the airways, such as asthma, and diffuse parenchymal lung diseases, such as pulmonary fibrosis. The rationale for broadening the scope of diseases discussed in this report is based on a growing body of research on associations of cigarette smoking with other chronic respiratory diseases. The potential for synergism between cigarette smoking and specific occupational exposures, which was reviewed in the 1985 Surgeon General’s report ( USDHHS 1985 ), is not considered in this report.

Conclusions from previous Surgeon General’s reports concerning smoking as a cause of chronic respiratory diseases.

Because of the extensive literature reviews in previous Surgeon General’s reports on chronic respiratory diseases, this section is limited largely to research published between 1989 and January 2000. The search strategy used to identify references in the MEDLINE database included smoking as a major MEDLINE term, or smoking as a descriptor with tobacco or smoking in the title field. These terms were then linked to lung growth and development, lung function, respiratory symptoms, obstructive lung diseases, asthma, and pulmonary fibrosis. In addition, tables of contents were reviewed from two publications, American Journal of Respiratory and Critical Care Medicine and Thorax , for issues published through April 2000.

The organization of this review follows lung growth and development through developmental periods (i.e., childhood versus adulthood) during which time the various respiratory diseases become clinically apparent. The available evidence suggests that the development of chronic respiratory diseases, particularly chronic airflow obstruction, may result from impaired lung development and growth, a premature onset of declining lung function, an accelerated decline in lung function, or any combination of these conditions ( Figure 4.1 ).

Theoretical curves depicting varying rates of decline of forced expiratory volume in one second (FEV 1 ). Note: Curves A and B represent never smokers and smokers, respectively, declining at normal rates. Curve C shows increased declines without the development (more...)

Airway development in utero, alveolar proliferation during the first 12 through 24 months of life ( Burri 1997 ), and lung growth to adulthood are critical to the level of mechanical functioning of the lungs. Impaired growth in utero from exposure to maternal smoking may begin a process that predisposes the infant to chronic respiratory diseases in childhood or adulthood. Exposure to secondhand smoke in infancy and childhood, and active smoking during childhood and adolescence, further contribute to impaired lung growth and the risk of developing respiratory diseases ( Fletcher et al. 1976 ; Samet et al. 1983 ; USDHHS 1984 ; Tager et al. 1988 ; Sherrill et al. 1991 ; Helms 1994 ; Samet and Lange 1996 ). Active smoking in adulthood leads to an accelerated decline of FEV 1 in some smokers and ultimately to the development of clinically apparent COPD ( USDHHS 1984 ).

Lung Development In Utero

Although measuring lung function during infancy to detect in utero effects presents many challenges and is an evolving technique, during the past decade our knowledge about the effects of maternal smoking during pregnancy has grown ( Dezateux and Stocks 1997 ; Morgan and Martinez 1998 ). Studies have consistently documented evidence of impaired lung function in early infancy following in utero exposure to maternal smoking ( Table 4.14 ) ( Young et al. 1991 ; Hanrahan et al. 1992 ; Tager et al. 1995 ; Stick et al. 1996 ; Lødrup Carlsen et al. 1997 ; Hoo et al. 1998 ; Dezateux et al. 1999 ; Milner et al. 1999 ). A number of measures of ventilatory function have been used, including (1) measures of expiratory flow: maximal flow at functional residual capacity ( V max FRC ) and the ratio of time to peak tidal expiratory flow to expiratory time ( tPTEF/tE ); (2) airway resistance and respiratory system conductance; and (3) respiratory system compliance. In addition, bronchial responsiveness to pharmacologic agents has been measured in a smaller number of studies ( Young et al. 1991 ; Clarke et al. 1995 ).

Studies on the association between maternal smoking during pregnancy and infant lung function.

To determine the effects of in utero exposures to maternal smoking, separate from later exposures to secondhand smoke and lower respiratory tract infections, pulmonary function tests have been performed in healthy infants soon after birth and even before hospital discharge ( Stick et al. 1996 ; Lødrup Carlsen et al. 1997 ; Hoo et al. 1998 ; Milner et al. 1999 ). Three studies that looked at examinations conducted before hospital discharge identified decrements in tPTEF/tE in relation to maternal smoking during pregnancy ( Stick et al. 1996 ; Lødrup Carlsen et al. 1997 ; Hoo et al. 1998 ). Instead of using a measure of airflow, Milner and colleagues (1999) measured respiratory system conductance and respiratory system compliance and found decrements in these parameters that differed between male and female infants ( Table 4.14 ). An inverse dose-response relationship between the number of cigarettes smoked per day during pregnancy and the level of pulmonary function was found in two of the investigations ( Stick et al. 1996 ; Lødrup Carlsen et al. 1997 ).

Further evidence for an adverse effect from maternal smoking during pregnancy has been found in infants who had pulmonary function measurements later in infancy but before having any LRI ( Young et al. 1991 ; Hanrahan et al. 1992 ; Tager et al. 1995 ; Dezateux et al. 1999 ). Young and colleagues (1991) measured pulmonary function and airway hyper-responsiveness to histamine in 63 healthy infants from a prenatal clinic in Perth, Australia. The infants were categorized into four groups on the basis of a family history of asthma and parental cigarette smoking during pregnancy, but prenatal and postnatal exposures to cigarette smoke could not be separated. At a mean age of 4.5 weeks, rates of forced expiratory flow ( FEF ) did not differ among the four groups. However, airway responsiveness was greater in infants whose parents had smoked during pregnancy.

An increased risk of lower respiratory tract illnesses, including wheezing, and subsequent reductions in expiratory airflow and airway hyperresponsiveness during infancy may be consequences of maternal smoking during pregnancy ( Martinez et al. 1988 ; Stick et al. 1991 ; Tager et al. 1993 ; Clarke et al. 1995 ; Dezateux et al. 1999 ). Martinez and colleagues (1988) measured pulmonary function in 124 infants from Tucson, Arizona, before any lower respiratory tract illness had occurred, and found that infants whose total respiratory conductance was in the lowest third of the group had an increased risk of a subsequent wheezing illness ( OR = 3.7 [95 percent CI , 0.9–15.5]). In a sample of 97 infants from the East Boston, Massachusetts, Neighborhood Health Center, Tager and colleagues (1993) found an association between maternal smoking during pregnancy and an elevated risk for lower respiratory tract illnesses (OR = 1.47 [95 percent CI, 1.08–1.99]). Clarke and colleagues (1995) conducted pulmonary function studies on 79 healthy infants approximately one month of age and followed them during their first year of life. Lower expiratory airflow was associated with a wheezing illness in boys but not in girls, and bronchial hyperreactivity was associated with a wheezing illness in girls but not boys. Dezateux and colleagues (1999) found a significantly higher expiratory airway resistance before there was any evidence of a lower respiratory tract illness in 28 infants who had developed at least one subsequent wheezing illness by one year of age or less, compared with 73 infants who did not have a wheezing illness.

The decrement in pulmonary function associated with in utero exposure to tobacco smoke that is detectable at birth and throughout infancy may persist across childhood and into adulthood. In a cross-sectional survey, Cunningham and colleagues (1994) measured pulmonary function in 8,863 children aged 8 through 12 years from 22 North American communities. In multivariate analyses the children whose mothers reported smoking during pregnancy had significantly lower FEFs and reductions in FEV 0.75 and FEV 1 / FVC , compared with the children of mothers who did not smoke during pregnancy. After adjusting for maternal smoking during pregnancy, current maternal smoking was not associated with a significant decrement in lung function. Gilliland and colleagues (2000) examined the relationship between maternal smoking and pulmonary function among 3,357 school children (grades 4, 7, and 10) living in 12 southern California communities. After adjusting for secondhand smoke exposure and other potential confounders, maternal smoking during pregnancy was associated with significant decrements in peak expiratory flows, maximum midexpiratory flows, and FEFs at 75 percent of FVC, but not in FEV 1 levels.

These findings consistently show the effects of maternal smoking during pregnancy, including impaired pulmonary function and lower respiratory tract illnesses during infancy and childhood. Evidence for a causal role of maternal smoking is further strengthened by the dose-response relationship between maternal smoking during pregnancy and the magnitude of decrements in pulmonary function ( Stick et al. 1996 ; Lødrup Carlsen et al. 1997 ). Because these studies have been restricted to healthy full-term infants, it is unlikely that the findings are a result of other factors that may adversely affect in utero development including poor maternal nutrition, alcohol use, or the intake of other potentially toxic agents.

In utero exposure to maternal smoking may be associated with lower respiratory tract illnesses in childhood, and the subsequent risk for chronic respiratory diseases in adulthood through its effect on birth weights. Lower birth weight has been associated with reduced lung function in childhood. Data on the relationship between birth weight and adult lung function also provide similar indirect evidence ( Chan et al. 1989 ; Barker et al. 1991 ; Rona et al. 1993 ). Maternal smoking during pregnancy has been associated with decreased birth weights (see Chapter 5 , “Reproductive Effects”), and several studies indicate that birth weight is directly related to the level of expiratory airflow during childhood ( Chan et al. 1989 ; Rona et al. 1993 ) and adulthood ( Barker et al. 1991 ). Furthermore, self-reports of childhood lower respiratory tract illnesses are associated with chronic airflow obstruction in adulthood ( Berglund et al. 1999 ).

• The evidence is sufficient to infer a causal relationship between maternal smoking during pregnancy and a reduction of lung function in infants.
• The evidence is suggestive but not sufficient to infer a causal relationship between maternal smoking during pregnancy and an increase in the frequency of lower respiratory tract illnesses during infancy.
• The evidence is suggestive but not sufficient to infer a causal relationship between maternal smoking during pregnancy and an increased risk for impaired lung function in childhood and adulthood.

Implication

Although the biologic basis for impaired infant lung function from maternal smoking during pregnancy is not yet fully understood, the causal link provides yet another strong rationale for smoking cessation during pregnancy.

Pathogenesis of Smoking-Induced Lung Injury

The rate of expiratory airflow depends on elastic recoil forces of the alveoli and on the diameter of the small airways. Complex interactions between smoking-caused changes in the structure and function of small airways and lung parenchyma result in the physiologic finding of chronic airflow limitation ( Wright 1992 ; Thurlbeck 1994 ). The literature relevant to understanding the mechanisms of smoking-induced COPD has grown substantially in recent years, and points to a complex interplay among a number of biologic processes including oxidative stress, inflammation, protease-antiprotease imbalances, repair processes, and the genetic variations that control these processes ( Figure 4.2 ) ( Sandford et al. 1997 ; Barnes 1999 ; MacNee and Rahman 1999 ). The inhalation of cigarette smoke exposes the lungs to high concentrations of oxidant agents and free radicals, which decrease the antioxidant capacity that normally protects epithelial cells from oxidant injury ( Repine et al. 1997 ; Rahman and MacNee 1999 ). Moreover, several enzymes found in the lungs generate reactive oxygen molecules that may contribute further to the oxidative stress in the lungs. Genetic variations that alter the function of enzymes that generate reactive oxygen molecules, or that affect the activity of antioxidant enzymes, may determine individual susceptibility to COPD from cigarette smoking ( Barnes 1999 ).

Summary diagram of cigarette-related mechanisms of lung injury. Source: MacNee and Rahman 1999, p. S63. Reprinted with permission.

Epithelial injury results in the release of proinflammatory mediators (i.e., cytokines) from epithelial cells and inflammatory cells in the airway walls (i.e., lymphocytes and macrophages). These mediators lead to an influx of neutrophils, which also release mediators that perpetuate the cycle of injury and inflammation ( Figure 4.2 ) ( MacNee and Rahman 1999 ; Mills et al. 1999 ). The inflammatory process is found in the central airways, peripheral airways, and lung parenchyma, even in smokers with normal lung function ( Saetta 1999 ; Saetta et al. 2001 ). Although an inflammatory process in the small airways (respiratory bronchiolitis) appears to develop in all cigarette smokers, in susceptible smokers the injury progresses and leads to a narrowing of these airways ( Bosken et al. 1990 ; USDHHS 1990 ; Aguayo 1994 ). Available evidence suggests that changes in the structure and function of small airways (bronchioles) are fundamental to the development of smoking-induced COPD ( Wright 1992 ; Thurlbeck 1994 ). Genetic variations that alter the function of several inflammatory mediators, and thus the type of inflammatory response, probably contribute in part to susceptibility to COPD ( Barnes 1999 ). For example, smokers with COPD have a predominance of CD8-positive T lymphocytes in the central and peripheral airways compared with smokers without COPD ( O’Shaughnessy et al. 1997 ; Saetta et al. 1998 , 2001 ).

The inflammatory process may extend into the peribronchiolar alveoli and destroy the alveolar walls—the hallmark of emphysema—when there is an imbalance between proteases and antiproteases. Proteases are enzymes released from neutrophils and macrophages that degrade structural proteins (e. g ., elastin and collagen) of the airways and lung parenchyma. Evidence for increased elastin degradation was reported by Gottlieb and colleagues (1996) , who found increased urine desmosine (a by-product of elastin degradation) in smokers who had rapid declines in lung function. Antiproteases released from macrophages and the liver provide a natural defense against proteases. A deficiency in alpha 1 -antitrypsin, an antiprotease, is a rare genetic variation that causes emphysema, but it is found only in 1 to 2 percent of patients with COPD .

To date, except for an alpha 1 -antitrypsin deficiency, the role of genetic variations in the development of COPD has received limited attention ( Sandford et al. 1997 ; Barnes 1999 ; Takizawa et al. 2001 ). Family studies have demonstrated a genetic influence on the level of FEV 1 , and segregation analysis has provided evidence that the effect is polygenic. Moreover, in case-control studies of COPD patients, a family history of COPD has proven to be a risk factor for COPD. Candidate genes for susceptibility to cigarette smoke and COPD that are under active investigation include the numerous genes that control peripheral airway inflammation, oxidant levels, and the protease-antiprotease balance ( Higham et al. 2000 ; Sakao et al. 2001 ; Sandford et al. 2001 ).

• Active smoking causes injurious biologic processes (i.e., oxidant stress, inflammation, and a protease-antiprotease imbalance) that result in airway and alveolar injury. This injury, if sustained, ultimately leads to the development of chronic obstructive pulmonary disease.

Although smoking prevention and cessation remain the cornerstones for preventing smoking-induced chronic respiratory diseases ( USDHHS 1990 ), further research on the biologic mechanisms of airway and alveolar injury caused by smoking may provide new approaches for preventing smoking-induced lung diseases among smokers unable to quit.

Growth of Lung Function in Infancy and Childhood

In addition to the adverse effects on pulmonary function of in utero exposure to maternal smoking and postnatal exposure to parental smoking ( National Research Council 1986 ; USDHHS 1986 ; U.S. Environmental Protection Agency 1992 ), active cigarette smoking during childhood and adolescence has the potential for retarding the rate of lung growth and the level of maximum lung function ( Table 4.13 ) ( USDHHS 1994 ), thus increasing the risk for COPD in adulthood ( Figure 4.1 ). Results from six cohort studies of lung function in children and adolescents published from 1982–1992 were reviewed in the 1994 Surgeon General’s report ( USDHHS 1994 ). Two representative publications from that report ( Tager et al. 1985 , 1988 ) are summarized here along with two investigations that were not reviewed in the 1994 report ( Sherrill et al. 1991 ; Gold et al. 1996 ).

In a longitudinal study of 669 children and adolescents aged 5 through 19 years in East Boston, Massachusetts, Tager and colleagues (1985) found that among adolescents who started to smoke at 15 years of age and continued to smoke, the percent predicted FEV 1 level at 20 years of age was only 92 percent of the expected FEV 1 level for nonsmokers. Subsequently, Tager and colleagues (1988) analyzed spirometric measurements from at least one FVC test performed during 1975–1985 in each of 974 females and 913 males aged 5 years and older. For girls, a linear increase in FEV 1 levels ended approximately one year earlier for current smokers (at 17 years of age) than for nonsmokers without respiratory symptoms (at 18 years of age); the average maximal FEV 1 values were 2.9 L and 3.1 L, respectively. For nonsmokers with respiratory symptoms, the estimated maximal FEV 1 level was identical to that for current smokers (2.9 L). For boys, the estimated maximal FEV 1 level was identical for asymptomatic nonsmokers (those who do not have a diagnosis of chronic bronchitis or emphysema, or evidence of chronic respiratory symptoms), symptomatic non-smokers, and current smokers (4.9 L), but was attained at a much earlier age for current smokers (at 18 through 19 years of age) compared with asymptomatic non-smokers (aged 20 through 34 years) and symptomatic nonsmokers (21 years). Sherrill and colleagues (1991) assessed growth curves in smokers classified as asymptomatic. They found that among women, cessation of lung function growth occurred at 22 years of age in asymptomatic smokers and at 23 years of age in asymptomatic women who had never smoked. Among female smokers with respiratory symptoms, lung function growth ended at 21 years of age, three years earlier than for those who had never smoked. Among asymptomatic men, the authors found no differences in the age of lung growth cessation between nonsmokers and smokers (23 years of age). Among symptomatic male smokers, however, lung growth cessation occurred at a younger age (25 years of age) compared with symptomatic nonsmokers (27 years of age).

In a cohort of 4,902 girls and 5,158 boys from 10 to 18 years of age tested annually with spirometry, Gold and colleagues (1996) examined the effects of cigarette smoking on the rate of lung function growth and the level of lung function attained. Among girls smoking five or more cigarettes per day, the rate of increase in FEV 1 levels was slower by 31 mL /year (95 percent CI , 16.0–46.0 mL/year) than among girls who had never smoked. At 17 to 18 years of age, FEV 1 levels began to decline among girls who smoked while staying at a plateau among girls who did not smoke. Although smoking five or more cigarettes per day slowed the rate of increase in FEV 1 levels in boys, the magnitude of the effect (slower by 9 mL/year; 95 percent CI, −6.0 to 24.0 mL/year) was less than that in girls. There was an inverse association between the amount smoked and the level of FEV 1 / FVC and FEF between 25 and 75 percent of the FVC (FEF 25–75% ). The number of cigarettes smoked was not associated with FVC or FEV 1 levels.

There have been only a limited number of longitudinal investigations of active smoking during childhood and adolescence because of the complex logistics of such studies. However, the findings are consistent for various populations. In smokers, lung function growth is slower during childhood and adolescence, prematurely ceases, and begins to decline in late adolescence and early adulthood. The evidence suggests a causal role for active smoking. This causal link is strengthened by the finding of a dose-response relationship between smoking and the level of FEV 1 / FVC and between smoking and FEF 25–75% . Additionally, the inflammatory process caused by smoking would be initiated at any age, and the lungs of young smokers show evidence of airways inflammation and injury.

• The evidence is sufficient to infer a causal relationship between active smoking and impaired lung growth during childhood and adolescence.
• The evidence is sufficient to infer a causal relationship between active smoking and the early onset of lung function decline during late adolescence and early adulthood.

These conclusions provide a strong rationale for interventions to prevent children and adolescents from starting to smoke and for helping young smokers to quit. Future studies should determine the effects of smoking cessation on the rate of lung growth, and they should follow smokers from adolescence into their fourth and fifth decades of life when COPD is first diagnosed. Addressing these gaps in knowledge could provide further evidence of a causal link between active smoking during childhood and the risk for later development of COPD.

Decline of Lung Function

Results from longitudinal investigations of adults between their second and third decades—the period of transition from lung growth to a plateau of variable length and then to decline—suggest that cigarette smoking causes a premature onset of lung function decline and, to a lesser extent, a more rapid decline ( Tager et al. 1988 ; Sherrill et al. 1991 ). In the East Boston study, estimates of the age range when lung function begins to decline were wide but tended to be earlier for current smokers compared with asymptomatic or symptomatic nonsmokers ( Tager et al. 1988 ). After the period of maximal lung growth, there is a prolonged plateau period for the FEV 1 level in nonsmoking men before the FEV 1 declines (late in the fourth decade of life). This decline is estimated to begin 10 years earlier (i.e., late in the third decade of life) in asymptomatic nonsmokers and 15 years earlier in current smokers (i.e., in the middle of the third decade). Among all women, the onset of decline begins at an earlier age compared with that of men, and female current smokers had a more rapid earlier decline (−20 mL /year) and an earlier age of onset of a more rapid decline compared with nonsmoking women. In the population-based study of respiratory diseases in Tucson, Arizona, Sherrill and colleagues (1991) also found that symptom status modified the rate of decline. The rate of decline was similar for asymptomatic male smokers and nonsmokers until approximately 48 years of age, when the average rate of decline for smokers increased from −29 mL/year to −46 mL/year. Among symptomatic smokers, the increased rate of decline occurred at a younger age (34 years of age). The FEV 1 level was lower for symptomatic female smokers beginning in the late teenage years, but there was little difference in the subsequent rate of FEV 1 decline between smokers and nonsmokers.

In cross-sectional and cohort studies of ventilatory function, a higher average rate of FEV 1 decline has been consistently found in current cigarette smokers compared with former smokers and nonsmokers ( Table 4.15 ) ( USDHHS 1984 , 1990 ). In cohort studies the average rate of FEV 1 decline among nonsmokers ranged from 17 to 61 mL /year, and the decline among smokers exceeded the decline among nonsmokers by 7 to 27 mL/year ( USDHHS 1990 ). Furthermore, while the rate of FEV 1 decline for smokers and nonsmokers is highly variable, the distribution of FEV 1 decline rates is shifted toward a higher proportion of sustained smokers with rapid rates of decline. As the amount of cigarette smoking increases, the rate of decline increases ( Xu et al. 1992 , 1994 ; Burchfiel et al. 1996 ; Vestbo et al. 1996 ; Belousova et al. 1997 ; Scanlon et al. 2000 ; Vollmer et al. 2000 ). For some smokers, the increased rate of decline eventually results in a FEV 1 level associated with dyspnea and a limitation of activities; at this level, the clinical diagnosis of COPD is usually made ( Figure 4.1 ).

Studies on the association between smoking and rates of forced expiratory volume in one second (FEV 1 ) decline.

Because not all smokers develop COPD , research is increasingly directed at identifying factors that may heighten susceptibility to rapid rates of FEV 1 decline. Factors that have been examined include gender ( Xu et al. 1994 ; Scanlon et al. 2000 ; Vollmer et al. 2000 ), race and ethnicity ( Scanlon et al. 2000 ; Vollmer et al. 2000 ), alcohol use ( Burchfiel et al. 1996 ), diet and use of nutritional supplements ( Carey et al. 1998 ), anthropometric characteristics ( Burchfiel et al. 1996 ), respiratory symptoms ( Jaakkola et al. 1991a , b ; Sherman et al. 1992 ; Burchfiel et al. 1996 ; Scanlon et al. 2000 ), FEV 1 levels ( Burrows et al. 1987 ; Scanlon et al. 2000 ), airways hyperresponsiveness ( Frew et al. 1992 ; Tashkin et al. 1996 ), comorbid conditions such as asthma and coronary heart disease ( Burchfiel et al. 1996 ; Lange et al. 1998 ), and occupational and environmental exposures ( Xu and Wang 1998 ). Investigations of these factors are ongoing and firm conclusions cannot yet be reached on their roles in modifying the risk for COPD in smokers.

Available investigations provide conflicting results about the relative rates of FEV 1 decline among women who smoke compared with men who smoke ( Xu et al. 1994 ; Scanlon et al. 2000 ; Vollmer et al. 2000 ). Xu and colleagues (1994) suggested that women may have a higher rate of FEV 1 decline. They hypothesized that different distributions of unhealthy participants by gender in nonsmoking reference groups may explain conflicting results in studies that compared rates of FEV 1 decline in women and men. Other factors that may modify the effects of smoking and contribute to gender differences in study findings include the year of birth of study participants (birth cohort) and the time period of a study ( Samet and Lange 1996 ). In a study from the Netherlands, Xu and colleagues (1995) reported a significant interaction between age and birth cohorts in relation to declines in FEV 1 levels in women but not in men. The modifying effects of a birth cohort may partially reflect changes in smoking behavior and perhaps in the products smoked.

Several studies have shown that women have a higher prevalence and degree of bronchial hyperreactivity ( Leynaert et al. 1997 ), associated with an accelerated rate of decline in FEV 1 levels, compared with men ( Tashkin et al. 1996 ; Scanlon et al. 2000 ). This gender difference in bronchial hyperreactivity may contribute to a higher risk in women for developing COPD . Scanlon and colleagues (2000) found in the Lung Health Study that women who continued to smoke over a five-year period had a greater annual decline in FEV 1 levels than did men with comparable levels of smoking (−1.08 percent predicted and −0.77 percent predicted, respectively), but the statistical significance of the difference was not reported. The increased rate of decline among women was associated with a greater degree of bronchial hyperreactivity.

Biologic differences between women and men, including differences in lung mechanics and hormonal factors, may affect susceptibility to the adverse effects of cigarette smoke, but limited data are available to test these hypotheses. Whether there are gender differences from the effects of smoking on changes in lung function remains unclear.

Scant data are available on racial and ethnic differences in the rates of FEV 1 decline ( Scanlon et al. 2000 ; Vollmer et al. 2000 ). In the Lung Health Study, Vollmer and colleagues (2000) combined spirometric data from eight population-based observational studies or clinical trials conducted in North America to examine the relationship between smoking, lung function, race, and ethnicity. Overall, this cross-sectional analysis included 23,812 men (66 percent white, 14 percent black, 4 percent Hispanic, 12 percent Asian/Pacific Islander, and 3 percent American Indian) and 16,921 women (62 percent white, 25 percent black, 6 percent Hispanic, and 7 percent American Indian). The estimated average excess FEV 1 decline attributed to smoking was highest among whites (−6 mL /pack-year) and similar in the other racial and ethnic groups (−3 to −4 mL/pack-year). However, the greatest differences among racial and ethnic groups were limited to the heaviest smokers (more than 10 cigarettes per day). Overall, during the five-year period of the Lung Health Study, there were no differences in the rates of change in FEV 1 declines among these participants ( Scanlon et al. 2000 ).

The presence of respiratory symptoms, particularly coughing, phlegm, and wheezing, has been associated with an accelerated decline in FEV 1 levels in cigarette smokers and nonsmokers in a number of studies ( Jaakkola et al. 1991a , b ; Sherman et al. 1992 ; Burchfiel et al. 1996 ; Vestbo et al. 1996 ). Among Japanese American men in the Honolulu Heart Program who were continuous smokers, Burchfiel and colleagues (1996) found an increased risk of rapid FEV 1 declines (−60 mL /year or greater) associated with wheezing ( OR = 3.9 [95 percent CI , 1.8–8.3]). However, respiratory symptoms have not been predictive of FEV 1 declines in all studies. Although Scanlon and colleagues (2000) did not find an association between respiratory symptoms and the rate of FEV 1 declines in the Lung Health Study, their ability to detect an association may have been limited because participants in this study were restricted to smokers with mild to moderate chronic airflow obstruction.

The presence of other diseases including asthma ( Lange et al. 1998 ) and coronary heart disease ( Burchfiel et al. 1996 ) has been associated with an accelerated FEV 1 decline among smokers. In the Copenhagen City Heart Study, Lange and colleagues (1998) followed 9,370 women and 8,136 men, 20 to 79 years of age, over a 15-year period. Except for the youngest women (20 to 39 years of age) and the oldest men (60 to 79 years of age), smokers with asthma averaged greater FEV 1 reductions than smokers without asthma. In the Honolulu Heart Program, Japanese American men with coronary heart disease who continued to smoke had an increased risk for a rapid FEV 1 decline (−60 mL /year or greater) ( OR = 1.99 [95 percent CI , 0.96–4.14]).

Nutritional factors such as dietary intake ( Carey et al. 1998 ) and anthropometric characteristics ( Burchfiel et al. 1996 ) have been associated with rates of FEV 1 decline. In a national sample of 2,171 British adults aged 18 through 73 years, Carey and colleagues (1998) found that current smokers who consumed the smallest quantities of fresh fruits (sources of antioxidant vitamins) over a seven-year period had a higher rate of FEV 1 decline than lifetime nonsmokers, with adjustments for social class, region, pack-years, and average fresh fruit scores (by rating consumption as more than one per day, one per day most days, once or twice per week, less than one per week, or never).

Anthropometric characteristics have been associated with a rapid FEV 1 decline among cigarette smokers ( Burchfiel et al. 1996 ). Burchfiel and colleagues (1996) found that increasing body mass, measured by subscapular skinfold thickness, was associated with a lower risk for rapid FEV 1 declines (−60 mL /year or greater). A 10- mm increase in subscapular skinfold thickness was associated with a 30 percent decrease in the risk for a rapid FEV 1 decline ( OR = 0.70 [95 percent CI , 0.55–0.88]).

The relationship between a single measure of and a subsequent rate of change in the FEV 1 level has been termed the “horse-racing effect”; a low FEV 1 level is a predictor of a rapid decline in the FEV 1 ( Fletcher et al. 1976 ; Burrows et al. 1987 ). The term “horse-racing” was proposed because a low FEV 1 level at any point reflects a high rate of prior loss and hence is predictive of a future decline. As an integrated consequence of a prior decline, the FEV 1 level is also a potential marker for susceptibility to the factors driving the decline.

Burrows and colleagues (1987) proposed that a low FEV 1 level may be an early marker for identifying smokers who are susceptible to COPD . The investigators examined relationships between FEV 1 levels and other spirometric parameters and the rates of FEV 1 decline in 620 women and 475 men from Tucson, Arizona. For both men and women, a low initial FEV 1 decline. In level was not associated with a rapid FEV 1 men, however, an initially low ratio of FEV 1 / FVC (less than 70 percent) was associated with a rapid FEV 1 decline; trends in women were reported to be similar but less marked, although the data were not provided. Similarly, in the Lung Health Study, Scanlon and colleagues (2000) found no differences in the rates of FEV 1 decline over four years of follow-up when comparing continuing smokers with a baseline FEV 1 in the lowest quintile (−63 mL /year) with those in the highest quintile (−61 mL/year). However, the investigators did find a significant association between the baseline FEV 1 percent predicted and the rate of decline. These findings need to be interpreted with attention to the characteristics of the study participants: middle-aged smokers with mild-to-moderate airflow obstruction. Overall, the available results suggest that various indicators of impaired ventilatory function predict subsequent FEV 1 declines.

Among cigarette smokers, bronchial hyper-responsiveness to a variety of stimuli (e. g ., histamine and methacholine) has been associated with an accelerated rate of decline in FEV 1 levels ( Frew et al. 1992 ; Rijcken et al. 1995 ; Villar et al. 1995 ; Tashkin et al. 1996 ). In the Lung Health Study, Tashkin and colleagues (1996) examined the relationship between bronchial hyperreactivity to methacholine and FEV 1 declines among 5,733 smokers aged 35 through 60 years with mild COPD (mean FEV 1 / FVC , 65 percent; FEV 1 , 78 percent predicted). After adjusting for age, gender, baseline smoking history, changes in smoking status, and baseline lung function levels, the investigators found that airway hyperreactivity during the five-year follow-up was a strong predictor of changes in FEV 1 levels percent predicted. The greatest decline of 2.2 percent predicted was in women who had the highest degree of hyperreactivity and who continued to smoke. The corresponding value in men was 1.7 percent predicted.

In addition to cigarette smoking, exposures to ambient air pollutants or workplace exposures may accelerate FEV 1 declines and increase future risks for COPD ( Garshick et al. 1996 ; Xu and Wang 1998 ). For example, Xu and Wang (1998) examined the effects of smoking, urban air pollution, and workplace exposures on lung function levels in a 1986 cross-sectional survey of 3,287 randomly selected adults 40 to 69 years of age residing in Beijing, China. The investigators found that smokers had an increased reduction in FEV 1 levels of 6.5 mL for each year of smoking compared with adults who had never smoked; smokers living in residential and industrial areas with high levels of ambient pollutants had further decrements in pulmonary function.

Effects of Smoking Cessation

The beneficial effects of smoking cessation on the decline were extensively reviewed in the rates of FEV 1 1990 Surgeon General’s report. A major conclusion of that report relevant to FEV 1 declines and smoking cessation was that “cigarette smoking accelerates the age-related decline in lung function that occurs among never smokers. With sustained abstinence from smoking, the rate of decline in pulmonary function among former smokers returns to that of never smokers” ( Table 4.13 ) ( USDHHS 1990 , p . 11). Since that report, there have been additional studies supporting these conclusions ( Townsend et al. 1991 ; Anthonisen et al. 1994 ; Sherrill et al. 1994 ; Xu et al. 1994 ; Burchfiel et al. 1995 ; Frette et al. 1996 ; Murray et al. 1998 ; Berglund et al. 1999 ; Scanlon et al. 2000 ). These studies also have advanced an understanding of factors that modify the effects of smoking cessation on rates of FEV 1 decline.

The Lung Health Study provides powerful clinical trial data on the effects of smoking cessation on the rates of FEV 1 decline and lung function levels ( Anthonisen et al. 1994 ; Scanlon et al. 2000 ). This five-year, multicenter clinical trial of smoking cessation interventions was conducted in 10 North American centers. Between 1986 and 1989, 5,887 women (37 percent) and men (63 percent) aged 35 through 60 years who were current smokers with mild to moderate air-flow obstruction (FEV 1 / FVC of 70 percent or less and FEV 1 between 55 percent and 90 percent of predicted normal) were randomized into three groups: usual care, smoking cessation intervention with a placebo inhaler, and smoking cessation intervention with an inhaled bronchodilator (ipratropium bromide). Participants in the smoking cessation intervention placebo group and the usual care group who stopped smoking in the first year of the trial had an average increase in FEV 1 levels of 47 mL compared with a 49 mL decrease among persons who continued to smoke ( Scanlon et al. 2000 ). Between year one and year five of the trial, the average rate of FEV 1 reduction among continuous smokers was −62 mL/year, twice that of sustained quitters (−31 mL/year) during the same time period. Quitting intermittently during the follow-up period was associated with an intermediate rate of decline (−43 mL/year). The degree of improvement during the first year of cessation and the rates of FEV 1 decline after cessation varied with age at cessation, gender, amount of smoking, level of baseline lung function, and airways hyperreactivity.

Results from several investigations suggest that the benefits of smoking cessation are greatest for persons who stop smoking at younger ages ( Camilli et al. 1987 ; Sherrill et al. 1994 ; Xu et al. 1994 ; Frette et al. 1996 ; Scanlon et al. 2000 ). In the Lung Health Study, Scanlon and colleagues (2000) found that sustained quitters younger than 50 years of age had the slowest rates of FEV 1 decline during the five-year follow-up period compared with sustained quitters 50 years of age and older ( Figure 4.3 ). Among 147 women and 141 men who were new quitters in the prospective Tucson Epidemiological Study of Airways Obstructive Disease, Sherrill and colleagues (1994) estimated that smoking cessation among women improved FEV 1 levels by 4.3 percent at 20 years of age and by 2.5 percent at 80 years of age. For men, FEV 1 improvements were less at both ages: 1.2 percent at 20 years of age and zero at 80 years of age. During the 24 years of follow-up in the Dutch Vlagtwedde-Vlaardingen Study ( Xu et al. 1994 ), the mean FEV 1 loss in former compared with current smokers was 20 mL /year less for women who stopped smoking before 45 years of age, but only 5.4 mL/year less for women who stopped smoking at 45 years of age or older. The corresponding values for men were 28.2 mL/year less for men younger than 45 years of age, and 10.4 mL/year less for men 45 years of age and older. In the Rancho Bernardo (California) Heart and Chronic Disease Study, 826 women and 571 men aged 51 through 95 years had spirometry testing from 1988–1991 ( Frette et al. 1996 ). Women who were former smokers who stopped smoking before 40 years of age had FEV 1 levels similar to those for women who had never smoked (2.09 L and 2.13 L, respectively). The average FEV 1 level for women who stopped smoking at 40 through 60 years of age was 2.02 L, which was between that for female nonsmokers (2.13 L) and female current smokers (1.71 L). Women who stopped smoking at 60 years of age or older had a FEV 1 level similar to that of current smokers (1.72 L and 1.71 L, respectively); the same pattern in relation to age at smoking cessation was found for men.

Mean change and 95 percent confidence interval in forced expiratory volume in one second (FEV 1 ) percent predicted from years 1–5 of the Lung Health Study for sustained quitters, intermittent quitters, and continuous smokers, by quintile of age. (more...)

Limited data suggest that smoking cessation more significantly benefits lung function and the rate of FEV 1 decline in women than in men ( Sherrill et al. 1994 ; Scanlon et al. 2000 ). The Tucson Epidemiological Study of Airways Obstructive Disease ( Sherrill et al. 1994 ) estimated that the average improvement in FEV 1 levels at 80 years of age was higher among women who had quit smoking (2.5 percent) than among men who had stopped smoking (0.0 percent). Women who were sustained quitters in the Lung Health Study had improvements in FEV 1 levels in the first year of cessation 2.5 times greater than did men ( Scanlon et al. 2000 ). The report from Scanlon and colleagues (2000) did not provide gender-specific effects on subsequent FEV 1 rates of decline.

The amount of exposure to cigarette smoke, which may be measured in several ways, may also influence the effects of smoking cessation ( Burchfiel et al. 1995 ; Scanlon et al. 2000 ). Burchfiel and colleagues (1995) found slower FEV 1 declines after quitting in Japanese American men with the highest level of baseline smoking (−9.1 mL /year) compared with men with the lowest level (−24.1 mL/year). In the Lung Health Study, Scanlon and colleagues (2000) found no differences in the rates of FEV 1 decline among sustained quitters from year one through year five of follow-up in relation to the number of cigarettes smoked at baseline. However, they did find that the largest improvements in FEV 1 levels after smoking cessation for the first year were among persons who smoked the most cigarettes per day before quitting ( Figure 4.4 ) ( Scanlon et al. 2000 ). Among sustained quitters in the Lung Health Study, for the subgroup with the highest quintile of cigarettes smoked per day before quitting, improvement in FEV 1 levels was 3.33 percent predicted in the first year of cessation compared with only 0.51 percent predicted for the lowest smoking quintile.

Mean change and 95 percent confidence interval in forced expiratory volume in one second (FEV 1 ) percent predicted during year 1 of the Lung Health Study, for persons who quit smoking and for persons who continued to smoke during year 1, by quintile of (more...)

Limited data are available on the relationship between the FEV 1 level at quitting and the consequences of smoking cessation ( Burchfiel et al. 1995 ; Scanlon et al. 2000 ). In the Honolulu Heart Program, Burchfiel and colleagues (1995) found that after adjusting for age, height, and amount smoked, the benefits of quitting were more evident in persons with lower baseline FEV 1 levels. In contrast, Scanlon and level was colleagues (2000) found that a baseline FEV 1 not predictive of subsequent rates of decline in the FEV 1 level and baseline level was not associated with greater improvements after the first year of cessation. The conflicting results between these two studies may reflect differing study populations. The Honolulu Heart Program was population-based and began with middle-aged Japanese American men, whereas the Lung Health Study used volunteer smokers with evidence of mild-to-moderate airflow obstruction.

The degree of bronchial reactivity has been strongly associated with the magnitude of improvements in FEV 1 levels in the first year of cessation, and with the subsequent rates of FEV 1 decline. In the Lung Health Study, Tashkin and colleagues (1996) found that persons with higher airway reactivity had the greatest improvements in FEV 1 levels within the first year after quitting, whereas the slowest rates of FEV 1 decline occurred among sustained quitters with the lowest airway reactivity.

Although the benefits of smoking cessation on rates of decline and lung function levels are well established, weight gain associated with quitting may reduce lung function levels and increase FEV 1 declines, thus counterbalancing the benefits of quitting. In the Lung Health Study, Wise and colleagues (1998) found that the FVC was affected more than the FEV 1 by the weight gain. The estimated loss of FEV 1 was 11.1 mL / kg of weight gain for men and 10.6 mL/kg for women, and the mean weight gains over five years among sustained quitters were 7.6 kg and 8.8 kg, respectively. Furthermore, the average FEV 1 decline was greater in those who gained the most weight during the five years of follow-up ( Figure 4.5 ). However, the effect of weight gain on the rates of FEV 1 decline was relatively small compared with the effects of continued smoking, and the FVC and FEV 1 would be expected to increase with weight loss.

The relationship between mean changes in forced expiratory volume in one second (FEV 1 ) percent predicted to quintiles of mean changes in weight for each smoking category. Note: Corrected data presentation shown here. The interval for changes in FEV 1 percent (more...)

The adverse effects of active smoking and the benefits of smoking cessation on lung function decline have been firmly established ( USDHHS 1984 , 1990 ). Research emphasis has shifted to finding determinants of susceptibility to rapid lung function decline in active smokers and determinants of improvements after smoking cessation. Factors that predict the greatest susceptibility to rapid lung function decline while actively smoking include a greater number of cigarettes smoked, wheezing, asthma, bronchial hyperreactivity, low body mass, low lung-function level ( FEV 1 percent predicted or low FEV 1 / FVC ), occupational exposures, and ambient air pollution. However, there is limited evidence available on how modifying active smoking affects the rate of lung function decline by gender, ethnicity, and antioxidant dietary intake.

• The evidence is sufficient to infer a causal relationship between active smoking in adulthood and a premature onset of and an accelerated age-related decline in lung function.
• The evidence is sufficient to infer a causal relationship between sustained cessation from smoking and a return of the rate of decline in pulmonary function to that of persons who had never smoked.

These conclusions provide a strong rationale for smoking cessation interventions for active smokers. The greatest benefits from smoking cessation will occur at younger ages, but all smokers benefit from cessation regardless of age. Identifying smokers with the greatest susceptibility for a rapid decline in lung function may lead to more targeted interventions, but cessation for all smokers is central to preventing COPD .

Chronic Respiratory Symptoms and Diseases

Substantial observational evidence has long shown that respiratory symptoms and diagnoses, the most relevant health outcomes to patients, are causally associated with smoking. Respiratory symptoms— coughing, productive coughing, wheezing, and dyspnea (difficulty breathing and shortness of breath)—are nonspecific and are associated with a number of acute and chronic respiratory diseases and even nonrespiratory diseases. Despite the nonspecificity of respiratory symptoms, their presence is a sensitive indicator of underlying lung injury and disease ( Torén et al. 1993 ), and they have clinical relevance because they may impair functioning and reduce the quality of life. Selected diseases, particularly asthma and respiratory symptoms such as wheezing, may be sufficiently specific in children to be used to define the disease. However, the specificity of wheezing for asthma declines with age because of the increasing prevalence of COPD .

Respiratory Symptoms: Childhood and Adolescence

Overall, the frequency of respiratory symptoms in children and adolescents is greater in current smokers compared with nonsmokers or former smokers, and the duration and amount of smoking further increase the frequency of symptoms ( USDHHS 1994 ; Arday et al. 1995 ; Larsson 1995 ; Lam et al. 1998 ; Withers et al. 1998 ). A major conclusion of the 1994 Surgeon General’s report was that “Cigarette smoking during childhood and adolescence produces significant health problems among young people, including cough and phlegm production, an increased number and severity of respiratory illnesses” and “decreased physical fitness” ( USDHHS 1994 , p . 41). Since the 1994 report, several investigations have confirmed and extended the conclusions relevant to respiratory symptoms in childhood and adolescence ( Arday et al. 1995 ; Lam et al. 1998 ; Withers et al. 1998 ).

To examine the relationship between smoking status and respiratory symptoms, Arday and colleagues (1995) used self-reported questionnaire data obtained from a random sample of 26,504 high school seniors in the 48 contiguous United States from 1982– 1989. Compared with students who had never smoked or who had smoked only once or twice in the past, current regular smokers (i.e., reported smoking at least one cigarette within the past 30 days) who began to smoke daily by ninth grade were more likely to report at least one episode in the past 30 days of coughing spells ( OR = 2.1 [95 percent CI , 1.90–2.33]), shortness of breath when not exercising (OR = 2.67 [95 percent CI, 2.38–2.99]), and wheezing or gasping (OR = 2.58 [95 percent CI, 2.29–2.90]). These risk estimates were adjusted for gender, marijuana and cocaine use, parental education, and the year of the survey. The prevalence of respiratory symptoms increased with the amount and duration of smoking.

Lam and colleagues (1998) conducted a cross-sectional survey of 6,304 students 12 to 15 years of age who were attending school in Hong Kong. Students who reported smoking more than six cigarettes per week had a higher prevalence of coughing for three months compared with students who had never smoked ( OR = 3.02 [95 percent CI , 1.95–4.69]), and a higher prevalence of wheezing in the past three months (OR = 2.91 [95 percent CI, 1.99–4.26]). These risk estimates were adjusted for gender, age, area of residence, and type of housing. Statistically significant increases in the prevalence of respiratory symptoms were associated with an increased frequency of smoking.

Withers and colleagues (1998) reported results from following a cohort of 2,289 children from the ages of 6 to 8 years to 14 to 16 years of age; all were registered with 1 of 86 family practitioners in Southampton, United Kingdom. Regular smoking (i.e., smoking at least one cigarette per week during the 12 months before completing the questionnaire) was associated with a current cough ( OR = 1.71 [95 percent CI , 1.21–2.43]), the onset of a cough between the surveys (OR = 1.91 [95 percent CI, 1.12–3.25]), a persistent wheeze in boys (OR = 4.35 [95 percent CI, 1.20–14.3]), and a new report of wheezing (OR = 1.65 [95 percent CI, 1.14–2.39]).

In the three investigations published since the 1994 Surgeon General’s report, the prevalence of respiratory symptoms was consistently higher among cigarette smokers than among nonsmokers ( Arday et al. 1995 ; Lam et al. 1998 ; Withers et al. 1998 ). Furthermore, limited evidence suggests that the prevalence of symptoms increases with the duration and amount of smoking ( Arday et al. 1995 ; Lam et al. 1998 ). Although the results from these investigations are not directly comparable because the survey questions on smoking status and respiratory symptoms vary across studies, in three distinct settings each study shows an increase in symptom rates for children who smoke.

Other factors that may also contribute to respiratory symptoms include gender, associated diseases (e. g ., atopy or asthma), passive exposure to smoking if parents or other household members smoke, marijuana and cocaine use, ambient air pollution, workplace exposures, and socioeconomic factors. These factors have been considered to an extent in some studies. Arday and colleagues (1995) adjusted for gender, marijuana and cocaine use, and parental education. Lam and colleagues (1998) considered gender, age, area of residence, and housing type. Withers and colleagues (1998) included gender, personal and family history of atopy, passive smoking, other household exposures, and social factors. However, despite inconsistent controls for other factors that may contribute to the occurrence of respiratory symptoms, none is likely to substantially confound the strong association between smoking and respiratory symptoms.

Limited data are available on the relationship between smoking cessation and the occurrence of respiratory symptoms in children and adolescents ( Arday et al. 1995 ; Lam et al. 1998 ). Compared with nonsmokers, former smokers report more frequent respiratory symptoms, but they generally have fewer occurrences of symptoms than regular smokers. Several factors may partially explain this higher occurrence in former smokers compared with nonsmokers, including a relatively short duration of cessation, false reporting of their smoking status, and the “healthy smoker” effect. This effect refers to the observation that persons who continue to smoke are less likely to have respiratory symptoms, in contrast to former smokers who quit smoking because of frequent respiratory symptoms ( Weiss et al. 1989 ).

Since the 1994 Surgeon General’s report on smoking and health, several investigations have been published that confirm and extend conclusions of that report that are relevant to respiratory symptoms in childhood and adolescence ( Table 4.13 ). These studies establish that respiratory symptoms increase with the amount and duration of smoking. Further, these studies also show that the effects of active smoking on respiratory symptoms are not due to other factors that increase respiratory symptoms. Limited data are available on the effects of smoking cessation on respiratory symptoms among youth.

• The evidence is sufficient to infer a causal relationship between active smoking and respiratory symptoms in children and adolescents, including coughing, phlegm, wheezing, and dyspnea.

This conclusion provides yet another strong rationale for smoking cessation interventions among youth.

Asthma. In the Guidelines for the Diagnosis and Management of Asthma of the National Heart, Lung, and Blood Institute ( NHLBI 1997 ), asthma is defined as “a chronic inflammatory disorder of the airways in which many cells and cellular elements play a role . . . . In susceptible individuals, this inflammation causes recurrent episodes of wheezing, breathlessness, chest tightness, and coughing, particularly at night or in the early morning. These episodes are usually associated with widespread but variable airflow obstruction that is often reversible either spontaneously or with treatment. The inflammation also causes an associated increase in the existing bronchial hyperresponsiveness to a variety of stimuli” ( p . 3).

Asthma is the most common chronic respiratory childhood disease, and it has been increasing in frequency in the United States and worldwide for several decades ( NHLBI 1997 ; Warner 1999 ). This complex disease is associated with a number of environmental exposures, particularly aeroallergens, and with genetic susceptibility. Although the literature documenting the association between secondhand smoke exposure and childhood asthma is extensive ( Cook and Strachan 1999 ), only a limited number of studies on active smoking and childhood asthma have been conducted ( Larsson 1995 ; Kaplan and Mascie-Taylor 1997 ; Lam et al. 1998 ; Norrman et al. 1998 ; Withers et al. 1998 ; Chen et al. 1999 ).

Epidemiologic Evidence. Establishing the presence of asthma in epidemiologic studies is one of the greatest challenges in investigating cigarette smoking and asthma, primarily because of the lack of an agreed-upon operational definition of asthma ( Torén et al. 1993 ). However, during childhood and adolescence physician-diagnosed asthma and standardized questions about asthma-related symptoms (i.e., wheezing or wheezing with dyspnea) provide sufficient specificity. Asking such questions has been the main method used to examine active smoking and asthma among youth ( Larsson 1995 ; Kaplan and Mascie-Taylor 1997 ; Lam et al. 1998 ; Withers et al. 1998 ; Chen et al. 1999 ).

Larsson (1995) examined the association between smoking and self-reported asthma incidence among 2,308 persons aged 16 through 19 years living in Sweden. Between 1990 and 1993, the overall incidence of physician-diagnosed asthma was 1.3 percent per year, and the incidence among females was higher (1.8 percent per year) than that among males (0.9 percent per year). The risk for physician-diagnosed asthma was also higher among female smokers ( OR = 2.0 [95 percent CI , 1.0–4.0]) than among male smokers (OR = 1.7 [95 percent CI, 0.6–4.8]). The risks for asthma-related symptoms and the use of asthma medications also were higher among females than among males. This analysis was limited by the lack of information on other factors associated with asthma, including personal atopy, family history of atopy and asthma, parental smoking, and other potential confounding variables.

Kaplan and Mascie-Taylor (1997) examined smoking and asthma in a cohort of 8,860 participants from England, Wales, and Scotland participating in the National Child Development Study. The analysis was based on self-reports at 16 and 23 years of age. In a univariate analysis that included males and females, regular smoking since 16 years of age was associated with reports of asthma or wheezy bronchitis between 16 and 23 years of age ( OR = 1.55). Stratified or multivariate analyses, adjusting for other factors, were not performed.

In a 1994 cross-sectional survey of Hong Kong schoolchildren aged 12 through 15 years, Lam and colleagues (1998) did not find an association between active smoking and physician-diagnosed asthma. The prevalence of asthma was 8.6 percent among children who reported smoking six or more cigarettes per week compared with 8.1 percent among children who had never smoked ( OR = 1.18 [95 percent CI , 0.76–1.83]).

In a cohort of persons from 2,150 households in the United Kingdom, Withers and colleagues (1998) obtained questionnaire responses on smoking behaviors and asthma from participants aged 14 through 16 years. Smoking at least one cigarette per week in the 12 months preceding the survey was not associated with physician-diagnosed asthma (26.3 percent) compared with children who did not report smoking (21.9 percent). However, the prevalence of asthma was not examined separately with greater amounts of smoking.

Norrman and colleagues (1998) surveyed 1,112 Swedish eighth graders 13 to 16 years of age in 1987 and again in 1991. Overall, the incidence of self-reported asthma was 1.1 percent per year. The onset of asthma was significantly associated with current smoking ( OR = 3.4 [95 percent CI , 1.2–9.3]) but not with former smoking (OR = 2.8 [95 percent CI, 0.4–23.0]).

Among 3,240 persons aged 12 through 24 years who participated in the 1994–1995 Canadian National Population Health Study, Chen and colleagues (1999) found a significant association between asthma diagnosed by a health professional and smoking, but only among females. The OR for asthma among female smokers compared with female nonsmokers, adjusted for age, was 2.18 (95 percent CI , 1.41–3.44). Among males, the OR for smokers was 0.98 (95 percent CI, 0.56–1.70) compared with nonsmokers.

In addition to the potential etiologic role of active smoking in asthma, there is strong evidence that smoking adversely affects the course of the disease in children with asthma ( Godden et al. 1994 ; Lam et al. 1998 ). Godden and colleagues (1994) examined the levels prevalence of respiratory symptoms and FEV 1 among 360 persons from Scotland aged 34 through 40 years, who were participants in a population-based survey as children and who had been diagnosed with childhood asthma ( n = 97), wheezing with an upper respiratory infection (n = 132), or no respiratory symptoms (n = 131). In the entire group, current smoking was associated with an increased risk of a current wheeze ( OR = 2.02 [95 percent CI , 1.15–3.52]), cough (OR = 7.24 [95 percent CI, 3.39–15.49]), and phlegm (OR = 3.08 [95 percent CI, 1.27–7.39]). The risk associated with all three respiratory symptoms was substantially lower for former smokers, and only phlegm (OR = 1.68 [95 percent CI, 1.30–10.38]) was significantly associated with past smoking. In addition, current smoking was associated with a lower mean FEV 1 percent predicted level (−5.64 percent [95 percent CI, -19.4 to 1.09]). In the 1994 cross-sectional survey of Hong Kong schoolchildren reported by Lam and colleagues (1998) , children with asthma who smoked more than six cigarettes per week were more likely to report using asthma medications during the previous two days compared with children who had never smoked (OR = 3.07 [95 percent CI, 1.58–5.97]).

Evidence Synthesis. Although the prevalence of wheezing, an asthma-related symptom, is consistently higher in current smokers than in former smokers and nonsmokers, available investigations provide inconsistent findings on the relationship between smoking and reports of physician-diagnosed asthma. Moreover, none of the investigations have fully controlled for known risk factors for asthma. There is limited but consistent evidence that active smoking worsens the prognosis of asthma in children.

• The evidence is sufficient to infer a causal relationship between active smoking and asthma-related symptoms (i.e., wheezing) in childhood and adolescence.
• The evidence is inadequate to infer the presence or absence of a causal relationship between active smoking and physician-diagnosed asthma in childhood and adolescence.
• The evidence is suggestive but not sufficient to infer a causal relationship between active smoking and a poorer prognosis for children and adolescents with asthma.

Implications. These conclusions provide a strong rationale for preventing active smoking among children and adolescents to preclude the occurrence of asthma-related symptoms. The promotion of smoking cessation should improve the prognosis for children and adolescents with asthma who smoke. Future studies of causes of childhood asthma should include active smoking as a potential etiologic agent.

Respiratory Symptoms: Adulthood

Evidence continues to accumulate confirming the long-established causal association between active smoking and respiratory symptoms in adults. Among adults, all respiratory symptoms are strongly and consistently associated with cigarette smoking ( Freund et al. 1993 ; David et al. 1996 ; Bodner et al. 1998 ; Forastiere et al. 1998 ; Butland et al. 1999 ), and smoking cessation reduces their frequency ( Kanner et al. 1999 ). In the Framingham Study, Freund and colleagues (1993) found that among persons aged 45 years and older, the prevalence of a cough was higher among cigarette smokers than among nonsmokers, and the prevalence increased as the amount smoked increased. Persons who smoked more than 30 cigarettes per day were seven times more likely than nonsmokers to report a chronic cough.

Among 677 women 18 to 43 years of age who were seen for prenatal care at an East Boston clinic, David and colleagues (1996) examined the relationship between cigarette smoking and a persistent wheeze without asthma. In a multiple logistic regression model adjusting for ethnicity, parental history of asthma, educational level, and the presence of a cat or dog at home, current smokers had a fivefold increased risk ( OR = 4.97 [95 percent CI , 2.46–10.1]) of a persistent wheeze compared with lifetime nonsmokers. There was no increase in this risk among former smokers (OR = 1.13 [95 percent CI, 0.50–2.55]).

Bodner and colleagues (1998) conducted a nested case-control study of 117 adults aged 39 through 45 years with adult onset of wheezing and 277 randomly selected persons without wheezing who were participants in a population-based cohort study in Scotland. After adjusting for family history, atopy, and social class, the investigators found that current smoking was associated with adult onset of wheezing ( OR = 2.01 [95 percent CI , 1.08–3.74]) and with chronic cough and phlegm (OR = 11.48 [95 percent CI, 2.49–52.89]). Former smokers were at a lower risk for adult onset of wheezing (OR = 1.48 [95 percent CI, 0.74–2.95]), but the risk remained significant for chronic cough and phlegm (OR = 5.24 [95 percent CI, 1.00–27.53]).

In a population-based study of 1,226 women aged 55 years and older living in Sonoma, California, Forastiere and colleagues (1998) examined relationships of chronic respiratory symptoms with a number of risk factors. Among women who reported shortness of breath with a wheeze or chronic wheeze during the past 12 months without a physician’s diagnosis of asthma or chronic bronchitis/emphysema, the investigators found that the risk for these symptoms was highest in current smokers ( OR = 3.8 [95 percent CI , 2.2–6.5]) and that the risk declined but remained statistically significant for former smokers who had quit for 10 or fewer years (OR = 1.8 [95 percent CI, 1.1–3.2]) or for more than 10 years (OR = 1.8 [95 percent CI, 1.2–2.5]). Overall, the population attributable risk for these symptoms in this population of women who had ever smoked was 35 percent.

In a longitudinal study in the Netherlands that included 792 women and 995 men, Jansen and colleagues (1999) found a dose-response relationship between the number of cigarettes smoked and any occurrence of chronic respiratory symptoms. When smokers were compared with nonsmokers, the risk ( OR ) of any chronic respiratory symptom was 1.89 (95 percent CI , 1.37–2.60) for those who smoked 1 to 14 cigarettes per day, 2.98 (95 percent CI, 2.14–4.29) for those who smoked 15 to 24 cigarettes per day, and 3.57 (95 percent CI, 2.32–5.48) for those who smoked 25 or more cigarettes per day. Among former smokers, the risk was lower but not statistically significant (OR = 1.21 [95 percent CI, 0.85–1.74]).

Butland and colleagues (1999) conducted a cross-sectional survey of 5,770 women and 5,582 men aged 33 years living in the United Kingdom. The prevalence of any wheezing or wheezing five or more times in the past 12 months increased with the amount smoked and was lower for former smokers. The prevalence of these symptoms was similar when comparing non-smokers with former smokers who had quit for more than five years.

In the Lung Health Study ( Kanner et al. 1999 ), the prevalence of all respiratory symptoms significantly decreased during the five-year sustained cessation follow-up period. Compared with current smokers, intermittent quitters had a lower prevalence of respiratory symptoms. When compared with those in the sustained cessation category, intermittent quitters had a greater prevalence of respiratory symptoms ( Figure 4.6 ) ( Kanner et al. 1999 ).

Proportion (95 percent confidence interval) of participants reporting chronic cough at each annual follow-up visit, stratified by final smoking status. Note: (A) Restricted to participants who did not report the symptom of cough at entry into the study. (more...)

Active cigarette smoking is consistently associated with an increased risk for respiratory symptoms, including coughing, phlegm, wheezing, and dyspnea. Moreover, the occurrence of respiratory symptoms increases with the number of cigarettes smoked and decreases with smoking cessation. These symptoms reflect the consequences of the smoking-caused changes throughout the respiratory tract.

• The evidence is sufficient to infer a causal relationship between active smoking and all major respiratory symptoms among adults, including coughing, phlegm, wheezing, and dyspnea.

Respiratory symptoms are common among cigarette smokers and probably contribute substantially to an impaired quality of life and a higher utilization of health care resources. Thus, a decrease in the occurrence of these symptoms with smoking cessation will provide important benefits to public health and to the well-being of successful quitters.

Asthma. Epidemiologic Evidence. Asthma in adults is a complex and heterogeneous disorder, likely caused by a number of occupational and environmental exposures as well as by genetic or other intrinsic predispositions. The majority of asthma begins in childhood and may remit for a number of years before manifesting again in adulthood. This phenomenon may complicate the interpretation of epidemiologic investigations of risk factors for adult-onset asthma, because this condition most likely comprises both childhood asthma and true adult-onset asthma. The role of smoking as an etiologic agent in adults with asthma has been investigated in a number of studies using both longitudinal and cross-sectional designs ( Tables 4.16 and 4.17 ). The results indicate a complicated relationship between cigarette smoking and asthma that may be modified by smoking status (i.e., current, former, or never smoker), gender, age, other established risk factors for asthma (e. g ., family history of asthma or personal atopy), and the bias arising from the “healthy smoker effect” ( Weiss et al. 1989 ).

Longitudinal studies on the association between smoking and adult asthma.

Cross-sectional studies on the association between smoking and adult asthma.

The interpretation of the evidence for cigarette smoking and asthma is constrained by a number of methodologic considerations including varying study designs, different definitions of asthma, and different indexes for defining smoking status. Although the longitudinal design is the strongest for investigating the relationship between smoking and adult-onset asthma, the studies that have been conducted arrived at conflicting results ( Table 4.16 ). In those studies, current smoking was associated with an increased risk of asthma among men ( Vesterinen et al. 1988 ) and among men and women aged 40 years or older ( Krzyzanowski and Lebowitz 1992 ). However, neither Vesterinen and colleagues (1988) nor Troisi and colleagues (1995) found an association between current smoking and asthma in women. Furthermore, Troisi and colleagues (1995) did not find a dose-response relationship between the amount smoked and asthma. The strongest associations between smoking and asthma were reported by Strachan and colleagues (1996) and Plaschke and colleagues (2000) . However, their results are difficult to interpret. For example, Strachan and colleagues (1996) combined asthma with wheezy bronchitis, and Plaschke and colleagues (2000) did not define “smokers,” which may have included former smokers. Finally, McWhorter and colleagues (1989) only examined ever smoking in their longitudinal investigation and did not find an association with asthma.

A number of cross-sectional studies have examined the association between asthma and smoking, with inconsistent results for both current and former smokers ( Table 4.17 ). Of the 10 publications that provided quantitative results, 3 found an association between current smoking and asthma in men and women ( Ben-Noun 1999 ; Chen et al. 1999 ; Torén and Hermansson 1999 ), and 1 found an association only in women ( Chen et al. 1999 ). No association was reported in seven cross-sectional studies ( Flodin et al. 1995 ; David et al. 1996 ; Bodner et al. 1998 ; Forastiere et al. 1998 ; Zhang et al. 1999 ; de Marco et al. 2000 ; Kotaniemi et al. 2001 ). Moreover, two investigations provided indirect evidence that current smoking was not associated with asthma ( Hansen et al. 2000 ; Kilpelainen et al. 2001 ), and limited data suggest that the risk of asthma may be greater because of a family history of asthma or the presence of other atopic conditions (i.e., hay fever, atopic dermatitis) ( Melbostad et al. 1998 ; Torén and Hermansson 1999 ). However, this finding was contradicted by the results reported by Plaschke and colleagues (2000) .

Among former smokers, an association with asthma has been inconsistent ( Table 4.17 ). Out of nine studies, five found an increased risk for asthma among former smokers compared with current smokers ( Flodin et al. 1995 ; Troisi et al. 1995 ; Bodner et al. 1998 ; Forastiere et al. 1998 ; Siroux et al. 2000 ), with ORs ranging from 1.4 to 5.24. In contrast, four studies found no association ( David et al. 1996 ; Chen et al. 1999 ; de Marco et al. 2000 ; Kotaniemi et al. 2001 ).

In four cross-sectional studies that examined ever smokers defined as current and former smokers ( Table 4.17 ) ( Flodin et al. 1995 ; Melbostad et al. 1998 ; Ben-Noun 1999 ; Siroux et al. 2000 ), three of the studies associated asthma with ever smoking ( Flodin et al. 1995 ; Melbostad et al. 1998 ; Ben-Noun 1999 ) with ORs ranging from 1.3 to 1.9.

Investigating the relationship between smoking and asthma offers a number of challenges, including diagnostic misclassifications and changes in smoking behaviors because of asthma. Dodge and colleagues (1986) found that among persons aged 40 years or older with newly diagnosed asthma, emphysema, or chronic bronchitis based on self-reports, women were more likely than men to receive a physician’s diagnosis of asthma or chronic bronchitis, and men were more likely to receive a diagnosis of emphysema. In the Nurses Health Study, Troisi and colleagues (1995) found that among women diagnosed with chronic bronchitis, smokers were more likely to receive a subsequent diagnosis of asthma than were nonsmokers ( RR = 2.02 [95 percent CI , 1.01–4.02]). This labeling pattern in women may tend to bias toward an association of asthma with smoking.

Because the bronchial hyperresponsiveness of asthma may cause an intolerance to tobacco smoke, and because smoking worsens respiratory symptoms in persons with asthma ( Althuis et al. 1999 ; Sippel et al. 1999 ), some persons alter their smoking habits and thereby obscure a possible causal association ( Weiss et al. 1989 ). The result is that persons with asthma may not start smoking or may be more likely to quit, a phenomenon referred to as the “healthy smoker effect” ( Weiss et al. 1989 ); however, few data support these suggested biases. In a population-based survey of 3,019 persons from Australia, Wakefield and colleagues (1995) found no differences in the prevalence of smoking between persons with asthma (28.5 percent) and persons without asthma (26.9 percent), or in the amount smoked. Moreover, there were no differences between those two groups in reports of ever trying to quit or trying to quit in the past year.

Siroux and colleagues (2000) examined smoking behaviors among 200 adult patients with asthma and 265 controls without asthma, and found that childhood asthma was not associated with a reduced initiation of smoking. However, patients with asthma were more likely than those without asthma to quit smoking ( OR = 2.76 [95 percent CI , 1.19–6.42] for men; OR = 2.20 [95 percent CI, 1.11–4.34] for women).

Surrogate evidence for a link between cigarette smoking and asthma may be obtained from investigations of the relationship between smoking and non-specific bronchial hyperresponsiveness ( Weiss et al. 1989 ). Although the results are not entirely consistent, available evidence suggests that current smokers have greater bronchial hyperresponsiveness compared with nonsmokers, thus establishing a biologically plausible link for a causal role for smoking in the development of asthma ( Weiss et al. 1989 ; Kennedy et al. 1990 ; Rijcken et al. 1993 ; Sunyer et al. 1997 ).

A possible biologic link between smoking and asthma was also described by Wang and colleagues (2001) in their case-control study of 128 patients with asthma and 136 controls, identified through a community-based survey of 10,014 patients in China. Patients and controls were all examined for the prevalence of two genetic variations of the β 2 -adrenergic receptor gene, which controls airway dilatation. Compared with lifetime nonsmokers, ever smokers who were homozygotes for a specific genetic variation of the β 2 -adrenergic receptor gene on chromosome 16 (arginine/arginine-16) had a markedly increased risk for asthma ( OR = 7.81 [95 percent CI , 2.07–29.5]). In addition, there was a strong dose-response relationship with the amount smoked.

Although the relationship between active smoking and adult-onset asthma is inconsistent, there is consistent evidence that smoking adversely affects the control and severity of asthma ( Prescott et al. 1997 ; Cassino et al. 1999 ; Siroux et al. 2000 ; Beeh et al. 2001 ). As part of the Copenhagen City Heart Study, Prescott and colleagues (1997) examined 13,540 patients for factors associated with hospital admissions for asthma between 1977 and 1993. Overall, the risk of hospitalization for asthma was 20 percent greater in current and former smokers compared with lifetime nonsmokers (95 percent CI , 1.1–1.4) for each 10-year period of smoking. Cassino and colleagues (1999) examined determinants of emergency department visits for asthma among 1,216 adults with asthma living in New York City. Compared with nonsmokers, the RRs for emergency department visits were 1.07 (95 percent CI, 0.97– 1.18) for 1 to 5 pack-years of smoking, 1.69 (95 percent CI, 1.56–1.84) for 6 to 13 pack-years, 0.93 (95 percent CI, 0.84–1.04) for 14 to 30 pack-years, and 1.11 (95 percent CI, 1.00–1.22) for 31 or more pack-years. They also identified heavy cigarette use (13 or more pack-years) as a predictor of emergency department visits following days that had high outdoor ozone levels. In a case-control study of 200 adults with asthma from six specialty clinics in France and 265 controls without asthma, Siroux and colleagues (2000) found that active smoking was associated with an increase in asthma severity. For example, compared with nonsmokers, current smokers more often reported one or more asthma attacks per day ( OR = 2.39 [95 percent CI, 1.06– 5.36]) and abnormal breathing between attacks (OR = 2.06 [95 percent CI, 0.97–4.36]) than nonsmokers. Among 112 persons with asthma seen at a pulmonary specialist practice in Germany, Beeh and colleagues (2001) found that severe asthma, defined as a FEV 1 less than 60 percent predicted, was strongly associated with current smoking (OR = 4.8 [95 percent CI, 1.3–18.3]).

Evidence Synthesis. Although limited evidence suggests that smoking is a biologically plausible cause of asthma, the available epidemiologic evidence of an association between smoking and adult-onset asthma is inconsistent ( Tables 4.16 and 4.17 ). A number of methodologic limitations, including different definitions of asthma, different study designs, and biases such as recall bias and healthy smoker bias, probably contribute to the inconsistent results. In contrast to studies on the causation of asthma, smoking is consistently associated with a greater severity of asthma and increased uses of emergency and hospital services. By increasing the degree of airways inflammation, smoking may worsen the inflammatory process that is considered central in the pathogenesis of asthma. The impairment of airways function caused by smoking may also increase the likelihood of more severe asthma on a clinical basis.

• The evidence is inadequate to infer the presence or absence of a causal relationship between active smoking and asthma in adults.
• The evidence is suggestive but not sufficient to infer a causal relationship between active smoking and increased nonspecific bronchial hyper-responsiveness.
• The evidence is sufficient to infer a causal relationship between active smoking and poor asthma control.

Implications. Because of the large numbers of persons with asthma and an increasing prevalence of asthma worldwide, the potential role of active smoking in the causation of asthma has major public health implications. Therefore, this problem is highly relevant for further research despite methodologic challenges. Patients with asthma need to be strongly encouraged to quit smoking.

COPD . COPD is defined differently by clinicians, pathologists, and epidemiologists; each discipline uses different criteria based on physiologic impairments, pathologic abnormalities, and symptoms ( Samet 1989 ). The hallmark of COPD is airflow obstruction based on spirometric testing, with a persistently low FEV 1 and a low ratio of FEV 1 / FVC despite treatment. Clinicians often diagnose COPD when an adult cigarette smoker presents with chronic dyspnea, coughing, and consistent spirometric abnormalities.

Chronic bronchitis and emphysema with airflow obstruction are both included in the clinical syndrome of COPD . Other specific diseases associated with airflow obstruction, such as asthma, bronchiectasis, and cystic fibrosis, are specifically excluded from the clinical definition of COPD, although there may be overlapping clinical features. Chronic bronchitis and emphysema have specific definitions, although the terms are used more loosely in clinical practice. Chronic bronchitis is characterized by a chronic cough productive of sputum with airflow obstruction. Emphysema is defined as “a condition of the lung characterized by abnormal permanent enlargement of the airspaces distal to the terminal bronchiole, accompanied by destruction of their walls, and without obvious fibrosis” ( American Thoracic Society 1987 , p . 225). On the basis of this definition, the diagnosis of emphysema requires an examination of gross or microscopic lung specimens or an assessment of the lungs based on computed tomography, a recently developed tool ( Thurlbeck 1994 ).

Epidemiologic Evidence. In epidemiologic studies, the diagnosis of COPD may be derived from surveys or clinical databases. Questionnaire responses that may be used to diagnose COPD include reports of symptoms (e. g ., dyspnea, coughing, or phlegm), reports of physician diagnoses (e.g., emphysema, chronic bronchitis, or COPD), or both. Spirometry is often performed in epidemiologic studies to provide objective evidence of airflow obstruction in persons with or without symptoms. Sources of data for descriptive or analytic studies of COPD include databases containing hospital discharge information or vital statistics (e.g., from death certificates). However, the quality of these data sources may vary greatly. The standard terms used for COPD in the databases include terms from the International Classification of Diseases, 9th Revision, such as “chronic bronchitis” (code 491), “emphysema” (code 492), and “chronic airway obstruction not elsewhere classified” (code 496) ( USDHHS 1989b ).

Cigarette smoking as a cause of COPD has been reviewed extensively in earlier reports of the Surgeon General ( Table 4.13 ) ( USDHHS 1984 , 1989a , 1990 ). A considerable amount of more recent research on the relationship between COPD and cigarette smoking has focused on determining predictors of susceptibility, as discussed previously, and on early detection. The following discussion summarizes more current key research on the epidemiology of COPD.

COPD Morbidity. COPD is a common chronic disease in the United States and a major cause of morbidity associated with limitations on physical functioning and a high utilization of medical care services ( Verbrugge and Patrick 1995 ; Mapel et al. 2000 ). Approximately 10 million people in the United States have been diagnosed with COPD ( Wise 1997 ). Verbrugge and Patrick (1995) used data collected from the National Health Interview Survey conducted from 1983–1985 to calculate the prevalence of chronic conditions in the United States and to determine their relative impact on functioning. Among adults aged 18 years and older the prevalence of COPD, which included chronic bronchitis, emphysema, and asthma, was consistently among the top 10 chronic conditions. The prevalence was highest in men and women aged 65 years and older (16.7 percent among men and 12.6 percent among women), intermediate for men and women aged 45 through 64 years (8.8 percent and 11.4 percent, respectively), and lowest for men and women aged 18 through 44 years (5.5 percent and 9.3 percent, respectively). In addition, COPD consistently ranked among the top 10 conditions in all age groups that resulted in limitations on job-related responsibilities and other activities of daily living.

More recent national data are available from the Third National Health and Nutrition Examination Survey ( Mannino et al. 2000 ). This survey included 20,050 U.S. adults who participated from 1988–1994 and who completed an examination that included spirometry and respiratory health questions. The findings suggest that COPD occurs frequently in the United States. The authors categorized current obstructive lung disease as a report of current asthma, bronchitis, or ever having a diagnosis of emphysema. A prior but not current diagnosis of either chronic bronchitis or asthma was categorized as past obstructive lung disease. With these definitions, obstructive lung disease was found to affect 12.5 percent of current smokers, 9.4 percent of former smokers, and 5.8 percent of lifetime nonsmokers.

COPD is associated with high medical care utilization rates, including office-based physician visits and hospitalizations ( Verbrugge and Patrick 1995 ; Sullivan et al. 2000 ). In the 1985 National Ambulatory Medical Care Survey, COPD was consistently among the top 10 conditions leading to a physician visit. Verbrugge and Patrick (1995) found that the largest percentage of physician visits for COPD were among men and women aged 65 years and older (10.8 percent among men and 9.4 percent among women), intermediate for men and women aged 45 through 64 years (6.1 percent and 8.2 percent, respectively), and lowest for men and women aged 18 through 44 years (3.4 percent and 4.8 percent, respectively). In 1995, more than 16 million visits were made to physicians’ offices for COPD, a 72 percent increase from 1985 ( Sullivan et al. 2000 ). In contrast to other chronic conditions (e. g ., cancer or cardiovascular disease), COPD was a less common primary cause of hospitalization in the 1984 National Hospital Discharge Survey ( Verbrugge and Patrick 1995 ), but in 1995 it accounted for more than 500,000 hospitalizations in the United States ( Sullivan et al. 2000 ). However, COPD often is a comorbid condition associated with other chronic conditions, including cancer and cardiovascular diseases ( Ferrer et al. 1997 ; Mapel et al. 2000 ). Total estimated costs associated with COPD in 1993 were $23.9 billion, or about $1,522 per person per year, three times the per capita cost of asthma ( Sullivan et al. 2000 ).

More recent epidemiologic investigations continue to provide strong evidence for the causal link between active smoking and COPD ( Troisi et al. 1995 ; Forastiere et al. 1998 ). In the Nurses Health Study, a prospective cohort study of 74,072 women aged 34 through 68 years, the RR for self-reported, physician-diagnosed chronic bronchitis among current smokers compared with women who had never smoked was 2.85 (95 percent CI , 2.45–3.32) ( Troisi et al. 1995 ). Forastiere and colleagues (1998) , in a population-based cross-sectional survey of 1,226 women aged 55 years and older, found a marked increase in risk for self-reported, physician-diagnosed chronic bronchitis/emphysema among current smokers compared with former and lifetime nonsmokers ( OR = 6.4 [95 percent CI, 3.2–12.6]).

Smoking Cessation and COPD Morbidity. Although smoking cessation slows the rate of FEV 1 decline, thus decreasing the risk for developing chronic airflow obstruction ( Figure 4.1 ), the risk may not return to that for nonsmokers. In a population-based study of 1,391 Seventh-Day Adventists from California, which included nonsmokers and former smokers (aged 16 years or older), Berglund and colleagues (1999) found that, compared with never smoking, past smoking for 10 years was associated with a small but significant risk ( OR = 1.29 [95 percent CI , 1.00–1.66]) of airflow obstruction (FEV 1 / FVC less than 65 percent or FEV 1 percent predicted less than 75 percent).

The risk of self-reported physician-diagnosed chronic bronchitis returns close to that of nonsmokers, but only after 5 to 10 years of cessation ( Troisi et al. 1995 ; Forastiere et al. 1998 ). In the Nurses Health Study, Troisi and colleagues (1995) found that among former smokers the incidence of chronic bronchitis among women was equal to the incidence in those who had completely abstained from smoking for five or more years. Among women aged 55 years and older from Sonoma, California, Forastiere and colleagues (1998) found that the occurrence of physician-diagnosed chronic bronchitis/emphysema was higher in former smokers who had stopped smoking for 10 years or less ( OR = 4.7 [95 percent CI , 2.5–8.7]) compared with nonsmokers, but the risk returned close to that of nonsmokers after more than 10 years of cessation (OR = 1.6 [95 percent CI, 0.9–2.8]).

COPD Mortality. In 2001, COPD (excluding asthma) was the fourth leading cause of death in the United States with more than 118,000 deaths (4.9 percent of all deaths) and an overall mortality rate of 41.7 per 100,000 ( Arias et al. 2003 ). Over the past 30 years, the age-adjusted mortality rate from COPD has been increasing. Of the 10 leading causes of death in the United States, only COPD has increased during this period ( Wise 1997 ). Factors that contribute to the rising COPD mortality rates include decreasing mortality from other causes of death (e. g ., cardiovascular diseases) and increasing mortality among women and nonwhite males ( Mannino et al. 1997 ).

Although COPD prevalence and mortality rates since the late 1970s have been substantially higher in men than in women, the estimated percentage increases have been higher for women ( Thun et al. 1995 , 1997a ; Mannino et al. 1997 ). In fact, from 1979–1988 mortality rates for men worldwide either remained stable or decreased ( Brown et al. 1994 ). These patterns may be partially explained by differences between the prevalence of smoking and smoking behaviors in women and men that have occurred over time. During the past 20 to 30 years, the prevalence and amount of smoking among women have become increasingly similar to those of men ( USDHHS 2001 ).

The prospective studies of the American Cancer Society (Cancer Prevention Study I [ CPS-I ] and Cancer Prevention Study II [ CPS-II ]), which were conducted in the early- to mid-1960s and in the 1980s, provide evidence for a marked increase in the risk of mortality from COPD among women ( Thun et al. 1995 , 1997a ). In CPS-II the death rate for female current smokers (61.6 per 100,000 person-years) was three times higher than in CPS-I. The mortality RR was 12.8 for female current smokers compared with women who had never smoked. For male current smokers in CPS-II, the death rate (103.9 per 100,000 person-years) was 41 percent higher than for male current smokers in CPS-I. The mortality RR was 11.7 for male current smokers compared with men who had never smoked.

Thun and colleagues (1997b) examined mortality rates for COPD in CPS-II in relation to the number of cigarettes currently smoked at baseline. The RR for death from COPD increased with the number of cigarettes smoked per day. For female current smokers compared with women who had never smoked, the RR was 5.6 for 1 to 9 cigarettes per day, 7.9 for 10 to 19 cigarettes per day, 23.3 for 20 cigarettes per day, 22.9 for 21 to 39 cigarettes per day, and 25.2 for 40 or more cigarettes per day. The corresponding RRs for current male smokers compared with men who had never smoked were 8.8 for 1 to 9 cigarettes per day, 8.9 for 10 to 19 cigarettes per day, 10.4 for 20 cigarettes per day, 16.5 for 21 to 39 cigarettes per day, and 9.3 for 40 or more cigarettes per day.

Using CPS-I and CPS-II data on the RR of COPD mortality, Thun and colleagues (1997a , b ) calculated the percentage of COPD deaths attributable to cigarette smoking. Among women in CPS-I, 85 percent of COPD deaths were attributable to smoking; this percentage increased to 92.2 percent in CPS-II. The corresponding values among men were 89.2 percent and 91.4 percent, respectively.

Mannino and colleagues (1997) analyzed mortality trends for obstructive lung disease (including asthma) among people who died in the United States from 1979–1993. Of all the deaths during this time period, 8.2 percent had obstructive lung disease listed on the death certificate, but in only 43.3 percent was the death attributed to obstructive lung disease. Over the time of the study, the age-adjusted mortality rates for obstructive lung disease were highest in white men (ranging from 98.8 to 115.5 per 100,000 per year), followed by black men (77.5 to 100.2 per 100,000), men of other races (38.1 to 58.6 per 100,000), white women (25.5 to 57.7 per 100,000), black women (14.9 to 38.5 per 100,000), and women of other races (10.9 to 20.9 per 100,000). The percentage increases in mortality rates were highest for black women (158.3 percent), followed by white women (126.3 percent), other women (91.7 percent), other men (57.8 percent), black men (29.3 percent), and lowest among white men (16.9 percent).

Smoking Cessation and COPD Mortality. The literature on the effects of smoking cessation on mortality from COPD was extensively reviewed in the 1990 Surgeon General’s report, and the major conclusion relevant to mortality from that report was “With sustained abstinence, the COPD mortality rates among former smokers decline in comparison with continuing smokers” ( Table 4.13 ) ( USDHHS 1990 , p . 11). However, the risk of COPD mortality among former smokers, even after 20 years or more of abstinence, remains elevated compared with the risk among people who have never smoked. Moreover, within approximately the first five years of cessation, mortality rates from COPD initially increase above the rates for continuing smokers and then gradually decline with an increase in the duration of abstinence.

Evidence Synthesis. The recent literature on smoking and COPD provides further support for the conclusion of the 1984 Surgeon General’s report that “cigarette smoking is the major cause of COLD in the United States for both men and women. The contribution of cigarette smoking to COLD morbidity and mortality far outweighs all other factors” ( USDHHS 1984 , p . 8). Whereas the risks for COPD morbidity and mortality decline with smoking cessation, they may not return to the levels of nonsmokers, probably because smoking has resulted in irreversible injury to the airways and parenchyma. A growing body of literature in recent years is providing evidence for major socioeconomic consequences of COPD associated with a marked increase in the utilization of medical care resources.

• The evidence is sufficient to infer a causal relationship between active smoking and chronic obstructive pulmonary disease morbidity and mortality.

Implication. COPD represents a major public health problem that is increasing but could be almost completely prevented with the elimination of smoking.

Cigarette Type and Risk for Chronic Respiratory Diseases. The effect of cigarette type on respiratory symptoms and COLD was reviewed in the 1984 Surgeon General’s report, by Samet (1996) , and by the National Cancer Institute ( NCI ) Tobacco Control Monograph 13 ( NCI 2001 ). A conclusion from the 1984 report was as follows:

Although a reduction in cigarette tar content appears to reduce the risk of cough and mucus hypersecretion, the risk of shortness of breath and airflow obstruction may not be reduced. Evidence is unavailable on the relative risks of developing COLD consequent to smoking cigarettes with the very low tar and nicotine yields of current and recently marketed brands ( USDHHS 1984 , p . 12).

Since the publication of that report, few new data are available on the relationship between cigarette type and chronic respiratory diseases ( Lange et al. 1990 , 1992 ).

Epidemiologic Evidence. Using longitudinal spirometric data obtained during five years (1976–1978 and 1981–1983) from 4,372 smokers and 3,753 non-smokers who participated in the Copenhagen City Heart Study, Lange and colleagues (1990) examined the relationship between cigarette type (filter-tipped versus unfiltered) and lung function deterioration. Overall, there was no significant difference in FEV 1 reductions among filter-tipped cigarette smokers compared with unfiltered cigarette smokers. On average, during the time of the study the tar content of Danish unfiltered cigarettes was 35 mg per cigarette compared with 23 mg per cigarette for filter-tipped cigarettes.

Lange and colleagues (1992) also examined risks of COPD mortality associated with the type of cigarette smoked (filter-tipped versus unfiltered) and inhalation patterns in 7,703 women and 6,511 men who participated in the Copenhagen City Heart Study. The RRs for COPD-related mortality differed little between women and men based on the type of cigarette smoked. Compared with women who were nonsmokers, women who smoked unfiltered cigarettes had a RR for COPD-related mortality of 15 (95 percent CI , 3.1–65.0), and women who smoked filter-tipped cigarettes had a RR of 16 (95 percent CI, 3.6–70.0). The corresponding RRs for men were 6.4 (95 percent CI, 2.0– 20.0) and 7.9 (95 percent CI, 2.3–27.0), respectively.

In four prospective cohort studies in the United Kingdom, Tang and colleagues (1995) assessed mortality in 56,225 men for smoking-induced diseases, comparing filter-tipped and unfiltered cigarettes and estimated tar yields. The mortality risk for COPD was somewhat lower for smokers of filter-tipped cigarettes, but not significantly in comparison with smokers of unfiltered cigarettes. For a tar reduction of 15 mg per cigarette, Tang and colleagues (1995) estimated that COPD mortality would drop by about 20 percent, but this estimate was quite imprecise.

Histopathologic findings have also been reported that provide insights concerning tar and nicotine yields, respiratory symptoms, and lung function levels. Auerbach and colleagues (1979) quantitated smoking-related changes in the autopsied lungs of men from a Veterans Administration hospital in New Jersey. In a rigorously studied series of autopsied lungs, these investigators showed that smokers from a period when cigarettes had comparatively high tar and nicotine yields (1955–1960) had more changes in the airways at various smoking levels compared with smokers from a later period (1970–1977). They interpreted this temporal pattern as an indication that cigarettes with lower tar and nicotine yields had fewer effects on the lungs than did higher-yield cigarettes.

A number of studies have shown that smokers of lower-yield cigarettes have comparatively lower rates of respiratory symptoms ( Table 4.18 ). Respiratory questionnaire data collected in the late 1970s from approximately 6,000 Pennsylvania women are illustrative ( Schenker et al. 1982 ). The brand of cigarettes currently smoked was identified and used with Federal Trade Commission tar yield information to classify the smokers according to tar exposure. A higher-tar yield was positively associated with coughing and phlegm but not with wheezing or shortness of breath. For coughing and phlegm, there were consistent exposure-response relationships with an approximate doubling of symptom frequency from the lowest to the highest exposure category. The findings of other studies are similar. For example, a large study of civil servants in the United Kingdom, the Whitehall Study, showed that the percentage of smokers reporting phlegm increased with tar yield within each stratum of cigarettes smoked per day, even the lowest ( Higenbottam et al. 1980 ).

Studies on the association between cigarette tar yields and chronic respiratory diseases.

Not all studies show less disease associated with lower-yield cigarettes ( Table 4.18 ). One study from Finland found that symptom levels in young smokers who were just initiating smoking did not depend greatly on tar yield ( Rimpela and Teperi 1989 ). In this six-year follow-up study, the youth were surveyed on several occasions to determine the relationship between tar yield and symptom onset. There was little evidence of less symptom occurrence in the new smokers using low-tar cigarettes in comparison with those smoking higher-tar cigarettes. Moreover, symptoms were far more frequent in the low-tar smokers than in nonsmokers. In a randomized trial in the United Kingdom, lower-tar cigarettes were not associated with either lower symptom frequency or a higher level of ventilatory function, which was assessed by measuring the peak expository flow rate ( Withey et al. 1992a , b ). The investigators monitored urinary nicotine metabolites and concluded that compensation led to comparable levels across the trial period.

Respiratory morbidity also has been investigated. Follow-ups of outpatient visits by enrollees in a Kaiser Permanente group over one year showed that there was a reduced risk for pneumonia and influenza, but not for other respiratory conditions, associated with the use of low-tar and low-nicotine products compared with the use of products higher in tar and nicotine ( Petitti and Friedman 1985a ). However, in comparison with nonsmokers, smokers using low-tar and low-nicotine cigarettes had an increased risk for pneumonia, influenza, and COPD .

The evidence does not suggest a relationship between tar yield and lung function level. For example, in the Whitehall Study there was no cross-sectional relationship between tar yield and the FEV 1 level ( Higenbottam et al. 1980 ). In the Normative Aging Study, a longitudinal study of U.S. veterans, tar yields of the usual brands of cigarettes smoked were not associated with a decline of FEV 1 levels ( Sparrow et al. 1983 ), and the Tucson Study found a weak association between lung function decline and higher tar yields ( Krzyzanowski et al. 1991 ).

In general, cohort studies assessing cigarette type and yield with COPD risks show little evidence for an association. In the CPS-I study comparing “low-” or “medium-” tar and nicotine smokers with “high-” tar and nicotine smokers, mortality from emphysema was reduced somewhat, although not significantly ( Table 4.18 ) ( Lee and Garfinkel 1981 ).

Evidence Synthesis. Little new evidence is available, and it does not conflict with the conclusion of the 1984 Surgeon General’s report ( USDHHS 1984 ) that “reduction in cigarette tar content appears to reduce the risk of cough and mucus hypersecretion” ( p . 12). Limited evidence published since that report suggests that cigarette type does not influence the rate of FEV 1 decline or COPD -related mortality.

• The evidence is suggestive but not sufficient to infer a causal relationship between lower machine-measured cigarette tar and a lower risk for cough and mucus hypersecretion.
• The evidence is inadequate to infer the presence or absence of a causal relationship between a lower cigarette tar content and reductions in forced expiratory volume in one second decline rates.
• The evidence is inadequate to infer the presence or absence of a causal relationship between a lower cigarette tar content and reductions in chronic obstructive pulmonary disease-related mortality.

Implications. Although there are limited data on the relationship between cigarette type and the risk for chronic respiratory diseases, the strong benefits from smoking cessation combined with the availability of effective methods for controlling tobacco use suggest that little public health benefit will be gained by further research on the relationship between cigarette type and chronic respiratory diseases.

Diffuse Parenchymal Lung Diseases. Diffuse parenchymal lung diseases, also known as interstitial lung diseases, are a heterogeneous group of disorders associated with different types of inflammation primarily in the walls and airspaces of alveoli. Although there are more than 100 different diffuse parenchymal lung diseases, only small numbers of patients with these diseases are seen regularly by clinicians ( Coultas et al. 1994 ), and the role of cigarette smoking has been investigated only for a few of these diseases.

Although the pathogenesis of these diseases is varied, conceptually they result from an inflammatory response in the lungs that follows the inhalation of a wide variety of particles (e. g ., inorganic and organic). For some of the diseases (i.e., idiopathic pulmonary fibrosis [ IPF ] or sarcoidosis), emerging evidence suggests a causal role for a number of inhaled agents, but causality remains to be established. The role of cigarette smoking in the pathogenesis of diffuse parenchymal lung diseases, although not fully defined, is potentially complex and may involve altered clearance, deposition of particles, and modification of the inflammatory response. Evidence for a complex interaction between cigarette smoking and the pathogenesis of diffuse parenchymal lung diseases is based on observations that cigarette smoking is associated with an increased disease risk for some (e.g., IPF or pneumoconiosis), and a decreased risk for others (e.g., hypersensitivity pneumonitis or sarcoidosis). Available evidence suggests that modification of the inflammatory/immune response may be the mechanism for lowering the risks for hypersensitivity pneumonitis ( Baron 1996 ) and sarcoidosis ( Soliman and Twigg 1992 ; Baron 1996 ).

Idiopathic Pulmonary Fibrosis. Epidemiologic Evidence. Scant epidemiologic data are available on the occurrence of IPF ( Coultas et al. 1994 ), but the available information suggests that IPF may be the most common diffuse parenchymal lung disease in the general population ( Coultas et al. 1994 ). Until recently, etiologic investigations of this disorder had not been conducted. It is relatively uncommon, and without a lung biopsy misclassification of the diagnosis may result, making investigation of this disorder difficult. Although the term “idiopathic” means of unknown cause, during the past decade four case-control studies have been conducted to examine potential etiologic agents, including cigarette smoking ( Scott et al. 1990 ; Iwai et al. 1994 ; Hubbard et al. 1996 ; Baumgartner et al. 1997 ). One case-control study of environmental exposures was conducted with 17 patients, but cigarette smoking was not examined ( Mullen et al. 1998 ).

Overall, significant associations were found in three of the four studies. Scott and colleagues (1990) identified 40 cases of IPF seen by pulmonary physicians or tested at pulmonary function laboratories in Nottingham, England, and 106 age- and gender-matched controls were identified from patients registered with the index patient’s general practitioner. In this case-control study, cigarette smoking was not significantly associated with IPF ( OR = 1.11 [95 percent CI , 0.13–1.40]).

Cases of IPF seen between 1992 and 1994 at four teaching hospitals in the Trent Region, United Kingdom, were identified by Hubbard and colleagues (1996) . Controls matched by age, gender, and community were identified from patients registered with the same general practitioner. Information on smoking and other exposures was obtained from 218 patients and 569 controls who returned a mailed questionnaire; 165 cases and 408 controls completed telephone interviews for verification. Having ever smoked was significantly associated with IPF ( OR = 1.57 [95 percent CI , 1.01– 2.43]).

Iwai and colleagues (1994) identified 86 patients with IPF evaluated by two research committees in Japan. Two controls for each patient were matched for age, gender, and residential area: a person selected from voters’ lists and a hospital patient with a non-IPF respiratory disease. Compared with healthy controls, IPF patients were significantly more likely to smoke ( OR = 2.94 [95 percent CI , 1.37–6.30]).

Baumgartner and colleagues (1997) conducted a multicenter case-control study in the United States that included 16 institutions in 15 states. A total of 248 patients had been diagnosed with IPF between 1989 and 1993; and 491 community controls matched for age, gender, and geographic location were identified using random-digit telephone dialing. Standardized telephone interviews were used to obtain risk factor information from cases and controls. Ever smoking was significantly associated with IPF ( OR = 1.6 [95 percent CI , 1.1–2.4]), but there was no dose-response relationship with pack-years of smoking. Moreover, there was no increased risk in current smokers (OR = 1.06 [95 percent CI, 0.6–1.8]). However, among former smokers there was an inverse trend in risk with time since cessation (OR = 3.5 [95 percent CI, 1.1–11.9] for cessation of less than 2.5 years, OR = 2.3 [95 percent CI, 1.3–4.2] for cessation of 2.5 to 10 years, OR = 1.9 [95 percent CI, 1.1–3.2] for cessation of 10 to 25 years, and OR = 1.3 [95 percent CI, 0.7–2.3] for cessation of 25 or more years).

Evidence Synthesis. Inflammation is thought to have a central role in the pathogenesis of IPF . Smoking, which increases lung inflammation, could plausibly increase the risk for IPF. Several studies show an association between ever smoking and IPF; however, the data are limited and further studies are needed.

• The evidence is inadequate to infer the presence or absence of a causal relationship between active smoking and idiopathic pulmonary fibrosis.

Implication. Further research will be needed to determine whether there is a causal relationship between active smoking and pulmonary fibrosis.

1. The evidence is sufficient to infer a causal relationship between smoking and acute respiratory illnesses, including pneumonia, in persons without underlying smoking-related chronic obstructive lung disease.

2. The evidence is suggestive but not sufficient to infer a causal relationship between smoking and acute respiratory infections among persons with preexisting chronic obstructive pulmonary disease.

3. In persons with asthma, the evidence is inadequate to infer the presence or absence of a causal relationship between smoking and acute asthma exacerbation.

4. The evidence is sufficient to infer a causal relationship between maternal smoking during pregnancy and a reduction of lung function in infants.

5. The evidence is suggestive but not sufficient to infer a causal relationship between maternal smoking during pregnancy and an increase in the frequency of lower respiratory tract illnesses during infancy.

6. The evidence is suggestive but not sufficient to infer a causal relationship between maternal smoking during pregnancy and an increased risk for impaired lung function in childhood and adulthood.

7. Active smoking causes injurious biologic processes (i.e., oxidant stress, inflammation, and a protease-antiprotease imbalance) that result in airway and alveolar injury. This injury, if sustained, ultimately leads to the development of chronic obstructive pulmonary disease.

8. The evidence is sufficient to infer a causal relationship between active smoking and impaired lung growth during childhood and adolescence.

9. The evidence is sufficient to infer a causal relationship between active smoking and the early onset of lung function decline during late adolescence and early adulthood.

10. The evidence is sufficient to infer a causal relationship between active smoking in adulthood and a premature onset of and an accelerated age-related decline in lung function.

11. The evidence is sufficient to infer a causal relationship between sustained cessation from smoking and a return of the rate of decline in pulmonary function to that of persons who had never smoked.

12. The evidence is sufficient to infer a causal relationship between active smoking and respiratory symptoms in children and adolescents, including coughing, phlegm, wheezing, and dyspnea.

13. The evidence is sufficient to infer a causal relationship between active smoking and asthma-related symptoms (i.e., wheezing) in childhood and adolescence.

14. The evidence is inadequate to infer the presence or absence of a causal relationship between active smoking and physician-diagnosed asthma in childhood and adolescence.

15. The evidence is suggestive but not sufficient to infer a causal relationship between active smoking and a poorer prognosis for children and adolescents with asthma.

16. The evidence is sufficient to infer a causal relationship between active smoking and all major respiratory symptoms among adults, including coughing, phlegm, wheezing, and dyspnea.

17. The evidence is inadequate to infer the presence or absence of a causal relationship between active smoking and asthma in adults.

18. The evidence is suggestive but not sufficient to infer a causal relationship between active smoking and increased nonspecific bronchial hyper-responsiveness.

19. The evidence is sufficient to infer a causal relationship between active smoking and poor asthma control.

20. The evidence is sufficient to infer a causal relationship between active smoking and chronic obstructive pulmonary disease morbidity and mortality.

21. The evidence is suggestive but not sufficient to infer a causal relationship between lower machine-measured cigarette tar and a lower risk for cough and mucus hypersecretion.

22. The evidence is inadequate to infer the presence or absence of a causal relationship between a lower cigarette tar content and reductions in forced expiratory volume in one second decline rates.

23. The evidence is inadequate to infer the presence or absence of a causal relationship between a lower cigarette tar content and reductions in chronic obstructive pulmonary disease-related mortality.

24. The evidence is inadequate to infer the presence or absence of a causal relationship between active smoking and idiopathic pulmonary fibrosis.

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Pack-years = The number of years of smoking multiplied by the number of packs of cigarettes smoked per day.

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How Smoking Is Harmful to Your Health Essay

Introduction, the impact of smoking on cardiovascular system, the impact of smoking on oral system, the impact of smoking on mental health.

More and more people become addicted to smoking every year. However, few of them are aware of the health damages all smokers may face in a long-term perspective. Smoking causes such incurable diseases as cancer, diabetes, asthma, or blindness. That is why many people nowadays substitute tobacco cigarettes with something they consider less damageable, for instance, electronic cigarettes or vapes. Although, there is almost no difference between smoking tobacco cigarettes and vapes or electronic cigarettes (Rofles et al., 2020). All of them are equally harmful to people’s health and may cause severe diseases. The primary purpose of the present speech is to inform the audience about the detrimental effects of smoking. The speech is divided into three parts. First, I will discuss the impact of tobacco on the cardiovascular system. Then I will proceed with the discussion of oral system diseases caused by smoking. Finally, I will finish the speech by speaking about the mental health problems that smoking causes.

The first system of the human body that suffers from cigarettes is the cardiovascular system. Almost one-third of smoke-related deaths worldwide are caused by cardiovascular disease (Kondo et al., 2019). Even people who smoke not so often may face cardiovascular problems. However, they have significantly lower chances of suffering from cardio diseases than those who smoke from 5 to 20 cigarettes on a daily basis. Both active and passive cigarette-smokers consume such dangerous chemicals as nicotine or carbon monoxide that may cause high blood pressure, atherosclerosis, or atrial fibrillation. The diseases in question carry the risk of a sudden heart attack both for men and women leading to hospitalization or death.

Besides, excessive consumption of nicotine and carbon monoxide causes congestive heart failure. In that case, the heart is unable to send blood around the body at a proper speed. Cognitive heart failure is also called heart aging, which means that a 30-year-old smoker may have the heart of a 50 or 60-year-old. That is why it is hard sometimes for people who smoke to do some physical activities, as their hearts cannot endure high tension.

Tobacco cigarettes smoke is proven to behave like a toxin or even drug. Interestingly, if at least one toxic chemical that tobacco smoke contains is excluded, the smoke becomes less damageable for the cardiovascular system (Kondo et al., 2019). That is why many people substitute tobacco cigarettes with electronic cigarettes because the latest contain such toxic chemicals as carbon monoxide, reactive oxygen species, carbonyls, and polyaromatic hydrocarbons to a lower degree than tobacco cigarettes. However, the nicotine level of electronic cigarettes remains unreasonably high, and the toxin affects all body systems and elevates cardiovascular risks.

However, smoking impacts not only the cardiovascular system of humans’ bodies. Now I am determined to discuss its influence on the oral system. The oral cavity system is the first one that meets toxic chemicals that cigarettes contain. Hence, it is logical to suppose that most of them remain on people’s teeth and tongues. However, mucosal cells of the oral cavity are susceptible to nicotine and other substances (Yu et al., 2017). That is why smokers often either do not notice some signs of oral cavity diseases or just do not relate them to smoking.

Apart from that, high temperature of cigarettes traumatizes the oral cavity. The temperature of smoldering tobacco is about 300°C and is higher than the average temperature of the oral cavity, so the smoke harms capillaries in the mouth.

Tobacco smoking causes changes in the pH of saliva (Yu et al., 2017). Cigarettes provoke oral dysbiosis and may become the reason for emerging unpleasant scents (, which may also be a consequence of dehydration of the oral cavity. The last often causes the loss of antibacterial and antiviral functions of saliva, which weakens oral mucosal immunity.

Refreshing toothpaste, mouthwash or chewing gum help to reduce unpleasant smells for some time, but they do not clean the oral cavity completely.

Weak oral immunity may also result in teeth disruption and discoloration. If not properly treated, these symptoms may cause parodontitis or complete teeth loss.

Now it is time to discuss what impact smoking has on mental health. Many people, especially the younger generation, see cigarettes as a means that helps to reduce stress and anxiety. It is true because nicotine, as one of the psychoactive substances stimulates adrenaline and dopamine release, provoking happiness and reducing stress (Chambers, 2017).

That is why people with mental disorders are more susceptible to smoking than anyone else. There are several reasons for that. First, people with mental illnesses seek all the possible ways to lessen stress, and smoking becomes for them the only source of dopamine. Second, culture itself associates depression and mental disorders in general with smoking, as in many books and movies, people suffering from mental illnesses are portrayed as smokers.

However, nicotine does not cause mental disorders. Cigarette addiction may be the result of depression or another mental illness. Nicotine, in that case, not only releases dopamine and adrenaline but also increases the risks of irrational behavior (Hefner et al., 2019). That is why many smokers are incredibly nervous and have trembling hands if they do not smoke at least for an hour. According to Hefner et al., nicotine addiction may be compared to alcohol addiction and cause far more damage.

It is necessary to remember that not only people with mental disorders may become addicted to smoking. For people who do not suffer from mental illnesses, nicotine addiction may shorten the attention span, cause problems with concentration and irrational behavior.

Many people nowadays are addicted to smoking. However, not all of them realize to which consequences it may lead. It is essential to assess all possible damages and be considerate of your health.

All the issues mentioned above compose only a tiny part of negative effects smoking causes on people’s health. It requires a lot of time and effort to number all of them. There were only three aspects of cigarettes’ harmful influence on people’s health discussed today, but there are far more of them. Concerning the present speech, it is possible to conclude that the harmful effects of smoking may not be noticeable when a person only starts doing it. However, in a long-term perspective, cigarettes pose a considerable threat to people’s physical and mental health.

Agarwal, N., Huq, S. M. & Dorji, C. (2018). The fatal link between tobacco smoking and cardiovascular diseases. The WHO South-Asia region.

Chambers, M. (Ed.). (2017). Psychiatric and mental health nursing: the craft of caring. Taylor & Francis.

Hefner, K.R., Sollazzo, A., Mullaney, S., Coker, K. L. & Sofuoglu, M. (2019). E-cigarettes, alcohol use, and mental health: Use and perceptions of e-cigarettes among college, by alcohol use and mental health status. Addict Behav. 91, 12-20.

Kondo, T., Nakano Y., Adachi , S. & Murohara, T. (2019). Effects of tobacco smoking on cardiovascular disease. Circulation Journal, 83, 1980-1985.

Naveed, A., Sohalib, A., Syed, N. B., Karobari, M. I., Anand, M., Charu, M., M., Pratibha, T., Pietro, M., Chan, Y., Y. & Scardina, G. A. (2021). Smoking a dangerous addiction: a systematic review on an underrated risk factor for oral diseases . I nt. J. Environ. Res. Public Health , 18 (21). Web.

Smoking and mental health. (n.d.). Web.

Yu, G., Philips, S., Gail, M. H., Goedert, J.J., Humphrys, M. S., Ravel, J., Ren, Y. & Caporaso, N. E. (2017). The effect of cigarette smoking on the oral and nasal microbiota. Microbiome, 5 (3).

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1. IvyPanda . "How Smoking Is Harmful to Your Health." February 26, 2024. https://ivypanda.com/essays/how-smoking-is-harmful-to-your-health/.

Bibliography

IvyPanda . "How Smoking Is Harmful to Your Health." February 26, 2024. https://ivypanda.com/essays/how-smoking-is-harmful-to-your-health/.

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